From 723965c572d0062934537355cc4d7ff4c2189b02 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Tue, 23 Apr 2019 18:44:33 +0800 Subject: [PATCH 001/117] WIP --- utensor_cgen/frontend/tflite.py | 113 +++++++++++++ .../frontend/tflite_flatbuffer/AbsOptions.py | 22 +++ .../ActivationFunctionType.py | 12 ++ .../frontend/tflite_flatbuffer/AddNOptions.py | 22 +++ .../frontend/tflite_flatbuffer/AddOptions.py | 30 ++++ .../tflite_flatbuffer/ArgMaxOptions.py | 30 ++++ .../tflite_flatbuffer/ArgMinOptions.py | 30 ++++ .../BatchToSpaceNDOptions.py | 22 +++ .../BidirectionalSequenceLSTMOptions.py | 62 +++++++ .../BidirectionalSequenceRNNOptions.py | 46 ++++++ .../frontend/tflite_flatbuffer/Buffer.py | 46 ++++++ .../tflite_flatbuffer/BuiltinOperator.py | 121 ++++++++++++++ .../tflite_flatbuffer/BuiltinOptions.py | 97 +++++++++++ .../frontend/tflite_flatbuffer/CallOptions.py | 30 ++++ .../frontend/tflite_flatbuffer/CastOptions.py | 38 +++++ .../tflite_flatbuffer/CombinerType.py | 9 ++ .../ConcatEmbeddingsOptions.py | 78 +++++++++ .../tflite_flatbuffer/ConcatenationOptions.py | 38 +++++ .../tflite_flatbuffer/Conv2DOptions.py | 70 ++++++++ .../frontend/tflite_flatbuffer/CosOptions.py | 22 +++ .../tflite_flatbuffer/CustomOptionsFormat.py | 7 + .../tflite_flatbuffer/CustomQuantization.py | 46 ++++++ .../DepthwiseConv2DOptions.py | 78 +++++++++ .../tflite_flatbuffer/DequantizeOptions.py | 22 +++ .../frontend/tflite_flatbuffer/DivOptions.py | 30 ++++ .../EmbeddingLookupSparseOptions.py | 30 ++++ .../tflite_flatbuffer/EqualOptions.py | 22 +++ .../frontend/tflite_flatbuffer/ExpOptions.py | 22 +++ .../tflite_flatbuffer/ExpandDimsOptions.py | 22 +++ .../tflite_flatbuffer/FakeQuantOptions.py | 54 +++++++ .../frontend/tflite_flatbuffer/FillOptions.py | 22 +++ .../tflite_flatbuffer/FloorDivOptions.py | 22 +++ .../tflite_flatbuffer/FloorModOptions.py | 22 +++ .../FullyConnectedOptions.py | 38 +++++ .../FullyConnectedOptionsWeightsFormat.py | 8 + .../tflite_flatbuffer/GatherNdOptions.py | 22 +++ .../tflite_flatbuffer/GatherOptions.py | 30 ++++ .../tflite_flatbuffer/GreaterEqualOptions.py | 22 +++ .../tflite_flatbuffer/GreaterOptions.py | 22 +++ .../tflite_flatbuffer/L2NormOptions.py | 30 ++++ .../tflite_flatbuffer/LSHProjectionOptions.py | 30 ++++ .../tflite_flatbuffer/LSHProjectionType.py | 9 ++ .../tflite_flatbuffer/LSTMKernelType.py | 8 + .../frontend/tflite_flatbuffer/LSTMOptions.py | 54 +++++++ .../tflite_flatbuffer/LeakyReluOptions.py | 30 ++++ .../tflite_flatbuffer/LessEqualOptions.py | 22 +++ .../frontend/tflite_flatbuffer/LessOptions.py | 22 +++ .../LocalResponseNormalizationOptions.py | 54 +++++++ .../tflite_flatbuffer/LogSoftmaxOptions.py | 22 +++ .../tflite_flatbuffer/LogicalAndOptions.py | 22 +++ .../tflite_flatbuffer/LogicalNotOptions.py | 22 +++ .../tflite_flatbuffer/LogicalOrOptions.py | 22 +++ .../tflite_flatbuffer/MatrixDiagOptions.py | 22 +++ .../tflite_flatbuffer/MatrixSetDiagOptions.py | 22 +++ .../MaximumMinimumOptions.py | 22 +++ .../tflite_flatbuffer/MirrorPadMode.py | 8 + .../tflite_flatbuffer/MirrorPadOptions.py | 30 ++++ .../frontend/tflite_flatbuffer/Model.py | 128 +++++++++++++++ .../frontend/tflite_flatbuffer/MulOptions.py | 30 ++++ .../frontend/tflite_flatbuffer/NegOptions.py | 22 +++ .../tflite_flatbuffer/NotEqualOptions.py | 22 +++ .../tflite_flatbuffer/OneHotOptions.py | 30 ++++ .../frontend/tflite_flatbuffer/Operator.py | 153 ++++++++++++++++++ .../tflite_flatbuffer/OperatorCode.py | 46 ++++++ .../frontend/tflite_flatbuffer/PackOptions.py | 38 +++++ .../frontend/tflite_flatbuffer/PadOptions.py | 22 +++ .../tflite_flatbuffer/PadV2Options.py | 22 +++ .../frontend/tflite_flatbuffer/Padding.py | 8 + .../tflite_flatbuffer/Pool2DOptions.py | 70 ++++++++ .../frontend/tflite_flatbuffer/PowOptions.py | 22 +++ .../tflite_flatbuffer/QuantizationDetails.py | 8 + .../QuantizationParameters.py | 145 +++++++++++++++++ .../tflite_flatbuffer/QuantizeOptions.py | 22 +++ .../frontend/tflite_flatbuffer/RNNOptions.py | 30 ++++ .../tflite_flatbuffer/RangeOptions.py | 22 +++ .../frontend/tflite_flatbuffer/RankOptions.py | 22 +++ .../tflite_flatbuffer/ReducerOptions.py | 30 ++++ .../tflite_flatbuffer/ReshapeOptions.py | 46 ++++++ .../ResizeBilinearOptions.py | 30 ++++ .../ResizeNearestNeighborOptions.py | 30 ++++ .../ReverseSequenceOptions.py | 38 +++++ .../tflite_flatbuffer/ReverseV2Options.py | 22 +++ .../frontend/tflite_flatbuffer/SVDFOptions.py | 38 +++++ .../tflite_flatbuffer/SelectOptions.py | 22 +++ .../tflite_flatbuffer/SequenceRNNOptions.py | 38 +++++ .../tflite_flatbuffer/ShapeOptions.py | 30 ++++ .../tflite_flatbuffer/SkipGramOptions.py | 46 ++++++ .../tflite_flatbuffer/SliceOptions.py | 22 +++ .../tflite_flatbuffer/SoftmaxOptions.py | 30 ++++ .../SpaceToBatchNDOptions.py | 22 +++ .../tflite_flatbuffer/SpaceToDepthOptions.py | 30 ++++ .../tflite_flatbuffer/SparseToDenseOptions.py | 30 ++++ .../tflite_flatbuffer/SplitOptions.py | 30 ++++ .../tflite_flatbuffer/SplitVOptions.py | 30 ++++ .../tflite_flatbuffer/SquareOptions.py | 22 +++ .../SquaredDifferenceOptions.py | 22 +++ .../tflite_flatbuffer/SqueezeOptions.py | 46 ++++++ .../tflite_flatbuffer/StridedSliceOptions.py | 62 +++++++ .../frontend/tflite_flatbuffer/SubGraph.py | 122 ++++++++++++++ .../frontend/tflite_flatbuffer/SubOptions.py | 30 ++++ .../frontend/tflite_flatbuffer/Tensor.py | 90 +++++++++++ .../frontend/tflite_flatbuffer/TensorType.py | 16 ++ .../frontend/tflite_flatbuffer/TileOptions.py | 22 +++ .../tflite_flatbuffer/TopKV2Options.py | 22 +++ .../tflite_flatbuffer/TransposeConvOptions.py | 46 ++++++ .../tflite_flatbuffer/TransposeOptions.py | 22 +++ .../UnidirectionalSequenceLSTMOptions.py | 54 +++++++ .../tflite_flatbuffer/UniqueOptions.py | 30 ++++ .../tflite_flatbuffer/UnpackOptions.py | 38 +++++ .../tflite_flatbuffer/WhereOptions.py | 22 +++ .../tflite_flatbuffer/ZerosLikeOptions.py | 22 +++ .../frontend/tflite_flatbuffer/__init__.py | 0 112 files changed, 4044 insertions(+) create mode 100644 utensor_cgen/frontend/tflite.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/AbsOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ActivationFunctionType.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/AddNOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/AddOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ArgMaxOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ArgMinOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/BatchToSpaceNDOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/BidirectionalSequenceLSTMOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/BidirectionalSequenceRNNOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/Buffer.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/BuiltinOperator.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/BuiltinOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/CallOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/CastOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/CombinerType.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ConcatEmbeddingsOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ConcatenationOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/Conv2DOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/CosOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/CustomOptionsFormat.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/CustomQuantization.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/DepthwiseConv2DOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/DequantizeOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/DivOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/EmbeddingLookupSparseOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/EqualOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ExpOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ExpandDimsOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/FakeQuantOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/FillOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/FloorDivOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/FloorModOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/FullyConnectedOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/FullyConnectedOptionsWeightsFormat.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/GatherNdOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/GatherOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/GreaterEqualOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/GreaterOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/L2NormOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LSHProjectionOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LSHProjectionType.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LSTMKernelType.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LSTMOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LeakyReluOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LessEqualOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LessOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LocalResponseNormalizationOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LogSoftmaxOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LogicalAndOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LogicalNotOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LogicalOrOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/MatrixDiagOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/MatrixSetDiagOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/MaximumMinimumOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/MirrorPadMode.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/MirrorPadOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/Model.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/MulOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/NegOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/NotEqualOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/OneHotOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/Operator.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/OperatorCode.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/PackOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/PadOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/PadV2Options.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/Padding.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/Pool2DOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/PowOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/QuantizationDetails.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/QuantizationParameters.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/QuantizeOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/RNNOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/RangeOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/RankOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ReducerOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ReshapeOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ResizeBilinearOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ResizeNearestNeighborOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ReverseSequenceOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ReverseV2Options.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SVDFOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SelectOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SequenceRNNOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ShapeOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SkipGramOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SliceOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SoftmaxOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SpaceToBatchNDOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SpaceToDepthOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SparseToDenseOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SplitOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SplitVOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SquareOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SquaredDifferenceOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SqueezeOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/StridedSliceOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SubGraph.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SubOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/Tensor.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/TensorType.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/TileOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/TopKV2Options.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/TransposeConvOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/TransposeOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/UnidirectionalSequenceLSTMOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/UniqueOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/UnpackOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/WhereOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ZerosLikeOptions.py create mode 100644 utensor_cgen/frontend/tflite_flatbuffer/__init__.py diff --git a/utensor_cgen/frontend/tflite.py b/utensor_cgen/frontend/tflite.py new file mode 100644 index 00000000..e1e17447 --- /dev/null +++ b/utensor_cgen/frontend/tflite.py @@ -0,0 +1,113 @@ +from __future__ import absolute_import +import os +import six + +import numpy as np + +from utensor_cgen.frontend.base import Parser +from utensor_cgen.frontend import FrontendSelector +from utensor_cgen.ir.base import TensorInfo, OperationInfo, uTensorGraph +from utensor_cgen.utils import topologic_order_graph + +import flatbuffers +from tflite_flatbuffer.Model import Model + +@FrontendSelector.register(target_exts=['.tflite']) +class TFLiteParser(Parser): + + @classmethod + def parse(self, pb_file, output_nodes=None): + buf = open(pb_file, 'rb').read() + buf = bytearray(buf) + model = Model.GetRootAsModel(buf, 0) + #TODO: version check + #print(model.Version()) + + assert model.SubgraphsLength() == 1 + subgraph = model.Subgraphs(0) + + #table to convert opcode to op-class + opcode_to_class= [] + for i in range(0, model.OperatorCodesLength()): + op_code = model.OperatorCodes(i) + opcode_to_class.append(op_code) + + #construct lookup objects for builtin ops + from tflite.BuiltinOperator import BuiltinOperator + from tflite.BuiltinOptions import BuiltinOptions + + builtin_ops = {v: k for k, v in BuiltinOperator.__dict__.items()} + op_options = {v: k for k, v in BuiltinOptions.__dict__.items()} + builtin_option_class = self.get_builtin_option_class() + + ugraph = uTensorGraph(output_nodes=output_nodes, + backend="tensorflow") #FIXME: with the proper backend + + #assuming this is the equivalent to ugraph's topological order + for i in range(0, subgraph.OperatorsLength()): + op = subgraph.Operators(i) + opIndex = op.OpcodeIndex() + op_class = opcode_to_class[opIndex] + builtin_code = op_class.BuiltinCode() + op_type = builtin_ops[builtin_code] #op's type in string + + op_attr = dict() + + if(op.CustomOptionsLength() < 1): + option = builtin_option_class[op_type] + builtin_data = op.BuiltinOptions() + option.Init(builtin_data.Bytes, builtin_data.Pos) + op_attr['option'] = option + + if(op_type == 'FULLY_CONNECTED'): + from tflite_flatbuffer.FullyConnectedOptionsWeightsFormat import FullyConnectedOptionsWeightsFormat + w_formats = {v: k for k, v in FullyConnectedOptionsWeightsFormat.__dict__.items()} + op_attr['weights_format'] = w_formats + else: + op_attr['custom_option'] = op.CustomOptionsAsNumpy() + + fb_input_tensors = [subgraph.Tensors(input_idx) for input_idx in op.InputsAsNumpy()] + in_tensors = [TensorInfo(name=tensor.Name(), + ugraph=ugraph, + op_name=tensor.op.name, #FIXME: who the fuck knows + dtype=self.tensor_type_lookup(tensor.Type()), + shape=tensor.ShapeAsNumpy()) + for tensor in fb_input_tensors] + + fb_output_tensors = [subgraph.Tensors(output_idx) for output_idx in op.OutputsAsNumpy()] + out_tensors = [TensorInfo(name=tensor.Name(), + ugraph=ugraph, + op_name=tensor.op.name, #FIXME: who the fuck knows + dtype=self.tensor_type_lookup(tensor.Type()), + shape=tensor.ShapeAsNumpy()) + for tensor in fb_output_tensors] + + node_name = op_type + "_" + i + + op_info = OperationInfo(name=node_name, + input_tensors=in_tensors, + output_tensors=out_tensors, + op_type=op_type, + backend='tensorflow', #FIXME: what should this be? + op_attr=op_attr, + ugraph=ugraph) + + ugraph.ops_info[node_name] = op_info + + topologic_order_graph(ugraph) + return ugraph + + + def get_builtin_option_class(self): + from tflite_flatbuffer.FullyConnectedOptions import FullyConnectedOptions + from tflite_flatbuffer.ArgMaxOptions import ArgMaxOptions + + table = dict() + table['FULLY_CONNECTED'] = FullyConnectedOptions() + table['ARG_MAX'] = ArgMaxOptions() + + return table + + def tensor_type_lookup(self, int_type): + #see TensorType.py + return -1 diff --git a/utensor_cgen/frontend/tflite_flatbuffer/AbsOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/AbsOptions.py new file mode 100644 index 00000000..0cbfb8c0 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/AbsOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class AbsOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsAbsOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = AbsOptions() + x.Init(buf, n + offset) + return x + + # AbsOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def AbsOptionsStart(builder): builder.StartObject(0) +def AbsOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ActivationFunctionType.py b/utensor_cgen/frontend/tflite_flatbuffer/ActivationFunctionType.py new file mode 100644 index 00000000..dc177dee --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/ActivationFunctionType.py @@ -0,0 +1,12 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class ActivationFunctionType(object): + NONE = 0 + RELU = 1 + RELU_N1_TO_1 = 2 + RELU6 = 3 + TANH = 4 + SIGN_BIT = 5 + diff --git a/utensor_cgen/frontend/tflite_flatbuffer/AddNOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/AddNOptions.py new file mode 100644 index 00000000..b5c2ddb7 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/AddNOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class AddNOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsAddNOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = AddNOptions() + x.Init(buf, n + offset) + return x + + # AddNOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def AddNOptionsStart(builder): builder.StartObject(0) +def AddNOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/AddOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/AddOptions.py new file mode 100644 index 00000000..d6cbfcf5 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/AddOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class AddOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsAddOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = AddOptions() + x.Init(buf, n + offset) + return x + + # AddOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # AddOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def AddOptionsStart(builder): builder.StartObject(1) +def AddOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) +def AddOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ArgMaxOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ArgMaxOptions.py new file mode 100644 index 00000000..fbf1415e --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/ArgMaxOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ArgMaxOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsArgMaxOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ArgMaxOptions() + x.Init(buf, n + offset) + return x + + # ArgMaxOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # ArgMaxOptions + def OutputType(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def ArgMaxOptionsStart(builder): builder.StartObject(1) +def ArgMaxOptionsAddOutputType(builder, outputType): builder.PrependInt8Slot(0, outputType, 0) +def ArgMaxOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ArgMinOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ArgMinOptions.py new file mode 100644 index 00000000..120fdca2 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/ArgMinOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ArgMinOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsArgMinOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ArgMinOptions() + x.Init(buf, n + offset) + return x + + # ArgMinOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # ArgMinOptions + def OutputType(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def ArgMinOptionsStart(builder): builder.StartObject(1) +def ArgMinOptionsAddOutputType(builder, outputType): builder.PrependInt8Slot(0, outputType, 0) +def ArgMinOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/BatchToSpaceNDOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/BatchToSpaceNDOptions.py new file mode 100644 index 00000000..3ddcfd3f --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/BatchToSpaceNDOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class BatchToSpaceNDOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsBatchToSpaceNDOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = BatchToSpaceNDOptions() + x.Init(buf, n + offset) + return x + + # BatchToSpaceNDOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def BatchToSpaceNDOptionsStart(builder): builder.StartObject(0) +def BatchToSpaceNDOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/BidirectionalSequenceLSTMOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/BidirectionalSequenceLSTMOptions.py new file mode 100644 index 00000000..8d8b7bea --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/BidirectionalSequenceLSTMOptions.py @@ -0,0 +1,62 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class BidirectionalSequenceLSTMOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsBidirectionalSequenceLSTMOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = BidirectionalSequenceLSTMOptions() + x.Init(buf, n + offset) + return x + + # BidirectionalSequenceLSTMOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # BidirectionalSequenceLSTMOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # BidirectionalSequenceLSTMOptions + def CellClip(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + + # BidirectionalSequenceLSTMOptions + def ProjClip(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + + # BidirectionalSequenceLSTMOptions + def MergeOutputs(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + + # BidirectionalSequenceLSTMOptions + def TimeMajor(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return True + +def BidirectionalSequenceLSTMOptionsStart(builder): builder.StartObject(5) +def BidirectionalSequenceLSTMOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) +def BidirectionalSequenceLSTMOptionsAddCellClip(builder, cellClip): builder.PrependFloat32Slot(1, cellClip, 0.0) +def BidirectionalSequenceLSTMOptionsAddProjClip(builder, projClip): builder.PrependFloat32Slot(2, projClip, 0.0) +def BidirectionalSequenceLSTMOptionsAddMergeOutputs(builder, mergeOutputs): builder.PrependBoolSlot(3, mergeOutputs, 0) +def BidirectionalSequenceLSTMOptionsAddTimeMajor(builder, timeMajor): builder.PrependBoolSlot(4, timeMajor, 1) +def BidirectionalSequenceLSTMOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/BidirectionalSequenceRNNOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/BidirectionalSequenceRNNOptions.py new file mode 100644 index 00000000..673af6b9 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/BidirectionalSequenceRNNOptions.py @@ -0,0 +1,46 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class BidirectionalSequenceRNNOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsBidirectionalSequenceRNNOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = BidirectionalSequenceRNNOptions() + x.Init(buf, n + offset) + return x + + # BidirectionalSequenceRNNOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # BidirectionalSequenceRNNOptions + def TimeMajor(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + + # BidirectionalSequenceRNNOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # BidirectionalSequenceRNNOptions + def MergeOutputs(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + +def BidirectionalSequenceRNNOptionsStart(builder): builder.StartObject(3) +def BidirectionalSequenceRNNOptionsAddTimeMajor(builder, timeMajor): builder.PrependBoolSlot(0, timeMajor, 0) +def BidirectionalSequenceRNNOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(1, fusedActivationFunction, 0) +def BidirectionalSequenceRNNOptionsAddMergeOutputs(builder, mergeOutputs): builder.PrependBoolSlot(2, mergeOutputs, 0) +def BidirectionalSequenceRNNOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/Buffer.py b/utensor_cgen/frontend/tflite_flatbuffer/Buffer.py new file mode 100644 index 00000000..754dee3b --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/Buffer.py @@ -0,0 +1,46 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class Buffer(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsBuffer(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = Buffer() + x.Init(buf, n + offset) + return x + + # Buffer + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # Buffer + def Data(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Uint8Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1)) + return 0 + + # Buffer + def DataAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint8Flags, o) + return 0 + + # Buffer + def DataLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + +def BufferStart(builder): builder.StartObject(1) +def BufferAddData(builder, data): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(data), 0) +def BufferStartDataVector(builder, numElems): return builder.StartVector(1, numElems, 1) +def BufferEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/BuiltinOperator.py b/utensor_cgen/frontend/tflite_flatbuffer/BuiltinOperator.py new file mode 100644 index 00000000..229e1270 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/BuiltinOperator.py @@ -0,0 +1,121 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class BuiltinOperator(object): + ADD = 0 + AVERAGE_POOL_2D = 1 + CONCATENATION = 2 + CONV_2D = 3 + DEPTHWISE_CONV_2D = 4 + DEQUANTIZE = 6 + EMBEDDING_LOOKUP = 7 + FLOOR = 8 + FULLY_CONNECTED = 9 + HASHTABLE_LOOKUP = 10 + L2_NORMALIZATION = 11 + L2_POOL_2D = 12 + LOCAL_RESPONSE_NORMALIZATION = 13 + LOGISTIC = 14 + LSH_PROJECTION = 15 + LSTM = 16 + MAX_POOL_2D = 17 + MUL = 18 + RELU = 19 + RELU_N1_TO_1 = 20 + RELU6 = 21 + RESHAPE = 22 + RESIZE_BILINEAR = 23 + RNN = 24 + SOFTMAX = 25 + SPACE_TO_DEPTH = 26 + SVDF = 27 + TANH = 28 + CONCAT_EMBEDDINGS = 29 + SKIP_GRAM = 30 + CALL = 31 + CUSTOM = 32 + EMBEDDING_LOOKUP_SPARSE = 33 + PAD = 34 + UNIDIRECTIONAL_SEQUENCE_RNN = 35 + GATHER = 36 + BATCH_TO_SPACE_ND = 37 + SPACE_TO_BATCH_ND = 38 + TRANSPOSE = 39 + MEAN = 40 + SUB = 41 + DIV = 42 + SQUEEZE = 43 + UNIDIRECTIONAL_SEQUENCE_LSTM = 44 + STRIDED_SLICE = 45 + BIDIRECTIONAL_SEQUENCE_RNN = 46 + EXP = 47 + TOPK_V2 = 48 + SPLIT = 49 + LOG_SOFTMAX = 50 + DELEGATE = 51 + BIDIRECTIONAL_SEQUENCE_LSTM = 52 + CAST = 53 + PRELU = 54 + MAXIMUM = 55 + ARG_MAX = 56 + MINIMUM = 57 + LESS = 58 + NEG = 59 + PADV2 = 60 + GREATER = 61 + GREATER_EQUAL = 62 + LESS_EQUAL = 63 + SELECT = 64 + SLICE = 65 + SIN = 66 + TRANSPOSE_CONV = 67 + SPARSE_TO_DENSE = 68 + TILE = 69 + EXPAND_DIMS = 70 + EQUAL = 71 + NOT_EQUAL = 72 + LOG = 73 + SUM = 74 + SQRT = 75 + RSQRT = 76 + SHAPE = 77 + POW = 78 + ARG_MIN = 79 + FAKE_QUANT = 80 + REDUCE_PROD = 81 + REDUCE_MAX = 82 + PACK = 83 + LOGICAL_OR = 84 + ONE_HOT = 85 + LOGICAL_AND = 86 + LOGICAL_NOT = 87 + UNPACK = 88 + REDUCE_MIN = 89 + FLOOR_DIV = 90 + REDUCE_ANY = 91 + SQUARE = 92 + ZEROS_LIKE = 93 + FILL = 94 + FLOOR_MOD = 95 + RANGE = 96 + RESIZE_NEAREST_NEIGHBOR = 97 + LEAKY_RELU = 98 + SQUARED_DIFFERENCE = 99 + MIRROR_PAD = 100 + ABS = 101 + SPLIT_V = 102 + UNIQUE = 103 + CEIL = 104 + REVERSE_V2 = 105 + ADD_N = 106 + GATHER_ND = 107 + COS = 108 + WHERE = 109 + RANK = 110 + ELU = 111 + REVERSE_SEQUENCE = 112 + MATRIX_DIAG = 113 + QUANTIZE = 114 + MATRIX_SET_DIAG = 115 + diff --git a/utensor_cgen/frontend/tflite_flatbuffer/BuiltinOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/BuiltinOptions.py new file mode 100644 index 00000000..9830f648 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/BuiltinOptions.py @@ -0,0 +1,97 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class BuiltinOptions(object): + NONE = 0 + Conv2DOptions = 1 + DepthwiseConv2DOptions = 2 + ConcatEmbeddingsOptions = 3 + LSHProjectionOptions = 4 + Pool2DOptions = 5 + SVDFOptions = 6 + RNNOptions = 7 + FullyConnectedOptions = 8 + SoftmaxOptions = 9 + ConcatenationOptions = 10 + AddOptions = 11 + L2NormOptions = 12 + LocalResponseNormalizationOptions = 13 + LSTMOptions = 14 + ResizeBilinearOptions = 15 + CallOptions = 16 + ReshapeOptions = 17 + SkipGramOptions = 18 + SpaceToDepthOptions = 19 + EmbeddingLookupSparseOptions = 20 + MulOptions = 21 + PadOptions = 22 + GatherOptions = 23 + BatchToSpaceNDOptions = 24 + SpaceToBatchNDOptions = 25 + TransposeOptions = 26 + ReducerOptions = 27 + SubOptions = 28 + DivOptions = 29 + SqueezeOptions = 30 + SequenceRNNOptions = 31 + StridedSliceOptions = 32 + ExpOptions = 33 + TopKV2Options = 34 + SplitOptions = 35 + LogSoftmaxOptions = 36 + CastOptions = 37 + DequantizeOptions = 38 + MaximumMinimumOptions = 39 + ArgMaxOptions = 40 + LessOptions = 41 + NegOptions = 42 + PadV2Options = 43 + GreaterOptions = 44 + GreaterEqualOptions = 45 + LessEqualOptions = 46 + SelectOptions = 47 + SliceOptions = 48 + TransposeConvOptions = 49 + SparseToDenseOptions = 50 + TileOptions = 51 + ExpandDimsOptions = 52 + EqualOptions = 53 + NotEqualOptions = 54 + ShapeOptions = 55 + PowOptions = 56 + ArgMinOptions = 57 + FakeQuantOptions = 58 + PackOptions = 59 + LogicalOrOptions = 60 + OneHotOptions = 61 + LogicalAndOptions = 62 + LogicalNotOptions = 63 + UnpackOptions = 64 + FloorDivOptions = 65 + SquareOptions = 66 + ZerosLikeOptions = 67 + FillOptions = 68 + BidirectionalSequenceLSTMOptions = 69 + BidirectionalSequenceRNNOptions = 70 + UnidirectionalSequenceLSTMOptions = 71 + FloorModOptions = 72 + RangeOptions = 73 + ResizeNearestNeighborOptions = 74 + LeakyReluOptions = 75 + SquaredDifferenceOptions = 76 + MirrorPadOptions = 77 + AbsOptions = 78 + SplitVOptions = 79 + UniqueOptions = 80 + ReverseV2Options = 81 + AddNOptions = 82 + GatherNdOptions = 83 + CosOptions = 84 + WhereOptions = 85 + RankOptions = 86 + ReverseSequenceOptions = 87 + MatrixDiagOptions = 88 + QuantizeOptions = 89 + MatrixSetDiagOptions = 90 + diff --git a/utensor_cgen/frontend/tflite_flatbuffer/CallOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/CallOptions.py new file mode 100644 index 00000000..5ae2eeae --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/CallOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class CallOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsCallOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = CallOptions() + x.Init(buf, n + offset) + return x + + # CallOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # CallOptions + def Subgraph(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos) + return 0 + +def CallOptionsStart(builder): builder.StartObject(1) +def CallOptionsAddSubgraph(builder, subgraph): builder.PrependUint32Slot(0, subgraph, 0) +def CallOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/CastOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/CastOptions.py new file mode 100644 index 00000000..70ae2e37 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/CastOptions.py @@ -0,0 +1,38 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class CastOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsCastOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = CastOptions() + x.Init(buf, n + offset) + return x + + # CastOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # CastOptions + def InDataType(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # CastOptions + def OutDataType(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def CastOptionsStart(builder): builder.StartObject(2) +def CastOptionsAddInDataType(builder, inDataType): builder.PrependInt8Slot(0, inDataType, 0) +def CastOptionsAddOutDataType(builder, outDataType): builder.PrependInt8Slot(1, outDataType, 0) +def CastOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/CombinerType.py b/utensor_cgen/frontend/tflite_flatbuffer/CombinerType.py new file mode 100644 index 00000000..17d91510 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/CombinerType.py @@ -0,0 +1,9 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class CombinerType(object): + SUM = 0 + MEAN = 1 + SQRTN = 2 + diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ConcatEmbeddingsOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ConcatEmbeddingsOptions.py new file mode 100644 index 00000000..9d26c510 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/ConcatEmbeddingsOptions.py @@ -0,0 +1,78 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ConcatEmbeddingsOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsConcatEmbeddingsOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ConcatEmbeddingsOptions() + x.Init(buf, n + offset) + return x + + # ConcatEmbeddingsOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # ConcatEmbeddingsOptions + def NumChannels(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # ConcatEmbeddingsOptions + def NumColumnsPerChannel(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # ConcatEmbeddingsOptions + def NumColumnsPerChannelAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) + return 0 + + # ConcatEmbeddingsOptions + def NumColumnsPerChannelLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # ConcatEmbeddingsOptions + def EmbeddingDimPerChannel(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # ConcatEmbeddingsOptions + def EmbeddingDimPerChannelAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) + return 0 + + # ConcatEmbeddingsOptions + def EmbeddingDimPerChannelLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + +def ConcatEmbeddingsOptionsStart(builder): builder.StartObject(3) +def ConcatEmbeddingsOptionsAddNumChannels(builder, numChannels): builder.PrependInt32Slot(0, numChannels, 0) +def ConcatEmbeddingsOptionsAddNumColumnsPerChannel(builder, numColumnsPerChannel): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(numColumnsPerChannel), 0) +def ConcatEmbeddingsOptionsStartNumColumnsPerChannelVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def ConcatEmbeddingsOptionsAddEmbeddingDimPerChannel(builder, embeddingDimPerChannel): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(embeddingDimPerChannel), 0) +def ConcatEmbeddingsOptionsStartEmbeddingDimPerChannelVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def ConcatEmbeddingsOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ConcatenationOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ConcatenationOptions.py new file mode 100644 index 00000000..c8e0b6ab --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/ConcatenationOptions.py @@ -0,0 +1,38 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ConcatenationOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsConcatenationOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ConcatenationOptions() + x.Init(buf, n + offset) + return x + + # ConcatenationOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # ConcatenationOptions + def Axis(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # ConcatenationOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def ConcatenationOptionsStart(builder): builder.StartObject(2) +def ConcatenationOptionsAddAxis(builder, axis): builder.PrependInt32Slot(0, axis, 0) +def ConcatenationOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(1, fusedActivationFunction, 0) +def ConcatenationOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/Conv2DOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/Conv2DOptions.py new file mode 100644 index 00000000..ef49f751 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/Conv2DOptions.py @@ -0,0 +1,70 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class Conv2DOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsConv2DOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = Conv2DOptions() + x.Init(buf, n + offset) + return x + + # Conv2DOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # Conv2DOptions + def Padding(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # Conv2DOptions + def StrideW(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # Conv2DOptions + def StrideH(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # Conv2DOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # Conv2DOptions + def DilationWFactor(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 1 + + # Conv2DOptions + def DilationHFactor(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 1 + +def Conv2DOptionsStart(builder): builder.StartObject(6) +def Conv2DOptionsAddPadding(builder, padding): builder.PrependInt8Slot(0, padding, 0) +def Conv2DOptionsAddStrideW(builder, strideW): builder.PrependInt32Slot(1, strideW, 0) +def Conv2DOptionsAddStrideH(builder, strideH): builder.PrependInt32Slot(2, strideH, 0) +def Conv2DOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(3, fusedActivationFunction, 0) +def Conv2DOptionsAddDilationWFactor(builder, dilationWFactor): builder.PrependInt32Slot(4, dilationWFactor, 1) +def Conv2DOptionsAddDilationHFactor(builder, dilationHFactor): builder.PrependInt32Slot(5, dilationHFactor, 1) +def Conv2DOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/CosOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/CosOptions.py new file mode 100644 index 00000000..7fbf8487 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/CosOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class CosOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsCosOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = CosOptions() + x.Init(buf, n + offset) + return x + + # CosOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def CosOptionsStart(builder): builder.StartObject(0) +def CosOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/CustomOptionsFormat.py b/utensor_cgen/frontend/tflite_flatbuffer/CustomOptionsFormat.py new file mode 100644 index 00000000..bef384cf --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/CustomOptionsFormat.py @@ -0,0 +1,7 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class CustomOptionsFormat(object): + FLEXBUFFERS = 0 + diff --git a/utensor_cgen/frontend/tflite_flatbuffer/CustomQuantization.py b/utensor_cgen/frontend/tflite_flatbuffer/CustomQuantization.py new file mode 100644 index 00000000..21ec0da4 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/CustomQuantization.py @@ -0,0 +1,46 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class CustomQuantization(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsCustomQuantization(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = CustomQuantization() + x.Init(buf, n + offset) + return x + + # CustomQuantization + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # CustomQuantization + def Custom(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Uint8Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1)) + return 0 + + # CustomQuantization + def CustomAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint8Flags, o) + return 0 + + # CustomQuantization + def CustomLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + +def CustomQuantizationStart(builder): builder.StartObject(1) +def CustomQuantizationAddCustom(builder, custom): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(custom), 0) +def CustomQuantizationStartCustomVector(builder, numElems): return builder.StartVector(1, numElems, 1) +def CustomQuantizationEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/DepthwiseConv2DOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/DepthwiseConv2DOptions.py new file mode 100644 index 00000000..9689383b --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/DepthwiseConv2DOptions.py @@ -0,0 +1,78 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class DepthwiseConv2DOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsDepthwiseConv2DOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = DepthwiseConv2DOptions() + x.Init(buf, n + offset) + return x + + # DepthwiseConv2DOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # DepthwiseConv2DOptions + def Padding(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # DepthwiseConv2DOptions + def StrideW(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # DepthwiseConv2DOptions + def StrideH(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # DepthwiseConv2DOptions + def DepthMultiplier(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # DepthwiseConv2DOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # DepthwiseConv2DOptions + def DilationWFactor(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 1 + + # DepthwiseConv2DOptions + def DilationHFactor(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 1 + +def DepthwiseConv2DOptionsStart(builder): builder.StartObject(7) +def DepthwiseConv2DOptionsAddPadding(builder, padding): builder.PrependInt8Slot(0, padding, 0) +def DepthwiseConv2DOptionsAddStrideW(builder, strideW): builder.PrependInt32Slot(1, strideW, 0) +def DepthwiseConv2DOptionsAddStrideH(builder, strideH): builder.PrependInt32Slot(2, strideH, 0) +def DepthwiseConv2DOptionsAddDepthMultiplier(builder, depthMultiplier): builder.PrependInt32Slot(3, depthMultiplier, 0) +def DepthwiseConv2DOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(4, fusedActivationFunction, 0) +def DepthwiseConv2DOptionsAddDilationWFactor(builder, dilationWFactor): builder.PrependInt32Slot(5, dilationWFactor, 1) +def DepthwiseConv2DOptionsAddDilationHFactor(builder, dilationHFactor): builder.PrependInt32Slot(6, dilationHFactor, 1) +def DepthwiseConv2DOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/DequantizeOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/DequantizeOptions.py new file mode 100644 index 00000000..5ef8b8dd --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/DequantizeOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class DequantizeOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsDequantizeOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = DequantizeOptions() + x.Init(buf, n + offset) + return x + + # DequantizeOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def DequantizeOptionsStart(builder): builder.StartObject(0) +def DequantizeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/DivOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/DivOptions.py new file mode 100644 index 00000000..905a3be0 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/DivOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class DivOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsDivOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = DivOptions() + x.Init(buf, n + offset) + return x + + # DivOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # DivOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def DivOptionsStart(builder): builder.StartObject(1) +def DivOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) +def DivOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/EmbeddingLookupSparseOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/EmbeddingLookupSparseOptions.py new file mode 100644 index 00000000..7d9c1442 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/EmbeddingLookupSparseOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class EmbeddingLookupSparseOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsEmbeddingLookupSparseOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = EmbeddingLookupSparseOptions() + x.Init(buf, n + offset) + return x + + # EmbeddingLookupSparseOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # EmbeddingLookupSparseOptions + def Combiner(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def EmbeddingLookupSparseOptionsStart(builder): builder.StartObject(1) +def EmbeddingLookupSparseOptionsAddCombiner(builder, combiner): builder.PrependInt8Slot(0, combiner, 0) +def EmbeddingLookupSparseOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/EqualOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/EqualOptions.py new file mode 100644 index 00000000..f787ef85 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/EqualOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class EqualOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsEqualOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = EqualOptions() + x.Init(buf, n + offset) + return x + + # EqualOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def EqualOptionsStart(builder): builder.StartObject(0) +def EqualOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ExpOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ExpOptions.py new file mode 100644 index 00000000..eac1456e --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/ExpOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ExpOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsExpOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ExpOptions() + x.Init(buf, n + offset) + return x + + # ExpOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def ExpOptionsStart(builder): builder.StartObject(0) +def ExpOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ExpandDimsOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ExpandDimsOptions.py new file mode 100644 index 00000000..69d63665 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/ExpandDimsOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ExpandDimsOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsExpandDimsOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ExpandDimsOptions() + x.Init(buf, n + offset) + return x + + # ExpandDimsOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def ExpandDimsOptionsStart(builder): builder.StartObject(0) +def ExpandDimsOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/FakeQuantOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/FakeQuantOptions.py new file mode 100644 index 00000000..46c371c3 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/FakeQuantOptions.py @@ -0,0 +1,54 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class FakeQuantOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsFakeQuantOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = FakeQuantOptions() + x.Init(buf, n + offset) + return x + + # FakeQuantOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # FakeQuantOptions + def Min(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + + # FakeQuantOptions + def Max(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + + # FakeQuantOptions + def NumBits(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # FakeQuantOptions + def NarrowRange(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + +def FakeQuantOptionsStart(builder): builder.StartObject(4) +def FakeQuantOptionsAddMin(builder, min): builder.PrependFloat32Slot(0, min, 0.0) +def FakeQuantOptionsAddMax(builder, max): builder.PrependFloat32Slot(1, max, 0.0) +def FakeQuantOptionsAddNumBits(builder, numBits): builder.PrependInt32Slot(2, numBits, 0) +def FakeQuantOptionsAddNarrowRange(builder, narrowRange): builder.PrependBoolSlot(3, narrowRange, 0) +def FakeQuantOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/FillOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/FillOptions.py new file mode 100644 index 00000000..5a1e651a --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/FillOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class FillOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsFillOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = FillOptions() + x.Init(buf, n + offset) + return x + + # FillOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def FillOptionsStart(builder): builder.StartObject(0) +def FillOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/FloorDivOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/FloorDivOptions.py new file mode 100644 index 00000000..64b474fb --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/FloorDivOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class FloorDivOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsFloorDivOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = FloorDivOptions() + x.Init(buf, n + offset) + return x + + # FloorDivOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def FloorDivOptionsStart(builder): builder.StartObject(0) +def FloorDivOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/FloorModOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/FloorModOptions.py new file mode 100644 index 00000000..37c8e5a5 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/FloorModOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class FloorModOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsFloorModOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = FloorModOptions() + x.Init(buf, n + offset) + return x + + # FloorModOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def FloorModOptionsStart(builder): builder.StartObject(0) +def FloorModOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/FullyConnectedOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/FullyConnectedOptions.py new file mode 100644 index 00000000..fb7ebc28 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/FullyConnectedOptions.py @@ -0,0 +1,38 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class FullyConnectedOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsFullyConnectedOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = FullyConnectedOptions() + x.Init(buf, n + offset) + return x + + # FullyConnectedOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # FullyConnectedOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # FullyConnectedOptions + def WeightsFormat(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def FullyConnectedOptionsStart(builder): builder.StartObject(2) +def FullyConnectedOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) +def FullyConnectedOptionsAddWeightsFormat(builder, weightsFormat): builder.PrependInt8Slot(1, weightsFormat, 0) +def FullyConnectedOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/FullyConnectedOptionsWeightsFormat.py b/utensor_cgen/frontend/tflite_flatbuffer/FullyConnectedOptionsWeightsFormat.py new file mode 100644 index 00000000..6d81eca1 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/FullyConnectedOptionsWeightsFormat.py @@ -0,0 +1,8 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class FullyConnectedOptionsWeightsFormat(object): + DEFAULT = 0 + SHUFFLED4x16INT8 = 1 + diff --git a/utensor_cgen/frontend/tflite_flatbuffer/GatherNdOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/GatherNdOptions.py new file mode 100644 index 00000000..f515eb5c --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/GatherNdOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class GatherNdOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsGatherNdOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = GatherNdOptions() + x.Init(buf, n + offset) + return x + + # GatherNdOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def GatherNdOptionsStart(builder): builder.StartObject(0) +def GatherNdOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/GatherOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/GatherOptions.py new file mode 100644 index 00000000..9fbc3e40 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/GatherOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class GatherOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsGatherOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = GatherOptions() + x.Init(buf, n + offset) + return x + + # GatherOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # GatherOptions + def Axis(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def GatherOptionsStart(builder): builder.StartObject(1) +def GatherOptionsAddAxis(builder, axis): builder.PrependInt32Slot(0, axis, 0) +def GatherOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/GreaterEqualOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/GreaterEqualOptions.py new file mode 100644 index 00000000..a29e200a --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/GreaterEqualOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class GreaterEqualOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsGreaterEqualOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = GreaterEqualOptions() + x.Init(buf, n + offset) + return x + + # GreaterEqualOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def GreaterEqualOptionsStart(builder): builder.StartObject(0) +def GreaterEqualOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/GreaterOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/GreaterOptions.py new file mode 100644 index 00000000..59d63501 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/GreaterOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class GreaterOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsGreaterOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = GreaterOptions() + x.Init(buf, n + offset) + return x + + # GreaterOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def GreaterOptionsStart(builder): builder.StartObject(0) +def GreaterOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/L2NormOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/L2NormOptions.py new file mode 100644 index 00000000..38bdf573 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/L2NormOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class L2NormOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsL2NormOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = L2NormOptions() + x.Init(buf, n + offset) + return x + + # L2NormOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # L2NormOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def L2NormOptionsStart(builder): builder.StartObject(1) +def L2NormOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) +def L2NormOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LSHProjectionOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/LSHProjectionOptions.py new file mode 100644 index 00000000..ad550be2 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/LSHProjectionOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class LSHProjectionOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLSHProjectionOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LSHProjectionOptions() + x.Init(buf, n + offset) + return x + + # LSHProjectionOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # LSHProjectionOptions + def Type(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def LSHProjectionOptionsStart(builder): builder.StartObject(1) +def LSHProjectionOptionsAddType(builder, type): builder.PrependInt8Slot(0, type, 0) +def LSHProjectionOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LSHProjectionType.py b/utensor_cgen/frontend/tflite_flatbuffer/LSHProjectionType.py new file mode 100644 index 00000000..d6a136e8 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/LSHProjectionType.py @@ -0,0 +1,9 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class LSHProjectionType(object): + UNKNOWN = 0 + SPARSE = 1 + DENSE = 2 + diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LSTMKernelType.py b/utensor_cgen/frontend/tflite_flatbuffer/LSTMKernelType.py new file mode 100644 index 00000000..1ca4df0a --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/LSTMKernelType.py @@ -0,0 +1,8 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class LSTMKernelType(object): + FULL = 0 + BASIC = 1 + diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LSTMOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/LSTMOptions.py new file mode 100644 index 00000000..93a83093 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/LSTMOptions.py @@ -0,0 +1,54 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class LSTMOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLSTMOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LSTMOptions() + x.Init(buf, n + offset) + return x + + # LSTMOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # LSTMOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # LSTMOptions + def CellClip(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + + # LSTMOptions + def ProjClip(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + + # LSTMOptions + def KernelType(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def LSTMOptionsStart(builder): builder.StartObject(4) +def LSTMOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) +def LSTMOptionsAddCellClip(builder, cellClip): builder.PrependFloat32Slot(1, cellClip, 0.0) +def LSTMOptionsAddProjClip(builder, projClip): builder.PrependFloat32Slot(2, projClip, 0.0) +def LSTMOptionsAddKernelType(builder, kernelType): builder.PrependInt8Slot(3, kernelType, 0) +def LSTMOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LeakyReluOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/LeakyReluOptions.py new file mode 100644 index 00000000..b61b21d5 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/LeakyReluOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class LeakyReluOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLeakyReluOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LeakyReluOptions() + x.Init(buf, n + offset) + return x + + # LeakyReluOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # LeakyReluOptions + def Alpha(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + +def LeakyReluOptionsStart(builder): builder.StartObject(1) +def LeakyReluOptionsAddAlpha(builder, alpha): builder.PrependFloat32Slot(0, alpha, 0.0) +def LeakyReluOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LessEqualOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/LessEqualOptions.py new file mode 100644 index 00000000..d49b7289 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/LessEqualOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class LessEqualOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLessEqualOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LessEqualOptions() + x.Init(buf, n + offset) + return x + + # LessEqualOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def LessEqualOptionsStart(builder): builder.StartObject(0) +def LessEqualOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LessOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/LessOptions.py new file mode 100644 index 00000000..469cb0b0 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/LessOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class LessOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLessOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LessOptions() + x.Init(buf, n + offset) + return x + + # LessOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def LessOptionsStart(builder): builder.StartObject(0) +def LessOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LocalResponseNormalizationOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/LocalResponseNormalizationOptions.py new file mode 100644 index 00000000..db875603 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/LocalResponseNormalizationOptions.py @@ -0,0 +1,54 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class LocalResponseNormalizationOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLocalResponseNormalizationOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LocalResponseNormalizationOptions() + x.Init(buf, n + offset) + return x + + # LocalResponseNormalizationOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # LocalResponseNormalizationOptions + def Radius(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # LocalResponseNormalizationOptions + def Bias(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + + # LocalResponseNormalizationOptions + def Alpha(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + + # LocalResponseNormalizationOptions + def Beta(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + +def LocalResponseNormalizationOptionsStart(builder): builder.StartObject(4) +def LocalResponseNormalizationOptionsAddRadius(builder, radius): builder.PrependInt32Slot(0, radius, 0) +def LocalResponseNormalizationOptionsAddBias(builder, bias): builder.PrependFloat32Slot(1, bias, 0.0) +def LocalResponseNormalizationOptionsAddAlpha(builder, alpha): builder.PrependFloat32Slot(2, alpha, 0.0) +def LocalResponseNormalizationOptionsAddBeta(builder, beta): builder.PrependFloat32Slot(3, beta, 0.0) +def LocalResponseNormalizationOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LogSoftmaxOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/LogSoftmaxOptions.py new file mode 100644 index 00000000..47893855 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/LogSoftmaxOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class LogSoftmaxOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLogSoftmaxOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LogSoftmaxOptions() + x.Init(buf, n + offset) + return x + + # LogSoftmaxOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def LogSoftmaxOptionsStart(builder): builder.StartObject(0) +def LogSoftmaxOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LogicalAndOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/LogicalAndOptions.py new file mode 100644 index 00000000..cee1cdb4 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/LogicalAndOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class LogicalAndOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLogicalAndOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LogicalAndOptions() + x.Init(buf, n + offset) + return x + + # LogicalAndOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def LogicalAndOptionsStart(builder): builder.StartObject(0) +def LogicalAndOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LogicalNotOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/LogicalNotOptions.py new file mode 100644 index 00000000..9971450c --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/LogicalNotOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class LogicalNotOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLogicalNotOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LogicalNotOptions() + x.Init(buf, n + offset) + return x + + # LogicalNotOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def LogicalNotOptionsStart(builder): builder.StartObject(0) +def LogicalNotOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LogicalOrOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/LogicalOrOptions.py new file mode 100644 index 00000000..e94a5dec --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/LogicalOrOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class LogicalOrOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLogicalOrOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LogicalOrOptions() + x.Init(buf, n + offset) + return x + + # LogicalOrOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def LogicalOrOptionsStart(builder): builder.StartObject(0) +def LogicalOrOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/MatrixDiagOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/MatrixDiagOptions.py new file mode 100644 index 00000000..0f64e657 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/MatrixDiagOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class MatrixDiagOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsMatrixDiagOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = MatrixDiagOptions() + x.Init(buf, n + offset) + return x + + # MatrixDiagOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def MatrixDiagOptionsStart(builder): builder.StartObject(0) +def MatrixDiagOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/MatrixSetDiagOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/MatrixSetDiagOptions.py new file mode 100644 index 00000000..14178cf8 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/MatrixSetDiagOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class MatrixSetDiagOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsMatrixSetDiagOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = MatrixSetDiagOptions() + x.Init(buf, n + offset) + return x + + # MatrixSetDiagOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def MatrixSetDiagOptionsStart(builder): builder.StartObject(0) +def MatrixSetDiagOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/MaximumMinimumOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/MaximumMinimumOptions.py new file mode 100644 index 00000000..f0806e2d --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/MaximumMinimumOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class MaximumMinimumOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsMaximumMinimumOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = MaximumMinimumOptions() + x.Init(buf, n + offset) + return x + + # MaximumMinimumOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def MaximumMinimumOptionsStart(builder): builder.StartObject(0) +def MaximumMinimumOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/MirrorPadMode.py b/utensor_cgen/frontend/tflite_flatbuffer/MirrorPadMode.py new file mode 100644 index 00000000..ce7579d2 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/MirrorPadMode.py @@ -0,0 +1,8 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class MirrorPadMode(object): + REFLECT = 0 + SYMMETRIC = 1 + diff --git a/utensor_cgen/frontend/tflite_flatbuffer/MirrorPadOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/MirrorPadOptions.py new file mode 100644 index 00000000..254ae217 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/MirrorPadOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class MirrorPadOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsMirrorPadOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = MirrorPadOptions() + x.Init(buf, n + offset) + return x + + # MirrorPadOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # MirrorPadOptions + def Mode(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def MirrorPadOptionsStart(builder): builder.StartObject(1) +def MirrorPadOptionsAddMode(builder, mode): builder.PrependInt8Slot(0, mode, 0) +def MirrorPadOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/Model.py b/utensor_cgen/frontend/tflite_flatbuffer/Model.py new file mode 100644 index 00000000..aab841ab --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/Model.py @@ -0,0 +1,128 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class Model(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsModel(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = Model() + x.Init(buf, n + offset) + return x + + # Model + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # Model + def Version(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos) + return 0 + + # Model + def OperatorCodes(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + x = self._tab.Vector(o) + x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 + x = self._tab.Indirect(x) + from .OperatorCode import OperatorCode + obj = OperatorCode() + obj.Init(self._tab.Bytes, x) + return obj + return None + + # Model + def OperatorCodesLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # Model + def Subgraphs(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + x = self._tab.Vector(o) + x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 + x = self._tab.Indirect(x) + from .SubGraph import SubGraph + obj = SubGraph() + obj.Init(self._tab.Bytes, x) + return obj + return None + + # Model + def SubgraphsLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # Model + def Description(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.String(o + self._tab.Pos) + return None + + # Model + def Buffers(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + x = self._tab.Vector(o) + x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 + x = self._tab.Indirect(x) + from .Buffer import Buffer + obj = Buffer() + obj.Init(self._tab.Bytes, x) + return obj + return None + + # Model + def BuffersLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # Model + def MetadataBuffer(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # Model + def MetadataBufferAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) + return 0 + + # Model + def MetadataBufferLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + +def ModelStart(builder): builder.StartObject(6) +def ModelAddVersion(builder, version): builder.PrependUint32Slot(0, version, 0) +def ModelAddOperatorCodes(builder, operatorCodes): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(operatorCodes), 0) +def ModelStartOperatorCodesVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def ModelAddSubgraphs(builder, subgraphs): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(subgraphs), 0) +def ModelStartSubgraphsVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def ModelAddDescription(builder, description): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(description), 0) +def ModelAddBuffers(builder, buffers): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(buffers), 0) +def ModelStartBuffersVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def ModelAddMetadataBuffer(builder, metadataBuffer): builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(metadataBuffer), 0) +def ModelStartMetadataBufferVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def ModelEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/MulOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/MulOptions.py new file mode 100644 index 00000000..55b9506f --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/MulOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class MulOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsMulOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = MulOptions() + x.Init(buf, n + offset) + return x + + # MulOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # MulOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def MulOptionsStart(builder): builder.StartObject(1) +def MulOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) +def MulOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/NegOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/NegOptions.py new file mode 100644 index 00000000..05d55c26 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/NegOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class NegOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsNegOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = NegOptions() + x.Init(buf, n + offset) + return x + + # NegOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def NegOptionsStart(builder): builder.StartObject(0) +def NegOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/NotEqualOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/NotEqualOptions.py new file mode 100644 index 00000000..4c511e93 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/NotEqualOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class NotEqualOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsNotEqualOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = NotEqualOptions() + x.Init(buf, n + offset) + return x + + # NotEqualOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def NotEqualOptionsStart(builder): builder.StartObject(0) +def NotEqualOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/OneHotOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/OneHotOptions.py new file mode 100644 index 00000000..793a3e75 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/OneHotOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class OneHotOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsOneHotOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = OneHotOptions() + x.Init(buf, n + offset) + return x + + # OneHotOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # OneHotOptions + def Axis(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def OneHotOptionsStart(builder): builder.StartObject(1) +def OneHotOptionsAddAxis(builder, axis): builder.PrependInt32Slot(0, axis, 0) +def OneHotOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/Operator.py b/utensor_cgen/frontend/tflite_flatbuffer/Operator.py new file mode 100644 index 00000000..fd237845 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/Operator.py @@ -0,0 +1,153 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class Operator(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsOperator(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = Operator() + x.Init(buf, n + offset) + return x + + # Operator + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # Operator + def OpcodeIndex(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos) + return 0 + + # Operator + def Inputs(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # Operator + def InputsAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) + return 0 + + # Operator + def InputsLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # Operator + def Outputs(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # Operator + def OutputsAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) + return 0 + + # Operator + def OutputsLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # Operator + def BuiltinOptionsType(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Uint8Flags, o + self._tab.Pos) + return 0 + + # Operator + def BuiltinOptions(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + from flatbuffers.table import Table + obj = Table(bytearray(), 0) + self._tab.Union(obj, o) + return obj + return None + + # Operator + def CustomOptions(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Uint8Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1)) + return 0 + + # Operator + def CustomOptionsAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint8Flags, o) + return 0 + + # Operator + def CustomOptionsLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # Operator + def CustomOptionsFormat(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # Operator + def MutatingVariableInputs(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.BoolFlags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1)) + return 0 + + # Operator + def MutatingVariableInputsAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.BoolFlags, o) + return 0 + + # Operator + def MutatingVariableInputsLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + +def OperatorStart(builder): builder.StartObject(8) +def OperatorAddOpcodeIndex(builder, opcodeIndex): builder.PrependUint32Slot(0, opcodeIndex, 0) +def OperatorAddInputs(builder, inputs): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(inputs), 0) +def OperatorStartInputsVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def OperatorAddOutputs(builder, outputs): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(outputs), 0) +def OperatorStartOutputsVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def OperatorAddBuiltinOptionsType(builder, builtinOptionsType): builder.PrependUint8Slot(3, builtinOptionsType, 0) +def OperatorAddBuiltinOptions(builder, builtinOptions): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(builtinOptions), 0) +def OperatorAddCustomOptions(builder, customOptions): builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(customOptions), 0) +def OperatorStartCustomOptionsVector(builder, numElems): return builder.StartVector(1, numElems, 1) +def OperatorAddCustomOptionsFormat(builder, customOptionsFormat): builder.PrependInt8Slot(6, customOptionsFormat, 0) +def OperatorAddMutatingVariableInputs(builder, mutatingVariableInputs): builder.PrependUOffsetTRelativeSlot(7, flatbuffers.number_types.UOffsetTFlags.py_type(mutatingVariableInputs), 0) +def OperatorStartMutatingVariableInputsVector(builder, numElems): return builder.StartVector(1, numElems, 1) +def OperatorEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/OperatorCode.py b/utensor_cgen/frontend/tflite_flatbuffer/OperatorCode.py new file mode 100644 index 00000000..dd525f53 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/OperatorCode.py @@ -0,0 +1,46 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class OperatorCode(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsOperatorCode(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = OperatorCode() + x.Init(buf, n + offset) + return x + + # OperatorCode + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # OperatorCode + def BuiltinCode(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # OperatorCode + def CustomCode(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.String(o + self._tab.Pos) + return None + + # OperatorCode + def Version(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 1 + +def OperatorCodeStart(builder): builder.StartObject(3) +def OperatorCodeAddBuiltinCode(builder, builtinCode): builder.PrependInt8Slot(0, builtinCode, 0) +def OperatorCodeAddCustomCode(builder, customCode): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(customCode), 0) +def OperatorCodeAddVersion(builder, version): builder.PrependInt32Slot(2, version, 1) +def OperatorCodeEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/PackOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/PackOptions.py new file mode 100644 index 00000000..6a8ee2bb --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/PackOptions.py @@ -0,0 +1,38 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class PackOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsPackOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = PackOptions() + x.Init(buf, n + offset) + return x + + # PackOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # PackOptions + def ValuesCount(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # PackOptions + def Axis(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def PackOptionsStart(builder): builder.StartObject(2) +def PackOptionsAddValuesCount(builder, valuesCount): builder.PrependInt32Slot(0, valuesCount, 0) +def PackOptionsAddAxis(builder, axis): builder.PrependInt32Slot(1, axis, 0) +def PackOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/PadOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/PadOptions.py new file mode 100644 index 00000000..d0833c68 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/PadOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class PadOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsPadOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = PadOptions() + x.Init(buf, n + offset) + return x + + # PadOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def PadOptionsStart(builder): builder.StartObject(0) +def PadOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/PadV2Options.py b/utensor_cgen/frontend/tflite_flatbuffer/PadV2Options.py new file mode 100644 index 00000000..5ea0d70c --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/PadV2Options.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class PadV2Options(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsPadV2Options(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = PadV2Options() + x.Init(buf, n + offset) + return x + + # PadV2Options + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def PadV2OptionsStart(builder): builder.StartObject(0) +def PadV2OptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/Padding.py b/utensor_cgen/frontend/tflite_flatbuffer/Padding.py new file mode 100644 index 00000000..6027f4b5 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/Padding.py @@ -0,0 +1,8 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class Padding(object): + SAME = 0 + VALID = 1 + diff --git a/utensor_cgen/frontend/tflite_flatbuffer/Pool2DOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/Pool2DOptions.py new file mode 100644 index 00000000..b8b9f178 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/Pool2DOptions.py @@ -0,0 +1,70 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class Pool2DOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsPool2DOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = Pool2DOptions() + x.Init(buf, n + offset) + return x + + # Pool2DOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # Pool2DOptions + def Padding(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # Pool2DOptions + def StrideW(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # Pool2DOptions + def StrideH(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # Pool2DOptions + def FilterWidth(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # Pool2DOptions + def FilterHeight(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # Pool2DOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def Pool2DOptionsStart(builder): builder.StartObject(6) +def Pool2DOptionsAddPadding(builder, padding): builder.PrependInt8Slot(0, padding, 0) +def Pool2DOptionsAddStrideW(builder, strideW): builder.PrependInt32Slot(1, strideW, 0) +def Pool2DOptionsAddStrideH(builder, strideH): builder.PrependInt32Slot(2, strideH, 0) +def Pool2DOptionsAddFilterWidth(builder, filterWidth): builder.PrependInt32Slot(3, filterWidth, 0) +def Pool2DOptionsAddFilterHeight(builder, filterHeight): builder.PrependInt32Slot(4, filterHeight, 0) +def Pool2DOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(5, fusedActivationFunction, 0) +def Pool2DOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/PowOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/PowOptions.py new file mode 100644 index 00000000..666ca488 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/PowOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class PowOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsPowOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = PowOptions() + x.Init(buf, n + offset) + return x + + # PowOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def PowOptionsStart(builder): builder.StartObject(0) +def PowOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/QuantizationDetails.py b/utensor_cgen/frontend/tflite_flatbuffer/QuantizationDetails.py new file mode 100644 index 00000000..7159efbd --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/QuantizationDetails.py @@ -0,0 +1,8 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class QuantizationDetails(object): + NONE = 0 + CustomQuantization = 1 + diff --git a/utensor_cgen/frontend/tflite_flatbuffer/QuantizationParameters.py b/utensor_cgen/frontend/tflite_flatbuffer/QuantizationParameters.py new file mode 100644 index 00000000..fcd686cf --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/QuantizationParameters.py @@ -0,0 +1,145 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class QuantizationParameters(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsQuantizationParameters(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = QuantizationParameters() + x.Init(buf, n + offset) + return x + + # QuantizationParameters + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # QuantizationParameters + def Min(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # QuantizationParameters + def MinAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o) + return 0 + + # QuantizationParameters + def MinLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # QuantizationParameters + def Max(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # QuantizationParameters + def MaxAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o) + return 0 + + # QuantizationParameters + def MaxLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # QuantizationParameters + def Scale(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # QuantizationParameters + def ScaleAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o) + return 0 + + # QuantizationParameters + def ScaleLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # QuantizationParameters + def ZeroPoint(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8)) + return 0 + + # QuantizationParameters + def ZeroPointAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o) + return 0 + + # QuantizationParameters + def ZeroPointLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # QuantizationParameters + def DetailsType(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Uint8Flags, o + self._tab.Pos) + return 0 + + # QuantizationParameters + def Details(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + from flatbuffers.table import Table + obj = Table(bytearray(), 0) + self._tab.Union(obj, o) + return obj + return None + + # QuantizationParameters + def QuantizedDimension(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def QuantizationParametersStart(builder): builder.StartObject(7) +def QuantizationParametersAddMin(builder, min): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(min), 0) +def QuantizationParametersStartMinVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def QuantizationParametersAddMax(builder, max): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(max), 0) +def QuantizationParametersStartMaxVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def QuantizationParametersAddScale(builder, scale): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(scale), 0) +def QuantizationParametersStartScaleVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def QuantizationParametersAddZeroPoint(builder, zeroPoint): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(zeroPoint), 0) +def QuantizationParametersStartZeroPointVector(builder, numElems): return builder.StartVector(8, numElems, 8) +def QuantizationParametersAddDetailsType(builder, detailsType): builder.PrependUint8Slot(4, detailsType, 0) +def QuantizationParametersAddDetails(builder, details): builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(details), 0) +def QuantizationParametersAddQuantizedDimension(builder, quantizedDimension): builder.PrependInt32Slot(6, quantizedDimension, 0) +def QuantizationParametersEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/QuantizeOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/QuantizeOptions.py new file mode 100644 index 00000000..28af8cc9 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/QuantizeOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class QuantizeOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsQuantizeOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = QuantizeOptions() + x.Init(buf, n + offset) + return x + + # QuantizeOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def QuantizeOptionsStart(builder): builder.StartObject(0) +def QuantizeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/RNNOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/RNNOptions.py new file mode 100644 index 00000000..3cfdb6af --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/RNNOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class RNNOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsRNNOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = RNNOptions() + x.Init(buf, n + offset) + return x + + # RNNOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # RNNOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def RNNOptionsStart(builder): builder.StartObject(1) +def RNNOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) +def RNNOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/RangeOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/RangeOptions.py new file mode 100644 index 00000000..cb705b57 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/RangeOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class RangeOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsRangeOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = RangeOptions() + x.Init(buf, n + offset) + return x + + # RangeOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def RangeOptionsStart(builder): builder.StartObject(0) +def RangeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/RankOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/RankOptions.py new file mode 100644 index 00000000..4e4a5ecd --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/RankOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class RankOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsRankOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = RankOptions() + x.Init(buf, n + offset) + return x + + # RankOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def RankOptionsStart(builder): builder.StartObject(0) +def RankOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ReducerOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ReducerOptions.py new file mode 100644 index 00000000..93bbde17 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/ReducerOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ReducerOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsReducerOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ReducerOptions() + x.Init(buf, n + offset) + return x + + # ReducerOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # ReducerOptions + def KeepDims(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + +def ReducerOptionsStart(builder): builder.StartObject(1) +def ReducerOptionsAddKeepDims(builder, keepDims): builder.PrependBoolSlot(0, keepDims, 0) +def ReducerOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ReshapeOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ReshapeOptions.py new file mode 100644 index 00000000..157d45d9 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/ReshapeOptions.py @@ -0,0 +1,46 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ReshapeOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsReshapeOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ReshapeOptions() + x.Init(buf, n + offset) + return x + + # ReshapeOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # ReshapeOptions + def NewShape(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # ReshapeOptions + def NewShapeAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) + return 0 + + # ReshapeOptions + def NewShapeLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + +def ReshapeOptionsStart(builder): builder.StartObject(1) +def ReshapeOptionsAddNewShape(builder, newShape): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(newShape), 0) +def ReshapeOptionsStartNewShapeVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def ReshapeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ResizeBilinearOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ResizeBilinearOptions.py new file mode 100644 index 00000000..0d1f9624 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/ResizeBilinearOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ResizeBilinearOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsResizeBilinearOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ResizeBilinearOptions() + x.Init(buf, n + offset) + return x + + # ResizeBilinearOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # ResizeBilinearOptions + def AlignCorners(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + +def ResizeBilinearOptionsStart(builder): builder.StartObject(3) +def ResizeBilinearOptionsAddAlignCorners(builder, alignCorners): builder.PrependBoolSlot(2, alignCorners, 0) +def ResizeBilinearOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ResizeNearestNeighborOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ResizeNearestNeighborOptions.py new file mode 100644 index 00000000..4b166e95 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/ResizeNearestNeighborOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ResizeNearestNeighborOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsResizeNearestNeighborOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ResizeNearestNeighborOptions() + x.Init(buf, n + offset) + return x + + # ResizeNearestNeighborOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # ResizeNearestNeighborOptions + def AlignCorners(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + +def ResizeNearestNeighborOptionsStart(builder): builder.StartObject(1) +def ResizeNearestNeighborOptionsAddAlignCorners(builder, alignCorners): builder.PrependBoolSlot(0, alignCorners, 0) +def ResizeNearestNeighborOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ReverseSequenceOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ReverseSequenceOptions.py new file mode 100644 index 00000000..cbaf96db --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/ReverseSequenceOptions.py @@ -0,0 +1,38 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ReverseSequenceOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsReverseSequenceOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ReverseSequenceOptions() + x.Init(buf, n + offset) + return x + + # ReverseSequenceOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # ReverseSequenceOptions + def SeqDim(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # ReverseSequenceOptions + def BatchDim(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def ReverseSequenceOptionsStart(builder): builder.StartObject(2) +def ReverseSequenceOptionsAddSeqDim(builder, seqDim): builder.PrependInt32Slot(0, seqDim, 0) +def ReverseSequenceOptionsAddBatchDim(builder, batchDim): builder.PrependInt32Slot(1, batchDim, 0) +def ReverseSequenceOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ReverseV2Options.py b/utensor_cgen/frontend/tflite_flatbuffer/ReverseV2Options.py new file mode 100644 index 00000000..dbac9362 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/ReverseV2Options.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ReverseV2Options(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsReverseV2Options(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ReverseV2Options() + x.Init(buf, n + offset) + return x + + # ReverseV2Options + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def ReverseV2OptionsStart(builder): builder.StartObject(0) +def ReverseV2OptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SVDFOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SVDFOptions.py new file mode 100644 index 00000000..6f391db1 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/SVDFOptions.py @@ -0,0 +1,38 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SVDFOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSVDFOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SVDFOptions() + x.Init(buf, n + offset) + return x + + # SVDFOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SVDFOptions + def Rank(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # SVDFOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def SVDFOptionsStart(builder): builder.StartObject(2) +def SVDFOptionsAddRank(builder, rank): builder.PrependInt32Slot(0, rank, 0) +def SVDFOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(1, fusedActivationFunction, 0) +def SVDFOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SelectOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SelectOptions.py new file mode 100644 index 00000000..d67daf36 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/SelectOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SelectOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSelectOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SelectOptions() + x.Init(buf, n + offset) + return x + + # SelectOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def SelectOptionsStart(builder): builder.StartObject(0) +def SelectOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SequenceRNNOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SequenceRNNOptions.py new file mode 100644 index 00000000..74a4954a --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/SequenceRNNOptions.py @@ -0,0 +1,38 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SequenceRNNOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSequenceRNNOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SequenceRNNOptions() + x.Init(buf, n + offset) + return x + + # SequenceRNNOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SequenceRNNOptions + def TimeMajor(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + + # SequenceRNNOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def SequenceRNNOptionsStart(builder): builder.StartObject(2) +def SequenceRNNOptionsAddTimeMajor(builder, timeMajor): builder.PrependBoolSlot(0, timeMajor, 0) +def SequenceRNNOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(1, fusedActivationFunction, 0) +def SequenceRNNOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ShapeOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ShapeOptions.py new file mode 100644 index 00000000..2d24c05f --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/ShapeOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ShapeOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsShapeOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ShapeOptions() + x.Init(buf, n + offset) + return x + + # ShapeOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # ShapeOptions + def OutType(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def ShapeOptionsStart(builder): builder.StartObject(1) +def ShapeOptionsAddOutType(builder, outType): builder.PrependInt8Slot(0, outType, 0) +def ShapeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SkipGramOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SkipGramOptions.py new file mode 100644 index 00000000..0e8bdc1d --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/SkipGramOptions.py @@ -0,0 +1,46 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SkipGramOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSkipGramOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SkipGramOptions() + x.Init(buf, n + offset) + return x + + # SkipGramOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SkipGramOptions + def NgramSize(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # SkipGramOptions + def MaxSkipSize(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # SkipGramOptions + def IncludeAllNgrams(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + +def SkipGramOptionsStart(builder): builder.StartObject(3) +def SkipGramOptionsAddNgramSize(builder, ngramSize): builder.PrependInt32Slot(0, ngramSize, 0) +def SkipGramOptionsAddMaxSkipSize(builder, maxSkipSize): builder.PrependInt32Slot(1, maxSkipSize, 0) +def SkipGramOptionsAddIncludeAllNgrams(builder, includeAllNgrams): builder.PrependBoolSlot(2, includeAllNgrams, 0) +def SkipGramOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SliceOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SliceOptions.py new file mode 100644 index 00000000..4b41568d --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/SliceOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SliceOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSliceOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SliceOptions() + x.Init(buf, n + offset) + return x + + # SliceOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def SliceOptionsStart(builder): builder.StartObject(0) +def SliceOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SoftmaxOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SoftmaxOptions.py new file mode 100644 index 00000000..a7168534 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/SoftmaxOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SoftmaxOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSoftmaxOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SoftmaxOptions() + x.Init(buf, n + offset) + return x + + # SoftmaxOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SoftmaxOptions + def Beta(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + +def SoftmaxOptionsStart(builder): builder.StartObject(1) +def SoftmaxOptionsAddBeta(builder, beta): builder.PrependFloat32Slot(0, beta, 0.0) +def SoftmaxOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SpaceToBatchNDOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SpaceToBatchNDOptions.py new file mode 100644 index 00000000..b61ef96f --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/SpaceToBatchNDOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SpaceToBatchNDOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSpaceToBatchNDOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SpaceToBatchNDOptions() + x.Init(buf, n + offset) + return x + + # SpaceToBatchNDOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def SpaceToBatchNDOptionsStart(builder): builder.StartObject(0) +def SpaceToBatchNDOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SpaceToDepthOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SpaceToDepthOptions.py new file mode 100644 index 00000000..d571174a --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/SpaceToDepthOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SpaceToDepthOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSpaceToDepthOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SpaceToDepthOptions() + x.Init(buf, n + offset) + return x + + # SpaceToDepthOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SpaceToDepthOptions + def BlockSize(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def SpaceToDepthOptionsStart(builder): builder.StartObject(1) +def SpaceToDepthOptionsAddBlockSize(builder, blockSize): builder.PrependInt32Slot(0, blockSize, 0) +def SpaceToDepthOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SparseToDenseOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SparseToDenseOptions.py new file mode 100644 index 00000000..826eee08 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/SparseToDenseOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SparseToDenseOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSparseToDenseOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SparseToDenseOptions() + x.Init(buf, n + offset) + return x + + # SparseToDenseOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SparseToDenseOptions + def ValidateIndices(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + +def SparseToDenseOptionsStart(builder): builder.StartObject(1) +def SparseToDenseOptionsAddValidateIndices(builder, validateIndices): builder.PrependBoolSlot(0, validateIndices, 0) +def SparseToDenseOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SplitOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SplitOptions.py new file mode 100644 index 00000000..3207525b --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/SplitOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SplitOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSplitOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SplitOptions() + x.Init(buf, n + offset) + return x + + # SplitOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SplitOptions + def NumSplits(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def SplitOptionsStart(builder): builder.StartObject(1) +def SplitOptionsAddNumSplits(builder, numSplits): builder.PrependInt32Slot(0, numSplits, 0) +def SplitOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SplitVOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SplitVOptions.py new file mode 100644 index 00000000..418959de --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/SplitVOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SplitVOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSplitVOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SplitVOptions() + x.Init(buf, n + offset) + return x + + # SplitVOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SplitVOptions + def NumSplits(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def SplitVOptionsStart(builder): builder.StartObject(1) +def SplitVOptionsAddNumSplits(builder, numSplits): builder.PrependInt32Slot(0, numSplits, 0) +def SplitVOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SquareOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SquareOptions.py new file mode 100644 index 00000000..56633f6a --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/SquareOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SquareOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSquareOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SquareOptions() + x.Init(buf, n + offset) + return x + + # SquareOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def SquareOptionsStart(builder): builder.StartObject(0) +def SquareOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SquaredDifferenceOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SquaredDifferenceOptions.py new file mode 100644 index 00000000..906855d1 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/SquaredDifferenceOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SquaredDifferenceOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSquaredDifferenceOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SquaredDifferenceOptions() + x.Init(buf, n + offset) + return x + + # SquaredDifferenceOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def SquaredDifferenceOptionsStart(builder): builder.StartObject(0) +def SquaredDifferenceOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SqueezeOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SqueezeOptions.py new file mode 100644 index 00000000..25b294dc --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/SqueezeOptions.py @@ -0,0 +1,46 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SqueezeOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSqueezeOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SqueezeOptions() + x.Init(buf, n + offset) + return x + + # SqueezeOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SqueezeOptions + def SqueezeDims(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # SqueezeOptions + def SqueezeDimsAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) + return 0 + + # SqueezeOptions + def SqueezeDimsLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + +def SqueezeOptionsStart(builder): builder.StartObject(1) +def SqueezeOptionsAddSqueezeDims(builder, squeezeDims): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(squeezeDims), 0) +def SqueezeOptionsStartSqueezeDimsVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def SqueezeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/StridedSliceOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/StridedSliceOptions.py new file mode 100644 index 00000000..3bbb36b8 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/StridedSliceOptions.py @@ -0,0 +1,62 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class StridedSliceOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsStridedSliceOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = StridedSliceOptions() + x.Init(buf, n + offset) + return x + + # StridedSliceOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # StridedSliceOptions + def BeginMask(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # StridedSliceOptions + def EndMask(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # StridedSliceOptions + def EllipsisMask(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # StridedSliceOptions + def NewAxisMask(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # StridedSliceOptions + def ShrinkAxisMask(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def StridedSliceOptionsStart(builder): builder.StartObject(5) +def StridedSliceOptionsAddBeginMask(builder, beginMask): builder.PrependInt32Slot(0, beginMask, 0) +def StridedSliceOptionsAddEndMask(builder, endMask): builder.PrependInt32Slot(1, endMask, 0) +def StridedSliceOptionsAddEllipsisMask(builder, ellipsisMask): builder.PrependInt32Slot(2, ellipsisMask, 0) +def StridedSliceOptionsAddNewAxisMask(builder, newAxisMask): builder.PrependInt32Slot(3, newAxisMask, 0) +def StridedSliceOptionsAddShrinkAxisMask(builder, shrinkAxisMask): builder.PrependInt32Slot(4, shrinkAxisMask, 0) +def StridedSliceOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SubGraph.py b/utensor_cgen/frontend/tflite_flatbuffer/SubGraph.py new file mode 100644 index 00000000..eaa42fac --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/SubGraph.py @@ -0,0 +1,122 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SubGraph(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSubGraph(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SubGraph() + x.Init(buf, n + offset) + return x + + # SubGraph + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SubGraph + def Tensors(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + x = self._tab.Vector(o) + x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 + x = self._tab.Indirect(x) + from .Tensor import Tensor + obj = Tensor() + obj.Init(self._tab.Bytes, x) + return obj + return None + + # SubGraph + def TensorsLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # SubGraph + def Inputs(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # SubGraph + def InputsAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) + return 0 + + # SubGraph + def InputsLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # SubGraph + def Outputs(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # SubGraph + def OutputsAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) + return 0 + + # SubGraph + def OutputsLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # SubGraph + def Operators(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + x = self._tab.Vector(o) + x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 + x = self._tab.Indirect(x) + from .Operator import Operator + obj = Operator() + obj.Init(self._tab.Bytes, x) + return obj + return None + + # SubGraph + def OperatorsLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # SubGraph + def Name(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + return self._tab.String(o + self._tab.Pos) + return None + +def SubGraphStart(builder): builder.StartObject(5) +def SubGraphAddTensors(builder, tensors): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(tensors), 0) +def SubGraphStartTensorsVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def SubGraphAddInputs(builder, inputs): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(inputs), 0) +def SubGraphStartInputsVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def SubGraphAddOutputs(builder, outputs): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(outputs), 0) +def SubGraphStartOutputsVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def SubGraphAddOperators(builder, operators): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(operators), 0) +def SubGraphStartOperatorsVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def SubGraphAddName(builder, name): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0) +def SubGraphEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SubOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SubOptions.py new file mode 100644 index 00000000..eccd7aba --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/SubOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SubOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSubOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SubOptions() + x.Init(buf, n + offset) + return x + + # SubOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SubOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def SubOptionsStart(builder): builder.StartObject(1) +def SubOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) +def SubOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/Tensor.py b/utensor_cgen/frontend/tflite_flatbuffer/Tensor.py new file mode 100644 index 00000000..9b5b8f87 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/Tensor.py @@ -0,0 +1,90 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class Tensor(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsTensor(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = Tensor() + x.Init(buf, n + offset) + return x + + # Tensor + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # Tensor + def Shape(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # Tensor + def ShapeAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) + return 0 + + # Tensor + def ShapeLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # Tensor + def Type(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # Tensor + def Buffer(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos) + return 0 + + # Tensor + def Name(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.String(o + self._tab.Pos) + return None + + # Tensor + def Quantization(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + x = self._tab.Indirect(o + self._tab.Pos) + from .QuantizationParameters import QuantizationParameters + obj = QuantizationParameters() + obj.Init(self._tab.Bytes, x) + return obj + return None + + # Tensor + def IsVariable(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + +def TensorStart(builder): builder.StartObject(6) +def TensorAddShape(builder, shape): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(shape), 0) +def TensorStartShapeVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def TensorAddType(builder, type): builder.PrependInt8Slot(1, type, 0) +def TensorAddBuffer(builder, buffer): builder.PrependUint32Slot(2, buffer, 0) +def TensorAddName(builder, name): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0) +def TensorAddQuantization(builder, quantization): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(quantization), 0) +def TensorAddIsVariable(builder, isVariable): builder.PrependBoolSlot(5, isVariable, 0) +def TensorEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/TensorType.py b/utensor_cgen/frontend/tflite_flatbuffer/TensorType.py new file mode 100644 index 00000000..2455835a --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/TensorType.py @@ -0,0 +1,16 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class TensorType(object): + FLOAT32 = 0 + FLOAT16 = 1 + INT32 = 2 + UINT8 = 3 + INT64 = 4 + STRING = 5 + BOOL = 6 + INT16 = 7 + COMPLEX64 = 8 + INT8 = 9 + diff --git a/utensor_cgen/frontend/tflite_flatbuffer/TileOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/TileOptions.py new file mode 100644 index 00000000..ec8396dc --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/TileOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class TileOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsTileOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = TileOptions() + x.Init(buf, n + offset) + return x + + # TileOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def TileOptionsStart(builder): builder.StartObject(0) +def TileOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/TopKV2Options.py b/utensor_cgen/frontend/tflite_flatbuffer/TopKV2Options.py new file mode 100644 index 00000000..ccd51033 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/TopKV2Options.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class TopKV2Options(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsTopKV2Options(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = TopKV2Options() + x.Init(buf, n + offset) + return x + + # TopKV2Options + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def TopKV2OptionsStart(builder): builder.StartObject(0) +def TopKV2OptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/TransposeConvOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/TransposeConvOptions.py new file mode 100644 index 00000000..423571c8 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/TransposeConvOptions.py @@ -0,0 +1,46 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class TransposeConvOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsTransposeConvOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = TransposeConvOptions() + x.Init(buf, n + offset) + return x + + # TransposeConvOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # TransposeConvOptions + def Padding(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # TransposeConvOptions + def StrideW(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # TransposeConvOptions + def StrideH(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def TransposeConvOptionsStart(builder): builder.StartObject(3) +def TransposeConvOptionsAddPadding(builder, padding): builder.PrependInt8Slot(0, padding, 0) +def TransposeConvOptionsAddStrideW(builder, strideW): builder.PrependInt32Slot(1, strideW, 0) +def TransposeConvOptionsAddStrideH(builder, strideH): builder.PrependInt32Slot(2, strideH, 0) +def TransposeConvOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/TransposeOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/TransposeOptions.py new file mode 100644 index 00000000..42c596d9 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/TransposeOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class TransposeOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsTransposeOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = TransposeOptions() + x.Init(buf, n + offset) + return x + + # TransposeOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def TransposeOptionsStart(builder): builder.StartObject(0) +def TransposeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/UnidirectionalSequenceLSTMOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/UnidirectionalSequenceLSTMOptions.py new file mode 100644 index 00000000..1b0c112c --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/UnidirectionalSequenceLSTMOptions.py @@ -0,0 +1,54 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class UnidirectionalSequenceLSTMOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsUnidirectionalSequenceLSTMOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = UnidirectionalSequenceLSTMOptions() + x.Init(buf, n + offset) + return x + + # UnidirectionalSequenceLSTMOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # UnidirectionalSequenceLSTMOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # UnidirectionalSequenceLSTMOptions + def CellClip(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + + # UnidirectionalSequenceLSTMOptions + def ProjClip(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + + # UnidirectionalSequenceLSTMOptions + def TimeMajor(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + +def UnidirectionalSequenceLSTMOptionsStart(builder): builder.StartObject(4) +def UnidirectionalSequenceLSTMOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) +def UnidirectionalSequenceLSTMOptionsAddCellClip(builder, cellClip): builder.PrependFloat32Slot(1, cellClip, 0.0) +def UnidirectionalSequenceLSTMOptionsAddProjClip(builder, projClip): builder.PrependFloat32Slot(2, projClip, 0.0) +def UnidirectionalSequenceLSTMOptionsAddTimeMajor(builder, timeMajor): builder.PrependBoolSlot(3, timeMajor, 0) +def UnidirectionalSequenceLSTMOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/UniqueOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/UniqueOptions.py new file mode 100644 index 00000000..841c6977 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/UniqueOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class UniqueOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsUniqueOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = UniqueOptions() + x.Init(buf, n + offset) + return x + + # UniqueOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # UniqueOptions + def IdxOutType(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 2 + +def UniqueOptionsStart(builder): builder.StartObject(1) +def UniqueOptionsAddIdxOutType(builder, idxOutType): builder.PrependInt8Slot(0, idxOutType, 2) +def UniqueOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/UnpackOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/UnpackOptions.py new file mode 100644 index 00000000..eed40193 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/UnpackOptions.py @@ -0,0 +1,38 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class UnpackOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsUnpackOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = UnpackOptions() + x.Init(buf, n + offset) + return x + + # UnpackOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # UnpackOptions + def Num(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # UnpackOptions + def Axis(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def UnpackOptionsStart(builder): builder.StartObject(2) +def UnpackOptionsAddNum(builder, num): builder.PrependInt32Slot(0, num, 0) +def UnpackOptionsAddAxis(builder, axis): builder.PrependInt32Slot(1, axis, 0) +def UnpackOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/WhereOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/WhereOptions.py new file mode 100644 index 00000000..ab69f6aa --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/WhereOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class WhereOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsWhereOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = WhereOptions() + x.Init(buf, n + offset) + return x + + # WhereOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def WhereOptionsStart(builder): builder.StartObject(0) +def WhereOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ZerosLikeOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ZerosLikeOptions.py new file mode 100644 index 00000000..e6aa9639 --- /dev/null +++ b/utensor_cgen/frontend/tflite_flatbuffer/ZerosLikeOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ZerosLikeOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsZerosLikeOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ZerosLikeOptions() + x.Init(buf, n + offset) + return x + + # ZerosLikeOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def ZerosLikeOptionsStart(builder): builder.StartObject(0) +def ZerosLikeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/__init__.py b/utensor_cgen/frontend/tflite_flatbuffer/__init__.py new file mode 100644 index 00000000..e69de29b From 765927356a60105a1c0719f56a4f19ccc986abdb Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Wed, 24 Apr 2019 22:36:12 +0800 Subject: [PATCH 002/117] - verified dependencies are importable - updated Pipfile.lock --- Pipfile | 3 +- utensor_cgen/frontend/tflite.py | 180 ++++++++++++++++---------------- 2 files changed, 93 insertions(+), 90 deletions(-) diff --git a/Pipfile b/Pipfile index 95e51bba..0fdf5036 100644 --- a/Pipfile +++ b/Pipfile @@ -4,7 +4,8 @@ verify_ssl = true name = "pypi" [packages] -"e1839a8" = {path = ".", editable = true} +e1839a8 = {path = ".",editable = true} +flatbuffers = "*" [dev-packages] pylint = "*" diff --git a/utensor_cgen/frontend/tflite.py b/utensor_cgen/frontend/tflite.py index e1e17447..be005694 100644 --- a/utensor_cgen/frontend/tflite.py +++ b/utensor_cgen/frontend/tflite.py @@ -10,104 +10,106 @@ from utensor_cgen.utils import topologic_order_graph import flatbuffers -from tflite_flatbuffer.Model import Model +from .tflite_flatbuffer.Model import Model @FrontendSelector.register(target_exts=['.tflite']) class TFLiteParser(Parser): @classmethod def parse(self, pb_file, output_nodes=None): - buf = open(pb_file, 'rb').read() - buf = bytearray(buf) - model = Model.GetRootAsModel(buf, 0) - #TODO: version check - #print(model.Version()) - - assert model.SubgraphsLength() == 1 - subgraph = model.Subgraphs(0) - - #table to convert opcode to op-class - opcode_to_class= [] - for i in range(0, model.OperatorCodesLength()): - op_code = model.OperatorCodes(i) - opcode_to_class.append(op_code) - - #construct lookup objects for builtin ops - from tflite.BuiltinOperator import BuiltinOperator - from tflite.BuiltinOptions import BuiltinOptions - - builtin_ops = {v: k for k, v in BuiltinOperator.__dict__.items()} - op_options = {v: k for k, v in BuiltinOptions.__dict__.items()} - builtin_option_class = self.get_builtin_option_class() - - ugraph = uTensorGraph(output_nodes=output_nodes, - backend="tensorflow") #FIXME: with the proper backend - - #assuming this is the equivalent to ugraph's topological order - for i in range(0, subgraph.OperatorsLength()): - op = subgraph.Operators(i) - opIndex = op.OpcodeIndex() - op_class = opcode_to_class[opIndex] - builtin_code = op_class.BuiltinCode() - op_type = builtin_ops[builtin_code] #op's type in string - - op_attr = dict() - - if(op.CustomOptionsLength() < 1): - option = builtin_option_class[op_type] - builtin_data = op.BuiltinOptions() - option.Init(builtin_data.Bytes, builtin_data.Pos) - op_attr['option'] = option - - if(op_type == 'FULLY_CONNECTED'): - from tflite_flatbuffer.FullyConnectedOptionsWeightsFormat import FullyConnectedOptionsWeightsFormat - w_formats = {v: k for k, v in FullyConnectedOptionsWeightsFormat.__dict__.items()} - op_attr['weights_format'] = w_formats - else: - op_attr['custom_option'] = op.CustomOptionsAsNumpy() - - fb_input_tensors = [subgraph.Tensors(input_idx) for input_idx in op.InputsAsNumpy()] - in_tensors = [TensorInfo(name=tensor.Name(), - ugraph=ugraph, - op_name=tensor.op.name, #FIXME: who the fuck knows - dtype=self.tensor_type_lookup(tensor.Type()), - shape=tensor.ShapeAsNumpy()) - for tensor in fb_input_tensors] - - fb_output_tensors = [subgraph.Tensors(output_idx) for output_idx in op.OutputsAsNumpy()] - out_tensors = [TensorInfo(name=tensor.Name(), - ugraph=ugraph, - op_name=tensor.op.name, #FIXME: who the fuck knows - dtype=self.tensor_type_lookup(tensor.Type()), - shape=tensor.ShapeAsNumpy()) - for tensor in fb_output_tensors] - - node_name = op_type + "_" + i - - op_info = OperationInfo(name=node_name, - input_tensors=in_tensors, - output_tensors=out_tensors, - op_type=op_type, - backend='tensorflow', #FIXME: what should this be? - op_attr=op_attr, - ugraph=ugraph) - - ugraph.ops_info[node_name] = op_info + print("TF Lite Parser") + exit() +# buf = open(pb_file, 'rb').read() +# buf = bytearray(buf) +# model = Model.GetRootAsModel(buf, 0) +# #TODO: version check +# #print(model.Version()) + +# assert model.SubgraphsLength() == 1 +# subgraph = model.Subgraphs(0) + +# #table to convert opcode to op-class +# opcode_to_class= [] +# for i in range(0, model.OperatorCodesLength()): +# op_code = model.OperatorCodes(i) +# opcode_to_class.append(op_code) + +# #construct lookup objects for builtin ops +# from tflite.BuiltinOperator import BuiltinOperator +# from tflite.BuiltinOptions import BuiltinOptions + +# builtin_ops = {v: k for k, v in BuiltinOperator.__dict__.items()} +# op_options = {v: k for k, v in BuiltinOptions.__dict__.items()} +# builtin_option_class = self.get_builtin_option_class() + +# ugraph = uTensorGraph(output_nodes=output_nodes, +# backend="tensorflow") #FIXME: with the proper backend + +# #assuming this is the equivalent to ugraph's topological order +# for i in range(0, subgraph.OperatorsLength()): +# op = subgraph.Operators(i) +# opIndex = op.OpcodeIndex() +# op_class = opcode_to_class[opIndex] +# builtin_code = op_class.BuiltinCode() +# op_type = builtin_ops[builtin_code] #op's type in string + +# node_name = op_type + "_" + i + +# op_attr = dict() + +# if(op.CustomOptionsLength() < 1): +# option = builtin_option_class[op_type] +# builtin_data = op.BuiltinOptions() +# option.Init(builtin_data.Bytes, builtin_data.Pos) +# op_attr['option'] = option + +# if(op_type == 'FULLY_CONNECTED'): +# from tflite_flatbuffer.FullyConnectedOptionsWeightsFormat import FullyConnectedOptionsWeightsFormat +# w_formats = {v: k for k, v in FullyConnectedOptionsWeightsFormat.__dict__.items()} +# op_attr['weights_format'] = w_formats +# else: +# op_attr['custom_option'] = op.CustomOptionsAsNumpy() + +# fb_input_tensors = [subgraph.Tensors(input_idx) for input_idx in op.InputsAsNumpy()] +# in_tensors = [TensorInfo(name=tensor.Name(), +# ugraph=ugraph, +# op_name=tensor.op.name, #FIXME: who the fuck knows +# dtype=self.tensor_type_lookup(tensor.Type()), +# shape=tensor.ShapeAsNumpy()) +# for tensor in fb_input_tensors] + +# fb_output_tensors = [subgraph.Tensors(output_idx) for output_idx in op.OutputsAsNumpy()] +# out_tensors = [TensorInfo(name=tensor.Name(), +# ugraph=ugraph, +# op_name=tensor.op.name, #FIXME: who the fuck knows +# dtype=self.tensor_type_lookup(tensor.Type()), +# shape=tensor.ShapeAsNumpy()) +# for tensor in fb_output_tensors] + +# op_info = OperationInfo(name=node_name, +# input_tensors=in_tensors, +# output_tensors=out_tensors, +# op_type=op_type, +# backend='tensorflow', #FIXME: what should this be? +# op_attr=op_attr, +# ugraph=ugraph) + +# ugraph.ops_info[node_name] = op_info - topologic_order_graph(ugraph) - return ugraph +# topologic_order_graph(ugraph) +# return ugraph - def get_builtin_option_class(self): - from tflite_flatbuffer.FullyConnectedOptions import FullyConnectedOptions - from tflite_flatbuffer.ArgMaxOptions import ArgMaxOptions +# def get_builtin_option_class(self): +# from tflite_flatbuffer.FullyConnectedOptions import FullyConnectedOptions +# from tflite_flatbuffer.ArgMaxOptions import ArgMaxOptions - table = dict() - table['FULLY_CONNECTED'] = FullyConnectedOptions() - table['ARG_MAX'] = ArgMaxOptions() +# table = dict() +# table['FULLY_CONNECTED'] = FullyConnectedOptions() +# table['ARG_MAX'] = ArgMaxOptions() - return table +# return table - def tensor_type_lookup(self, int_type): - #see TensorType.py - return -1 +# def tensor_type_lookup(self, int_type): +# #see TensorType.py +# return -1 From 236ec41d4b5d1b710986c60170ca6a0a0cde4c59 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Fri, 4 Oct 2019 23:35:46 +0800 Subject: [PATCH 003/117] initial commit next: op code --- utensor_cgen/third_party/instruction.md | 26 + utensor_cgen/third_party/schema.fbs | 932 ++++++++++++++++++ utensor_cgen/third_party/tflite/AbsOptions.py | 22 + .../tflite/ActivationFunctionType.py | 12 + .../third_party/tflite/AddNOptions.py | 22 + utensor_cgen/third_party/tflite/AddOptions.py | 30 + .../third_party/tflite/ArgMaxOptions.py | 30 + .../third_party/tflite/ArgMinOptions.py | 30 + .../tflite/BatchToSpaceNDOptions.py | 22 + .../BidirectionalSequenceLSTMOptions.py | 62 ++ .../tflite/BidirectionalSequenceRNNOptions.py | 46 + utensor_cgen/third_party/tflite/Buffer.py | 46 + .../third_party/tflite/BuiltinOperator.py | 128 +++ .../third_party/tflite/BuiltinOptions.py | 103 ++ .../third_party/tflite/CallOptions.py | 30 + .../third_party/tflite/CastOptions.py | 38 + .../third_party/tflite/CombinerType.py | 9 + .../tflite/ConcatEmbeddingsOptions.py | 78 ++ .../tflite/ConcatenationOptions.py | 38 + .../third_party/tflite/Conv2DOptions.py | 70 ++ utensor_cgen/third_party/tflite/CosOptions.py | 22 + .../third_party/tflite/CustomOptionsFormat.py | 7 + .../third_party/tflite/CustomQuantization.py | 46 + .../third_party/tflite/DepthToSpaceOptions.py | 30 + .../tflite/DepthwiseConv2DOptions.py | 78 ++ .../third_party/tflite/DequantizeOptions.py | 22 + utensor_cgen/third_party/tflite/DivOptions.py | 30 + .../tflite/EmbeddingLookupSparseOptions.py | 30 + .../third_party/tflite/EqualOptions.py | 22 + utensor_cgen/third_party/tflite/ExpOptions.py | 22 + .../third_party/tflite/ExpandDimsOptions.py | 22 + .../third_party/tflite/FakeQuantOptions.py | 54 + .../third_party/tflite/FillOptions.py | 22 + .../third_party/tflite/FloorDivOptions.py | 22 + .../third_party/tflite/FloorModOptions.py | 22 + .../tflite/FullyConnectedOptions.py | 46 + .../FullyConnectedOptionsWeightsFormat.py | 8 + .../third_party/tflite/GatherNdOptions.py | 22 + .../third_party/tflite/GatherOptions.py | 30 + .../third_party/tflite/GreaterEqualOptions.py | 22 + .../third_party/tflite/GreaterOptions.py | 22 + .../third_party/tflite/HardSwishOptions.py | 22 + utensor_cgen/third_party/tflite/IfOptions.py | 38 + .../third_party/tflite/L2NormOptions.py | 30 + .../tflite/LSHProjectionOptions.py | 30 + .../third_party/tflite/LSHProjectionType.py | 9 + .../third_party/tflite/LSTMKernelType.py | 8 + .../third_party/tflite/LSTMOptions.py | 54 + .../third_party/tflite/LeakyReluOptions.py | 30 + .../third_party/tflite/LessEqualOptions.py | 22 + .../third_party/tflite/LessOptions.py | 22 + .../LocalResponseNormalizationOptions.py | 54 + .../third_party/tflite/LogSoftmaxOptions.py | 22 + .../third_party/tflite/LogicalAndOptions.py | 22 + .../third_party/tflite/LogicalNotOptions.py | 22 + .../third_party/tflite/LogicalOrOptions.py | 22 + .../third_party/tflite/MatrixDiagOptions.py | 22 + .../tflite/MatrixSetDiagOptions.py | 22 + .../tflite/MaximumMinimumOptions.py | 22 + utensor_cgen/third_party/tflite/Metadata.py | 38 + .../third_party/tflite/MirrorPadMode.py | 8 + .../third_party/tflite/MirrorPadOptions.py | 30 + utensor_cgen/third_party/tflite/Model.py | 150 +++ utensor_cgen/third_party/tflite/MulOptions.py | 30 + utensor_cgen/third_party/tflite/NegOptions.py | 22 + .../tflite/NonMaxSuppressionV4Options.py | 22 + .../tflite/NonMaxSuppressionV5Options.py | 22 + .../third_party/tflite/NotEqualOptions.py | 22 + .../third_party/tflite/OneHotOptions.py | 30 + utensor_cgen/third_party/tflite/Operator.py | 177 ++++ .../third_party/tflite/OperatorCode.py | 46 + .../third_party/tflite/PackOptions.py | 38 + utensor_cgen/third_party/tflite/PadOptions.py | 22 + .../third_party/tflite/PadV2Options.py | 22 + utensor_cgen/third_party/tflite/Padding.py | 8 + .../third_party/tflite/Pool2DOptions.py | 70 ++ utensor_cgen/third_party/tflite/PowOptions.py | 22 + .../third_party/tflite/QuantizationDetails.py | 8 + .../tflite/QuantizationParameters.py | 145 +++ .../third_party/tflite/QuantizeOptions.py | 22 + utensor_cgen/third_party/tflite/RNNOptions.py | 30 + .../third_party/tflite/RangeOptions.py | 22 + .../third_party/tflite/RankOptions.py | 22 + .../third_party/tflite/ReducerOptions.py | 30 + .../third_party/tflite/ReshapeOptions.py | 46 + .../tflite/ResizeBilinearOptions.py | 30 + .../tflite/ResizeNearestNeighborOptions.py | 30 + .../tflite/ReverseSequenceOptions.py | 38 + .../third_party/tflite/ReverseV2Options.py | 22 + .../third_party/tflite/SVDFOptions.py | 38 + .../third_party/tflite/SelectOptions.py | 22 + .../third_party/tflite/SequenceRNNOptions.py | 38 + .../third_party/tflite/ShapeOptions.py | 30 + .../third_party/tflite/SkipGramOptions.py | 46 + .../third_party/tflite/SliceOptions.py | 22 + .../third_party/tflite/SoftmaxOptions.py | 30 + .../tflite/SpaceToBatchNDOptions.py | 22 + .../third_party/tflite/SpaceToDepthOptions.py | 30 + .../tflite/SparseToDenseOptions.py | 30 + .../third_party/tflite/SplitOptions.py | 30 + .../third_party/tflite/SplitVOptions.py | 30 + .../third_party/tflite/SquareOptions.py | 22 + .../tflite/SquaredDifferenceOptions.py | 22 + .../third_party/tflite/SqueezeOptions.py | 46 + .../third_party/tflite/StridedSliceOptions.py | 62 ++ utensor_cgen/third_party/tflite/SubGraph.py | 122 +++ utensor_cgen/third_party/tflite/SubOptions.py | 30 + utensor_cgen/third_party/tflite/Tensor.py | 90 ++ utensor_cgen/third_party/tflite/TensorType.py | 16 + .../third_party/tflite/TileOptions.py | 22 + .../third_party/tflite/TopKV2Options.py | 22 + .../tflite/TransposeConvOptions.py | 46 + .../third_party/tflite/TransposeOptions.py | 22 + .../UnidirectionalSequenceLSTMOptions.py | 54 + .../third_party/tflite/UniqueOptions.py | 30 + .../third_party/tflite/UnpackOptions.py | 38 + .../third_party/tflite/WhereOptions.py | 22 + .../third_party/tflite/WhileOptions.py | 38 + .../third_party/tflite/ZerosLikeOptions.py | 22 + utensor_cgen/third_party/tflite/__init__.py | 0 utensor_cgen/transformer/tflite_exporter.py | 164 +++ 121 files changed, 5330 insertions(+) create mode 100644 utensor_cgen/third_party/instruction.md create mode 100644 utensor_cgen/third_party/schema.fbs create mode 100644 utensor_cgen/third_party/tflite/AbsOptions.py create mode 100644 utensor_cgen/third_party/tflite/ActivationFunctionType.py create mode 100644 utensor_cgen/third_party/tflite/AddNOptions.py create mode 100644 utensor_cgen/third_party/tflite/AddOptions.py create mode 100644 utensor_cgen/third_party/tflite/ArgMaxOptions.py create mode 100644 utensor_cgen/third_party/tflite/ArgMinOptions.py create mode 100644 utensor_cgen/third_party/tflite/BatchToSpaceNDOptions.py create mode 100644 utensor_cgen/third_party/tflite/BidirectionalSequenceLSTMOptions.py create mode 100644 utensor_cgen/third_party/tflite/BidirectionalSequenceRNNOptions.py create mode 100644 utensor_cgen/third_party/tflite/Buffer.py create mode 100644 utensor_cgen/third_party/tflite/BuiltinOperator.py create mode 100644 utensor_cgen/third_party/tflite/BuiltinOptions.py create mode 100644 utensor_cgen/third_party/tflite/CallOptions.py create mode 100644 utensor_cgen/third_party/tflite/CastOptions.py create mode 100644 utensor_cgen/third_party/tflite/CombinerType.py create mode 100644 utensor_cgen/third_party/tflite/ConcatEmbeddingsOptions.py create mode 100644 utensor_cgen/third_party/tflite/ConcatenationOptions.py create mode 100644 utensor_cgen/third_party/tflite/Conv2DOptions.py create mode 100644 utensor_cgen/third_party/tflite/CosOptions.py create mode 100644 utensor_cgen/third_party/tflite/CustomOptionsFormat.py create mode 100644 utensor_cgen/third_party/tflite/CustomQuantization.py create mode 100644 utensor_cgen/third_party/tflite/DepthToSpaceOptions.py create mode 100644 utensor_cgen/third_party/tflite/DepthwiseConv2DOptions.py create mode 100644 utensor_cgen/third_party/tflite/DequantizeOptions.py create mode 100644 utensor_cgen/third_party/tflite/DivOptions.py create mode 100644 utensor_cgen/third_party/tflite/EmbeddingLookupSparseOptions.py create mode 100644 utensor_cgen/third_party/tflite/EqualOptions.py create mode 100644 utensor_cgen/third_party/tflite/ExpOptions.py create mode 100644 utensor_cgen/third_party/tflite/ExpandDimsOptions.py create mode 100644 utensor_cgen/third_party/tflite/FakeQuantOptions.py create mode 100644 utensor_cgen/third_party/tflite/FillOptions.py create mode 100644 utensor_cgen/third_party/tflite/FloorDivOptions.py create mode 100644 utensor_cgen/third_party/tflite/FloorModOptions.py create mode 100644 utensor_cgen/third_party/tflite/FullyConnectedOptions.py create mode 100644 utensor_cgen/third_party/tflite/FullyConnectedOptionsWeightsFormat.py create mode 100644 utensor_cgen/third_party/tflite/GatherNdOptions.py create mode 100644 utensor_cgen/third_party/tflite/GatherOptions.py create mode 100644 utensor_cgen/third_party/tflite/GreaterEqualOptions.py create mode 100644 utensor_cgen/third_party/tflite/GreaterOptions.py create mode 100644 utensor_cgen/third_party/tflite/HardSwishOptions.py create mode 100644 utensor_cgen/third_party/tflite/IfOptions.py create mode 100644 utensor_cgen/third_party/tflite/L2NormOptions.py create mode 100644 utensor_cgen/third_party/tflite/LSHProjectionOptions.py create mode 100644 utensor_cgen/third_party/tflite/LSHProjectionType.py create mode 100644 utensor_cgen/third_party/tflite/LSTMKernelType.py create mode 100644 utensor_cgen/third_party/tflite/LSTMOptions.py create mode 100644 utensor_cgen/third_party/tflite/LeakyReluOptions.py create mode 100644 utensor_cgen/third_party/tflite/LessEqualOptions.py create mode 100644 utensor_cgen/third_party/tflite/LessOptions.py create mode 100644 utensor_cgen/third_party/tflite/LocalResponseNormalizationOptions.py create mode 100644 utensor_cgen/third_party/tflite/LogSoftmaxOptions.py create mode 100644 utensor_cgen/third_party/tflite/LogicalAndOptions.py create mode 100644 utensor_cgen/third_party/tflite/LogicalNotOptions.py create mode 100644 utensor_cgen/third_party/tflite/LogicalOrOptions.py create mode 100644 utensor_cgen/third_party/tflite/MatrixDiagOptions.py create mode 100644 utensor_cgen/third_party/tflite/MatrixSetDiagOptions.py create mode 100644 utensor_cgen/third_party/tflite/MaximumMinimumOptions.py create mode 100644 utensor_cgen/third_party/tflite/Metadata.py create mode 100644 utensor_cgen/third_party/tflite/MirrorPadMode.py create mode 100644 utensor_cgen/third_party/tflite/MirrorPadOptions.py create mode 100644 utensor_cgen/third_party/tflite/Model.py create mode 100644 utensor_cgen/third_party/tflite/MulOptions.py create mode 100644 utensor_cgen/third_party/tflite/NegOptions.py create mode 100644 utensor_cgen/third_party/tflite/NonMaxSuppressionV4Options.py create mode 100644 utensor_cgen/third_party/tflite/NonMaxSuppressionV5Options.py create mode 100644 utensor_cgen/third_party/tflite/NotEqualOptions.py create mode 100644 utensor_cgen/third_party/tflite/OneHotOptions.py create mode 100644 utensor_cgen/third_party/tflite/Operator.py create mode 100644 utensor_cgen/third_party/tflite/OperatorCode.py create mode 100644 utensor_cgen/third_party/tflite/PackOptions.py create mode 100644 utensor_cgen/third_party/tflite/PadOptions.py create mode 100644 utensor_cgen/third_party/tflite/PadV2Options.py create mode 100644 utensor_cgen/third_party/tflite/Padding.py create mode 100644 utensor_cgen/third_party/tflite/Pool2DOptions.py create mode 100644 utensor_cgen/third_party/tflite/PowOptions.py create mode 100644 utensor_cgen/third_party/tflite/QuantizationDetails.py create mode 100644 utensor_cgen/third_party/tflite/QuantizationParameters.py create mode 100644 utensor_cgen/third_party/tflite/QuantizeOptions.py create mode 100644 utensor_cgen/third_party/tflite/RNNOptions.py create mode 100644 utensor_cgen/third_party/tflite/RangeOptions.py create mode 100644 utensor_cgen/third_party/tflite/RankOptions.py create mode 100644 utensor_cgen/third_party/tflite/ReducerOptions.py create mode 100644 utensor_cgen/third_party/tflite/ReshapeOptions.py create mode 100644 utensor_cgen/third_party/tflite/ResizeBilinearOptions.py create mode 100644 utensor_cgen/third_party/tflite/ResizeNearestNeighborOptions.py create mode 100644 utensor_cgen/third_party/tflite/ReverseSequenceOptions.py create mode 100644 utensor_cgen/third_party/tflite/ReverseV2Options.py create mode 100644 utensor_cgen/third_party/tflite/SVDFOptions.py create mode 100644 utensor_cgen/third_party/tflite/SelectOptions.py create mode 100644 utensor_cgen/third_party/tflite/SequenceRNNOptions.py create mode 100644 utensor_cgen/third_party/tflite/ShapeOptions.py create mode 100644 utensor_cgen/third_party/tflite/SkipGramOptions.py create mode 100644 utensor_cgen/third_party/tflite/SliceOptions.py create mode 100644 utensor_cgen/third_party/tflite/SoftmaxOptions.py create mode 100644 utensor_cgen/third_party/tflite/SpaceToBatchNDOptions.py create mode 100644 utensor_cgen/third_party/tflite/SpaceToDepthOptions.py create mode 100644 utensor_cgen/third_party/tflite/SparseToDenseOptions.py create mode 100644 utensor_cgen/third_party/tflite/SplitOptions.py create mode 100644 utensor_cgen/third_party/tflite/SplitVOptions.py create mode 100644 utensor_cgen/third_party/tflite/SquareOptions.py create mode 100644 utensor_cgen/third_party/tflite/SquaredDifferenceOptions.py create mode 100644 utensor_cgen/third_party/tflite/SqueezeOptions.py create mode 100644 utensor_cgen/third_party/tflite/StridedSliceOptions.py create mode 100644 utensor_cgen/third_party/tflite/SubGraph.py create mode 100644 utensor_cgen/third_party/tflite/SubOptions.py create mode 100644 utensor_cgen/third_party/tflite/Tensor.py create mode 100644 utensor_cgen/third_party/tflite/TensorType.py create mode 100644 utensor_cgen/third_party/tflite/TileOptions.py create mode 100644 utensor_cgen/third_party/tflite/TopKV2Options.py create mode 100644 utensor_cgen/third_party/tflite/TransposeConvOptions.py create mode 100644 utensor_cgen/third_party/tflite/TransposeOptions.py create mode 100644 utensor_cgen/third_party/tflite/UnidirectionalSequenceLSTMOptions.py create mode 100644 utensor_cgen/third_party/tflite/UniqueOptions.py create mode 100644 utensor_cgen/third_party/tflite/UnpackOptions.py create mode 100644 utensor_cgen/third_party/tflite/WhereOptions.py create mode 100644 utensor_cgen/third_party/tflite/WhileOptions.py create mode 100644 utensor_cgen/third_party/tflite/ZerosLikeOptions.py create mode 100644 utensor_cgen/third_party/tflite/__init__.py create mode 100644 utensor_cgen/transformer/tflite_exporter.py diff --git a/utensor_cgen/third_party/instruction.md b/utensor_cgen/third_party/instruction.md new file mode 100644 index 00000000..eed0b7da --- /dev/null +++ b/utensor_cgen/third_party/instruction.md @@ -0,0 +1,26 @@ +# Instructions for generating Tensorflow Lite flatbuffer API + +## Building flatc +Visit the Flatbuffer repository +Tutorials are here + +``` +git clone https://github.com/google/flatbuffers +cmake -G "Unix Makefiles" -DCMAKE_BUILD_TYPE=Release +make +``` + +Add `flac` to your .bash_profile + +``` +alias flac=`~/PATH/TO/flatbuffers/flatc` +``` + + +## Generate TF Lite schema + +``` +wget https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/lite/schema/schema.fbs +pip install flatbuffers +flatc -p schema.fbs +``` \ No newline at end of file diff --git a/utensor_cgen/third_party/schema.fbs b/utensor_cgen/third_party/schema.fbs new file mode 100644 index 00000000..2a7c2228 --- /dev/null +++ b/utensor_cgen/third_party/schema.fbs @@ -0,0 +1,932 @@ +// Copyright 2017 The TensorFlow Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +// Revision History +// Version 0: Initial version. +// Version 1: Add subgraphs to schema. +// Version 2: Rename operators to conform to NN API. +// Version 3: Move buffer data from Model.Subgraph.Tensors to Model.Buffers. + +namespace tflite; + +// This corresponds to the version. +file_identifier "TFL3"; +// File extension of any written files. +file_extension "tflite"; + +// IMPORTANT: All new members of tables, enums and unions must be added at the +// end to ensure backwards compatibility. + +// The type of data stored in a tensor. +enum TensorType : byte { + FLOAT32 = 0, + FLOAT16 = 1, + INT32 = 2, + UINT8 = 3, + INT64 = 4, + STRING = 5, + BOOL = 6, + INT16 = 7, + COMPLEX64 = 8, + INT8 = 9, +} + +// Custom quantization parameters for experimenting with new quantization +// techniques. +table CustomQuantization { + custom:[ubyte] (force_align: 16); +} + +// Represents a specific quantization technique's parameters. +union QuantizationDetails { + CustomQuantization, +} + +// Parameters for converting a quantized tensor back to float. +table QuantizationParameters { + // These four parameters are the asymmetric linear quantization parameters. + // Given a quantized value q, the corresponding float value f should be: + // f = scale * (q - zero_point) + // For other quantization types, the QuantizationDetails below is used. + min:[float]; // For importing back into tensorflow. + max:[float]; // For importing back into tensorflow. + scale:[float]; // For dequantizing the tensor's values. + zero_point:[long]; + + // If this is not none, the other quantization parameters (i.e. min, max, + // scale, zero_point fields above) are ignored and the value of the + // QuantizationDetails union should be used. + details:QuantizationDetails; + + // Specifies the dimension of the Tensor's shape that the scales and + // zero_points correspond to. For example, a tensor t, with dims=[4, 3, 2, 1] + // with quantization params: + // scale=[1.0, 2.0, 3.0], zero_point=[1, 2, 3], quantization_dimension=1 + // will be quantized across the second dimension of t. + // t[:, 0, :, :] will have scale[0]=1.0, zero_point[0]=1 + // t[:, 1, :, :] will have scale[1]=2.0, zero_point[0]=2 + // t[:, 2, :, :] will have scale[2]=3.0, zero_point[0]=3 + quantized_dimension:int; +} + +table Tensor { + // The tensor shape. The meaning of each entry is operator-specific but + // builtin ops use: [batch size, height, width, number of channels] (That's + // Tensorflow's NHWC). + shape:[int]; + type:TensorType; + // An index that refers to the buffers table at the root of the model. Or, + // if there is no data buffer associated (i.e. intermediate results), then + // this is 0 (which refers to an always existent empty buffer). + // + // The data_buffer itself is an opaque container, with the assumption that the + // target device is little-endian. In addition, all builtin operators assume + // the memory is ordered such that if `shape` is [4, 3, 2], then index + // [i, j, k] maps to data_buffer[i*3*2 + j*2 + k]. + buffer:uint; + name:string; // For debugging and importing back into tensorflow. + quantization:QuantizationParameters; // Optional. + + is_variable:bool = false; +} + +// A list of builtin operators. Builtin operators are slightly faster than custom +// ones, but not by much. Moreover, while custom operators accept an opaque +// object containing configuration parameters, builtins have a predetermined +// set of acceptable options. +enum BuiltinOperator : byte { + ADD = 0, + AVERAGE_POOL_2D = 1, + CONCATENATION = 2, + CONV_2D = 3, + DEPTHWISE_CONV_2D = 4, + DEPTH_TO_SPACE = 5, + DEQUANTIZE = 6, + EMBEDDING_LOOKUP = 7, + FLOOR = 8, + FULLY_CONNECTED = 9, + HASHTABLE_LOOKUP = 10, + L2_NORMALIZATION = 11, + L2_POOL_2D = 12, + LOCAL_RESPONSE_NORMALIZATION = 13, + LOGISTIC = 14, + LSH_PROJECTION = 15, + LSTM = 16, + MAX_POOL_2D = 17, + MUL = 18, + RELU = 19, + // NOTE(aselle): RELU_N1_TO_1 used to be called RELU1, but it was renamed + // since different model developers use RELU1 in different ways. Never + // create another op called RELU1. + RELU_N1_TO_1 = 20, + RELU6 = 21, + RESHAPE = 22, + RESIZE_BILINEAR = 23, + RNN = 24, + SOFTMAX = 25, + SPACE_TO_DEPTH = 26, + SVDF = 27, + TANH = 28, + // TODO(aselle): Consider rename to CONCATENATE_EMBEDDINGS + CONCAT_EMBEDDINGS = 29, + SKIP_GRAM = 30, + CALL = 31, + CUSTOM = 32, + EMBEDDING_LOOKUP_SPARSE = 33, + PAD = 34, + UNIDIRECTIONAL_SEQUENCE_RNN = 35, + GATHER = 36, + BATCH_TO_SPACE_ND = 37, + SPACE_TO_BATCH_ND = 38, + TRANSPOSE = 39, + MEAN = 40, + SUB = 41, + DIV = 42, + SQUEEZE = 43, + UNIDIRECTIONAL_SEQUENCE_LSTM = 44, + STRIDED_SLICE = 45, + BIDIRECTIONAL_SEQUENCE_RNN = 46, + EXP = 47, + TOPK_V2 = 48, + SPLIT = 49, + LOG_SOFTMAX = 50, + // DELEGATE is a special op type for the operations which are delegated to + // other backends. + // WARNING: Experimental interface, subject to change + DELEGATE = 51, + BIDIRECTIONAL_SEQUENCE_LSTM = 52, + CAST = 53, + PRELU = 54, + MAXIMUM = 55, + ARG_MAX = 56, + MINIMUM = 57, + LESS = 58, + NEG = 59, + PADV2 = 60, + GREATER = 61, + GREATER_EQUAL = 62, + LESS_EQUAL = 63, + SELECT = 64, + SLICE = 65, + SIN = 66, + TRANSPOSE_CONV = 67, + SPARSE_TO_DENSE = 68, + TILE = 69, + EXPAND_DIMS = 70, + EQUAL = 71, + NOT_EQUAL = 72, + LOG = 73, + SUM = 74, + SQRT = 75, + RSQRT = 76, + SHAPE = 77, + POW = 78, + ARG_MIN = 79, + FAKE_QUANT = 80, + REDUCE_PROD = 81, + REDUCE_MAX = 82, + PACK = 83, + LOGICAL_OR = 84, + ONE_HOT = 85, + LOGICAL_AND = 86, + LOGICAL_NOT = 87, + UNPACK = 88, + REDUCE_MIN = 89, + FLOOR_DIV = 90, + REDUCE_ANY = 91, + SQUARE = 92, + ZEROS_LIKE = 93, + FILL = 94, + FLOOR_MOD = 95, + RANGE = 96, + RESIZE_NEAREST_NEIGHBOR = 97, + LEAKY_RELU = 98, + SQUARED_DIFFERENCE = 99, + MIRROR_PAD = 100, + ABS = 101, + SPLIT_V = 102, + UNIQUE = 103, + CEIL = 104, + REVERSE_V2 = 105, + ADD_N = 106, + GATHER_ND = 107, + COS = 108, + WHERE = 109, + RANK = 110, + ELU = 111, + REVERSE_SEQUENCE = 112, + MATRIX_DIAG = 113, + QUANTIZE = 114, + MATRIX_SET_DIAG = 115, + ROUND = 116, + HARD_SWISH = 117, + IF = 118, + WHILE = 119, + NON_MAX_SUPPRESSION_V4 = 120, + NON_MAX_SUPPRESSION_V5 = 121, +} + +// Options for the builtin operators. +union BuiltinOptions { + Conv2DOptions, + DepthwiseConv2DOptions, + ConcatEmbeddingsOptions, + LSHProjectionOptions, + Pool2DOptions, + SVDFOptions, + RNNOptions, + FullyConnectedOptions, + SoftmaxOptions, + ConcatenationOptions, + AddOptions, + L2NormOptions, + LocalResponseNormalizationOptions, + LSTMOptions, + ResizeBilinearOptions, + CallOptions, + ReshapeOptions, + SkipGramOptions, + SpaceToDepthOptions, + EmbeddingLookupSparseOptions, + MulOptions, + PadOptions, + GatherOptions, + BatchToSpaceNDOptions, + SpaceToBatchNDOptions, + TransposeOptions, + ReducerOptions, + SubOptions, + DivOptions, + SqueezeOptions, + SequenceRNNOptions, + StridedSliceOptions, + ExpOptions, + TopKV2Options, + SplitOptions, + LogSoftmaxOptions, + CastOptions, + DequantizeOptions, + MaximumMinimumOptions, + ArgMaxOptions, + LessOptions, + NegOptions, + PadV2Options, + GreaterOptions, + GreaterEqualOptions, + LessEqualOptions, + SelectOptions, + SliceOptions, + TransposeConvOptions, + SparseToDenseOptions, + TileOptions, + ExpandDimsOptions, + EqualOptions, + NotEqualOptions, + ShapeOptions, + PowOptions, + ArgMinOptions, + FakeQuantOptions, + PackOptions, + LogicalOrOptions, + OneHotOptions, + LogicalAndOptions, + LogicalNotOptions, + UnpackOptions, + FloorDivOptions, + SquareOptions, + ZerosLikeOptions, + FillOptions, + BidirectionalSequenceLSTMOptions, + BidirectionalSequenceRNNOptions, + UnidirectionalSequenceLSTMOptions, + FloorModOptions, + RangeOptions, + ResizeNearestNeighborOptions, + LeakyReluOptions, + SquaredDifferenceOptions, + MirrorPadOptions, + AbsOptions, + SplitVOptions, + UniqueOptions, + ReverseV2Options, + AddNOptions, + GatherNdOptions, + CosOptions, + WhereOptions, + RankOptions, + ReverseSequenceOptions, + MatrixDiagOptions, + QuantizeOptions, + MatrixSetDiagOptions, + HardSwishOptions, + IfOptions, + WhileOptions, + DepthToSpaceOptions, + NonMaxSuppressionV4Options, + NonMaxSuppressionV5Options +} + +enum Padding : byte { SAME, VALID } + +enum ActivationFunctionType : byte { + NONE = 0, + RELU = 1, + RELU_N1_TO_1 = 2, + RELU6 = 3, + TANH = 4, + SIGN_BIT = 5, +} + +table Conv2DOptions { + padding:Padding; + stride_w:int; + stride_h:int; + fused_activation_function:ActivationFunctionType; + dilation_w_factor:int = 1; + dilation_h_factor:int = 1; +} + +table Pool2DOptions { + padding:Padding; + stride_w:int; + stride_h:int; + filter_width:int; + filter_height:int; + fused_activation_function:ActivationFunctionType; +} + +table DepthwiseConv2DOptions { + // Parameters for DepthwiseConv version 1 or above. + padding:Padding; + stride_w:int; + stride_h:int; + depth_multiplier:int; + fused_activation_function:ActivationFunctionType; + // Parameters for DepthwiseConv version 2 or above. + dilation_w_factor:int = 1; + dilation_h_factor:int = 1; +} + +table ConcatEmbeddingsOptions { + num_channels:int; + num_columns_per_channel:[int]; + embedding_dim_per_channel:[int]; // This could be inferred from parameters. +} + +enum LSHProjectionType: byte { + UNKNOWN = 0, + SPARSE = 1, + DENSE = 2, +} + +table LSHProjectionOptions { + type: LSHProjectionType; +} + +table SVDFOptions { + rank:int; + fused_activation_function:ActivationFunctionType; +} + +// An implementation of TensorFlow RNNCell. +table RNNOptions { + fused_activation_function:ActivationFunctionType; +} + +// An implementation of TensorFlow dynamic_rnn with RNNCell. +table SequenceRNNOptions { + time_major:bool; + fused_activation_function:ActivationFunctionType; +} + +// An implementation of TensorFlow bidrectional_dynamic_rnn with RNNCell. +table BidirectionalSequenceRNNOptions { + time_major:bool; + fused_activation_function:ActivationFunctionType; + merge_outputs: bool; +} + +enum FullyConnectedOptionsWeightsFormat: byte { + DEFAULT = 0, + SHUFFLED4x16INT8 = 1, +} + +// An implementation of TensorFlow fully_connected (a.k.a Dense) layer. +table FullyConnectedOptions { + // Parameters for FullyConnected version 1 or above. + fused_activation_function:ActivationFunctionType; + + // Parameters for FullyConnected version 2 or above. + weights_format:FullyConnectedOptionsWeightsFormat = DEFAULT; + + // Parameters for FullyConnected version 5 or above. + // If set to true, then the number of dimension is preserved. Furthermore, + // all but the last dimension of the input and output shapes will be equal. + keep_num_dims: bool; +} + +table SoftmaxOptions { + beta: float; +} + +// An implementation of TensorFlow concat. +table ConcatenationOptions { + axis:int; + fused_activation_function:ActivationFunctionType; +} + +table AddOptions { + fused_activation_function:ActivationFunctionType; +} + +table MulOptions { + fused_activation_function:ActivationFunctionType; +} + +table L2NormOptions { + fused_activation_function:ActivationFunctionType; +} + +table LocalResponseNormalizationOptions { + radius:int; + bias:float; + alpha:float; + beta:float; +} + +enum LSTMKernelType : byte { + // Full LSTM kernel which supports peephole and projection. + FULL = 0, + // Basic LSTM kernels. Equivalent to TensorFlow BasicLSTMCell. + BASIC = 1, +} + +// An implementation of TensorFlow LSTMCell and CoupledInputForgetGateLSTMCell +table LSTMOptions { + // Parameters for LSTM version 1 or above. + fused_activation_function:ActivationFunctionType; + cell_clip: float; // Optional, 0.0 means no clipping + proj_clip: float; // Optional, 0.0 means no clipping + + // Parameters for LSTM version 2 or above. + // Basic kernel is only supported in version 2 or above. + kernel_type: LSTMKernelType = FULL; +} + +// An implementation of TensorFlow dynamic_rnn with LSTMCell. +table UnidirectionalSequenceLSTMOptions { + fused_activation_function:ActivationFunctionType; + cell_clip: float; // Optional, 0.0 means no clipping + proj_clip: float; // Optional, 0.0 means no clipping + + // If true then first dimension is sequence, otherwise batch. + time_major:bool; +} + +table BidirectionalSequenceLSTMOptions { + // Parameters supported by version 1: + fused_activation_function:ActivationFunctionType; + cell_clip: float; // Optional, 0.0 means no clipping + proj_clip: float; // Optional, 0.0 means no clipping + + // If true, store the outputs of both directions into the first output. + merge_outputs: bool; + + // Parameters supported by version 2: + // If true then first dimension is sequence, otherwise batch. + // Version 1 implementations assumed time_major to be true, so this default + // value should never change. + time_major: bool = true; +} + +table ResizeBilinearOptions { + new_height: int (deprecated); + new_width: int (deprecated); + align_corners: bool; +} + +table ResizeNearestNeighborOptions { + align_corners: bool; +} + +// A call operation options +table CallOptions { + // The subgraph index that needs to be called. + subgraph:uint; +} + +table PadOptions { +} + +table PadV2Options { +} + +table ReshapeOptions { + new_shape:[int]; +} + +table SpaceToBatchNDOptions { +} + +table BatchToSpaceNDOptions { +} + +table SkipGramOptions { + ngram_size: int; + max_skip_size: int; + include_all_ngrams: bool; +} + +table SpaceToDepthOptions { + block_size: int; +} + +table DepthToSpaceOptions { + block_size: int; +} + +table SubOptions { + fused_activation_function:ActivationFunctionType; +} + +table DivOptions { + fused_activation_function:ActivationFunctionType; +} + +table TopKV2Options { +} + +enum CombinerType : byte { + SUM = 0, + MEAN = 1, + SQRTN = 2, +} + +table EmbeddingLookupSparseOptions { + combiner:CombinerType; +} + +table GatherOptions { + axis: int; +} + +table TransposeOptions { +} + +table ExpOptions { +} + +table CosOptions { +} + +table ReducerOptions { + keep_dims: bool; +} + +table SqueezeOptions { + squeeze_dims:[int]; +} + +table SplitOptions { + num_splits: int; +} + +table SplitVOptions { + num_splits: int; +} + +table StridedSliceOptions { + begin_mask: int; + end_mask: int; + ellipsis_mask: int; + new_axis_mask: int; + shrink_axis_mask: int; +} + +table LogSoftmaxOptions { +} + +table CastOptions { + in_data_type: TensorType; + out_data_type: TensorType; +} + +table DequantizeOptions { +} + +table MaximumMinimumOptions { +} + +table TileOptions { +} + +table ArgMaxOptions { + output_type : TensorType; +} + +table ArgMinOptions { + output_type : TensorType; +} + +table GreaterOptions { +} + +table GreaterEqualOptions { +} + +table LessOptions { +} + +table LessEqualOptions { +} + +table NegOptions { +} + +table SelectOptions { +} + +table SliceOptions { +} + +table TransposeConvOptions { + padding:Padding; + stride_w:int; + stride_h:int; +} + +table ExpandDimsOptions { +} + +table SparseToDenseOptions { + validate_indices:bool; +} + +table EqualOptions { +} + +table NotEqualOptions { +} + +table ShapeOptions { + // Optional output type of the operation (int32 or int64). Defaults to int32. + out_type : TensorType; +} + +table RankOptions { +} + +table PowOptions { +} + +table FakeQuantOptions { + // Parameters supported by version 1: + min:float; + max:float; + num_bits:int; + + // Parameters supported by version 2: + narrow_range:bool; +} + +table PackOptions { + values_count:int; + axis:int; +} + +table LogicalOrOptions { +} + +table OneHotOptions { + axis:int; +} + +table AbsOptions { +} + + +table HardSwishOptions { +} + +table LogicalAndOptions { +} + +table LogicalNotOptions { +} + +table UnpackOptions { + num:int; + axis:int; +} + +table FloorDivOptions { +} + +table SquareOptions { +} + +table ZerosLikeOptions { +} + +table FillOptions { +} + +table FloorModOptions { +} + +table RangeOptions { +} + +table LeakyReluOptions { + alpha:float; +} + +table SquaredDifferenceOptions { +} + +enum MirrorPadMode : byte { + // Doesn't include borders. + REFLECT = 0, + // Includes borders. + SYMMETRIC = 1, +} + +table MirrorPadOptions { + mode:MirrorPadMode; +} + +table UniqueOptions { + idx_out_type:TensorType = INT32; +} + +table ReverseV2Options { +} + +table AddNOptions { +} + +table GatherNdOptions { +} + +table WhereOptions { +} + +table ReverseSequenceOptions { + seq_dim:int; + batch_dim:int = 0; +} + +table MatrixDiagOptions { +} + +table QuantizeOptions { +} + +table MatrixSetDiagOptions { +} + +table IfOptions { + then_subgraph_index:int; + else_subgraph_index:int; +} + +table WhileOptions { + cond_subgraph_index:int; + body_subgraph_index:int; +} + +table NonMaxSuppressionV4Options { +} + +table NonMaxSuppressionV5Options { +} + +// An OperatorCode can be an enum value (BuiltinOperator) if the operator is a +// builtin, or a string if the operator is custom. +table OperatorCode { + builtin_code:BuiltinOperator; + custom_code:string; + + // The version of the operator. The version need to be bumped whenever new + // parameters are introduced into an op. + version:int = 1; +} + +enum CustomOptionsFormat : byte { + FLEXBUFFERS = 0, +} + +// An operator takes tensors as inputs and outputs. The type of operation being +// performed is determined by an index into the list of valid OperatorCodes, +// while the specifics of each operations is configured using builtin_options +// or custom_options. +table Operator { + // Index into the operator_codes array. Using an integer here avoids + // complicate map lookups. + opcode_index:uint; + + // Optional input and output tensors are indicated by -1. + inputs:[int]; + outputs:[int]; + + builtin_options:BuiltinOptions; + custom_options:[ubyte]; + custom_options_format:CustomOptionsFormat; + + // A list of booleans indicating the input tensors which are being mutated by + // this operator.(e.g. used by RNN and LSTM). + // For example, if the "inputs" array refers to 5 tensors and the second and + // fifth are mutable variables, then this list will contain + // [false, true, false, false, true]. + // + // If the list is empty, no variable is mutated in this operator. + // The list either has the same length as `inputs`, or is empty. + mutating_variable_inputs:[bool]; + + // A list of indices to the subgraph's "tensors" that are internal to an Op. + // Internal tensors are those that do not flow in or out of the operation, + // but instead are part of internal computation. As such, the operation's + // implementation may manage its memory more efficiently. They are needed + // however (i.e. not just an implementation detail) since they are part of the + // computation, which may require relevant metadata such as quantization + // parameters. + intermediates:[int]; +} + +// The root type, defining a subgraph, which typically represents an entire +// model. +table SubGraph { + // A list of all tensors used in this subgraph. + tensors:[Tensor]; + + // Indices of the tensors that are inputs into this subgraph. Note this is + // the list of non-static tensors that feed into the subgraph for inference. + inputs:[int]; + + // Indices of the tensors that are outputs out of this subgraph. Note this is + // the list of output tensors that are considered the product of the + // subgraph's inference. + outputs:[int]; + + // All operators, in execution order. + operators:[Operator]; + + // Name of this subgraph (used for debugging). + name:string; +} + +// Table of raw data buffers (used for constant tensors). Referenced by tensors +// by index. The generous alignment accommodates mmap-friendly data structures. +table Buffer { + data:[ubyte] (force_align: 16); +} + +table Metadata { + // A human readable string to uniquely identify a Metadata. + name:string; + // An index to the buffers table. + buffer:uint; +} + +table Model { + // Version of the schema. + version:uint; + + // A list of all operator codes used in this model. This is + // kept in order because operators carry an index into this + // vector. + operator_codes:[OperatorCode]; + + // All the subgraphs of the model. The 0th is assumed to be the main + // model. + subgraphs:[SubGraph]; + + // A description of the model. + description:string; + + // Buffers of the model. + // Note the 0th entry of this array must be an empty buffer (sentinel). + // This is a convention so that tensors without a buffer can provide 0 as + // their buffer. + buffers:[Buffer]; + + // Metadata about the model. Indirects into the existings buffers list. + // Deprecated, prefer to use metadata field. + metadata_buffer:[int]; + + // Metadata about the model. + metadata:[Metadata]; +} + +root_type Model; diff --git a/utensor_cgen/third_party/tflite/AbsOptions.py b/utensor_cgen/third_party/tflite/AbsOptions.py new file mode 100644 index 00000000..0cbfb8c0 --- /dev/null +++ b/utensor_cgen/third_party/tflite/AbsOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class AbsOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsAbsOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = AbsOptions() + x.Init(buf, n + offset) + return x + + # AbsOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def AbsOptionsStart(builder): builder.StartObject(0) +def AbsOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/ActivationFunctionType.py b/utensor_cgen/third_party/tflite/ActivationFunctionType.py new file mode 100644 index 00000000..dc177dee --- /dev/null +++ b/utensor_cgen/third_party/tflite/ActivationFunctionType.py @@ -0,0 +1,12 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class ActivationFunctionType(object): + NONE = 0 + RELU = 1 + RELU_N1_TO_1 = 2 + RELU6 = 3 + TANH = 4 + SIGN_BIT = 5 + diff --git a/utensor_cgen/third_party/tflite/AddNOptions.py b/utensor_cgen/third_party/tflite/AddNOptions.py new file mode 100644 index 00000000..b5c2ddb7 --- /dev/null +++ b/utensor_cgen/third_party/tflite/AddNOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class AddNOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsAddNOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = AddNOptions() + x.Init(buf, n + offset) + return x + + # AddNOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def AddNOptionsStart(builder): builder.StartObject(0) +def AddNOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/AddOptions.py b/utensor_cgen/third_party/tflite/AddOptions.py new file mode 100644 index 00000000..d6cbfcf5 --- /dev/null +++ b/utensor_cgen/third_party/tflite/AddOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class AddOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsAddOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = AddOptions() + x.Init(buf, n + offset) + return x + + # AddOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # AddOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def AddOptionsStart(builder): builder.StartObject(1) +def AddOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) +def AddOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/ArgMaxOptions.py b/utensor_cgen/third_party/tflite/ArgMaxOptions.py new file mode 100644 index 00000000..fbf1415e --- /dev/null +++ b/utensor_cgen/third_party/tflite/ArgMaxOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ArgMaxOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsArgMaxOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ArgMaxOptions() + x.Init(buf, n + offset) + return x + + # ArgMaxOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # ArgMaxOptions + def OutputType(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def ArgMaxOptionsStart(builder): builder.StartObject(1) +def ArgMaxOptionsAddOutputType(builder, outputType): builder.PrependInt8Slot(0, outputType, 0) +def ArgMaxOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/ArgMinOptions.py b/utensor_cgen/third_party/tflite/ArgMinOptions.py new file mode 100644 index 00000000..120fdca2 --- /dev/null +++ b/utensor_cgen/third_party/tflite/ArgMinOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ArgMinOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsArgMinOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ArgMinOptions() + x.Init(buf, n + offset) + return x + + # ArgMinOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # ArgMinOptions + def OutputType(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def ArgMinOptionsStart(builder): builder.StartObject(1) +def ArgMinOptionsAddOutputType(builder, outputType): builder.PrependInt8Slot(0, outputType, 0) +def ArgMinOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/BatchToSpaceNDOptions.py b/utensor_cgen/third_party/tflite/BatchToSpaceNDOptions.py new file mode 100644 index 00000000..3ddcfd3f --- /dev/null +++ b/utensor_cgen/third_party/tflite/BatchToSpaceNDOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class BatchToSpaceNDOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsBatchToSpaceNDOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = BatchToSpaceNDOptions() + x.Init(buf, n + offset) + return x + + # BatchToSpaceNDOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def BatchToSpaceNDOptionsStart(builder): builder.StartObject(0) +def BatchToSpaceNDOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/BidirectionalSequenceLSTMOptions.py b/utensor_cgen/third_party/tflite/BidirectionalSequenceLSTMOptions.py new file mode 100644 index 00000000..8d8b7bea --- /dev/null +++ b/utensor_cgen/third_party/tflite/BidirectionalSequenceLSTMOptions.py @@ -0,0 +1,62 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class BidirectionalSequenceLSTMOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsBidirectionalSequenceLSTMOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = BidirectionalSequenceLSTMOptions() + x.Init(buf, n + offset) + return x + + # BidirectionalSequenceLSTMOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # BidirectionalSequenceLSTMOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # BidirectionalSequenceLSTMOptions + def CellClip(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + + # BidirectionalSequenceLSTMOptions + def ProjClip(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + + # BidirectionalSequenceLSTMOptions + def MergeOutputs(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + + # BidirectionalSequenceLSTMOptions + def TimeMajor(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return True + +def BidirectionalSequenceLSTMOptionsStart(builder): builder.StartObject(5) +def BidirectionalSequenceLSTMOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) +def BidirectionalSequenceLSTMOptionsAddCellClip(builder, cellClip): builder.PrependFloat32Slot(1, cellClip, 0.0) +def BidirectionalSequenceLSTMOptionsAddProjClip(builder, projClip): builder.PrependFloat32Slot(2, projClip, 0.0) +def BidirectionalSequenceLSTMOptionsAddMergeOutputs(builder, mergeOutputs): builder.PrependBoolSlot(3, mergeOutputs, 0) +def BidirectionalSequenceLSTMOptionsAddTimeMajor(builder, timeMajor): builder.PrependBoolSlot(4, timeMajor, 1) +def BidirectionalSequenceLSTMOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/BidirectionalSequenceRNNOptions.py b/utensor_cgen/third_party/tflite/BidirectionalSequenceRNNOptions.py new file mode 100644 index 00000000..673af6b9 --- /dev/null +++ b/utensor_cgen/third_party/tflite/BidirectionalSequenceRNNOptions.py @@ -0,0 +1,46 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class BidirectionalSequenceRNNOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsBidirectionalSequenceRNNOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = BidirectionalSequenceRNNOptions() + x.Init(buf, n + offset) + return x + + # BidirectionalSequenceRNNOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # BidirectionalSequenceRNNOptions + def TimeMajor(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + + # BidirectionalSequenceRNNOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # BidirectionalSequenceRNNOptions + def MergeOutputs(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + +def BidirectionalSequenceRNNOptionsStart(builder): builder.StartObject(3) +def BidirectionalSequenceRNNOptionsAddTimeMajor(builder, timeMajor): builder.PrependBoolSlot(0, timeMajor, 0) +def BidirectionalSequenceRNNOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(1, fusedActivationFunction, 0) +def BidirectionalSequenceRNNOptionsAddMergeOutputs(builder, mergeOutputs): builder.PrependBoolSlot(2, mergeOutputs, 0) +def BidirectionalSequenceRNNOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/Buffer.py b/utensor_cgen/third_party/tflite/Buffer.py new file mode 100644 index 00000000..754dee3b --- /dev/null +++ b/utensor_cgen/third_party/tflite/Buffer.py @@ -0,0 +1,46 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class Buffer(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsBuffer(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = Buffer() + x.Init(buf, n + offset) + return x + + # Buffer + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # Buffer + def Data(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Uint8Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1)) + return 0 + + # Buffer + def DataAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint8Flags, o) + return 0 + + # Buffer + def DataLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + +def BufferStart(builder): builder.StartObject(1) +def BufferAddData(builder, data): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(data), 0) +def BufferStartDataVector(builder, numElems): return builder.StartVector(1, numElems, 1) +def BufferEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/BuiltinOperator.py b/utensor_cgen/third_party/tflite/BuiltinOperator.py new file mode 100644 index 00000000..61028141 --- /dev/null +++ b/utensor_cgen/third_party/tflite/BuiltinOperator.py @@ -0,0 +1,128 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class BuiltinOperator(object): + ADD = 0 + AVERAGE_POOL_2D = 1 + CONCATENATION = 2 + CONV_2D = 3 + DEPTHWISE_CONV_2D = 4 + DEPTH_TO_SPACE = 5 + DEQUANTIZE = 6 + EMBEDDING_LOOKUP = 7 + FLOOR = 8 + FULLY_CONNECTED = 9 + HASHTABLE_LOOKUP = 10 + L2_NORMALIZATION = 11 + L2_POOL_2D = 12 + LOCAL_RESPONSE_NORMALIZATION = 13 + LOGISTIC = 14 + LSH_PROJECTION = 15 + LSTM = 16 + MAX_POOL_2D = 17 + MUL = 18 + RELU = 19 + RELU_N1_TO_1 = 20 + RELU6 = 21 + RESHAPE = 22 + RESIZE_BILINEAR = 23 + RNN = 24 + SOFTMAX = 25 + SPACE_TO_DEPTH = 26 + SVDF = 27 + TANH = 28 + CONCAT_EMBEDDINGS = 29 + SKIP_GRAM = 30 + CALL = 31 + CUSTOM = 32 + EMBEDDING_LOOKUP_SPARSE = 33 + PAD = 34 + UNIDIRECTIONAL_SEQUENCE_RNN = 35 + GATHER = 36 + BATCH_TO_SPACE_ND = 37 + SPACE_TO_BATCH_ND = 38 + TRANSPOSE = 39 + MEAN = 40 + SUB = 41 + DIV = 42 + SQUEEZE = 43 + UNIDIRECTIONAL_SEQUENCE_LSTM = 44 + STRIDED_SLICE = 45 + BIDIRECTIONAL_SEQUENCE_RNN = 46 + EXP = 47 + TOPK_V2 = 48 + SPLIT = 49 + LOG_SOFTMAX = 50 + DELEGATE = 51 + BIDIRECTIONAL_SEQUENCE_LSTM = 52 + CAST = 53 + PRELU = 54 + MAXIMUM = 55 + ARG_MAX = 56 + MINIMUM = 57 + LESS = 58 + NEG = 59 + PADV2 = 60 + GREATER = 61 + GREATER_EQUAL = 62 + LESS_EQUAL = 63 + SELECT = 64 + SLICE = 65 + SIN = 66 + TRANSPOSE_CONV = 67 + SPARSE_TO_DENSE = 68 + TILE = 69 + EXPAND_DIMS = 70 + EQUAL = 71 + NOT_EQUAL = 72 + LOG = 73 + SUM = 74 + SQRT = 75 + RSQRT = 76 + SHAPE = 77 + POW = 78 + ARG_MIN = 79 + FAKE_QUANT = 80 + REDUCE_PROD = 81 + REDUCE_MAX = 82 + PACK = 83 + LOGICAL_OR = 84 + ONE_HOT = 85 + LOGICAL_AND = 86 + LOGICAL_NOT = 87 + UNPACK = 88 + REDUCE_MIN = 89 + FLOOR_DIV = 90 + REDUCE_ANY = 91 + SQUARE = 92 + ZEROS_LIKE = 93 + FILL = 94 + FLOOR_MOD = 95 + RANGE = 96 + RESIZE_NEAREST_NEIGHBOR = 97 + LEAKY_RELU = 98 + SQUARED_DIFFERENCE = 99 + MIRROR_PAD = 100 + ABS = 101 + SPLIT_V = 102 + UNIQUE = 103 + CEIL = 104 + REVERSE_V2 = 105 + ADD_N = 106 + GATHER_ND = 107 + COS = 108 + WHERE = 109 + RANK = 110 + ELU = 111 + REVERSE_SEQUENCE = 112 + MATRIX_DIAG = 113 + QUANTIZE = 114 + MATRIX_SET_DIAG = 115 + ROUND = 116 + HARD_SWISH = 117 + IF = 118 + WHILE = 119 + NON_MAX_SUPPRESSION_V4 = 120 + NON_MAX_SUPPRESSION_V5 = 121 + diff --git a/utensor_cgen/third_party/tflite/BuiltinOptions.py b/utensor_cgen/third_party/tflite/BuiltinOptions.py new file mode 100644 index 00000000..a7957d28 --- /dev/null +++ b/utensor_cgen/third_party/tflite/BuiltinOptions.py @@ -0,0 +1,103 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class BuiltinOptions(object): + NONE = 0 + Conv2DOptions = 1 + DepthwiseConv2DOptions = 2 + ConcatEmbeddingsOptions = 3 + LSHProjectionOptions = 4 + Pool2DOptions = 5 + SVDFOptions = 6 + RNNOptions = 7 + FullyConnectedOptions = 8 + SoftmaxOptions = 9 + ConcatenationOptions = 10 + AddOptions = 11 + L2NormOptions = 12 + LocalResponseNormalizationOptions = 13 + LSTMOptions = 14 + ResizeBilinearOptions = 15 + CallOptions = 16 + ReshapeOptions = 17 + SkipGramOptions = 18 + SpaceToDepthOptions = 19 + EmbeddingLookupSparseOptions = 20 + MulOptions = 21 + PadOptions = 22 + GatherOptions = 23 + BatchToSpaceNDOptions = 24 + SpaceToBatchNDOptions = 25 + TransposeOptions = 26 + ReducerOptions = 27 + SubOptions = 28 + DivOptions = 29 + SqueezeOptions = 30 + SequenceRNNOptions = 31 + StridedSliceOptions = 32 + ExpOptions = 33 + TopKV2Options = 34 + SplitOptions = 35 + LogSoftmaxOptions = 36 + CastOptions = 37 + DequantizeOptions = 38 + MaximumMinimumOptions = 39 + ArgMaxOptions = 40 + LessOptions = 41 + NegOptions = 42 + PadV2Options = 43 + GreaterOptions = 44 + GreaterEqualOptions = 45 + LessEqualOptions = 46 + SelectOptions = 47 + SliceOptions = 48 + TransposeConvOptions = 49 + SparseToDenseOptions = 50 + TileOptions = 51 + ExpandDimsOptions = 52 + EqualOptions = 53 + NotEqualOptions = 54 + ShapeOptions = 55 + PowOptions = 56 + ArgMinOptions = 57 + FakeQuantOptions = 58 + PackOptions = 59 + LogicalOrOptions = 60 + OneHotOptions = 61 + LogicalAndOptions = 62 + LogicalNotOptions = 63 + UnpackOptions = 64 + FloorDivOptions = 65 + SquareOptions = 66 + ZerosLikeOptions = 67 + FillOptions = 68 + BidirectionalSequenceLSTMOptions = 69 + BidirectionalSequenceRNNOptions = 70 + UnidirectionalSequenceLSTMOptions = 71 + FloorModOptions = 72 + RangeOptions = 73 + ResizeNearestNeighborOptions = 74 + LeakyReluOptions = 75 + SquaredDifferenceOptions = 76 + MirrorPadOptions = 77 + AbsOptions = 78 + SplitVOptions = 79 + UniqueOptions = 80 + ReverseV2Options = 81 + AddNOptions = 82 + GatherNdOptions = 83 + CosOptions = 84 + WhereOptions = 85 + RankOptions = 86 + ReverseSequenceOptions = 87 + MatrixDiagOptions = 88 + QuantizeOptions = 89 + MatrixSetDiagOptions = 90 + HardSwishOptions = 91 + IfOptions = 92 + WhileOptions = 93 + DepthToSpaceOptions = 94 + NonMaxSuppressionV4Options = 95 + NonMaxSuppressionV5Options = 96 + diff --git a/utensor_cgen/third_party/tflite/CallOptions.py b/utensor_cgen/third_party/tflite/CallOptions.py new file mode 100644 index 00000000..5ae2eeae --- /dev/null +++ b/utensor_cgen/third_party/tflite/CallOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class CallOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsCallOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = CallOptions() + x.Init(buf, n + offset) + return x + + # CallOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # CallOptions + def Subgraph(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos) + return 0 + +def CallOptionsStart(builder): builder.StartObject(1) +def CallOptionsAddSubgraph(builder, subgraph): builder.PrependUint32Slot(0, subgraph, 0) +def CallOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/CastOptions.py b/utensor_cgen/third_party/tflite/CastOptions.py new file mode 100644 index 00000000..70ae2e37 --- /dev/null +++ b/utensor_cgen/third_party/tflite/CastOptions.py @@ -0,0 +1,38 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class CastOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsCastOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = CastOptions() + x.Init(buf, n + offset) + return x + + # CastOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # CastOptions + def InDataType(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # CastOptions + def OutDataType(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def CastOptionsStart(builder): builder.StartObject(2) +def CastOptionsAddInDataType(builder, inDataType): builder.PrependInt8Slot(0, inDataType, 0) +def CastOptionsAddOutDataType(builder, outDataType): builder.PrependInt8Slot(1, outDataType, 0) +def CastOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/CombinerType.py b/utensor_cgen/third_party/tflite/CombinerType.py new file mode 100644 index 00000000..17d91510 --- /dev/null +++ b/utensor_cgen/third_party/tflite/CombinerType.py @@ -0,0 +1,9 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class CombinerType(object): + SUM = 0 + MEAN = 1 + SQRTN = 2 + diff --git a/utensor_cgen/third_party/tflite/ConcatEmbeddingsOptions.py b/utensor_cgen/third_party/tflite/ConcatEmbeddingsOptions.py new file mode 100644 index 00000000..9d26c510 --- /dev/null +++ b/utensor_cgen/third_party/tflite/ConcatEmbeddingsOptions.py @@ -0,0 +1,78 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ConcatEmbeddingsOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsConcatEmbeddingsOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ConcatEmbeddingsOptions() + x.Init(buf, n + offset) + return x + + # ConcatEmbeddingsOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # ConcatEmbeddingsOptions + def NumChannels(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # ConcatEmbeddingsOptions + def NumColumnsPerChannel(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # ConcatEmbeddingsOptions + def NumColumnsPerChannelAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) + return 0 + + # ConcatEmbeddingsOptions + def NumColumnsPerChannelLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # ConcatEmbeddingsOptions + def EmbeddingDimPerChannel(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # ConcatEmbeddingsOptions + def EmbeddingDimPerChannelAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) + return 0 + + # ConcatEmbeddingsOptions + def EmbeddingDimPerChannelLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + +def ConcatEmbeddingsOptionsStart(builder): builder.StartObject(3) +def ConcatEmbeddingsOptionsAddNumChannels(builder, numChannels): builder.PrependInt32Slot(0, numChannels, 0) +def ConcatEmbeddingsOptionsAddNumColumnsPerChannel(builder, numColumnsPerChannel): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(numColumnsPerChannel), 0) +def ConcatEmbeddingsOptionsStartNumColumnsPerChannelVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def ConcatEmbeddingsOptionsAddEmbeddingDimPerChannel(builder, embeddingDimPerChannel): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(embeddingDimPerChannel), 0) +def ConcatEmbeddingsOptionsStartEmbeddingDimPerChannelVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def ConcatEmbeddingsOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/ConcatenationOptions.py b/utensor_cgen/third_party/tflite/ConcatenationOptions.py new file mode 100644 index 00000000..c8e0b6ab --- /dev/null +++ b/utensor_cgen/third_party/tflite/ConcatenationOptions.py @@ -0,0 +1,38 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ConcatenationOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsConcatenationOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ConcatenationOptions() + x.Init(buf, n + offset) + return x + + # ConcatenationOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # ConcatenationOptions + def Axis(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # ConcatenationOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def ConcatenationOptionsStart(builder): builder.StartObject(2) +def ConcatenationOptionsAddAxis(builder, axis): builder.PrependInt32Slot(0, axis, 0) +def ConcatenationOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(1, fusedActivationFunction, 0) +def ConcatenationOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/Conv2DOptions.py b/utensor_cgen/third_party/tflite/Conv2DOptions.py new file mode 100644 index 00000000..ef49f751 --- /dev/null +++ b/utensor_cgen/third_party/tflite/Conv2DOptions.py @@ -0,0 +1,70 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class Conv2DOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsConv2DOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = Conv2DOptions() + x.Init(buf, n + offset) + return x + + # Conv2DOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # Conv2DOptions + def Padding(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # Conv2DOptions + def StrideW(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # Conv2DOptions + def StrideH(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # Conv2DOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # Conv2DOptions + def DilationWFactor(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 1 + + # Conv2DOptions + def DilationHFactor(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 1 + +def Conv2DOptionsStart(builder): builder.StartObject(6) +def Conv2DOptionsAddPadding(builder, padding): builder.PrependInt8Slot(0, padding, 0) +def Conv2DOptionsAddStrideW(builder, strideW): builder.PrependInt32Slot(1, strideW, 0) +def Conv2DOptionsAddStrideH(builder, strideH): builder.PrependInt32Slot(2, strideH, 0) +def Conv2DOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(3, fusedActivationFunction, 0) +def Conv2DOptionsAddDilationWFactor(builder, dilationWFactor): builder.PrependInt32Slot(4, dilationWFactor, 1) +def Conv2DOptionsAddDilationHFactor(builder, dilationHFactor): builder.PrependInt32Slot(5, dilationHFactor, 1) +def Conv2DOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/CosOptions.py b/utensor_cgen/third_party/tflite/CosOptions.py new file mode 100644 index 00000000..7fbf8487 --- /dev/null +++ b/utensor_cgen/third_party/tflite/CosOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class CosOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsCosOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = CosOptions() + x.Init(buf, n + offset) + return x + + # CosOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def CosOptionsStart(builder): builder.StartObject(0) +def CosOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/CustomOptionsFormat.py b/utensor_cgen/third_party/tflite/CustomOptionsFormat.py new file mode 100644 index 00000000..bef384cf --- /dev/null +++ b/utensor_cgen/third_party/tflite/CustomOptionsFormat.py @@ -0,0 +1,7 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class CustomOptionsFormat(object): + FLEXBUFFERS = 0 + diff --git a/utensor_cgen/third_party/tflite/CustomQuantization.py b/utensor_cgen/third_party/tflite/CustomQuantization.py new file mode 100644 index 00000000..21ec0da4 --- /dev/null +++ b/utensor_cgen/third_party/tflite/CustomQuantization.py @@ -0,0 +1,46 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class CustomQuantization(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsCustomQuantization(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = CustomQuantization() + x.Init(buf, n + offset) + return x + + # CustomQuantization + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # CustomQuantization + def Custom(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Uint8Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1)) + return 0 + + # CustomQuantization + def CustomAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint8Flags, o) + return 0 + + # CustomQuantization + def CustomLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + +def CustomQuantizationStart(builder): builder.StartObject(1) +def CustomQuantizationAddCustom(builder, custom): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(custom), 0) +def CustomQuantizationStartCustomVector(builder, numElems): return builder.StartVector(1, numElems, 1) +def CustomQuantizationEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/DepthToSpaceOptions.py b/utensor_cgen/third_party/tflite/DepthToSpaceOptions.py new file mode 100644 index 00000000..97b93aa7 --- /dev/null +++ b/utensor_cgen/third_party/tflite/DepthToSpaceOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class DepthToSpaceOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsDepthToSpaceOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = DepthToSpaceOptions() + x.Init(buf, n + offset) + return x + + # DepthToSpaceOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # DepthToSpaceOptions + def BlockSize(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def DepthToSpaceOptionsStart(builder): builder.StartObject(1) +def DepthToSpaceOptionsAddBlockSize(builder, blockSize): builder.PrependInt32Slot(0, blockSize, 0) +def DepthToSpaceOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/DepthwiseConv2DOptions.py b/utensor_cgen/third_party/tflite/DepthwiseConv2DOptions.py new file mode 100644 index 00000000..9689383b --- /dev/null +++ b/utensor_cgen/third_party/tflite/DepthwiseConv2DOptions.py @@ -0,0 +1,78 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class DepthwiseConv2DOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsDepthwiseConv2DOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = DepthwiseConv2DOptions() + x.Init(buf, n + offset) + return x + + # DepthwiseConv2DOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # DepthwiseConv2DOptions + def Padding(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # DepthwiseConv2DOptions + def StrideW(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # DepthwiseConv2DOptions + def StrideH(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # DepthwiseConv2DOptions + def DepthMultiplier(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # DepthwiseConv2DOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # DepthwiseConv2DOptions + def DilationWFactor(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 1 + + # DepthwiseConv2DOptions + def DilationHFactor(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 1 + +def DepthwiseConv2DOptionsStart(builder): builder.StartObject(7) +def DepthwiseConv2DOptionsAddPadding(builder, padding): builder.PrependInt8Slot(0, padding, 0) +def DepthwiseConv2DOptionsAddStrideW(builder, strideW): builder.PrependInt32Slot(1, strideW, 0) +def DepthwiseConv2DOptionsAddStrideH(builder, strideH): builder.PrependInt32Slot(2, strideH, 0) +def DepthwiseConv2DOptionsAddDepthMultiplier(builder, depthMultiplier): builder.PrependInt32Slot(3, depthMultiplier, 0) +def DepthwiseConv2DOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(4, fusedActivationFunction, 0) +def DepthwiseConv2DOptionsAddDilationWFactor(builder, dilationWFactor): builder.PrependInt32Slot(5, dilationWFactor, 1) +def DepthwiseConv2DOptionsAddDilationHFactor(builder, dilationHFactor): builder.PrependInt32Slot(6, dilationHFactor, 1) +def DepthwiseConv2DOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/DequantizeOptions.py b/utensor_cgen/third_party/tflite/DequantizeOptions.py new file mode 100644 index 00000000..5ef8b8dd --- /dev/null +++ b/utensor_cgen/third_party/tflite/DequantizeOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class DequantizeOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsDequantizeOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = DequantizeOptions() + x.Init(buf, n + offset) + return x + + # DequantizeOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def DequantizeOptionsStart(builder): builder.StartObject(0) +def DequantizeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/DivOptions.py b/utensor_cgen/third_party/tflite/DivOptions.py new file mode 100644 index 00000000..905a3be0 --- /dev/null +++ b/utensor_cgen/third_party/tflite/DivOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class DivOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsDivOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = DivOptions() + x.Init(buf, n + offset) + return x + + # DivOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # DivOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def DivOptionsStart(builder): builder.StartObject(1) +def DivOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) +def DivOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/EmbeddingLookupSparseOptions.py b/utensor_cgen/third_party/tflite/EmbeddingLookupSparseOptions.py new file mode 100644 index 00000000..7d9c1442 --- /dev/null +++ b/utensor_cgen/third_party/tflite/EmbeddingLookupSparseOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class EmbeddingLookupSparseOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsEmbeddingLookupSparseOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = EmbeddingLookupSparseOptions() + x.Init(buf, n + offset) + return x + + # EmbeddingLookupSparseOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # EmbeddingLookupSparseOptions + def Combiner(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def EmbeddingLookupSparseOptionsStart(builder): builder.StartObject(1) +def EmbeddingLookupSparseOptionsAddCombiner(builder, combiner): builder.PrependInt8Slot(0, combiner, 0) +def EmbeddingLookupSparseOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/EqualOptions.py b/utensor_cgen/third_party/tflite/EqualOptions.py new file mode 100644 index 00000000..f787ef85 --- /dev/null +++ b/utensor_cgen/third_party/tflite/EqualOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class EqualOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsEqualOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = EqualOptions() + x.Init(buf, n + offset) + return x + + # EqualOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def EqualOptionsStart(builder): builder.StartObject(0) +def EqualOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/ExpOptions.py b/utensor_cgen/third_party/tflite/ExpOptions.py new file mode 100644 index 00000000..eac1456e --- /dev/null +++ b/utensor_cgen/third_party/tflite/ExpOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ExpOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsExpOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ExpOptions() + x.Init(buf, n + offset) + return x + + # ExpOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def ExpOptionsStart(builder): builder.StartObject(0) +def ExpOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/ExpandDimsOptions.py b/utensor_cgen/third_party/tflite/ExpandDimsOptions.py new file mode 100644 index 00000000..69d63665 --- /dev/null +++ b/utensor_cgen/third_party/tflite/ExpandDimsOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ExpandDimsOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsExpandDimsOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ExpandDimsOptions() + x.Init(buf, n + offset) + return x + + # ExpandDimsOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def ExpandDimsOptionsStart(builder): builder.StartObject(0) +def ExpandDimsOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/FakeQuantOptions.py b/utensor_cgen/third_party/tflite/FakeQuantOptions.py new file mode 100644 index 00000000..46c371c3 --- /dev/null +++ b/utensor_cgen/third_party/tflite/FakeQuantOptions.py @@ -0,0 +1,54 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class FakeQuantOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsFakeQuantOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = FakeQuantOptions() + x.Init(buf, n + offset) + return x + + # FakeQuantOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # FakeQuantOptions + def Min(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + + # FakeQuantOptions + def Max(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + + # FakeQuantOptions + def NumBits(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # FakeQuantOptions + def NarrowRange(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + +def FakeQuantOptionsStart(builder): builder.StartObject(4) +def FakeQuantOptionsAddMin(builder, min): builder.PrependFloat32Slot(0, min, 0.0) +def FakeQuantOptionsAddMax(builder, max): builder.PrependFloat32Slot(1, max, 0.0) +def FakeQuantOptionsAddNumBits(builder, numBits): builder.PrependInt32Slot(2, numBits, 0) +def FakeQuantOptionsAddNarrowRange(builder, narrowRange): builder.PrependBoolSlot(3, narrowRange, 0) +def FakeQuantOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/FillOptions.py b/utensor_cgen/third_party/tflite/FillOptions.py new file mode 100644 index 00000000..5a1e651a --- /dev/null +++ b/utensor_cgen/third_party/tflite/FillOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class FillOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsFillOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = FillOptions() + x.Init(buf, n + offset) + return x + + # FillOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def FillOptionsStart(builder): builder.StartObject(0) +def FillOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/FloorDivOptions.py b/utensor_cgen/third_party/tflite/FloorDivOptions.py new file mode 100644 index 00000000..64b474fb --- /dev/null +++ b/utensor_cgen/third_party/tflite/FloorDivOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class FloorDivOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsFloorDivOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = FloorDivOptions() + x.Init(buf, n + offset) + return x + + # FloorDivOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def FloorDivOptionsStart(builder): builder.StartObject(0) +def FloorDivOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/FloorModOptions.py b/utensor_cgen/third_party/tflite/FloorModOptions.py new file mode 100644 index 00000000..37c8e5a5 --- /dev/null +++ b/utensor_cgen/third_party/tflite/FloorModOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class FloorModOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsFloorModOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = FloorModOptions() + x.Init(buf, n + offset) + return x + + # FloorModOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def FloorModOptionsStart(builder): builder.StartObject(0) +def FloorModOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/FullyConnectedOptions.py b/utensor_cgen/third_party/tflite/FullyConnectedOptions.py new file mode 100644 index 00000000..a6b4e40f --- /dev/null +++ b/utensor_cgen/third_party/tflite/FullyConnectedOptions.py @@ -0,0 +1,46 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class FullyConnectedOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsFullyConnectedOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = FullyConnectedOptions() + x.Init(buf, n + offset) + return x + + # FullyConnectedOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # FullyConnectedOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # FullyConnectedOptions + def WeightsFormat(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # FullyConnectedOptions + def KeepNumDims(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + +def FullyConnectedOptionsStart(builder): builder.StartObject(3) +def FullyConnectedOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) +def FullyConnectedOptionsAddWeightsFormat(builder, weightsFormat): builder.PrependInt8Slot(1, weightsFormat, 0) +def FullyConnectedOptionsAddKeepNumDims(builder, keepNumDims): builder.PrependBoolSlot(2, keepNumDims, 0) +def FullyConnectedOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/FullyConnectedOptionsWeightsFormat.py b/utensor_cgen/third_party/tflite/FullyConnectedOptionsWeightsFormat.py new file mode 100644 index 00000000..6d81eca1 --- /dev/null +++ b/utensor_cgen/third_party/tflite/FullyConnectedOptionsWeightsFormat.py @@ -0,0 +1,8 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class FullyConnectedOptionsWeightsFormat(object): + DEFAULT = 0 + SHUFFLED4x16INT8 = 1 + diff --git a/utensor_cgen/third_party/tflite/GatherNdOptions.py b/utensor_cgen/third_party/tflite/GatherNdOptions.py new file mode 100644 index 00000000..f515eb5c --- /dev/null +++ b/utensor_cgen/third_party/tflite/GatherNdOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class GatherNdOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsGatherNdOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = GatherNdOptions() + x.Init(buf, n + offset) + return x + + # GatherNdOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def GatherNdOptionsStart(builder): builder.StartObject(0) +def GatherNdOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/GatherOptions.py b/utensor_cgen/third_party/tflite/GatherOptions.py new file mode 100644 index 00000000..9fbc3e40 --- /dev/null +++ b/utensor_cgen/third_party/tflite/GatherOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class GatherOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsGatherOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = GatherOptions() + x.Init(buf, n + offset) + return x + + # GatherOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # GatherOptions + def Axis(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def GatherOptionsStart(builder): builder.StartObject(1) +def GatherOptionsAddAxis(builder, axis): builder.PrependInt32Slot(0, axis, 0) +def GatherOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/GreaterEqualOptions.py b/utensor_cgen/third_party/tflite/GreaterEqualOptions.py new file mode 100644 index 00000000..a29e200a --- /dev/null +++ b/utensor_cgen/third_party/tflite/GreaterEqualOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class GreaterEqualOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsGreaterEqualOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = GreaterEqualOptions() + x.Init(buf, n + offset) + return x + + # GreaterEqualOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def GreaterEqualOptionsStart(builder): builder.StartObject(0) +def GreaterEqualOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/GreaterOptions.py b/utensor_cgen/third_party/tflite/GreaterOptions.py new file mode 100644 index 00000000..59d63501 --- /dev/null +++ b/utensor_cgen/third_party/tflite/GreaterOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class GreaterOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsGreaterOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = GreaterOptions() + x.Init(buf, n + offset) + return x + + # GreaterOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def GreaterOptionsStart(builder): builder.StartObject(0) +def GreaterOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/HardSwishOptions.py b/utensor_cgen/third_party/tflite/HardSwishOptions.py new file mode 100644 index 00000000..4f6a5200 --- /dev/null +++ b/utensor_cgen/third_party/tflite/HardSwishOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class HardSwishOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsHardSwishOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = HardSwishOptions() + x.Init(buf, n + offset) + return x + + # HardSwishOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def HardSwishOptionsStart(builder): builder.StartObject(0) +def HardSwishOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/IfOptions.py b/utensor_cgen/third_party/tflite/IfOptions.py new file mode 100644 index 00000000..13f4e697 --- /dev/null +++ b/utensor_cgen/third_party/tflite/IfOptions.py @@ -0,0 +1,38 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class IfOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsIfOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = IfOptions() + x.Init(buf, n + offset) + return x + + # IfOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # IfOptions + def ThenSubgraphIndex(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # IfOptions + def ElseSubgraphIndex(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def IfOptionsStart(builder): builder.StartObject(2) +def IfOptionsAddThenSubgraphIndex(builder, thenSubgraphIndex): builder.PrependInt32Slot(0, thenSubgraphIndex, 0) +def IfOptionsAddElseSubgraphIndex(builder, elseSubgraphIndex): builder.PrependInt32Slot(1, elseSubgraphIndex, 0) +def IfOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/L2NormOptions.py b/utensor_cgen/third_party/tflite/L2NormOptions.py new file mode 100644 index 00000000..38bdf573 --- /dev/null +++ b/utensor_cgen/third_party/tflite/L2NormOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class L2NormOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsL2NormOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = L2NormOptions() + x.Init(buf, n + offset) + return x + + # L2NormOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # L2NormOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def L2NormOptionsStart(builder): builder.StartObject(1) +def L2NormOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) +def L2NormOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/LSHProjectionOptions.py b/utensor_cgen/third_party/tflite/LSHProjectionOptions.py new file mode 100644 index 00000000..ad550be2 --- /dev/null +++ b/utensor_cgen/third_party/tflite/LSHProjectionOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class LSHProjectionOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLSHProjectionOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LSHProjectionOptions() + x.Init(buf, n + offset) + return x + + # LSHProjectionOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # LSHProjectionOptions + def Type(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def LSHProjectionOptionsStart(builder): builder.StartObject(1) +def LSHProjectionOptionsAddType(builder, type): builder.PrependInt8Slot(0, type, 0) +def LSHProjectionOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/LSHProjectionType.py b/utensor_cgen/third_party/tflite/LSHProjectionType.py new file mode 100644 index 00000000..d6a136e8 --- /dev/null +++ b/utensor_cgen/third_party/tflite/LSHProjectionType.py @@ -0,0 +1,9 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class LSHProjectionType(object): + UNKNOWN = 0 + SPARSE = 1 + DENSE = 2 + diff --git a/utensor_cgen/third_party/tflite/LSTMKernelType.py b/utensor_cgen/third_party/tflite/LSTMKernelType.py new file mode 100644 index 00000000..1ca4df0a --- /dev/null +++ b/utensor_cgen/third_party/tflite/LSTMKernelType.py @@ -0,0 +1,8 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class LSTMKernelType(object): + FULL = 0 + BASIC = 1 + diff --git a/utensor_cgen/third_party/tflite/LSTMOptions.py b/utensor_cgen/third_party/tflite/LSTMOptions.py new file mode 100644 index 00000000..93a83093 --- /dev/null +++ b/utensor_cgen/third_party/tflite/LSTMOptions.py @@ -0,0 +1,54 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class LSTMOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLSTMOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LSTMOptions() + x.Init(buf, n + offset) + return x + + # LSTMOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # LSTMOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # LSTMOptions + def CellClip(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + + # LSTMOptions + def ProjClip(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + + # LSTMOptions + def KernelType(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def LSTMOptionsStart(builder): builder.StartObject(4) +def LSTMOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) +def LSTMOptionsAddCellClip(builder, cellClip): builder.PrependFloat32Slot(1, cellClip, 0.0) +def LSTMOptionsAddProjClip(builder, projClip): builder.PrependFloat32Slot(2, projClip, 0.0) +def LSTMOptionsAddKernelType(builder, kernelType): builder.PrependInt8Slot(3, kernelType, 0) +def LSTMOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/LeakyReluOptions.py b/utensor_cgen/third_party/tflite/LeakyReluOptions.py new file mode 100644 index 00000000..b61b21d5 --- /dev/null +++ b/utensor_cgen/third_party/tflite/LeakyReluOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class LeakyReluOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLeakyReluOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LeakyReluOptions() + x.Init(buf, n + offset) + return x + + # LeakyReluOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # LeakyReluOptions + def Alpha(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + +def LeakyReluOptionsStart(builder): builder.StartObject(1) +def LeakyReluOptionsAddAlpha(builder, alpha): builder.PrependFloat32Slot(0, alpha, 0.0) +def LeakyReluOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/LessEqualOptions.py b/utensor_cgen/third_party/tflite/LessEqualOptions.py new file mode 100644 index 00000000..d49b7289 --- /dev/null +++ b/utensor_cgen/third_party/tflite/LessEqualOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class LessEqualOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLessEqualOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LessEqualOptions() + x.Init(buf, n + offset) + return x + + # LessEqualOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def LessEqualOptionsStart(builder): builder.StartObject(0) +def LessEqualOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/LessOptions.py b/utensor_cgen/third_party/tflite/LessOptions.py new file mode 100644 index 00000000..469cb0b0 --- /dev/null +++ b/utensor_cgen/third_party/tflite/LessOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class LessOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLessOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LessOptions() + x.Init(buf, n + offset) + return x + + # LessOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def LessOptionsStart(builder): builder.StartObject(0) +def LessOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/LocalResponseNormalizationOptions.py b/utensor_cgen/third_party/tflite/LocalResponseNormalizationOptions.py new file mode 100644 index 00000000..db875603 --- /dev/null +++ b/utensor_cgen/third_party/tflite/LocalResponseNormalizationOptions.py @@ -0,0 +1,54 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class LocalResponseNormalizationOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLocalResponseNormalizationOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LocalResponseNormalizationOptions() + x.Init(buf, n + offset) + return x + + # LocalResponseNormalizationOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # LocalResponseNormalizationOptions + def Radius(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # LocalResponseNormalizationOptions + def Bias(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + + # LocalResponseNormalizationOptions + def Alpha(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + + # LocalResponseNormalizationOptions + def Beta(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + +def LocalResponseNormalizationOptionsStart(builder): builder.StartObject(4) +def LocalResponseNormalizationOptionsAddRadius(builder, radius): builder.PrependInt32Slot(0, radius, 0) +def LocalResponseNormalizationOptionsAddBias(builder, bias): builder.PrependFloat32Slot(1, bias, 0.0) +def LocalResponseNormalizationOptionsAddAlpha(builder, alpha): builder.PrependFloat32Slot(2, alpha, 0.0) +def LocalResponseNormalizationOptionsAddBeta(builder, beta): builder.PrependFloat32Slot(3, beta, 0.0) +def LocalResponseNormalizationOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/LogSoftmaxOptions.py b/utensor_cgen/third_party/tflite/LogSoftmaxOptions.py new file mode 100644 index 00000000..47893855 --- /dev/null +++ b/utensor_cgen/third_party/tflite/LogSoftmaxOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class LogSoftmaxOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLogSoftmaxOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LogSoftmaxOptions() + x.Init(buf, n + offset) + return x + + # LogSoftmaxOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def LogSoftmaxOptionsStart(builder): builder.StartObject(0) +def LogSoftmaxOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/LogicalAndOptions.py b/utensor_cgen/third_party/tflite/LogicalAndOptions.py new file mode 100644 index 00000000..cee1cdb4 --- /dev/null +++ b/utensor_cgen/third_party/tflite/LogicalAndOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class LogicalAndOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLogicalAndOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LogicalAndOptions() + x.Init(buf, n + offset) + return x + + # LogicalAndOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def LogicalAndOptionsStart(builder): builder.StartObject(0) +def LogicalAndOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/LogicalNotOptions.py b/utensor_cgen/third_party/tflite/LogicalNotOptions.py new file mode 100644 index 00000000..9971450c --- /dev/null +++ b/utensor_cgen/third_party/tflite/LogicalNotOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class LogicalNotOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLogicalNotOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LogicalNotOptions() + x.Init(buf, n + offset) + return x + + # LogicalNotOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def LogicalNotOptionsStart(builder): builder.StartObject(0) +def LogicalNotOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/LogicalOrOptions.py b/utensor_cgen/third_party/tflite/LogicalOrOptions.py new file mode 100644 index 00000000..e94a5dec --- /dev/null +++ b/utensor_cgen/third_party/tflite/LogicalOrOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class LogicalOrOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLogicalOrOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LogicalOrOptions() + x.Init(buf, n + offset) + return x + + # LogicalOrOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def LogicalOrOptionsStart(builder): builder.StartObject(0) +def LogicalOrOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/MatrixDiagOptions.py b/utensor_cgen/third_party/tflite/MatrixDiagOptions.py new file mode 100644 index 00000000..0f64e657 --- /dev/null +++ b/utensor_cgen/third_party/tflite/MatrixDiagOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class MatrixDiagOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsMatrixDiagOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = MatrixDiagOptions() + x.Init(buf, n + offset) + return x + + # MatrixDiagOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def MatrixDiagOptionsStart(builder): builder.StartObject(0) +def MatrixDiagOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/MatrixSetDiagOptions.py b/utensor_cgen/third_party/tflite/MatrixSetDiagOptions.py new file mode 100644 index 00000000..14178cf8 --- /dev/null +++ b/utensor_cgen/third_party/tflite/MatrixSetDiagOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class MatrixSetDiagOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsMatrixSetDiagOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = MatrixSetDiagOptions() + x.Init(buf, n + offset) + return x + + # MatrixSetDiagOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def MatrixSetDiagOptionsStart(builder): builder.StartObject(0) +def MatrixSetDiagOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/MaximumMinimumOptions.py b/utensor_cgen/third_party/tflite/MaximumMinimumOptions.py new file mode 100644 index 00000000..f0806e2d --- /dev/null +++ b/utensor_cgen/third_party/tflite/MaximumMinimumOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class MaximumMinimumOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsMaximumMinimumOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = MaximumMinimumOptions() + x.Init(buf, n + offset) + return x + + # MaximumMinimumOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def MaximumMinimumOptionsStart(builder): builder.StartObject(0) +def MaximumMinimumOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/Metadata.py b/utensor_cgen/third_party/tflite/Metadata.py new file mode 100644 index 00000000..273e51ee --- /dev/null +++ b/utensor_cgen/third_party/tflite/Metadata.py @@ -0,0 +1,38 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class Metadata(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsMetadata(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = Metadata() + x.Init(buf, n + offset) + return x + + # Metadata + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # Metadata + def Name(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.String(o + self._tab.Pos) + return None + + # Metadata + def Buffer(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos) + return 0 + +def MetadataStart(builder): builder.StartObject(2) +def MetadataAddName(builder, name): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0) +def MetadataAddBuffer(builder, buffer): builder.PrependUint32Slot(1, buffer, 0) +def MetadataEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/MirrorPadMode.py b/utensor_cgen/third_party/tflite/MirrorPadMode.py new file mode 100644 index 00000000..ce7579d2 --- /dev/null +++ b/utensor_cgen/third_party/tflite/MirrorPadMode.py @@ -0,0 +1,8 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class MirrorPadMode(object): + REFLECT = 0 + SYMMETRIC = 1 + diff --git a/utensor_cgen/third_party/tflite/MirrorPadOptions.py b/utensor_cgen/third_party/tflite/MirrorPadOptions.py new file mode 100644 index 00000000..254ae217 --- /dev/null +++ b/utensor_cgen/third_party/tflite/MirrorPadOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class MirrorPadOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsMirrorPadOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = MirrorPadOptions() + x.Init(buf, n + offset) + return x + + # MirrorPadOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # MirrorPadOptions + def Mode(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def MirrorPadOptionsStart(builder): builder.StartObject(1) +def MirrorPadOptionsAddMode(builder, mode): builder.PrependInt8Slot(0, mode, 0) +def MirrorPadOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/Model.py b/utensor_cgen/third_party/tflite/Model.py new file mode 100644 index 00000000..cc9991ba --- /dev/null +++ b/utensor_cgen/third_party/tflite/Model.py @@ -0,0 +1,150 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class Model(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsModel(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = Model() + x.Init(buf, n + offset) + return x + + # Model + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # Model + def Version(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos) + return 0 + + # Model + def OperatorCodes(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + x = self._tab.Vector(o) + x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 + x = self._tab.Indirect(x) + from .OperatorCode import OperatorCode + obj = OperatorCode() + obj.Init(self._tab.Bytes, x) + return obj + return None + + # Model + def OperatorCodesLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # Model + def Subgraphs(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + x = self._tab.Vector(o) + x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 + x = self._tab.Indirect(x) + from .SubGraph import SubGraph + obj = SubGraph() + obj.Init(self._tab.Bytes, x) + return obj + return None + + # Model + def SubgraphsLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # Model + def Description(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.String(o + self._tab.Pos) + return None + + # Model + def Buffers(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + x = self._tab.Vector(o) + x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 + x = self._tab.Indirect(x) + from .Buffer import Buffer + obj = Buffer() + obj.Init(self._tab.Bytes, x) + return obj + return None + + # Model + def BuffersLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # Model + def MetadataBuffer(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # Model + def MetadataBufferAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) + return 0 + + # Model + def MetadataBufferLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # Model + def Metadata(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16)) + if o != 0: + x = self._tab.Vector(o) + x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 + x = self._tab.Indirect(x) + from .Metadata import Metadata + obj = Metadata() + obj.Init(self._tab.Bytes, x) + return obj + return None + + # Model + def MetadataLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + +def ModelStart(builder): builder.StartObject(7) +def ModelAddVersion(builder, version): builder.PrependUint32Slot(0, version, 0) +def ModelAddOperatorCodes(builder, operatorCodes): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(operatorCodes), 0) +def ModelStartOperatorCodesVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def ModelAddSubgraphs(builder, subgraphs): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(subgraphs), 0) +def ModelStartSubgraphsVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def ModelAddDescription(builder, description): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(description), 0) +def ModelAddBuffers(builder, buffers): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(buffers), 0) +def ModelStartBuffersVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def ModelAddMetadataBuffer(builder, metadataBuffer): builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(metadataBuffer), 0) +def ModelStartMetadataBufferVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def ModelAddMetadata(builder, metadata): builder.PrependUOffsetTRelativeSlot(6, flatbuffers.number_types.UOffsetTFlags.py_type(metadata), 0) +def ModelStartMetadataVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def ModelEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/MulOptions.py b/utensor_cgen/third_party/tflite/MulOptions.py new file mode 100644 index 00000000..55b9506f --- /dev/null +++ b/utensor_cgen/third_party/tflite/MulOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class MulOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsMulOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = MulOptions() + x.Init(buf, n + offset) + return x + + # MulOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # MulOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def MulOptionsStart(builder): builder.StartObject(1) +def MulOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) +def MulOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/NegOptions.py b/utensor_cgen/third_party/tflite/NegOptions.py new file mode 100644 index 00000000..05d55c26 --- /dev/null +++ b/utensor_cgen/third_party/tflite/NegOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class NegOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsNegOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = NegOptions() + x.Init(buf, n + offset) + return x + + # NegOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def NegOptionsStart(builder): builder.StartObject(0) +def NegOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/NonMaxSuppressionV4Options.py b/utensor_cgen/third_party/tflite/NonMaxSuppressionV4Options.py new file mode 100644 index 00000000..6ad10a2e --- /dev/null +++ b/utensor_cgen/third_party/tflite/NonMaxSuppressionV4Options.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class NonMaxSuppressionV4Options(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsNonMaxSuppressionV4Options(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = NonMaxSuppressionV4Options() + x.Init(buf, n + offset) + return x + + # NonMaxSuppressionV4Options + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def NonMaxSuppressionV4OptionsStart(builder): builder.StartObject(0) +def NonMaxSuppressionV4OptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/NonMaxSuppressionV5Options.py b/utensor_cgen/third_party/tflite/NonMaxSuppressionV5Options.py new file mode 100644 index 00000000..99cbdbbf --- /dev/null +++ b/utensor_cgen/third_party/tflite/NonMaxSuppressionV5Options.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class NonMaxSuppressionV5Options(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsNonMaxSuppressionV5Options(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = NonMaxSuppressionV5Options() + x.Init(buf, n + offset) + return x + + # NonMaxSuppressionV5Options + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def NonMaxSuppressionV5OptionsStart(builder): builder.StartObject(0) +def NonMaxSuppressionV5OptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/NotEqualOptions.py b/utensor_cgen/third_party/tflite/NotEqualOptions.py new file mode 100644 index 00000000..4c511e93 --- /dev/null +++ b/utensor_cgen/third_party/tflite/NotEqualOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class NotEqualOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsNotEqualOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = NotEqualOptions() + x.Init(buf, n + offset) + return x + + # NotEqualOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def NotEqualOptionsStart(builder): builder.StartObject(0) +def NotEqualOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/OneHotOptions.py b/utensor_cgen/third_party/tflite/OneHotOptions.py new file mode 100644 index 00000000..793a3e75 --- /dev/null +++ b/utensor_cgen/third_party/tflite/OneHotOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class OneHotOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsOneHotOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = OneHotOptions() + x.Init(buf, n + offset) + return x + + # OneHotOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # OneHotOptions + def Axis(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def OneHotOptionsStart(builder): builder.StartObject(1) +def OneHotOptionsAddAxis(builder, axis): builder.PrependInt32Slot(0, axis, 0) +def OneHotOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/Operator.py b/utensor_cgen/third_party/tflite/Operator.py new file mode 100644 index 00000000..cbae3dab --- /dev/null +++ b/utensor_cgen/third_party/tflite/Operator.py @@ -0,0 +1,177 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class Operator(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsOperator(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = Operator() + x.Init(buf, n + offset) + return x + + # Operator + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # Operator + def OpcodeIndex(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos) + return 0 + + # Operator + def Inputs(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # Operator + def InputsAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) + return 0 + + # Operator + def InputsLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # Operator + def Outputs(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # Operator + def OutputsAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) + return 0 + + # Operator + def OutputsLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # Operator + def BuiltinOptionsType(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Uint8Flags, o + self._tab.Pos) + return 0 + + # Operator + def BuiltinOptions(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + from flatbuffers.table import Table + obj = Table(bytearray(), 0) + self._tab.Union(obj, o) + return obj + return None + + # Operator + def CustomOptions(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Uint8Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1)) + return 0 + + # Operator + def CustomOptionsAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint8Flags, o) + return 0 + + # Operator + def CustomOptionsLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # Operator + def CustomOptionsFormat(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # Operator + def MutatingVariableInputs(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.BoolFlags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1)) + return 0 + + # Operator + def MutatingVariableInputsAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.BoolFlags, o) + return 0 + + # Operator + def MutatingVariableInputsLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # Operator + def Intermediates(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(20)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # Operator + def IntermediatesAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(20)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) + return 0 + + # Operator + def IntermediatesLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(20)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + +def OperatorStart(builder): builder.StartObject(9) +def OperatorAddOpcodeIndex(builder, opcodeIndex): builder.PrependUint32Slot(0, opcodeIndex, 0) +def OperatorAddInputs(builder, inputs): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(inputs), 0) +def OperatorStartInputsVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def OperatorAddOutputs(builder, outputs): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(outputs), 0) +def OperatorStartOutputsVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def OperatorAddBuiltinOptionsType(builder, builtinOptionsType): builder.PrependUint8Slot(3, builtinOptionsType, 0) +def OperatorAddBuiltinOptions(builder, builtinOptions): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(builtinOptions), 0) +def OperatorAddCustomOptions(builder, customOptions): builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(customOptions), 0) +def OperatorStartCustomOptionsVector(builder, numElems): return builder.StartVector(1, numElems, 1) +def OperatorAddCustomOptionsFormat(builder, customOptionsFormat): builder.PrependInt8Slot(6, customOptionsFormat, 0) +def OperatorAddMutatingVariableInputs(builder, mutatingVariableInputs): builder.PrependUOffsetTRelativeSlot(7, flatbuffers.number_types.UOffsetTFlags.py_type(mutatingVariableInputs), 0) +def OperatorStartMutatingVariableInputsVector(builder, numElems): return builder.StartVector(1, numElems, 1) +def OperatorAddIntermediates(builder, intermediates): builder.PrependUOffsetTRelativeSlot(8, flatbuffers.number_types.UOffsetTFlags.py_type(intermediates), 0) +def OperatorStartIntermediatesVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def OperatorEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/OperatorCode.py b/utensor_cgen/third_party/tflite/OperatorCode.py new file mode 100644 index 00000000..dd525f53 --- /dev/null +++ b/utensor_cgen/third_party/tflite/OperatorCode.py @@ -0,0 +1,46 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class OperatorCode(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsOperatorCode(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = OperatorCode() + x.Init(buf, n + offset) + return x + + # OperatorCode + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # OperatorCode + def BuiltinCode(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # OperatorCode + def CustomCode(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.String(o + self._tab.Pos) + return None + + # OperatorCode + def Version(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 1 + +def OperatorCodeStart(builder): builder.StartObject(3) +def OperatorCodeAddBuiltinCode(builder, builtinCode): builder.PrependInt8Slot(0, builtinCode, 0) +def OperatorCodeAddCustomCode(builder, customCode): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(customCode), 0) +def OperatorCodeAddVersion(builder, version): builder.PrependInt32Slot(2, version, 1) +def OperatorCodeEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/PackOptions.py b/utensor_cgen/third_party/tflite/PackOptions.py new file mode 100644 index 00000000..6a8ee2bb --- /dev/null +++ b/utensor_cgen/third_party/tflite/PackOptions.py @@ -0,0 +1,38 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class PackOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsPackOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = PackOptions() + x.Init(buf, n + offset) + return x + + # PackOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # PackOptions + def ValuesCount(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # PackOptions + def Axis(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def PackOptionsStart(builder): builder.StartObject(2) +def PackOptionsAddValuesCount(builder, valuesCount): builder.PrependInt32Slot(0, valuesCount, 0) +def PackOptionsAddAxis(builder, axis): builder.PrependInt32Slot(1, axis, 0) +def PackOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/PadOptions.py b/utensor_cgen/third_party/tflite/PadOptions.py new file mode 100644 index 00000000..d0833c68 --- /dev/null +++ b/utensor_cgen/third_party/tflite/PadOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class PadOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsPadOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = PadOptions() + x.Init(buf, n + offset) + return x + + # PadOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def PadOptionsStart(builder): builder.StartObject(0) +def PadOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/PadV2Options.py b/utensor_cgen/third_party/tflite/PadV2Options.py new file mode 100644 index 00000000..5ea0d70c --- /dev/null +++ b/utensor_cgen/third_party/tflite/PadV2Options.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class PadV2Options(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsPadV2Options(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = PadV2Options() + x.Init(buf, n + offset) + return x + + # PadV2Options + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def PadV2OptionsStart(builder): builder.StartObject(0) +def PadV2OptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/Padding.py b/utensor_cgen/third_party/tflite/Padding.py new file mode 100644 index 00000000..6027f4b5 --- /dev/null +++ b/utensor_cgen/third_party/tflite/Padding.py @@ -0,0 +1,8 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class Padding(object): + SAME = 0 + VALID = 1 + diff --git a/utensor_cgen/third_party/tflite/Pool2DOptions.py b/utensor_cgen/third_party/tflite/Pool2DOptions.py new file mode 100644 index 00000000..b8b9f178 --- /dev/null +++ b/utensor_cgen/third_party/tflite/Pool2DOptions.py @@ -0,0 +1,70 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class Pool2DOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsPool2DOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = Pool2DOptions() + x.Init(buf, n + offset) + return x + + # Pool2DOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # Pool2DOptions + def Padding(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # Pool2DOptions + def StrideW(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # Pool2DOptions + def StrideH(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # Pool2DOptions + def FilterWidth(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # Pool2DOptions + def FilterHeight(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # Pool2DOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def Pool2DOptionsStart(builder): builder.StartObject(6) +def Pool2DOptionsAddPadding(builder, padding): builder.PrependInt8Slot(0, padding, 0) +def Pool2DOptionsAddStrideW(builder, strideW): builder.PrependInt32Slot(1, strideW, 0) +def Pool2DOptionsAddStrideH(builder, strideH): builder.PrependInt32Slot(2, strideH, 0) +def Pool2DOptionsAddFilterWidth(builder, filterWidth): builder.PrependInt32Slot(3, filterWidth, 0) +def Pool2DOptionsAddFilterHeight(builder, filterHeight): builder.PrependInt32Slot(4, filterHeight, 0) +def Pool2DOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(5, fusedActivationFunction, 0) +def Pool2DOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/PowOptions.py b/utensor_cgen/third_party/tflite/PowOptions.py new file mode 100644 index 00000000..666ca488 --- /dev/null +++ b/utensor_cgen/third_party/tflite/PowOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class PowOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsPowOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = PowOptions() + x.Init(buf, n + offset) + return x + + # PowOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def PowOptionsStart(builder): builder.StartObject(0) +def PowOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/QuantizationDetails.py b/utensor_cgen/third_party/tflite/QuantizationDetails.py new file mode 100644 index 00000000..7159efbd --- /dev/null +++ b/utensor_cgen/third_party/tflite/QuantizationDetails.py @@ -0,0 +1,8 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class QuantizationDetails(object): + NONE = 0 + CustomQuantization = 1 + diff --git a/utensor_cgen/third_party/tflite/QuantizationParameters.py b/utensor_cgen/third_party/tflite/QuantizationParameters.py new file mode 100644 index 00000000..fcd686cf --- /dev/null +++ b/utensor_cgen/third_party/tflite/QuantizationParameters.py @@ -0,0 +1,145 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class QuantizationParameters(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsQuantizationParameters(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = QuantizationParameters() + x.Init(buf, n + offset) + return x + + # QuantizationParameters + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # QuantizationParameters + def Min(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # QuantizationParameters + def MinAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o) + return 0 + + # QuantizationParameters + def MinLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # QuantizationParameters + def Max(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # QuantizationParameters + def MaxAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o) + return 0 + + # QuantizationParameters + def MaxLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # QuantizationParameters + def Scale(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # QuantizationParameters + def ScaleAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o) + return 0 + + # QuantizationParameters + def ScaleLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # QuantizationParameters + def ZeroPoint(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8)) + return 0 + + # QuantizationParameters + def ZeroPointAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o) + return 0 + + # QuantizationParameters + def ZeroPointLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # QuantizationParameters + def DetailsType(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Uint8Flags, o + self._tab.Pos) + return 0 + + # QuantizationParameters + def Details(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + from flatbuffers.table import Table + obj = Table(bytearray(), 0) + self._tab.Union(obj, o) + return obj + return None + + # QuantizationParameters + def QuantizedDimension(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def QuantizationParametersStart(builder): builder.StartObject(7) +def QuantizationParametersAddMin(builder, min): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(min), 0) +def QuantizationParametersStartMinVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def QuantizationParametersAddMax(builder, max): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(max), 0) +def QuantizationParametersStartMaxVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def QuantizationParametersAddScale(builder, scale): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(scale), 0) +def QuantizationParametersStartScaleVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def QuantizationParametersAddZeroPoint(builder, zeroPoint): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(zeroPoint), 0) +def QuantizationParametersStartZeroPointVector(builder, numElems): return builder.StartVector(8, numElems, 8) +def QuantizationParametersAddDetailsType(builder, detailsType): builder.PrependUint8Slot(4, detailsType, 0) +def QuantizationParametersAddDetails(builder, details): builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(details), 0) +def QuantizationParametersAddQuantizedDimension(builder, quantizedDimension): builder.PrependInt32Slot(6, quantizedDimension, 0) +def QuantizationParametersEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/QuantizeOptions.py b/utensor_cgen/third_party/tflite/QuantizeOptions.py new file mode 100644 index 00000000..28af8cc9 --- /dev/null +++ b/utensor_cgen/third_party/tflite/QuantizeOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class QuantizeOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsQuantizeOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = QuantizeOptions() + x.Init(buf, n + offset) + return x + + # QuantizeOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def QuantizeOptionsStart(builder): builder.StartObject(0) +def QuantizeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/RNNOptions.py b/utensor_cgen/third_party/tflite/RNNOptions.py new file mode 100644 index 00000000..3cfdb6af --- /dev/null +++ b/utensor_cgen/third_party/tflite/RNNOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class RNNOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsRNNOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = RNNOptions() + x.Init(buf, n + offset) + return x + + # RNNOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # RNNOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def RNNOptionsStart(builder): builder.StartObject(1) +def RNNOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) +def RNNOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/RangeOptions.py b/utensor_cgen/third_party/tflite/RangeOptions.py new file mode 100644 index 00000000..cb705b57 --- /dev/null +++ b/utensor_cgen/third_party/tflite/RangeOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class RangeOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsRangeOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = RangeOptions() + x.Init(buf, n + offset) + return x + + # RangeOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def RangeOptionsStart(builder): builder.StartObject(0) +def RangeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/RankOptions.py b/utensor_cgen/third_party/tflite/RankOptions.py new file mode 100644 index 00000000..4e4a5ecd --- /dev/null +++ b/utensor_cgen/third_party/tflite/RankOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class RankOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsRankOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = RankOptions() + x.Init(buf, n + offset) + return x + + # RankOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def RankOptionsStart(builder): builder.StartObject(0) +def RankOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/ReducerOptions.py b/utensor_cgen/third_party/tflite/ReducerOptions.py new file mode 100644 index 00000000..93bbde17 --- /dev/null +++ b/utensor_cgen/third_party/tflite/ReducerOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ReducerOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsReducerOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ReducerOptions() + x.Init(buf, n + offset) + return x + + # ReducerOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # ReducerOptions + def KeepDims(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + +def ReducerOptionsStart(builder): builder.StartObject(1) +def ReducerOptionsAddKeepDims(builder, keepDims): builder.PrependBoolSlot(0, keepDims, 0) +def ReducerOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/ReshapeOptions.py b/utensor_cgen/third_party/tflite/ReshapeOptions.py new file mode 100644 index 00000000..157d45d9 --- /dev/null +++ b/utensor_cgen/third_party/tflite/ReshapeOptions.py @@ -0,0 +1,46 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ReshapeOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsReshapeOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ReshapeOptions() + x.Init(buf, n + offset) + return x + + # ReshapeOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # ReshapeOptions + def NewShape(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # ReshapeOptions + def NewShapeAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) + return 0 + + # ReshapeOptions + def NewShapeLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + +def ReshapeOptionsStart(builder): builder.StartObject(1) +def ReshapeOptionsAddNewShape(builder, newShape): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(newShape), 0) +def ReshapeOptionsStartNewShapeVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def ReshapeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/ResizeBilinearOptions.py b/utensor_cgen/third_party/tflite/ResizeBilinearOptions.py new file mode 100644 index 00000000..0d1f9624 --- /dev/null +++ b/utensor_cgen/third_party/tflite/ResizeBilinearOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ResizeBilinearOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsResizeBilinearOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ResizeBilinearOptions() + x.Init(buf, n + offset) + return x + + # ResizeBilinearOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # ResizeBilinearOptions + def AlignCorners(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + +def ResizeBilinearOptionsStart(builder): builder.StartObject(3) +def ResizeBilinearOptionsAddAlignCorners(builder, alignCorners): builder.PrependBoolSlot(2, alignCorners, 0) +def ResizeBilinearOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/ResizeNearestNeighborOptions.py b/utensor_cgen/third_party/tflite/ResizeNearestNeighborOptions.py new file mode 100644 index 00000000..4b166e95 --- /dev/null +++ b/utensor_cgen/third_party/tflite/ResizeNearestNeighborOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ResizeNearestNeighborOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsResizeNearestNeighborOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ResizeNearestNeighborOptions() + x.Init(buf, n + offset) + return x + + # ResizeNearestNeighborOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # ResizeNearestNeighborOptions + def AlignCorners(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + +def ResizeNearestNeighborOptionsStart(builder): builder.StartObject(1) +def ResizeNearestNeighborOptionsAddAlignCorners(builder, alignCorners): builder.PrependBoolSlot(0, alignCorners, 0) +def ResizeNearestNeighborOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/ReverseSequenceOptions.py b/utensor_cgen/third_party/tflite/ReverseSequenceOptions.py new file mode 100644 index 00000000..cbaf96db --- /dev/null +++ b/utensor_cgen/third_party/tflite/ReverseSequenceOptions.py @@ -0,0 +1,38 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ReverseSequenceOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsReverseSequenceOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ReverseSequenceOptions() + x.Init(buf, n + offset) + return x + + # ReverseSequenceOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # ReverseSequenceOptions + def SeqDim(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # ReverseSequenceOptions + def BatchDim(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def ReverseSequenceOptionsStart(builder): builder.StartObject(2) +def ReverseSequenceOptionsAddSeqDim(builder, seqDim): builder.PrependInt32Slot(0, seqDim, 0) +def ReverseSequenceOptionsAddBatchDim(builder, batchDim): builder.PrependInt32Slot(1, batchDim, 0) +def ReverseSequenceOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/ReverseV2Options.py b/utensor_cgen/third_party/tflite/ReverseV2Options.py new file mode 100644 index 00000000..dbac9362 --- /dev/null +++ b/utensor_cgen/third_party/tflite/ReverseV2Options.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ReverseV2Options(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsReverseV2Options(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ReverseV2Options() + x.Init(buf, n + offset) + return x + + # ReverseV2Options + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def ReverseV2OptionsStart(builder): builder.StartObject(0) +def ReverseV2OptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/SVDFOptions.py b/utensor_cgen/third_party/tflite/SVDFOptions.py new file mode 100644 index 00000000..6f391db1 --- /dev/null +++ b/utensor_cgen/third_party/tflite/SVDFOptions.py @@ -0,0 +1,38 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SVDFOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSVDFOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SVDFOptions() + x.Init(buf, n + offset) + return x + + # SVDFOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SVDFOptions + def Rank(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # SVDFOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def SVDFOptionsStart(builder): builder.StartObject(2) +def SVDFOptionsAddRank(builder, rank): builder.PrependInt32Slot(0, rank, 0) +def SVDFOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(1, fusedActivationFunction, 0) +def SVDFOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/SelectOptions.py b/utensor_cgen/third_party/tflite/SelectOptions.py new file mode 100644 index 00000000..d67daf36 --- /dev/null +++ b/utensor_cgen/third_party/tflite/SelectOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SelectOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSelectOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SelectOptions() + x.Init(buf, n + offset) + return x + + # SelectOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def SelectOptionsStart(builder): builder.StartObject(0) +def SelectOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/SequenceRNNOptions.py b/utensor_cgen/third_party/tflite/SequenceRNNOptions.py new file mode 100644 index 00000000..74a4954a --- /dev/null +++ b/utensor_cgen/third_party/tflite/SequenceRNNOptions.py @@ -0,0 +1,38 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SequenceRNNOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSequenceRNNOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SequenceRNNOptions() + x.Init(buf, n + offset) + return x + + # SequenceRNNOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SequenceRNNOptions + def TimeMajor(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + + # SequenceRNNOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def SequenceRNNOptionsStart(builder): builder.StartObject(2) +def SequenceRNNOptionsAddTimeMajor(builder, timeMajor): builder.PrependBoolSlot(0, timeMajor, 0) +def SequenceRNNOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(1, fusedActivationFunction, 0) +def SequenceRNNOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/ShapeOptions.py b/utensor_cgen/third_party/tflite/ShapeOptions.py new file mode 100644 index 00000000..2d24c05f --- /dev/null +++ b/utensor_cgen/third_party/tflite/ShapeOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ShapeOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsShapeOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ShapeOptions() + x.Init(buf, n + offset) + return x + + # ShapeOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # ShapeOptions + def OutType(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def ShapeOptionsStart(builder): builder.StartObject(1) +def ShapeOptionsAddOutType(builder, outType): builder.PrependInt8Slot(0, outType, 0) +def ShapeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/SkipGramOptions.py b/utensor_cgen/third_party/tflite/SkipGramOptions.py new file mode 100644 index 00000000..0e8bdc1d --- /dev/null +++ b/utensor_cgen/third_party/tflite/SkipGramOptions.py @@ -0,0 +1,46 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SkipGramOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSkipGramOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SkipGramOptions() + x.Init(buf, n + offset) + return x + + # SkipGramOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SkipGramOptions + def NgramSize(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # SkipGramOptions + def MaxSkipSize(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # SkipGramOptions + def IncludeAllNgrams(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + +def SkipGramOptionsStart(builder): builder.StartObject(3) +def SkipGramOptionsAddNgramSize(builder, ngramSize): builder.PrependInt32Slot(0, ngramSize, 0) +def SkipGramOptionsAddMaxSkipSize(builder, maxSkipSize): builder.PrependInt32Slot(1, maxSkipSize, 0) +def SkipGramOptionsAddIncludeAllNgrams(builder, includeAllNgrams): builder.PrependBoolSlot(2, includeAllNgrams, 0) +def SkipGramOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/SliceOptions.py b/utensor_cgen/third_party/tflite/SliceOptions.py new file mode 100644 index 00000000..4b41568d --- /dev/null +++ b/utensor_cgen/third_party/tflite/SliceOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SliceOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSliceOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SliceOptions() + x.Init(buf, n + offset) + return x + + # SliceOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def SliceOptionsStart(builder): builder.StartObject(0) +def SliceOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/SoftmaxOptions.py b/utensor_cgen/third_party/tflite/SoftmaxOptions.py new file mode 100644 index 00000000..a7168534 --- /dev/null +++ b/utensor_cgen/third_party/tflite/SoftmaxOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SoftmaxOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSoftmaxOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SoftmaxOptions() + x.Init(buf, n + offset) + return x + + # SoftmaxOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SoftmaxOptions + def Beta(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + +def SoftmaxOptionsStart(builder): builder.StartObject(1) +def SoftmaxOptionsAddBeta(builder, beta): builder.PrependFloat32Slot(0, beta, 0.0) +def SoftmaxOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/SpaceToBatchNDOptions.py b/utensor_cgen/third_party/tflite/SpaceToBatchNDOptions.py new file mode 100644 index 00000000..b61ef96f --- /dev/null +++ b/utensor_cgen/third_party/tflite/SpaceToBatchNDOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SpaceToBatchNDOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSpaceToBatchNDOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SpaceToBatchNDOptions() + x.Init(buf, n + offset) + return x + + # SpaceToBatchNDOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def SpaceToBatchNDOptionsStart(builder): builder.StartObject(0) +def SpaceToBatchNDOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/SpaceToDepthOptions.py b/utensor_cgen/third_party/tflite/SpaceToDepthOptions.py new file mode 100644 index 00000000..d571174a --- /dev/null +++ b/utensor_cgen/third_party/tflite/SpaceToDepthOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SpaceToDepthOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSpaceToDepthOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SpaceToDepthOptions() + x.Init(buf, n + offset) + return x + + # SpaceToDepthOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SpaceToDepthOptions + def BlockSize(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def SpaceToDepthOptionsStart(builder): builder.StartObject(1) +def SpaceToDepthOptionsAddBlockSize(builder, blockSize): builder.PrependInt32Slot(0, blockSize, 0) +def SpaceToDepthOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/SparseToDenseOptions.py b/utensor_cgen/third_party/tflite/SparseToDenseOptions.py new file mode 100644 index 00000000..826eee08 --- /dev/null +++ b/utensor_cgen/third_party/tflite/SparseToDenseOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SparseToDenseOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSparseToDenseOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SparseToDenseOptions() + x.Init(buf, n + offset) + return x + + # SparseToDenseOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SparseToDenseOptions + def ValidateIndices(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + +def SparseToDenseOptionsStart(builder): builder.StartObject(1) +def SparseToDenseOptionsAddValidateIndices(builder, validateIndices): builder.PrependBoolSlot(0, validateIndices, 0) +def SparseToDenseOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/SplitOptions.py b/utensor_cgen/third_party/tflite/SplitOptions.py new file mode 100644 index 00000000..3207525b --- /dev/null +++ b/utensor_cgen/third_party/tflite/SplitOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SplitOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSplitOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SplitOptions() + x.Init(buf, n + offset) + return x + + # SplitOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SplitOptions + def NumSplits(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def SplitOptionsStart(builder): builder.StartObject(1) +def SplitOptionsAddNumSplits(builder, numSplits): builder.PrependInt32Slot(0, numSplits, 0) +def SplitOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/SplitVOptions.py b/utensor_cgen/third_party/tflite/SplitVOptions.py new file mode 100644 index 00000000..418959de --- /dev/null +++ b/utensor_cgen/third_party/tflite/SplitVOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SplitVOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSplitVOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SplitVOptions() + x.Init(buf, n + offset) + return x + + # SplitVOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SplitVOptions + def NumSplits(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def SplitVOptionsStart(builder): builder.StartObject(1) +def SplitVOptionsAddNumSplits(builder, numSplits): builder.PrependInt32Slot(0, numSplits, 0) +def SplitVOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/SquareOptions.py b/utensor_cgen/third_party/tflite/SquareOptions.py new file mode 100644 index 00000000..56633f6a --- /dev/null +++ b/utensor_cgen/third_party/tflite/SquareOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SquareOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSquareOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SquareOptions() + x.Init(buf, n + offset) + return x + + # SquareOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def SquareOptionsStart(builder): builder.StartObject(0) +def SquareOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/SquaredDifferenceOptions.py b/utensor_cgen/third_party/tflite/SquaredDifferenceOptions.py new file mode 100644 index 00000000..906855d1 --- /dev/null +++ b/utensor_cgen/third_party/tflite/SquaredDifferenceOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SquaredDifferenceOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSquaredDifferenceOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SquaredDifferenceOptions() + x.Init(buf, n + offset) + return x + + # SquaredDifferenceOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def SquaredDifferenceOptionsStart(builder): builder.StartObject(0) +def SquaredDifferenceOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/SqueezeOptions.py b/utensor_cgen/third_party/tflite/SqueezeOptions.py new file mode 100644 index 00000000..25b294dc --- /dev/null +++ b/utensor_cgen/third_party/tflite/SqueezeOptions.py @@ -0,0 +1,46 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SqueezeOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSqueezeOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SqueezeOptions() + x.Init(buf, n + offset) + return x + + # SqueezeOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SqueezeOptions + def SqueezeDims(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # SqueezeOptions + def SqueezeDimsAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) + return 0 + + # SqueezeOptions + def SqueezeDimsLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + +def SqueezeOptionsStart(builder): builder.StartObject(1) +def SqueezeOptionsAddSqueezeDims(builder, squeezeDims): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(squeezeDims), 0) +def SqueezeOptionsStartSqueezeDimsVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def SqueezeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/StridedSliceOptions.py b/utensor_cgen/third_party/tflite/StridedSliceOptions.py new file mode 100644 index 00000000..3bbb36b8 --- /dev/null +++ b/utensor_cgen/third_party/tflite/StridedSliceOptions.py @@ -0,0 +1,62 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class StridedSliceOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsStridedSliceOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = StridedSliceOptions() + x.Init(buf, n + offset) + return x + + # StridedSliceOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # StridedSliceOptions + def BeginMask(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # StridedSliceOptions + def EndMask(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # StridedSliceOptions + def EllipsisMask(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # StridedSliceOptions + def NewAxisMask(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # StridedSliceOptions + def ShrinkAxisMask(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def StridedSliceOptionsStart(builder): builder.StartObject(5) +def StridedSliceOptionsAddBeginMask(builder, beginMask): builder.PrependInt32Slot(0, beginMask, 0) +def StridedSliceOptionsAddEndMask(builder, endMask): builder.PrependInt32Slot(1, endMask, 0) +def StridedSliceOptionsAddEllipsisMask(builder, ellipsisMask): builder.PrependInt32Slot(2, ellipsisMask, 0) +def StridedSliceOptionsAddNewAxisMask(builder, newAxisMask): builder.PrependInt32Slot(3, newAxisMask, 0) +def StridedSliceOptionsAddShrinkAxisMask(builder, shrinkAxisMask): builder.PrependInt32Slot(4, shrinkAxisMask, 0) +def StridedSliceOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/SubGraph.py b/utensor_cgen/third_party/tflite/SubGraph.py new file mode 100644 index 00000000..eaa42fac --- /dev/null +++ b/utensor_cgen/third_party/tflite/SubGraph.py @@ -0,0 +1,122 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SubGraph(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSubGraph(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SubGraph() + x.Init(buf, n + offset) + return x + + # SubGraph + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SubGraph + def Tensors(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + x = self._tab.Vector(o) + x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 + x = self._tab.Indirect(x) + from .Tensor import Tensor + obj = Tensor() + obj.Init(self._tab.Bytes, x) + return obj + return None + + # SubGraph + def TensorsLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # SubGraph + def Inputs(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # SubGraph + def InputsAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) + return 0 + + # SubGraph + def InputsLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # SubGraph + def Outputs(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # SubGraph + def OutputsAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) + return 0 + + # SubGraph + def OutputsLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # SubGraph + def Operators(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + x = self._tab.Vector(o) + x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 + x = self._tab.Indirect(x) + from .Operator import Operator + obj = Operator() + obj.Init(self._tab.Bytes, x) + return obj + return None + + # SubGraph + def OperatorsLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # SubGraph + def Name(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + return self._tab.String(o + self._tab.Pos) + return None + +def SubGraphStart(builder): builder.StartObject(5) +def SubGraphAddTensors(builder, tensors): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(tensors), 0) +def SubGraphStartTensorsVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def SubGraphAddInputs(builder, inputs): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(inputs), 0) +def SubGraphStartInputsVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def SubGraphAddOutputs(builder, outputs): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(outputs), 0) +def SubGraphStartOutputsVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def SubGraphAddOperators(builder, operators): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(operators), 0) +def SubGraphStartOperatorsVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def SubGraphAddName(builder, name): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0) +def SubGraphEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/SubOptions.py b/utensor_cgen/third_party/tflite/SubOptions.py new file mode 100644 index 00000000..eccd7aba --- /dev/null +++ b/utensor_cgen/third_party/tflite/SubOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class SubOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSubOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SubOptions() + x.Init(buf, n + offset) + return x + + # SubOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # SubOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + +def SubOptionsStart(builder): builder.StartObject(1) +def SubOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) +def SubOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/Tensor.py b/utensor_cgen/third_party/tflite/Tensor.py new file mode 100644 index 00000000..9b5b8f87 --- /dev/null +++ b/utensor_cgen/third_party/tflite/Tensor.py @@ -0,0 +1,90 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class Tensor(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsTensor(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = Tensor() + x.Init(buf, n + offset) + return x + + # Tensor + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # Tensor + def Shape(self, j): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + a = self._tab.Vector(o) + return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) + return 0 + + # Tensor + def ShapeAsNumpy(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) + return 0 + + # Tensor + def ShapeLength(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.VectorLen(o) + return 0 + + # Tensor + def Type(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # Tensor + def Buffer(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos) + return 0 + + # Tensor + def Name(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return self._tab.String(o + self._tab.Pos) + return None + + # Tensor + def Quantization(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) + if o != 0: + x = self._tab.Indirect(o + self._tab.Pos) + from .QuantizationParameters import QuantizationParameters + obj = QuantizationParameters() + obj.Init(self._tab.Bytes, x) + return obj + return None + + # Tensor + def IsVariable(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + +def TensorStart(builder): builder.StartObject(6) +def TensorAddShape(builder, shape): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(shape), 0) +def TensorStartShapeVector(builder, numElems): return builder.StartVector(4, numElems, 4) +def TensorAddType(builder, type): builder.PrependInt8Slot(1, type, 0) +def TensorAddBuffer(builder, buffer): builder.PrependUint32Slot(2, buffer, 0) +def TensorAddName(builder, name): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0) +def TensorAddQuantization(builder, quantization): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(quantization), 0) +def TensorAddIsVariable(builder, isVariable): builder.PrependBoolSlot(5, isVariable, 0) +def TensorEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/TensorType.py b/utensor_cgen/third_party/tflite/TensorType.py new file mode 100644 index 00000000..2455835a --- /dev/null +++ b/utensor_cgen/third_party/tflite/TensorType.py @@ -0,0 +1,16 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +class TensorType(object): + FLOAT32 = 0 + FLOAT16 = 1 + INT32 = 2 + UINT8 = 3 + INT64 = 4 + STRING = 5 + BOOL = 6 + INT16 = 7 + COMPLEX64 = 8 + INT8 = 9 + diff --git a/utensor_cgen/third_party/tflite/TileOptions.py b/utensor_cgen/third_party/tflite/TileOptions.py new file mode 100644 index 00000000..ec8396dc --- /dev/null +++ b/utensor_cgen/third_party/tflite/TileOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class TileOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsTileOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = TileOptions() + x.Init(buf, n + offset) + return x + + # TileOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def TileOptionsStart(builder): builder.StartObject(0) +def TileOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/TopKV2Options.py b/utensor_cgen/third_party/tflite/TopKV2Options.py new file mode 100644 index 00000000..ccd51033 --- /dev/null +++ b/utensor_cgen/third_party/tflite/TopKV2Options.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class TopKV2Options(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsTopKV2Options(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = TopKV2Options() + x.Init(buf, n + offset) + return x + + # TopKV2Options + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def TopKV2OptionsStart(builder): builder.StartObject(0) +def TopKV2OptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/TransposeConvOptions.py b/utensor_cgen/third_party/tflite/TransposeConvOptions.py new file mode 100644 index 00000000..423571c8 --- /dev/null +++ b/utensor_cgen/third_party/tflite/TransposeConvOptions.py @@ -0,0 +1,46 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class TransposeConvOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsTransposeConvOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = TransposeConvOptions() + x.Init(buf, n + offset) + return x + + # TransposeConvOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # TransposeConvOptions + def Padding(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # TransposeConvOptions + def StrideW(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # TransposeConvOptions + def StrideH(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def TransposeConvOptionsStart(builder): builder.StartObject(3) +def TransposeConvOptionsAddPadding(builder, padding): builder.PrependInt8Slot(0, padding, 0) +def TransposeConvOptionsAddStrideW(builder, strideW): builder.PrependInt32Slot(1, strideW, 0) +def TransposeConvOptionsAddStrideH(builder, strideH): builder.PrependInt32Slot(2, strideH, 0) +def TransposeConvOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/TransposeOptions.py b/utensor_cgen/third_party/tflite/TransposeOptions.py new file mode 100644 index 00000000..42c596d9 --- /dev/null +++ b/utensor_cgen/third_party/tflite/TransposeOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class TransposeOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsTransposeOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = TransposeOptions() + x.Init(buf, n + offset) + return x + + # TransposeOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def TransposeOptionsStart(builder): builder.StartObject(0) +def TransposeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/UnidirectionalSequenceLSTMOptions.py b/utensor_cgen/third_party/tflite/UnidirectionalSequenceLSTMOptions.py new file mode 100644 index 00000000..1b0c112c --- /dev/null +++ b/utensor_cgen/third_party/tflite/UnidirectionalSequenceLSTMOptions.py @@ -0,0 +1,54 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class UnidirectionalSequenceLSTMOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsUnidirectionalSequenceLSTMOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = UnidirectionalSequenceLSTMOptions() + x.Init(buf, n + offset) + return x + + # UnidirectionalSequenceLSTMOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # UnidirectionalSequenceLSTMOptions + def FusedActivationFunction(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 0 + + # UnidirectionalSequenceLSTMOptions + def CellClip(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + + # UnidirectionalSequenceLSTMOptions + def ProjClip(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) + return 0.0 + + # UnidirectionalSequenceLSTMOptions + def TimeMajor(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) + if o != 0: + return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False + +def UnidirectionalSequenceLSTMOptionsStart(builder): builder.StartObject(4) +def UnidirectionalSequenceLSTMOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) +def UnidirectionalSequenceLSTMOptionsAddCellClip(builder, cellClip): builder.PrependFloat32Slot(1, cellClip, 0.0) +def UnidirectionalSequenceLSTMOptionsAddProjClip(builder, projClip): builder.PrependFloat32Slot(2, projClip, 0.0) +def UnidirectionalSequenceLSTMOptionsAddTimeMajor(builder, timeMajor): builder.PrependBoolSlot(3, timeMajor, 0) +def UnidirectionalSequenceLSTMOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/UniqueOptions.py b/utensor_cgen/third_party/tflite/UniqueOptions.py new file mode 100644 index 00000000..841c6977 --- /dev/null +++ b/utensor_cgen/third_party/tflite/UniqueOptions.py @@ -0,0 +1,30 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class UniqueOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsUniqueOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = UniqueOptions() + x.Init(buf, n + offset) + return x + + # UniqueOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # UniqueOptions + def IdxOutType(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) + return 2 + +def UniqueOptionsStart(builder): builder.StartObject(1) +def UniqueOptionsAddIdxOutType(builder, idxOutType): builder.PrependInt8Slot(0, idxOutType, 2) +def UniqueOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/UnpackOptions.py b/utensor_cgen/third_party/tflite/UnpackOptions.py new file mode 100644 index 00000000..eed40193 --- /dev/null +++ b/utensor_cgen/third_party/tflite/UnpackOptions.py @@ -0,0 +1,38 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class UnpackOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsUnpackOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = UnpackOptions() + x.Init(buf, n + offset) + return x + + # UnpackOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # UnpackOptions + def Num(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # UnpackOptions + def Axis(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def UnpackOptionsStart(builder): builder.StartObject(2) +def UnpackOptionsAddNum(builder, num): builder.PrependInt32Slot(0, num, 0) +def UnpackOptionsAddAxis(builder, axis): builder.PrependInt32Slot(1, axis, 0) +def UnpackOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/WhereOptions.py b/utensor_cgen/third_party/tflite/WhereOptions.py new file mode 100644 index 00000000..ab69f6aa --- /dev/null +++ b/utensor_cgen/third_party/tflite/WhereOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class WhereOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsWhereOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = WhereOptions() + x.Init(buf, n + offset) + return x + + # WhereOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def WhereOptionsStart(builder): builder.StartObject(0) +def WhereOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/WhileOptions.py b/utensor_cgen/third_party/tflite/WhileOptions.py new file mode 100644 index 00000000..7d5a6dfa --- /dev/null +++ b/utensor_cgen/third_party/tflite/WhileOptions.py @@ -0,0 +1,38 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class WhileOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsWhileOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = WhileOptions() + x.Init(buf, n + offset) + return x + + # WhileOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # WhileOptions + def CondSubgraphIndex(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # WhileOptions + def BodySubgraphIndex(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + +def WhileOptionsStart(builder): builder.StartObject(2) +def WhileOptionsAddCondSubgraphIndex(builder, condSubgraphIndex): builder.PrependInt32Slot(0, condSubgraphIndex, 0) +def WhileOptionsAddBodySubgraphIndex(builder, bodySubgraphIndex): builder.PrependInt32Slot(1, bodySubgraphIndex, 0) +def WhileOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/ZerosLikeOptions.py b/utensor_cgen/third_party/tflite/ZerosLikeOptions.py new file mode 100644 index 00000000..e6aa9639 --- /dev/null +++ b/utensor_cgen/third_party/tflite/ZerosLikeOptions.py @@ -0,0 +1,22 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + +class ZerosLikeOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsZerosLikeOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ZerosLikeOptions() + x.Init(buf, n + offset) + return x + + # ZerosLikeOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + +def ZerosLikeOptionsStart(builder): builder.StartObject(0) +def ZerosLikeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/third_party/tflite/__init__.py b/utensor_cgen/third_party/tflite/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/utensor_cgen/transformer/tflite_exporter.py b/utensor_cgen/transformer/tflite_exporter.py new file mode 100644 index 00000000..8daa164e --- /dev/null +++ b/utensor_cgen/transformer/tflite_exporter.py @@ -0,0 +1,164 @@ +# -*- coding:utf8 -*- +r"""Graph Visualization Transformer + +Transformers that export a flatbuffer file presenting the Tensorflow Lite's model. +""" +import re +from collections import OrderedDict +from copy import deepcopy + +from utensor_cgen.ir import OperationInfo, uTensorGraph +import flatbuffers +import utensor_cgen.third_party.tflite as tflite +from utensor_cgen.third_party.tflite.BuiltinOperator import BuiltinOperator +from utensor_cgen.third_party.tflite.BuiltinOptions import BuiltinOptions +from utensor_cgen.third_party.tflite.TensorType import TensorType +from utensor_cgen.logger import logger +from utensor_cgen.utils import parse_tensor_name + +from .base import Transformer + +__all__ = ["TFLiteExporter"] + +class FlatbufferOpManager: + op_list = list() + code_name_lookup = {v: k for k, v in BuiltinOperator.__dict__.items()} + + def regsiter_op(self, op_type_str): + if op_type_str not in BuiltinOperator.__dict__: + assert("invalid op str") + if op_type_str not in self.op_list: + self.op_list.append(op_type_str) + return self.op_index(op_type_str) + + def op_index(self, op_type_str): + return self.op_list.index(op_type_str) + def op_code(self, op_type_str): + return BuiltinOperator.__dict__[op_type_str] + def code2name(self, _op_code): + return self.code_name_lookup[_op_code] + def code2index(self, _op_code): + return self.op_list.index(self.code2name(_op_code)) + def index2code(self, _index): + op_type = self.op_list[_index] + return BuiltinOperator.__dict__[op_type] + def index2name(self, _index): + code = self.index2code(_index) + return self.code2name(code) + +class TFLiteExporter(Transformer): + METHOD_NAME = 'tflite_export' + KWARGS_NAMESCOPE = '_tflite_export' + Max_fbuff_size = 1024 * 10 + op_manager = FlatbufferOpManager() + fbuilder = flatbuffers.Builder(Max_fbuff_size) + tensor_buffer_index = OrderedDict() #added to the Model object + tensor_index = OrderedDict() #added to the Subgraph object + op_exec_order = list() + + def __init__(self): + self.prune_graph = False #what is this? + + def transform(self, ugraph): + #visitor pattern here + return ugraph + + def build_buffer(self, ugraph): + + #tensor data buffers + + pass + + def create_tensor_data(self, fbuilder, ugraph): + target_ops = ["Inline", "Const"] + export_tensor_name = True + for op_name in ugraph.topo_order: + op_info = ugraph.ops_info[op_name] + op_type = op_info.op_type + if op_type in target_ops: + out_tensor_info = op_info.output_tensors[0] + out_tname, out_dtype, tensor_shape = (out_tensor_info.name, + out_tensor_info.dtype, + out_tensor_info.shape) + #weight + value = op_info.op_attr['value'].value.np_array.flatten() #TODO: specify endianness here + raw_data = value.tobytes() + tflite.Buffer.BufferStartDataVector(fbuilder, len(raw_data)) + for i in raw_data: + fbuilder.PrependUint8(i) + data_vec = fbuilder.EndVector(len(raw_data)) + self.tensor_buffer_index[out_tname] = data_vec + + #shape + tflite.Tensor.TensorStartShapeVector(fbuilder, len(tensor_shape)) + for d in tensor_shape: + fbuilder.PrependInt32(d) + shape_vec = fbuilder.EndVector(len(tensor_shape)) + + #name + if export_tensor_name: + tensor_name = fbuilder.CreateString(out_tname) #TODO: legalize + + #quantization + (q_min, q_max) = self.quantization_info(op_info) + ##Min vec + tflite.QuantizationParameters.QuantizationParametersStartMinVector(fbuilder, 1) + fbuilder.PrependFloat32(q_min) + q_min_vec = fbuilder.EndVector(1) + ##Max Vec + tflite.QuantizationParameters.QuantizationParametersStartMaxVector(fbuilder, 1) + fbuilder.PrependFloat32(q_max) + q_max_vec = fbuilder.EndVector(1) + + tflite.QuantizationParameters.QuantizationParametersStart(fbuilder) + tflite.QuantizationParameters.QuantizationParametersAddMin(fbuilder, q_min_vec) + tflite.QuantizationParameters.QuantizationParametersAddMax(fbuilder, q_max_vec) + q_param = tflite.QuantizationParameters.QuantizationParametersEnd(fbuilder) + + #tensor object + tflite.Tensor.TensorStart(fbuilder) + tflite.Tensor.TensorAddShape(fbuilder, shape_vec) + tflite.Tensor.TensorAddType(fbuilder, TensorType.INT8) #TODO: a conversion class here + if export_tensor_name: + tflite.Tensor.TensorAddName(fbuilder, tensor_name) + tflite.Tensor.TensorAddQuantization(fbuilder, q_param) + tflite.Tensor.TensorAddIsVariable(fbuilder, False) + + tflite.Tensor.TensorAddBuffer(fbuilder, 0) + new_tensor = tflite.Tensor.TensorEnd(fbuilder) + self.tensor_index[out_tname] = new_tensor + + def output_vector(self, tensor_infos): + n = len(tensor_infos) + tflite.Operator.OperatorStartOutputsVector(self.fbuilder, n) + for tensor_info in tensor_infos: + tensor_index = self.tensor_index.keys().index(tensor_info.name) + self.fbuilder.PrependInt32(tensor_index) + return self.fbuilder.EndVector(n) + + def input_vector(self, tensor_infos): + n = len(tensor_infos) + tflite.Operator.OperatorStartInputsVector(self.fbuilder, n) + for tensor_info in tensor_infos: + tensor_index = self.tensor_index.keys().index(tensor_info.name) + self.fbuilder.PrependInt32(tensor_index) + return self.fbuilder.EndVector(n) + + + def add_FullyConnectedOp(self, inputs, outputs): + op_inputs = self.input_vector(inputs) + op_outputs = self.output_vector(outputs) + + tflite.Operator.OperatorStart(self.fbuilder) + tflite.Operator.OperatorAddOpcodeIndex(self.fbuilder, self.op_manager.op_index('FULLY_CONNECTED')) + tflite.Operator.OperatorAddInputs(self.fbuilder, op_inputs) + tflite.Operator.OperatorAddOutputs(self.fbuilder, op_outputs) + op = tflite.Operator.OperatorEnd(self.fbuilder) + self.op_exec_order.append(op) + + def quantization_info(self, op_info): + values = op_info.op_attr['value'].value.np_array.flatten() + return (values.min(), values.max()) + + + #TODO: op code From 4bb2de552e211e7912c16009c6cfc252b2cbefae Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Sun, 6 Oct 2019 00:28:39 +0800 Subject: [PATCH 004/117] op regristation --- utensor_cgen/transformer/tflite_exporter.py | 71 +++++++++++++-------- 1 file changed, 43 insertions(+), 28 deletions(-) diff --git a/utensor_cgen/transformer/tflite_exporter.py b/utensor_cgen/transformer/tflite_exporter.py index 8daa164e..11a710af 100644 --- a/utensor_cgen/transformer/tflite_exporter.py +++ b/utensor_cgen/transformer/tflite_exporter.py @@ -54,6 +54,7 @@ class TFLiteExporter(Transformer): fbuilder = flatbuffers.Builder(Max_fbuff_size) tensor_buffer_index = OrderedDict() #added to the Model object tensor_index = OrderedDict() #added to the Subgraph object + op_exec_order = list() def __init__(self): @@ -69,7 +70,7 @@ def build_buffer(self, ugraph): pass - def create_tensor_data(self, fbuilder, ugraph): + def create_tensor_data(self, ugraph): target_ops = ["Inline", "Const"] export_tensor_name = True for op_name in ugraph.topo_order: @@ -83,49 +84,49 @@ def create_tensor_data(self, fbuilder, ugraph): #weight value = op_info.op_attr['value'].value.np_array.flatten() #TODO: specify endianness here raw_data = value.tobytes() - tflite.Buffer.BufferStartDataVector(fbuilder, len(raw_data)) + tflite.Buffer.BufferStartDataVector(self.fbuilder, len(raw_data)) for i in raw_data: - fbuilder.PrependUint8(i) - data_vec = fbuilder.EndVector(len(raw_data)) + self.fbuilder.PrependUint8(i) + data_vec = self.fbuilder.EndVector(len(raw_data)) self.tensor_buffer_index[out_tname] = data_vec #shape - tflite.Tensor.TensorStartShapeVector(fbuilder, len(tensor_shape)) + tflite.Tensor.TensorStartShapeVector(self.fbuilder, len(tensor_shape)) for d in tensor_shape: - fbuilder.PrependInt32(d) - shape_vec = fbuilder.EndVector(len(tensor_shape)) + self.fbuilder.PrependInt32(d) + shape_vec = self.fbuilder.EndVector(len(tensor_shape)) #name if export_tensor_name: - tensor_name = fbuilder.CreateString(out_tname) #TODO: legalize + tensor_name = self.fbuilder.CreateString(out_tname) #TODO: legalize #quantization (q_min, q_max) = self.quantization_info(op_info) ##Min vec - tflite.QuantizationParameters.QuantizationParametersStartMinVector(fbuilder, 1) - fbuilder.PrependFloat32(q_min) - q_min_vec = fbuilder.EndVector(1) + tflite.QuantizationParameters.QuantizationParametersStartMinVector(self.fbuilder, 1) + self.fbuilder.PrependFloat32(q_min) + q_min_vec = self.fbuilder.EndVector(1) ##Max Vec - tflite.QuantizationParameters.QuantizationParametersStartMaxVector(fbuilder, 1) - fbuilder.PrependFloat32(q_max) - q_max_vec = fbuilder.EndVector(1) + tflite.QuantizationParameters.QuantizationParametersStartMaxVector(self.fbuilder, 1) + self.fbuilder.PrependFloat32(q_max) + q_max_vec = self.fbuilder.EndVector(1) - tflite.QuantizationParameters.QuantizationParametersStart(fbuilder) - tflite.QuantizationParameters.QuantizationParametersAddMin(fbuilder, q_min_vec) - tflite.QuantizationParameters.QuantizationParametersAddMax(fbuilder, q_max_vec) - q_param = tflite.QuantizationParameters.QuantizationParametersEnd(fbuilder) + tflite.QuantizationParameters.QuantizationParametersStart(self.fbuilder) + tflite.QuantizationParameters.QuantizationParametersAddMin(self.fbuilder, q_min_vec) + tflite.QuantizationParameters.QuantizationParametersAddMax(self.fbuilder, q_max_vec) + q_param = tflite.QuantizationParameters.QuantizationParametersEnd(self.fbuilder) #tensor object - tflite.Tensor.TensorStart(fbuilder) - tflite.Tensor.TensorAddShape(fbuilder, shape_vec) - tflite.Tensor.TensorAddType(fbuilder, TensorType.INT8) #TODO: a conversion class here + tflite.Tensor.TensorStart(self.fbuilder) + tflite.Tensor.TensorAddShape(self.fbuilder, shape_vec) + tflite.Tensor.TensorAddType(self.fbuilder, TensorType.INT8) #TODO: a conversion class here if export_tensor_name: - tflite.Tensor.TensorAddName(fbuilder, tensor_name) - tflite.Tensor.TensorAddQuantization(fbuilder, q_param) - tflite.Tensor.TensorAddIsVariable(fbuilder, False) + tflite.Tensor.TensorAddName(self.fbuilder, tensor_name) + tflite.Tensor.TensorAddQuantization(self.fbuilder, q_param) + tflite.Tensor.TensorAddIsVariable(self.fbuilder, False) - tflite.Tensor.TensorAddBuffer(fbuilder, 0) - new_tensor = tflite.Tensor.TensorEnd(fbuilder) + tflite.Tensor.TensorAddBuffer(self.fbuilder, 0) + new_tensor = tflite.Tensor.TensorEnd(self.fbuilder) self.tensor_index[out_tname] = new_tensor def output_vector(self, tensor_infos): @@ -144,6 +145,22 @@ def input_vector(self, tensor_infos): self.fbuilder.PrependInt32(tensor_index) return self.fbuilder.EndVector(n) + def create_op_codes(self, ugraph): + #scan, translation and register + op_codes = list() + for op_name in ugraph.topo_order: + op_type = ugraph.ops_info[op_name].op_type + tflite.OperatorCode.OperatorCodeStart(self.fbuilder) + #TODO: op type translation happen here + opcode_index = self.op_manager.regsiter_op(op_type) + tflite.OperatorCode.OperatorCodeAddBuiltinCode(self.fbuilder, opcode_index) + op_codes.append(tflite.OperatorCode.OperatorCodeEnd(self.fbuilder)) + + tflite.Model.ModelStartOperatorCodesVector(self.fbuilder, len(op_codes)) + for it_op_code in op_codes: + self.fbuilder.PrependUOffsetTRelative(it_op_code) + op_code_vec = self.fbuilder.EndVector(len(op_codes)) + return op_code_vec #needs to be added into the Model def add_FullyConnectedOp(self, inputs, outputs): op_inputs = self.input_vector(inputs) @@ -160,5 +177,3 @@ def quantization_info(self, op_info): values = op_info.op_attr['value'].value.np_array.flatten() return (values.min(), values.max()) - - #TODO: op code From 3f19de7d42602f15cd0611a5848e4ba5073bcb38 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Mon, 7 Oct 2019 18:17:30 +0800 Subject: [PATCH 005/117] tied everything together; lacks of op factory/converter quantization param for intermediate tensors? --- utensor_cgen/transformer/tflite_exporter.py | 133 ++++++++++++++++---- 1 file changed, 109 insertions(+), 24 deletions(-) diff --git a/utensor_cgen/transformer/tflite_exporter.py b/utensor_cgen/transformer/tflite_exporter.py index 11a710af..7095f71c 100644 --- a/utensor_cgen/transformer/tflite_exporter.py +++ b/utensor_cgen/transformer/tflite_exporter.py @@ -52,8 +52,10 @@ class TFLiteExporter(Transformer): Max_fbuff_size = 1024 * 10 op_manager = FlatbufferOpManager() fbuilder = flatbuffers.Builder(Max_fbuff_size) - tensor_buffer_index = OrderedDict() #added to the Model object - tensor_index = OrderedDict() #added to the Subgraph object + tensor_buffer_index = OrderedDict() # out_name : data buff vec + # added to the Model object + tensor_index = OrderedDict() # out_name : ref tensor object + #added to the Subgraph object op_exec_order = list() @@ -61,22 +63,67 @@ def __init__(self): self.prune_graph = False #what is this? def transform(self, ugraph): - #visitor pattern here - return ugraph + #create tensor data buffer + #create const tensors + #update tensor_index + self.create_static_tensor(ugraph) + + #create intermediate tensors + #update tensor_index + self.create_variable_tensors(ugraph) + + #interacts with FlatbufferOpManager + op_codes_vec = self.create_op_codes(ugraph) #to be added into the Model + + #create subgraph + #first, create the tensor vector + tflite.SubGraph.SubGraphStartTensorsVector(self.fbuilder, len(self.tensor_index)) + for t_name, f_obj in self.tensor_index.items(): + self.fbuilder.PrependUOffsetTRelative(f_obj) + tensors_vec = self.fbuilder.EndVector(len(self.tensor_index)) + + ## TODO: traverse the graph and produce + ops_vec = None + ## here + + tflite.SubGraph.SubGraphStart(self.fbuilder) + tflite.SubGraph.SubGraphAddTensors(self.fbuilder, tensors_vec) + tflite.SubGraph.SubGraphAddOperators(self.fbuilder, ops_vec) + subgraph = tflite.SubGraph.SubGraphEnd(self.fbuilder) + + tflite.Model.ModelStartSubgraphsVector(self.fbuilder, 1) #TFLM runtime only support 1 subgraph + self.fbuilder.PrependUOffsetTRelative(subgraph) + subgraph_vec = self.fbuilder.EndVector(1) - def build_buffer(self, ugraph): + #model + # first, add tensor buffer here + tflite.Model.ModelStartBuffersVector(self.fbuilder, len(self.tensor_buffer_index)) + for t_name, f_obj in self.tensor_buffer_index.items(): + self.fbuilder.PrependUOffsetTRelative(f_obj) + buff_vec = self.fbuilder.EndVector(len(self.tensor_buffer_index)) - #tensor data buffers + tflite.Model.ModelStart(self.fbuilder) + tflite.Model.ModelAddVersion(self.fbuilder, 100) #TODO: change this + tflite.Model.ModelAddOperatorCodes(self.fbuilder, op_codes_vec) + tflite.Model.ModelAddSubgraphs(self.fbuilder, subgraph_vec) + tflite.Model.ModelAddBuffers(self.fbuilder, buff_vec) - pass + model = tflite.Model.ModelEnd(self.fbuilder) - def create_tensor_data(self, ugraph): + self.fbuilder.Finish(model) + + output = self.fbuilder.Output() #do something with the output here + print(output) + + return ugraph + + def create_static_tensor(self, ugraph): target_ops = ["Inline", "Const"] export_tensor_name = True for op_name in ugraph.topo_order: op_info = ugraph.ops_info[op_name] op_type = op_info.op_type - if op_type in target_ops: + if op_type in target_ops: #TODO: check if op data is empty out_tensor_info = op_info.output_tensors[0] out_tname, out_dtype, tensor_shape = (out_tensor_info.name, out_tensor_info.dtype, @@ -98,28 +145,16 @@ def create_tensor_data(self, ugraph): #name if export_tensor_name: - tensor_name = self.fbuilder.CreateString(out_tname) #TODO: legalize + tensor_name = self.fbuilder.CreateString(self.legalize_name(out_tname)) #quantization (q_min, q_max) = self.quantization_info(op_info) - ##Min vec - tflite.QuantizationParameters.QuantizationParametersStartMinVector(self.fbuilder, 1) - self.fbuilder.PrependFloat32(q_min) - q_min_vec = self.fbuilder.EndVector(1) - ##Max Vec - tflite.QuantizationParameters.QuantizationParametersStartMaxVector(self.fbuilder, 1) - self.fbuilder.PrependFloat32(q_max) - q_max_vec = self.fbuilder.EndVector(1) - - tflite.QuantizationParameters.QuantizationParametersStart(self.fbuilder) - tflite.QuantizationParameters.QuantizationParametersAddMin(self.fbuilder, q_min_vec) - tflite.QuantizationParameters.QuantizationParametersAddMax(self.fbuilder, q_max_vec) - q_param = tflite.QuantizationParameters.QuantizationParametersEnd(self.fbuilder) + q_param = self.create_quantization_param([q_min], [q_max]) #tensor object tflite.Tensor.TensorStart(self.fbuilder) tflite.Tensor.TensorAddShape(self.fbuilder, shape_vec) - tflite.Tensor.TensorAddType(self.fbuilder, TensorType.INT8) #TODO: a conversion class here + tflite.Tensor.TensorAddType(self.fbuilder, TensorType.INT8) #TODO: a conversion class here, out_dtype if export_tensor_name: tflite.Tensor.TensorAddName(self.fbuilder, tensor_name) tflite.Tensor.TensorAddQuantization(self.fbuilder, q_param) @@ -177,3 +212,53 @@ def quantization_info(self, op_info): values = op_info.op_attr['value'].value.np_array.flatten() return (values.min(), values.max()) + def create_quantization_param(self, mins, maxs): + assert len(mins) != len(maxs), "mins and maxs length mismatch" + + + tflite.QuantizationParameters.QuantizationParametersStartMinVector(self.fbuilder, len(mins)) + for _min in mins: + self.fbuilder.PrependFloat32(_min) + q_min_vec = self.fbuilder.EndVector(len(mins)) + + tflite.QuantizationParameters.QuantizationParametersStartMaxVector(self.fbuilder, len(maxs)) + for _max in maxs: + self.fbuilder.PrependFloat32(_max) + q_max_vec = self.fbuilder.EndVector(len(maxs)) + + tflite.QuantizationParameters.QuantizationParametersStart(self.fbuilder) + tflite.QuantizationParameters.QuantizationParametersAddMin(self.fbuilder, q_min_vec) + tflite.QuantizationParameters.QuantizationParametersAddMax(self.fbuilder, q_max_vec) + q_param = tflite.QuantizationParameters.QuantizationParametersEnd(self.fbuilder) + + return q_param + + def create_variable_tensors(self, ugraph): + tensor_infos = set() + for op_name in ugraph.topo_order: + tensor_infos.update(ugraph.ops_info[op_name].inputs) + tensor_infos.update(ugraph.ops_info[op_name].outputs) + for tensor_info in tensor_infos: + tflite.Tensor.TensorStartShapeVector(self.fbuilder, len(tensor_info.shape)) + for d in tensor_info.shape: + self.fbuilder.PrependInt32(d) + shape_vec = self.fbuilder.EndVector(len(tensor_info.shape)) + + tensor_name = self.fbuilder.CreateString(self.legalize_name(tensor_info.name)) + + q_param = self.create_quantization_param([-20],[200]) #TODO: workout how to treat the quantization here + + tflite.Tensor.TensorStart(self.fbuilder) + tflite.Tensor.TensorAddShape(self.fbuilder, shape_vec) + tflite.Tensor.TensorAddType(self.fbuilder, TensorType.INT8) #TODO: tensor type conversion here + tflite.Tensor.TensorAddName(self.fbuilder, tensor_name) + tflite.Tensor.TensorAddQuantization(self.fbuilder, q_param) + tflite.Tensor.TensorAddIsVariable(self.fbuilder, True) + + self.tensor_index[tensor_info.name] = tflite.Tensor.TensorEnd(self.fbuilder) + + def legalize_name(self, str): + #TODO: legalize the name + return str + +# How to construct the quantization parameter for intermediate tensors? From 9db8ba4f12d632c99b12dea68dd69fa4b313706b Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Wed, 9 Oct 2019 17:43:29 +0800 Subject: [PATCH 006/117] uses Affine Quantization --- utensor_cgen/transformer/tflite_exporter.py | 52 ++++++++++++++------- 1 file changed, 35 insertions(+), 17 deletions(-) diff --git a/utensor_cgen/transformer/tflite_exporter.py b/utensor_cgen/transformer/tflite_exporter.py index 7095f71c..efa44488 100644 --- a/utensor_cgen/transformer/tflite_exporter.py +++ b/utensor_cgen/transformer/tflite_exporter.py @@ -5,6 +5,7 @@ """ import re from collections import OrderedDict +import numpy as np from copy import deepcopy from utensor_cgen.ir import OperationInfo, uTensorGraph @@ -148,8 +149,9 @@ def create_static_tensor(self, ugraph): tensor_name = self.fbuilder.CreateString(self.legalize_name(out_tname)) #quantization - (q_min, q_max) = self.quantization_info(op_info) - q_param = self.create_quantization_param([q_min], [q_max]) + ## only per-layer supported + (q_scale, q_zero) = self.sym_quantization_info(op_info) + q_param = self.create_quantization_param([q_scale], [q_zero]) #tensor object tflite.Tensor.TensorStart(self.fbuilder) @@ -208,31 +210,46 @@ def add_FullyConnectedOp(self, inputs, outputs): op = tflite.Operator.OperatorEnd(self.fbuilder) self.op_exec_order.append(op) - def quantization_info(self, op_info): - values = op_info.op_attr['value'].value.np_array.flatten() - return (values.min(), values.max()) - def create_quantization_param(self, mins, maxs): - assert len(mins) != len(maxs), "mins and maxs length mismatch" + def sym_quantization_info(self, op_info): + """ + https://www.tensorflow.org/lite/performance/quantization_spec + C++: TfLiteAffineQuantization struct + zero-point = 0 : symmetric quantization + Returns (scale, zero) + + TODO: per-axis quantization + """ + + values = op_info.op_attr['value'].value.np_array.flatten() + abs_max = np.absolute(values).max() + #based on quantizted int8 dtype + scale = 127 / abs_max + + return (scale, 0) + + def create_quantization_param(self, scales, zeros): + assert len(scales) != len(zeros), "scales and zero-points length mismatch" - tflite.QuantizationParameters.QuantizationParametersStartMinVector(self.fbuilder, len(mins)) - for _min in mins: - self.fbuilder.PrependFloat32(_min) - q_min_vec = self.fbuilder.EndVector(len(mins)) + tflite.QuantizationParameters.QuantizationParametersStartScaleVector(self.fbuilder, len(scales)) + for _scale in scales: + self.fbuilder.PrependFloat32(_scale) + q_scales_vec = self.fbuilder.EndVector(len(scales)) - tflite.QuantizationParameters.QuantizationParametersStartMaxVector(self.fbuilder, len(maxs)) - for _max in maxs: - self.fbuilder.PrependFloat32(_max) - q_max_vec = self.fbuilder.EndVector(len(maxs)) + tflite.QuantizationParameters.QuantizationParametersStartZeroPointVector(self.fbuilder, len(zeros)) + for _zero in zeros: + self.fbuilder.PrependFloat32(_zero) + q_zeros_vec = self.fbuilder.EndVector(len(zeros)) tflite.QuantizationParameters.QuantizationParametersStart(self.fbuilder) - tflite.QuantizationParameters.QuantizationParametersAddMin(self.fbuilder, q_min_vec) - tflite.QuantizationParameters.QuantizationParametersAddMax(self.fbuilder, q_max_vec) + tflite.QuantizationParameters.QuantizationParametersAddScale(self.fbuilder, q_scales_vec) + tflite.QuantizationParameters.QuantizationParametersAddZeroPoint(self.fbuilder, q_zeros_vec) q_param = tflite.QuantizationParameters.QuantizationParametersEnd(self.fbuilder) return q_param + # These are often the intermediate output tensors with online quantization def create_variable_tensors(self, ugraph): tensor_infos = set() for op_name in ugraph.topo_order: @@ -262,3 +279,4 @@ def legalize_name(self, str): return str # How to construct the quantization parameter for intermediate tensors? +## zero point and scale From f46ff9fc280c5e3daf920d68aa9e4d0d9d7e490d Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Wed, 9 Oct 2019 18:10:27 +0800 Subject: [PATCH 007/117] private variable and member functions --- utensor_cgen/transformer/tflite_exporter.py | 76 ++++++++++----------- 1 file changed, 38 insertions(+), 38 deletions(-) diff --git a/utensor_cgen/transformer/tflite_exporter.py b/utensor_cgen/transformer/tflite_exporter.py index efa44488..85f675a8 100644 --- a/utensor_cgen/transformer/tflite_exporter.py +++ b/utensor_cgen/transformer/tflite_exporter.py @@ -50,13 +50,13 @@ def index2name(self, _index): class TFLiteExporter(Transformer): METHOD_NAME = 'tflite_export' KWARGS_NAMESCOPE = '_tflite_export' - Max_fbuff_size = 1024 * 10 - op_manager = FlatbufferOpManager() - fbuilder = flatbuffers.Builder(Max_fbuff_size) - tensor_buffer_index = OrderedDict() # out_name : data buff vec - # added to the Model object - tensor_index = OrderedDict() # out_name : ref tensor object - #added to the Subgraph object + __Max_fbuff_size = 1024 * 10 + __op_manager = FlatbufferOpManager() + fbuilder = flatbuffers.Builder(__Max_fbuff_size) + __tensor_buffer_index = OrderedDict() # out_name : data buff vec + # added to the Model object + __tensor_index = OrderedDict() # out_name : ref tensor object + #added to the Subgraph object op_exec_order = list() @@ -67,21 +67,21 @@ def transform(self, ugraph): #create tensor data buffer #create const tensors #update tensor_index - self.create_static_tensor(ugraph) + self.__create_static_tensor(ugraph) #create intermediate tensors #update tensor_index - self.create_variable_tensors(ugraph) + self.__create_variable_tensors(ugraph) #interacts with FlatbufferOpManager - op_codes_vec = self.create_op_codes(ugraph) #to be added into the Model + op_codes_vec = self.__create_op_codes(ugraph) #to be added into the Model #create subgraph #first, create the tensor vector - tflite.SubGraph.SubGraphStartTensorsVector(self.fbuilder, len(self.tensor_index)) - for t_name, f_obj in self.tensor_index.items(): + tflite.SubGraph.SubGraphStartTensorsVector(self.fbuilder, len(self.__tensor_index)) + for t_name, f_obj in self.__tensor_index.items(): self.fbuilder.PrependUOffsetTRelative(f_obj) - tensors_vec = self.fbuilder.EndVector(len(self.tensor_index)) + tensors_vec = self.fbuilder.EndVector(len(self.__tensor_index)) ## TODO: traverse the graph and produce ops_vec = None @@ -98,10 +98,10 @@ def transform(self, ugraph): #model # first, add tensor buffer here - tflite.Model.ModelStartBuffersVector(self.fbuilder, len(self.tensor_buffer_index)) - for t_name, f_obj in self.tensor_buffer_index.items(): + tflite.Model.ModelStartBuffersVector(self.fbuilder, len(self.__tensor_buffer_index)) + for t_name, f_obj in self.__tensor_buffer_index.items(): self.fbuilder.PrependUOffsetTRelative(f_obj) - buff_vec = self.fbuilder.EndVector(len(self.tensor_buffer_index)) + buff_vec = self.fbuilder.EndVector(len(self.__tensor_buffer_index)) tflite.Model.ModelStart(self.fbuilder) tflite.Model.ModelAddVersion(self.fbuilder, 100) #TODO: change this @@ -118,7 +118,7 @@ def transform(self, ugraph): return ugraph - def create_static_tensor(self, ugraph): + def __create_static_tensor(self, ugraph): target_ops = ["Inline", "Const"] export_tensor_name = True for op_name in ugraph.topo_order: @@ -136,7 +136,7 @@ def create_static_tensor(self, ugraph): for i in raw_data: self.fbuilder.PrependUint8(i) data_vec = self.fbuilder.EndVector(len(raw_data)) - self.tensor_buffer_index[out_tname] = data_vec + self.__tensor_buffer_index[out_tname] = data_vec #shape tflite.Tensor.TensorStartShapeVector(self.fbuilder, len(tensor_shape)) @@ -146,12 +146,12 @@ def create_static_tensor(self, ugraph): #name if export_tensor_name: - tensor_name = self.fbuilder.CreateString(self.legalize_name(out_tname)) + tensor_name = self.fbuilder.CreateString(self.__legalize_name(out_tname)) #quantization ## only per-layer supported - (q_scale, q_zero) = self.sym_quantization_info(op_info) - q_param = self.create_quantization_param([q_scale], [q_zero]) + (q_scale, q_zero) = self.__sym_quantization_info(op_info) + q_param = self.__create_quantization_param([q_scale], [q_zero]) #tensor object tflite.Tensor.TensorStart(self.fbuilder) @@ -164,32 +164,32 @@ def create_static_tensor(self, ugraph): tflite.Tensor.TensorAddBuffer(self.fbuilder, 0) new_tensor = tflite.Tensor.TensorEnd(self.fbuilder) - self.tensor_index[out_tname] = new_tensor + self.__tensor_index[out_tname] = new_tensor - def output_vector(self, tensor_infos): + def __output_vector(self, tensor_infos): n = len(tensor_infos) tflite.Operator.OperatorStartOutputsVector(self.fbuilder, n) for tensor_info in tensor_infos: - tensor_index = self.tensor_index.keys().index(tensor_info.name) + tensor_index = self.__tensor_index.keys().index(tensor_info.name) self.fbuilder.PrependInt32(tensor_index) return self.fbuilder.EndVector(n) - def input_vector(self, tensor_infos): + def __input_vector(self, tensor_infos): n = len(tensor_infos) tflite.Operator.OperatorStartInputsVector(self.fbuilder, n) for tensor_info in tensor_infos: - tensor_index = self.tensor_index.keys().index(tensor_info.name) + tensor_index = self.__tensor_index.keys().index(tensor_info.name) self.fbuilder.PrependInt32(tensor_index) return self.fbuilder.EndVector(n) - def create_op_codes(self, ugraph): + def __create_op_codes(self, ugraph): #scan, translation and register op_codes = list() for op_name in ugraph.topo_order: op_type = ugraph.ops_info[op_name].op_type tflite.OperatorCode.OperatorCodeStart(self.fbuilder) #TODO: op type translation happen here - opcode_index = self.op_manager.regsiter_op(op_type) + opcode_index = self.__op_manager.regsiter_op(op_type) tflite.OperatorCode.OperatorCodeAddBuiltinCode(self.fbuilder, opcode_index) op_codes.append(tflite.OperatorCode.OperatorCodeEnd(self.fbuilder)) @@ -200,18 +200,18 @@ def create_op_codes(self, ugraph): return op_code_vec #needs to be added into the Model def add_FullyConnectedOp(self, inputs, outputs): - op_inputs = self.input_vector(inputs) - op_outputs = self.output_vector(outputs) + op_inputs = self.__input_vector(inputs) + op_outputs = self.__output_vector(outputs) tflite.Operator.OperatorStart(self.fbuilder) - tflite.Operator.OperatorAddOpcodeIndex(self.fbuilder, self.op_manager.op_index('FULLY_CONNECTED')) + tflite.Operator.OperatorAddOpcodeIndex(self.fbuilder, self.__op_manager.op_index('FULLY_CONNECTED')) tflite.Operator.OperatorAddInputs(self.fbuilder, op_inputs) tflite.Operator.OperatorAddOutputs(self.fbuilder, op_outputs) op = tflite.Operator.OperatorEnd(self.fbuilder) self.op_exec_order.append(op) - def sym_quantization_info(self, op_info): + def __sym_quantization_info(self, op_info): """ https://www.tensorflow.org/lite/performance/quantization_spec C++: TfLiteAffineQuantization struct @@ -228,7 +228,7 @@ def sym_quantization_info(self, op_info): return (scale, 0) - def create_quantization_param(self, scales, zeros): + def __create_quantization_param(self, scales, zeros): assert len(scales) != len(zeros), "scales and zero-points length mismatch" @@ -250,7 +250,7 @@ def create_quantization_param(self, scales, zeros): return q_param # These are often the intermediate output tensors with online quantization - def create_variable_tensors(self, ugraph): + def __create_variable_tensors(self, ugraph): tensor_infos = set() for op_name in ugraph.topo_order: tensor_infos.update(ugraph.ops_info[op_name].inputs) @@ -261,9 +261,9 @@ def create_variable_tensors(self, ugraph): self.fbuilder.PrependInt32(d) shape_vec = self.fbuilder.EndVector(len(tensor_info.shape)) - tensor_name = self.fbuilder.CreateString(self.legalize_name(tensor_info.name)) + tensor_name = self.fbuilder.CreateString(self.__legalize_name(tensor_info.name)) - q_param = self.create_quantization_param([-20],[200]) #TODO: workout how to treat the quantization here + q_param = self.__create_quantization_param([-20],[200]) #TODO: workout how to treat the quantization here tflite.Tensor.TensorStart(self.fbuilder) tflite.Tensor.TensorAddShape(self.fbuilder, shape_vec) @@ -272,9 +272,9 @@ def create_variable_tensors(self, ugraph): tflite.Tensor.TensorAddQuantization(self.fbuilder, q_param) tflite.Tensor.TensorAddIsVariable(self.fbuilder, True) - self.tensor_index[tensor_info.name] = tflite.Tensor.TensorEnd(self.fbuilder) + self.__tensor_index[tensor_info.name] = tflite.Tensor.TensorEnd(self.fbuilder) - def legalize_name(self, str): + def __legalize_name(self, str): #TODO: legalize the name return str From 8b60cc528a16ee00d7ab4c77d76f42b71ed27f2a Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Wed, 16 Oct 2019 16:24:09 +0800 Subject: [PATCH 008/117] float32 for intermediate tensors --- utensor_cgen/transformer/tflite_exporter.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/utensor_cgen/transformer/tflite_exporter.py b/utensor_cgen/transformer/tflite_exporter.py index 85f675a8..ab2cded1 100644 --- a/utensor_cgen/transformer/tflite_exporter.py +++ b/utensor_cgen/transformer/tflite_exporter.py @@ -250,6 +250,7 @@ def __create_quantization_param(self, scales, zeros): return q_param # These are often the intermediate output tensors with online quantization + # Allocation them as Float32 to use online quantization def __create_variable_tensors(self, ugraph): tensor_infos = set() for op_name in ugraph.topo_order: @@ -263,13 +264,14 @@ def __create_variable_tensors(self, ugraph): tensor_name = self.fbuilder.CreateString(self.__legalize_name(tensor_info.name)) - q_param = self.__create_quantization_param([-20],[200]) #TODO: workout how to treat the quantization here + #q_param = self.__create_quantization_param([-20],[200]) #TODO: workout how to treat the quantization here tflite.Tensor.TensorStart(self.fbuilder) tflite.Tensor.TensorAddShape(self.fbuilder, shape_vec) - tflite.Tensor.TensorAddType(self.fbuilder, TensorType.INT8) #TODO: tensor type conversion here + #tflite.Tensor.TensorAddType(self.fbuilder, TensorType.INT8) #TODO: tensor type conversion here + tflite.Tensor.TensorAddType(self.fbuilder, TensorType.FLOAT32) tflite.Tensor.TensorAddName(self.fbuilder, tensor_name) - tflite.Tensor.TensorAddQuantization(self.fbuilder, q_param) + #tflite.Tensor.TensorAddQuantization(self.fbuilder, q_param) tflite.Tensor.TensorAddIsVariable(self.fbuilder, True) self.__tensor_index[tensor_info.name] = tflite.Tensor.TensorEnd(self.fbuilder) From 295efea905bf2e620a25bb88f63f33c9677d51d6 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Thu, 17 Oct 2019 18:14:48 +0800 Subject: [PATCH 009/117] - bugfixes in tflite_exporter.py - layout the test mechanism - making third_party/tflite all importable --- tests/tflm/tflite_export/__init__.py | 0 tests/tflm/tflite_export/conftest.py | 105 ++++++++++++++++++++ tests/tflm/tflite_export/test_write.py | 16 +++ utensor_cgen/third_party/tflite/__init__.py | 4 + utensor_cgen/transformer/__init__.py | 1 + utensor_cgen/transformer/tflite_exporter.py | 46 ++++++--- 6 files changed, 156 insertions(+), 16 deletions(-) create mode 100644 tests/tflm/tflite_export/__init__.py create mode 100644 tests/tflm/tflite_export/conftest.py create mode 100644 tests/tflm/tflite_export/test_write.py diff --git a/tests/tflm/tflite_export/__init__.py b/tests/tflm/tflite_export/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/tflm/tflite_export/conftest.py b/tests/tflm/tflite_export/conftest.py new file mode 100644 index 00000000..899cb8c2 --- /dev/null +++ b/tests/tflm/tflite_export/conftest.py @@ -0,0 +1,105 @@ +import numpy as np +from pytest import fixture + +import tensorflow as tf +from utensor_cgen.ir import TensorInfo, OperationInfo, uTensorGraph +from utensor_cgen.utils import prune_graph, topologic_order_graph + +@fixture(name='sample_ugraph') +def simple_tflm_graph(): + ugraph = uTensorGraph() + + weight_op = OperationInfo( + name = "weight_const", + op_type = "Const", + backend = "tensorflow", + ugraph = ugraph, + op_attr = dict(), + input_tensors = [], + output_tensors = [] + ) + weight_op.op_attr["value"] = np.array([1,2,3,4], dtype=np.int8) + weight_op.op_attr["shape"] = [1,4] + + weight = TensorInfo( + name = "weight", + op_name = "weight_const", + dtype = np.dtype("int8"), + shape = weight_op.op_attr["shape"], + ugraph = ugraph + ) + weight_op.output_tensors = [weight] + + mock_input_op = OperationInfo( + name = "mock_input_const", + op_type = "Const", + backend = "tensorflow", + ugraph = ugraph, + op_attr = dict(), + input_tensors = [], + output_tensors = [] + ) + mock_input_op.op_attr["value"] = np.array([[1],[2],[3],[4]], dtype=np.int8) + mock_input_op.op_attr["shape"] = [4,1] + + input1 = TensorInfo( + name = "input", + op_name = "mock_input_const", + dtype = np.dtype("float"), + shape = [1, 4], + ugraph = ugraph + ) + + mock_input_op.output_tensors = [input1] + + bias_op = OperationInfo( + name = "bias_const", + op_type = "Const", + backend = "tensorflow", + ugraph = ugraph, + op_attr = dict(), + input_tensors = [], + output_tensors = [] + ) + bias_op.op_attr["value"] = np.array([1], dtype=np.int8) + bias_op.op_attr["shape"] = [1] + + bias = TensorInfo( + name = "bias", + op_name = "bias_const", + dtype = np.dtype("int8"), + shape = weight_op.op_attr["shape"], + ugraph = ugraph + ) + bias_op.output_tensors = [bias] + + + fc1_op = OperationInfo( + name = "FC1", + op_type = "FULLY_CONNECTED", + backend = "tensorflow", + ugraph = ugraph, + op_attr = dict(), + input_tensors = [], + output_tensors = [] + ) + + output = TensorInfo( + name = "output", + op_name = "FC1", + dtype = np.dtype("float"), + shape = [1, 1], + ugraph = ugraph + ) + + fc1_op.input_tensors = [input1, weight, bias] + fc1_op.output_tensors = [output] + + ugraph.ops_info["FC1"] = fc1_op + ugraph.output_nodes = ["FC1"] + #ugraph.backend = "tensorflow" + + topologic_order_graph(ugraph) + #ugraph = prune_graph(ugraph) + + return ugraph diff --git a/tests/tflm/tflite_export/test_write.py b/tests/tflm/tflite_export/test_write.py new file mode 100644 index 00000000..f98a484b --- /dev/null +++ b/tests/tflm/tflite_export/test_write.py @@ -0,0 +1,16 @@ +import numpy as np + +import tensorflow as tf +from utensor_cgen.frontend.tensorflow import GraphDefParser +from utensor_cgen.matcher import uTensorGraphMatcher +from utensor_cgen.utils import prune_graph, topologic_order_graph +from utensor_cgen.transformer import TFLiteExporter + +def test_tflite_fb_write(sample_ugraph): + exporter = TFLiteExporter() + ugraph = exporter.transform(sample_ugraph) + exporter.output() + print(exporter.output()) + + test_pass = True + assert test_pass, 'error message here' diff --git a/utensor_cgen/third_party/tflite/__init__.py b/utensor_cgen/third_party/tflite/__init__.py index e69de29b..1cb91d4c 100644 --- a/utensor_cgen/third_party/tflite/__init__.py +++ b/utensor_cgen/third_party/tflite/__init__.py @@ -0,0 +1,4 @@ +from os.path import dirname, basename, isfile, join +import glob +modules = glob.glob(join(dirname(__file__), "*.py")) +__all__ = [ basename(f)[:-3] for f in modules if isfile(f) and not f.endswith('__init__.py')] \ No newline at end of file diff --git a/utensor_cgen/transformer/__init__.py b/utensor_cgen/transformer/__init__.py index 13a003e0..ae6d6a23 100644 --- a/utensor_cgen/transformer/__init__.py +++ b/utensor_cgen/transformer/__init__.py @@ -7,3 +7,4 @@ from .optimizer import * from .pipeline import TransformerPipeline from .quantize import * +from .tflite_exporter import * diff --git a/utensor_cgen/transformer/tflite_exporter.py b/utensor_cgen/transformer/tflite_exporter.py index ab2cded1..99f8aaa7 100644 --- a/utensor_cgen/transformer/tflite_exporter.py +++ b/utensor_cgen/transformer/tflite_exporter.py @@ -11,6 +11,7 @@ from utensor_cgen.ir import OperationInfo, uTensorGraph import flatbuffers import utensor_cgen.third_party.tflite as tflite +from utensor_cgen.third_party.tflite import * from utensor_cgen.third_party.tflite.BuiltinOperator import BuiltinOperator from utensor_cgen.third_party.tflite.BuiltinOptions import BuiltinOptions from utensor_cgen.third_party.tflite.TensorType import TensorType @@ -58,7 +59,7 @@ class TFLiteExporter(Transformer): __tensor_index = OrderedDict() # out_name : ref tensor object #added to the Subgraph object - op_exec_order = list() + #op_exec_order = list() def __init__(self): self.prune_graph = False #what is this? @@ -83,9 +84,16 @@ def transform(self, ugraph): self.fbuilder.PrependUOffsetTRelative(f_obj) tensors_vec = self.fbuilder.EndVector(len(self.__tensor_index)) - ## TODO: traverse the graph and produce - ops_vec = None - ## here + ##traverse ugraph + fb_ops_list = list() + for op_name in ugraph.topo_order: + op_info = ugraph.ops_info[op_name] + fb_ops_list.append(self.add_Op(op_info)) + + tflite.SubGraph.SubGraphStartOperatorsVector(self.fbuilder, len(fb_ops_list)) + for fb_op in fb_ops_list: + self.fbuilder.PrependUOffsetTRelative(fb_op) + ops_vec = self.fbuilder.EndVector(len(fb_ops_list)) tflite.SubGraph.SubGraphStart(self.fbuilder) tflite.SubGraph.SubGraphAddTensors(self.fbuilder, tensors_vec) @@ -130,7 +138,8 @@ def __create_static_tensor(self, ugraph): out_tensor_info.dtype, out_tensor_info.shape) #weight - value = op_info.op_attr['value'].value.np_array.flatten() #TODO: specify endianness here + #value = op_info.op_attr['value'].value.np_array.flatten() #TODO: specify endianness here + value = op_info.op_attr['value'].flatten() #FIXME: deal with the proper attribute type here raw_data = value.tobytes() tflite.Buffer.BufferStartDataVector(self.fbuilder, len(raw_data)) for i in raw_data: @@ -170,7 +179,7 @@ def __output_vector(self, tensor_infos): n = len(tensor_infos) tflite.Operator.OperatorStartOutputsVector(self.fbuilder, n) for tensor_info in tensor_infos: - tensor_index = self.__tensor_index.keys().index(tensor_info.name) + tensor_index = list(self.__tensor_index.keys()).index(tensor_info.name) self.fbuilder.PrependInt32(tensor_index) return self.fbuilder.EndVector(n) @@ -178,7 +187,7 @@ def __input_vector(self, tensor_infos): n = len(tensor_infos) tflite.Operator.OperatorStartInputsVector(self.fbuilder, n) for tensor_info in tensor_infos: - tensor_index = self.__tensor_index.keys().index(tensor_info.name) + tensor_index = list(self.__tensor_index.keys()).index(tensor_info.name) self.fbuilder.PrependInt32(tensor_index) return self.fbuilder.EndVector(n) @@ -199,16 +208,17 @@ def __create_op_codes(self, ugraph): op_code_vec = self.fbuilder.EndVector(len(op_codes)) return op_code_vec #needs to be added into the Model - def add_FullyConnectedOp(self, inputs, outputs): - op_inputs = self.__input_vector(inputs) - op_outputs = self.__output_vector(outputs) + def add_Op(self, op_info): + op_inputs = self.__input_vector(op_info.input_tensors) + op_outputs = self.__output_vector(op_info.output_tensors) tflite.Operator.OperatorStart(self.fbuilder) - tflite.Operator.OperatorAddOpcodeIndex(self.fbuilder, self.__op_manager.op_index('FULLY_CONNECTED')) + tflite.Operator.OperatorAddOpcodeIndex(self.fbuilder, self.__op_manager.op_index(op_info.op_type)) tflite.Operator.OperatorAddInputs(self.fbuilder, op_inputs) tflite.Operator.OperatorAddOutputs(self.fbuilder, op_outputs) op = tflite.Operator.OperatorEnd(self.fbuilder) - self.op_exec_order.append(op) + #self.op_exec_order.append(op) + return op #to be added into SubGraphStartOperatorsVector def __sym_quantization_info(self, op_info): @@ -221,7 +231,8 @@ def __sym_quantization_info(self, op_info): TODO: per-axis quantization """ - values = op_info.op_attr['value'].value.np_array.flatten() + #values = op_info.op_attr['value'].value.np_array.flatten() + values = op_info.op_attr['value'].flatten() #FIXME: deal with the proper attribute type here abs_max = np.absolute(values).max() #based on quantizted int8 dtype scale = 127 / abs_max @@ -229,7 +240,7 @@ def __sym_quantization_info(self, op_info): return (scale, 0) def __create_quantization_param(self, scales, zeros): - assert len(scales) != len(zeros), "scales and zero-points length mismatch" + assert len(scales) == len(zeros), "scales and zero-points length mismatch" tflite.QuantizationParameters.QuantizationParametersStartScaleVector(self.fbuilder, len(scales)) @@ -254,8 +265,8 @@ def __create_quantization_param(self, scales, zeros): def __create_variable_tensors(self, ugraph): tensor_infos = set() for op_name in ugraph.topo_order: - tensor_infos.update(ugraph.ops_info[op_name].inputs) - tensor_infos.update(ugraph.ops_info[op_name].outputs) + tensor_infos.update(ugraph.ops_info[op_name].input_tensors) + tensor_infos.update(ugraph.ops_info[op_name].output_tensors) for tensor_info in tensor_infos: tflite.Tensor.TensorStartShapeVector(self.fbuilder, len(tensor_info.shape)) for d in tensor_info.shape: @@ -280,5 +291,8 @@ def __legalize_name(self, str): #TODO: legalize the name return str + def output(self): + return self.fbuilder.Output() + # How to construct the quantization parameter for intermediate tensors? ## zero point and scale From b7f3adb4a614ad8ce85742a7d6f6cd37cb9bf1b4 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Thu, 17 Oct 2019 23:04:43 +0800 Subject: [PATCH 010/117] problem with tensor.isVariable() --- tests/tflm/tflite_export/test_write.py | 76 ++++++++++++++++++++- utensor_cgen/transformer/tflite_exporter.py | 4 +- 2 files changed, 77 insertions(+), 3 deletions(-) diff --git a/tests/tflm/tflite_export/test_write.py b/tests/tflm/tflite_export/test_write.py index f98a484b..f964d02d 100644 --- a/tests/tflm/tflite_export/test_write.py +++ b/tests/tflm/tflite_export/test_write.py @@ -5,12 +5,86 @@ from utensor_cgen.matcher import uTensorGraphMatcher from utensor_cgen.utils import prune_graph, topologic_order_graph from utensor_cgen.transformer import TFLiteExporter +import flatbuffers +import utensor_cgen.third_party.tflite as tflite +from utensor_cgen.third_party.tflite import * +from utensor_cgen.third_party.tflite.BuiltinOperator import BuiltinOperator +from utensor_cgen.third_party.tflite.Model import Model +from utensor_cgen.third_party.tflite.BuiltinOptions import BuiltinOptions +from utensor_cgen.third_party.tflite.TensorType import TensorType + +builtin_ops = {v: k for k, v in BuiltinOperator.__dict__.items()} +op_options = {v: k for k, v in BuiltinOptions.__dict__.items()} + +tensor_np_type = dict() +tensor_np_type[0] = np.float32 +tensor_np_type[1] = np.float16 +tensor_np_type[2] = np.int32 +tensor_np_type[3] = np.uint8 +tensor_np_type[4] = np.uint64 +tensor_np_type[5] = np.ubyte #FIXME: supposed to be string +tensor_np_type[6] = np.bool +tensor_np_type[7] = np.int16 +tensor_np_type[8] = np.cdouble +tensor_np_type[9] = np.int8 + +def print_tflite_graph(byte_buff): + + model = Model.GetRootAsModel(byte_buff, 0) + subgraphs_len = model.SubgraphsLength() + subgraph = model.Subgraphs(0) + n_ops = subgraph.OperatorsLength() + print("version: ", model.Version()) + print("subgraph len: ", subgraphs_len) + print("number of operators: ", n_ops) + print("flat buffer length: ", len(byte_buff), " bytes") + op_codes = [] + for i in range(0, model.OperatorCodesLength()): + op_code = model.OperatorCodes(i) + op_codes.append(op_code) + print("op code length: ", len(op_codes)) + + for i in range(0, subgraph.OperatorsLength()): + op = subgraph.Operators(i) + print("op code index: ", op.OpcodeIndex()) + opIndex = op.OpcodeIndex() + op_code = op_codes[opIndex] + builtin_code = op_code.BuiltinCode() + op_type = builtin_ops[builtin_code] + print(op_type) + + input_tensors = [subgraph.Tensors(input_idx) for input_idx in op.InputsAsNumpy()] + for tensor in input_tensors: + print(tensor.Name(), ", ", tensor.ShapeAsNumpy()) + + print("variable: ", tensor.IsVariable()) + if tensor.Type() == np.uint8 or tensor.Type() == np.int8: + q = tensor.Quantization() + assert q != None + print("quantization info: ") + print(" Detail Type: ", q.DetailsType()) + print(" Scales: ", q.ScaleAsNumpy()) + print(" Zeros: ", q.ZeroPointAsNumpy()) + print(" Scale: ", q.ScaleAsNumpy()) + print(" Zero Point: ", q.ZeroPointAsNumpy()) + print(" Dimension: ", q.QuantizedDimension()) + + print(tensor.IsVariable()) + if not tensor.IsVariable(): + buffer_index = tensor.Buffer() + assert buffer_index >= 0 + assert model.Buffers(buffer_index).DataLength() <= 0 + buffer_content = model.Buffers(buffer_index).DataAsNumpy() + print("Tensor values: ", buffer_content.astype(tensor_np_type[tensor.Type()])) + else: + print("None") def test_tflite_fb_write(sample_ugraph): exporter = TFLiteExporter() ugraph = exporter.transform(sample_ugraph) exporter.output() - print(exporter.output()) + #print(exporter.output()) + print_tflite_graph(exporter.output()) test_pass = True assert test_pass, 'error message here' diff --git a/utensor_cgen/transformer/tflite_exporter.py b/utensor_cgen/transformer/tflite_exporter.py index 99f8aaa7..34d782a1 100644 --- a/utensor_cgen/transformer/tflite_exporter.py +++ b/utensor_cgen/transformer/tflite_exporter.py @@ -121,8 +121,7 @@ def transform(self, ugraph): self.fbuilder.Finish(model) - output = self.fbuilder.Output() #do something with the output here - print(output) + #output = self.fbuilder.Output() #do something with the output here return ugraph @@ -130,6 +129,7 @@ def __create_static_tensor(self, ugraph): target_ops = ["Inline", "Const"] export_tensor_name = True for op_name in ugraph.topo_order: + import pdb; pdb.set_trace() op_info = ugraph.ops_info[op_name] op_type = op_info.op_type if op_type in target_ops: #TODO: check if op data is empty From 4d37abf84ff9d5e3039a8091c68a4370338982c4 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Thu, 17 Oct 2019 23:49:38 +0800 Subject: [PATCH 011/117] problem reading back tensor buffers --- tests/tflm/tflite_export/test_write.py | 6 ++- utensor_cgen/transformer/tflite_exporter.py | 57 +++++++++++---------- 2 files changed, 35 insertions(+), 28 deletions(-) diff --git a/tests/tflm/tflite_export/test_write.py b/tests/tflm/tflite_export/test_write.py index f964d02d..73f46ded 100644 --- a/tests/tflm/tflite_export/test_write.py +++ b/tests/tflm/tflite_export/test_write.py @@ -37,6 +37,7 @@ def print_tflite_graph(byte_buff): print("version: ", model.Version()) print("subgraph len: ", subgraphs_len) print("number of operators: ", n_ops) + print("number of t buff: ", model.BuffersLength()) print("flat buffer length: ", len(byte_buff), " bytes") op_codes = [] for i in range(0, model.OperatorCodesLength()): @@ -53,10 +54,11 @@ def print_tflite_graph(byte_buff): op_type = builtin_ops[builtin_code] print(op_type) + #import pdb;pdb.set_trace() input_tensors = [subgraph.Tensors(input_idx) for input_idx in op.InputsAsNumpy()] for tensor in input_tensors: + print() print(tensor.Name(), ", ", tensor.ShapeAsNumpy()) - print("variable: ", tensor.IsVariable()) if tensor.Type() == np.uint8 or tensor.Type() == np.int8: q = tensor.Quantization() @@ -73,7 +75,7 @@ def print_tflite_graph(byte_buff): if not tensor.IsVariable(): buffer_index = tensor.Buffer() assert buffer_index >= 0 - assert model.Buffers(buffer_index).DataLength() <= 0 + assert model.Buffers(buffer_index).DataLength() >= 0 buffer_content = model.Buffers(buffer_index).DataAsNumpy() print("Tensor values: ", buffer_content.astype(tensor_np_type[tensor.Type()])) else: diff --git a/utensor_cgen/transformer/tflite_exporter.py b/utensor_cgen/transformer/tflite_exporter.py index 34d782a1..871956db 100644 --- a/utensor_cgen/transformer/tflite_exporter.py +++ b/utensor_cgen/transformer/tflite_exporter.py @@ -52,17 +52,17 @@ class TFLiteExporter(Transformer): METHOD_NAME = 'tflite_export' KWARGS_NAMESCOPE = '_tflite_export' __Max_fbuff_size = 1024 * 10 - __op_manager = FlatbufferOpManager() - fbuilder = flatbuffers.Builder(__Max_fbuff_size) - __tensor_buffer_index = OrderedDict() # out_name : data buff vec - # added to the Model object - __tensor_index = OrderedDict() # out_name : ref tensor object - #added to the Subgraph object - - #op_exec_order = list() + static_op_types = ["Inline", "Const"] def __init__(self): self.prune_graph = False #what is this? + self.op_manager = FlatbufferOpManager() + self.fbuilder = flatbuffers.Builder(self.__Max_fbuff_size) + self.tensor_buffer_index = OrderedDict() # out_name : data buff vec + # added to the Model object + self.tensor_index = OrderedDict() # out_name : ref tensor object + #added to the Subgraph object + def transform(self, ugraph): #create tensor data buffer @@ -79,16 +79,17 @@ def transform(self, ugraph): #create subgraph #first, create the tensor vector - tflite.SubGraph.SubGraphStartTensorsVector(self.fbuilder, len(self.__tensor_index)) - for t_name, f_obj in self.__tensor_index.items(): + tflite.SubGraph.SubGraphStartTensorsVector(self.fbuilder, len(self.tensor_index)) + for t_name, f_obj in self.tensor_index.items(): self.fbuilder.PrependUOffsetTRelative(f_obj) - tensors_vec = self.fbuilder.EndVector(len(self.__tensor_index)) + tensors_vec = self.fbuilder.EndVector(len(self.tensor_index)) ##traverse ugraph fb_ops_list = list() for op_name in ugraph.topo_order: op_info = ugraph.ops_info[op_name] - fb_ops_list.append(self.add_Op(op_info)) + if not op_info.op_type in self.static_op_types: + fb_ops_list.append(self.add_Op(op_info)) tflite.SubGraph.SubGraphStartOperatorsVector(self.fbuilder, len(fb_ops_list)) for fb_op in fb_ops_list: @@ -106,10 +107,10 @@ def transform(self, ugraph): #model # first, add tensor buffer here - tflite.Model.ModelStartBuffersVector(self.fbuilder, len(self.__tensor_buffer_index)) - for t_name, f_obj in self.__tensor_buffer_index.items(): + tflite.Model.ModelStartBuffersVector(self.fbuilder, len(self.tensor_buffer_index)) + for t_name, f_obj in self.tensor_buffer_index.items(): self.fbuilder.PrependUOffsetTRelative(f_obj) - buff_vec = self.fbuilder.EndVector(len(self.__tensor_buffer_index)) + buff_vec = self.fbuilder.EndVector(len(self.tensor_buffer_index)) tflite.Model.ModelStart(self.fbuilder) tflite.Model.ModelAddVersion(self.fbuilder, 100) #TODO: change this @@ -126,13 +127,12 @@ def transform(self, ugraph): return ugraph def __create_static_tensor(self, ugraph): - target_ops = ["Inline", "Const"] export_tensor_name = True for op_name in ugraph.topo_order: - import pdb; pdb.set_trace() + op_info = ugraph.ops_info[op_name] op_type = op_info.op_type - if op_type in target_ops: #TODO: check if op data is empty + if op_type in self.static_op_types: #TODO: check if op data is empty out_tensor_info = op_info.output_tensors[0] out_tname, out_dtype, tensor_shape = (out_tensor_info.name, out_tensor_info.dtype, @@ -145,7 +145,7 @@ def __create_static_tensor(self, ugraph): for i in raw_data: self.fbuilder.PrependUint8(i) data_vec = self.fbuilder.EndVector(len(raw_data)) - self.__tensor_buffer_index[out_tname] = data_vec + self.tensor_buffer_index[out_tname] = data_vec #shape tflite.Tensor.TensorStartShapeVector(self.fbuilder, len(tensor_shape)) @@ -173,13 +173,13 @@ def __create_static_tensor(self, ugraph): tflite.Tensor.TensorAddBuffer(self.fbuilder, 0) new_tensor = tflite.Tensor.TensorEnd(self.fbuilder) - self.__tensor_index[out_tname] = new_tensor + self.tensor_index[out_tname] = new_tensor def __output_vector(self, tensor_infos): n = len(tensor_infos) tflite.Operator.OperatorStartOutputsVector(self.fbuilder, n) for tensor_info in tensor_infos: - tensor_index = list(self.__tensor_index.keys()).index(tensor_info.name) + tensor_index = list(self.tensor_index.keys()).index(tensor_info.name) self.fbuilder.PrependInt32(tensor_index) return self.fbuilder.EndVector(n) @@ -187,7 +187,7 @@ def __input_vector(self, tensor_infos): n = len(tensor_infos) tflite.Operator.OperatorStartInputsVector(self.fbuilder, n) for tensor_info in tensor_infos: - tensor_index = list(self.__tensor_index.keys()).index(tensor_info.name) + tensor_index = list(self.tensor_index.keys()).index(tensor_info.name) self.fbuilder.PrependInt32(tensor_index) return self.fbuilder.EndVector(n) @@ -196,9 +196,12 @@ def __create_op_codes(self, ugraph): op_codes = list() for op_name in ugraph.topo_order: op_type = ugraph.ops_info[op_name].op_type + if op_type in self.static_op_types: + continue + tflite.OperatorCode.OperatorCodeStart(self.fbuilder) #TODO: op type translation happen here - opcode_index = self.__op_manager.regsiter_op(op_type) + opcode_index = self.op_manager.regsiter_op(op_type) tflite.OperatorCode.OperatorCodeAddBuiltinCode(self.fbuilder, opcode_index) op_codes.append(tflite.OperatorCode.OperatorCodeEnd(self.fbuilder)) @@ -213,11 +216,11 @@ def add_Op(self, op_info): op_outputs = self.__output_vector(op_info.output_tensors) tflite.Operator.OperatorStart(self.fbuilder) - tflite.Operator.OperatorAddOpcodeIndex(self.fbuilder, self.__op_manager.op_index(op_info.op_type)) + tflite.Operator.OperatorAddOpcodeIndex(self.fbuilder, self.op_manager.op_index(op_info.op_type)) tflite.Operator.OperatorAddInputs(self.fbuilder, op_inputs) tflite.Operator.OperatorAddOutputs(self.fbuilder, op_outputs) op = tflite.Operator.OperatorEnd(self.fbuilder) - #self.op_exec_order.append(op) + return op #to be added into SubGraphStartOperatorsVector @@ -268,6 +271,8 @@ def __create_variable_tensors(self, ugraph): tensor_infos.update(ugraph.ops_info[op_name].input_tensors) tensor_infos.update(ugraph.ops_info[op_name].output_tensors) for tensor_info in tensor_infos: + if ugraph.ops_info[tensor_info.op_name].op_type in self.static_op_types: + continue tflite.Tensor.TensorStartShapeVector(self.fbuilder, len(tensor_info.shape)) for d in tensor_info.shape: self.fbuilder.PrependInt32(d) @@ -285,7 +290,7 @@ def __create_variable_tensors(self, ugraph): #tflite.Tensor.TensorAddQuantization(self.fbuilder, q_param) tflite.Tensor.TensorAddIsVariable(self.fbuilder, True) - self.__tensor_index[tensor_info.name] = tflite.Tensor.TensorEnd(self.fbuilder) + self.tensor_index[tensor_info.name] = tflite.Tensor.TensorEnd(self.fbuilder) def __legalize_name(self, str): #TODO: legalize the name From fdd18a28e90ec1c53d89a23377cde22d08a325a1 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Fri, 18 Oct 2019 22:50:43 +0800 Subject: [PATCH 012/117] tensor buffer bug fix --- utensor_cgen/transformer/tflite_exporter.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/utensor_cgen/transformer/tflite_exporter.py b/utensor_cgen/transformer/tflite_exporter.py index 871956db..10d7df58 100644 --- a/utensor_cgen/transformer/tflite_exporter.py +++ b/utensor_cgen/transformer/tflite_exporter.py @@ -145,7 +145,12 @@ def __create_static_tensor(self, ugraph): for i in raw_data: self.fbuilder.PrependUint8(i) data_vec = self.fbuilder.EndVector(len(raw_data)) - self.tensor_buffer_index[out_tname] = data_vec + + tflite.Buffer.BufferStart(self.fbuilder) + tflite.Buffer.BufferAddData(self.fbuilder, data_vec) + tensor_buff = tflite.Buffer.BufferEnd(self.fbuilder) + + self.tensor_buffer_index[out_tname] = tensor_buff #shape tflite.Tensor.TensorStartShapeVector(self.fbuilder, len(tensor_shape)) @@ -171,7 +176,7 @@ def __create_static_tensor(self, ugraph): tflite.Tensor.TensorAddQuantization(self.fbuilder, q_param) tflite.Tensor.TensorAddIsVariable(self.fbuilder, False) - tflite.Tensor.TensorAddBuffer(self.fbuilder, 0) + tflite.Tensor.TensorAddBuffer(self.fbuilder, list(self.tensor_buffer_index.keys()).index(out_tname)) new_tensor = tflite.Tensor.TensorEnd(self.fbuilder) self.tensor_index[out_tname] = new_tensor From 280fdc8df6819e667e0f06582a45dd7b11fff474 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Tue, 22 Oct 2019 16:26:27 +0800 Subject: [PATCH 013/117] -fixed problem with builtin_opcode - modified test graph * the input tensor not showing up in the print-graph --- tests/tflm/tflite_export/conftest.py | 4 ++-- utensor_cgen/transformer/tflite_exporter.py | 3 ++- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/tests/tflm/tflite_export/conftest.py b/tests/tflm/tflite_export/conftest.py index 899cb8c2..3f34874c 100644 --- a/tests/tflm/tflite_export/conftest.py +++ b/tests/tflm/tflite_export/conftest.py @@ -19,7 +19,7 @@ def simple_tflm_graph(): output_tensors = [] ) weight_op.op_attr["value"] = np.array([1,2,3,4], dtype=np.int8) - weight_op.op_attr["shape"] = [1,4] + weight_op.op_attr["shape"] = [4,1] weight = TensorInfo( name = "weight", @@ -40,7 +40,7 @@ def simple_tflm_graph(): output_tensors = [] ) mock_input_op.op_attr["value"] = np.array([[1],[2],[3],[4]], dtype=np.int8) - mock_input_op.op_attr["shape"] = [4,1] + mock_input_op.op_attr["shape"] = [1,4] input1 = TensorInfo( name = "input", diff --git a/utensor_cgen/transformer/tflite_exporter.py b/utensor_cgen/transformer/tflite_exporter.py index 10d7df58..a029b95e 100644 --- a/utensor_cgen/transformer/tflite_exporter.py +++ b/utensor_cgen/transformer/tflite_exporter.py @@ -31,7 +31,7 @@ def regsiter_op(self, op_type_str): assert("invalid op str") if op_type_str not in self.op_list: self.op_list.append(op_type_str) - return self.op_index(op_type_str) + return self.op_code(op_type_str) def op_index(self, op_type_str): return self.op_list.index(op_type_str) @@ -179,6 +179,7 @@ def __create_static_tensor(self, ugraph): tflite.Tensor.TensorAddBuffer(self.fbuilder, list(self.tensor_buffer_index.keys()).index(out_tname)) new_tensor = tflite.Tensor.TensorEnd(self.fbuilder) self.tensor_index[out_tname] = new_tensor + def __output_vector(self, tensor_infos): n = len(tensor_infos) From 4f72a8bdfc6460e7642aa538985d2053d6e2c85b Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Thu, 21 Nov 2019 18:41:42 -0800 Subject: [PATCH 014/117] iterables require reversing prior to flatbuffer prepending --- tests/tflm/tflite_export/test_write.py | 1 - utensor_cgen/transformer/tflite_exporter.py | 60 ++++++++++++--------- 2 files changed, 35 insertions(+), 26 deletions(-) diff --git a/tests/tflm/tflite_export/test_write.py b/tests/tflm/tflite_export/test_write.py index 73f46ded..698a889a 100644 --- a/tests/tflm/tflite_export/test_write.py +++ b/tests/tflm/tflite_export/test_write.py @@ -54,7 +54,6 @@ def print_tflite_graph(byte_buff): op_type = builtin_ops[builtin_code] print(op_type) - #import pdb;pdb.set_trace() input_tensors = [subgraph.Tensors(input_idx) for input_idx in op.InputsAsNumpy()] for tensor in input_tensors: print() diff --git a/utensor_cgen/transformer/tflite_exporter.py b/utensor_cgen/transformer/tflite_exporter.py index a029b95e..cfe8bc2d 100644 --- a/utensor_cgen/transformer/tflite_exporter.py +++ b/utensor_cgen/transformer/tflite_exporter.py @@ -28,10 +28,13 @@ class FlatbufferOpManager: def regsiter_op(self, op_type_str): if op_type_str not in BuiltinOperator.__dict__: - assert("invalid op str") - if op_type_str not in self.op_list: - self.op_list.append(op_type_str) + assert("invalid op str") + assert "op already been registered", not self.is_registered(op_type_str) + self.op_list.append(op_type_str) return self.op_code(op_type_str) + + def is_registered(self, op_type_str): + return op_type_str in self.op_list def op_index(self, op_type_str): return self.op_list.index(op_type_str) @@ -65,37 +68,41 @@ def __init__(self): def transform(self, ugraph): - #create tensor data buffer - #create const tensors - #update tensor_index + # create tensor data buffer + # create const tensors + # update tensor_index self.__create_static_tensor(ugraph) - #create intermediate tensors - #update tensor_index + # create intermediate tensors + # update tensor_index self.__create_variable_tensors(ugraph) - #interacts with FlatbufferOpManager - op_codes_vec = self.__create_op_codes(ugraph) #to be added into the Model + # interacts with FlatbufferOpManager + op_codes_vec = self.__create_op_codes(ugraph) # to be added into the Model - #create subgraph - #first, create the tensor vector + # create subgraph + # first, create the tensor vector tflite.SubGraph.SubGraphStartTensorsVector(self.fbuilder, len(self.tensor_index)) - for t_name, f_obj in self.tensor_index.items(): + #reverse the order for prepend, so flatbuffer's indexi aggree with self.tensor_index's + for t_name, f_obj in reversed(self.tensor_index.items()): self.fbuilder.PrependUOffsetTRelative(f_obj) tensors_vec = self.fbuilder.EndVector(len(self.tensor_index)) - ##traverse ugraph + # traverse ugraph + # compile a list of static ops : fb_ops_list fb_ops_list = list() for op_name in ugraph.topo_order: op_info = ugraph.ops_info[op_name] if not op_info.op_type in self.static_op_types: fb_ops_list.append(self.add_Op(op_info)) + # buile the operators vector tflite.SubGraph.SubGraphStartOperatorsVector(self.fbuilder, len(fb_ops_list)) - for fb_op in fb_ops_list: + for fb_op in reversed(fb_ops_list): self.fbuilder.PrependUOffsetTRelative(fb_op) ops_vec = self.fbuilder.EndVector(len(fb_ops_list)) + # construct the subgraph tflite.SubGraph.SubGraphStart(self.fbuilder) tflite.SubGraph.SubGraphAddTensors(self.fbuilder, tensors_vec) tflite.SubGraph.SubGraphAddOperators(self.fbuilder, ops_vec) @@ -108,7 +115,7 @@ def transform(self, ugraph): #model # first, add tensor buffer here tflite.Model.ModelStartBuffersVector(self.fbuilder, len(self.tensor_buffer_index)) - for t_name, f_obj in self.tensor_buffer_index.items(): + for t_name, f_obj in reversed(self.tensor_buffer_index.items()): self.fbuilder.PrependUOffsetTRelative(f_obj) buff_vec = self.fbuilder.EndVector(len(self.tensor_buffer_index)) @@ -142,7 +149,7 @@ def __create_static_tensor(self, ugraph): value = op_info.op_attr['value'].flatten() #FIXME: deal with the proper attribute type here raw_data = value.tobytes() tflite.Buffer.BufferStartDataVector(self.fbuilder, len(raw_data)) - for i in raw_data: + for i in reversed(raw_data): self.fbuilder.PrependUint8(i) data_vec = self.fbuilder.EndVector(len(raw_data)) @@ -154,7 +161,7 @@ def __create_static_tensor(self, ugraph): #shape tflite.Tensor.TensorStartShapeVector(self.fbuilder, len(tensor_shape)) - for d in tensor_shape: + for d in reversed(tensor_shape): self.fbuilder.PrependInt32(d) shape_vec = self.fbuilder.EndVector(len(tensor_shape)) @@ -184,7 +191,7 @@ def __create_static_tensor(self, ugraph): def __output_vector(self, tensor_infos): n = len(tensor_infos) tflite.Operator.OperatorStartOutputsVector(self.fbuilder, n) - for tensor_info in tensor_infos: + for tensor_info in reversed(tensor_infos): tensor_index = list(self.tensor_index.keys()).index(tensor_info.name) self.fbuilder.PrependInt32(tensor_index) return self.fbuilder.EndVector(n) @@ -192,7 +199,7 @@ def __output_vector(self, tensor_infos): def __input_vector(self, tensor_infos): n = len(tensor_infos) tflite.Operator.OperatorStartInputsVector(self.fbuilder, n) - for tensor_info in tensor_infos: + for tensor_info in reversed(tensor_infos): tensor_index = list(self.tensor_index.keys()).index(tensor_info.name) self.fbuilder.PrependInt32(tensor_index) return self.fbuilder.EndVector(n) @@ -206,13 +213,16 @@ def __create_op_codes(self, ugraph): continue tflite.OperatorCode.OperatorCodeStart(self.fbuilder) - #TODO: op type translation happen here + + if self.op_manager.is_registered(op_type): + continue + opcode_index = self.op_manager.regsiter_op(op_type) tflite.OperatorCode.OperatorCodeAddBuiltinCode(self.fbuilder, opcode_index) op_codes.append(tflite.OperatorCode.OperatorCodeEnd(self.fbuilder)) tflite.Model.ModelStartOperatorCodesVector(self.fbuilder, len(op_codes)) - for it_op_code in op_codes: + for it_op_code in reversed(op_codes): self.fbuilder.PrependUOffsetTRelative(it_op_code) op_code_vec = self.fbuilder.EndVector(len(op_codes)) return op_code_vec #needs to be added into the Model @@ -253,12 +263,12 @@ def __create_quantization_param(self, scales, zeros): tflite.QuantizationParameters.QuantizationParametersStartScaleVector(self.fbuilder, len(scales)) - for _scale in scales: + for _scale in reversed(scales): self.fbuilder.PrependFloat32(_scale) q_scales_vec = self.fbuilder.EndVector(len(scales)) tflite.QuantizationParameters.QuantizationParametersStartZeroPointVector(self.fbuilder, len(zeros)) - for _zero in zeros: + for _zero in reversed(zeros): self.fbuilder.PrependFloat32(_zero) q_zeros_vec = self.fbuilder.EndVector(len(zeros)) @@ -280,7 +290,7 @@ def __create_variable_tensors(self, ugraph): if ugraph.ops_info[tensor_info.op_name].op_type in self.static_op_types: continue tflite.Tensor.TensorStartShapeVector(self.fbuilder, len(tensor_info.shape)) - for d in tensor_info.shape: + for d in reversed(tensor_info.shape): self.fbuilder.PrependInt32(d) shape_vec = self.fbuilder.EndVector(len(tensor_info.shape)) From 58d533f4ac100e8fc9bf66c3a91f4267efbdbc12 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Fri, 22 Nov 2019 17:08:19 -0800 Subject: [PATCH 015/117] refactoring: fb-vector-builder in progress tflite_exporter.py:174 --- utensor_cgen/transformer/tflite_exporter.py | 66 ++++++++++++--------- 1 file changed, 38 insertions(+), 28 deletions(-) diff --git a/utensor_cgen/transformer/tflite_exporter.py b/utensor_cgen/transformer/tflite_exporter.py index cfe8bc2d..38217d47 100644 --- a/utensor_cgen/transformer/tflite_exporter.py +++ b/utensor_cgen/transformer/tflite_exporter.py @@ -81,12 +81,8 @@ def transform(self, ugraph): op_codes_vec = self.__create_op_codes(ugraph) # to be added into the Model # create subgraph - # first, create the tensor vector - tflite.SubGraph.SubGraphStartTensorsVector(self.fbuilder, len(self.tensor_index)) - #reverse the order for prepend, so flatbuffer's indexi aggree with self.tensor_index's - for t_name, f_obj in reversed(self.tensor_index.items()): - self.fbuilder.PrependUOffsetTRelative(f_obj) - tensors_vec = self.fbuilder.EndVector(len(self.tensor_index)) + tensors_vec = self.__fb_vector(tflite.SubGraph.SubGraphStartTensorsVector, + [f_obj for t_name, f_obj in self.tensor_index.items()]) # traverse ugraph # compile a list of static ops : fb_ops_list @@ -96,11 +92,8 @@ def transform(self, ugraph): if not op_info.op_type in self.static_op_types: fb_ops_list.append(self.add_Op(op_info)) - # buile the operators vector - tflite.SubGraph.SubGraphStartOperatorsVector(self.fbuilder, len(fb_ops_list)) - for fb_op in reversed(fb_ops_list): - self.fbuilder.PrependUOffsetTRelative(fb_op) - ops_vec = self.fbuilder.EndVector(len(fb_ops_list)) + # build the operators vector + ops_vec = self.__fb_vector(tflite.SubGraph.SubGraphStartOperatorsVector, fb_ops_list) # construct the subgraph tflite.SubGraph.SubGraphStart(self.fbuilder) @@ -108,16 +101,11 @@ def transform(self, ugraph): tflite.SubGraph.SubGraphAddOperators(self.fbuilder, ops_vec) subgraph = tflite.SubGraph.SubGraphEnd(self.fbuilder) - tflite.Model.ModelStartSubgraphsVector(self.fbuilder, 1) #TFLM runtime only support 1 subgraph - self.fbuilder.PrependUOffsetTRelative(subgraph) - subgraph_vec = self.fbuilder.EndVector(1) + #TFLM runtime only support 1 subgraph + subgraph_vec = self.__fb_vector(tflite.Model.ModelStartSubgraphsVector, [subgraph]) #model - # first, add tensor buffer here - tflite.Model.ModelStartBuffersVector(self.fbuilder, len(self.tensor_buffer_index)) - for t_name, f_obj in reversed(self.tensor_buffer_index.items()): - self.fbuilder.PrependUOffsetTRelative(f_obj) - buff_vec = self.fbuilder.EndVector(len(self.tensor_buffer_index)) + buff_vec = self.__fb_vector(tflite.Model.ModelStartBuffersVector, [f_obj for t_name, f_obj in self.tensor_buffer_index.items()]) tflite.Model.ModelStart(self.fbuilder) tflite.Model.ModelAddVersion(self.fbuilder, 100) #TODO: change this @@ -148,10 +136,8 @@ def __create_static_tensor(self, ugraph): #value = op_info.op_attr['value'].value.np_array.flatten() #TODO: specify endianness here value = op_info.op_attr['value'].flatten() #FIXME: deal with the proper attribute type here raw_data = value.tobytes() - tflite.Buffer.BufferStartDataVector(self.fbuilder, len(raw_data)) - for i in reversed(raw_data): - self.fbuilder.PrependUint8(i) - data_vec = self.fbuilder.EndVector(len(raw_data)) + data_vec = self.__fb_vector(tflite.Buffer.BufferStartDataVector, raw_data) + tflite.Buffer.BufferStart(self.fbuilder) tflite.Buffer.BufferAddData(self.fbuilder, data_vec) @@ -160,10 +146,7 @@ def __create_static_tensor(self, ugraph): self.tensor_buffer_index[out_tname] = tensor_buff #shape - tflite.Tensor.TensorStartShapeVector(self.fbuilder, len(tensor_shape)) - for d in reversed(tensor_shape): - self.fbuilder.PrependInt32(d) - shape_vec = self.fbuilder.EndVector(len(tensor_shape)) + shape_vec = self.__fb_vector(tflite.Tensor.TensorStartShapeVector, tensor_shape) #name if export_tensor_name: @@ -188,13 +171,14 @@ def __create_static_tensor(self, ugraph): self.tensor_index[out_tname] = new_tensor - def __output_vector(self, tensor_infos): + def __output_vector(self, tensor_infos): ##TODO left it here n = len(tensor_infos) tflite.Operator.OperatorStartOutputsVector(self.fbuilder, n) for tensor_info in reversed(tensor_infos): tensor_index = list(self.tensor_index.keys()).index(tensor_info.name) self.fbuilder.PrependInt32(tensor_index) return self.fbuilder.EndVector(n) + def __input_vector(self, tensor_infos): n = len(tensor_infos) @@ -315,5 +299,31 @@ def __legalize_name(self, str): def output(self): return self.fbuilder.Output() + def __fb_vector(self, vector_constructor, arr_list): + prepend_method_table = dict() + prepend_method_table[tflite.SubGraph.SubGraphStartTensorsVector] = self.fbuilder.PrependUOffsetTRelative + prepend_method_table[tflite.SubGraph.SubGraphStartOperatorsVector] = self.fbuilder.PrependUOffsetTRelative + prepend_method_table[tflite.Model.ModelStartSubgraphsVector] = self.fbuilder.PrependUOffsetTRelative + prepend_method_table[tflite.Model.ModelStartBuffersVector] = self.fbuilder.PrependUOffsetTRelative + prepend_method_table[tflite.Model.ModelStartOperatorCodesVector] = self.fbuilder.PrependUOffsetTRelative + prepend_method_table[tflite.Buffer.BufferStartDataVector] = self.fbuilder.PrependUint8 + prepend_method_table[tflite.Tensor.TensorStartShapeVector] = self.fbuilder.PrependInt32 + prepend_method_table[tflite.Operator.OperatorStartOutputsVector] = self.fbuilder.PrependInt32 + prepend_method_table[tflite.Operator.OperatorStartInputsVector] = self.fbuilder.PrependInt32 + prepend_method_table[tflite.QuantizationParameters.QuantizationParametersStartScaleVector] = self.fbuilder.PrependFloat32 + prepend_method_table[tflite.QuantizationParameters.QuantizationParametersStartZeroPointVector] = self.fbuilder.PrependFloat32 + + preprend_method = prepend_method_table[vector_constructor] + + num_items = len(arr_list) + assert num_items > 0 + + vector_constructor(self.fbuilder, num_items) + # reversed to handle prepend + for it in reversed(arr_list): + preprend_method(it) + + return self.fbuilder.EndVector(num_items) + # How to construct the quantization parameter for intermediate tensors? ## zero point and scale From 5a56092b5975c115f0186c244e7f29f14f8e53ad Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Sat, 23 Nov 2019 11:27:53 -0800 Subject: [PATCH 016/117] fb-vector-builder refactoring completed --- utensor_cgen/transformer/tflite_exporter.py | 48 ++++++--------------- 1 file changed, 13 insertions(+), 35 deletions(-) diff --git a/utensor_cgen/transformer/tflite_exporter.py b/utensor_cgen/transformer/tflite_exporter.py index 38217d47..5f77ce47 100644 --- a/utensor_cgen/transformer/tflite_exporter.py +++ b/utensor_cgen/transformer/tflite_exporter.py @@ -170,23 +170,15 @@ def __create_static_tensor(self, ugraph): new_tensor = tflite.Tensor.TensorEnd(self.fbuilder) self.tensor_index[out_tname] = new_tensor + #construct the output tensor vector for an op + def __output_vector(self, tensor_infos): + tensor_indexi = [list(self.tensor_index.keys()).index(tensor_info.name) for tensor_info in tensor_infos] + return self.__fb_vector(tflite.Operator.OperatorStartOutputsVector, tensor_indexi) - def __output_vector(self, tensor_infos): ##TODO left it here - n = len(tensor_infos) - tflite.Operator.OperatorStartOutputsVector(self.fbuilder, n) - for tensor_info in reversed(tensor_infos): - tensor_index = list(self.tensor_index.keys()).index(tensor_info.name) - self.fbuilder.PrependInt32(tensor_index) - return self.fbuilder.EndVector(n) - - + #construct the input tensor vector for an op def __input_vector(self, tensor_infos): - n = len(tensor_infos) - tflite.Operator.OperatorStartInputsVector(self.fbuilder, n) - for tensor_info in reversed(tensor_infos): - tensor_index = list(self.tensor_index.keys()).index(tensor_info.name) - self.fbuilder.PrependInt32(tensor_index) - return self.fbuilder.EndVector(n) + tensor_indexi = [list(self.tensor_index.keys()).index(tensor_info.name) for tensor_info in tensor_infos] + return self.__fb_vector(tflite.Operator.OperatorStartInputsVector, tensor_indexi) def __create_op_codes(self, ugraph): #scan, translation and register @@ -205,11 +197,8 @@ def __create_op_codes(self, ugraph): tflite.OperatorCode.OperatorCodeAddBuiltinCode(self.fbuilder, opcode_index) op_codes.append(tflite.OperatorCode.OperatorCodeEnd(self.fbuilder)) - tflite.Model.ModelStartOperatorCodesVector(self.fbuilder, len(op_codes)) - for it_op_code in reversed(op_codes): - self.fbuilder.PrependUOffsetTRelative(it_op_code) - op_code_vec = self.fbuilder.EndVector(len(op_codes)) - return op_code_vec #needs to be added into the Model + op_code_vec = self.__fb_vector(tflite.Model.ModelStartOperatorCodesVector, op_codes) + return op_code_vec def add_Op(self, op_info): op_inputs = self.__input_vector(op_info.input_tensors) @@ -223,7 +212,6 @@ def add_Op(self, op_info): return op #to be added into SubGraphStartOperatorsVector - def __sym_quantization_info(self, op_info): """ https://www.tensorflow.org/lite/performance/quantization_spec @@ -244,18 +232,10 @@ def __sym_quantization_info(self, op_info): def __create_quantization_param(self, scales, zeros): assert len(scales) == len(zeros), "scales and zero-points length mismatch" - - tflite.QuantizationParameters.QuantizationParametersStartScaleVector(self.fbuilder, len(scales)) - for _scale in reversed(scales): - self.fbuilder.PrependFloat32(_scale) - q_scales_vec = self.fbuilder.EndVector(len(scales)) + q_scales_vec = self.__fb_vector(tflite.QuantizationParameters.QuantizationParametersStartScaleVector, scales) + q_zeros_vec = self.__fb_vector(tflite.QuantizationParameters.QuantizationParametersStartZeroPointVector, zeros) - tflite.QuantizationParameters.QuantizationParametersStartZeroPointVector(self.fbuilder, len(zeros)) - for _zero in reversed(zeros): - self.fbuilder.PrependFloat32(_zero) - q_zeros_vec = self.fbuilder.EndVector(len(zeros)) - tflite.QuantizationParameters.QuantizationParametersStart(self.fbuilder) tflite.QuantizationParameters.QuantizationParametersAddScale(self.fbuilder, q_scales_vec) tflite.QuantizationParameters.QuantizationParametersAddZeroPoint(self.fbuilder, q_zeros_vec) @@ -273,10 +253,8 @@ def __create_variable_tensors(self, ugraph): for tensor_info in tensor_infos: if ugraph.ops_info[tensor_info.op_name].op_type in self.static_op_types: continue - tflite.Tensor.TensorStartShapeVector(self.fbuilder, len(tensor_info.shape)) - for d in reversed(tensor_info.shape): - self.fbuilder.PrependInt32(d) - shape_vec = self.fbuilder.EndVector(len(tensor_info.shape)) + + shape_vec = self.__fb_vector(tflite.Tensor.TensorStartShapeVector, tensor_info.shape) tensor_name = self.fbuilder.CreateString(self.__legalize_name(tensor_info.name)) From 1a184ed117646737d8d9a04f5f2421f3e1695284 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Sun, 24 Nov 2019 18:03:32 -0800 Subject: [PATCH 017/117] - using flatbuffers 136d75fa6580ef87d1b7cbc243e617f21149852e - included flatbuffers python in utensor_cgen/third_party/flatbuffers - added file_identifier : TFL3 - changed schema version field to : 3 - bugfix in the confteest.py test_write.py WIP --- tests/tflm/tflite_export/conftest.py | 2 +- tests/tflm/tflite_export/test_write.py | 14 +- .../third_party/flatbuffers/__init__.py | 17 + .../third_party/flatbuffers/builder.py | 764 ++++++++++++++++++ .../third_party/flatbuffers/compat.py | 81 ++ .../third_party/flatbuffers/encode.py | 42 + .../third_party/flatbuffers/number_types.py | 181 +++++ .../third_party/flatbuffers/packer.py | 42 + utensor_cgen/third_party/flatbuffers/table.py | 129 +++ utensor_cgen/third_party/flatbuffers/util.py | 43 + utensor_cgen/transformer/tflite_exporter.py | 9 +- 11 files changed, 1316 insertions(+), 8 deletions(-) create mode 100644 utensor_cgen/third_party/flatbuffers/__init__.py create mode 100644 utensor_cgen/third_party/flatbuffers/builder.py create mode 100644 utensor_cgen/third_party/flatbuffers/compat.py create mode 100644 utensor_cgen/third_party/flatbuffers/encode.py create mode 100644 utensor_cgen/third_party/flatbuffers/number_types.py create mode 100644 utensor_cgen/third_party/flatbuffers/packer.py create mode 100644 utensor_cgen/third_party/flatbuffers/table.py create mode 100644 utensor_cgen/third_party/flatbuffers/util.py diff --git a/tests/tflm/tflite_export/conftest.py b/tests/tflm/tflite_export/conftest.py index 3f34874c..2aa974b6 100644 --- a/tests/tflm/tflite_export/conftest.py +++ b/tests/tflm/tflite_export/conftest.py @@ -68,7 +68,7 @@ def simple_tflm_graph(): name = "bias", op_name = "bias_const", dtype = np.dtype("int8"), - shape = weight_op.op_attr["shape"], + shape = bias_op.op_attr["shape"], ugraph = ugraph ) bias_op.output_tensors = [bias] diff --git a/tests/tflm/tflite_export/test_write.py b/tests/tflm/tflite_export/test_write.py index 698a889a..bc4bc918 100644 --- a/tests/tflm/tflite_export/test_write.py +++ b/tests/tflm/tflite_export/test_write.py @@ -74,7 +74,7 @@ def print_tflite_graph(byte_buff): if not tensor.IsVariable(): buffer_index = tensor.Buffer() assert buffer_index >= 0 - assert model.Buffers(buffer_index).DataLength() >= 0 + assert model.Buffers(buffer_index).DataLength() > 0 buffer_content = model.Buffers(buffer_index).DataAsNumpy() print("Tensor values: ", buffer_content.astype(tensor_np_type[tensor.Type()])) else: @@ -83,9 +83,15 @@ def print_tflite_graph(byte_buff): def test_tflite_fb_write(sample_ugraph): exporter = TFLiteExporter() ugraph = exporter.transform(sample_ugraph) - exporter.output() - #print(exporter.output()) - print_tflite_graph(exporter.output()) + model_content = exporter.output() + + print_tflite_graph(model_content) + + # referece_model_content = open('/Users/neitan01/Documents/tflm/sinExample/sine_model.tflite', "rb").read() + # print_tflite_graph(referece_model_content) + + open("tflm_test_model.tflite", "wb").write(model_content) + test_model = tf.lite.Interpreter('tflm_test_model.tflite') test_pass = True assert test_pass, 'error message here' diff --git a/utensor_cgen/third_party/flatbuffers/__init__.py b/utensor_cgen/third_party/flatbuffers/__init__.py new file mode 100644 index 00000000..d14872ae --- /dev/null +++ b/utensor_cgen/third_party/flatbuffers/__init__.py @@ -0,0 +1,17 @@ +# Copyright 2014 Google Inc. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .builder import Builder +from .table import Table +from .compat import range_func as compat_range diff --git a/utensor_cgen/third_party/flatbuffers/builder.py b/utensor_cgen/third_party/flatbuffers/builder.py new file mode 100644 index 00000000..478cdcd5 --- /dev/null +++ b/utensor_cgen/third_party/flatbuffers/builder.py @@ -0,0 +1,764 @@ +# Copyright 2014 Google Inc. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from . import number_types as N +from .number_types import (UOffsetTFlags, SOffsetTFlags, VOffsetTFlags) + +from . import encode +from . import packer + +from . import compat +from .compat import range_func +from .compat import memoryview_type +from .compat import import_numpy, NumpyRequiredForThisFeature + +np = import_numpy() +## @file +## @addtogroup flatbuffers_python_api +## @{ + +## @cond FLATBUFFERS_INTERNAL +class OffsetArithmeticError(RuntimeError): + """ + Error caused by an Offset arithmetic error. Probably caused by bad + writing of fields. This is considered an unreachable situation in + normal circumstances. + """ + pass + + +class IsNotNestedError(RuntimeError): + """ + Error caused by using a Builder to write Object data when not inside + an Object. + """ + pass + + +class IsNestedError(RuntimeError): + """ + Error caused by using a Builder to begin an Object when an Object is + already being built. + """ + pass + + +class StructIsNotInlineError(RuntimeError): + """ + Error caused by using a Builder to write a Struct at a location that + is not the current Offset. + """ + pass + + +class BuilderSizeError(RuntimeError): + """ + Error caused by causing a Builder to exceed the hardcoded limit of 2 + gigabytes. + """ + pass + +class BuilderNotFinishedError(RuntimeError): + """ + Error caused by not calling `Finish` before calling `Output`. + """ + pass + + +# VtableMetadataFields is the count of metadata fields in each vtable. +VtableMetadataFields = 2 +## @endcond + +class Builder(object): + """ A Builder is used to construct one or more FlatBuffers. + + Typically, Builder objects will be used from code generated by the `flatc` + compiler. + + A Builder constructs byte buffers in a last-first manner for simplicity and + performance during reading. + + Internally, a Builder is a state machine for creating FlatBuffer objects. + + It holds the following internal state: + - Bytes: an array of bytes. + - current_vtable: a list of integers. + - vtables: a hash of vtable entries. + + Attributes: + Bytes: The internal `bytearray` for the Builder. + finished: A boolean determining if the Builder has been finalized. + """ + + ## @cond FLATBUFFERS_INTENRAL + __slots__ = ("Bytes", "current_vtable", "head", "minalign", "objectEnd", + "vtables", "nested", "forceDefaults", "finished") + + """Maximum buffer size constant, in bytes. + + Builder will never allow it's buffer grow over this size. + Currently equals 2Gb. + """ + MAX_BUFFER_SIZE = 2**31 + ## @endcond + + def __init__(self, initialSize): + """Initializes a Builder of size `initial_size`. + + The internal buffer is grown as needed. + """ + + if not (0 <= initialSize <= Builder.MAX_BUFFER_SIZE): + msg = "flatbuffers: Cannot create Builder larger than 2 gigabytes." + raise BuilderSizeError(msg) + + self.Bytes = bytearray(initialSize) + ## @cond FLATBUFFERS_INTERNAL + self.current_vtable = None + self.head = UOffsetTFlags.py_type(initialSize) + self.minalign = 1 + self.objectEnd = None + self.vtables = {} + self.nested = False + self.forceDefaults = False + ## @endcond + self.finished = False + + def Output(self): + """Return the portion of the buffer that has been used for writing data. + + This is the typical way to access the FlatBuffer data inside the + builder. If you try to access `Builder.Bytes` directly, you would need + to manually index it with `Head()`, since the buffer is constructed + backwards. + + It raises BuilderNotFinishedError if the buffer has not been finished + with `Finish`. + """ + + if not self.finished: + raise BuilderNotFinishedError() + + return self.Bytes[self.Head():] + + ## @cond FLATBUFFERS_INTERNAL + def StartObject(self, numfields): + """StartObject initializes bookkeeping for writing a new object.""" + + self.assertNotNested() + + # use 32-bit offsets so that arithmetic doesn't overflow. + self.current_vtable = [0 for _ in range_func(numfields)] + self.objectEnd = self.Offset() + self.nested = True + + def WriteVtable(self): + """ + WriteVtable serializes the vtable for the current object, if needed. + + Before writing out the vtable, this checks pre-existing vtables for + equality to this one. If an equal vtable is found, point the object to + the existing vtable and return. + + Because vtable values are sensitive to alignment of object data, not + all logically-equal vtables will be deduplicated. + + A vtable has the following format: + + + * N, where N is the number of fields + in the schema for this type. Includes deprecated fields. + Thus, a vtable is made of 2 + N elements, each VOffsetT bytes wide. + + An object has the following format: + + + + """ + + # Prepend a zero scalar to the object. Later in this function we'll + # write an offset here that points to the object's vtable: + self.PrependSOffsetTRelative(0) + + objectOffset = self.Offset() + + vtKey = [] + trim = True + for elem in reversed(self.current_vtable): + if elem == 0: + if trim: + continue + else: + elem = objectOffset - elem + trim = False + + vtKey.append(elem) + + vtKey = tuple(vtKey) + vt2Offset = self.vtables.get(vtKey) + if vt2Offset is None: + # Did not find a vtable, so write this one to the buffer. + + # Write out the current vtable in reverse , because + # serialization occurs in last-first order: + i = len(self.current_vtable) - 1 + trailing = 0 + trim = True + while i >= 0: + off = 0 + elem = self.current_vtable[i] + i -= 1 + + if elem == 0: + if trim: + trailing += 1 + continue + else: + # Forward reference to field; + # use 32bit number to ensure no overflow: + off = objectOffset - elem + trim = False + + self.PrependVOffsetT(off) + + # The two metadata fields are written last. + + # First, store the object bytesize: + objectSize = UOffsetTFlags.py_type(objectOffset - self.objectEnd) + self.PrependVOffsetT(VOffsetTFlags.py_type(objectSize)) + + # Second, store the vtable bytesize: + vBytes = len(self.current_vtable) - trailing + VtableMetadataFields + vBytes *= N.VOffsetTFlags.bytewidth + self.PrependVOffsetT(VOffsetTFlags.py_type(vBytes)) + + # Next, write the offset to the new vtable in the + # already-allocated SOffsetT at the beginning of this object: + objectStart = SOffsetTFlags.py_type(len(self.Bytes) - objectOffset) + encode.Write(packer.soffset, self.Bytes, objectStart, + SOffsetTFlags.py_type(self.Offset() - objectOffset)) + + # Finally, store this vtable in memory for future + # deduplication: + self.vtables[vtKey] = self.Offset() + else: + # Found a duplicate vtable. + objectStart = SOffsetTFlags.py_type(len(self.Bytes) - objectOffset) + self.head = UOffsetTFlags.py_type(objectStart) + + # Write the offset to the found vtable in the + # already-allocated SOffsetT at the beginning of this object: + encode.Write(packer.soffset, self.Bytes, self.Head(), + SOffsetTFlags.py_type(vt2Offset - objectOffset)) + + self.current_vtable = None + return objectOffset + + def EndObject(self): + """EndObject writes data necessary to finish object construction.""" + self.assertNested() + self.nested = False + return self.WriteVtable() + + def growByteBuffer(self): + """Doubles the size of the byteslice, and copies the old data towards + the end of the new buffer (since we build the buffer backwards).""" + if len(self.Bytes) == Builder.MAX_BUFFER_SIZE: + msg = "flatbuffers: cannot grow buffer beyond 2 gigabytes" + raise BuilderSizeError(msg) + + newSize = min(len(self.Bytes) * 2, Builder.MAX_BUFFER_SIZE) + if newSize == 0: + newSize = 1 + bytes2 = bytearray(newSize) + bytes2[newSize-len(self.Bytes):] = self.Bytes + self.Bytes = bytes2 + ## @endcond + + def Head(self): + """Get the start of useful data in the underlying byte buffer. + + Note: unlike other functions, this value is interpreted as from the + left. + """ + ## @cond FLATBUFFERS_INTERNAL + return self.head + ## @endcond + + ## @cond FLATBUFFERS_INTERNAL + def Offset(self): + """Offset relative to the end of the buffer.""" + return UOffsetTFlags.py_type(len(self.Bytes) - self.Head()) + + def Pad(self, n): + """Pad places zeros at the current offset.""" + for i in range_func(n): + self.Place(0, N.Uint8Flags) + + def Prep(self, size, additionalBytes): + """ + Prep prepares to write an element of `size` after `additional_bytes` + have been written, e.g. if you write a string, you need to align + such the int length field is aligned to SizeInt32, and the string + data follows it directly. + If all you need to do is align, `additionalBytes` will be 0. + """ + + # Track the biggest thing we've ever aligned to. + if size > self.minalign: + self.minalign = size + + # Find the amount of alignment needed such that `size` is properly + # aligned after `additionalBytes`: + alignSize = (~(len(self.Bytes) - self.Head() + additionalBytes)) + 1 + alignSize &= (size - 1) + + # Reallocate the buffer if needed: + while self.Head() < alignSize+size+additionalBytes: + oldBufSize = len(self.Bytes) + self.growByteBuffer() + updated_head = self.head + len(self.Bytes) - oldBufSize + self.head = UOffsetTFlags.py_type(updated_head) + self.Pad(alignSize) + + def PrependSOffsetTRelative(self, off): + """ + PrependSOffsetTRelative prepends an SOffsetT, relative to where it + will be written. + """ + + # Ensure alignment is already done: + self.Prep(N.SOffsetTFlags.bytewidth, 0) + if not (off <= self.Offset()): + msg = "flatbuffers: Offset arithmetic error." + raise OffsetArithmeticError(msg) + off2 = self.Offset() - off + N.SOffsetTFlags.bytewidth + self.PlaceSOffsetT(off2) + ## @endcond + + def PrependUOffsetTRelative(self, off): + """Prepends an unsigned offset into vector data, relative to where it + will be written. + """ + + # Ensure alignment is already done: + self.Prep(N.UOffsetTFlags.bytewidth, 0) + if not (off <= self.Offset()): + msg = "flatbuffers: Offset arithmetic error." + raise OffsetArithmeticError(msg) + off2 = self.Offset() - off + N.UOffsetTFlags.bytewidth + self.PlaceUOffsetT(off2) + + ## @cond FLATBUFFERS_INTERNAL + def StartVector(self, elemSize, numElems, alignment): + """ + StartVector initializes bookkeeping for writing a new vector. + + A vector has the following format: + - + - +, where T is the type of elements of this vector. + """ + + self.assertNotNested() + self.nested = True + self.Prep(N.Uint32Flags.bytewidth, elemSize*numElems) + self.Prep(alignment, elemSize*numElems) # In case alignment > int. + return self.Offset() + ## @endcond + + def EndVector(self, vectorNumElems): + """EndVector writes data necessary to finish vector construction.""" + + self.assertNested() + ## @cond FLATBUFFERS_INTERNAL + self.nested = False + ## @endcond + # we already made space for this, so write without PrependUint32 + self.PlaceUOffsetT(vectorNumElems) + return self.Offset() + + def CreateString(self, s, encoding='utf-8', errors='strict'): + """CreateString writes a null-terminated byte string as a vector.""" + + self.assertNotNested() + ## @cond FLATBUFFERS_INTERNAL + self.nested = True + ## @endcond + + if isinstance(s, compat.string_types): + x = s.encode(encoding, errors) + elif isinstance(s, compat.binary_types): + x = s + else: + raise TypeError("non-string passed to CreateString") + + self.Prep(N.UOffsetTFlags.bytewidth, (len(x)+1)*N.Uint8Flags.bytewidth) + self.Place(0, N.Uint8Flags) + + l = UOffsetTFlags.py_type(len(s)) + ## @cond FLATBUFFERS_INTERNAL + self.head = UOffsetTFlags.py_type(self.Head() - l) + ## @endcond + self.Bytes[self.Head():self.Head()+l] = x + + return self.EndVector(len(x)) + + def CreateByteVector(self, x): + """CreateString writes a byte vector.""" + + self.assertNotNested() + ## @cond FLATBUFFERS_INTERNAL + self.nested = True + ## @endcond + + if not isinstance(x, compat.binary_types): + raise TypeError("non-byte vector passed to CreateByteVector") + + self.Prep(N.UOffsetTFlags.bytewidth, len(x)*N.Uint8Flags.bytewidth) + + l = UOffsetTFlags.py_type(len(x)) + ## @cond FLATBUFFERS_INTERNAL + self.head = UOffsetTFlags.py_type(self.Head() - l) + ## @endcond + self.Bytes[self.Head():self.Head()+l] = x + + return self.EndVector(len(x)) + + def CreateNumpyVector(self, x): + """CreateNumpyVector writes a numpy array into the buffer.""" + + if np is None: + # Numpy is required for this feature + raise NumpyRequiredForThisFeature("Numpy was not found.") + + if not isinstance(x, np.ndarray): + raise TypeError("non-numpy-ndarray passed to CreateNumpyVector") + + if x.dtype.kind not in ['b', 'i', 'u', 'f']: + raise TypeError("numpy-ndarray holds elements of unsupported datatype") + + if x.ndim > 1: + raise TypeError("multidimensional-ndarray passed to CreateNumpyVector") + + self.StartVector(x.itemsize, x.size, x.dtype.alignment) + + # Ensure little endian byte ordering + if x.dtype.str[0] == "<": + x_lend = x + else: + x_lend = x.byteswap(inplace=False) + + # Calculate total length + l = UOffsetTFlags.py_type(x_lend.itemsize * x_lend.size) + ## @cond FLATBUFFERS_INTERNAL + self.head = UOffsetTFlags.py_type(self.Head() - l) + ## @endcond + + # tobytes ensures c_contiguous ordering + self.Bytes[self.Head():self.Head()+l] = x_lend.tobytes(order='C') + + return self.EndVector(x.size) + + ## @cond FLATBUFFERS_INTERNAL + def assertNested(self): + """ + Check that we are in the process of building an object. + """ + + if not self.nested: + raise IsNotNestedError() + + def assertNotNested(self): + """ + Check that no other objects are being built while making this + object. If not, raise an exception. + """ + + if self.nested: + raise IsNestedError() + + def assertStructIsInline(self, obj): + """ + Structs are always stored inline, so need to be created right + where they are used. You'll get this error if you created it + elsewhere. + """ + + N.enforce_number(obj, N.UOffsetTFlags) + if obj != self.Offset(): + msg = ("flatbuffers: Tried to write a Struct at an Offset that " + "is different from the current Offset of the Builder.") + raise StructIsNotInlineError(msg) + + def Slot(self, slotnum): + """ + Slot sets the vtable key `voffset` to the current location in the + buffer. + + """ + self.assertNested() + self.current_vtable[slotnum] = self.Offset() + ## @endcond + + def __Finish(self, rootTable, sizePrefix, file_identifier=None): + """Finish finalizes a buffer, pointing to the given `rootTable`.""" + N.enforce_number(rootTable, N.UOffsetTFlags) + + if file_identifier is not None: + self.Prep(N.UOffsetTFlags.bytewidth, N.Uint8Flags.bytewidth*4) + + # Convert bytes object file_identifier to an array of 4 8-bit integers, + # and use big-endian to enforce size compliance. + # https://docs.python.org/2/library/struct.html#format-characters + file_identifier = N.struct.unpack(">BBBB", file_identifier) + for i in range(encode.FILE_IDENTIFIER_LENGTH-1, -1, -1): + # Place the bytes of the file_identifer in reverse order: + self.Place(file_identifier[i], N.Uint8Flags) + + self.PrependUOffsetTRelative(rootTable) + if sizePrefix: + size = len(self.Bytes) - self.Head() + N.enforce_number(size, N.Int32Flags) + self.PrependInt32(size) + self.finished = True + return self.Head() + + def Finish(self, rootTable, file_identifier=None): + """Finish finalizes a buffer, pointing to the given `rootTable`.""" + return self.__Finish(rootTable, False, file_identifier=file_identifier) + + def FinishSizePrefixed(self, rootTable, file_identifier=None): + """ + Finish finalizes a buffer, pointing to the given `rootTable`, + with the size prefixed. + """ + return self.__Finish(rootTable, True, file_identifier=file_identifier) + + ## @cond FLATBUFFERS_INTERNAL + def Prepend(self, flags, off): + self.Prep(flags.bytewidth, 0) + self.Place(off, flags) + + def PrependSlot(self, flags, o, x, d): + N.enforce_number(x, flags) + N.enforce_number(d, flags) + if x != d or self.forceDefaults: + self.Prepend(flags, x) + self.Slot(o) + + def PrependBoolSlot(self, *args): self.PrependSlot(N.BoolFlags, *args) + + def PrependByteSlot(self, *args): self.PrependSlot(N.Uint8Flags, *args) + + def PrependUint8Slot(self, *args): self.PrependSlot(N.Uint8Flags, *args) + + def PrependUint16Slot(self, *args): self.PrependSlot(N.Uint16Flags, *args) + + def PrependUint32Slot(self, *args): self.PrependSlot(N.Uint32Flags, *args) + + def PrependUint64Slot(self, *args): self.PrependSlot(N.Uint64Flags, *args) + + def PrependInt8Slot(self, *args): self.PrependSlot(N.Int8Flags, *args) + + def PrependInt16Slot(self, *args): self.PrependSlot(N.Int16Flags, *args) + + def PrependInt32Slot(self, *args): self.PrependSlot(N.Int32Flags, *args) + + def PrependInt64Slot(self, *args): self.PrependSlot(N.Int64Flags, *args) + + def PrependFloat32Slot(self, *args): self.PrependSlot(N.Float32Flags, + *args) + + def PrependFloat64Slot(self, *args): self.PrependSlot(N.Float64Flags, + *args) + + def PrependUOffsetTRelativeSlot(self, o, x, d): + """ + PrependUOffsetTRelativeSlot prepends an UOffsetT onto the object at + vtable slot `o`. If value `x` equals default `d`, then the slot will + be set to zero and no other data will be written. + """ + + if x != d or self.forceDefaults: + self.PrependUOffsetTRelative(x) + self.Slot(o) + + def PrependStructSlot(self, v, x, d): + """ + PrependStructSlot prepends a struct onto the object at vtable slot `o`. + Structs are stored inline, so nothing additional is being added. + In generated code, `d` is always 0. + """ + + N.enforce_number(d, N.UOffsetTFlags) + if x != d: + self.assertStructIsInline(x) + self.Slot(v) + + ## @endcond + + def PrependBool(self, x): + """Prepend a `bool` to the Builder buffer. + + Note: aligns and checks for space. + """ + self.Prepend(N.BoolFlags, x) + + def PrependByte(self, x): + """Prepend a `byte` to the Builder buffer. + + Note: aligns and checks for space. + """ + self.Prepend(N.Uint8Flags, x) + + def PrependUint8(self, x): + """Prepend an `uint8` to the Builder buffer. + + Note: aligns and checks for space. + """ + self.Prepend(N.Uint8Flags, x) + + def PrependUint16(self, x): + """Prepend an `uint16` to the Builder buffer. + + Note: aligns and checks for space. + """ + self.Prepend(N.Uint16Flags, x) + + def PrependUint32(self, x): + """Prepend an `uint32` to the Builder buffer. + + Note: aligns and checks for space. + """ + self.Prepend(N.Uint32Flags, x) + + def PrependUint64(self, x): + """Prepend an `uint64` to the Builder buffer. + + Note: aligns and checks for space. + """ + self.Prepend(N.Uint64Flags, x) + + def PrependInt8(self, x): + """Prepend an `int8` to the Builder buffer. + + Note: aligns and checks for space. + """ + self.Prepend(N.Int8Flags, x) + + def PrependInt16(self, x): + """Prepend an `int16` to the Builder buffer. + + Note: aligns and checks for space. + """ + self.Prepend(N.Int16Flags, x) + + def PrependInt32(self, x): + """Prepend an `int32` to the Builder buffer. + + Note: aligns and checks for space. + """ + self.Prepend(N.Int32Flags, x) + + def PrependInt64(self, x): + """Prepend an `int64` to the Builder buffer. + + Note: aligns and checks for space. + """ + self.Prepend(N.Int64Flags, x) + + def PrependFloat32(self, x): + """Prepend a `float32` to the Builder buffer. + + Note: aligns and checks for space. + """ + self.Prepend(N.Float32Flags, x) + + def PrependFloat64(self, x): + """Prepend a `float64` to the Builder buffer. + + Note: aligns and checks for space. + """ + self.Prepend(N.Float64Flags, x) + + def ForceDefaults(self, forceDefaults): + """ + In order to save space, fields that are set to their default value + don't get serialized into the buffer. Forcing defaults provides a + way to manually disable this optimization. When set to `True`, will + always serialize default values. + """ + self.forceDefaults = forceDefaults + +############################################################## + + ## @cond FLATBUFFERS_INTERNAL + def PrependVOffsetT(self, x): self.Prepend(N.VOffsetTFlags, x) + + def Place(self, x, flags): + """ + Place prepends a value specified by `flags` to the Builder, + without checking for available space. + """ + + N.enforce_number(x, flags) + self.head = self.head - flags.bytewidth + encode.Write(flags.packer_type, self.Bytes, self.Head(), x) + + def PlaceVOffsetT(self, x): + """PlaceVOffsetT prepends a VOffsetT to the Builder, without checking + for space. + """ + N.enforce_number(x, N.VOffsetTFlags) + self.head = self.head - N.VOffsetTFlags.bytewidth + encode.Write(packer.voffset, self.Bytes, self.Head(), x) + + def PlaceSOffsetT(self, x): + """PlaceSOffsetT prepends a SOffsetT to the Builder, without checking + for space. + """ + N.enforce_number(x, N.SOffsetTFlags) + self.head = self.head - N.SOffsetTFlags.bytewidth + encode.Write(packer.soffset, self.Bytes, self.Head(), x) + + def PlaceUOffsetT(self, x): + """PlaceUOffsetT prepends a UOffsetT to the Builder, without checking + for space. + """ + N.enforce_number(x, N.UOffsetTFlags) + self.head = self.head - N.UOffsetTFlags.bytewidth + encode.Write(packer.uoffset, self.Bytes, self.Head(), x) + ## @endcond + +## @cond FLATBUFFERS_INTERNAL +def vtableEqual(a, objectStart, b): + """vtableEqual compares an unwritten vtable to a written vtable.""" + + N.enforce_number(objectStart, N.UOffsetTFlags) + + if len(a) * N.VOffsetTFlags.bytewidth != len(b): + return False + + for i, elem in enumerate(a): + x = encode.Get(packer.voffset, b, i * N.VOffsetTFlags.bytewidth) + + # Skip vtable entries that indicate a default value. + if x == 0 and elem == 0: + pass + else: + y = objectStart - elem + if x != y: + return False + return True +## @endcond +## @} diff --git a/utensor_cgen/third_party/flatbuffers/compat.py b/utensor_cgen/third_party/flatbuffers/compat.py new file mode 100644 index 00000000..2fc9cca0 --- /dev/null +++ b/utensor_cgen/third_party/flatbuffers/compat.py @@ -0,0 +1,81 @@ +# Copyright 2016 Google Inc. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" A tiny version of `six` to help with backwards compability. Also includes + compatibility helpers for numpy. """ + +import sys +import imp + +PY2 = sys.version_info[0] == 2 +PY26 = sys.version_info[0:2] == (2, 6) +PY27 = sys.version_info[0:2] == (2, 7) +PY275 = sys.version_info[0:3] >= (2, 7, 5) +PY3 = sys.version_info[0] == 3 +PY34 = sys.version_info[0:2] >= (3, 4) + +if PY3: + string_types = (str,) + binary_types = (bytes,bytearray) + range_func = range + memoryview_type = memoryview + struct_bool_decl = "?" +else: + string_types = (unicode,) + if PY26 or PY27: + binary_types = (str,bytearray) + else: + binary_types = (str,) + range_func = xrange + if PY26 or (PY27 and not PY275): + memoryview_type = buffer + struct_bool_decl = " Date: Tue, 26 Nov 2019 22:34:17 -0800 Subject: [PATCH 018/117] daily commit - ops require options; see TODO in tflite_exporter.py --- tests/tflm/tflite_export/conftest.py | 9 ++++---- tests/tflm/tflite_export/test_write.py | 11 ++++++++-- utensor_cgen/transformer/tflite_exporter.py | 23 +++++++++++++++++++-- 3 files changed, 35 insertions(+), 8 deletions(-) diff --git a/tests/tflm/tflite_export/conftest.py b/tests/tflm/tflite_export/conftest.py index 2aa974b6..e798b3f4 100644 --- a/tests/tflm/tflite_export/conftest.py +++ b/tests/tflm/tflite_export/conftest.py @@ -5,7 +5,7 @@ from utensor_cgen.ir import TensorInfo, OperationInfo, uTensorGraph from utensor_cgen.utils import prune_graph, topologic_order_graph -@fixture(name='sample_ugraph') +@fixture(name='hybrid_quant_output') def simple_tflm_graph(): ugraph = uTensorGraph() @@ -39,13 +39,13 @@ def simple_tflm_graph(): input_tensors = [], output_tensors = [] ) - mock_input_op.op_attr["value"] = np.array([[1],[2],[3],[4]], dtype=np.int8) + mock_input_op.op_attr["value"] = np.array([[1.0],[2.0],[3.0],[4.0]], dtype=np.float32) mock_input_op.op_attr["shape"] = [1,4] input1 = TensorInfo( name = "input", op_name = "mock_input_const", - dtype = np.dtype("float"), + dtype = mock_input_op.op_attr["value"].dtype, shape = [1, 4], ugraph = ugraph ) @@ -102,4 +102,5 @@ def simple_tflm_graph(): topologic_order_graph(ugraph) #ugraph = prune_graph(ugraph) - return ugraph + #return: ugraph, input tensors, output tensors + return [ugraph, [], ["output"]] diff --git a/tests/tflm/tflite_export/test_write.py b/tests/tflm/tflite_export/test_write.py index bc4bc918..efc039a6 100644 --- a/tests/tflm/tflite_export/test_write.py +++ b/tests/tflm/tflite_export/test_write.py @@ -80,8 +80,9 @@ def print_tflite_graph(byte_buff): else: print("None") -def test_tflite_fb_write(sample_ugraph): - exporter = TFLiteExporter() +def test_tflite_fb_write(hybrid_quant_output): + [sample_ugraph, input_tensors, output_tensors] = hybrid_quant_output + exporter = TFLiteExporter(input_tensors=input_tensors, output_tensors=output_tensors) ugraph = exporter.transform(sample_ugraph) model_content = exporter.output() @@ -92,6 +93,12 @@ def test_tflite_fb_write(sample_ugraph): open("tflm_test_model.tflite", "wb").write(model_content) test_model = tf.lite.Interpreter('tflm_test_model.tflite') + test_model.allocate_tensors() + test_model_output_index = test_model.tensor(test_model.get_output_details()[0]["index"]) + test_model.invoke() + output_content = test_model.get_tensor(test_model_output_index)[0] + + print(output_content) test_pass = True assert test_pass, 'error message here' diff --git a/utensor_cgen/transformer/tflite_exporter.py b/utensor_cgen/transformer/tflite_exporter.py index 5fc4d9b8..cf9de297 100644 --- a/utensor_cgen/transformer/tflite_exporter.py +++ b/utensor_cgen/transformer/tflite_exporter.py @@ -60,7 +60,12 @@ class TFLiteExporter(Transformer): schema_version = 3 file_ident = "TFL3" - def __init__(self): + def __init__(self, input_tensors, output_tensors): + """ + input_tensors: a list of input tensor names + output_tensors: alist of output tensor names + """ + self.prune_graph = False #what is this? self.op_manager = FlatbufferOpManager() self.fbuilder = flatbuffers.Builder(self.__Max_fbuff_size) @@ -68,6 +73,8 @@ def __init__(self): # added to the Model object self.tensor_index = OrderedDict() # out_name : ref tensor object #added to the Subgraph object + self.input_tensors = input_tensors + self.output_tensors = output_tensors def transform(self, ugraph): @@ -87,6 +94,12 @@ def transform(self, ugraph): tensors_vec = self.__fb_vector(tflite.SubGraph.SubGraphStartTensorsVector, [f_obj for t_name, f_obj in self.tensor_index.items()]) + subgraph_input_t_names = [list(self.tensor_index.keys()).index(t_name) for t_name in self.input_tensors] + subgraph_input_vec = self.__fb_vector(tflite.SubGraph.SubGraphStartInputsVector, subgraph_input_t_names) + + subgraph_output_t_names = [list(self.tensor_index.keys()).index(t_name) for t_name in self.output_tensors] + subgraph_output_vec = self.__fb_vector(tflite.SubGraph.SubGraphStartOutputsVector, subgraph_output_t_names) + # traverse ugraph # compile a list of static ops : fb_ops_list fb_ops_list = list() @@ -102,6 +115,8 @@ def transform(self, ugraph): tflite.SubGraph.SubGraphStart(self.fbuilder) tflite.SubGraph.SubGraphAddTensors(self.fbuilder, tensors_vec) tflite.SubGraph.SubGraphAddOperators(self.fbuilder, ops_vec) + tflite.SubGraph.SubGraphAddInputs(self.fbuilder, subgraph_input_vec) + tflite.SubGraph.SubGraphAddOutputs(self.fbuilder, subgraph_output_vec) subgraph = tflite.SubGraph.SubGraphEnd(self.fbuilder) #TFLM runtime only support 1 subgraph @@ -207,6 +222,8 @@ def add_Op(self, op_info): op_inputs = self.__input_vector(op_info.input_tensors) op_outputs = self.__output_vector(op_info.output_tensors) + ##TODO: add op factory to deal with op options + tflite.Operator.OperatorStart(self.fbuilder) tflite.Operator.OperatorAddOpcodeIndex(self.fbuilder, self.op_manager.op_index(op_info.op_type)) tflite.Operator.OperatorAddInputs(self.fbuilder, op_inputs) @@ -293,11 +310,13 @@ def __fb_vector(self, vector_constructor, arr_list): prepend_method_table[tflite.Operator.OperatorStartInputsVector] = self.fbuilder.PrependInt32 prepend_method_table[tflite.QuantizationParameters.QuantizationParametersStartScaleVector] = self.fbuilder.PrependFloat32 prepend_method_table[tflite.QuantizationParameters.QuantizationParametersStartZeroPointVector] = self.fbuilder.PrependFloat32 + prepend_method_table[tflite.SubGraph.SubGraphStartOutputsVector] = self.fbuilder.PrependInt32 + prepend_method_table[tflite.SubGraph.SubGraphStartInputsVector] = self.fbuilder.PrependInt32 preprend_method = prepend_method_table[vector_constructor] num_items = len(arr_list) - assert num_items > 0 + #assert num_items > 0 vector_constructor(self.fbuilder, num_items) # reversed to handle prepend From ab7d08ec4100ad75acf2ebf1d4fdd9a0a16bce89 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Wed, 27 Nov 2019 17:58:48 -0800 Subject: [PATCH 019/117] OperatorAddBuiltinOptions breaks input tensors? try regenerate the flatbuffer python bindings --- utensor_cgen/transformer/tflite_exporter.py | 23 +++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/utensor_cgen/transformer/tflite_exporter.py b/utensor_cgen/transformer/tflite_exporter.py index cf9de297..597975b0 100644 --- a/utensor_cgen/transformer/tflite_exporter.py +++ b/utensor_cgen/transformer/tflite_exporter.py @@ -15,6 +15,7 @@ from utensor_cgen.third_party.tflite import * from utensor_cgen.third_party.tflite.BuiltinOperator import BuiltinOperator from utensor_cgen.third_party.tflite.BuiltinOptions import BuiltinOptions +from utensor_cgen.third_party.tflite.ActivationFunctionType import ActivationFunctionType from utensor_cgen.third_party.tflite.TensorType import TensorType from utensor_cgen.logger import logger from utensor_cgen.utils import parse_tensor_name @@ -23,6 +24,20 @@ __all__ = ["TFLiteExporter"] +def get_fullyconnected_builtin_option(fbuilder, op_info): + + weight_format = tflite.FullyConnectedOptionsWeightsFormat.FullyConnectedOptionsWeightsFormat.DEFAULT + + tflite.FullyConnectedOptions.FullyConnectedOptionsStart(fbuilder) + #FIXME: node fusion and select an activation function here + tflite.FullyConnectedOptions.FullyConnectedOptionsAddFusedActivationFunction(fbuilder, ActivationFunctionType.NONE) + tflite.FullyConnectedOptions.FullyConnectedOptionsAddWeightsFormat(fbuilder, weight_format) + #FIXME: check the parameter here + tflite.FullyConnectedOptions.FullyConnectedOptionsAddKeepNumDims(fbuilder, False) + obj = tflite.FullyConnectedOptions.FullyConnectedOptionsEnd(fbuilder) + + return obj, BuiltinOptions.FullyConnectedOptions + class FlatbufferOpManager: op_list = list() code_name_lookup = {v: k for k, v in BuiltinOperator.__dict__.items()} @@ -223,11 +238,19 @@ def add_Op(self, op_info): op_outputs = self.__output_vector(op_info.output_tensors) ##TODO: add op factory to deal with op options + op_option_factory = dict() + op_option_factory["FULLY_CONNECTED"] = get_fullyconnected_builtin_option + + builtin_opt_func = op_option_factory[op_info.op_type] + + builtin_opt, builtin_opt_type = builtin_opt_func(self.fbuilder, op_info) tflite.Operator.OperatorStart(self.fbuilder) tflite.Operator.OperatorAddOpcodeIndex(self.fbuilder, self.op_manager.op_index(op_info.op_type)) tflite.Operator.OperatorAddInputs(self.fbuilder, op_inputs) tflite.Operator.OperatorAddOutputs(self.fbuilder, op_outputs) + tflite.Operator.OperatorAddBuiltinOptionsType(self.fbuilder, builtin_opt_type) + #tflite.Operator.OperatorAddBuiltinOptions(self.fbuilder, builtin_opt) op = tflite.Operator.OperatorEnd(self.fbuilder) return op #to be added into SubGraphStartOperatorsVector From 2fd92f6fedf7d7b2d21bbf8320e8e33becc81949 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Thu, 28 Nov 2019 16:09:09 -0800 Subject: [PATCH 020/117] regenerated the fb python bindings hybrid quantization failed --- utensor_cgen/third_party/tflite/AbsOptions.py | 4 ++++ utensor_cgen/third_party/tflite/AddNOptions.py | 4 ++++ utensor_cgen/third_party/tflite/AddOptions.py | 4 ++++ utensor_cgen/third_party/tflite/ArgMaxOptions.py | 4 ++++ utensor_cgen/third_party/tflite/ArgMinOptions.py | 4 ++++ utensor_cgen/third_party/tflite/BatchToSpaceNDOptions.py | 4 ++++ .../third_party/tflite/BidirectionalSequenceLSTMOptions.py | 4 ++++ .../third_party/tflite/BidirectionalSequenceRNNOptions.py | 4 ++++ utensor_cgen/third_party/tflite/Buffer.py | 4 ++++ utensor_cgen/third_party/tflite/CallOptions.py | 4 ++++ utensor_cgen/third_party/tflite/CastOptions.py | 4 ++++ utensor_cgen/third_party/tflite/ConcatEmbeddingsOptions.py | 4 ++++ utensor_cgen/third_party/tflite/ConcatenationOptions.py | 4 ++++ utensor_cgen/third_party/tflite/Conv2DOptions.py | 4 ++++ utensor_cgen/third_party/tflite/CosOptions.py | 4 ++++ utensor_cgen/third_party/tflite/CustomQuantization.py | 4 ++++ utensor_cgen/third_party/tflite/DepthToSpaceOptions.py | 4 ++++ utensor_cgen/third_party/tflite/DepthwiseConv2DOptions.py | 4 ++++ utensor_cgen/third_party/tflite/DequantizeOptions.py | 4 ++++ utensor_cgen/third_party/tflite/DivOptions.py | 4 ++++ .../third_party/tflite/EmbeddingLookupSparseOptions.py | 4 ++++ utensor_cgen/third_party/tflite/EqualOptions.py | 4 ++++ utensor_cgen/third_party/tflite/ExpOptions.py | 4 ++++ utensor_cgen/third_party/tflite/ExpandDimsOptions.py | 4 ++++ utensor_cgen/third_party/tflite/FakeQuantOptions.py | 4 ++++ utensor_cgen/third_party/tflite/FillOptions.py | 4 ++++ utensor_cgen/third_party/tflite/FloorDivOptions.py | 4 ++++ utensor_cgen/third_party/tflite/FloorModOptions.py | 4 ++++ utensor_cgen/third_party/tflite/FullyConnectedOptions.py | 4 ++++ utensor_cgen/third_party/tflite/GatherNdOptions.py | 4 ++++ utensor_cgen/third_party/tflite/GatherOptions.py | 4 ++++ utensor_cgen/third_party/tflite/GreaterEqualOptions.py | 4 ++++ utensor_cgen/third_party/tflite/GreaterOptions.py | 4 ++++ utensor_cgen/third_party/tflite/HardSwishOptions.py | 4 ++++ utensor_cgen/third_party/tflite/IfOptions.py | 4 ++++ utensor_cgen/third_party/tflite/L2NormOptions.py | 4 ++++ utensor_cgen/third_party/tflite/LSHProjectionOptions.py | 4 ++++ utensor_cgen/third_party/tflite/LSTMOptions.py | 4 ++++ utensor_cgen/third_party/tflite/LeakyReluOptions.py | 4 ++++ utensor_cgen/third_party/tflite/LessEqualOptions.py | 4 ++++ utensor_cgen/third_party/tflite/LessOptions.py | 4 ++++ .../third_party/tflite/LocalResponseNormalizationOptions.py | 4 ++++ utensor_cgen/third_party/tflite/LogSoftmaxOptions.py | 4 ++++ utensor_cgen/third_party/tflite/LogicalAndOptions.py | 4 ++++ utensor_cgen/third_party/tflite/LogicalNotOptions.py | 4 ++++ utensor_cgen/third_party/tflite/LogicalOrOptions.py | 4 ++++ utensor_cgen/third_party/tflite/MatrixDiagOptions.py | 4 ++++ utensor_cgen/third_party/tflite/MatrixSetDiagOptions.py | 4 ++++ utensor_cgen/third_party/tflite/MaximumMinimumOptions.py | 4 ++++ utensor_cgen/third_party/tflite/Metadata.py | 4 ++++ utensor_cgen/third_party/tflite/MirrorPadOptions.py | 4 ++++ utensor_cgen/third_party/tflite/Model.py | 4 ++++ utensor_cgen/third_party/tflite/MulOptions.py | 4 ++++ utensor_cgen/third_party/tflite/NegOptions.py | 4 ++++ utensor_cgen/third_party/tflite/NonMaxSuppressionV4Options.py | 4 ++++ utensor_cgen/third_party/tflite/NonMaxSuppressionV5Options.py | 4 ++++ utensor_cgen/third_party/tflite/NotEqualOptions.py | 4 ++++ utensor_cgen/third_party/tflite/OneHotOptions.py | 4 ++++ utensor_cgen/third_party/tflite/Operator.py | 4 ++++ utensor_cgen/third_party/tflite/OperatorCode.py | 4 ++++ utensor_cgen/third_party/tflite/PackOptions.py | 4 ++++ utensor_cgen/third_party/tflite/PadOptions.py | 4 ++++ utensor_cgen/third_party/tflite/PadV2Options.py | 4 ++++ utensor_cgen/third_party/tflite/Pool2DOptions.py | 4 ++++ utensor_cgen/third_party/tflite/PowOptions.py | 4 ++++ utensor_cgen/third_party/tflite/QuantizationParameters.py | 4 ++++ utensor_cgen/third_party/tflite/QuantizeOptions.py | 4 ++++ utensor_cgen/third_party/tflite/RNNOptions.py | 4 ++++ utensor_cgen/third_party/tflite/RangeOptions.py | 4 ++++ utensor_cgen/third_party/tflite/RankOptions.py | 4 ++++ utensor_cgen/third_party/tflite/ReducerOptions.py | 4 ++++ utensor_cgen/third_party/tflite/ReshapeOptions.py | 4 ++++ utensor_cgen/third_party/tflite/ResizeBilinearOptions.py | 4 ++++ .../third_party/tflite/ResizeNearestNeighborOptions.py | 4 ++++ utensor_cgen/third_party/tflite/ReverseSequenceOptions.py | 4 ++++ utensor_cgen/third_party/tflite/ReverseV2Options.py | 4 ++++ utensor_cgen/third_party/tflite/SVDFOptions.py | 4 ++++ utensor_cgen/third_party/tflite/SelectOptions.py | 4 ++++ utensor_cgen/third_party/tflite/SequenceRNNOptions.py | 4 ++++ utensor_cgen/third_party/tflite/ShapeOptions.py | 4 ++++ utensor_cgen/third_party/tflite/SkipGramOptions.py | 4 ++++ utensor_cgen/third_party/tflite/SliceOptions.py | 4 ++++ utensor_cgen/third_party/tflite/SoftmaxOptions.py | 4 ++++ utensor_cgen/third_party/tflite/SpaceToBatchNDOptions.py | 4 ++++ utensor_cgen/third_party/tflite/SpaceToDepthOptions.py | 4 ++++ utensor_cgen/third_party/tflite/SparseToDenseOptions.py | 4 ++++ utensor_cgen/third_party/tflite/SplitOptions.py | 4 ++++ utensor_cgen/third_party/tflite/SplitVOptions.py | 4 ++++ utensor_cgen/third_party/tflite/SquareOptions.py | 4 ++++ utensor_cgen/third_party/tflite/SquaredDifferenceOptions.py | 4 ++++ utensor_cgen/third_party/tflite/SqueezeOptions.py | 4 ++++ utensor_cgen/third_party/tflite/StridedSliceOptions.py | 4 ++++ utensor_cgen/third_party/tflite/SubGraph.py | 4 ++++ utensor_cgen/third_party/tflite/SubOptions.py | 4 ++++ utensor_cgen/third_party/tflite/Tensor.py | 4 ++++ utensor_cgen/third_party/tflite/TileOptions.py | 4 ++++ utensor_cgen/third_party/tflite/TopKV2Options.py | 4 ++++ utensor_cgen/third_party/tflite/TransposeConvOptions.py | 4 ++++ utensor_cgen/third_party/tflite/TransposeOptions.py | 4 ++++ .../third_party/tflite/UnidirectionalSequenceLSTMOptions.py | 4 ++++ utensor_cgen/third_party/tflite/UniqueOptions.py | 4 ++++ utensor_cgen/third_party/tflite/UnpackOptions.py | 4 ++++ utensor_cgen/third_party/tflite/WhereOptions.py | 4 ++++ utensor_cgen/third_party/tflite/WhileOptions.py | 4 ++++ utensor_cgen/third_party/tflite/ZerosLikeOptions.py | 4 ++++ utensor_cgen/transformer/tflite_exporter.py | 2 +- 106 files changed, 421 insertions(+), 1 deletion(-) diff --git a/utensor_cgen/third_party/tflite/AbsOptions.py b/utensor_cgen/third_party/tflite/AbsOptions.py index 0cbfb8c0..3da6cb0f 100644 --- a/utensor_cgen/third_party/tflite/AbsOptions.py +++ b/utensor_cgen/third_party/tflite/AbsOptions.py @@ -14,6 +14,10 @@ def GetRootAsAbsOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def AbsOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # AbsOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/AddNOptions.py b/utensor_cgen/third_party/tflite/AddNOptions.py index b5c2ddb7..a29858bd 100644 --- a/utensor_cgen/third_party/tflite/AddNOptions.py +++ b/utensor_cgen/third_party/tflite/AddNOptions.py @@ -14,6 +14,10 @@ def GetRootAsAddNOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def AddNOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # AddNOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/AddOptions.py b/utensor_cgen/third_party/tflite/AddOptions.py index d6cbfcf5..b9fe03b8 100644 --- a/utensor_cgen/third_party/tflite/AddOptions.py +++ b/utensor_cgen/third_party/tflite/AddOptions.py @@ -14,6 +14,10 @@ def GetRootAsAddOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def AddOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # AddOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/ArgMaxOptions.py b/utensor_cgen/third_party/tflite/ArgMaxOptions.py index fbf1415e..741135dd 100644 --- a/utensor_cgen/third_party/tflite/ArgMaxOptions.py +++ b/utensor_cgen/third_party/tflite/ArgMaxOptions.py @@ -14,6 +14,10 @@ def GetRootAsArgMaxOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def ArgMaxOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # ArgMaxOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/ArgMinOptions.py b/utensor_cgen/third_party/tflite/ArgMinOptions.py index 120fdca2..f62455f7 100644 --- a/utensor_cgen/third_party/tflite/ArgMinOptions.py +++ b/utensor_cgen/third_party/tflite/ArgMinOptions.py @@ -14,6 +14,10 @@ def GetRootAsArgMinOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def ArgMinOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # ArgMinOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/BatchToSpaceNDOptions.py b/utensor_cgen/third_party/tflite/BatchToSpaceNDOptions.py index 3ddcfd3f..8398ca19 100644 --- a/utensor_cgen/third_party/tflite/BatchToSpaceNDOptions.py +++ b/utensor_cgen/third_party/tflite/BatchToSpaceNDOptions.py @@ -14,6 +14,10 @@ def GetRootAsBatchToSpaceNDOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def BatchToSpaceNDOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # BatchToSpaceNDOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/BidirectionalSequenceLSTMOptions.py b/utensor_cgen/third_party/tflite/BidirectionalSequenceLSTMOptions.py index 8d8b7bea..f1671b5a 100644 --- a/utensor_cgen/third_party/tflite/BidirectionalSequenceLSTMOptions.py +++ b/utensor_cgen/third_party/tflite/BidirectionalSequenceLSTMOptions.py @@ -14,6 +14,10 @@ def GetRootAsBidirectionalSequenceLSTMOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def BidirectionalSequenceLSTMOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # BidirectionalSequenceLSTMOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/BidirectionalSequenceRNNOptions.py b/utensor_cgen/third_party/tflite/BidirectionalSequenceRNNOptions.py index 673af6b9..340d6667 100644 --- a/utensor_cgen/third_party/tflite/BidirectionalSequenceRNNOptions.py +++ b/utensor_cgen/third_party/tflite/BidirectionalSequenceRNNOptions.py @@ -14,6 +14,10 @@ def GetRootAsBidirectionalSequenceRNNOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def BidirectionalSequenceRNNOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # BidirectionalSequenceRNNOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/Buffer.py b/utensor_cgen/third_party/tflite/Buffer.py index 754dee3b..1333f955 100644 --- a/utensor_cgen/third_party/tflite/Buffer.py +++ b/utensor_cgen/third_party/tflite/Buffer.py @@ -14,6 +14,10 @@ def GetRootAsBuffer(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def BufferBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # Buffer def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/CallOptions.py b/utensor_cgen/third_party/tflite/CallOptions.py index 5ae2eeae..3216f8a1 100644 --- a/utensor_cgen/third_party/tflite/CallOptions.py +++ b/utensor_cgen/third_party/tflite/CallOptions.py @@ -14,6 +14,10 @@ def GetRootAsCallOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def CallOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # CallOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/CastOptions.py b/utensor_cgen/third_party/tflite/CastOptions.py index 70ae2e37..1f007650 100644 --- a/utensor_cgen/third_party/tflite/CastOptions.py +++ b/utensor_cgen/third_party/tflite/CastOptions.py @@ -14,6 +14,10 @@ def GetRootAsCastOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def CastOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # CastOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/ConcatEmbeddingsOptions.py b/utensor_cgen/third_party/tflite/ConcatEmbeddingsOptions.py index 9d26c510..13be8026 100644 --- a/utensor_cgen/third_party/tflite/ConcatEmbeddingsOptions.py +++ b/utensor_cgen/third_party/tflite/ConcatEmbeddingsOptions.py @@ -14,6 +14,10 @@ def GetRootAsConcatEmbeddingsOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def ConcatEmbeddingsOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # ConcatEmbeddingsOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/ConcatenationOptions.py b/utensor_cgen/third_party/tflite/ConcatenationOptions.py index c8e0b6ab..f6928a6b 100644 --- a/utensor_cgen/third_party/tflite/ConcatenationOptions.py +++ b/utensor_cgen/third_party/tflite/ConcatenationOptions.py @@ -14,6 +14,10 @@ def GetRootAsConcatenationOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def ConcatenationOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # ConcatenationOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/Conv2DOptions.py b/utensor_cgen/third_party/tflite/Conv2DOptions.py index ef49f751..a05b39cb 100644 --- a/utensor_cgen/third_party/tflite/Conv2DOptions.py +++ b/utensor_cgen/third_party/tflite/Conv2DOptions.py @@ -14,6 +14,10 @@ def GetRootAsConv2DOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def Conv2DOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # Conv2DOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/CosOptions.py b/utensor_cgen/third_party/tflite/CosOptions.py index 7fbf8487..12d49268 100644 --- a/utensor_cgen/third_party/tflite/CosOptions.py +++ b/utensor_cgen/third_party/tflite/CosOptions.py @@ -14,6 +14,10 @@ def GetRootAsCosOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def CosOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # CosOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/CustomQuantization.py b/utensor_cgen/third_party/tflite/CustomQuantization.py index 21ec0da4..5b1013c6 100644 --- a/utensor_cgen/third_party/tflite/CustomQuantization.py +++ b/utensor_cgen/third_party/tflite/CustomQuantization.py @@ -14,6 +14,10 @@ def GetRootAsCustomQuantization(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def CustomQuantizationBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # CustomQuantization def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/DepthToSpaceOptions.py b/utensor_cgen/third_party/tflite/DepthToSpaceOptions.py index 97b93aa7..e6b44f42 100644 --- a/utensor_cgen/third_party/tflite/DepthToSpaceOptions.py +++ b/utensor_cgen/third_party/tflite/DepthToSpaceOptions.py @@ -14,6 +14,10 @@ def GetRootAsDepthToSpaceOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def DepthToSpaceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # DepthToSpaceOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/DepthwiseConv2DOptions.py b/utensor_cgen/third_party/tflite/DepthwiseConv2DOptions.py index 9689383b..50dcdbfb 100644 --- a/utensor_cgen/third_party/tflite/DepthwiseConv2DOptions.py +++ b/utensor_cgen/third_party/tflite/DepthwiseConv2DOptions.py @@ -14,6 +14,10 @@ def GetRootAsDepthwiseConv2DOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def DepthwiseConv2DOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # DepthwiseConv2DOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/DequantizeOptions.py b/utensor_cgen/third_party/tflite/DequantizeOptions.py index 5ef8b8dd..bd9f2e2d 100644 --- a/utensor_cgen/third_party/tflite/DequantizeOptions.py +++ b/utensor_cgen/third_party/tflite/DequantizeOptions.py @@ -14,6 +14,10 @@ def GetRootAsDequantizeOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def DequantizeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # DequantizeOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/DivOptions.py b/utensor_cgen/third_party/tflite/DivOptions.py index 905a3be0..2988a8e3 100644 --- a/utensor_cgen/third_party/tflite/DivOptions.py +++ b/utensor_cgen/third_party/tflite/DivOptions.py @@ -14,6 +14,10 @@ def GetRootAsDivOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def DivOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # DivOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/EmbeddingLookupSparseOptions.py b/utensor_cgen/third_party/tflite/EmbeddingLookupSparseOptions.py index 7d9c1442..eaa83569 100644 --- a/utensor_cgen/third_party/tflite/EmbeddingLookupSparseOptions.py +++ b/utensor_cgen/third_party/tflite/EmbeddingLookupSparseOptions.py @@ -14,6 +14,10 @@ def GetRootAsEmbeddingLookupSparseOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def EmbeddingLookupSparseOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # EmbeddingLookupSparseOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/EqualOptions.py b/utensor_cgen/third_party/tflite/EqualOptions.py index f787ef85..b7b14bb3 100644 --- a/utensor_cgen/third_party/tflite/EqualOptions.py +++ b/utensor_cgen/third_party/tflite/EqualOptions.py @@ -14,6 +14,10 @@ def GetRootAsEqualOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def EqualOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # EqualOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/ExpOptions.py b/utensor_cgen/third_party/tflite/ExpOptions.py index eac1456e..ea1cfead 100644 --- a/utensor_cgen/third_party/tflite/ExpOptions.py +++ b/utensor_cgen/third_party/tflite/ExpOptions.py @@ -14,6 +14,10 @@ def GetRootAsExpOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def ExpOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # ExpOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/ExpandDimsOptions.py b/utensor_cgen/third_party/tflite/ExpandDimsOptions.py index 69d63665..2350c6e7 100644 --- a/utensor_cgen/third_party/tflite/ExpandDimsOptions.py +++ b/utensor_cgen/third_party/tflite/ExpandDimsOptions.py @@ -14,6 +14,10 @@ def GetRootAsExpandDimsOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def ExpandDimsOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # ExpandDimsOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/FakeQuantOptions.py b/utensor_cgen/third_party/tflite/FakeQuantOptions.py index 46c371c3..d97da180 100644 --- a/utensor_cgen/third_party/tflite/FakeQuantOptions.py +++ b/utensor_cgen/third_party/tflite/FakeQuantOptions.py @@ -14,6 +14,10 @@ def GetRootAsFakeQuantOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def FakeQuantOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # FakeQuantOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/FillOptions.py b/utensor_cgen/third_party/tflite/FillOptions.py index 5a1e651a..f6b2ca3a 100644 --- a/utensor_cgen/third_party/tflite/FillOptions.py +++ b/utensor_cgen/third_party/tflite/FillOptions.py @@ -14,6 +14,10 @@ def GetRootAsFillOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def FillOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # FillOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/FloorDivOptions.py b/utensor_cgen/third_party/tflite/FloorDivOptions.py index 64b474fb..53a3c4c6 100644 --- a/utensor_cgen/third_party/tflite/FloorDivOptions.py +++ b/utensor_cgen/third_party/tflite/FloorDivOptions.py @@ -14,6 +14,10 @@ def GetRootAsFloorDivOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def FloorDivOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # FloorDivOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/FloorModOptions.py b/utensor_cgen/third_party/tflite/FloorModOptions.py index 37c8e5a5..6e5f679f 100644 --- a/utensor_cgen/third_party/tflite/FloorModOptions.py +++ b/utensor_cgen/third_party/tflite/FloorModOptions.py @@ -14,6 +14,10 @@ def GetRootAsFloorModOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def FloorModOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # FloorModOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/FullyConnectedOptions.py b/utensor_cgen/third_party/tflite/FullyConnectedOptions.py index a6b4e40f..c2eff3a3 100644 --- a/utensor_cgen/third_party/tflite/FullyConnectedOptions.py +++ b/utensor_cgen/third_party/tflite/FullyConnectedOptions.py @@ -14,6 +14,10 @@ def GetRootAsFullyConnectedOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def FullyConnectedOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # FullyConnectedOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/GatherNdOptions.py b/utensor_cgen/third_party/tflite/GatherNdOptions.py index f515eb5c..db09182b 100644 --- a/utensor_cgen/third_party/tflite/GatherNdOptions.py +++ b/utensor_cgen/third_party/tflite/GatherNdOptions.py @@ -14,6 +14,10 @@ def GetRootAsGatherNdOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def GatherNdOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # GatherNdOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/GatherOptions.py b/utensor_cgen/third_party/tflite/GatherOptions.py index 9fbc3e40..d0915f82 100644 --- a/utensor_cgen/third_party/tflite/GatherOptions.py +++ b/utensor_cgen/third_party/tflite/GatherOptions.py @@ -14,6 +14,10 @@ def GetRootAsGatherOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def GatherOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # GatherOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/GreaterEqualOptions.py b/utensor_cgen/third_party/tflite/GreaterEqualOptions.py index a29e200a..03a86fa2 100644 --- a/utensor_cgen/third_party/tflite/GreaterEqualOptions.py +++ b/utensor_cgen/third_party/tflite/GreaterEqualOptions.py @@ -14,6 +14,10 @@ def GetRootAsGreaterEqualOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def GreaterEqualOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # GreaterEqualOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/GreaterOptions.py b/utensor_cgen/third_party/tflite/GreaterOptions.py index 59d63501..593e0d75 100644 --- a/utensor_cgen/third_party/tflite/GreaterOptions.py +++ b/utensor_cgen/third_party/tflite/GreaterOptions.py @@ -14,6 +14,10 @@ def GetRootAsGreaterOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def GreaterOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # GreaterOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/HardSwishOptions.py b/utensor_cgen/third_party/tflite/HardSwishOptions.py index 4f6a5200..cbbf99cc 100644 --- a/utensor_cgen/third_party/tflite/HardSwishOptions.py +++ b/utensor_cgen/third_party/tflite/HardSwishOptions.py @@ -14,6 +14,10 @@ def GetRootAsHardSwishOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def HardSwishOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # HardSwishOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/IfOptions.py b/utensor_cgen/third_party/tflite/IfOptions.py index 13f4e697..aad72769 100644 --- a/utensor_cgen/third_party/tflite/IfOptions.py +++ b/utensor_cgen/third_party/tflite/IfOptions.py @@ -14,6 +14,10 @@ def GetRootAsIfOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def IfOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # IfOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/L2NormOptions.py b/utensor_cgen/third_party/tflite/L2NormOptions.py index 38bdf573..0b1212fc 100644 --- a/utensor_cgen/third_party/tflite/L2NormOptions.py +++ b/utensor_cgen/third_party/tflite/L2NormOptions.py @@ -14,6 +14,10 @@ def GetRootAsL2NormOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def L2NormOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # L2NormOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/LSHProjectionOptions.py b/utensor_cgen/third_party/tflite/LSHProjectionOptions.py index ad550be2..b545e78d 100644 --- a/utensor_cgen/third_party/tflite/LSHProjectionOptions.py +++ b/utensor_cgen/third_party/tflite/LSHProjectionOptions.py @@ -14,6 +14,10 @@ def GetRootAsLSHProjectionOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def LSHProjectionOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # LSHProjectionOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/LSTMOptions.py b/utensor_cgen/third_party/tflite/LSTMOptions.py index 93a83093..59c680c9 100644 --- a/utensor_cgen/third_party/tflite/LSTMOptions.py +++ b/utensor_cgen/third_party/tflite/LSTMOptions.py @@ -14,6 +14,10 @@ def GetRootAsLSTMOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def LSTMOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # LSTMOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/LeakyReluOptions.py b/utensor_cgen/third_party/tflite/LeakyReluOptions.py index b61b21d5..5b8c647a 100644 --- a/utensor_cgen/third_party/tflite/LeakyReluOptions.py +++ b/utensor_cgen/third_party/tflite/LeakyReluOptions.py @@ -14,6 +14,10 @@ def GetRootAsLeakyReluOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def LeakyReluOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # LeakyReluOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/LessEqualOptions.py b/utensor_cgen/third_party/tflite/LessEqualOptions.py index d49b7289..8465ff3e 100644 --- a/utensor_cgen/third_party/tflite/LessEqualOptions.py +++ b/utensor_cgen/third_party/tflite/LessEqualOptions.py @@ -14,6 +14,10 @@ def GetRootAsLessEqualOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def LessEqualOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # LessEqualOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/LessOptions.py b/utensor_cgen/third_party/tflite/LessOptions.py index 469cb0b0..93f3edcd 100644 --- a/utensor_cgen/third_party/tflite/LessOptions.py +++ b/utensor_cgen/third_party/tflite/LessOptions.py @@ -14,6 +14,10 @@ def GetRootAsLessOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def LessOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # LessOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/LocalResponseNormalizationOptions.py b/utensor_cgen/third_party/tflite/LocalResponseNormalizationOptions.py index db875603..5f4399a3 100644 --- a/utensor_cgen/third_party/tflite/LocalResponseNormalizationOptions.py +++ b/utensor_cgen/third_party/tflite/LocalResponseNormalizationOptions.py @@ -14,6 +14,10 @@ def GetRootAsLocalResponseNormalizationOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def LocalResponseNormalizationOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # LocalResponseNormalizationOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/LogSoftmaxOptions.py b/utensor_cgen/third_party/tflite/LogSoftmaxOptions.py index 47893855..584d628c 100644 --- a/utensor_cgen/third_party/tflite/LogSoftmaxOptions.py +++ b/utensor_cgen/third_party/tflite/LogSoftmaxOptions.py @@ -14,6 +14,10 @@ def GetRootAsLogSoftmaxOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def LogSoftmaxOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # LogSoftmaxOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/LogicalAndOptions.py b/utensor_cgen/third_party/tflite/LogicalAndOptions.py index cee1cdb4..1ec0e6e4 100644 --- a/utensor_cgen/third_party/tflite/LogicalAndOptions.py +++ b/utensor_cgen/third_party/tflite/LogicalAndOptions.py @@ -14,6 +14,10 @@ def GetRootAsLogicalAndOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def LogicalAndOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # LogicalAndOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/LogicalNotOptions.py b/utensor_cgen/third_party/tflite/LogicalNotOptions.py index 9971450c..ce66ec3b 100644 --- a/utensor_cgen/third_party/tflite/LogicalNotOptions.py +++ b/utensor_cgen/third_party/tflite/LogicalNotOptions.py @@ -14,6 +14,10 @@ def GetRootAsLogicalNotOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def LogicalNotOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # LogicalNotOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/LogicalOrOptions.py b/utensor_cgen/third_party/tflite/LogicalOrOptions.py index e94a5dec..ae4b36b0 100644 --- a/utensor_cgen/third_party/tflite/LogicalOrOptions.py +++ b/utensor_cgen/third_party/tflite/LogicalOrOptions.py @@ -14,6 +14,10 @@ def GetRootAsLogicalOrOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def LogicalOrOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # LogicalOrOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/MatrixDiagOptions.py b/utensor_cgen/third_party/tflite/MatrixDiagOptions.py index 0f64e657..85113659 100644 --- a/utensor_cgen/third_party/tflite/MatrixDiagOptions.py +++ b/utensor_cgen/third_party/tflite/MatrixDiagOptions.py @@ -14,6 +14,10 @@ def GetRootAsMatrixDiagOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def MatrixDiagOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # MatrixDiagOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/MatrixSetDiagOptions.py b/utensor_cgen/third_party/tflite/MatrixSetDiagOptions.py index 14178cf8..361d3b69 100644 --- a/utensor_cgen/third_party/tflite/MatrixSetDiagOptions.py +++ b/utensor_cgen/third_party/tflite/MatrixSetDiagOptions.py @@ -14,6 +14,10 @@ def GetRootAsMatrixSetDiagOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def MatrixSetDiagOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # MatrixSetDiagOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/MaximumMinimumOptions.py b/utensor_cgen/third_party/tflite/MaximumMinimumOptions.py index f0806e2d..86b94f9e 100644 --- a/utensor_cgen/third_party/tflite/MaximumMinimumOptions.py +++ b/utensor_cgen/third_party/tflite/MaximumMinimumOptions.py @@ -14,6 +14,10 @@ def GetRootAsMaximumMinimumOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def MaximumMinimumOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # MaximumMinimumOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/Metadata.py b/utensor_cgen/third_party/tflite/Metadata.py index 273e51ee..bd2bcfd2 100644 --- a/utensor_cgen/third_party/tflite/Metadata.py +++ b/utensor_cgen/third_party/tflite/Metadata.py @@ -14,6 +14,10 @@ def GetRootAsMetadata(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def MetadataBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # Metadata def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/MirrorPadOptions.py b/utensor_cgen/third_party/tflite/MirrorPadOptions.py index 254ae217..fa408556 100644 --- a/utensor_cgen/third_party/tflite/MirrorPadOptions.py +++ b/utensor_cgen/third_party/tflite/MirrorPadOptions.py @@ -14,6 +14,10 @@ def GetRootAsMirrorPadOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def MirrorPadOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # MirrorPadOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/Model.py b/utensor_cgen/third_party/tflite/Model.py index cc9991ba..e9a57966 100644 --- a/utensor_cgen/third_party/tflite/Model.py +++ b/utensor_cgen/third_party/tflite/Model.py @@ -14,6 +14,10 @@ def GetRootAsModel(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def ModelBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # Model def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/MulOptions.py b/utensor_cgen/third_party/tflite/MulOptions.py index 55b9506f..5b53c4bb 100644 --- a/utensor_cgen/third_party/tflite/MulOptions.py +++ b/utensor_cgen/third_party/tflite/MulOptions.py @@ -14,6 +14,10 @@ def GetRootAsMulOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def MulOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # MulOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/NegOptions.py b/utensor_cgen/third_party/tflite/NegOptions.py index 05d55c26..9cf217cc 100644 --- a/utensor_cgen/third_party/tflite/NegOptions.py +++ b/utensor_cgen/third_party/tflite/NegOptions.py @@ -14,6 +14,10 @@ def GetRootAsNegOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def NegOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # NegOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/NonMaxSuppressionV4Options.py b/utensor_cgen/third_party/tflite/NonMaxSuppressionV4Options.py index 6ad10a2e..4764bc00 100644 --- a/utensor_cgen/third_party/tflite/NonMaxSuppressionV4Options.py +++ b/utensor_cgen/third_party/tflite/NonMaxSuppressionV4Options.py @@ -14,6 +14,10 @@ def GetRootAsNonMaxSuppressionV4Options(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def NonMaxSuppressionV4OptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # NonMaxSuppressionV4Options def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/NonMaxSuppressionV5Options.py b/utensor_cgen/third_party/tflite/NonMaxSuppressionV5Options.py index 99cbdbbf..ce84c863 100644 --- a/utensor_cgen/third_party/tflite/NonMaxSuppressionV5Options.py +++ b/utensor_cgen/third_party/tflite/NonMaxSuppressionV5Options.py @@ -14,6 +14,10 @@ def GetRootAsNonMaxSuppressionV5Options(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def NonMaxSuppressionV5OptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # NonMaxSuppressionV5Options def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/NotEqualOptions.py b/utensor_cgen/third_party/tflite/NotEqualOptions.py index 4c511e93..b99152df 100644 --- a/utensor_cgen/third_party/tflite/NotEqualOptions.py +++ b/utensor_cgen/third_party/tflite/NotEqualOptions.py @@ -14,6 +14,10 @@ def GetRootAsNotEqualOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def NotEqualOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # NotEqualOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/OneHotOptions.py b/utensor_cgen/third_party/tflite/OneHotOptions.py index 793a3e75..7bc9ba36 100644 --- a/utensor_cgen/third_party/tflite/OneHotOptions.py +++ b/utensor_cgen/third_party/tflite/OneHotOptions.py @@ -14,6 +14,10 @@ def GetRootAsOneHotOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def OneHotOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # OneHotOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/Operator.py b/utensor_cgen/third_party/tflite/Operator.py index cbae3dab..f10fdf4f 100644 --- a/utensor_cgen/third_party/tflite/Operator.py +++ b/utensor_cgen/third_party/tflite/Operator.py @@ -14,6 +14,10 @@ def GetRootAsOperator(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def OperatorBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # Operator def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/OperatorCode.py b/utensor_cgen/third_party/tflite/OperatorCode.py index dd525f53..9d87429a 100644 --- a/utensor_cgen/third_party/tflite/OperatorCode.py +++ b/utensor_cgen/third_party/tflite/OperatorCode.py @@ -14,6 +14,10 @@ def GetRootAsOperatorCode(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def OperatorCodeBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # OperatorCode def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/PackOptions.py b/utensor_cgen/third_party/tflite/PackOptions.py index 6a8ee2bb..2d590c52 100644 --- a/utensor_cgen/third_party/tflite/PackOptions.py +++ b/utensor_cgen/third_party/tflite/PackOptions.py @@ -14,6 +14,10 @@ def GetRootAsPackOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def PackOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # PackOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/PadOptions.py b/utensor_cgen/third_party/tflite/PadOptions.py index d0833c68..58d33e54 100644 --- a/utensor_cgen/third_party/tflite/PadOptions.py +++ b/utensor_cgen/third_party/tflite/PadOptions.py @@ -14,6 +14,10 @@ def GetRootAsPadOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def PadOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # PadOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/PadV2Options.py b/utensor_cgen/third_party/tflite/PadV2Options.py index 5ea0d70c..2b81bdd5 100644 --- a/utensor_cgen/third_party/tflite/PadV2Options.py +++ b/utensor_cgen/third_party/tflite/PadV2Options.py @@ -14,6 +14,10 @@ def GetRootAsPadV2Options(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def PadV2OptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # PadV2Options def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/Pool2DOptions.py b/utensor_cgen/third_party/tflite/Pool2DOptions.py index b8b9f178..031629bb 100644 --- a/utensor_cgen/third_party/tflite/Pool2DOptions.py +++ b/utensor_cgen/third_party/tflite/Pool2DOptions.py @@ -14,6 +14,10 @@ def GetRootAsPool2DOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def Pool2DOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # Pool2DOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/PowOptions.py b/utensor_cgen/third_party/tflite/PowOptions.py index 666ca488..b9c9f848 100644 --- a/utensor_cgen/third_party/tflite/PowOptions.py +++ b/utensor_cgen/third_party/tflite/PowOptions.py @@ -14,6 +14,10 @@ def GetRootAsPowOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def PowOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # PowOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/QuantizationParameters.py b/utensor_cgen/third_party/tflite/QuantizationParameters.py index fcd686cf..66ec8ac2 100644 --- a/utensor_cgen/third_party/tflite/QuantizationParameters.py +++ b/utensor_cgen/third_party/tflite/QuantizationParameters.py @@ -14,6 +14,10 @@ def GetRootAsQuantizationParameters(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def QuantizationParametersBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # QuantizationParameters def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/QuantizeOptions.py b/utensor_cgen/third_party/tflite/QuantizeOptions.py index 28af8cc9..12135988 100644 --- a/utensor_cgen/third_party/tflite/QuantizeOptions.py +++ b/utensor_cgen/third_party/tflite/QuantizeOptions.py @@ -14,6 +14,10 @@ def GetRootAsQuantizeOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def QuantizeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # QuantizeOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/RNNOptions.py b/utensor_cgen/third_party/tflite/RNNOptions.py index 3cfdb6af..9b493386 100644 --- a/utensor_cgen/third_party/tflite/RNNOptions.py +++ b/utensor_cgen/third_party/tflite/RNNOptions.py @@ -14,6 +14,10 @@ def GetRootAsRNNOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def RNNOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # RNNOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/RangeOptions.py b/utensor_cgen/third_party/tflite/RangeOptions.py index cb705b57..dfb3b6b2 100644 --- a/utensor_cgen/third_party/tflite/RangeOptions.py +++ b/utensor_cgen/third_party/tflite/RangeOptions.py @@ -14,6 +14,10 @@ def GetRootAsRangeOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def RangeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # RangeOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/RankOptions.py b/utensor_cgen/third_party/tflite/RankOptions.py index 4e4a5ecd..808d0c0b 100644 --- a/utensor_cgen/third_party/tflite/RankOptions.py +++ b/utensor_cgen/third_party/tflite/RankOptions.py @@ -14,6 +14,10 @@ def GetRootAsRankOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def RankOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # RankOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/ReducerOptions.py b/utensor_cgen/third_party/tflite/ReducerOptions.py index 93bbde17..bc9c399d 100644 --- a/utensor_cgen/third_party/tflite/ReducerOptions.py +++ b/utensor_cgen/third_party/tflite/ReducerOptions.py @@ -14,6 +14,10 @@ def GetRootAsReducerOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def ReducerOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # ReducerOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/ReshapeOptions.py b/utensor_cgen/third_party/tflite/ReshapeOptions.py index 157d45d9..d942ec5d 100644 --- a/utensor_cgen/third_party/tflite/ReshapeOptions.py +++ b/utensor_cgen/third_party/tflite/ReshapeOptions.py @@ -14,6 +14,10 @@ def GetRootAsReshapeOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def ReshapeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # ReshapeOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/ResizeBilinearOptions.py b/utensor_cgen/third_party/tflite/ResizeBilinearOptions.py index 0d1f9624..cbc7f73e 100644 --- a/utensor_cgen/third_party/tflite/ResizeBilinearOptions.py +++ b/utensor_cgen/third_party/tflite/ResizeBilinearOptions.py @@ -14,6 +14,10 @@ def GetRootAsResizeBilinearOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def ResizeBilinearOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # ResizeBilinearOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/ResizeNearestNeighborOptions.py b/utensor_cgen/third_party/tflite/ResizeNearestNeighborOptions.py index 4b166e95..38e24a0a 100644 --- a/utensor_cgen/third_party/tflite/ResizeNearestNeighborOptions.py +++ b/utensor_cgen/third_party/tflite/ResizeNearestNeighborOptions.py @@ -14,6 +14,10 @@ def GetRootAsResizeNearestNeighborOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def ResizeNearestNeighborOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # ResizeNearestNeighborOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/ReverseSequenceOptions.py b/utensor_cgen/third_party/tflite/ReverseSequenceOptions.py index cbaf96db..525c5980 100644 --- a/utensor_cgen/third_party/tflite/ReverseSequenceOptions.py +++ b/utensor_cgen/third_party/tflite/ReverseSequenceOptions.py @@ -14,6 +14,10 @@ def GetRootAsReverseSequenceOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def ReverseSequenceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # ReverseSequenceOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/ReverseV2Options.py b/utensor_cgen/third_party/tflite/ReverseV2Options.py index dbac9362..3168fdc8 100644 --- a/utensor_cgen/third_party/tflite/ReverseV2Options.py +++ b/utensor_cgen/third_party/tflite/ReverseV2Options.py @@ -14,6 +14,10 @@ def GetRootAsReverseV2Options(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def ReverseV2OptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # ReverseV2Options def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/SVDFOptions.py b/utensor_cgen/third_party/tflite/SVDFOptions.py index 6f391db1..ff1d6727 100644 --- a/utensor_cgen/third_party/tflite/SVDFOptions.py +++ b/utensor_cgen/third_party/tflite/SVDFOptions.py @@ -14,6 +14,10 @@ def GetRootAsSVDFOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def SVDFOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # SVDFOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/SelectOptions.py b/utensor_cgen/third_party/tflite/SelectOptions.py index d67daf36..d0f158da 100644 --- a/utensor_cgen/third_party/tflite/SelectOptions.py +++ b/utensor_cgen/third_party/tflite/SelectOptions.py @@ -14,6 +14,10 @@ def GetRootAsSelectOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def SelectOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # SelectOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/SequenceRNNOptions.py b/utensor_cgen/third_party/tflite/SequenceRNNOptions.py index 74a4954a..701cc80f 100644 --- a/utensor_cgen/third_party/tflite/SequenceRNNOptions.py +++ b/utensor_cgen/third_party/tflite/SequenceRNNOptions.py @@ -14,6 +14,10 @@ def GetRootAsSequenceRNNOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def SequenceRNNOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # SequenceRNNOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/ShapeOptions.py b/utensor_cgen/third_party/tflite/ShapeOptions.py index 2d24c05f..fa8256b8 100644 --- a/utensor_cgen/third_party/tflite/ShapeOptions.py +++ b/utensor_cgen/third_party/tflite/ShapeOptions.py @@ -14,6 +14,10 @@ def GetRootAsShapeOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def ShapeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # ShapeOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/SkipGramOptions.py b/utensor_cgen/third_party/tflite/SkipGramOptions.py index 0e8bdc1d..5657db54 100644 --- a/utensor_cgen/third_party/tflite/SkipGramOptions.py +++ b/utensor_cgen/third_party/tflite/SkipGramOptions.py @@ -14,6 +14,10 @@ def GetRootAsSkipGramOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def SkipGramOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # SkipGramOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/SliceOptions.py b/utensor_cgen/third_party/tflite/SliceOptions.py index 4b41568d..fbc01a9d 100644 --- a/utensor_cgen/third_party/tflite/SliceOptions.py +++ b/utensor_cgen/third_party/tflite/SliceOptions.py @@ -14,6 +14,10 @@ def GetRootAsSliceOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def SliceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # SliceOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/SoftmaxOptions.py b/utensor_cgen/third_party/tflite/SoftmaxOptions.py index a7168534..b5304f44 100644 --- a/utensor_cgen/third_party/tflite/SoftmaxOptions.py +++ b/utensor_cgen/third_party/tflite/SoftmaxOptions.py @@ -14,6 +14,10 @@ def GetRootAsSoftmaxOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def SoftmaxOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # SoftmaxOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/SpaceToBatchNDOptions.py b/utensor_cgen/third_party/tflite/SpaceToBatchNDOptions.py index b61ef96f..44fc19f0 100644 --- a/utensor_cgen/third_party/tflite/SpaceToBatchNDOptions.py +++ b/utensor_cgen/third_party/tflite/SpaceToBatchNDOptions.py @@ -14,6 +14,10 @@ def GetRootAsSpaceToBatchNDOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def SpaceToBatchNDOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # SpaceToBatchNDOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/SpaceToDepthOptions.py b/utensor_cgen/third_party/tflite/SpaceToDepthOptions.py index d571174a..32c05480 100644 --- a/utensor_cgen/third_party/tflite/SpaceToDepthOptions.py +++ b/utensor_cgen/third_party/tflite/SpaceToDepthOptions.py @@ -14,6 +14,10 @@ def GetRootAsSpaceToDepthOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def SpaceToDepthOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # SpaceToDepthOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/SparseToDenseOptions.py b/utensor_cgen/third_party/tflite/SparseToDenseOptions.py index 826eee08..3653a320 100644 --- a/utensor_cgen/third_party/tflite/SparseToDenseOptions.py +++ b/utensor_cgen/third_party/tflite/SparseToDenseOptions.py @@ -14,6 +14,10 @@ def GetRootAsSparseToDenseOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def SparseToDenseOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # SparseToDenseOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/SplitOptions.py b/utensor_cgen/third_party/tflite/SplitOptions.py index 3207525b..5f2bbcf7 100644 --- a/utensor_cgen/third_party/tflite/SplitOptions.py +++ b/utensor_cgen/third_party/tflite/SplitOptions.py @@ -14,6 +14,10 @@ def GetRootAsSplitOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def SplitOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # SplitOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/SplitVOptions.py b/utensor_cgen/third_party/tflite/SplitVOptions.py index 418959de..bceaf9ab 100644 --- a/utensor_cgen/third_party/tflite/SplitVOptions.py +++ b/utensor_cgen/third_party/tflite/SplitVOptions.py @@ -14,6 +14,10 @@ def GetRootAsSplitVOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def SplitVOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # SplitVOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/SquareOptions.py b/utensor_cgen/third_party/tflite/SquareOptions.py index 56633f6a..3a3efabd 100644 --- a/utensor_cgen/third_party/tflite/SquareOptions.py +++ b/utensor_cgen/third_party/tflite/SquareOptions.py @@ -14,6 +14,10 @@ def GetRootAsSquareOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def SquareOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # SquareOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/SquaredDifferenceOptions.py b/utensor_cgen/third_party/tflite/SquaredDifferenceOptions.py index 906855d1..90b53260 100644 --- a/utensor_cgen/third_party/tflite/SquaredDifferenceOptions.py +++ b/utensor_cgen/third_party/tflite/SquaredDifferenceOptions.py @@ -14,6 +14,10 @@ def GetRootAsSquaredDifferenceOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def SquaredDifferenceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # SquaredDifferenceOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/SqueezeOptions.py b/utensor_cgen/third_party/tflite/SqueezeOptions.py index 25b294dc..443ca9b3 100644 --- a/utensor_cgen/third_party/tflite/SqueezeOptions.py +++ b/utensor_cgen/third_party/tflite/SqueezeOptions.py @@ -14,6 +14,10 @@ def GetRootAsSqueezeOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def SqueezeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # SqueezeOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/StridedSliceOptions.py b/utensor_cgen/third_party/tflite/StridedSliceOptions.py index 3bbb36b8..65edbe71 100644 --- a/utensor_cgen/third_party/tflite/StridedSliceOptions.py +++ b/utensor_cgen/third_party/tflite/StridedSliceOptions.py @@ -14,6 +14,10 @@ def GetRootAsStridedSliceOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def StridedSliceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # StridedSliceOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/SubGraph.py b/utensor_cgen/third_party/tflite/SubGraph.py index eaa42fac..7e12f246 100644 --- a/utensor_cgen/third_party/tflite/SubGraph.py +++ b/utensor_cgen/third_party/tflite/SubGraph.py @@ -14,6 +14,10 @@ def GetRootAsSubGraph(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def SubGraphBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # SubGraph def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/SubOptions.py b/utensor_cgen/third_party/tflite/SubOptions.py index eccd7aba..a0d84e74 100644 --- a/utensor_cgen/third_party/tflite/SubOptions.py +++ b/utensor_cgen/third_party/tflite/SubOptions.py @@ -14,6 +14,10 @@ def GetRootAsSubOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def SubOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # SubOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/Tensor.py b/utensor_cgen/third_party/tflite/Tensor.py index 9b5b8f87..12cccf6e 100644 --- a/utensor_cgen/third_party/tflite/Tensor.py +++ b/utensor_cgen/third_party/tflite/Tensor.py @@ -14,6 +14,10 @@ def GetRootAsTensor(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def TensorBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # Tensor def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/TileOptions.py b/utensor_cgen/third_party/tflite/TileOptions.py index ec8396dc..a17473ee 100644 --- a/utensor_cgen/third_party/tflite/TileOptions.py +++ b/utensor_cgen/third_party/tflite/TileOptions.py @@ -14,6 +14,10 @@ def GetRootAsTileOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def TileOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # TileOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/TopKV2Options.py b/utensor_cgen/third_party/tflite/TopKV2Options.py index ccd51033..5f65915b 100644 --- a/utensor_cgen/third_party/tflite/TopKV2Options.py +++ b/utensor_cgen/third_party/tflite/TopKV2Options.py @@ -14,6 +14,10 @@ def GetRootAsTopKV2Options(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def TopKV2OptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # TopKV2Options def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/TransposeConvOptions.py b/utensor_cgen/third_party/tflite/TransposeConvOptions.py index 423571c8..83a71b8b 100644 --- a/utensor_cgen/third_party/tflite/TransposeConvOptions.py +++ b/utensor_cgen/third_party/tflite/TransposeConvOptions.py @@ -14,6 +14,10 @@ def GetRootAsTransposeConvOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def TransposeConvOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # TransposeConvOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/TransposeOptions.py b/utensor_cgen/third_party/tflite/TransposeOptions.py index 42c596d9..64b04425 100644 --- a/utensor_cgen/third_party/tflite/TransposeOptions.py +++ b/utensor_cgen/third_party/tflite/TransposeOptions.py @@ -14,6 +14,10 @@ def GetRootAsTransposeOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def TransposeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # TransposeOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/UnidirectionalSequenceLSTMOptions.py b/utensor_cgen/third_party/tflite/UnidirectionalSequenceLSTMOptions.py index 1b0c112c..610edaae 100644 --- a/utensor_cgen/third_party/tflite/UnidirectionalSequenceLSTMOptions.py +++ b/utensor_cgen/third_party/tflite/UnidirectionalSequenceLSTMOptions.py @@ -14,6 +14,10 @@ def GetRootAsUnidirectionalSequenceLSTMOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def UnidirectionalSequenceLSTMOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # UnidirectionalSequenceLSTMOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/UniqueOptions.py b/utensor_cgen/third_party/tflite/UniqueOptions.py index 841c6977..40bd4f38 100644 --- a/utensor_cgen/third_party/tflite/UniqueOptions.py +++ b/utensor_cgen/third_party/tflite/UniqueOptions.py @@ -14,6 +14,10 @@ def GetRootAsUniqueOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def UniqueOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # UniqueOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/UnpackOptions.py b/utensor_cgen/third_party/tflite/UnpackOptions.py index eed40193..f90fbe30 100644 --- a/utensor_cgen/third_party/tflite/UnpackOptions.py +++ b/utensor_cgen/third_party/tflite/UnpackOptions.py @@ -14,6 +14,10 @@ def GetRootAsUnpackOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def UnpackOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # UnpackOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/WhereOptions.py b/utensor_cgen/third_party/tflite/WhereOptions.py index ab69f6aa..544b0028 100644 --- a/utensor_cgen/third_party/tflite/WhereOptions.py +++ b/utensor_cgen/third_party/tflite/WhereOptions.py @@ -14,6 +14,10 @@ def GetRootAsWhereOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def WhereOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # WhereOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/WhileOptions.py b/utensor_cgen/third_party/tflite/WhileOptions.py index 7d5a6dfa..9aa52a43 100644 --- a/utensor_cgen/third_party/tflite/WhileOptions.py +++ b/utensor_cgen/third_party/tflite/WhileOptions.py @@ -14,6 +14,10 @@ def GetRootAsWhileOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def WhileOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # WhileOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/third_party/tflite/ZerosLikeOptions.py b/utensor_cgen/third_party/tflite/ZerosLikeOptions.py index e6aa9639..5ed55e8a 100644 --- a/utensor_cgen/third_party/tflite/ZerosLikeOptions.py +++ b/utensor_cgen/third_party/tflite/ZerosLikeOptions.py @@ -14,6 +14,10 @@ def GetRootAsZerosLikeOptions(cls, buf, offset): x.Init(buf, n + offset) return x + @classmethod + def ZerosLikeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False): + return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed) + # ZerosLikeOptions def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) diff --git a/utensor_cgen/transformer/tflite_exporter.py b/utensor_cgen/transformer/tflite_exporter.py index 597975b0..6759750c 100644 --- a/utensor_cgen/transformer/tflite_exporter.py +++ b/utensor_cgen/transformer/tflite_exporter.py @@ -250,7 +250,7 @@ def add_Op(self, op_info): tflite.Operator.OperatorAddInputs(self.fbuilder, op_inputs) tflite.Operator.OperatorAddOutputs(self.fbuilder, op_outputs) tflite.Operator.OperatorAddBuiltinOptionsType(self.fbuilder, builtin_opt_type) - #tflite.Operator.OperatorAddBuiltinOptions(self.fbuilder, builtin_opt) + tflite.Operator.OperatorAddBuiltinOptions(self.fbuilder, builtin_opt) op = tflite.Operator.OperatorEnd(self.fbuilder) return op #to be added into SubGraphStartOperatorsVector From 56bbd266d19712f589ef0b738918c21feccca83f Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Thu, 28 Nov 2019 18:53:29 -0800 Subject: [PATCH 021/117] input1 tensor failed to print after invoke --- tests/tflm/tflite_export/conftest.py | 26 +++++++++++---------- tests/tflm/tflite_export/test_write.py | 10 ++++---- utensor_cgen/transformer/tflite_exporter.py | 13 +++++++++-- 3 files changed, 31 insertions(+), 18 deletions(-) diff --git a/tests/tflm/tflite_export/conftest.py b/tests/tflm/tflite_export/conftest.py index e798b3f4..adcd52bd 100644 --- a/tests/tflm/tflite_export/conftest.py +++ b/tests/tflm/tflite_export/conftest.py @@ -18,13 +18,14 @@ def simple_tflm_graph(): input_tensors = [], output_tensors = [] ) - weight_op.op_attr["value"] = np.array([1,2,3,4], dtype=np.int8) - weight_op.op_attr["shape"] = [4,1] + #weight_op.op_attr["value"] = np.array([1,2,3,4], dtype=np.int8) + weight_op.op_attr["value"] = np.array([10,20,30,40], dtype=np.float32) + weight_op.op_attr["shape"] = [1,4] weight = TensorInfo( name = "weight", op_name = "weight_const", - dtype = np.dtype("int8"), + dtype = np.dtype("float32"), shape = weight_op.op_attr["shape"], ugraph = ugraph ) @@ -39,14 +40,14 @@ def simple_tflm_graph(): input_tensors = [], output_tensors = [] ) - mock_input_op.op_attr["value"] = np.array([[1.0],[2.0],[3.0],[4.0]], dtype=np.float32) - mock_input_op.op_attr["shape"] = [1,4] + mock_input_op.op_attr["value"] = np.array([[2],[4],[6],[8]], dtype=np.float32) + mock_input_op.op_attr["shape"] = [4,1] input1 = TensorInfo( - name = "input", + name = "input1", op_name = "mock_input_const", dtype = mock_input_op.op_attr["value"].dtype, - shape = [1, 4], + shape = mock_input_op.op_attr["shape"], ugraph = ugraph ) @@ -61,13 +62,14 @@ def simple_tflm_graph(): input_tensors = [], output_tensors = [] ) - bias_op.op_attr["value"] = np.array([1], dtype=np.int8) + #bias_op.op_attr["value"] = np.array([1], dtype=np.int8) + bias_op.op_attr["value"] = np.array([7], dtype=np.float32) bias_op.op_attr["shape"] = [1] bias = TensorInfo( name = "bias", op_name = "bias_const", - dtype = np.dtype("int8"), + dtype = np.dtype("float32"), shape = bias_op.op_attr["shape"], ugraph = ugraph ) @@ -87,8 +89,8 @@ def simple_tflm_graph(): output = TensorInfo( name = "output", op_name = "FC1", - dtype = np.dtype("float"), - shape = [1, 1], + dtype = np.dtype("float32"), + shape = [1], ugraph = ugraph ) @@ -103,4 +105,4 @@ def simple_tflm_graph(): #ugraph = prune_graph(ugraph) #return: ugraph, input tensors, output tensors - return [ugraph, [], ["output"]] + return [ugraph, [], ["input1", "weight", "bias", "output"]] diff --git a/tests/tflm/tflite_export/test_write.py b/tests/tflm/tflite_export/test_write.py index efc039a6..4efe9f78 100644 --- a/tests/tflm/tflite_export/test_write.py +++ b/tests/tflm/tflite_export/test_write.py @@ -86,7 +86,7 @@ def test_tflite_fb_write(hybrid_quant_output): ugraph = exporter.transform(sample_ugraph) model_content = exporter.output() - print_tflite_graph(model_content) + #print_tflite_graph(model_content) # referece_model_content = open('/Users/neitan01/Documents/tflm/sinExample/sine_model.tflite', "rb").read() # print_tflite_graph(referece_model_content) @@ -94,11 +94,13 @@ def test_tflite_fb_write(hybrid_quant_output): open("tflm_test_model.tflite", "wb").write(model_content) test_model = tf.lite.Interpreter('tflm_test_model.tflite') test_model.allocate_tensors() - test_model_output_index = test_model.tensor(test_model.get_output_details()[0]["index"]) test_model.invoke() - output_content = test_model.get_tensor(test_model_output_index)[0] - print(output_content) + print(test_model.get_tensor_details()) + print("1 :", test_model.get_tensor(test_model.get_output_details()[0]["index"])) + print("2 :", test_model.get_tensor(test_model.get_output_details()[1]["index"])) + print("3 :", test_model.get_tensor(test_model.get_output_details()[2]["index"])) + print("out :", test_model.get_tensor(test_model.get_output_details()[3]["index"])) test_pass = True assert test_pass, 'error message here' diff --git a/utensor_cgen/transformer/tflite_exporter.py b/utensor_cgen/transformer/tflite_exporter.py index 6759750c..a0a0373b 100644 --- a/utensor_cgen/transformer/tflite_exporter.py +++ b/utensor_cgen/transformer/tflite_exporter.py @@ -38,6 +38,15 @@ def get_fullyconnected_builtin_option(fbuilder, op_info): return obj, BuiltinOptions.FullyConnectedOptions +def tensor_type_lookup(numpy_dtype): + TensorType = tflite.TensorType.TensorType + lookup_map = dict() + lookup_map[np.dtype('float32')] = TensorType.FLOAT32 + lookup_map[np.dtype('int8')] = TensorType.INT8 + + return lookup_map[numpy_dtype] + + class FlatbufferOpManager: op_list = list() code_name_lookup = {v: k for k, v in BuiltinOperator.__dict__.items()} @@ -193,7 +202,7 @@ def __create_static_tensor(self, ugraph): #tensor object tflite.Tensor.TensorStart(self.fbuilder) tflite.Tensor.TensorAddShape(self.fbuilder, shape_vec) - tflite.Tensor.TensorAddType(self.fbuilder, TensorType.INT8) #TODO: a conversion class here, out_dtype + tflite.Tensor.TensorAddType(self.fbuilder, tensor_type_lookup(out_dtype)) if export_tensor_name: tflite.Tensor.TensorAddName(self.fbuilder, tensor_name) tflite.Tensor.TensorAddQuantization(self.fbuilder, q_param) @@ -306,7 +315,7 @@ def __create_variable_tensors(self, ugraph): tflite.Tensor.TensorStart(self.fbuilder) tflite.Tensor.TensorAddShape(self.fbuilder, shape_vec) #tflite.Tensor.TensorAddType(self.fbuilder, TensorType.INT8) #TODO: tensor type conversion here - tflite.Tensor.TensorAddType(self.fbuilder, TensorType.FLOAT32) + tflite.Tensor.TensorAddType(self.fbuilder, tensor_type_lookup(tensor_info.dtype)) tflite.Tensor.TensorAddName(self.fbuilder, tensor_name) #tflite.Tensor.TensorAddQuantization(self.fbuilder, q_param) tflite.Tensor.TensorAddIsVariable(self.fbuilder, True) From acef9d0e4a330002e7dc62ac01b3d5a3fb7bffd0 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Thu, 28 Nov 2019 21:58:33 -0800 Subject: [PATCH 022/117] - a more comprehensive test graph - add op the first tensor still fails --- tests/tflm/tflite_export/conftest.py | 86 +++++++++++++++------ tests/tflm/tflite_export/test_write.py | 16 ++-- utensor_cgen/transformer/tflite_exporter.py | 12 ++- 3 files changed, 86 insertions(+), 28 deletions(-) diff --git a/tests/tflm/tflite_export/conftest.py b/tests/tflm/tflite_export/conftest.py index adcd52bd..d5d11dac 100644 --- a/tests/tflm/tflite_export/conftest.py +++ b/tests/tflm/tflite_export/conftest.py @@ -9,8 +9,8 @@ def simple_tflm_graph(): ugraph = uTensorGraph() - weight_op = OperationInfo( - name = "weight_const", + mock_input_op0 = OperationInfo( + name = "mock_input_const0", op_type = "Const", backend = "tensorflow", ugraph = ugraph, @@ -18,21 +18,21 @@ def simple_tflm_graph(): input_tensors = [], output_tensors = [] ) - #weight_op.op_attr["value"] = np.array([1,2,3,4], dtype=np.int8) - weight_op.op_attr["value"] = np.array([10,20,30,40], dtype=np.float32) - weight_op.op_attr["shape"] = [1,4] - - weight = TensorInfo( - name = "weight", - op_name = "weight_const", - dtype = np.dtype("float32"), - shape = weight_op.op_attr["shape"], + mock_input_op0.op_attr["value"] = np.array([[2],[4],[6],[8]], dtype=np.float32) + mock_input_op0.op_attr["shape"] = [4,1] + + input0 = TensorInfo( + name = "input0", + op_name = "mock_input_const0", + dtype = mock_input_op0.op_attr["value"].dtype, + shape = mock_input_op0.op_attr["shape"], ugraph = ugraph ) - weight_op.output_tensors = [weight] - mock_input_op = OperationInfo( - name = "mock_input_const", + mock_input_op0.output_tensors = [input0] + + mock_input1_op = OperationInfo( + name = "mock_input_const1", op_type = "Const", backend = "tensorflow", ugraph = ugraph, @@ -40,18 +40,60 @@ def simple_tflm_graph(): input_tensors = [], output_tensors = [] ) - mock_input_op.op_attr["value"] = np.array([[2],[4],[6],[8]], dtype=np.float32) - mock_input_op.op_attr["shape"] = [4,1] + mock_input1_op.op_attr["value"] = np.array([[2],[4],[6],[8]], dtype=np.float32) + mock_input1_op.op_attr["shape"] = [4,1] input1 = TensorInfo( name = "input1", - op_name = "mock_input_const", - dtype = mock_input_op.op_attr["value"].dtype, - shape = mock_input_op.op_attr["shape"], + op_name = "mock_input_const1", + dtype = mock_input1_op.op_attr["value"].dtype, + shape = mock_input1_op.op_attr["shape"], + ugraph = ugraph + ) + + mock_input1_op.output_tensors = [input1] + + add_output = TensorInfo( + name = "add_out", + op_name = "add0", + dtype = mock_input_op0.op_attr["value"].dtype, + shape = mock_input_op0.op_attr["shape"], ugraph = ugraph ) - mock_input_op.output_tensors = [input1] + add_op = OperationInfo( + name = "add0", + op_type = "ADD", + backend = "tensorflow", + ugraph = ugraph, + op_attr = dict(), + input_tensors = [input0, input1], + output_tensors = [add_output] + ) + + ugraph.ops_info["ADD0"] = add_op + + weight_op = OperationInfo( + name = "weight_const", + op_type = "Const", + backend = "tensorflow", + ugraph = ugraph, + op_attr = dict(), + input_tensors = [], + output_tensors = [] + ) + #weight_op.op_attr["value"] = np.array([1,2,3,4], dtype=np.int8) + weight_op.op_attr["value"] = np.array([10,20,30,40], dtype=np.float32) + weight_op.op_attr["shape"] = [1,4] + + weight = TensorInfo( + name = "weight", + op_name = "weight_const", + dtype = np.dtype("float32"), + shape = weight_op.op_attr["shape"], + ugraph = ugraph + ) + weight_op.output_tensors = [weight] bias_op = OperationInfo( name = "bias_const", @@ -94,7 +136,7 @@ def simple_tflm_graph(): ugraph = ugraph ) - fc1_op.input_tensors = [input1, weight, bias] + fc1_op.input_tensors = [add_output, weight, bias] fc1_op.output_tensors = [output] ugraph.ops_info["FC1"] = fc1_op @@ -105,4 +147,4 @@ def simple_tflm_graph(): #ugraph = prune_graph(ugraph) #return: ugraph, input tensors, output tensors - return [ugraph, [], ["input1", "weight", "bias", "output"]] + return [ugraph, ["input0", "input1"], ["weight", "bias", "output"]] diff --git a/tests/tflm/tflite_export/test_write.py b/tests/tflm/tflite_export/test_write.py index 4efe9f78..480cc5de 100644 --- a/tests/tflm/tflite_export/test_write.py +++ b/tests/tflm/tflite_export/test_write.py @@ -86,7 +86,7 @@ def test_tflite_fb_write(hybrid_quant_output): ugraph = exporter.transform(sample_ugraph) model_content = exporter.output() - #print_tflite_graph(model_content) + print_tflite_graph(model_content) # referece_model_content = open('/Users/neitan01/Documents/tflm/sinExample/sine_model.tflite', "rb").read() # print_tflite_graph(referece_model_content) @@ -96,11 +96,17 @@ def test_tflite_fb_write(hybrid_quant_output): test_model.allocate_tensors() test_model.invoke() + print("0 :", test_model.get_tensor(0)) + print("1 :", test_model.get_tensor(1)) + print("2 :", test_model.get_tensor(2)) + print("3 :", test_model.get_tensor(3)) + + print(test_model.get_tensor_details()) - print("1 :", test_model.get_tensor(test_model.get_output_details()[0]["index"])) - print("2 :", test_model.get_tensor(test_model.get_output_details()[1]["index"])) - print("3 :", test_model.get_tensor(test_model.get_output_details()[2]["index"])) - print("out :", test_model.get_tensor(test_model.get_output_details()[3]["index"])) + print("out0 :", test_model.get_tensor(test_model.get_output_details()[0]["index"])) + print("out1 :", test_model.get_tensor(test_model.get_output_details()[1]["index"])) + print("out2 :", test_model.get_tensor(test_model.get_output_details()[2]["index"])) + test_pass = True assert test_pass, 'error message here' diff --git a/utensor_cgen/transformer/tflite_exporter.py b/utensor_cgen/transformer/tflite_exporter.py index a0a0373b..664cb8b2 100644 --- a/utensor_cgen/transformer/tflite_exporter.py +++ b/utensor_cgen/transformer/tflite_exporter.py @@ -38,6 +38,15 @@ def get_fullyconnected_builtin_option(fbuilder, op_info): return obj, BuiltinOptions.FullyConnectedOptions +def get_add_builtin_option(fbuilder, op_info): + + tflite.AddOptions.AddOptionsStart(fbuilder) + #FIXME: node fusion and select an activation function here + tflite.AddOptions.AddOptionsAddFusedActivationFunction(fbuilder, ActivationFunctionType.NONE) + obj = tflite.AddOptions.AddOptionsEnd(fbuilder) + + return obj, BuiltinOptions.AddOptions + def tensor_type_lookup(numpy_dtype): TensorType = tflite.TensorType.TensorType lookup_map = dict() @@ -249,6 +258,7 @@ def add_Op(self, op_info): ##TODO: add op factory to deal with op options op_option_factory = dict() op_option_factory["FULLY_CONNECTED"] = get_fullyconnected_builtin_option + op_option_factory["ADD"] = get_add_builtin_option builtin_opt_func = op_option_factory[op_info.op_type] @@ -314,11 +324,11 @@ def __create_variable_tensors(self, ugraph): tflite.Tensor.TensorStart(self.fbuilder) tflite.Tensor.TensorAddShape(self.fbuilder, shape_vec) - #tflite.Tensor.TensorAddType(self.fbuilder, TensorType.INT8) #TODO: tensor type conversion here tflite.Tensor.TensorAddType(self.fbuilder, tensor_type_lookup(tensor_info.dtype)) tflite.Tensor.TensorAddName(self.fbuilder, tensor_name) #tflite.Tensor.TensorAddQuantization(self.fbuilder, q_param) tflite.Tensor.TensorAddIsVariable(self.fbuilder, True) + #tflite.Tensor.TensorAddIsVariable(self.fbuilder, False) #FIXME: TOCO outputs False here, no idea why self.tensor_index[tensor_info.name] = tflite.Tensor.TensorEnd(self.fbuilder) From 0e3c5f1b1346911cf3833f515b4faadbbad24131 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Thu, 28 Nov 2019 22:17:00 -0800 Subject: [PATCH 023/117] binding data to the first input tensor --- tests/tflm/tflite_export/test_write.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tests/tflm/tflite_export/test_write.py b/tests/tflm/tflite_export/test_write.py index 480cc5de..b905fb77 100644 --- a/tests/tflm/tflite_export/test_write.py +++ b/tests/tflm/tflite_export/test_write.py @@ -94,6 +94,8 @@ def test_tflite_fb_write(hybrid_quant_output): open("tflm_test_model.tflite", "wb").write(model_content) test_model = tf.lite.Interpreter('tflm_test_model.tflite') test_model.allocate_tensors() + input_data = np.array(np.ones([4,1]), dtype=np.float32) + test_model.set_tensor(test_model.get_input_details()[0]['index'], input_data) test_model.invoke() print("0 :", test_model.get_tensor(0)) From b16236b2ef1d09c9741e2251404a1409310efb09 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Thu, 28 Nov 2019 22:24:04 -0800 Subject: [PATCH 024/117] run test with "pytest -s tests/tflm" --- tests/tflm/tflite_export/test_write.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tests/tflm/tflite_export/test_write.py b/tests/tflm/tflite_export/test_write.py index b905fb77..c5930e71 100644 --- a/tests/tflm/tflite_export/test_write.py +++ b/tests/tflm/tflite_export/test_write.py @@ -110,5 +110,6 @@ def test_tflite_fb_write(hybrid_quant_output): print("out2 :", test_model.get_tensor(test_model.get_output_details()[2]["index"])) - test_pass = True - assert test_pass, 'error message here' + output = test_model.get_tensor(test_model.get_output_details()[2]["index"]) + + assert np.abs(output - 707) <= 0.0001, 'error is greater than 0.0001' From 3c60d92f706293b2672fcb0cd207ce042f2ccd9e Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Thu, 28 Nov 2019 23:02:47 -0800 Subject: [PATCH 025/117] clean up flatbuffers import --- tests/tflm/tflite_export/test_write.py | 3 +-- utensor_cgen/transformer/tflite_exporter.py | 1 - 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/tests/tflm/tflite_export/test_write.py b/tests/tflm/tflite_export/test_write.py index c5930e71..7fdaf3e2 100644 --- a/tests/tflm/tflite_export/test_write.py +++ b/tests/tflm/tflite_export/test_write.py @@ -5,9 +5,8 @@ from utensor_cgen.matcher import uTensorGraphMatcher from utensor_cgen.utils import prune_graph, topologic_order_graph from utensor_cgen.transformer import TFLiteExporter -import flatbuffers +import utensor_cgen.third_party.flatbuffers as flatbuffers import utensor_cgen.third_party.tflite as tflite -from utensor_cgen.third_party.tflite import * from utensor_cgen.third_party.tflite.BuiltinOperator import BuiltinOperator from utensor_cgen.third_party.tflite.Model import Model from utensor_cgen.third_party.tflite.BuiltinOptions import BuiltinOptions diff --git a/utensor_cgen/transformer/tflite_exporter.py b/utensor_cgen/transformer/tflite_exporter.py index 664cb8b2..2d21b303 100644 --- a/utensor_cgen/transformer/tflite_exporter.py +++ b/utensor_cgen/transformer/tflite_exporter.py @@ -9,7 +9,6 @@ from copy import deepcopy from utensor_cgen.ir import OperationInfo, uTensorGraph -#import flatbuffers import utensor_cgen.third_party.flatbuffers as flatbuffers import utensor_cgen.third_party.tflite as tflite from utensor_cgen.third_party.tflite import * From 5c1bf6050a45074bb9b8ae97f7b7fe03d9a46cad Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Thu, 5 Dec 2019 17:02:40 -0800 Subject: [PATCH 026/117] added flatbuffers to setup.py --- setup.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 37d7a061..63849a6b 100644 --- a/setup.py +++ b/setup.py @@ -37,7 +37,8 @@ 'torch', 'torchvision', 'onnx-tf==1.2.1', - 'graphviz' + 'graphviz', + 'flatbuffers' ], extras_require={ 'dev': ['pytest'] From f4b87e5b1bf470a5a176c7b833f5e5e63ad0e514 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Thu, 12 Dec 2019 18:33:47 +0800 Subject: [PATCH 027/117] updated this branch to use the new ugraph-constructor --- tests/tflm/tflite_export/conftest.py | 265 ++++++++++---------- utensor_cgen/transformer/tflite_exporter.py | 46 +++- 2 files changed, 171 insertions(+), 140 deletions(-) diff --git a/tests/tflm/tflite_export/conftest.py b/tests/tflm/tflite_export/conftest.py index d5d11dac..dd5bc005 100644 --- a/tests/tflm/tflite_export/conftest.py +++ b/tests/tflm/tflite_export/conftest.py @@ -3,148 +3,137 @@ import tensorflow as tf from utensor_cgen.ir import TensorInfo, OperationInfo, uTensorGraph +from utensor_cgen.ir.converter import (AttrValueConverter, DataTypeConverter, + GenericTensorConverterMixin) from utensor_cgen.utils import prune_graph, topologic_order_graph - -@fixture(name='hybrid_quant_output') -def simple_tflm_graph(): - ugraph = uTensorGraph() - - mock_input_op0 = OperationInfo( - name = "mock_input_const0", - op_type = "Const", - backend = "tensorflow", - ugraph = ugraph, - op_attr = dict(), - input_tensors = [], - output_tensors = [] - ) - mock_input_op0.op_attr["value"] = np.array([[2],[4],[6],[8]], dtype=np.float32) - mock_input_op0.op_attr["shape"] = [4,1] - - input0 = TensorInfo( - name = "input0", - op_name = "mock_input_const0", - dtype = mock_input_op0.op_attr["value"].dtype, - shape = mock_input_op0.op_attr["shape"], - ugraph = ugraph - ) - - mock_input_op0.output_tensors = [input0] - - mock_input1_op = OperationInfo( - name = "mock_input_const1", - op_type = "Const", - backend = "tensorflow", - ugraph = ugraph, - op_attr = dict(), - input_tensors = [], - output_tensors = [] - ) - mock_input1_op.op_attr["value"] = np.array([[2],[4],[6],[8]], dtype=np.float32) - mock_input1_op.op_attr["shape"] = [4,1] - - input1 = TensorInfo( - name = "input1", - op_name = "mock_input_const1", - dtype = mock_input1_op.op_attr["value"].dtype, - shape = mock_input1_op.op_attr["shape"], - ugraph = ugraph - ) - - mock_input1_op.output_tensors = [input1] - - add_output = TensorInfo( - name = "add_out", - op_name = "add0", - dtype = mock_input_op0.op_attr["value"].dtype, - shape = mock_input_op0.op_attr["shape"], - ugraph = ugraph - ) - - add_op = OperationInfo( - name = "add0", - op_type = "ADD", - backend = "tensorflow", - ugraph = ugraph, - op_attr = dict(), - input_tensors = [input0, input1], - output_tensors = [add_output] +from utensor_cgen.backend.operators import OperatorFactory, _Operator +from utensor_cgen.matcher import OpEqualityDelegate, _morphism + + +@OperatorFactory.register +@OpEqualityDelegate.is_associative( + permutations=((0, 1), (1, 0)) +) +class _TFLM_AddOperator(_Operator): + + op_type = "TFLM_ADD" # tf op type + + def __init__(self, op_info, **kwargs): + _Operator.__init__(self) + inputs = [tensor_info.name for tensor_info in op_info.input_tensors] + output = op_info.output_tensors[0].name + tf_dtype = op_info.input_tensors[0].dtype + + @classmethod + def build_op_info(cls, ugraph, name, tensor_x, tensor_y, **kwargs): + # broadcast the shape and promote types + dummy_x = np.empty(tensor_x.shape) + dummy_y = np.empty(tensor_y.shape) + output_shape = np.broadcast(dummy_x, dummy_y).shape + output_dtype = np.promote_types(tensor_x.dtype, tensor_y.dtype) + return OperationInfo( + name=name, + input_tensors=[tensor_x, tensor_y], + output_tensors=[ + TensorInfo( + name='{}:0'.format(name), + op_name=name, + dtype=output_dtype, + shape=list(output_shape), + ugraph=ugraph + ) + ], + op_type=cls.op_type, + op_attr={ + 'T': AttrValueConverter.__utensor_generic_type__( + value_name='type', + value=DataTypeConverter.get_tf_value(output_dtype) + ) + }, + ugraph=ugraph, + backend=kwargs.get('backend', 'TFLM') ) - ugraph.ops_info["ADD0"] = add_op - weight_op = OperationInfo( - name = "weight_const", - op_type = "Const", - backend = "tensorflow", - ugraph = ugraph, - op_attr = dict(), - input_tensors = [], - output_tensors = [] +@OperatorFactory.register +class _TFLM_FULLY_CONNECTED_Operator(_Operator): + + op_type="TFLM_FULLY_CONNECTED" + + def __init__(self, op_info, **kwargs): + _Operator.__init__(self) + inputs = [tensor_info.name for tensor_info in op_info.input_tensors] + output = op_info.output_tensors[0].name + out_dtype = op_info.output_tensors[0].dtype + in_dtypes = [tensor_info.dtype for tensor_info in op_info.input_tensors] + #assert (op_info.input_tensors[0].shape[1] == None or op_info.input_tensors[0].shape[1] == 1) + + @classmethod + def build_op_info(cls, ugraph, name, tensor_x, tensor_w, tensor_b, **kwargs): + output_shape = [tensor_w.shape[0], tensor_x.shape[1]] + #output_dtype = np.promote_types(tensor_x.dtype, tensor_y.dtype) + output_dtype = tensor_x.dtype + return OperationInfo( + name=name, + input_tensors=[tensor_x, tensor_w, tensor_b], + output_tensors=[ + TensorInfo( + name='{}:0'.format(name), + op_name=name, + dtype=output_dtype, + shape=list(output_shape), + ugraph=ugraph + ) + ], + op_type=cls.op_type, + op_attr={ + 'T': AttrValueConverter.__utensor_generic_type__( + value_name='type', + value=DataTypeConverter.get_tf_value(output_dtype) + ) + }, + ugraph=ugraph, + backend=kwargs.get('backend', 'TFLM') ) - #weight_op.op_attr["value"] = np.array([1,2,3,4], dtype=np.int8) - weight_op.op_attr["value"] = np.array([10,20,30,40], dtype=np.float32) - weight_op.op_attr["shape"] = [1,4] - weight = TensorInfo( - name = "weight", - op_name = "weight_const", - dtype = np.dtype("float32"), - shape = weight_op.op_attr["shape"], - ugraph = ugraph - ) - weight_op.output_tensors = [weight] - - bias_op = OperationInfo( - name = "bias_const", - op_type = "Const", - backend = "tensorflow", - ugraph = ugraph, - op_attr = dict(), - input_tensors = [], - output_tensors = [] - ) - #bias_op.op_attr["value"] = np.array([1], dtype=np.int8) - bias_op.op_attr["value"] = np.array([7], dtype=np.float32) - bias_op.op_attr["shape"] = [1] - - bias = TensorInfo( - name = "bias", - op_name = "bias_const", - dtype = np.dtype("float32"), - shape = bias_op.op_attr["shape"], - ugraph = ugraph - ) - bias_op.output_tensors = [bias] - - - fc1_op = OperationInfo( - name = "FC1", - op_type = "FULLY_CONNECTED", - backend = "tensorflow", - ugraph = ugraph, - op_attr = dict(), - input_tensors = [], - output_tensors = [] - ) - - output = TensorInfo( - name = "output", - op_name = "FC1", - dtype = np.dtype("float32"), - shape = [1], - ugraph = ugraph - ) - - fc1_op.input_tensors = [add_output, weight, bias] - fc1_op.output_tensors = [output] - - ugraph.ops_info["FC1"] = fc1_op - ugraph.output_nodes = ["FC1"] - #ugraph.backend = "tensorflow" - - topologic_order_graph(ugraph) - #ugraph = prune_graph(ugraph) +@fixture(name='hybrid_quant_output') +def simple_tflm_graph(): + ugraph = uTensorGraph() - #return: ugraph, input tensors, output tensors - return [ugraph, ["input0", "input1"], ["weight", "bias", "output"]] + with ugraph.begin_construction(): + tensor_x0, = ugraph.add_op( + op_type='Const', + name='x0', + value=np.array([1, 1, 1, 1], dtype=np.float32)[:, np.newaxis] + ) + tensor_x1, = ugraph.add_op( + op_type='Const', + name='x1', + value=np.array([2, 4, 6, 8], dtype=np.float32)[:, np.newaxis] + ) + tensor_w, = ugraph.add_op( + op_type='Const', + name='w', + value=np.array([10, 20, 30, 40], dtype=np.float32)[np.newaxis, :] + ) + tensor_b, = ugraph.add_op( + op_type='Const', + name='b', + value=np.array([7], dtype=np.float32) + ) + + + tensor_addout, = ugraph.add_op( + tensor_x0, tensor_x1, + op_type='TFLM_ADD', + name='TFLM_ADD0' + ) + + tensor_out, = ugraph.add_op( + tensor_addout, tensor_w, tensor_b, + op_type='TFLM_FULLY_CONNECTED', + name='TFLM_FULLY_CONNECTED00', + is_output=True + ) + + return [ugraph, ["x0:0", "x1:0"], ["w:0", "b:0", tensor_out.name]] diff --git a/utensor_cgen/transformer/tflite_exporter.py b/utensor_cgen/transformer/tflite_exporter.py index 2d21b303..0277f010 100644 --- a/utensor_cgen/transformer/tflite_exporter.py +++ b/utensor_cgen/transformer/tflite_exporter.py @@ -23,6 +23,42 @@ __all__ = ["TFLiteExporter"] +def rename_ugraph_ops_(ugraph, name_old, name_new): + op = ugraph.ops_info.pop(name_old) + op.name = name_new + for out_tensor in op.output_tensors: + pass # renaming tensors + for out_node in op.output_nodes: + for in_tensor in out_node.input_tensors: + if in_tensor.op_name == name_old: + in_tensor.op_name = name_new + ugraph.ops_info[name_new] = op + for i, op_name in enumerate(ugraph.topo_order): + if op_name == name_old: + ugraph.topo_order[i] = name_new + break + for i, op_name in enumerate(ugraph.output_nodes): + if op_name == name_old: + ugraph.output_nodes[i] = name_new + break + + op.name = name_new + for out_tensor in op.output_tensors: + pass # renaming tensors + for out_node in op.output_nodes: + for in_tensor in out_node.input_tensors: + if in_tensor.op_name == name_old: + in_tensor.op_name = name_new + ugraph.ops_info[name_new] = op + for i, op_name in enumerate(ugraph.topo_order): + if op_name == name_old: + ugraph.topo_order[i] = name_new + break + for i, op_name in enumerate(ugraph.output_nodes): + if op_name == name_old: + ugraph.output_nodes[i] = name_new + break + def get_fullyconnected_builtin_option(fbuilder, op_info): weight_format = tflite.FullyConnectedOptionsWeightsFormat.FullyConnectedOptionsWeightsFormat.DEFAULT @@ -110,6 +146,7 @@ def __init__(self, input_tensors, output_tensors): def transform(self, ugraph): + self.__tflm_graph_legalize_(ugraph) # create tensor data buffer # create const tensors # update tensor_index @@ -184,7 +221,7 @@ def __create_static_tensor(self, ugraph): out_tensor_info.shape) #weight #value = op_info.op_attr['value'].value.np_array.flatten() #TODO: specify endianness here - value = op_info.op_attr['value'].flatten() #FIXME: deal with the proper attribute type here + value = op_info.op_attr['value'].value.np_array.flatten() #FIXME: deal with the proper attribute type here raw_data = value.tobytes() data_vec = self.__fb_vector(tflite.Buffer.BufferStartDataVector, raw_data) @@ -284,7 +321,7 @@ def __sym_quantization_info(self, op_info): """ #values = op_info.op_attr['value'].value.np_array.flatten() - values = op_info.op_attr['value'].flatten() #FIXME: deal with the proper attribute type here + values = op_info.op_attr['value'].value.np_array.flatten() #FIXME: deal with the proper attribute type here abs_max = np.absolute(values).max() #based on quantizted int8 dtype scale = 127 / abs_max @@ -366,5 +403,10 @@ def __fb_vector(self, vector_constructor, arr_list): return self.fbuilder.EndVector(num_items) + def __tflm_graph_legalize_(self, ugraph): + for _, op in ugraph.ops_info.items(): + assert op.op_type.startswith("TFLM_") or op.op_type in self.static_op_types + #TODO: strip the leading TFLM_ only + op.op_type = op.op_type.replace("TFLM_", '') # How to construct the quantization parameter for intermediate tensors? ## zero point and scale From ab6940ad54594f8d34de20164f85908ed21fd0df Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sat, 22 Feb 2020 16:15:30 +0800 Subject: [PATCH 028/117] - onnx frontend build ugraph done (missing shape and dtype) - onnx code generation --- utensor_cgen/backend/utensor/_graph_lower.py | 40 ++-- utensor_cgen/cli.py | 13 +- utensor_cgen/frontend/__init__.py | 2 + utensor_cgen/frontend/onnx.py | 233 ++++++++++++++++++- utensor_cgen/frontend/tensorflow.py | 15 +- utensor_cgen/ir/base.py | 2 +- utensor_cgen/legalizer/__init__.py | 2 + utensor_cgen/legalizer/onnx.py | 53 +++++ utensor_cgen/transformer/linear_reoder.py | 5 +- utensor_cgen/utils.py | 4 - 10 files changed, 321 insertions(+), 48 deletions(-) create mode 100644 utensor_cgen/legalizer/onnx.py diff --git a/utensor_cgen/backend/utensor/_graph_lower.py b/utensor_cgen/backend/utensor/_graph_lower.py index 8ba62a17..95f51b4e 100644 --- a/utensor_cgen/backend/utensor/_graph_lower.py +++ b/utensor_cgen/backend/utensor/_graph_lower.py @@ -1,22 +1,29 @@ +import logging from copy import deepcopy from utensor_cgen.backend.base import BackendPart from utensor_cgen.utils import class_property +logger = logging.getLogger(__name__) -class uTensorLegacyGraphLower(BackendPart): - +class uTensorGraphLowerBase(BackendPart): TARGET = 'utensor' - PART = 'legacy_graph_lower' + def handle_default(self, ugraph): + logger.warning('fall back to default graph lowering (do nothing)') + return ugraph + + def get_handler(self, ugraph): + handler = getattr(self, 'handle_{}'.format(ugraph.lib_name), self.handle_default) + return handler + def apply(self, ugraph): - handler = getattr(self, 'handle_{}'.format(ugraph.lib_name)) - if handler is None: - raise RuntimeError( - 'can not lower ugraph from {} to utensor'.format(ugraph.lib_name) - ) + handler = self.get_handler(ugraph) return handler(ugraph) +class uTensorLegacyGraphLower(uTensorGraphLowerBase): + PART = 'legacy_graph_lower' + def handle_tensorflow(self, ugraph): return ugraph @@ -25,11 +32,10 @@ def default_config(cls): return {} -class uTensorRearchGraphLower(BackendPart): - TARGET = 'utensor' +class uTensorRearchGraphLower(uTensorGraphLowerBase): PART = 'rearch_graph_lower' - class OptypRenameManager(object): + class OptypeRenameManager(object): NAME_MAP = { 'Add': 'AddOperator', 'Conv2D': 'ConvOperator', @@ -40,22 +46,12 @@ class OptypRenameManager(object): def get_new_optype(cls, op_type): return cls.NAME_MAP.get(op_type, op_type) - def apply(self, ugraph): - handler = getattr(self, 'handle_{}'.format(ugraph.lib_name)) - if handler is None: - raise RuntimeError( - 'can not lower ugraph from {} to utensor'.format(ugraph.lib_name) - ) - return handler(ugraph) - def handle_tensorflow(self, ugraph): new_ugraph = deepcopy(ugraph) for op_info in new_ugraph.ops_info.values(): - op_info.op_type = self.OptypRenameManager.get_new_optype(op_info.op_type) + op_info.op_type = self.OptypeRenameManager.get_new_optype(op_info.op_type) return new_ugraph @class_property def default_config(cls): return {} - - diff --git a/utensor_cgen/cli.py b/utensor_cgen/cli.py index 74cbeead..e7ed0394 100644 --- a/utensor_cgen/cli.py +++ b/utensor_cgen/cli.py @@ -81,7 +81,6 @@ def generate_config(target, output): '--output-nodes', type=NArgsParam(), metavar="NODE_NAME,NODE_NAME,...", - required=True, help="list of output nodes" ) @click.option('--config', default='utensor_cli.toml', show_default=True, metavar='CONFIG.toml') @@ -129,10 +128,12 @@ def list_trans_methods(verbose): type=NArgsParam(), metavar="NODE_NAME,NODE_NAME,...", help="list of output nodes") +@click.option('--config', default='utensor_cli.toml', show_default=True, metavar='CONFIG.toml') @click.argument('model_file', required=True, metavar='MODEL.{pb,pkl}') -def show_graph(model_file, **kwargs): +def show_graph(model_file, config, **kwargs): import pickle from utensor_cgen.frontend import FrontendSelector + _, ext = os.path.splitext(model_file) output_nodes = kwargs.pop('output_nodes') @@ -142,9 +143,13 @@ def show_graph(model_file, **kwargs): _show_ugraph(ugraph, **kwargs) return 0 + if os.path.exists(config): + with open(config) as fid: + config = loads(fid.read()) + else: + config = {} try: - parser = FrontendSelector.select_parser(ext) - ugraph = parser.parse(model_file, output_nodes) + ugraph = FrontendSelector.parse(model_file, output_nodes, config) _show_ugraph(ugraph, **kwargs) return 0 except RuntimeError as err: diff --git a/utensor_cgen/frontend/__init__.py b/utensor_cgen/frontend/__init__.py index f122ea18..0b70318e 100644 --- a/utensor_cgen/frontend/__init__.py +++ b/utensor_cgen/frontend/__init__.py @@ -26,6 +26,8 @@ def parse(cls, model_file, output_nodes, config=None): if config is None: config = {} _, ext = os.path.splitext(model_file) + if ext != '.onnx' and not output_nodes: + raise ValueError('output_nodes are required for {} file'.format(ext)) parser = cls.select_parser(ext)(config) return parser.parse(model_file, output_nodes) diff --git a/utensor_cgen/frontend/onnx.py b/utensor_cgen/frontend/onnx.py index fc681bae..fe41a1e1 100644 --- a/utensor_cgen/frontend/onnx.py +++ b/utensor_cgen/frontend/onnx.py @@ -1,19 +1,238 @@ +import os +import re +from collections import Counter + +import numpy as np import onnx -from onnx_tf.backend import prepare +import tensorflow as tf +from onnx.onnx_pb import TensorProto +from onnx_tf.backend import TensorflowBackend, prepare from utensor_cgen.frontend import FrontendSelector from utensor_cgen.frontend.base import Parser +from utensor_cgen.ir import OperationInfo, TensorInfo, uTensorGraph +from utensor_cgen.ir.converter import AttrValueConverter, TensorProtoConverter +from utensor_cgen.legalizer import Legalizer +from utensor_cgen.utils import topologic_order_graph from .tensorflow import GraphDefParser +def _convert_op_attribute(attrib_pb): + # TODO: integrate with ir.converter + if attrib_pb.HasField('f'): + return attrib_pb.f + elif attrib_pb.HasField('i'): + return attrib_pb.i + elif attrib_pb.HasField('s'): + return attrib_pb.s + else: + raise ValueError('Unknown attribute value: {}'.format(attrib_pb)) + + @FrontendSelector.register(target_exts=['.onnx']) class OnnxParser(Parser): + # https://github.com/onnx/onnx/blob/master/onnx/onnx.proto + # https://pytorch.org/docs/stable/onnx.html - @classmethod - def parse(cls, onnx_file, output_nodes): - onnx_model = onnx.load(onnx_file) - tf_rep = prepare(onnx_model) - graph_def = tf_rep.graph.as_graph_def() - ugraph = GraphDefParser.parse(graph_def, output_nodes) + def parse(self, onnx_file, output_nodes=None): + graph_name, _ = os.path.splitext(onnx_file) + tf.reset_default_graph() + model = onnx.load(onnx_file) + onnx_graph = model.graph + ugraph = uTensorGraph( + name=graph_name, + output_nodes=[], + lib_name='onnx', + ops_info={}, + ) + self._build_graph(onnx_graph, ugraph) + ugraph = Legalizer.legalize(ugraph) + tf.reset_default_graph() return ugraph + + def _build_graph(self, onnx_graph, ugraph): + op_types_cnt = Counter() + tensor_names_map = {} + # add const ops + params_dict = self._get_params_dict(onnx_graph) + for tensor_name, tensor_value in params_dict.items(): + cnt = op_types_cnt['Const'] + node_name = self._format_node_name(tensor_name, 'Const', cnt) + op_types_cnt['Const'] += 1 + tensor_names_map[tensor_name] = TensorInfo( + name=self._format_tensor_name('', node_name, 0), + op_name=node_name, + dtype=tensor_value.value.dtype, + shape=list(tensor_value.value.np_array.shape), + ugraph=ugraph + ) + OperationInfo( + name=node_name, + input_tensors=[], + output_tensors=[tensor_names_map[tensor_name]], + op_type='Const', + lib_name='onnx', + ugraph=ugraph, + op_attr={ + 'value': tensor_value + } + ) + # placeholders + for value in onnx_graph.input: + # value: ValueInfoProto + tensor_name = value.name + if tensor_name in tensor_names_map: + # tensor in initializers MAY appears in input, ignore + continue + cnt = op_types_cnt['Placeholder'] + node_name = self._format_node_name(tensor_name, 'Placeholder', cnt) + op_types_cnt['Placeholder'] += 1 + assert value.type.HasField('tensor_type'), 'invalid graph input value' + tensor_type = value.type.tensor_type + dtype_str = TensorProto.DataType.Name(tensor_type.elem_type).lower() + if dtype_str == 'float': + dtype_str = 'float32' + dtype = np.dtype(dtype_str) + shape = [ + dim.dim_value for dim in tensor_type.shape.dim + ] + tensor_names_map[tensor_name] = TensorInfo( + name=self._format_tensor_name('', node_name, 0), + op_name=node_name, + dtype=dtype, + shape=shape, + ugraph=ugraph, + ) + OperationInfo( + name=node_name, + input_tensors=[], + output_tensors=[tensor_names_map[tensor_name]], + op_type='Placeholder', + ugraph=ugraph, + lib_name='onnx', + op_attr={} + ) + for node in onnx_graph.node: + cnt = op_types_cnt[node.op_type] + node_name = self._format_node_name(node.name, node.op_type, cnt) + for i, tensor_name in enumerate(node.output): + tensor_names_map[tensor_name] = TensorInfo( + name=self._format_tensor_name(tensor_name, node_name, i), + op_name=node_name, + dtype=None, + shape=None, + ugraph=ugraph, + ) + node_names_map = {} + # create all outupt tensors + for node in onnx_graph.node: + cnt = op_types_cnt[node.op_type] + node_name = self._format_node_name(node.name, node.op_type, cnt) + op_types_cnt[node.op_type] += 1 + for i, name in enumerate(node.output): + tensor_names_map[name] = TensorInfo( + name=self._format_tensor_name(name, node_name, i), + op_name=node_name, + dtype=None, + shape=None, + ugraph=ugraph + ) + if node.name: + node_names_map[node.name] = node_name + # create ops + for node in onnx_graph.node: + input_tensors = [ + tensor_names_map[name] for name in node.input + ] + output_tensors = [ + tensor_names_map[name] for name in node.output + ] + + op_attr = { + attrib_pb.name: _convert_op_attribute(attrib_pb) + for attrib_pb in node.attribute + } + node_name = output_tensors[0].op_name + OperationInfo( + name=node_name, + input_tensors=input_tensors, + output_tensors=output_tensors, + op_type=node.op_type, + lib_name='onnx', + ugraph=ugraph, + op_attr=op_attr + ) + # find outupt nodes + distinct_out_tensors = set() + graph_output = set([v.name for v in onnx_graph.output]) + for name, tensor_info in tensor_names_map.items(): + if name in graph_output: + distinct_out_tensors.add(tensor_info.op_name) + ugraph.output_nodes = list(distinct_out_tensors) + topologic_order_graph(ugraph) + _PostProcessing.post_process(ugraph) + + def _format_node_name(self, node_name, op_type, op_cnt): + if node_name == '': + node_name = '{}_{}'.format(op_type, op_cnt) + return re.sub(r'[\.:/]', '_', node_name) + + def _format_tensor_name(self, name, node_name, offset): + if re.match(r'[a-zA-Z][a-zA-Z0-9]*:[0-9]+', name): + return name + return '{}:{}'.format(node_name, offset) + + def _get_params_dict(self, onnx_graph): + dict_items = TensorflowBackend._onnx_initializer_to_input_dict_items(onnx_graph.initializer) + params_dict = {} + for name, tf_tensor in dict_items: + params_dict[name] = AttrValueConverter.GenericType( + value_name='value', + value=TensorProtoConverter.get_generic_value( + tf_tensor.op.get_attr('value') + ) + ) + return params_dict + + +class _PostProcessing(object): + + @classmethod + def post_process(cls, ugraph): + for op_name in ugraph.topo_order: + op_info = ugraph.ops_info[op_name] + handler = getattr(cls, '_handle_{}'.format(op_info.op_type.lower()), lambda op_info: op_info) + handler(op_info) + + @staticmethod + def _handle_gemm(op_info): + ugraph = op_info.ugraph + output_tensor = op_info.output_tensors[0] + input_a, input_w, input_bias = op_info.input_tensors + a = np.empty(input_a.shape, dtype=input_a.dtype) + w = np.empty(input_w.shape, dtype=input_w.dtype) + b = np.empty(input_bias.shape, dtype=input_bias.dtype) + out = np.matmul(a, w.T) + b + output_tensor.dtype = out.dtype + output_tensor.shape = list(out.shape) + for op in output_tensor.op.output_nodes: + for i, in_tensor in enumerate(op.input_tensors): + if in_tensor.name == output_tensor.name: + op.input_tensors[i] = output_tensor + + @staticmethod + def _handle_relu(op_info): + input_tensor = op_info.input_tensors[0] + op_info.output_tensors[0].dtype = input_tensor.dtype + op_info.output_tensors[0].shape = input_tensor.shape[:] + + @staticmethod + def _handle_softmax(op_info): + input_tensor = op_info.input_tensors[0] + logistics = np.empty(input_tensor.shape, dtype=input_tensor.dtype) + out = np.exp(-logistics) + out /= out.sum(axis=1) + output_tensor = op_info.output_tensors[0] + output_tensor.shape = list(out.shape) + output_tensor.dtype = out.dtype diff --git a/utensor_cgen/frontend/tensorflow.py b/utensor_cgen/frontend/tensorflow.py index 263faa92..bd357a1d 100644 --- a/utensor_cgen/frontend/tensorflow.py +++ b/utensor_cgen/frontend/tensorflow.py @@ -17,10 +17,9 @@ @FrontendSelector.register(target_exts=['.pb', '.pbtxt']) class GraphDefParser(Parser): - @classmethod - def parse(cls, pb_file, output_nodes=None): - graph_def, graph_name = cls._load_graph_def(pb_file) - if not cls._tf_is_freeze_graph(graph_def): + def parse(self, pb_file, output_nodes=None): + graph_def, graph_name = self._load_graph_def(pb_file) + if not self._tf_is_freeze_graph(graph_def): raise ValueError('Given graph_def is not freezed') if output_nodes is None: output_nodes = [node.name for node in graph_def.node] @@ -39,14 +38,14 @@ def parse(cls, pb_file, output_nodes=None): ugraph=ugraph, op_name=tensor.op.name, dtype=np.dtype(tensor.dtype.as_numpy_dtype), - shape=cls._tf_parse_tshape(tensor.shape), + shape=self._tf_parse_tshape(tensor.shape), ) for tensor in op.inputs] out_tensors = [TensorInfo(name=tensor.name, ugraph=ugraph, op_name=op.name, dtype=np.dtype(tensor.dtype.as_numpy_dtype), - shape=cls._tf_parse_tshape(tensor.shape), + shape=self._tf_parse_tshape(tensor.shape), ) for tensor in op.outputs] op_type = node.op @@ -91,7 +90,7 @@ def _tf_parse_tshape(t_shape): shape = None return shape - @classmethod - def _tf_is_freeze_graph(self, graph_def): + @staticmethod + def _tf_is_freeze_graph(graph_def): is_frozen = all(node.op not in ['VariableV2'] for node in graph_def.node) return is_frozen diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index 5fef8683..827adc98 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -66,7 +66,7 @@ class TensorInfo(IRBase, _NoShallowCopyMixin): """ name = attr.ib(validator=instance_of(six.string_types)) op_name = attr.ib(validator=instance_of(six.string_types)) - dtype = attr.ib(validator=instance_of(np.dtype)) + dtype = attr.ib(validator=instance_of((np.dtype, type(None)))) shape = attr.ib(validator=instance_of((list, type(None)))) diff --git a/utensor_cgen/legalizer/__init__.py b/utensor_cgen/legalizer/__init__.py index 15e82b78..824a36b3 100644 --- a/utensor_cgen/legalizer/__init__.py +++ b/utensor_cgen/legalizer/__init__.py @@ -1,5 +1,6 @@ from .base import LegalizerBase from .tensorflow import GraphDefLegalizer +from .onnx import OnnxLegalizer class Legalizer(object): @@ -32,3 +33,4 @@ def legalize(cls, ugraph, config=None): return legalizer.legalize(ugraph) Legalizer.register(GraphDefLegalizer) +Legalizer.register(OnnxLegalizer) diff --git a/utensor_cgen/legalizer/onnx.py b/utensor_cgen/legalizer/onnx.py new file mode 100644 index 00000000..13c46a27 --- /dev/null +++ b/utensor_cgen/legalizer/onnx.py @@ -0,0 +1,53 @@ +from utensor_cgen.ir import OperationInfo, TensorInfo +from utensor_cgen.utils import topologic_order_graph + +from .base import LegalizerBase + + +class OnnxLegalizer(LegalizerBase): + TARGET = 'onnx' + + def legalize(self, ugraph): + self._visit(ugraph) + return ugraph + + def _visit(self, ugraph): + for op_info in list(ugraph.ops_info.values()): + visitor = getattr( + self, + '_visit_{}'.format(op_info.op_type.lower()), + lambda op_info: op_info + ) + visitor(op_info) + topologic_order_graph(ugraph) + + def _visit_gemm(self, op_info): + ugraph = op_info.ugraph + op_info.op_type = 'MatMul' + tensor_a, tensor_w, tensor_bias = op_info.input_tensors + out_tensor = TensorInfo( + name='{}_MatMul:0'.format(op_info.name), + op_name='{}_MatMul'.format(op_info.name), + dtype=op_info.output_tensors[0].dtype, + shape=op_info.output_tensors[0].shape[:], + ugraph=ugraph, + ) + OperationInfo( + name='{}_MatMul'.format(op_info.name), + input_tensors=[tensor_a, tensor_w], + output_tensors=[out_tensor], + op_type='MatMul', + lib_name=op_info.lib_name, + ugraph=ugraph, + op_attr=op_info.op_attr + ) + add_op = OperationInfo( + name='{}_AddBias'.format(op_info.name), + input_tensors=[out_tensor, tensor_bias], + output_tensors=op_info.output_tensors[:], + op_type='Add', + lib_name=op_info.lib_name, + ugraph=ugraph, + op_attr={} + ) + op_info.output_tensors[0].op_name = add_op.name diff --git a/utensor_cgen/transformer/linear_reoder.py b/utensor_cgen/transformer/linear_reoder.py index 2cce490e..491f9f11 100644 --- a/utensor_cgen/transformer/linear_reoder.py +++ b/utensor_cgen/transformer/linear_reoder.py @@ -5,6 +5,7 @@ """ import tensorflow as tf + from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.matcher import uTensorGraphMatcher from utensor_cgen.utils import prune_graph, topologic_order_graph @@ -30,7 +31,7 @@ def pattern_ugraph(self): dummy_input = tf.placeholder(dtype=tf.float32, shape=[None, 128, 128, 3]) relu = tf.nn.relu(dummy_input, name='relu') tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='max_pool') - pattern_ugraph = GraphDefParser.parse(graph.as_graph_def(), output_nodes=['max_pool']) + pattern_ugraph = GraphDefParser(config={}).parse(graph.as_graph_def(), output_nodes=['max_pool']) pattern_ugraph['relu'].replace_with_null_input_tensor(0) pattern_ugraph = prune_graph(pattern_ugraph) topologic_order_graph(pattern_ugraph) @@ -56,7 +57,7 @@ def __call__(self, match): dummy_input = tf.placeholder(dtype=tf.float32, shape=[None, 128, 128, 3]) max_pool = tf.nn.max_pool(dummy_input, ksize=ksize, strides=strides, padding=padding, name='max_pool') tf.nn.relu(max_pool, name='relu') - ugraph = GraphDefParser.parse(graph.as_graph_def(), output_nodes=['relu']) + ugraph = GraphDefParser(config={}).parse(graph.as_graph_def(), output_nodes=['relu']) ugraph['max_pool'].replace_with_null_input_tensor(0) ugraph = prune_graph(ugraph) topologic_order_graph(ugraph) diff --git a/utensor_cgen/utils.py b/utensor_cgen/utils.py index 006bf0dd..70c28657 100644 --- a/utensor_cgen/utils.py +++ b/utensor_cgen/utils.py @@ -352,10 +352,6 @@ def get_topologic_order(ugraph, init_nodes=None): - `Topological Sorting (wiki) `_ """ - if ugraph.lib_name != "tensorflow": - raise ValueError( - "topologic_order_graph works only on tensorflow graph" - ) if init_nodes is None: init_nodes = ugraph.output_nodes queue = deepcopy(init_nodes) From 6317edac08f8250aa183f91f4914890c50d99a68 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 25 Feb 2020 00:53:26 +0800 Subject: [PATCH 029/117] update tests and fix transformers --- tests/test_ir/test_uTensorGraph/test_graph.py | 10 +++++----- .../test_matcher/test_permutation/conftest.py | 10 +++++----- tests/test_matcher/test_replace/conftest.py | 6 +++--- .../test_matcher/test_replace/test_replace.py | 4 ++-- .../test_transformer/test_convpool/conftest.py | 4 ++-- .../test_transformer/test_dropout/conftest.py | 4 ++-- .../test_dropout/test_dropout_transormer.py | 7 ++++--- .../test_id_remove/test_transformer.py | 7 ++++--- .../test_inline/test_inline_optimizer.py | 2 +- .../test_linear_reorder/conftest.py | 4 ++-- .../test_refcnt/test_refcnt_optimizer.py | 2 +- .../test_allocator_optimizer.py | 18 +++++++++--------- utensor_cgen/transformer/conv_pool.py | 3 ++- utensor_cgen/transformer/ns_transformer.py | 4 ++-- utensor_cgen/transformer/quantize.py | 7 +++++-- 15 files changed, 49 insertions(+), 43 deletions(-) diff --git a/tests/test_ir/test_uTensorGraph/test_graph.py b/tests/test_ir/test_uTensorGraph/test_graph.py index 6a60e595..af8e441d 100644 --- a/tests/test_ir/test_uTensorGraph/test_graph.py +++ b/tests/test_ir/test_uTensorGraph/test_graph.py @@ -1,8 +1,8 @@ from copy import deepcopy import numpy as np - import tensorflow as tf + from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.ir import OperationInfo, uTensorGraph from utensor_cgen.ir.converter import TensorProtoConverter @@ -10,7 +10,7 @@ def test_ugraph_topo_order(graph_tuple): graph_def, output_nodes = graph_tuple - ugraph = GraphDefParser.parse(graph_def, output_nodes) + ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes) first_out, second_out = output_nodes meet_first = False for node_name in ugraph.topo_order: @@ -21,7 +21,7 @@ def test_ugraph_topo_order(graph_tuple): def test_ugraph_copy(graph_tuple): graph_def, output_nodes = graph_tuple - ugraph_1 = GraphDefParser.parse(graph_def, output_nodes) + ugraph_1 = GraphDefParser(config={}).parse(graph_def, output_nodes) ugraph_2 = deepcopy(ugraph_1) assert ugraph_1 is not ugraph_2 assert ugraph_1.graph_def == ugraph_2.graph_def @@ -53,7 +53,7 @@ def test_op_info(): def test_in_out_nodes(graph_tuple): graph_def, output_nodes = graph_tuple - ugraph = GraphDefParser.parse(graph_def, output_nodes) + ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes) x3 = ugraph.ops_info['x3'] assert x3.ugraph is ugraph assert len(x3.input_nodes) == len(set([op.name for op in x3.input_nodes])) @@ -65,7 +65,7 @@ def test_in_out_nodes(graph_tuple): def test_tensor_ops(graph_tuple): graph_def, output_nodes = graph_tuple - ugraph = GraphDefParser.parse(graph_def, output_nodes) + ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes) for op in ugraph.ops_info.values(): for tensor in op.output_tensors: assert tensor.op is op diff --git a/tests/test_matcher/test_permutation/conftest.py b/tests/test_matcher/test_permutation/conftest.py index ddc6a422..71cb1713 100644 --- a/tests/test_matcher/test_permutation/conftest.py +++ b/tests/test_matcher/test_permutation/conftest.py @@ -1,6 +1,6 @@ import pytest - import tensorflow as tf + from utensor_cgen.frontend.tensorflow import GraphDefParser @@ -12,7 +12,7 @@ def patrn_ugraph(): ptrn_input1 = tf.placeholder(dtype=tf.float32, name='input1') ptrn_add0 = tf.add(ptrn_input0, ptrn_input1, name='add0') ptrn_out = tf.add(ptrn_add0, ptrn_input1, name='output') - ugraph = GraphDefParser.parse(graph.as_graph_def(), [ptrn_out.op.name]) + ugraph = GraphDefParser(config={}).parse(graph.as_graph_def(), [ptrn_out.op.name]) # ugraph.ops_info[ptrn_input0.op.name].add_null_input_tensor() return ugraph @@ -26,7 +26,7 @@ def subject_ugraph1(): sub_add0 = tf.add(sub_input0, sub_input1, name='sub_add0') sub_add1 = tf.add(sub_add0, sub_input1, name='sub_add1') sub_output = tf.add(sub_add1, sub_input2, name='sub_output') - ugraph = GraphDefParser.parse(graph.as_graph_def(), [sub_output.op.name]) + ugraph = GraphDefParser(config={}).parse(graph.as_graph_def(), [sub_output.op.name]) return ugraph @pytest.fixture(scope='function', name='subject_ugraph1_1') @@ -40,7 +40,7 @@ def subject_ugraph1_1(): sub_add0 = tf.add(sub_input1, sub_input0, name='sub_add0') sub_add1 = tf.add(sub_add0, sub_input1, name='sub_add1') sub_output = tf.multiply(sub_add1, sub_input2, name='sub_output') - ugraph = GraphDefParser.parse(graph.as_graph_def(), [sub_output.op.name]) + ugraph = GraphDefParser(config={}).parse(graph.as_graph_def(), [sub_output.op.name]) return ugraph @pytest.fixture(scope='function', name='subject_ugraph1_2') @@ -53,6 +53,6 @@ def subject_ugraph1_2(): sub_add0 = tf.add(sub_input0, sub_input1, name='sub_add0') sub_add1 = tf.add(sub_input1, sub_add0, name='sub_add1') sub_output = tf.multiply(sub_add1, sub_input2, name='sub_output') - ugraph = GraphDefParser.parse(graph.as_graph_def(), [sub_output.op.name]) + ugraph = GraphDefParser(config={}).parse(graph.as_graph_def(), [sub_output.op.name]) # ugraph.ops_info[sub_input1.op.name].add_null_input_tensor() return ugraph diff --git a/tests/test_matcher/test_replace/conftest.py b/tests/test_matcher/test_replace/conftest.py index 64d8a6e3..e212a332 100644 --- a/tests/test_matcher/test_replace/conftest.py +++ b/tests/test_matcher/test_replace/conftest.py @@ -1,7 +1,7 @@ import numpy as np +import tensorflow as tf from pytest import fixture -import tensorflow as tf from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.utils import prune_graph, topologic_order_graph @@ -14,7 +14,7 @@ def fully_connect_pattern1(): w_prime = tf.constant(np.random.rand(3, 3), name='w_prime', dtype=tf.float32) a_prime = tf.matmul(z_prime, w_prime, name='a_prime') r_prime = tf.nn.relu(a_prime, name='r_prime') - patrn_ugraph = GraphDefParser.parse(patrn_graph.as_graph_def(), output_nodes=[r_prime.op.name]) + patrn_ugraph = GraphDefParser(config={}).parse(patrn_graph.as_graph_def(), output_nodes=[r_prime.op.name]) for i in range(2): patrn_ugraph.ops_info['a_prime'].replace_with_null_input_tensor(i) patrn_ugraph = prune_graph(patrn_ugraph) @@ -33,5 +33,5 @@ def subject_ugraph_1(): a = tf.matmul(z, w, name='a') r = tf.nn.relu(a, name='r') out = tf.add(x, r, name='out') - subj_ugraph = GraphDefParser.parse(subj_graph.as_graph_def(), output_nodes=[out.op.name]) + subj_ugraph = GraphDefParser(config={}).parse(subj_graph.as_graph_def(), output_nodes=[out.op.name]) return subj_ugraph diff --git a/tests/test_matcher/test_replace/test_replace.py b/tests/test_matcher/test_replace/test_replace.py index 9615b0f2..0bfb96d4 100644 --- a/tests/test_matcher/test_replace/test_replace.py +++ b/tests/test_matcher/test_replace/test_replace.py @@ -1,6 +1,6 @@ import numpy as np - import tensorflow as tf + from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.matcher import uTensorGraphMatcher from utensor_cgen.utils import prune_graph, topologic_order_graph @@ -13,7 +13,7 @@ def callback(match): a = tf.placeholder(dtype=tf.float32, name='a') b = tf.placeholder(dtype=tf.float32, name='b') out = tf.add(a, b, name='fused_node') - ugraph = GraphDefParser.parse(graph.as_graph_def(), output_nodes=[out.op.name]) + ugraph = GraphDefParser(config={}).parse(graph.as_graph_def(), output_nodes=[out.op.name]) ugraph.ops_info['fused_node'].replace_with_null_input_tensor(0) ugraph.ops_info['fused_node'].replace_with_null_input_tensor(1) topologic_order_graph(ugraph) diff --git a/tests/test_transformer/test_convpool/conftest.py b/tests/test_transformer/test_convpool/conftest.py index e9125cbb..dd3d7d68 100644 --- a/tests/test_transformer/test_convpool/conftest.py +++ b/tests/test_transformer/test_convpool/conftest.py @@ -1,9 +1,9 @@ from random import sample import numpy as np +import tensorflow as tf from pytest import fixture -import tensorflow as tf from utensor_cgen.frontend.tensorflow import GraphDefParser @@ -39,5 +39,5 @@ def gen_vgg_graph(): name='pool_{}'.format(i), padding='SAME', ) - ugraph = GraphDefParser.parse(graph.as_graph_def(), output_nodes=[in_feat.op.name]) + ugraph = GraphDefParser(config={}).parse(graph.as_graph_def(), output_nodes=[in_feat.op.name]) return ugraph diff --git a/tests/test_transformer/test_dropout/conftest.py b/tests/test_transformer/test_dropout/conftest.py index d4937bff..a0c685c7 100644 --- a/tests/test_transformer/test_dropout/conftest.py +++ b/tests/test_transformer/test_dropout/conftest.py @@ -2,8 +2,8 @@ import numpy as np import pytest - import tensorflow as tf + from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.utils import random_str @@ -68,4 +68,4 @@ def gen_vgg_graph(): ) if i != num_layers: in_feat = tf.nn.dropout(in_feat, rate=rate, name='dropout_{}'.format(i)) - return GraphDefParser.parse(graph.as_graph_def(), output_nodes=[in_feat.op.name]) + return GraphDefParser(config={}).parse(graph.as_graph_def(), output_nodes=[in_feat.op.name]) diff --git a/tests/test_transformer/test_dropout/test_dropout_transormer.py b/tests/test_transformer/test_dropout/test_dropout_transormer.py index 98593527..a6085879 100644 --- a/tests/test_transformer/test_dropout/test_dropout_transormer.py +++ b/tests/test_transformer/test_dropout/test_dropout_transormer.py @@ -1,4 +1,5 @@ import tensorflow as tf + from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.transformer.ns_transformer import (DropoutTransformer, DropoutTransformerV2) @@ -8,7 +9,7 @@ def test_dropout_trans_1_1(droput_graph_tuple): (graph_def, (keep_prob_name, dropout_output_name), output_nodes) = droput_graph_tuple - ugraph = GraphDefParser.parse(graph_def, output_nodes=output_nodes) + ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) transformer = DropoutTransformer() new_ugraph = transformer.transform(ugraph) for op in new_ugraph.ops_info.values(): @@ -40,7 +41,7 @@ def test_dropout_trans_1_2(droput_graph_tuple): (graph_def, (keep_prob_name, dropout_output_name), output_nodes) = droput_graph_tuple - ugraph = GraphDefParser.parse(graph_def, output_nodes=output_nodes) + ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) transformer = DropoutTransformerV2() new_ugraph = transformer.transform(ugraph) for op in new_ugraph.ops_info.values(): @@ -70,7 +71,7 @@ def test_dropout_trans_1_2(droput_graph_tuple): def test_dropout_trans_2(dropout_graph_tuple2): graph_def, output_nodes = dropout_graph_tuple2 - ugraph = GraphDefParser.parse(graph_def, output_nodes=output_nodes) + ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) trans = DropoutTransformerV2() new_ugraph = trans.transform(ugraph) assert len(new_ugraph.ops_info) == 1 diff --git a/tests/test_transformer/test_id_remove/test_transformer.py b/tests/test_transformer/test_id_remove/test_transformer.py index d09654eb..edd1b440 100644 --- a/tests/test_transformer/test_id_remove/test_transformer.py +++ b/tests/test_transformer/test_id_remove/test_transformer.py @@ -1,8 +1,9 @@ from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.transformer.optimizer import IdOpRemoveOptimizer + def test_id_rm_transform_1(id_graph_def_1): - ugraph = GraphDefParser.parse(id_graph_def_1, output_nodes=['z']) + ugraph = GraphDefParser(config={}).parse(id_graph_def_1, output_nodes=['z']) optimizer = IdOpRemoveOptimizer() new_ugraph = optimizer.transform(ugraph) for op in new_ugraph.ops_info.values(): @@ -12,11 +13,11 @@ def test_id_rm_transform_1(id_graph_def_1): assert set(['x', 'y']) == in_op_names def test_id_rm_transform_2(id_graph_def_2): - ugraph = GraphDefParser.parse(id_graph_def_2, output_nodes=['z']) + ugraph = GraphDefParser(config={}).parse(id_graph_def_2, output_nodes=['z']) optimizer = IdOpRemoveOptimizer() new_ugraph = optimizer.transform(ugraph) for op in new_ugraph.ops_info.values(): assert op.op_type != 'Identity' op_z = new_ugraph.ops_info['z'] in_op_names = set([op.name for op in op_z.input_nodes]) - assert set(['w', 'y']) == in_op_names \ No newline at end of file + assert set(['w', 'y']) == in_op_names diff --git a/tests/test_transformer/test_inline/test_inline_optimizer.py b/tests/test_transformer/test_inline/test_inline_optimizer.py index 48b82dfd..f416d9b3 100644 --- a/tests/test_transformer/test_inline/test_inline_optimizer.py +++ b/tests/test_transformer/test_inline/test_inline_optimizer.py @@ -4,7 +4,7 @@ def test_inline_optimizer(inlinegraph_tuple): (graph_def, inline_ans, output_nodes)= inlinegraph_tuple - ugraph = GraphDefParser.parse(graph_def, output_nodes) + ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes) transformer = InlineTransformer() ugraph = transformer.transform(ugraph) for node_name in ugraph.topo_order: diff --git a/tests/test_transformer/test_linear_reorder/conftest.py b/tests/test_transformer/test_linear_reorder/conftest.py index e69dd354..7209afbe 100644 --- a/tests/test_transformer/test_linear_reorder/conftest.py +++ b/tests/test_transformer/test_linear_reorder/conftest.py @@ -1,6 +1,6 @@ +import tensorflow as tf from pytest import fixture -import tensorflow as tf from utensor_cgen.frontend.tensorflow import GraphDefParser @@ -15,5 +15,5 @@ def subject_ugraph_1(): relu_2 = tf.nn.relu(input_2, name='relu_2') max_pool_2 = tf.nn.max_pool(relu_2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID', name='pool_2') output = tf.add(max_pool_1, max_pool_2, name='output') - subj_ugraph = GraphDefParser.parse(graph.as_graph_def(), output_nodes=[output.op.name]) + subj_ugraph = GraphDefParser(config={}).parse(graph.as_graph_def(), output_nodes=[output.op.name]) return subj_ugraph diff --git a/tests/test_transformer/test_refcnt/test_refcnt_optimizer.py b/tests/test_transformer/test_refcnt/test_refcnt_optimizer.py index e032e725..ea696d7a 100644 --- a/tests/test_transformer/test_refcnt/test_refcnt_optimizer.py +++ b/tests/test_transformer/test_refcnt/test_refcnt_optimizer.py @@ -4,7 +4,7 @@ def test_refcnt_optimizer(refgraph_tuple): (graph_def, refcnt_ans, output_nodes)= refgraph_tuple - ugraph = GraphDefParser.parse(graph_def, output_nodes=output_nodes) + ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) transformer = RefCntOptimizer() ugraph = transformer.transform(ugraph) for node_name in ugraph.topo_order: diff --git a/tests/test_transformer/test_tensorallocator/test_allocator_optimizer.py b/tests/test_transformer/test_tensorallocator/test_allocator_optimizer.py index 491de548..83f9090b 100644 --- a/tests/test_transformer/test_tensorallocator/test_allocator_optimizer.py +++ b/tests/test_transformer/test_tensorallocator/test_allocator_optimizer.py @@ -4,7 +4,7 @@ def test_create_resource_table(refgraph_tuple): (graph_def, output_nodes)= refgraph_tuple - ugraph = GraphDefParser.parse(graph_def, output_nodes=output_nodes) + ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) transformer = TensorLifeProbe() table = transformer._create_resource_table(ugraph) resource_ans = { @@ -21,7 +21,7 @@ def test_create_resource_table(refgraph_tuple): def test_create_allocate_table(refgraph_tuple): (graph_def, output_nodes)= refgraph_tuple - ugraph = GraphDefParser.parse(graph_def, output_nodes=output_nodes) + ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) transformer = TensorLifeProbe() table = transformer._create_resource_table(ugraph) allocate_table = dict() @@ -36,7 +36,7 @@ def test_create_allocate_table(refgraph_tuple): def test_query_offset_address(refgraph_tuple): (graph_def, output_nodes)= refgraph_tuple - ugraph = GraphDefParser.parse(graph_def, output_nodes=output_nodes) + ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) transformer = TensorLifeProbe() table = transformer._create_resource_table(ugraph) allocate_table = dict() @@ -50,7 +50,7 @@ def test_query_offset_address(refgraph_tuple): def test_query_timeline(refgraph_tuple): (graph_def, output_nodes)= refgraph_tuple - ugraph = GraphDefParser.parse(graph_def, output_nodes=output_nodes) + ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) transformer = TensorLifeProbe() table = transformer._create_resource_table(ugraph) allocate_table = dict() @@ -64,7 +64,7 @@ def test_query_timeline(refgraph_tuple): def test_query_result(refgraph_tuple): (graph_def, output_nodes)= refgraph_tuple - ugraph = GraphDefParser.parse(graph_def, output_nodes=output_nodes) + ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) transformer = TensorLifeProbe() table = transformer._create_resource_table(ugraph) allocate_table = dict() @@ -91,7 +91,7 @@ def test_query_result(refgraph_tuple): def test_allocate_tensor(refgraph_tuple): (graph_def, output_nodes) = refgraph_tuple - ugraph = GraphDefParser.parse(graph_def, output_nodes=output_nodes) + ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) transformer = TensorLifeProbe() tensors = [] table = transformer._create_resource_table(ugraph) @@ -108,7 +108,7 @@ def test_allocate_tensor(refgraph_tuple): def test_allocate_graph(refgraph_tuple): (graph_def, output_nodes) = refgraph_tuple - ugraph = GraphDefParser.parse(graph_def, output_nodes=output_nodes) + ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) transformer = TensorLifeProbe() use_def_table = transformer._create_resource_table(ugraph) unit_size = 4 @@ -120,7 +120,7 @@ def test_allocate_graph(refgraph_tuple): def test_query_check(refgraph_tuple): (graph_def, output_nodes)= refgraph_tuple - ugraph = GraphDefParser.parse(graph_def, output_nodes=output_nodes) + ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) transformer = TensorLifeProbe() table = transformer._create_resource_table(ugraph) allocate_table = dict() @@ -136,6 +136,6 @@ def test_query_check(refgraph_tuple): def test_memory_allocation(refgraph_tuple): (graph_def, output_nodes)= refgraph_tuple - ugraph = GraphDefParser.parse(graph_def, output_nodes=output_nodes) + ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) transformer = TensorLifeProbe() ugraph = transformer.transform(ugraph) diff --git a/utensor_cgen/transformer/conv_pool.py b/utensor_cgen/transformer/conv_pool.py index 21cae4e0..9be901eb 100644 --- a/utensor_cgen/transformer/conv_pool.py +++ b/utensor_cgen/transformer/conv_pool.py @@ -7,6 +7,7 @@ from copy import deepcopy import tensorflow as tf + from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.ir import OperationInfo, TensorInfo, uTensorGraph from utensor_cgen.matcher import uTensorGraphMatcher @@ -35,7 +36,7 @@ def pattern_ugraph(self): dummy_weight = tf.zeros([32, 32, 3, 10], dtype=tf.float32, name='dummy_weight') conv = tf.nn.conv2d(dummy_input, dummy_weight, strides=[1, 2, 2, 1], padding='VALID', name='conv') maxpool = tf.nn.max_pool(conv, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID', name='maxpool') - ugraph = GraphDefParser.parse(graph.as_graph_def(), output_nodes=[maxpool.op.name]) + ugraph = GraphDefParser(config={}).parse(graph.as_graph_def(), output_nodes=[maxpool.op.name]) quant_ugraph = QuantizeTransformer().transform(ugraph) patrn_ugraph = deepcopy(quant_ugraph) quant_conv_op = patrn_ugraph['conv/eightbit'] diff --git a/utensor_cgen/transformer/ns_transformer.py b/utensor_cgen/transformer/ns_transformer.py index f4fdee2d..2a627b61 100644 --- a/utensor_cgen/transformer/ns_transformer.py +++ b/utensor_cgen/transformer/ns_transformer.py @@ -9,8 +9,8 @@ from copy import deepcopy import numpy as np - import tensorflow as tf + from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.ir import OperationInfo, uTensorGraph from utensor_cgen.logger import logger @@ -367,7 +367,7 @@ def pattern_ugraph(self): dummy_x = tf.constant(np.random.rand(10, 10), dtype=tf.float32, name='dummy_x') dummy_rate = tf.placeholder(dtype=tf.float32, name='dummy_rate') dropout = tf.nn.dropout(dummy_x, rate=dummy_rate, name='dropout') - patrn_ugraph = GraphDefParser.parse(graph.as_graph_def(), output_nodes=[dropout.op.name]) + patrn_ugraph = GraphDefParser(config={}).parse(graph.as_graph_def(), output_nodes=[dropout.op.name]) # replace dummy_x patrn_ugraph['dropout/truediv'].replace_with_null_input_tensor(0) # # replace dummy_rate diff --git a/utensor_cgen/transformer/quantize.py b/utensor_cgen/transformer/quantize.py index d876ed69..df724d8e 100644 --- a/utensor_cgen/transformer/quantize.py +++ b/utensor_cgen/transformer/quantize.py @@ -1,4 +1,5 @@ from tensorflow.tools.graph_transforms import TransformGraph + from utensor_cgen.frontend.tensorflow import GraphDefParser from .base import Transformer @@ -18,5 +19,7 @@ def transform(self, ugraph): inputs=[], outputs=ugraph.output_nodes, transforms=["quantize_weights", "quantize_nodes"]) - return GraphDefParser.parse(quant_graph_def, - output_nodes=ugraph.output_nodes) + return GraphDefParser(config={}).parse( + quant_graph_def, + output_nodes=ugraph.output_nodes + ) From f697120809ecde49da40dc640b4d619cf22dba6d Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 25 Feb 2020 17:41:07 +0800 Subject: [PATCH 030/117] making some transformer generic (not specific to tensorflow) --- utensor_cgen/legalizer/onnx.py | 4 ++++ utensor_cgen/transformer/conv_pool.py | 3 ++- utensor_cgen/transformer/optimizer.py | 7 ++----- utensor_cgen/transformer/quantize.py | 3 ++- 4 files changed, 10 insertions(+), 7 deletions(-) diff --git a/utensor_cgen/legalizer/onnx.py b/utensor_cgen/legalizer/onnx.py index 13c46a27..2a8fceba 100644 --- a/utensor_cgen/legalizer/onnx.py +++ b/utensor_cgen/legalizer/onnx.py @@ -51,3 +51,7 @@ def _visit_gemm(self, op_info): op_attr={} ) op_info.output_tensors[0].op_name = add_op.name + + def _visit_id(self, op_info): + # identity op should have op_type == 'Identity' + return op_info diff --git a/utensor_cgen/transformer/conv_pool.py b/utensor_cgen/transformer/conv_pool.py index 9be901eb..d3de708a 100644 --- a/utensor_cgen/transformer/conv_pool.py +++ b/utensor_cgen/transformer/conv_pool.py @@ -7,7 +7,6 @@ from copy import deepcopy import tensorflow as tf - from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.ir import OperationInfo, TensorInfo, uTensorGraph from utensor_cgen.matcher import uTensorGraphMatcher @@ -48,6 +47,8 @@ def pattern_ugraph(self): return patrn_ugraph def transform(self, ugraph): + if ugraph.lib_name != 'tensorflow': + raise ValueError('only support tensorflow graph') matcher = uTensorGraphMatcher(pattern_ugraph=self.pattern_ugraph) matches = matcher.match(ugraph, n=1) while matches: diff --git a/utensor_cgen/transformer/optimizer.py b/utensor_cgen/transformer/optimizer.py index e83d081c..f10c9cfc 100644 --- a/utensor_cgen/transformer/optimizer.py +++ b/utensor_cgen/transformer/optimizer.py @@ -54,12 +54,9 @@ def __init__(self, **kwargs): self.prune_graph = True def transform(self, ugraph): - if ugraph.lib_name == 'tensorflow': - return self._transform_tf(ugraph) - else: - raise RuntimeError('unsupported lib_name: {}'.format(ugraph.lib_name)) + return self._transform(ugraph) - def _transform_tf(self, ugraph): + def _transform(self, ugraph): ops_to_remove = [ op for op_name, op in ugraph.ops_info.items() diff --git a/utensor_cgen/transformer/quantize.py b/utensor_cgen/transformer/quantize.py index df724d8e..8f030565 100644 --- a/utensor_cgen/transformer/quantize.py +++ b/utensor_cgen/transformer/quantize.py @@ -1,5 +1,4 @@ from tensorflow.tools.graph_transforms import TransformGraph - from utensor_cgen.frontend.tensorflow import GraphDefParser from .base import Transformer @@ -14,6 +13,8 @@ class QuantizeTransformer(Transformer): KWARGS_NAMESCOPE = '_quantize' def transform(self, ugraph): + if ugraph.lib_name != 'tensorflow': + raise ValueError('only support tensorflow graph') graph_def = ugraph.graph_def quant_graph_def = TransformGraph(input_graph_def=graph_def, inputs=[], From fb5413e4a9500bebb89a2ff51ae66fc2dd645a4d Mon Sep 17 00:00:00 2001 From: dboyliao Date: Fri, 28 Feb 2020 00:27:05 +0800 Subject: [PATCH 031/117] update pipfile, locks --- Pipfile | 6 +- Pipfile.lock | 962 --------------------------------------------------- setup.py | 1 + 3 files changed, 6 insertions(+), 963 deletions(-) delete mode 100644 Pipfile.lock diff --git a/Pipfile b/Pipfile index 62bfd748..13c552e8 100644 --- a/Pipfile +++ b/Pipfile @@ -1,12 +1,13 @@ [[source]] url = "https://pypi.python.org/simple" -verify_ssl = true +verify_ssl = false name = "pypi" [packages] e1839a8 = {path = ".",editable = true} [dev-packages] +matplotlib = "*" pylint = "*" "flake8" = "*" pytest = "*" @@ -16,3 +17,6 @@ scipy = "*" graphviz = "*" sphinx = "*" sphinx-autoapi = "*" + +[pipenv] +allow_prereleases = true diff --git a/Pipfile.lock b/Pipfile.lock deleted file mode 100644 index 403da44e..00000000 --- a/Pipfile.lock +++ /dev/null @@ -1,962 +0,0 @@ -{ - "_meta": { - "hash": { - "sha256": "ff0b58731e6f51ecb106fdbf32c5a60479ca4aea1b27a1670e6cf6d61e3ac739" - }, - "pipfile-spec": 6, - "requires": {}, - "sources": [ - { - "name": "pypi", - "url": "https://pypi.python.org/simple", - "verify_ssl": true - } - ] - }, - "default": { - "absl-py": { - "hashes": [ - "sha256:75e737d6ce7723d9ff9b7aa1ba3233c34be62ef18d5859e706b8fdc828989830" - ], - "version": "==0.9.0" - }, - "astor": { - "hashes": [ - "sha256:070a54e890cefb5b3739d19f30f5a5ec840ffc9c50ffa7d23cc9fc1a38ebbfc5", - 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_ExtensionsLoader __version__ = ( diff --git a/utensor_cgen/frontend/onnx.py b/utensor_cgen/frontend/onnx.py index fe41a1e1..544887d9 100644 --- a/utensor_cgen/frontend/onnx.py +++ b/utensor_cgen/frontend/onnx.py @@ -7,7 +7,6 @@ import tensorflow as tf from onnx.onnx_pb import TensorProto from onnx_tf.backend import TensorflowBackend, prepare - from utensor_cgen.frontend import FrontendSelector from utensor_cgen.frontend.base import Parser from utensor_cgen.ir import OperationInfo, TensorInfo, uTensorGraph @@ -209,10 +208,12 @@ def post_process(cls, ugraph): def _handle_gemm(op_info): ugraph = op_info.ugraph output_tensor = op_info.output_tensors[0] + if output_tensor.dtype is not None and output_tensor.shape is not None: + return input_a, input_w, input_bias = op_info.input_tensors - a = np.empty(input_a.shape, dtype=input_a.dtype) - w = np.empty(input_w.shape, dtype=input_w.dtype) - b = np.empty(input_bias.shape, dtype=input_bias.dtype) + a = np.zeros(input_a.shape, dtype=input_a.dtype) + w = np.zeros(input_w.shape, dtype=input_w.dtype) + b = np.zeros(input_bias.shape, dtype=input_bias.dtype) out = np.matmul(a, w.T) + b output_tensor.dtype = out.dtype output_tensor.shape = list(out.shape) @@ -230,9 +231,11 @@ def _handle_relu(op_info): @staticmethod def _handle_softmax(op_info): input_tensor = op_info.input_tensors[0] - logistics = np.empty(input_tensor.shape, dtype=input_tensor.dtype) - out = np.exp(-logistics) - out /= out.sum(axis=1) output_tensor = op_info.output_tensors[0] + if output_tensor.dtype is not None and output_tensor.shape is not None: + return + logistics = np.zeros(input_tensor.shape, dtype=input_tensor.dtype) + out = np.exp(-logistics) * 1e-6 + out /= out.sum(axis=1) output_tensor.shape = list(out.shape) output_tensor.dtype = out.dtype diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index 827adc98..30718e72 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -4,10 +4,10 @@ from functools import reduce import attr -import numpy as np import six from attr.validators import instance_of +import numpy as np import tensorflow as tf from tensorflow.core.framework.attr_value_pb2 import AttrValue as _AttrValue from tensorflow.core.framework.attr_value_pb2 import \ @@ -464,6 +464,7 @@ class uTensorGraph(IRBase, _NoShallowCopyMixin, uTensorGraphBuilderMixin): topo_order = attr.ib(factory=list, init=False) data_manager = attr.ib(default=None, init=False) _type_to_op_map = attr.ib(factory=dict, init=False, repr=False) + attributes = attr.ib(factory=dict, init=False, repr=False) def __attrs_post_init__(self): if not all( @@ -671,6 +672,7 @@ def __deepcopy__(self, memo): new_graph.data_manager = DataManager({}) new_graph.data_manager.StorageCenter = deepcopy(self.data_manager.StorageCenter) new_graph._lib_name = self._lib_name + new_graph.attributes = deepcopy(self.attributes) topologic_order_graph(new_graph) return new_graph diff --git a/utensor_cgen/transformer/__init__.py b/utensor_cgen/transformer/__init__.py index 13a003e0..c4674258 100644 --- a/utensor_cgen/transformer/__init__.py +++ b/utensor_cgen/transformer/__init__.py @@ -3,6 +3,7 @@ from .conv_pool import * from .graph_viz import * from .linear_reoder import * +from .mem_alloc import * from .ns_transformer import * from .optimizer import * from .pipeline import TransformerPipeline diff --git a/utensor_cgen/transformer/mem_alloc.py b/utensor_cgen/transformer/mem_alloc.py new file mode 100644 index 00000000..b6489f36 --- /dev/null +++ b/utensor_cgen/transformer/mem_alloc.py @@ -0,0 +1,79 @@ +from collections import Counter, defaultdict, namedtuple +from itertools import combinations, product + +from ortools.sat.python import cp_model + +from .base import Transformer +from .pipeline import TransformerPipeline + +__all__ = ['TensorAllocationTransformer'] + +Allocation = namedtuple('Allocation', ['start', 'end', 'size']) +AllocationPlan = namedtuple('AllocationPlan', ['plan', 'total_size']) + +@TransformerPipeline.register_transformer +class TensorAllocationTransformer(Transformer): + METHOD_NAME = "tensor_alloc" + KWARGS_NAMESCOPE = "_tensor_alloc" + + def __init__(self, prune_graph=True, max_pool_size=1024*1024*1024): + super(TensorAllocationTransformer, self).__init__(prune_graph=prune_graph) + self.max_pool_size = max_pool_size + + def transform(self, ugraph): + ref_cnts = Counter() + for op_name in ugraph.topo_order: + op_info = ugraph.ops_info[op_name] + for tensor in op_info.input_tensors: + ref_cnts[tensor.name] += 1 + visited_tensors = set() + nonoverlap_map = defaultdict(set) + for op_name in ugraph.topo_order: + op_info = ugraph.ops_info[op_name] + for in_tensor, out_tensor in product(op_info.input_tensors, op_info.output_tensors): + nonoverlap_map[out_tensor].add(in_tensor) + visited_tensors.add(in_tensor) + for known_tensor, out_tensor in product(visited_tensors, op_info.output_tensors): + if ref_cnts[known_tensor.name] > 0: + nonoverlap_map[out_tensor].add(known_tensor) + for in_tensor in op_info.input_tensors: + ref_cnts[in_tensor.name] -= 1 + for out_tensor1, out_tensor2 in combinations(ugraph.output_tensors, 2): + nonoverlap_map[out_tensor1].add(out_tensor2) + visited_tensors.update(ugraph.output_tensors) + + model = cp_model.CpModel() + inter_vars = {} + tensor_allocs = {} + for tensor in visited_tensors: + var_start = model.NewIntVar(0, self.max_pool_size, f'{tensor.name}_start') + var_end = model.NewIntVar(0, self.max_pool_size, f'{tensor.name}_end') + size = tensor.size * tensor.dtype.itemsize + intv_var = model.NewIntervalVar(var_start, size, var_end, f'{tensor.name}_alloc') + inter_vars[tensor.name] = intv_var + tensor_allocs[tensor.name] = Allocation(var_start, var_end, size) + for tensor in visited_tensors: + inter_var = inter_vars[tensor.name] + nonoverlap_vars = [inter_vars[t.name] for t in nonoverlap_map[tensor]] + for other in nonoverlap_vars: + model.AddNoOverlap([inter_var, other]) + var_mem_span = model.NewIntVar(0, self.max_pool_size, f'mem_span') + model.AddMaxEquality(var_mem_span, [alloc.end for alloc in tensor_allocs.values()]) + model.Minimize(var_mem_span) + + solver = cp_model.CpSolver() + status = solver.Solve(model) + if solver.StatusName(status) == 'OPTIMAL': + opt_mem_span = solver.Value(var_mem_span) + alloc_plan = {} + for name, alloc in tensor_allocs.items(): + alloc_plan[name] = Allocation( + start=solver.Value(alloc.start), + end=solver.Value(alloc.end), + size=alloc.size + ) + ugraph.attributes[self.KWARGS_NAMESCOPE] = AllocationPlan( + plan=alloc_plan, + total_size=opt_mem_span + ) + return ugraph From 4a23369b7dc9c12d98a47bcf386ea7c680d5f0d6 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 1 Mar 2020 00:29:00 +0800 Subject: [PATCH 034/117] tensor mem alloc: fix bug and add logging --- utensor_cgen/transformer/mem_alloc.py | 26 ++++++++++++++++++-------- 1 file changed, 18 insertions(+), 8 deletions(-) diff --git a/utensor_cgen/transformer/mem_alloc.py b/utensor_cgen/transformer/mem_alloc.py index b6489f36..6cb4b4c6 100644 --- a/utensor_cgen/transformer/mem_alloc.py +++ b/utensor_cgen/transformer/mem_alloc.py @@ -2,6 +2,7 @@ from itertools import combinations, product from ortools.sat.python import cp_model +from utensor_cgen.logger import logger from .base import Transformer from .pipeline import TransformerPipeline @@ -30,17 +31,23 @@ def transform(self, ugraph): nonoverlap_map = defaultdict(set) for op_name in ugraph.topo_order: op_info = ugraph.ops_info[op_name] + # no overlapping between input/output tensors for in_tensor, out_tensor in product(op_info.input_tensors, op_info.output_tensors): nonoverlap_map[out_tensor].add(in_tensor) visited_tensors.add(in_tensor) - for known_tensor, out_tensor in product(visited_tensors, op_info.output_tensors): - if ref_cnts[known_tensor.name] > 0: - nonoverlap_map[out_tensor].add(known_tensor) - for in_tensor in op_info.input_tensors: - ref_cnts[in_tensor.name] -= 1 - for out_tensor1, out_tensor2 in combinations(ugraph.output_tensors, 2): - nonoverlap_map[out_tensor1].add(out_tensor2) - visited_tensors.update(ugraph.output_tensors) + # no overlapping among input tensors + for in_tensor1, in_tensor2 in combinations(op_info.input_tensors, 2): + nonoverlap_map[in_tensor1].add(in_tensor2) + # no overlapping between output tensors and all evaluated tensors + for known_tensor, out_tensor in product(visited_tensors, op_info.output_tensors): + if ref_cnts[known_tensor.name] > 0: + nonoverlap_map[out_tensor].add(known_tensor) + visited_tensors.update(op_info.output_tensors) + for in_tensor in op_info.input_tensors: + ref_cnts[in_tensor.name] -= 1 + for out_tensor1, out_tensor2 in combinations(ugraph.output_tensors, 2): + nonoverlap_map[out_tensor1].add(out_tensor2) + visited_tensors.update(ugraph.output_tensors) model = cp_model.CpModel() inter_vars = {} @@ -72,8 +79,11 @@ def transform(self, ugraph): end=solver.Value(alloc.end), size=alloc.size ) + logger.info('optimal tensor allocation plan solved, memory span: %i bytes', opt_mem_span) ugraph.attributes[self.KWARGS_NAMESCOPE] = AllocationPlan( plan=alloc_plan, total_size=opt_mem_span ) + else: + logger.info('tensor allocation plan not found, status: %s', solver.StatusName(status)) return ugraph From 3e282a248f067740d57da8708fbbd50f00184cb1 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 1 Mar 2020 16:39:53 +0800 Subject: [PATCH 035/117] tensor mem alloc visualization --- setup.py | 1 + utensor_cgen/ir/base.py | 7 ++ utensor_cgen/ir/misc/graph_viz.py | 49 ++++++++- utensor_cgen/transformer/mem_alloc.py | 142 ++++++++++++++------------ 4 files changed, 130 insertions(+), 69 deletions(-) diff --git a/setup.py b/setup.py index e7da3b4c..03b526b2 100644 --- a/setup.py +++ b/setup.py @@ -59,6 +59,7 @@ class _Develop(_CompileFlatbuffMixin, _develop): pass 'torchvision', 'onnx-tf==1.2.1', 'graphviz', + 'matplotlib', 'toml', 'ortools', ], diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index 30718e72..3ba8acf0 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -17,6 +17,7 @@ TensorShapeProto as _TensorShapeProto from tensorflow.core.framework.types_pb2 import DataType as _DataType from utensor_cgen.ir.instr import DataManager +from utensor_cgen.logger import logger from utensor_cgen.utils import random_str, topologic_order_graph from .converter import AttrValueConverter, ConverterDispatcher @@ -173,6 +174,12 @@ def is_null_tensor(self): @property def size(self): + if None in self.shape: + logger.warning( + 'nondeterministic shape, implicit converting None to 1: %s, %s', + self.name, + self.shape, + ) return reduce(lambda i, j: i*(j is None and 1 or j), self.shape, 1) def __deepcopy__(self, memo): diff --git a/utensor_cgen/ir/misc/graph_viz.py b/utensor_cgen/ir/misc/graph_viz.py index befe3780..7e072146 100644 --- a/utensor_cgen/ir/misc/graph_viz.py +++ b/utensor_cgen/ir/misc/graph_viz.py @@ -1,7 +1,12 @@ -from graphviz import Digraph +from random import random +import matplotlib.pyplot as plt +from graphviz import Digraph +from matplotlib import cm as _cm from utensor_cgen.logger import logger +plt.style.use('ggplot') + def viz_graph(ugraph, out_fname=None, view=False, cleanup=True, colored_nodes=None, suffix=''): if colored_nodes is None: @@ -65,3 +70,45 @@ def viz_graph(ugraph, out_fname=None, view=False, cleanup=True, colored_nodes=No dot.render(out_fname, view=view, cleanup=cleanup) logger.info('graph visualization file generated: %s', out_fname) return dot + + +def viz_memalloc(ugraph, out_fname=None, fontsize=12, lw=15, cmap=_cm.BrBG_r): + fig = plt.gcf() + if TensorAllocationTransformer.KWARGS_NAMESCOPE not in ugraph.attributes: + logger.info('No tensor allocation plan to visualize: %s', ugraph.name) + return fig + alloc_plan = ugraph.attributes[TensorAllocationTransformer.KWARGS_NAMESCOPE] + topo_tensors = [] + visited_tensors = set() + for op_name in ugraph.topo_order: + op_info = ugraph.ops_info[op_name] + for tensor in op_info.input_tensors: + if tensor not in visited_tensors: + topo_tensors.append(tensor) + visited_tensors.add(tensor) + for tensor in ugraph.output_tensors: + if tensor not in visited_tensors: + topo_tensors.append(tensor) + visited_tensors.add(tensor) + num_tensors = len(topo_tensors) + ys = [num_tensors - i for i in range(len(topo_tensors))] + xmins = [alloc_plan.plan[tensor.name].start for tensor in topo_tensors] + xmaxs = [alloc_plan.plan[tensor.name].end for tensor in topo_tensors] + colors = [cmap(random()) for _ in alloc_plan.plan] + labels = [tensor.name for tensor in topo_tensors] + sizes = [alloc_plan.plan[tensor.name].size for tensor in topo_tensors] + for y, xmin, xmax, color, size in zip(ys, xmins, xmaxs, colors, sizes): + plt.hlines(y, xmin, xmax, lw=lw, colors=color) + plt.text(xmax, y-0.15, '{} bytes'.format(size), fontdict={'fontsize': fontsize}) + plt.xlabel('Offset (bytes)', fontdict={'fontsize': fontsize}) + plt.yticks(ys, labels, fontsize=fontsize) + plt.ylabel('Tensor Names (Topological Ordered, Top to Bottom)', fontdict={'fontsize':fontsize}) + fig.set_size_inches(len(ys), len(ys)*0.5) + plt.tight_layout() + if out_fname: + logger.info('saving tensor mem allocation to %s', out_fname) + fig.savefig(out_fname) + return fig + +# cyclic import +from utensor_cgen.transformer.mem_alloc import TensorAllocationTransformer # isort:skip diff --git a/utensor_cgen/transformer/mem_alloc.py b/utensor_cgen/transformer/mem_alloc.py index 6cb4b4c6..13dfbbc5 100644 --- a/utensor_cgen/transformer/mem_alloc.py +++ b/utensor_cgen/transformer/mem_alloc.py @@ -14,76 +14,82 @@ @TransformerPipeline.register_transformer class TensorAllocationTransformer(Transformer): - METHOD_NAME = "tensor_alloc" - KWARGS_NAMESCOPE = "_tensor_alloc" + METHOD_NAME = "tensor_alloc" + KWARGS_NAMESCOPE = "_tensor_alloc" - def __init__(self, prune_graph=True, max_pool_size=1024*1024*1024): - super(TensorAllocationTransformer, self).__init__(prune_graph=prune_graph) - self.max_pool_size = max_pool_size + def __init__(self, prune_graph=True, max_pool_size=1024*1024*1024): + super(TensorAllocationTransformer, self).__init__(prune_graph=prune_graph) + self.max_pool_size = max_pool_size - def transform(self, ugraph): - ref_cnts = Counter() - for op_name in ugraph.topo_order: - op_info = ugraph.ops_info[op_name] - for tensor in op_info.input_tensors: - ref_cnts[tensor.name] += 1 - visited_tensors = set() - nonoverlap_map = defaultdict(set) - for op_name in ugraph.topo_order: - op_info = ugraph.ops_info[op_name] - # no overlapping between input/output tensors - for in_tensor, out_tensor in product(op_info.input_tensors, op_info.output_tensors): - nonoverlap_map[out_tensor].add(in_tensor) - visited_tensors.add(in_tensor) - # no overlapping among input tensors - for in_tensor1, in_tensor2 in combinations(op_info.input_tensors, 2): - nonoverlap_map[in_tensor1].add(in_tensor2) - # no overlapping between output tensors and all evaluated tensors - for known_tensor, out_tensor in product(visited_tensors, op_info.output_tensors): - if ref_cnts[known_tensor.name] > 0: - nonoverlap_map[out_tensor].add(known_tensor) - visited_tensors.update(op_info.output_tensors) - for in_tensor in op_info.input_tensors: - ref_cnts[in_tensor.name] -= 1 - for out_tensor1, out_tensor2 in combinations(ugraph.output_tensors, 2): - nonoverlap_map[out_tensor1].add(out_tensor2) - visited_tensors.update(ugraph.output_tensors) + def transform(self, ugraph): + ref_cnts = Counter() + for op_name in ugraph.topo_order: + op_info = ugraph.ops_info[op_name] + for tensor in op_info.input_tensors: + ref_cnts[tensor.name] += 1 + visited_tensors = set() + nonoverlap_map = defaultdict(set) + for op_name in ugraph.topo_order: + op_info = ugraph.ops_info[op_name] + # no overlapping between input/output tensors + for in_tensor, out_tensor in product(op_info.input_tensors, op_info.output_tensors): + nonoverlap_map[out_tensor].add(in_tensor) + visited_tensors.add(in_tensor) + # no overlapping among input tensors + for in_tensor1, in_tensor2 in combinations(op_info.input_tensors, 2): + nonoverlap_map[in_tensor1].add(in_tensor2) + # no overlapping between output tensors and all evaluated tensors + for known_tensor, out_tensor in product(visited_tensors, op_info.output_tensors): + if ref_cnts[known_tensor.name] > 0: + nonoverlap_map[out_tensor].add(known_tensor) + visited_tensors.update(op_info.output_tensors) + for in_tensor in op_info.input_tensors: + ref_cnts[in_tensor.name] -= 1 + for out_tensor1, out_tensor2 in combinations(ugraph.output_tensors, 2): + nonoverlap_map[out_tensor1].add(out_tensor2) + visited_tensors.update(ugraph.output_tensors) + alloc_plan = self._solve_opt_plan(visited_tensors, nonoverlap_map) + if alloc_plan is not None: + ugraph.attributes[self.KWARGS_NAMESCOPE] = alloc_plan + return ugraph - model = cp_model.CpModel() - inter_vars = {} - tensor_allocs = {} - for tensor in visited_tensors: - var_start = model.NewIntVar(0, self.max_pool_size, f'{tensor.name}_start') - var_end = model.NewIntVar(0, self.max_pool_size, f'{tensor.name}_end') - size = tensor.size * tensor.dtype.itemsize - intv_var = model.NewIntervalVar(var_start, size, var_end, f'{tensor.name}_alloc') - inter_vars[tensor.name] = intv_var - tensor_allocs[tensor.name] = Allocation(var_start, var_end, size) - for tensor in visited_tensors: - inter_var = inter_vars[tensor.name] - nonoverlap_vars = [inter_vars[t.name] for t in nonoverlap_map[tensor]] - for other in nonoverlap_vars: - model.AddNoOverlap([inter_var, other]) - var_mem_span = model.NewIntVar(0, self.max_pool_size, f'mem_span') - model.AddMaxEquality(var_mem_span, [alloc.end for alloc in tensor_allocs.values()]) - model.Minimize(var_mem_span) + def _solve_opt_plan(self, visited_tensors, nonoverlap_map): + model = cp_model.CpModel() + inter_vars = {} + tensor_allocs = {} + for tensor in visited_tensors: + var_start = model.NewIntVar(0, self.max_pool_size, f'{tensor.name}_start') + var_end = model.NewIntVar(0, self.max_pool_size, f'{tensor.name}_end') + size = tensor.size * tensor.dtype.itemsize + intv_var = model.NewIntervalVar(var_start, size, var_end, f'{tensor.name}_alloc') + inter_vars[tensor.name] = intv_var + tensor_allocs[tensor.name] = Allocation(var_start, var_end, size) + for tensor in visited_tensors: + inter_var = inter_vars[tensor.name] + nonoverlap_vars = [inter_vars[t.name] for t in nonoverlap_map[tensor]] + for other in nonoverlap_vars: + model.AddNoOverlap([inter_var, other]) + var_mem_span = model.NewIntVar(0, self.max_pool_size, f'mem_span') + model.AddMaxEquality(var_mem_span, [alloc.end for alloc in tensor_allocs.values()]) + model.Minimize(var_mem_span) - solver = cp_model.CpSolver() - status = solver.Solve(model) - if solver.StatusName(status) == 'OPTIMAL': - opt_mem_span = solver.Value(var_mem_span) - alloc_plan = {} - for name, alloc in tensor_allocs.items(): - alloc_plan[name] = Allocation( - start=solver.Value(alloc.start), - end=solver.Value(alloc.end), - size=alloc.size - ) - logger.info('optimal tensor allocation plan solved, memory span: %i bytes', opt_mem_span) - ugraph.attributes[self.KWARGS_NAMESCOPE] = AllocationPlan( - plan=alloc_plan, - total_size=opt_mem_span + solver = cp_model.CpSolver() + status = solver.Solve(model) + alloc_plan = None + if solver.StatusName(status) == 'OPTIMAL': + opt_mem_span = solver.Value(var_mem_span) + alloc_plan = {} + for name, alloc in tensor_allocs.items(): + alloc_plan[name] = Allocation( + start=solver.Value(alloc.start), + end=solver.Value(alloc.end), + size=alloc.size ) - else: - logger.info('tensor allocation plan not found, status: %s', solver.StatusName(status)) - return ugraph + logger.info('optimal tensor allocation plan solved, memory span: %i bytes', opt_mem_span) + alloc_plan = AllocationPlan( + plan=alloc_plan, + total_size=opt_mem_span + ) + else: + logger.info('tensor allocation plan not found, status: %s', solver.StatusName(status)) + return alloc_plan From ba5ad38a5df72b8da3fa8db19e10cd532d5b7de8 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 1 Mar 2020 16:49:05 +0800 Subject: [PATCH 036/117] minor fix --- utensor_cgen/ir/misc/graph_viz.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/utensor_cgen/ir/misc/graph_viz.py b/utensor_cgen/ir/misc/graph_viz.py index 7e072146..316b68f4 100644 --- a/utensor_cgen/ir/misc/graph_viz.py +++ b/utensor_cgen/ir/misc/graph_viz.py @@ -1,4 +1,4 @@ -from random import random +from random import random, seed import matplotlib.pyplot as plt from graphviz import Digraph @@ -72,7 +72,8 @@ def viz_graph(ugraph, out_fname=None, view=False, cleanup=True, colored_nodes=No return dot -def viz_memalloc(ugraph, out_fname=None, fontsize=12, lw=15, cmap=_cm.BrBG_r): +def viz_memalloc(ugraph, out_fname=None, fontsize=12, lw=15, cmap=_cm.BrBG_r, rand_seed=1024): + seed(rand_seed) fig = plt.gcf() if TensorAllocationTransformer.KWARGS_NAMESCOPE not in ugraph.attributes: logger.info('No tensor allocation plan to visualize: %s', ugraph.name) From c87784e62b5ab79e0419303f233960a4699b5f04 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 1 Mar 2020 22:10:10 +0800 Subject: [PATCH 037/117] python3.5 compatible (no f-string) --- utensor_cgen/ir/misc/graph_viz.py | 1 + utensor_cgen/transformer/mem_alloc.py | 8 ++++---- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/utensor_cgen/ir/misc/graph_viz.py b/utensor_cgen/ir/misc/graph_viz.py index 316b68f4..8161acba 100644 --- a/utensor_cgen/ir/misc/graph_viz.py +++ b/utensor_cgen/ir/misc/graph_viz.py @@ -103,6 +103,7 @@ def viz_memalloc(ugraph, out_fname=None, fontsize=12, lw=15, cmap=_cm.BrBG_r, ra plt.text(xmax, y-0.15, '{} bytes'.format(size), fontdict={'fontsize': fontsize}) plt.xlabel('Offset (bytes)', fontdict={'fontsize': fontsize}) plt.yticks(ys, labels, fontsize=fontsize) + plt.xticks(fontsize=fontsize) plt.ylabel('Tensor Names (Topological Ordered, Top to Bottom)', fontdict={'fontsize':fontsize}) fig.set_size_inches(len(ys), len(ys)*0.5) plt.tight_layout() diff --git a/utensor_cgen/transformer/mem_alloc.py b/utensor_cgen/transformer/mem_alloc.py index 13dfbbc5..8ab58224 100644 --- a/utensor_cgen/transformer/mem_alloc.py +++ b/utensor_cgen/transformer/mem_alloc.py @@ -58,10 +58,10 @@ def _solve_opt_plan(self, visited_tensors, nonoverlap_map): inter_vars = {} tensor_allocs = {} for tensor in visited_tensors: - var_start = model.NewIntVar(0, self.max_pool_size, f'{tensor.name}_start') - var_end = model.NewIntVar(0, self.max_pool_size, f'{tensor.name}_end') + var_start = model.NewIntVar(0, self.max_pool_size, '{}_start'.format(tensor.name)) + var_end = model.NewIntVar(0, self.max_pool_size, '{}_end'.format(tensor.name)) size = tensor.size * tensor.dtype.itemsize - intv_var = model.NewIntervalVar(var_start, size, var_end, f'{tensor.name}_alloc') + intv_var = model.NewIntervalVar(var_start, size, var_end, '{}_alloc'.format(tensor.name)) inter_vars[tensor.name] = intv_var tensor_allocs[tensor.name] = Allocation(var_start, var_end, size) for tensor in visited_tensors: @@ -69,7 +69,7 @@ def _solve_opt_plan(self, visited_tensors, nonoverlap_map): nonoverlap_vars = [inter_vars[t.name] for t in nonoverlap_map[tensor]] for other in nonoverlap_vars: model.AddNoOverlap([inter_var, other]) - var_mem_span = model.NewIntVar(0, self.max_pool_size, f'mem_span') + var_mem_span = model.NewIntVar(0, self.max_pool_size, 'mem_span') model.AddMaxEquality(var_mem_span, [alloc.end for alloc in tensor_allocs.values()]) model.Minimize(var_mem_span) From 8b09892ab70c12c34ee150baed558b44d31c7278 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Mon, 2 Mar 2020 17:38:02 +0800 Subject: [PATCH 038/117] remove data manager from snippet creation and update tests --- tests/deep_mlp/end_to_end.ipynb | 208 +++++------------- tests/deep_mlp/simple_mnist.pb | Bin 438801 -> 438744 bytes tests/test_ir/__init__.py | 1 + tests/test_matcher/__init__.py | 1 + tests/test_transformer/__init__.py | 1 + .../code_generator/legacy/_code_generator.py | 46 ++-- .../code_generator/legacy/_operators.py | 190 ++++++++++------ .../utensor/snippets/legacy/_snippets.py | 4 + utensor_cgen/ir/misc/graph_viz.py | 19 +- utensor_cgen/transformer/__init__.py | 2 +- utensor_cgen/transformer/mem_alloc.py | 74 +++++-- utensor_cgen/transformer/ns_transformer.py | 11 +- utensor_cgen/utils.py | 31 +-- 13 files changed, 291 insertions(+), 297 deletions(-) create mode 100644 tests/test_matcher/__init__.py diff --git a/tests/deep_mlp/end_to_end.ipynb b/tests/deep_mlp/end_to_end.ipynb index cd69e021..d61a2519 100644 --- a/tests/deep_mlp/end_to_end.ipynb +++ b/tests/deep_mlp/end_to_end.ipynb @@ -10,23 +10,8 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2018-05-20T08:36:04.135548Z", - "start_time": "2018-05-20T08:35:58.619420Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From /Users/dboyliao/Work/open_source/uTensor/utensor_cgen/.venv/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use the retry module or similar alternatives.\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow.python.framework import graph_util\n", @@ -36,19 +21,12 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "ExecuteTime": { - "end_time": "2018-05-20T08:36:04.143346Z", - "start_time": "2018-05-20T08:36:04.138305Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": [ - "1.7.0\n" - ] + "text": "1.13.1\n" } ], "source": [ @@ -58,44 +36,16 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2018-05-20T08:36:10.734171Z", - "start_time": "2018-05-20T08:36:10.056448Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": [ - "WARNING:tensorflow:From :1: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n", - "WARNING:tensorflow:From /Users/dboyliao/Work/open_source/uTensor/utensor_cgen/.venv/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Please write your own downloading logic.\n", - "WARNING:tensorflow:From /Users/dboyliao/Work/open_source/uTensor/utensor_cgen/.venv/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Please use tf.data to implement this functionality.\n", - "Extracting data/train-images-idx3-ubyte.gz\n", - "WARNING:tensorflow:From /Users/dboyliao/Work/open_source/uTensor/utensor_cgen/.venv/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Please use tf.data to implement this functionality.\n", - "Extracting data/train-labels-idx1-ubyte.gz\n", - "WARNING:tensorflow:From /Users/dboyliao/Work/open_source/uTensor/utensor_cgen/.venv/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Please use tf.one_hot on tensors.\n", - "Extracting data/t10k-images-idx3-ubyte.gz\n", - "Extracting data/t10k-labels-idx1-ubyte.gz\n", - "WARNING:tensorflow:From /Users/dboyliao/Work/open_source/uTensor/utensor_cgen/.venv/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n" - ] + "text": "WARNING:tensorflow:From :1: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\nInstructions for updating:\nPlease use alternatives such as official/mnist/dataset.py from tensorflow/models.\nWARNING:tensorflow:From /Users/dboyliao/Work/open_source/uTensor/utensor_cgen/.venv/lib/python3.6/site-packages/tensorflow-1.13.1-py3.6-macosx-10.15-x86_64.egg/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\nInstructions for updating:\nPlease write your own downloading logic.\nWARNING:tensorflow:From /Users/dboyliao/Work/open_source/uTensor/utensor_cgen/.venv/lib/python3.6/site-packages/tensorflow-1.13.1-py3.6-macosx-10.15-x86_64.egg/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\nInstructions for updating:\nPlease use tf.data to implement this functionality.\nExtracting mnist_data/train-images-idx3-ubyte.gz\nWARNING:tensorflow:From /Users/dboyliao/Work/open_source/uTensor/utensor_cgen/.venv/lib/python3.6/site-packages/tensorflow-1.13.1-py3.6-macosx-10.15-x86_64.egg/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\nInstructions for updating:\nPlease use tf.data to implement this functionality.\nExtracting mnist_data/train-labels-idx1-ubyte.gz\nWARNING:tensorflow:From /Users/dboyliao/Work/open_source/uTensor/utensor_cgen/.venv/lib/python3.6/site-packages/tensorflow-1.13.1-py3.6-macosx-10.15-x86_64.egg/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\nInstructions for updating:\nPlease use tf.one_hot on tensors.\nExtracting mnist_data/t10k-images-idx3-ubyte.gz\nExtracting mnist_data/t10k-labels-idx1-ubyte.gz\nWARNING:tensorflow:From /Users/dboyliao/Work/open_source/uTensor/utensor_cgen/.venv/lib/python3.6/site-packages/tensorflow-1.13.1-py3.6-macosx-10.15-x86_64.egg/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\nInstructions for updating:\nPlease use alternatives such as official/mnist/dataset.py from tensorflow/models.\n" } ], "source": [ - "mnist = read_data_sets('data', one_hot=True)" + "mnist = read_data_sets('mnist_data', one_hot=True)" ] }, { @@ -108,42 +58,47 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2018-05-20T08:36:15.435585Z", - "start_time": "2018-05-20T08:36:15.431209Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "def weight_variable(shape, name):\n", " \"\"\"weight_variable generates a weight variable of a given shape.\"\"\"\n", " initial = tf.truncated_normal(shape, stddev=0.1)\n", - " return tf.Variable(initial, name)\n", + " return tf.Variable(initial, name=name)\n", "\n", "\n", "def bias_variable(shape, name):\n", " \"\"\"bias_variable generates a bias variable of a given shape.\"\"\"\n", " initial = tf.constant(0.1, shape=shape)\n", - " return tf.Variable(initial, name)" + " return tf.Variable(initial, name=name)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 10" ] }, { "cell_type": "code", "execution_count": 6, - "metadata": { - "ExecuteTime": { - "end_time": "2018-05-20T08:38:12.753876Z", - "start_time": "2018-05-20T08:38:12.568009Z" + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": "WARNING:tensorflow:From /Users/dboyliao/Work/open_source/uTensor/utensor_cgen/.venv/lib/python3.6/site-packages/tensorflow-1.13.1-py3.6-macosx-10.15-x86_64.egg/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\nInstructions for updating:\nColocations handled automatically by placer.\n" } - }, - "outputs": [], + ], "source": [ "graph = tf.Graph()\n", "\n", "with graph.as_default():\n", - " x = tf.placeholder(tf.float32, [None, 784], name=\"x\")\n", - " y = tf.placeholder(tf.float32, [None, 10], name=\"y\")\n", + " x = tf.placeholder(tf.float32, [batch_size, 784], name=\"x\")\n", + " y = tf.placeholder(tf.float32, [batch_size, 10], name=\"y\")\n", "\n", " with tf.name_scope(\"Layer1\"):\n", " W_fc1 = weight_variable([784, 128], name='W_fc1')\n", @@ -176,21 +131,14 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "ExecuteTime": { - "end_time": "2018-05-20T08:44:06.198243Z", - "start_time": "2018-05-20T08:44:06.193588Z" - } - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "'y_pred'" - ] + "text/plain": "'y_pred'" }, - "execution_count": 12, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -201,40 +149,13 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "ExecuteTime": { - "end_time": "2018-05-20T08:38:43.202667Z", - "start_time": "2018-05-20T08:38:14.106117Z" - } - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": [ - "step 1000, training accuracy 0.88\n", - "step 2000, training accuracy 0.98\n", - "step 3000, training accuracy 0.92\n", - "step 4000, training accuracy 0.96\n", - "step 5000, training accuracy 0.96\n", - "step 6000, training accuracy 0.96\n", - "step 7000, training accuracy 0.96\n", - "step 8000, training accuracy 0.94\n", - "step 9000, training accuracy 0.94\n", - "step 10000, training accuracy 0.94\n", - "step 11000, training accuracy 1\n", - "step 12000, training accuracy 1\n", - "step 13000, training accuracy 0.96\n", - "step 14000, training accuracy 0.98\n", - "step 15000, training accuracy 0.96\n", - "step 16000, training accuracy 1\n", - "step 17000, training accuracy 0.96\n", - "step 18000, training accuracy 0.96\n", - "step 19000, training accuracy 1\n", - "step 20000, training accuracy 1\n", - "test accuracy 0.9704\n" - ] + "text": "step 1000, training accuracy 0.7\nstep 2000, training accuracy 0.9\nstep 3000, training accuracy 0.8\nstep 4000, training accuracy 1\nstep 5000, training accuracy 0.9\nstep 6000, training accuracy 1\nstep 7000, training accuracy 1\nstep 8000, training accuracy 0.9\nstep 9000, training accuracy 0.8\nstep 10000, training accuracy 1\nstep 11000, training accuracy 0.8\nstep 12000, training accuracy 1\nstep 13000, training accuracy 0.9\nstep 14000, training accuracy 1\nstep 15000, training accuracy 1\nstep 16000, training accuracy 1\nstep 17000, training accuracy 0.8\nstep 18000, training accuracy 0.9\nstep 19000, training accuracy 1\nstep 20000, training accuracy 0.9\n" } ], "source": [ @@ -243,32 +164,24 @@ " saver = tf.train.Saver()\n", "\n", " for i in range(1, 20001):\n", - " images, labels = mnist.train.next_batch(50)\n", + " images, labels = mnist.train.next_batch(batch_size)\n", " train_step.run(feed_dict={x: images, y: labels})\n", " if i % 1000 == 0:\n", " train_accuracy = accuracy.eval(feed_dict={x: images, y: labels})\n", " print('step %d, training accuracy %g' % (i, train_accuracy))\n", - " print('test accuracy %g' % accuracy.eval(feed_dict={x: mnist.test.images, y: mnist.test.labels}))\n", " chkp_path = saver.save(sess, \"./my-model/model.ckpt\")" ] }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "ExecuteTime": { - "end_time": "2018-05-20T08:38:43.221800Z", - "start_time": "2018-05-20T08:38:43.205483Z" - } - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "'./my-model/model.ckpt'" - ] + "text/plain": "'./my-model/model.ckpt'" }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -286,22 +199,13 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "ExecuteTime": { - "end_time": "2018-05-20T08:39:09.259145Z", - "start_time": "2018-05-20T08:39:09.191322Z" - } - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": [ - "INFO:tensorflow:Restoring parameters from ./my-model/model.ckpt\n", - "INFO:tensorflow:Froze 6 variables.\n", - "Converted 6 variables to const ops.\n" - ] + "text": "WARNING:tensorflow:From /Users/dboyliao/Work/open_source/uTensor/utensor_cgen/.venv/lib/python3.6/site-packages/tensorflow-1.13.1-py3.6-macosx-10.15-x86_64.egg/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\nInstructions for updating:\nUse standard file APIs to check for files with this prefix.\nINFO:tensorflow:Restoring parameters from ./my-model/model.ckpt\nWARNING:tensorflow:From :4: remove_training_nodes (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\nInstructions for updating:\nUse tf.compat.v1.graph_util.remove_training_nodes\nWARNING:tensorflow:From :5: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\nInstructions for updating:\nUse tf.compat.v1.graph_util.convert_variables_to_constants\nWARNING:tensorflow:From /Users/dboyliao/Work/open_source/uTensor/utensor_cgen/.venv/lib/python3.6/site-packages/tensorflow-1.13.1-py3.6-macosx-10.15-x86_64.egg/tensorflow/python/framework/graph_util_impl.py:245: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\nInstructions for updating:\nUse tf.compat.v1.graph_util.extract_sub_graph\nINFO:tensorflow:Froze 6 variables.\nINFO:tensorflow:Converted 6 variables to const ops.\n" } ], "source": [ @@ -314,20 +218,13 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "ExecuteTime": { - "end_time": "2018-05-20T08:39:11.127661Z", - "start_time": "2018-05-20T08:39:11.120094Z" - } - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": [ - "written graph to: ./my-model/simple_mnist.pb\n" - ] + "text": "written graph to: ./my-model/simple_mnist.pb\n" } ], "source": [ @@ -340,13 +237,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "ExecuteTime": { - "end_time": "2018-05-20T08:40:11.092109Z", - "start_time": "2018-05-20T08:40:11.087863Z" - } - }, + "execution_count": 12, + "metadata": {}, "outputs": [], "source": [ "# you are ready to use simple_mnist.pb to generate uTensor implementation" @@ 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nonoverlap_map[out_tensor].add(known_tensor) - visited_tensors.update(op_info.output_tensors) + tensors_to_schedule.update(op_info.output_tensors) for in_tensor in op_info.input_tensors: ref_cnts[in_tensor.name] -= 1 for out_tensor1, out_tensor2 in combinations(ugraph.output_tensors, 2): nonoverlap_map[out_tensor1].add(out_tensor2) - visited_tensors.update(ugraph.output_tensors) - alloc_plan = self._solve_opt_plan(visited_tensors, nonoverlap_map) + tensors_to_schedule.update(ugraph.output_tensors) + alloc_plan = self._solve_opt_plan(tensors_to_schedule, nonoverlap_map) if alloc_plan is not None: ugraph.attributes[self.KWARGS_NAMESCOPE] = alloc_plan + if self.vizualize_fname: + viz_memalloc(ugraph=ugraph, out_fname=self.vizualize_fname, **self.vizualize_kwargs) return ugraph - def _solve_opt_plan(self, visited_tensors, nonoverlap_map): + @timed + def _solve_opt_plan(self, tensors_to_schedule, nonoverlap_map): model = cp_model.CpModel() inter_vars = {} tensor_allocs = {} - for tensor in visited_tensors: + for tensor in tensors_to_schedule: var_start = model.NewIntVar(0, self.max_pool_size, '{}_start'.format(tensor.name)) var_end = model.NewIntVar(0, self.max_pool_size, '{}_end'.format(tensor.name)) size = tensor.size * tensor.dtype.itemsize intv_var = model.NewIntervalVar(var_start, size, var_end, '{}_alloc'.format(tensor.name)) inter_vars[tensor.name] = intv_var - tensor_allocs[tensor.name] = Allocation(var_start, var_end, size) - for tensor in visited_tensors: + tensor_allocs[tensor.name] = MemorySpan(var_start, var_end, size) + for tensor in tensors_to_schedule: inter_var = inter_vars[tensor.name] nonoverlap_vars = [inter_vars[t.name] for t in nonoverlap_map[tensor]] for other in nonoverlap_vars: model.AddNoOverlap([inter_var, other]) - var_mem_span = model.NewIntVar(0, self.max_pool_size, 'mem_span') - model.AddMaxEquality(var_mem_span, [alloc.end for alloc in tensor_allocs.values()]) - model.Minimize(var_mem_span) + var_mempool_size = model.NewIntVar(0, self.max_pool_size, 'mempool_size') + model.AddMaxEquality(var_mempool_size, [alloc.end for alloc in tensor_allocs.values()]) + model.Minimize(var_mempool_size) solver = cp_model.CpSolver() status = solver.Solve(model) alloc_plan = None if solver.StatusName(status) == 'OPTIMAL': - opt_mem_span = solver.Value(var_mem_span) - alloc_plan = {} + opt_mempool_size = solver.Value(var_mempool_size) + plan = {} for name, alloc in tensor_allocs.items(): - alloc_plan[name] = Allocation( + plan[name] = MemorySpan( start=solver.Value(alloc.start), end=solver.Value(alloc.end), size=alloc.size ) - logger.info('optimal tensor allocation plan solved, memory span: %i bytes', opt_mem_span) + logger.info('optimal tensor allocation plan solved, memory span: %i bytes', opt_mempool_size) alloc_plan = AllocationPlan( - plan=alloc_plan, - total_size=opt_mem_span + plan=plan, + total_size=opt_mempool_size ) else: logger.info('tensor allocation plan not found, status: %s', solver.StatusName(status)) return alloc_plan + +from utensor_cgen.ir.misc.graph_viz import viz_memalloc # isort:skip diff --git a/utensor_cgen/transformer/ns_transformer.py b/utensor_cgen/transformer/ns_transformer.py index 2a627b61..ef0acd5e 100644 --- a/utensor_cgen/transformer/ns_transformer.py +++ b/utensor_cgen/transformer/ns_transformer.py @@ -10,7 +10,6 @@ import numpy as np import tensorflow as tf - from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.ir import OperationInfo, uTensorGraph from utensor_cgen.logger import logger @@ -21,13 +20,18 @@ from .base import Transformer from .pipeline import TransformerPipeline -__all__ = ["DropoutTransformer", "BatchNormTransformer", "InlineTransformer", "TensorLifeProbe"] +__all__ = [ + "DropoutTransformer", + "BatchNormTransformer", + "InlineTransformer", + "TensorLifeProbe" +] @TransformerPipeline.register_transformer class TensorLifeProbe(Transformer): METHOD_NAME = 'tensorlife' - KWARGS_NAMESCOPE = '_utensor_utlife' + KWARGS_NAMESCOPE = '_tensorlife' DATA_NAME = 'address' def __init__( @@ -56,6 +60,7 @@ def transform(self, ugraph): for out_o in out_t_infos: if out_o.name in allocate_table: new_ugraph.data_manager.address = (out_o.name, allocate_table[out_o.name]['offsetstart']) + new_ugraph.attributes[self.KWARGS_NAMESCOPE] = allocate_table return new_ugraph return ugraph diff --git a/utensor_cgen/utils.py b/utensor_cgen/utils.py index 70c28657..095c53c0 100644 --- a/utensor_cgen/utils.py +++ b/utensor_cgen/utils.py @@ -6,15 +6,17 @@ from ast import literal_eval from collections import deque from copy import deepcopy +from functools import wraps from random import choice from string import ascii_letters, digits +from time import time import attr -import numpy as np from click.types import ParamType from toml import loads as _parse import idx2numpy as idx2np +import numpy as np from utensor_cgen.logger import logger __all__ = ["save_idx", "save_consts", "save_graph", "log_graph", @@ -197,7 +199,7 @@ class NamescopedKWArgsParser: TODO: replace it with a better data manager """ - def __init__(self, namespace, kwargs, data_manager=None, op_info=None): + def __init__(self, namespace, kwargs): ns_pattern = re.compile(r'^([^\d\W][\w\d_]*)__([^\d\W][\w\d_]*)') self._namespace = namespace self._private_kwargs = {} @@ -211,17 +213,7 @@ def __init__(self, namespace, kwargs, data_manager=None, op_info=None): self._private_kwargs[argname] = value else: self._shared_kwargs[key] = value - if op_info is not None and data_manager is not None: - outputs = [tensor_info.name for tensor_info in op_info.output_tensors] - for tensor in outputs: - values = data_manager.group(tensor) - for key, value in values.items(): - if key not in self._private_kwargs: - self._private_kwargs[key] = [] - self._private_kwargs[key].append(value) - else: - self._private_kwargs[key].append(value) - + def get(self, argname, default=None): """ Get value of given name in the namespace @@ -539,3 +531,16 @@ def __repr__(self): ' user_config={} \n' ')' ).format(self._defaults, self._user_config) + + +def timed(func): + + @wraps(func) + def wrapped(*args, **kwargs): + start_time = time() + ret = func(*args, **kwargs) + end_time = time() + logger.info('collapsed time of calling %s: %0.4f seconds', func.__name__, end_time - start_time) + return ret + + return wrapped From 5f3f2d2519c8f44966b047db998912e662737495 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Mon, 2 Mar 2020 22:06:53 +0800 Subject: [PATCH 039/117] cleanup DataManager, TensorLifeProbe transformer and update tests --- .../test_tensorallocator/__init__.py | 0 .../test_tensorallocator/conftest.py | 16 -- .../test_allocator_optimizer.py | 141 ------------- utensor_cgen/ir/base.py | 9 - utensor_cgen/ir/instr.py | 59 ------ utensor_cgen/transformer/ns_transformer.py | 192 ------------------ 6 files changed, 417 deletions(-) delete mode 100644 tests/test_transformer/test_tensorallocator/__init__.py delete mode 100644 tests/test_transformer/test_tensorallocator/conftest.py delete mode 100644 tests/test_transformer/test_tensorallocator/test_allocator_optimizer.py delete mode 100644 utensor_cgen/ir/instr.py diff --git a/tests/test_transformer/test_tensorallocator/__init__.py b/tests/test_transformer/test_tensorallocator/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/test_transformer/test_tensorallocator/conftest.py b/tests/test_transformer/test_tensorallocator/conftest.py deleted file mode 100644 index e3a6f948..00000000 --- a/tests/test_transformer/test_tensorallocator/conftest.py +++ /dev/null @@ -1,16 +0,0 @@ -import pytest -import tensorflow as tf - - -@pytest.fixture(scope='session', name='refgraph_tuple') -def refgraph(): - graph = tf.Graph() - with graph.as_default(): - x = tf.constant(1, name='x', dtype=tf.float32) - y = tf.constant(1, name='y', dtype=tf.float32) - z = tf.add(x, y, name='z') - w = tf.add(x, 2.0, name='w') - k = tf.add(z, w, name='k') - - - return graph.as_graph_def(), [k.op.name] \ No newline at end of file diff --git a/tests/test_transformer/test_tensorallocator/test_allocator_optimizer.py b/tests/test_transformer/test_tensorallocator/test_allocator_optimizer.py deleted file mode 100644 index 83f9090b..00000000 --- a/tests/test_transformer/test_tensorallocator/test_allocator_optimizer.py +++ /dev/null @@ -1,141 +0,0 @@ -from utensor_cgen.frontend.tensorflow import GraphDefParser -from utensor_cgen.transformer import TensorLifeProbe - - -def test_create_resource_table(refgraph_tuple): - (graph_def, output_nodes)= refgraph_tuple - ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) - transformer = TensorLifeProbe() - table = transformer._create_resource_table(ugraph) - resource_ans = { - 'x:0' : [0, 4], - 'y:0' : [1, 2], - 'z:0' : [2, 5], - 'w/y:0': [3, 4], - 'w:0' : [4, 5], - 'k:0' : [5, 5] - } - for t in table: - assert table[t]['start'] == resource_ans[t][0] - assert table[t]['end'] == resource_ans[t][1] - -def test_create_allocate_table(refgraph_tuple): - (graph_def, output_nodes)= refgraph_tuple - ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) - transformer = TensorLifeProbe() - table = transformer._create_resource_table(ugraph) - allocate_table = dict() - l = ugraph.topo_order[3] - g = ugraph.ops_info[l] - x = g.output_tensors[0] - result = transformer._update_allocation_table(allocate_table, table, x, 0, 5) - assert result['w/y:0']['offsetstart'] == 0 - assert result['w/y:0']['offsetend'] == 5 - - - -def test_query_offset_address(refgraph_tuple): - (graph_def, output_nodes)= refgraph_tuple - ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) - transformer = TensorLifeProbe() - table = transformer._create_resource_table(ugraph) - allocate_table = dict() - l = ugraph.topo_order[3] - g = ugraph.ops_info[l] - x = g.output_tensors[0] - result = transformer._update_allocation_table(allocate_table, table, x, 0, 5) - start, end = transformer._query_offset_fromallocate_table(allocate_table, 1, 5) - assert start == 0 - assert end == 5 - -def test_query_timeline(refgraph_tuple): - (graph_def, output_nodes)= refgraph_tuple - ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) - transformer = TensorLifeProbe() - table = transformer._create_resource_table(ugraph) - allocate_table = dict() - l = ugraph.topo_order[3] - g = ugraph.ops_info[l] - x = g.output_tensors[0] - result = transformer._update_allocation_table(allocate_table, table, x, 0, 5) - start, end = transformer._query_time_fromallocate_table(allocate_table, 1, 5) - assert start == 1 - assert end == 5 - -def test_query_result(refgraph_tuple): - (graph_def, output_nodes)= refgraph_tuple - ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) - transformer = TensorLifeProbe() - table = transformer._create_resource_table(ugraph) - allocate_table = dict() - l = ugraph.topo_order[0] - g = ugraph.ops_info[l] - x = g.output_tensors[0] - result = transformer._update_allocation_table(allocate_table, table, x, 5, 10) - l = ugraph.topo_order[1] - g = ugraph.ops_info[l] - y = g.output_tensors[0] - #address and time overlap - s = transformer._query_result(allocate_table, 3, 6, 1, 2) - assert s - s = transformer._query_result(allocate_table, 6, 8, 1, 2) - assert s - s = transformer._query_result(allocate_table, 9, 11, 1, 2) - assert s - #address overlap, but time doesn't - s = transformer._query_result(allocate_table, 3, 6, 5, 6) - assert not s - #time overlap, but address doesn't - s = transformer._query_result(allocate_table, 3, 4, 1, 2) - assert s - -def test_allocate_tensor(refgraph_tuple): - (graph_def, output_nodes) = refgraph_tuple - ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) - transformer = TensorLifeProbe() - tensors = [] - table = transformer._create_resource_table(ugraph) - allocate_table = dict() - l = ugraph.topo_order[3] - g = ugraph.ops_info[l] - x = g.output_tensors[0] - tensors.append(x) - unit_size = 4 - buffer_size = 30000 #1k bytes - result = transformer.allocate_tensor(tensors, 0, allocate_table, table, buffer_size, unit_size) - - assert result == True - -def test_allocate_graph(refgraph_tuple): - (graph_def, output_nodes) = refgraph_tuple - ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) - transformer = TensorLifeProbe() - use_def_table = transformer._create_resource_table(ugraph) - unit_size = 4 - buffer_size = 3000 #1k bytes - allocate_table = dict() - result = transformer.allocate_graph(ugraph, allocate_table, use_def_table, buffer_size, unit_size) - assert result == True - - -def test_query_check(refgraph_tuple): - (graph_def, output_nodes)= refgraph_tuple - ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) - transformer = TensorLifeProbe() - table = transformer._create_resource_table(ugraph) - allocate_table = dict() - l = ugraph.topo_order[3] - g = ugraph.ops_info[l] - x = g.output_tensors[0] - result = transformer._update_allocation_table(allocate_table, table, x, 0, 5) - l = ugraph.topo_order[1] - g = ugraph.ops_info[l] - y = g.output_tensors[0] - valid = transformer._check(result, table, y, 4, 10) - assert valid == False - -def test_memory_allocation(refgraph_tuple): - (graph_def, output_nodes)= refgraph_tuple - ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) - transformer = TensorLifeProbe() - ugraph = transformer.transform(ugraph) diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index 3ba8acf0..1c674de0 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -16,7 +16,6 @@ from tensorflow.core.framework.tensor_shape_pb2 import \ TensorShapeProto as _TensorShapeProto from tensorflow.core.framework.types_pb2 import DataType as _DataType -from utensor_cgen.ir.instr import DataManager from utensor_cgen.logger import logger from utensor_cgen.utils import random_str, topologic_order_graph @@ -469,7 +468,6 @@ class uTensorGraph(IRBase, _NoShallowCopyMixin, uTensorGraphBuilderMixin): ops_info = attr.ib(factory=dict) # non-init topo_order = attr.ib(factory=list, init=False) - data_manager = attr.ib(default=None, init=False) _type_to_op_map = attr.ib(factory=dict, init=False, repr=False) attributes = attr.ib(factory=dict, init=False, repr=False) @@ -617,10 +615,6 @@ def ops(self): if not self.topo_order: topologic_order_graph(self) return [self.ops_info[name] for name in self.topo_order] - - def setup_data_manager(self, datas): - manager = DataManager(datas) - self.data_manager = manager def unsafe_merge_into(self, other_ugraph): """ @@ -675,9 +669,6 @@ def __deepcopy__(self, memo): k: deepcopy(v, memo) for k, v in self.ops_info.items() } - if self.data_manager: - new_graph.data_manager = DataManager({}) - new_graph.data_manager.StorageCenter = deepcopy(self.data_manager.StorageCenter) new_graph._lib_name = self._lib_name new_graph.attributes = deepcopy(self.attributes) topologic_order_graph(new_graph) diff --git a/utensor_cgen/ir/instr.py b/utensor_cgen/ir/instr.py deleted file mode 100644 index b452cd4c..00000000 --- a/utensor_cgen/ir/instr.py +++ /dev/null @@ -1,59 +0,0 @@ -class MemoryAllocation(object): - _store_name = "address" - - def __init__(self): - self._address_map = {} - - def __setattr__(self, attr, value): - if attr == '_address_map': - return super(MemoryAllocation, self).__setattr__(attr, value) - self._address_map[attr] = value - - def __getattr__(self, att): - return self._address_map[att] - - def __deepcopy__(self, memo): - new_obj = MemoryAllocation() - new_obj._address_map = {k: v for k, v in self._address_map.items()} - return new_obj - -class DataManager(object): - - InfoCenterMap = { - MemoryAllocation._store_name: MemoryAllocation - } - - def __init__(self, datas): - self.StorageCenter = {} - for data_name in datas: - data_cls = self.InfoCenterMap.get(data_name, None) - if data_cls is None: - raise ValueError("Unknown transformation method: {}".format(data_name)) - datastorage = data_cls() - self.StorageCenter.update({data_name: datastorage}) - - @classmethod - def register_datamap(cls, data_cls, overwrite=False): - cls.InfoCenterMap[data_cls.name] = data_cls - - def __getattr__(self, attr): - if attr == 'StorageCenter': - raise AttributeError('StorageCenter') - elif attr.startswith('__'): - return super(DataManager, self).__getattr__(attr) - cls_instance = self.StorageCenter[attr] - return cls_instance - - def __setattr__(self, attr, value): - if attr == 'StorageCenter': - return super(DataManager, self).__setattr__(attr, value) - cls_instance = self.StorageCenter[attr] - k, v = value - cls_instance.__setattr__(k, v) - - def group(self, tensor): - ret = {} - for cls_object in self.StorageCenter.values(): - ans = cls_object.__getattr__(tensor) - ret.update({cls_object._store_name: ans}) - return ret diff --git a/utensor_cgen/transformer/ns_transformer.py b/utensor_cgen/transformer/ns_transformer.py index ef0acd5e..31acd247 100644 --- a/utensor_cgen/transformer/ns_transformer.py +++ b/utensor_cgen/transformer/ns_transformer.py @@ -24,201 +24,9 @@ "DropoutTransformer", "BatchNormTransformer", "InlineTransformer", - "TensorLifeProbe" ] -@TransformerPipeline.register_transformer -class TensorLifeProbe(Transformer): - METHOD_NAME = 'tensorlife' - KWARGS_NAMESCOPE = '_tensorlife' - DATA_NAME = 'address' - - def __init__( - self, - buff_size=100000, #1k bytes - unit_size=4 - ): - self.buff_size = buff_size - self.unit_size = unit_size - - def transform(self, ugraph): - new_ugraph = deepcopy(ugraph) - new_ugraph.setup_data_manager({self.DATA_NAME: " "}) - # use_def_table: dict, tensor_name -> {'start': op_idx, 'end': op_idx} - use_def_table = self._create_resource_table(new_ugraph) - allocate_table = dict() - allocate_success = self.allocate_graph(new_ugraph, allocate_table, use_def_table, self.buff_size, self.unit_size) - - if allocate_success: - for node_name in new_ugraph.topo_order: - in_t_infos = new_ugraph.ops_info[node_name].input_tensors - for in_o in in_t_infos: - if in_o.name in allocate_table: - new_ugraph.data_manager.address = (in_o.name, allocate_table[in_o.name]['offsetstart']) - out_t_infos = new_ugraph.ops_info[node_name].output_tensors - for out_o in out_t_infos: - if out_o.name in allocate_table: - new_ugraph.data_manager.address = (out_o.name, allocate_table[out_o.name]['offsetstart']) - new_ugraph.attributes[self.KWARGS_NAMESCOPE] = allocate_table - return new_ugraph - return ugraph - - def _query_offset_fromallocate_table(self, allocate_table, start, end): - new_start = start - new_end = end - for key in allocate_table: - if allocate_table[key]['offsetstart'] >= start and allocate_table[key]['offsetend'] <= end: - continue - elif allocate_table[key]['offsetstart'] <= start and allocate_table[key]['offsetend'] >= start: - new_start = allocate_table[key]['offsetstart'] - if allocate_table[key]['offsetend'] >= end: - new_end = max(new_end, allocate_table[key]['offsetend']) - else: - new_end = max(end, new_end) - elif allocate_table[key]['offsetstart'] >= start and allocate_table[key]['offsetend'] >= start: - if allocate_table[key]['offsetend'] >= end: - new_end = max(new_end, allocate_table[key]['offsetend']) - else: - new_end = max(end, new_end) - return new_start, new_end - - def _query_time_fromallocate_table(self, allocate_table, start, end): - time_start = start - time_end = end - for key in allocate_table: - if allocate_table[key]['start'] >= start and allocate_table[key]['end'] <= end: - continue - elif allocate_table[key]['start'] <= start and allocate_table[key]['end'] >= start: - if allocate_table[key]['end'] >= end: - time_end = max(time_end, allocate_table[key]['end']) - else: - time_end = max(end, time_end) - elif allocate_table[key]['start'] >= start and allocate_table[key]['end'] >= start: - if allocate_table[key]['end'] >= end: - time_end = max(time_end, allocate_table[key]['end']) - else: - time_end = max(end, time_end) - return time_start, time_end - - def _query_result(self, allocate_table, offset, length, timestart, timeend): - for key in allocate_table: - mem_occupied = ( - (allocate_table[key]['offsetstart'] >= offset and allocate_table[key]['offsetstart'] <= offset + length) or - (allocate_table[key]['offsetstart'] <= offset and allocate_table[key]['offsetend'] >= offset) - ) - life_span_occupied = ( - (allocate_table[key]['start'] >= timestart and allocate_table[key]['start'] <= timeend) or - (allocate_table[key]['start'] <= timestart and allocate_table[key]['end'] >= timestart) - ) - if mem_occupied and life_span_occupied: - return True - return False - - def allocate_tensor(self, tensors, tensor_index, allocate_table, use_def_table, buffer_size, unit_size): - if tensor_index == len(tensors): - return True - if tensors[tensor_index].name in allocate_table: - return self.allocate_tensor(tensors, tensor_index + 1, allocate_table, use_def_table, buffer_size, unit_size) - - tensor = tensors[tensor_index] - candidates = self._get_candidates(allocate_table, use_def_table, buffer_size, unit_size, tensor) - if not candidates: - return False - success = False - for candidate in candidates: - self._update_allocation_table(allocate_table, use_def_table, tensor, candidate, candidate + tensor.size) - success = self.allocate_tensor(tensors, tensor_index + 1, allocate_table, use_def_table, buffer_size, unit_size) - if success: - break - else: - self._remove_allocate_table(allocate_table, tensor) - return success - - def allocate_graph(self, ugraph, allocate_table, use_def_table, buffer_size, unit_size): - tensors = [] - - for node_name in ugraph.topo_order: - in_t_infos = [ - tensor - for tensor in ugraph.ops_info[node_name].input_tensors - if tensor.op.op_type != 'Inline' - ] - out_t_infos = [ - tensor - for tensor in ugraph.ops_info[node_name].output_tensors - if tensor.op.op_type != 'Inline' - ] - tensors.extend(in_t_infos) - tensors.extend(out_t_infos) - - succ = self.allocate_tensor(tensors, 0, allocate_table, use_def_table, buffer_size, unit_size) - return succ - - def _check(self, allocate_table, use_def_table, tensor, tensor_offset_start, tensor_offset_end): - valid = False - timestart = use_def_table[tensor.name]['start'] - timeend = use_def_table[tensor.name]['end'] - offset, length = self._query_offset_fromallocate_table(allocate_table, tensor_offset_start, tensor_offset_end) - timestart, timeend = self._query_time_fromallocate_table(allocate_table, timestart, timeend) - occupied = self._query_result(allocate_table, offset, length, timestart, timeend) - if not occupied: - valid = True - return valid - - def _get_candidates(self, allocate_table, use_def_table, buffer_size, unit_size, in_o): - ret = [] - for i in range(0, buffer_size, unit_size): - if self._check(allocate_table, use_def_table, in_o, i, i + in_o.size): - ret.append(i) - return ret - - def _update_allocation_table( - self, - allocate_table, - use_def_table, - tensor, - offset_start, - offset_end - ): - time_start = use_def_table[tensor.name]['start'] - time_end = use_def_table[tensor.name]['end'] - attribute = dict() - attribute['start'] = time_start - attribute['end'] = time_end - attribute['offsetstart'] = offset_start - attribute['offsetend'] = offset_end - allocate_table[tensor.name] = attribute - return allocate_table - - def _remove_allocate_table(self, allocate_table, tensor): - del allocate_table[tensor.name] - - def _create_resource_table(self, ugraph): - resource_table = dict() - len_map = { - op_name: idx - for idx, op_name in enumerate(ugraph.topo_order) - } - for node_name in ugraph.topo_order: - for tensor_info in ugraph.ops_info[node_name].input_tensors: - if tensor_info.name not in resource_table: - lifetime = dict() - lifetime['start'] = len_map[node_name] - lifetime['end'] = len_map[node_name] - resource_table[tensor_info.name] = lifetime - resource_table[tensor_info.name]['end']= len_map[node_name] - - for outtensor in ugraph.ops_info[node_name].output_tensors: - if outtensor.name not in resource_table: - lifetime = dict() - lifetime['start'] = len_map[node_name] - lifetime['end'] = len_map[node_name] - resource_table[outtensor.name] = lifetime - - return resource_table - - @TransformerPipeline.register_transformer class BiasAddTransformer(Transformer): METHOD_NAME = 'biasAdd' From 2b3481b5bf6aa394c0e8a0d5a01216c12f3d2f1c Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 3 Mar 2020 01:13:45 +0800 Subject: [PATCH 040/117] Fix kwargs parsing bug, add include_inputs flag for tensor alloc transformer --- tests/deep_mlp/.gitignore | 1 + utensor_cgen/transformer/mem_alloc.py | 46 +++++++++++++++++++++++---- utensor_cgen/transformer/pipeline.py | 9 +----- 3 files changed, 42 insertions(+), 14 deletions(-) create mode 100644 tests/deep_mlp/.gitignore diff --git a/tests/deep_mlp/.gitignore b/tests/deep_mlp/.gitignore new file mode 100644 index 00000000..405ec2c3 --- /dev/null +++ b/tests/deep_mlp/.gitignore @@ -0,0 +1 @@ +*.toml diff --git a/utensor_cgen/transformer/mem_alloc.py b/utensor_cgen/transformer/mem_alloc.py index b5ac194a..129534fe 100644 --- a/utensor_cgen/transformer/mem_alloc.py +++ b/utensor_cgen/transformer/mem_alloc.py @@ -1,3 +1,4 @@ +import pickle from collections import Counter, defaultdict, namedtuple from itertools import chain, combinations, product @@ -15,6 +16,23 @@ @TransformerPipeline.register_transformer class TensorAllocationTransformer(Transformer): + """ + Offline Tensor Allocation Optimizer + + analyse tensor lifetime and find the optimal allocation offset in the managed memory pool + + :param max_pool_size: the size of the memory pool (default: 1 KB) + :type max_pool_size: int + + :param include_inputs: include the input tensors (Placeholder) in the allocation plan + :type include_inputs: bool + + :param out_fname: the file name of memory allocation visualization (will NOT generate if not given) + :type out_fname: str + + :param aesthetic_kwargs: the keyword arguments controlling the aesthetic of the visualization of allocation plan + :type aesthetic_kwargs: dict + """ METHOD_NAME = "tensor_alloc" KWARGS_NAMESCOPE = "_tensor_alloc" @@ -22,13 +40,15 @@ def __init__( self, prune_graph=False, max_pool_size=1024*1024, # 1k bytes - vizualize_fname=None, - **vizualize_kwargs + include_inputs=False, + out_fname=None, + **aesthetic_kwargs ): super(TensorAllocationTransformer, self).__init__(prune_graph=prune_graph) self.max_pool_size = max_pool_size - self.vizualize_fname = vizualize_fname - self.vizualize_kwargs = vizualize_kwargs + self.include_inputs = include_inputs + self.out_fname = out_fname + self.aesthetic_kwargs = aesthetic_kwargs def transform(self, ugraph): ref_cnts = Counter() @@ -43,11 +63,22 @@ def transform(self, ugraph): for op in ugraph.get_ops_by_type('Inline') ]) ) + if not self.include_inputs: + tensors_to_ignore.update( + chain(*[ + op.output_tensors + for op in ugraph.get_ops_by_type('Placeholder') + ]) + ) nonoverlap_map = defaultdict(set) for op_name in ugraph.topo_order: op_info = ugraph.ops_info[op_name] + # ignore inline ops if op_info.op_type == 'Inline': continue + # ignore placeholders if not included + if not self.include_inputs and op_info.op_type == 'Placeholder': + continue # no overlapping between input/output tensors for in_tensor, out_tensor in product(op_info.input_tensors, op_info.output_tensors): if not in_tensor in tensors_to_ignore: @@ -72,8 +103,11 @@ def transform(self, ugraph): alloc_plan = self._solve_opt_plan(tensors_to_schedule, nonoverlap_map) if alloc_plan is not None: ugraph.attributes[self.KWARGS_NAMESCOPE] = alloc_plan - if self.vizualize_fname: - viz_memalloc(ugraph=ugraph, out_fname=self.vizualize_fname, **self.vizualize_kwargs) + if self.out_fname: + fig = viz_memalloc(ugraph=ugraph, out_fname=self.out_fname, **self.aesthetic_kwargs) + with open('{}.pkl'.format(self.out_fname), 'wb') as fid: + pickle.dump(fig, fid) + logger.info('matplotlib figure object dumped (pickle): %s', fid.name) return ugraph @timed diff --git a/utensor_cgen/transformer/pipeline.py b/utensor_cgen/transformer/pipeline.py index 500f58a0..1279f325 100644 --- a/utensor_cgen/transformer/pipeline.py +++ b/utensor_cgen/transformer/pipeline.py @@ -1,5 +1,4 @@ import re -from ast import literal_eval from .base import Transformer @@ -74,10 +73,4 @@ def _parse_expr(cls, expr): @classmethod def _get_kwargs(cls, kws_str): - kw_arg_strs = [s.strip() for s in kws_str.split(',')] - kwargs = {} - for kw_str in kw_arg_strs: - name, v_str = kw_str.split('=') - value = literal_eval(v_str) - kwargs[name] = value - return kwargs + return eval('dict({})'.format(kws_str), {}, {}) From 44583197bcb1dec1858b4198c6c69c4dccefb5eb Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 3 Mar 2020 13:31:44 +0800 Subject: [PATCH 041/117] better visualization --- utensor_cgen/frontend/__init__.py | 4 +- utensor_cgen/frontend/onnx.py | 1 + utensor_cgen/frontend/tensorflow.py | 161 ++++++++++++++------------ utensor_cgen/ir/misc/graph_viz.py | 141 ++++++++++++++-------- utensor_cgen/transformer/mem_alloc.py | 1 + 5 files changed, 183 insertions(+), 125 deletions(-) diff --git a/utensor_cgen/frontend/__init__.py b/utensor_cgen/frontend/__init__.py index 0b70318e..32b82590 100644 --- a/utensor_cgen/frontend/__init__.py +++ b/utensor_cgen/frontend/__init__.py @@ -22,12 +22,10 @@ def _register(parser_cls): return _register @classmethod - def parse(cls, model_file, output_nodes, config=None): + def parse(cls, model_file, output_nodes=None, config=None): if config is None: config = {} _, ext = os.path.splitext(model_file) - if ext != '.onnx' and not output_nodes: - raise ValueError('output_nodes are required for {} file'.format(ext)) parser = cls.select_parser(ext)(config) return parser.parse(model_file, output_nodes) diff --git a/utensor_cgen/frontend/onnx.py b/utensor_cgen/frontend/onnx.py index 544887d9..c7767eda 100644 --- a/utensor_cgen/frontend/onnx.py +++ b/utensor_cgen/frontend/onnx.py @@ -7,6 +7,7 @@ import tensorflow as tf from onnx.onnx_pb import TensorProto from onnx_tf.backend import TensorflowBackend, prepare + from utensor_cgen.frontend import FrontendSelector from utensor_cgen.frontend.base import Parser from utensor_cgen.ir import OperationInfo, TensorInfo, uTensorGraph diff --git a/utensor_cgen/frontend/tensorflow.py b/utensor_cgen/frontend/tensorflow.py index bd357a1d..64a42b8b 100644 --- a/utensor_cgen/frontend/tensorflow.py +++ b/utensor_cgen/frontend/tensorflow.py @@ -4,93 +4,102 @@ import numpy as np import six - import tensorflow as tf from google.protobuf import text_format + from utensor_cgen.frontend import FrontendSelector from utensor_cgen.frontend.base import Parser from utensor_cgen.ir.base import OperationInfo, TensorInfo, uTensorGraph from utensor_cgen.legalizer import Legalizer +from utensor_cgen.logger import logger from utensor_cgen.utils import random_str, topologic_order_graph -@FrontendSelector.register(target_exts=['.pb', '.pbtxt']) +@FrontendSelector.register(target_exts=[".pb", ".pbtxt"]) class GraphDefParser(Parser): + def parse(self, pb_file, output_nodes=None): + graph_def, graph_name = self._load_graph_def(pb_file) + if not self._is_freeze_graph(graph_def): + raise ValueError("Given graph_def is not freezed") + if output_nodes is None: + output_nodes = [node.name for node in graph_def.node] + logger.warning( + 'output_nodes is not given, use all nodes instead (may cause unexpected behaviour)' + ) - def parse(self, pb_file, output_nodes=None): - graph_def, graph_name = self._load_graph_def(pb_file) - if not self._tf_is_freeze_graph(graph_def): - raise ValueError('Given graph_def is not freezed') - if output_nodes is None: - output_nodes = [node.name for node in graph_def.node] - - graph = tf.Graph() - with graph.as_default(): - tf.import_graph_def(graph_def, name='') - ugraph = uTensorGraph( - name=graph_name, - output_nodes=output_nodes, - lib_name="tensorflow", - ) - for node in graph_def.node: - op = graph.get_operation_by_name(node.name) - in_tensors = [TensorInfo(name=tensor.name, - ugraph=ugraph, - op_name=tensor.op.name, - dtype=np.dtype(tensor.dtype.as_numpy_dtype), - shape=self._tf_parse_tshape(tensor.shape), - ) - for tensor in op.inputs] - out_tensors = [TensorInfo(name=tensor.name, - ugraph=ugraph, - op_name=op.name, - dtype=np.dtype(tensor.dtype.as_numpy_dtype), - shape=self._tf_parse_tshape(tensor.shape), - ) - for tensor in op.outputs] - op_type = node.op - op_attr = node.attr - op_info = OperationInfo(name=node.name, - input_tensors=in_tensors, - n_inputs=len(in_tensors), - output_tensors=out_tensors, - n_outputs=len(out_tensors), - op_type=op_type, - lib_name='tensorflow', - op_attr=op_attr, - ugraph=ugraph) - op_info.op_attr['tensorflow__device'] = node.device - ugraph.ops_info[node.name] = op_info - topologic_order_graph(ugraph) - ugraph = Legalizer.legalize(ugraph, {}) - return ugraph + graph = tf.Graph() + with graph.as_default(): + tf.import_graph_def(graph_def, name="") + ugraph = uTensorGraph( + name=graph_name, output_nodes=output_nodes, lib_name="tensorflow", + ) + for node in graph_def.node: + op = graph.get_operation_by_name(node.name) + in_tensors = [ + TensorInfo( + name=tensor.name, + ugraph=ugraph, + op_name=tensor.op.name, + dtype=np.dtype(tensor.dtype.as_numpy_dtype), + shape=self._tf_parse_tshape(tensor.shape), + ) + for tensor in op.inputs + ] + out_tensors = [ + TensorInfo( + name=tensor.name, + ugraph=ugraph, + op_name=op.name, + dtype=np.dtype(tensor.dtype.as_numpy_dtype), + shape=self._tf_parse_tshape(tensor.shape), + ) + for tensor in op.outputs + ] + op_type = node.op + op_attr = node.attr + op_info = OperationInfo( + name=node.name, + input_tensors=in_tensors, + n_inputs=len(in_tensors), + output_tensors=out_tensors, + n_outputs=len(out_tensors), + op_type=op_type, + lib_name="tensorflow", + op_attr=op_attr, + ugraph=ugraph, + ) + op_info.op_attr["tensorflow__device"] = node.device + ugraph.ops_info[node.name] = op_info + topologic_order_graph(ugraph) + ugraph = Legalizer.legalize(ugraph, {}) + return ugraph - @staticmethod - def _load_graph_def(pb_file): - if isinstance(pb_file, tf.GraphDef): - return pb_file, 'tf_graph_{}'.format(random_str(6)) - assert isinstance(pb_file, six.string_types) - graph_name, _ = os.path.splitext(os.path.basename(pb_file)) - graph_def = tf.GraphDef() - if pb_file[-3:] == ".pb": - with open(pb_file, 'rb') as fid: - graph_def.ParseFromString(fid.read()) - elif pb_file[-6:] == ".pbtxt": - with open(pb_file, 'r') as fid: - text_format.Parse(fid.read(), graph_def) - else: - raise ValueError('unknown file format: %s' % pb_file) - return graph_def, graph_name + @staticmethod + def _load_graph_def(pb_file): + if isinstance(pb_file, tf.GraphDef): + return pb_file, "tf_graph_{}".format(random_str(6)) + assert isinstance(pb_file, six.string_types) + graph_name, _ = os.path.splitext(os.path.basename(pb_file)) + graph_def = tf.GraphDef() + if pb_file[-3:] == ".pb": + with open(pb_file, "rb") as fid: + graph_def.ParseFromString(fid.read()) + elif pb_file[-6:] == ".pbtxt": + with open(pb_file, "r") as fid: + text_format.Parse(fid.read(), graph_def) + else: + raise ValueError("unknown file format: %s" % pb_file) + return graph_def, graph_name - @staticmethod - def _tf_parse_tshape(t_shape): - try: - shape = t_shape.as_list() - except ValueError: - shape = None - return shape + @staticmethod + def _tf_parse_tshape(t_shape): + try: + shape = t_shape.as_list() + except ValueError: + shape = None + return shape - @staticmethod - def _tf_is_freeze_graph(graph_def): - is_frozen = all(node.op not in ['VariableV2'] for node in graph_def.node) - return is_frozen + @staticmethod + def _is_freeze_graph(graph_def): + is_frozen = all(node.op not in ["VariableV2"] for node in graph_def.node) + return is_frozen diff --git a/utensor_cgen/ir/misc/graph_viz.py b/utensor_cgen/ir/misc/graph_viz.py index c71424f6..f4f7efc5 100644 --- a/utensor_cgen/ir/misc/graph_viz.py +++ b/utensor_cgen/ir/misc/graph_viz.py @@ -1,3 +1,5 @@ +import os +from math import ceil, log10 from random import random, seed import matplotlib.pyplot as plt @@ -72,54 +74,101 @@ def viz_graph(ugraph, out_fname=None, view=False, cleanup=True, colored_nodes=No return dot -def viz_memalloc(ugraph, out_fname=None, figsize=None, fontsize=12, lw=15, cmap=_cm.BrBG_r, rand_seed=1111): - seed(rand_seed) - fig = plt.gcf() - if TensorAllocationTransformer.KWARGS_NAMESCOPE not in ugraph.attributes: - logger.info('No tensor allocation plan to visualize: %s', ugraph.name) - return fig - alloc_plan = ugraph.attributes[TensorAllocationTransformer.KWARGS_NAMESCOPE] - topo_tensors = [] - visited_tensors = set() - for op_name in ugraph.topo_order: - op_info = ugraph.ops_info[op_name] - for tensor in op_info.input_tensors: +class _MemallcVisualizer(object): + + @classmethod + def viz_memalloc( + cls, + ugraph, + out_fname=None, + split_on_large_graph=True, + num_tensors_per_split=20, + figsize=None, + fontsize=12, + lw=15, + cmap=_cm.BrBG_r, + rand_seed=1111 + ): + seed(rand_seed) + if TensorAllocationTransformer.KWARGS_NAMESCOPE not in ugraph.attributes: + logger.info('No tensor allocation plan to visualize: %s', ugraph.name) + return plt.Figure() + alloc_plan = ugraph.attributes[TensorAllocationTransformer.KWARGS_NAMESCOPE] + topo_tensors = [] + visited_tensors = set() + for op_name in ugraph.topo_order: + op_info = ugraph.ops_info[op_name] + for tensor in op_info.input_tensors: + if tensor.name in alloc_plan.plan and tensor not in visited_tensors: + topo_tensors.append(tensor) + visited_tensors.add(tensor) + for tensor in ugraph.output_tensors: if tensor.name in alloc_plan.plan and tensor not in visited_tensors: topo_tensors.append(tensor) visited_tensors.add(tensor) - for tensor in ugraph.output_tensors: - if tensor.name in alloc_plan.plan and tensor not in visited_tensors: - topo_tensors.append(tensor) - visited_tensors.add(tensor) - num_tensors = len(topo_tensors) - ys = [num_tensors - i for i in range(len(topo_tensors))] - xmins = [alloc_plan.plan[tensor.name].start for tensor in topo_tensors] - xmaxs = [alloc_plan.plan[tensor.name].end for tensor in topo_tensors] - colors = [cmap(random()) for _ in alloc_plan.plan] - labels = [tensor.name for tensor in topo_tensors] - sizes = [alloc_plan.plan[tensor.name].size for tensor in topo_tensors] - for y, xmin, xmax, color, size in zip(ys, xmins, xmaxs, colors, sizes): - plt.hlines(y, xmin, xmax, lw=lw, colors=color) - plt.text(xmax, y-0.15, '{} bytes'.format(size), fontdict={'fontsize': fontsize}) - plt.xlabel('Offset (bytes)', fontdict={'fontsize': fontsize}) - plt.yticks(ys, labels, fontsize=fontsize) - plt.xticks(fontsize=fontsize) - plt.ylabel( - 'Tensor Names (Topological Ordered, Top to Bottom)', - fontdict={'fontsize':fontsize} - ) - plt.title( - 'Optimal Tensor Allocation: {} bytes in total'.format(alloc_plan.total_size), - fontdict={'fontsize': fontsize} - ) - if figsize is None: - figsize = (len(ys), len(ys)*0.5) - fig.set_size_inches(*figsize) - plt.tight_layout() - if out_fname: - logger.info('saving tensor mem allocation to %s', out_fname) - fig.savefig(out_fname) - return fig + if out_fname: + figs = cls._draw_figs(topo_tensors, alloc_plan, cmap, figsize, fontsize, lw, split_on_large_graph, num_tensors_per_split) + if len(figs) == 1: + logger.info('saving tensor mem allocation to %s', out_fname) + figs[0].savefig(out_fname) + else: + num_digits = ceil(log10(len(figs))) + file_format = '{{}}_{{:0{}d}}{{}}'.format(num_digits) + for i, fig in enumerate(figs, 1): + fname, ext = os.path.splitext(out_fname) + fname = file_format.format(fname, i, ext) + logger.info('saving tensor mem allocation to %s', fname) + fig.savefig(fname) + + @classmethod + def _draw_figs(cls, topo_tensors, alloc_plan, cmap, figsize, fontsize, lw, split_on_large_graph, num_tensors_per_split): + xmins = [alloc_plan.plan[tensor.name].start for tensor in topo_tensors] + xmaxs = [alloc_plan.plan[tensor.name].end for tensor in topo_tensors] + colors = [cmap(random()) for _ in alloc_plan.plan] + labels = [tensor.name for tensor in topo_tensors] + sizes = [alloc_plan.plan[tensor.name].size for tensor in topo_tensors] + if split_on_large_graph: + xmin_chunks = [xmins[i:i+num_tensors_per_split] for i in range(0, len(xmins), num_tensors_per_split)] + xmax_chunks = [xmaxs[i:i+num_tensors_per_split] for i in range(0, len(xmaxs), num_tensors_per_split)] + color_chunks = [colors[i:i+num_tensors_per_split] for i in range(0, len(colors), num_tensors_per_split)] + label_chunks = [labels[i:i+num_tensors_per_split] for i in range(0, len(labels), num_tensors_per_split)] + size_chunks = [sizes[i:i+num_tensors_per_split] for i in range(0, len(sizes), num_tensors_per_split)] + else: + xmin_chunks = [xmins] + xmax_chunks = [xmaxs] + color_chunks = [colors] + label_chunks = [labe] + size_chunks = [sizes] + figs = [] + for i, (xmins, xmaxs, colors, sizes) in enumerate(zip(xmin_chunks, xmax_chunks, color_chunks, size_chunks)): + fig, _ = plt.subplots(1, 1) + ys = [len(xmins)-i for i in range(len(xmins))] + for y, xmin, xmax, color, size in zip(ys, xmins, xmaxs, colors, sizes): + plt.hlines(y, xmin, xmax, lw=lw, colors=color) + plt.text(xmax+lw*5, y-0.2*lw/3, '{} bytes'.format(size), fontdict={'fontsize': fontsize}) + plt.xlabel('Offset (bytes)', fontdict={'fontsize': fontsize}) + plt.yticks(ys, labels, fontsize=fontsize) + plt.xticks(fontsize=fontsize) + plt.ylabel( + 'Tensor Names (Topological Ordered, Top to Bottom)', + fontdict={'fontsize':fontsize} + ) + if i: + title = 'Optimal Tensor Allocation: {} bytes in total (Cont.)'.format(alloc_plan.total_size) + else: + title = 'Optimal Tensor Allocation: {} bytes in total'.format(alloc_plan.total_size) + plt.title( + title, + fontdict={'fontsize': fontsize} + ) + if figsize is None: + figsize = (num_tensors_per_split, num_tensors_per_split / 2) + fig.set_size_inches(*figsize) + fig.tight_layout() + figs.append(fig) + return figs + +viz_memalloc = _MemallcVisualizer.viz_memalloc -# cyclic import +# FIXME: cyclic import from utensor_cgen.transformer.mem_alloc import TensorAllocationTransformer # isort:skip diff --git a/utensor_cgen/transformer/mem_alloc.py b/utensor_cgen/transformer/mem_alloc.py index 129534fe..1e5bb8d3 100644 --- a/utensor_cgen/transformer/mem_alloc.py +++ b/utensor_cgen/transformer/mem_alloc.py @@ -152,4 +152,5 @@ def _solve_opt_plan(self, tensors_to_schedule, nonoverlap_map): logger.info('tensor allocation plan not found, status: %s', solver.StatusName(status)) return alloc_plan +# FIXME: cyclic import from utensor_cgen.ir.misc.graph_viz import viz_memalloc # isort:skip From a5a3043453c9260433c7e588bafbc675b721ab1f Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 3 Mar 2020 13:31:55 +0800 Subject: [PATCH 042/117] update README --- README.rst | 30 ++++++++++++++++++++++--- doc/source/_images/mlp_alloc.png | Bin 0 -> 25540 bytes doc/source/_images/mlp_alloc_graph.png | Bin 0 -> 178181 bytes doc/source/images.rst | 6 +++++ tests/deep_cnn/.gitignore | 1 + 5 files changed, 34 insertions(+), 3 deletions(-) create mode 100644 doc/source/_images/mlp_alloc.png create mode 100644 doc/source/_images/mlp_alloc_graph.png diff --git a/README.rst b/README.rst index 68ab57d3..000be69e 100644 --- a/README.rst +++ b/README.rst @@ -226,12 +226,32 @@ Use Case: Dropout Layer Removal \ |cnn-dropout| -.. subgraph-match-end - We use mainly `Tensorflow`_ for declaring the pattern graph for matcher now. High-level graph builder is on its way, see `Future Works <#future-works>`_ for detail. +.. subgraph-match-end + +.. offline-tensor-alloc-start + +Offline Tensor Memory Allocation +-------------------------------- + +Considering following simple multi layers perceptron (`simple_mnist.pb`_): + +\ |mlp-alloc-graph| + +Once enabled the optimization transformer, ``tensor_alloc``, +which is an offline tensor memory allocation planner, +``utensor-cli`` will generate ``uTensor`` runtime codes that use following optimized allocation plan: + +\ |mlp-alloc| + +- On the y-axis, these are tensor names ordered by topological sorting +- On the x-axis, these are the memory span occupied by each tensor + +.. offline-tensor-alloc-end + Tutorials ========= @@ -313,6 +333,7 @@ Future Works .. _Tensorflow: https://www.tensorflow.org .. _PyTorch: https://pytorch.org/ .. _uTensor: https://github.com/uTensor/uTensor +.. _simple_mnist.pb: https://github.com/uTensor/utensor_cgen/blob/develop/tests/deep_mlp/simple_mnist.pb .. readme_end @@ -322,7 +343,10 @@ Future Works :alt: conv-pool-fuse .. |convert-example| image:: doc/source/_images/convert_example.png :alt: convert-example - +.. |mlp-alloc| image:: doc/source/_images/mlp_alloc.png + :alt: mlp-alloc +.. |mlp-alloc-graph| image:: doc/source/_images/mlp_alloc_graph.png + :alt: mlp-alloc-graph .. 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zmhZT&(AA&aLbo>p2z<++v{IC2J`RtKV8;;$90Z_@B9Y)?q}xT zefGPzCGH=8ec7ZIf5^}8pX0v7eSVJ1e_lTW^B9lk%e<67`2YW$Uk^Y0$&c>6>RtZc zSO4O{y>C)~Z}0DY`{2G$?D_oOqmu*szHi@0_P+G_Z14EN@7wpj6MwY#oqzoKy}>Wu zzVFILZQn0#{>t9+;XC%tOP|~ObK5_?clU3W_N{#8@Am%Zt=WB-v_G+T*RLMj_puw_ zvv2X^pWFLzx3=${JAZxe8*?sT%!l(BB)B~T`dkx#+U%LUDJru^=9e0_|N`&CQ-IMy%`=I5n{^ViD!ye9}1&VR4(3HiQ}U!^Mie~F#^k9o6v z-^lmt{M@<3?f>gPORRve&3ttKGe1Asf9>iw_rKxZkMDn0Ie6py{{83npSt0|jg^~! zasQ6{)BSsgci;H&JO60^Q(K?g|IshqcH_*iS8x2?t^ct9fP2S{Ge7!w`|tX-PwoG+ zN%6*m!DsjX4%&8dAbi_94;J6{ zGY5We?}39m{95gTs$sa`3lb{GEf}Klqh{4}S2g2d?;yKR@`s?4pBL{_)Cz zpLpHpTjAN$)U4n7jPFmK;@Z!{0&%V1xVKKS!z-gfBce*VToKlww&L*M?*Zyx%{_Dc@^+_%1Q z=nwB1KlsGo|MH=G-u~nvb*6skKmM?DXzGfG57poH`-i^Xi5}ekA0IiS|I8~7-g(>N zq5F^i=Ry~ORGUKh{2KFm8m&b#9WHxJ~W^@&e@{MP*bo1eVm;e4F* zy|L)`273ARCI5T=S^2U0Q{3|FN`CFl$A0-Tzry=+r{&MhM+)%y&W{)P Date: Mon, 27 Apr 2020 16:13:37 +0800 Subject: [PATCH 096/117] re-arch code generator refactoring --- .../backend/utensor/_graph_lower/_op_lower.py | 4 + .../code_generator/rearch/_code_generator.py | 78 +++++---- .../code_generator/rearch/_operators.py | 154 ------------------ .../rearch/_operators/__init__.py | 2 + .../code_generator/rearch/_operators/_base.py | 98 +++++++++++ .../rearch/_operators/_impls.py | 59 +++++++ .../utensor/snippets/rearch/_snippets.py | 139 ++++++++++------ .../templates/containers/rearch/simple.cpp | 18 +- .../templates/snippets/rearch/declare_op.cpp | 4 +- .../snippets/rearch/declare_ram_tensor.cpp | 9 + .../snippets/rearch/declare_rom_tensor.cpp | 6 +- .../templates/snippets/rearch/eval_op.cpp | 9 +- utensor_cgen/frontend/tflite.py | 18 +- utensor_cgen/transformer/base.py | 20 +++ utensor_cgen/transformer/conv_pool.py | 1 + utensor_cgen/transformer/graph_viz.py | 3 +- utensor_cgen/transformer/linear_reoder.py | 3 +- utensor_cgen/transformer/ns_transformer.py | 9 +- utensor_cgen/transformer/optimizer.py | 4 +- utensor_cgen/transformer/quantize.py | 1 + 20 files changed, 382 insertions(+), 257 deletions(-) delete mode 100644 utensor_cgen/backend/utensor/code_generator/rearch/_operators.py create mode 100644 utensor_cgen/backend/utensor/code_generator/rearch/_operators/__init__.py create mode 100644 utensor_cgen/backend/utensor/code_generator/rearch/_operators/_base.py create mode 100644 utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py create mode 100644 utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_ram_tensor.cpp diff --git a/utensor_cgen/backend/utensor/_graph_lower/_op_lower.py b/utensor_cgen/backend/utensor/_graph_lower/_op_lower.py index 613a69cc..3c523099 100644 --- a/utensor_cgen/backend/utensor/_graph_lower/_op_lower.py +++ b/utensor_cgen/backend/utensor/_graph_lower/_op_lower.py @@ -44,6 +44,10 @@ class OptypeRenameManager(object): @classmethod def get_new_optype(cls, op_type): return cls.NAME_MAP.get(op_type, op_type) + + @classmethod + def add_name_map(cls, generic_name, target_specific_name): + cls.OptypeRenameManager.NAME_MAP[generic_name] = target_specific_name def handle_tensorflow(self, ugraph): for op_info in ugraph.ops_info.values(): diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py b/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py index 4949add9..a4b3f7a7 100644 --- a/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py +++ b/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py @@ -1,5 +1,3 @@ -import os -import pickle import re from itertools import chain from pathlib import Path @@ -8,9 +6,9 @@ from utensor_cgen.backend.utensor.snippets.composer import Composer from utensor_cgen.backend.utensor.snippets.legacy import ( ContextGlobalArrayContainer, WeightSnippet) -from utensor_cgen.backend.utensor.snippets.rearch import SimpleContainer +from utensor_cgen.backend.utensor.snippets.rearch import ( + DeclareRamTensorSnippet, DeclareRomTensorSnippet, SimpleContainer) from utensor_cgen.backend.utensor.snippets.template_env import env -from utensor_cgen.transformer.pipeline import TransformerPipeline from utensor_cgen.utils import Configuration, class_property from ._operators import OperatorFactory @@ -40,25 +38,27 @@ def apply(self, ugraph): src_fname = '{}.cpp'.format(ugraph.name) # find all required ops and the variable names for the tensors in the generate files ( - ops, # Set[OperationInfo] - placeholders, # Set[OperationInfo] + ops, # Set[Operator], no Placeholder or Inline ops + placeholders, # Set[String], variable names of planceholder tensors tensor_var_map, # dict, tensor name -> var name ) = self._find_required_ops(ugraph) ( - ops_map, # dict, op_info -> variable name of op in the output files - declare_snippets, # list of Snippet objects, which will render code snippets for tensor declaration - weight_snippets, # snippets for generating weights header file - ) = self._get_declare_snippets(ugraph, ops, tensor_var_map) + ops_map, # dict, op_info -> variable name of op in the output files + declare_global_snippets, # list of Snippet objects, which will rendered in global scop + declare_local_snippets, # list of Snippet objects, which will rendered in local function scope + weight_snippets, # snippets for generating weights header file + ) = self._get_declare_snippets(ugraph, ops, tensor_var_map) # eval_snippets: List of snippet objects, which will render code snippets for tensor evaluation eval_snippets = self._get_evaluation_snippets(ugraph, ops_map, tensor_var_map) # generate files self._generate_files( ugraph, - placeholders, - tensor_var_map, - weight_snippets, - declare_snippets, - eval_snippets + placeholders=placeholders, + tensor_var_map=tensor_var_map, + weight_snippets=weight_snippets, + declare_global_snippets=declare_global_snippets, + declare_local_snippets=declare_local_snippets, + eval_snippets=eval_snippets ) def _find_required_ops(self, ugraph): @@ -80,32 +80,40 @@ def _find_required_ops(self, ugraph): def _get_declare_snippets(self, ugraph, ops, tensor_var_map): # get ops/tensors declaration snippets - declare_snippets = [] + declare_global_snippets = [] + declare_local_snippets = [] ops_map = {} # op -> op variable name weight_snippets = [] for i, op in enumerate(ops): op_var_name = 'op_{:03d}'.format(i) ops_map[op] = op_var_name - declare_snippets.append(op.get_declare_snippet(op_var_name)) + declare_local_snippets.append(op.get_declare_snippet(op_var_name, tensor_var_map)) + for op_info in filter(lambda op_info: op_info.op_type not in ["Inline", "Placeholder"], ugraph.ops_info.values()): + for out_tensor in op_info.output_tensors: + declare_local_snippets.append( + DeclareRamTensorSnippet(out_tensor, tensor_var_map[out_tensor.name]) + ) for op_info in filter(lambda op_info: op_info.op_type == 'Inline', ugraph.ops_info.values()): - tensor = op_info.output_tensors[0] - buffer_name = 'data_{}'.format(tensor.name.replace(':', '_').replace('/', '_')) + tensor_info = op_info.output_tensors[0] + tensor_var = tensor_var_map[tensor_info.name] + buffer_name = 'data_{}'.format(tensor_info.name.replace(':', '_').replace('/', '_')) weight_snippets.append( WeightSnippet( buffer_name, - tensor.dtype, - tensor.shape, + tensor_info.dtype, + tensor_info.shape, op_info.op_attr['value'].value.np_array.ravel() ) ) - declare_snippets.append( - OperatorFactory.get_opertor(op_info).get_declare_snippet( - tensor_var_name=tensor_var_map[tensor.name], - buffer_var_name=buffer_name, - tensor=tensor + declare_global_snippets.append( + DeclareRomTensorSnippet( + tensor_info=tensor_info, + tensor_var=tensor_var, + buffer_var=buffer_name, + static=True, ) ) - return ops_map, declare_snippets, weight_snippets + return ops_map, declare_global_snippets, declare_local_snippets, weight_snippets def _get_evaluation_snippets(self, ugraph, ops_map, tensor_var_map): eval_snippets = [] @@ -116,11 +124,15 @@ def _get_evaluation_snippets(self, ugraph, ops_map, tensor_var_map): op = OperatorFactory.get_opertor(op_info) op_name = ops_map[op] eval_snippets.append( - op.get_eval_snippet(op_info, op_name, tensor_var_map) + op.get_eval_snippet(op_name, op_info, tensor_var_map) ) return eval_snippets - def _generate_files(self, ugraph, placeholders, tensor_var_map, weight_snippets, declare_snippets, eval_snippets): + def _generate_files( + self, ugraph, placeholders, tensor_var_map, + weight_snippets, declare_global_snippets, declare_local_snippets, + eval_snippets + ): template_vars = {} template_vars['model_name'] = ugraph.name template_vars['meta_data_pool_size'] = self._compute_meta_data_size(ugraph) @@ -146,10 +158,10 @@ def _generate_files(self, ugraph, placeholders, tensor_var_map, weight_snippets, # generate the compute function model_file_dir = Path(self.model_dir) header_fname = self.header_fname == 'None' and '{}.hpp'.format(ugraph.name) or self.header_fname - container_snippet = SimpleContainer( - declare_snippets=declare_snippets, - eval_snippests=eval_snippets - ) + container_snippet = SimpleContainer() + container_snippet.add_declare_global_snippets(*declare_global_snippets) + container_snippet.add_declare_local_snippets(*declare_local_snippets) + container_snippet.add_eval_snippets(*eval_snippets) container_snippet.template_vars.update(template_vars) (model_file_dir / ugraph.name).mkdir(parents=True, exist_ok=True) with (model_file_dir / ugraph.name / header_fname).open('w') as fid: diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_operators.py b/utensor_cgen/backend/utensor/code_generator/rearch/_operators.py deleted file mode 100644 index 3d3d8456..00000000 --- a/utensor_cgen/backend/utensor/code_generator/rearch/_operators.py +++ /dev/null @@ -1,154 +0,0 @@ -from copy import deepcopy -from typing import Hashable - -from six import with_metaclass - -from utensor_cgen.backend.utensor.snippets._types import NP_TYPES_MAP -from utensor_cgen.backend.utensor.snippets.rearch import (AddOpEvalSnippet, - DeclareOpSnippet, - RomTensorSnippet) -from utensor_cgen.utils import must_return_type - -__all__ = ['OperatorFactory', 'OpNotSupportedError'] - - -class OpNotSupportedError(Exception): pass - - -class OperatorFactory(object): - - _operators = {} - - @classmethod - def get_opertor(cls, op_info): - op_type = op_info.op_type - op_cls = cls._operators.get(op_type) - if op_cls is None: - raise OpNotSupportedError( - '{} not supported in utensor_cgen'.format(op_type) - ) - return op_cls(op_info) - - @classmethod - def register(cls, op_cls): - cls._operators[op_cls.op_type] = op_cls - return op_cls - - @classmethod - def support_op_types(cls): - """Return the set of all supported ops - """ - return set(cls._operators.keys()) - - @classmethod - def is_supported(cls, op_type): - if op_type != 'Placeholder' and op_type not in cls._operators: - return False - return True - - -class _OperatorMeta(type): - - def __new__(mcls, name, bases, attrib): - attrib['_cache'] = {} - for key in ['get_type_signature', 'get_constructor_signature']: - func = attrib.get(key) - if func is None: - continue - if not must_return_type.return_type_is_ensured(func): - attrib[key] = must_return_type(Hashable)(func) - elif not issubclass(must_return_type.get_expect_type(func), Hashable): - raise RuntimeError('{}.{} must be ensured to return {}'.format(name, key, Hashable)) - cls = type.__new__(mcls, name, bases, attrib) - return cls - - -class _Operator(with_metaclass(_OperatorMeta), object): - - def __new__(cls, op_info): - type_signature = cls.get_type_signature(op_info) - construct_signature = cls.get_constructor_signature(op_info) - full_signature = (type_signature, construct_signature) - in_dtypes, out_dtypes = type_signature - if full_signature not in cls._cache: - self = object.__new__(cls) - self.in_dtypes = in_dtypes - self.out_dtypes = out_dtypes - self.construct_params = construct_signature - self.attributes = deepcopy(op_info.op_attr) - cls._cache[full_signature] = self - return cls._cache[full_signature] - - @classmethod - @must_return_type(Hashable) - def get_type_signature(cls, op_info): - in_dtypes = tuple(t.dtype for t in op_info.input_tensors) - out_dtypes = tuple(t.dtype for t in op_info.output_tensors) - return (in_dtypes, out_dtypes) - - @classmethod - @must_return_type(Hashable) - def get_constructor_signature(cls, op_info): - return tuple() - - def get_declare_snippet(self, op_var_name, **kwargs): - raise NotImplementedError( - 'base get_declare_snippet invoked: {}'.format(type(self)) - ) - - def get_eval_snippet(self, op_info, op_name, tensor_var_map, **kwargs): - raise NotImplementedError( - 'base get_eval_snippet invoked: {}'.format(type(self)) - ) - - -@OperatorFactory.register -class _AddOperator(_Operator): - - op_type = 'AddOperator' - - def get_declare_snippet(self, op_var_name): - snippet = DeclareOpSnippet( - op_type=self.op_type, - dtypes=[NP_TYPES_MAP[self.in_dtypes[0]].tensor_type_str], - op_var_name=op_var_name, - ) - return snippet - - def get_eval_snippet(self, op_info, op_name, tensor_var_map): - snippet = AddOpEvalSnippet( - op_info=op_info, - op_name=op_name, - tensor_var_map=tensor_var_map, - dtypes=[op_info.input_tensors[0].dtype] - ) - return snippet - - -@OperatorFactory.register -class _MatmulOperator(_Operator): - - op_type = 'MatrixMultOperator' - - -@OperatorFactory.register -class _ConvOperator(_Operator): - - op_type = 'ConvOperator' - - -@OperatorFactory.register -class _InlineOperator(_Operator): - - op_type = 'Inline' - - def __init__(self, op_info): - self._tensor = op_info.output_tensors[0] - - def get_declare_snippet(self, tensor_var_name, buffer_var_name, tensor): - snippet = RomTensorSnippet( - tensor_var_name=tensor_var_name, - buffer_var_name=buffer_var_name, - tensor=tensor - ) - return snippet diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/__init__.py b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/__init__.py new file mode 100644 index 00000000..d5f883eb --- /dev/null +++ b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/__init__.py @@ -0,0 +1,2 @@ +from . import _impls as _ +from ._base import OperatorFactory, OpNotSupportedError diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_base.py b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_base.py new file mode 100644 index 00000000..954c4a5a --- /dev/null +++ b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_base.py @@ -0,0 +1,98 @@ +from copy import deepcopy +from typing import Hashable + +from six import with_metaclass + +from utensor_cgen.utils import must_return_type + +__all__ = ["OperatorFactory", "OpNotSupportedError"] + + +class OpNotSupportedError(Exception): + pass + + +class OperatorFactory(object): + + _operators = {} + + @classmethod + def get_opertor(cls, op_info): + op_type = op_info.op_type + op_cls = cls._operators.get(op_type) + if op_cls is None: + raise OpNotSupportedError( + "{} not supported in utensor_cgen".format(op_type) + ) + return op_cls(op_info) + + @classmethod + def register(cls, op_cls): + cls._operators[op_cls.op_type] = op_cls + return op_cls + + @classmethod + def support_op_types(cls): + """Return the set of all supported ops + """ + return set(cls._operators.keys()) + + @classmethod + def is_supported(cls, op_type): + if op_type != "Placeholder" and op_type not in cls._operators: + return False + return True + + +class _OperatorMeta(type): + def __new__(mcls, name, bases, attrib): + attrib["_cache"] = {} + for key in ["get_type_signature", "get_constructor_signature"]: + func = attrib.get(key) + if func is None: + continue + if not must_return_type.return_type_is_ensured(func): + attrib[key] = must_return_type(Hashable)(func) + elif not issubclass(must_return_type.get_expect_type(func), Hashable): + raise RuntimeError( + "{}.{} must be ensured to return {}".format(name, key, Hashable) + ) + cls = type.__new__(mcls, name, bases, attrib) + return cls + + +class _Operator(with_metaclass(_OperatorMeta), object): + def __new__(cls, op_info): + type_signature = cls.get_type_signature(op_info) + construct_signature = cls.get_constructor_signature(op_info) + full_signature = (type_signature, construct_signature) + in_dtypes, out_dtypes = type_signature + if full_signature not in cls._cache: + self = object.__new__(cls) + self.in_dtypes = in_dtypes + self.out_dtypes = out_dtypes + self.construct_params = construct_signature + self.op_info = op_info + cls._cache[full_signature] = self + return cls._cache[full_signature] + + @classmethod + @must_return_type(Hashable) + def get_type_signature(cls, op_info): + in_dtypes = tuple(t.dtype for t in op_info.input_tensors) + out_dtypes = tuple(t.dtype for t in op_info.output_tensors) + return (in_dtypes, out_dtypes) + + @classmethod + @must_return_type(Hashable) + def get_constructor_signature(cls, op_info): + return tuple() + + def get_declare_snippet(self, op_var_name, tensor_var_map, **kwargs): + raise NotImplementedError( + "base get_declare_snippet invoked: {}".format(type(self)) + ) + def get_eval_snippet(self, op_var_name, op_info, tensor_var_map, **kwargs): + raise NotImplementedError( + "base get_eval_snippet invoked: {}".format(type(self)) + ) diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py new file mode 100644 index 00000000..80296657 --- /dev/null +++ b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py @@ -0,0 +1,59 @@ +from utensor_cgen.backend.utensor.snippets._types import NP_TYPES_MAP +from utensor_cgen.backend.utensor.snippets.rearch import ( + AddOpEvalSnippet, DeclareOpSnippet, DeclareRamTensorSnippet, + DeclareRomTensorSnippet) + +from ._base import OperatorFactory, _Operator + + +@OperatorFactory.register +class _AddOperator(_Operator): + + op_type = 'AddOperator' + + def get_declare_snippet(self, op_var_name, tensor_var_map): + snippet = DeclareOpSnippet( + op=self, + templ_dtypes=[self.in_dtypes[0]], + op_var_name=op_var_name, + ) + return snippet + + def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): + snippet = AddOpEvalSnippet( + op_info=op_info, + templ_dtypes=[self.in_dtypes[0]], + op_name=op_var_name, + tensor_var_map=tensor_var_map, + ) + return snippet + + +@OperatorFactory.register +class _MatmulOperator(_Operator): + + op_type = 'MatrixMultOperator' + + +@OperatorFactory.register +class _ConvOperator(_Operator): + + op_type = 'ConvOperator' + + +# @OperatorFactory.register +# class _InlineOperator(_Operator): + +# op_type = 'Inline' + +# def __init__(self, op_info): +# self._tensor = op_info.output_tensors[0] + +# def get_declare_snippet(self, op_var_name, tensor_var_map, buffer_var_name): +# tensor_var_name = tensor_var_map[self._tensor.name] +# snippet = DeclareRomTensorSnippet( +# tensor_info=self._tensor, +# tensor_var=tensor_var_name, +# buffer_var=buffer_var_name, +# ) +# return snippet diff --git a/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py b/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py index 552469a6..be7dc55e 100644 --- a/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py +++ b/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py @@ -1,33 +1,75 @@ -from utensor_cgen.backend.utensor.snippets._types import UTENSOR_TYPES_MAP, NP_TYPES_MAP -from utensor_cgen.backend.utensor.snippets._base import Snippet, SnippetBase +import numpy as np -__all__ = ['RomTensorSnippet', 'DeclareOpSnippet', 'AddOpEvalSnippet', 'SimpleContainer'] +from utensor_cgen.backend.utensor.snippets._base import Snippet, SnippetBase +from utensor_cgen.backend.utensor.snippets._types import (NP_TYPES_MAP, + UTENSOR_TYPES_MAP) + +__all__ = [ + "DeclareRomTensorSnippet", + "DeclareRamTensorSnippet", + "DeclareOpSnippet", + "OpEvalSnippet", + "AddOpEvalSnippet", + "SimpleContainer" +] class _SnippetBase(Snippet): __headers__ = set(['"uTensor/uTensor.hpp"']) - -class RomTensorSnippet(_SnippetBase): + @staticmethod + def get_quant_param(tensor_info): + quant_params = {} + if 'quantization_zeros' in tensor_info.attributes: + zeros = tensor_info.attributes['quantization_zeros'] + scales = tensor_info.attributes["quantization_scales"] + quant_params['zero_point'] = { + 'value': zeros[0], + 'type_str': ['uint8_t', 'int8_t'][zeros.dtype == np.dtype('int8')] + } + quant_params['scale'] = { + 'value': scales[0], + 'type_str': 'float' + } + return quant_params + + +class DeclareRomTensorSnippet(_SnippetBase): __template_name__ = 'snippets/rearch/declare_rom_tensor.cpp' - def __init__(self, tensor_var_name, buffer_var_name, tensor): - Snippet.__init__(self) - shape = tensor.shape or [1] - self.template_vars = { - 'tensor_var_name': tensor_var_name, - 'shape': shape, - 'buffer_var_name': buffer_var_name, - 'utensor_dtype': UTENSOR_TYPES_MAP[tensor.dtype] - } + def __init__(self, tensor_info, tensor_var, buffer_var, static=False): + _SnippetBase.__init__(self) + self.template_vars['tensor_var'] = tensor_var + self.template_vars['shape'] = tensor_info.shape or [1] + self.template_vars['buffer_var'] = buffer_var + self.template_vars['static'] = static + self.template_vars['utensor_dtype'] = UTENSOR_TYPES_MAP[tensor_info.dtype] + self.template_vars['quantize_params'] = self.get_quant_param(tensor_info) + + +class DeclareRamTensorSnippet(_SnippetBase): + __template_name__ = 'snippets/rearch/declare_ram_tensor.cpp' + + def __init__(self, tensor_info, tensor_var): + _SnippetBase.__init__(self) + self.template_vars['tensor_var'] = tensor_var + self.template_vars['shape'] = tensor_info.shape or [1] + self.template_vars['utensor_dtype'] = UTENSOR_TYPES_MAP[tensor_info.dtype] + self.template_vars['quantize_params'] = self.get_quant_param(tensor_info) class DeclareOpSnippet(_SnippetBase): __template_name__ = 'snippets/rearch/declare_op.cpp' - def __init__(self, op_type, dtypes, op_var_name): - Snippet.__init__(self) + def __init__(self, op, templ_dtypes, op_var_name): + _SnippetBase.__init__(self) + op_info = op.op_info + if templ_dtypes: + templ_params = ', '.join([NP_TYPES_MAP[dtype].tensor_type_str for dtype in templ_dtypes]) + op_type = '{}<{}>'.format(op_info.op_type, templ_params) + else: + op_type = op_info.op_type self.template_vars['op_type'] = op_type - self.template_vars['dtypes'] = dtypes + self.template_vars['construct_params'] = op.construct_params self.template_vars['op_var_name'] = op_var_name @@ -35,9 +77,8 @@ class OpEvalSnippet(_SnippetBase): __template_name__ = 'snippets/rearch/eval_op.cpp' __inputs__ = [] __outputs__ = [] - - def __init__(self, op_info, op_name, tensor_var_map, dtypes): + def __init__(self, op_info, templ_dtypes, op_name, tensor_var_map): Snippet.__init__(self) input_map = { name: tensor_var_map[tensor.name] @@ -47,25 +88,22 @@ def __init__(self, op_info, op_name, tensor_var_map, dtypes): name: tensor_var_map[tensor.name] for name, tensor in zip(self.__outputs__, op_info.output_tensors) } - out_shapes_map = { - tensor_var_map[tensor.name]: tensor.shape or [1] - for tensor in op_info.output_tensors - } - out_dtypes_map = { - tensor_var_map[tensor.name]: UTENSOR_TYPES_MAP[tensor.dtype] - for tensor in op_info.output_tensors - } - utensor_dtypes = [NP_TYPES_MAP[dtype].tensor_type_str for dtype in dtypes] - if utensor_dtypes: - op_type = '{}<{}>'.format(op_info.op_type, ', '.join(utensor_dtypes)) + quantize_params_map = {} + for tensor_info in op_info.output_tensors: + quant_param = self.get_quant_param(tensor_info) + if quant_param: + tensor_var = tensor_var_map[tensor_info.name] + quantize_params_map[tensor_var] = quant_param + if templ_dtypes: + templ_params = ', '.join([NP_TYPES_MAP[dtype].tensor_type_str for dtype in templ_dtypes]) + op_type = '{}<{}>'.format(op_info.op_type, templ_params) else: op_type = op_info.op_type self.template_vars['op_type'] = op_type self.template_vars['op_name'] = op_name self.template_vars['input_map'] = input_map self.template_vars['output_map'] = output_map - self.template_vars['out_shapes_map'] = out_shapes_map - self.template_vars['out_dtypes_map'] = out_dtypes_map + self.template_vars['quantize_params_map'] = quantize_params_map class AddOpEvalSnippet(OpEvalSnippet): @@ -77,26 +115,26 @@ class SimpleContainer(SnippetBase): __headers__ = set(['"uTensor/uTensor.hpp"', ""]) __template_name__ = 'containers/rearch/simple.cpp' - def __init__(self, declare_snippets=None, eval_snippests=None): - if declare_snippets is None: - declare_snippets = [] - if eval_snippests is None: - eval_snippests = [] + def __init__(self): SnippetBase.__init__(self) - self._declare_snippets = [] + self._declare_local_snippets = [] + self._declare_global_snippets = [] self._eval_snippests = [] - for snp in declare_snippets: - self.add_declare_snippet(snp) - for snp in eval_snippests: - self.add_eval_snippet(snp) - - def add_declare_snippet(self, snippet): - self.__headers__.update(snippet.headers) - self._declare_snippets.append(snippet) + + def add_declare_global_snippets(self, *snippets): + for snippet in snippets: + self.__headers__.update(snippet.headers) + self._declare_global_snippets.append(snippet) - def add_eval_snippet(self, snippet): - self.__headers__.update(snippet.headers) - self._eval_snippests.append(snippet) + def add_declare_local_snippets(self, *snippets): + for snippet in snippets: + self.__headers__.update(snippet.headers) + self._declare_local_snippets.append(snippet) + + def add_eval_snippets(self, *snippets): + for snippet in snippets: + self.__headers__.update(snippet.headers) + self._eval_snippests.append(snippet) def add_header(self, header, *headers): self._add_header(header) @@ -111,7 +149,8 @@ def _add_header(self, header): def render(self): return self.template.render( - declare_snippets=self._declare_snippets, + declare_global_snippets=self._declare_global_snippets, + declare_local_snippets=self._declare_local_snippets, eval_snippets=self._eval_snippests, **self.template_vars ) diff --git a/utensor_cgen/backend/utensor/snippets/templates/containers/rearch/simple.cpp b/utensor_cgen/backend/utensor/snippets/templates/containers/rearch/simple.cpp index 76571151..0b395fd6 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/containers/rearch/simple.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/containers/rearch/simple.cpp @@ -1,16 +1,24 @@ using namespace uTensor; -// start rendering declare snippets -{%for snippet in declare_snippets%} +static localCircularArenaAllocator<{{meta_data_pool_size}}> meta_allocator; +static localCircularArenaAllocator<{{ram_data_pool_size}}> ram_allocator; + +{#ex: rom tensors, ops..etc#} +// start rendering global declare snippets +{%for snippet in declare_global_snippets%} {{snippet.render()}} {%endfor%} -// end of rendering declare snippets -localCircularArenaAllocator<{{meta_data_pool_size}}> meta_allocator; -localCircularArenaAllocator<{{ram_data_pool_size}}> ram_allocator; +// end of rendering global declare snippets void compute_{{model_name}}({%for pl in placeholders%}Tensor& {{pl}}, {%endfor%}std::vector& outputs){ Context::get_default_context()->set_metadata_allocator(&meta_allocator); Context::get_default_context()->set_ram_data_allocator(&ram_allocator); + {#ex: ram tensors#} + // start rendering local declare snippets + {%for snippet in declare_local_snippets%} + {{snippet.render()}} + {%endfor%} + // end of rendering local declare snippets // start rendering eval snippets {%for snippet in eval_snippets%} {{snippet.render()}} diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_op.cpp index 4fc8112d..2145e7ff 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_op.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_op.cpp @@ -1,5 +1,5 @@ -{%if dtypes %} -{{op_type}}<{%for t in dtypes[:-1]%}{{t}},{%endfor%}{{dtypes[-1]}}> {{op_var_name}}; +{%if construct_params%} +{{op_type}} {{op_var_name}}({%for param in construct_params%}{{construct_params}}{%if not loop.last%}, {%endif%}{%endfor%}); {%else%} {{op_type}} {{op_var_name}}; {%endif%} \ No newline at end of file diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_ram_tensor.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_ram_tensor.cpp new file mode 100644 index 00000000..90570e56 --- /dev/null +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_ram_tensor.cpp @@ -0,0 +1,9 @@ +{%if shape%} +Tensor {{tensor_var}} = new RamTensor({ {%for s in shape%}{{s}}{%if not loop.last%}, {%endif%}{%endfor%} }, {{utensor_dtype}}); +{%else%} +Tensor {{tensor_var}} = new RamTensor({{utensor_dtype}}); +{%endif%} +{%if quantize_params%} +{{quantize_params["zero_point"]["type_str"]}} {{tensor_var_name}}_zp; +{{quantize_params["scale"]["type_str"]}} {{tensor_var_name}}_scale; +{%endif%} \ No newline at end of file diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_rom_tensor.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_rom_tensor.cpp index 7aea2018..6b8c06ad 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_rom_tensor.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_rom_tensor.cpp @@ -1 +1,5 @@ -Tensor {{tensor_var_name}} = new RomTensor({ {%for s in shape[:-1]%}{{s}}, {%endfor%}{{shape[-1]}} }, {{utensor_dtype}}, {{buffer_var_name}}); \ No newline at end of file +{%if static %}static {%endif%}Tensor {{tensor_var}} = new RomTensor({ {%for s in shape%}{{s}}{%if not loop.last%}, {%endif%}{%endfor%} }, {{utensor_dtype}}, {{buffer_var}}); +{%if quantize_params%} +{{quantize_params["zero_point"]["type_str"]}} {{tensor_var_name}}_zp; +{{quantize_params["scale"]["type_str"]}} {{tensor_var_name}}_scale; +{%endif%} \ No newline at end of file diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_op.cpp index 6915a007..71d5666a 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_op.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_op.cpp @@ -1,6 +1,11 @@ -{%for tensor_var, shape in out_shapes_map.items()%} -Tensor {{tensor_var}} = new RamTensor({ {%for s in shape[:-1]%}{{s}}, {%endfor%}{{shape[-1]}} }, {{out_dtypes_map[tensor_var]}}); +{%if quantize_params_map%} +{%for tensor_var in output_map.values()%} +{%if tensor_var in quantize_params_map%} +{{tensor_var}}_zp = {{quantize_params_map[tensor_var]["zero_point"]["value"]}}; +{{tensor_var}}_scale = {{quantize_params_map[tensor_var]["scale"]["value"]}}; +{%endif%} {%endfor%} +{%endif%} {{op_name}} .set_inputs({ {%for name, tensor_var in input_map.items()%} diff --git a/utensor_cgen/frontend/tflite.py b/utensor_cgen/frontend/tflite.py index 8d679176..2a6c4226 100644 --- a/utensor_cgen/frontend/tflite.py +++ b/utensor_cgen/frontend/tflite.py @@ -240,10 +240,15 @@ def _build_tensor_map(self, fb_model, ugraph): dtype = self._TENSOR_NP_TYPE[tensor.Type()] attributes = dict() - quantParam = tensor.Quantization() - if quantParam is not None: - attributes["quantizationZeros"] = list([quantParam.ZeroPointAsNumpy()]) - attributes["quantizationScales"] = list([quantParam.ScaleAsNumpy()]) + quant_params = tensor.Quantization() + if quant_params is not None: + zp = quant_params.ZeroPointAsNumpy() + if zp.dtype == np.dtype(' Date: Tue, 28 Apr 2020 15:40:54 +0800 Subject: [PATCH 097/117] clean up code --- .../rearch/_operators/_impls.py | 23 ++----------------- 1 file changed, 2 insertions(+), 21 deletions(-) diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py index 80296657..5d860426 100644 --- a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py +++ b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py @@ -1,7 +1,6 @@ from utensor_cgen.backend.utensor.snippets._types import NP_TYPES_MAP -from utensor_cgen.backend.utensor.snippets.rearch import ( - AddOpEvalSnippet, DeclareOpSnippet, DeclareRamTensorSnippet, - DeclareRomTensorSnippet) +from utensor_cgen.backend.utensor.snippets.rearch import (AddOpEvalSnippet, + DeclareOpSnippet) from ._base import OperatorFactory, _Operator @@ -39,21 +38,3 @@ class _MatmulOperator(_Operator): class _ConvOperator(_Operator): op_type = 'ConvOperator' - - -# @OperatorFactory.register -# class _InlineOperator(_Operator): - -# op_type = 'Inline' - -# def __init__(self, op_info): -# self._tensor = op_info.output_tensors[0] - -# def get_declare_snippet(self, op_var_name, tensor_var_map, buffer_var_name): -# tensor_var_name = tensor_var_map[self._tensor.name] -# snippet = DeclareRomTensorSnippet( -# tensor_info=self._tensor, -# tensor_var=tensor_var_name, -# buffer_var=buffer_var_name, -# ) -# return snippet From 131e978d3a4f204b99ade6259f3df43fba37a226 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 28 Apr 2020 21:47:26 +0800 Subject: [PATCH 098/117] adding ops for code generator --- .../rearch/_operators/_impls.py | 276 +++++++++++++++++- .../utensor/snippets/rearch/_snippets.py | 68 ++++- 2 files changed, 335 insertions(+), 9 deletions(-) diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py index 5d860426..05ec2412 100644 --- a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py +++ b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py @@ -1,38 +1,298 @@ +from typing import Hashable + from utensor_cgen.backend.utensor.snippets._types import NP_TYPES_MAP -from utensor_cgen.backend.utensor.snippets.rearch import (AddOpEvalSnippet, - DeclareOpSnippet) +from utensor_cgen.backend.utensor.snippets.rearch import * +from utensor_cgen.utils import must_return_type from ._base import OperatorFactory, _Operator @OperatorFactory.register class _AddOperator(_Operator): - op_type = 'AddOperator' def get_declare_snippet(self, op_var_name, tensor_var_map): - snippet = DeclareOpSnippet( + return DeclareOpSnippet( op=self, templ_dtypes=[self.in_dtypes[0]], op_var_name=op_var_name, ) - return snippet def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): - snippet = AddOpEvalSnippet( + return AddOpEvalSnippet( op_info=op_info, templ_dtypes=[self.in_dtypes[0]], op_name=op_var_name, tensor_var_map=tensor_var_map, ) - return snippet @OperatorFactory.register -class _MatmulOperator(_Operator): +class _ReshapeOperator(_Operator): + op_type = "ReshapeOperator" + + def get_declare_snippet(self, op_var_name, tensor_var_map): + return DeclareOpSnippet( + op=self, + templ_dtypes=[self.in_dtypes[0]], + op_var_name=op_var_name, + ) + + def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): + return ReshahpeEvalSnippet( + op_info=op_info, + templ_dtypes=[self.in_dtypes[0]], + op_name=op_var_name, + tensor_var_map=tensor_var_map, + ) + +@OperatorFactory.register +class _MatmulOperator(_Operator): op_type = 'MatrixMultOperator' + def get_declare_snippet(self, op_var_name, tensor_var_map): + return DeclareOpSnippet( + op=self, + templ_dtypes=[self.in_dtypes[0]], + op_var_name=op_var_name, + ) + + def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): + return MatrixMultEvalSnippet( + op_info=op_info, + templ_dtypes=[self.in_dtypes[0]], + op_name=op_var_name, + tensor_var_map=tensor_var_map, + ) + + +@OperatorFactory.register +class _ArgMinOperator(_Operator): + op_type = "ArgMinOperator" + + def get_declare_snippet(self, op_var_name, tensor_var_map): + return DeclareOpSnippet( + op=self, + templ_dtypes=[self.in_dtypes[0]], + op_var_name=op_var_name, + ) + + def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): + return ArgMinEvalSnippet( + op_info=op_info, + templ_dtypes=[self.in_dtypes[0]], + op_name=op_var_name, + tensor_var_map=tensor_var_map, + ) + + +@OperatorFactory.register +class _ArgMaxOperator(_Operator): + op_type = "ArgMaxOperator" + + def get_declare_snippet(self, op_var_name, tensor_var_map): + return DeclareOpSnippet( + op=self, + templ_dtypes=[self.in_dtypes[0]], + op_var_name=op_var_name, + ) + + def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): + return ArgMaxEvalSnippet( + op_info=op_info, + templ_dtypes=[self.in_dtypes[0]], + op_name=op_var_name, + tensor_var_map=tensor_var_map, + ) + + +@OperatorFactory.register +class _QuantizeOperator(_Operator): + op_type = "QuantizeOperator" + + def get_declare_snippet(self, op_var_name, tensor_var_map): + return DeclareOpSnippet( + op=self, + templ_dtypes=[self.out_dtypes[0], self.in_dtypes[0]], + op_var_name=op_var_name, + ) + + def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): + return QuantizeEvalSnippet( + op_info=op_info, + templ_dtypes=[self.out_dtypes[0], self.in_dtypes[0]], + op_name=op_var_name, + tensor_var_map=tensor_var_map, + ) + + +@OperatorFactory.register +class _DequantizeOperator(_Operator): + op_type = "DequantizeOperator" + + def get_declare_snippet(self, op_var_name, tensor_var_map): + return DeclareOpSnippet( + op=self, + templ_dtypes=[self.out_dtypes[0], self.in_dtypes[0]], + op_var_name=op_var_name, + ) + + def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): + return DequantizeEvalSnippet( + op_info=op_info, + templ_dtypes=[self.out_dtypes[0], self.in_dtypes[0]], + op_name=op_var_name, + tensor_var_map=tensor_var_map, + ) + + +@OperatorFactory.register +class _ReLUOperator(_Operator): + op_type = "ReLUOperator" + + def get_declare_snippet(self, op_var_name, tensor_var_map): + return DeclareOpSnippet( + op=self, + templ_dtypes=[self.in_dtypes[0]], + op_var_name=op_var_name, + ) + + def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): + return ReLUEvalSnippet( + op_info=op_info, + templ_dtypes=[self.in_dtypes[0]], + op_name=op_var_name, + tensor_var_map=tensor_var_map, + ) + + +@OperatorFactory.register +class _ReLU6Operator(_Operator): + op_type = "ReLU6Operator" + + def get_declare_snippet(self, op_var_name, tensor_var_map): + return DeclareOpSnippet( + op=self, + templ_dtypes=[self.in_dtypes[0]], + op_var_name=op_var_name, + ) + + def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): + return ReLU6EvalSnippet( + op_info=op_info, + templ_dtypes=[self.in_dtypes[0]], + op_name=op_var_name, + tensor_var_map=tensor_var_map, + ) + + +@OperatorFactory.register +class _MinOperator(_Operator): + op_type = 'MinOperator' + + def get_declare_snippet(self, op_var_name, tensor_var_map): + return DeclareOpSnippet( + op=self, + templ_dtypes=[self.in_dtypes[0]], + op_var_name=op_var_name, + ) + + def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): + return MinEvalSnippet( + op_info=op_info, + templ_dtypes=[self.in_dtypes[0]], + op_name=op_var_name, + tensor_var_map=tensor_var_map, + ) + + +@OperatorFactory.register +class _MaxOperator(_Operator): + op_type = 'MaxOperator' + + def get_declare_snippet(self, op_var_name, tensor_var_map): + return DeclareOpSnippet( + op=self, + templ_dtypes=[self.in_dtypes[0]], + op_var_name=op_var_name, + ) + + def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): + return MaxEvalSnippet( + op_info=op_info, + templ_dtypes=[self.in_dtypes[0]], + op_name=op_var_name, + tensor_var_map=tensor_var_map, + ) + +class _PoolingOperatorMixin(object): + + @classmethod + @must_return_type(Hashable) + def get_constructor_signature(cls, op_info): + if op_info.ugraph.lib_name == "tensorflow": + strides = op_info.op_attr['strides'].value.ints_value + ksize = op_info.op_attr['ksize'].value.ints_value[1:3] + padding = op_info.op_attr['padding'].value.decode('utf8') + elif op_info.ugraph.lib_name == 'tflite': + strides = [ + 1, + op_info.op_attr['StrideW'], + op_info.op_attr['StrideH'], + 1, + ] + ksize = [ + op_info.op_attr['FilterWidth'], + op_info.op_attr['FilterHeight'], + ] + padding = op_info.op_attr['Padding'] == 1 and "VALID" or "SAME" + else: + raise RuntimeError("dont know to to get constructor signature") + stride_str = "{{ {} }}".format(", ".join(map(str, strides))) + ksize_str = "{{ {} }}".format(", ".join(map(str, ksize))) + return (stride_str, ksize_str, padding) + + +@OperatorFactory.register +class _MaxPoolOperator(_Operator, _PoolingOperatorMixin): + op_type = 'MaxPoolOperator' + + def get_declare_snippet(self, op_var_name, tensor_var_map): + return DeclareOpSnippet( + op=self, + templ_dtypes=[self.in_dtypes[0]], + op_var_name=op_var_name, + ) + + def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): + return MaxPoolEvalSnippet( + op_info=op_info, + templ_dtypes=[self.in_dtypes[0]], + op_name=op_var_name, + tensor_var_map=tensor_var_map, + ) + + +@OperatorFactory.register +class _MinPoolOperator(_Operator, _PoolingOperatorMixin): + op_type = 'MinPoolOperator' + + def get_declare_snippet(self, op_var_name, tensor_var_map): + return DeclareOpSnippet( + op=self, + templ_dtypes=[self.in_dtypes[0]], + op_var_name=op_var_name, + ) + + def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): + return MinPoolEvalSnippet( + op_info=op_info, + templ_dtypes=[self.in_dtypes[0]], + op_name=op_var_name, + tensor_var_map=tensor_var_map, + ) + @OperatorFactory.register class _ConvOperator(_Operator): diff --git a/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py b/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py index be7dc55e..773757d2 100644 --- a/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py +++ b/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py @@ -10,7 +10,13 @@ "DeclareOpSnippet", "OpEvalSnippet", "AddOpEvalSnippet", - "SimpleContainer" + "ReshahpeEvalSnippet", + "MatrixMultEvalSnippet", + "ArgMaxEvalSnippet", + "ArgMinEvalSnippet", + "QuantizeEvalSnippet", + "DequantizeEvalSnippet", + "SimpleContainer", ] class _SnippetBase(Snippet): @@ -111,6 +117,66 @@ class AddOpEvalSnippet(OpEvalSnippet): __outputs__ = ['c'] +class ReshahpeEvalSnippet(OpEvalSnippet): + __inputs__ = ["input"] + __outputs__ = ["output"] + + +class QuantizeEvalSnippet(OpEvalSnippet): + __inputs__ = ["input"] + __outputs__ = ["output"] + + +class MatrixMultEvalSnippet(OpEvalSnippet): + __inputs__ = ["a", "b"] + __outputs__ = ["c"] + + +class ArgMaxEvalSnippet(OpEvalSnippet): + __inputs__ = ["input"] + __outputs__ = ["output"] + + +class ArgMinEvalSnippet(OpEvalSnippet): + __inputs__ = ["input"] + __outputs__ = ["output"] + + +class DequantizeEvalSnippet(OpEvalSnippet): + __inputs__ = ["a"] + __outputs__ = ["b"] + + +class ReLUEvalSnippet(OpEvalSnippet): + __inputs__ = ["in"] + __outputs__ = ["out"] + + +class ReLU6EvalSnippet(OpEvalSnippet): + __inputs__ = ["in"] + __outputs__ = ["out"] + + +class MinEvalSnippet(OpEvalSnippet): + __inputs__ = ["in"] + __outputs__ = ["out"] + + +class MaxEvalSnippet(OpEvalSnippet): + __inputs__ = ["in"] + __outputs__ = ["out"] + + +class MaxPoolEvalSnippet(OpEvalSnippet): + __inputs__ = ["in"] + __outputs__ = ["out"] + + +class MinPoolEvalSnippet(OpEvalSnippet): + __inputs__ = ["in"] + __outputs__ = ["out"] + + class SimpleContainer(SnippetBase): __headers__ = set(['"uTensor/uTensor.hpp"', ""]) __template_name__ = 'containers/rearch/simple.cpp' From cff49408d685fc588b2c0eaf402b475a905bc562 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Wed, 29 Apr 2020 22:27:50 +0800 Subject: [PATCH 099/117] minor fix: utils.prune_graph --- .../utensor/snippets/rearch/_snippets.py | 43 ++++++++++++++++++- .../snippets/rearch/declare_dws_conv_op.cpp | 6 +++ .../snippets/rearch/eval_dws_conv_op.cpp | 28 ++++++++++++ utensor_cgen/utils.py | 17 +++++--- 4 files changed, 88 insertions(+), 6 deletions(-) create mode 100644 utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_dws_conv_op.cpp create mode 100644 utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_dws_conv_op.cpp diff --git a/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py b/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py index 773757d2..7d8ac321 100644 --- a/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py +++ b/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py @@ -1,3 +1,5 @@ +import re + import numpy as np from utensor_cgen.backend.utensor.snippets._base import Snippet, SnippetBase @@ -79,6 +81,10 @@ def __init__(self, op, templ_dtypes, op_var_name): self.template_vars['op_var_name'] = op_var_name +class DepthwiseSeperateConvOpDeclareSnippet(DeclareOpSnippet): + __template_name__ = "snippets/rearch/declare_dws_conv_op.cpp" + + class OpEvalSnippet(_SnippetBase): __template_name__ = 'snippets/rearch/eval_op.cpp' __inputs__ = [] @@ -106,12 +112,47 @@ def __init__(self, op_info, templ_dtypes, op_name, tensor_var_map): else: op_type = op_info.op_type self.template_vars['op_type'] = op_type - self.template_vars['op_name'] = op_name + self.template_vars['op_var_name'] = op_name self.template_vars['input_map'] = input_map self.template_vars['output_map'] = output_map self.template_vars['quantize_params_map'] = quantize_params_map +class DepthwiseSeperateConvOpEvalSnippet(OpEvalSnippet): + __template_name__ = 'snippets/rearch/eval_dws_conv_op.cpp' + __inputs__ = ["in", "depthwise_filter", "pointwise_filter"] + __outputs__ = ["out"] + + _PADDING_MAP = { + 0: "TFLM::TfLitePadding::kTfLitePaddingUnknown", + 1: "TFLM::TfLitePadding::kTfLitePaddingSame", + 2: "TFLM::TfLitePadding::kTfLitePaddingValid" + } + _ACTIVATION_MAP = { + 0: 'kTfLiteActNone', + 1: 'kTfLiteActRelu', + 2: 'kTfLiteActRelu1', + 3: 'kTfLiteActRelu6', + 4: 'kTfLiteActTanh', + 5: 'kTfLiteActSignBit', + 6: 'kTfLiteActSigmoid', + } + _ACTIVATION_STR_PATTERN = re.compile(r'^(\d+) \(\w+\)$') + + def __init__(self, op_info, templ_dtypes, op_name, tensor_var_map): + OpEvalSnippet.__init__(self, op_info, templ_dtypes, op_name, tensor_var_map) + cls = type(self) + self.template_vars['padding'] = cls._PADDING_MAP[op_info.op_attr['Padding']] + self.template_vars['stride_width'] = op_info.op_attr['StrideW'] + self.template_vars['stride+height'] = op_info.op_attr['StrideH'] + self.template_vars['depth_multiplier'] = op_info.op_attr['DepthMultiplier'] + activation_idx = cls._ACTIVATION_STR_PATTERN.match( + op_info.op_attr['FusedActivationFunction'] + ).group(1) + self.template_vars['activation'] = cls._ACTIVATION_MAP[activation_idx] + self.template_vars['dilation_width_factor'] = op_info.op_attr['DilationWFactor'] + self.template_vars['dilation_height_factor'] = op_info.op_attr['DilationHFactor'] + class AddOpEvalSnippet(OpEvalSnippet): __inputs__ = ['a', 'b'] __outputs__ = ['c'] diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_dws_conv_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_dws_conv_op.cpp new file mode 100644 index 00000000..04ee01fb --- /dev/null +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_dws_conv_op.cpp @@ -0,0 +1,6 @@ +{%if construct_params%} +{{op_type}} {{op_var_name}}({%for param in construct_params%}{{construct_params}}{%if not loop.last%}, {%endif%}{%endfor%}); +{%else%} +{{op_type}} {{op_var_name}}; +{%endif%} +TFLM::TfLiteDepthwiseConvParams {{op_var_name}}_params; \ No newline at end of file diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_dws_conv_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_dws_conv_op.cpp new file mode 100644 index 00000000..6b4c420b --- /dev/null +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_dws_conv_op.cpp @@ -0,0 +1,28 @@ +{%if quantize_params_map%} +{%for tensor_var in output_map.values()%} +{%if tensor_var in quantize_params_map%} +{{tensor_var}}_zp = {{quantize_params_map[tensor_var]["zero_point"]["value"]}}; +{{tensor_var}}_scale = {{quantize_params_map[tensor_var]["scale"]["value"]}}; +{%endif%} +{%endfor%} +{%endif%} + + {{op_var_name}}_params.padding = {{padding}}; + {{op_var_name}}_params.stride_width = {{stride_width}}; + {{op_var_name}}_params.stride_height = {{stride_height}}; + {{op_var_name}}_params.depth_multiplier = {{depth_multiplier}}; + {{op_var_name}}_params.activation = {{activation}}; + {{op_var_name}}_params.dilation_width_factor = {{dilation_width_factor}}; + {{op_var_name}}_params.dilation_height_factor = {{dilation_height_factor}}; + {{op_var_name}} + .set_inputs({ +{%for name, tensor_var in input_map.items()%} + { {{op_type}}::{{name}}, {{tensor_var}} }, +{%endfor%} + }) + .set_outputs({ +{%for name, tensor_var in output_map.items()%} + { {{op_type}}::{{name}}, {{tensor_var}}} +{%endfor%} + }) + .eval(); \ No newline at end of file diff --git a/utensor_cgen/utils.py b/utensor_cgen/utils.py index 13c0bb58..0c6c73ad 100644 --- a/utensor_cgen/utils.py +++ b/utensor_cgen/utils.py @@ -12,11 +12,11 @@ from time import time import attr +import idx2numpy as idx2np +import numpy as np from click.types import ParamType from toml import loads as _parse -import idx2numpy as idx2np -import numpy as np from utensor_cgen.logger import logger __all__ = ["save_idx", "save_consts", "save_graph", "log_graph", @@ -395,7 +395,7 @@ def ops_bfs_queue(ugraph, init_nodes=None): bfs_deck.append(op) return bfs_deck -def prune_graph(ugraph): +def prune_graph(ugraph, output_nodes=None): """ Remove nodes that is no longer needed *in-place* @@ -405,11 +405,17 @@ def prune_graph(ugraph): :param ugraph: the graph to be pruned :type ugraph: :class:`.uTensorGraph` + :param output_nodes: the output nodes + :type output_nodes: List[String] """ new_ugraph = deepcopy(ugraph) + if output_nodes is None: + output_nodes = ugraph.output_nodes[:] + else: + new_ugraph.output_nodes = output_nodes[:] # BFS to find all ops you need - ops_in_need = set(ugraph.output_nodes) - queue = [name for name in ugraph.output_nodes] + ops_in_need = set(output_nodes) + queue = [name for name in output_nodes] visited = set([]) while queue: op_name = queue.pop(0) @@ -436,6 +442,7 @@ def prune_graph(ugraph): ops_to_remove.add(op_name) for op_name in ops_to_remove: new_ugraph.ops_info.pop(op_name) + topologic_order_graph(new_ugraph) return new_ugraph def random_str(length=8): From 5dda2b96f22d1f9d215db16a06925cc8514a86f1 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Wed, 29 Apr 2020 22:28:02 +0800 Subject: [PATCH 100/117] rearch code generator beta --- .../code_generator/rearch/_operators/_base.py | 146 +++++++++--------- .../rearch/_operators/_impls.py | 27 ++++ .../utensor/snippets/rearch/_snippets.py | 51 +++--- .../snippets/rearch/declare_dws_conv_op.cpp | 6 - .../snippets/rearch/eval_dws_conv_op.cpp | 31 ++-- .../templates/snippets/rearch/eval_op.cpp | 24 +-- utensor_cgen/frontend/tflite.py | 20 ++- 7 files changed, 179 insertions(+), 126 deletions(-) delete mode 100644 utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_dws_conv_op.cpp diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_base.py b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_base.py index 954c4a5a..e3cfae7e 100644 --- a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_base.py +++ b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_base.py @@ -9,90 +9,90 @@ class OpNotSupportedError(Exception): - pass + pass class OperatorFactory(object): - _operators = {} + _operators = {} - @classmethod - def get_opertor(cls, op_info): - op_type = op_info.op_type - op_cls = cls._operators.get(op_type) - if op_cls is None: - raise OpNotSupportedError( - "{} not supported in utensor_cgen".format(op_type) - ) - return op_cls(op_info) + @classmethod + def get_opertor(cls, op_info): + op_type = op_info.op_type + op_cls = cls._operators.get(op_type) + if op_cls is None: + raise OpNotSupportedError( + "{} not supported in utensor_cgen".format(op_type) + ) + return op_cls(op_info) - @classmethod - def register(cls, op_cls): - cls._operators[op_cls.op_type] = op_cls - return op_cls + @classmethod + def register(cls, op_cls): + cls._operators[op_cls.op_type] = op_cls + return op_cls - @classmethod - def support_op_types(cls): - """Return the set of all supported ops - """ - return set(cls._operators.keys()) + @classmethod + def support_op_types(cls): + """Return the set of all supported ops + """ + return set(cls._operators.keys()) - @classmethod - def is_supported(cls, op_type): - if op_type != "Placeholder" and op_type not in cls._operators: - return False - return True + @classmethod + def is_supported(cls, op_type): + if op_type != "Placeholder" and op_type not in cls._operators: + return False + return True class _OperatorMeta(type): - def __new__(mcls, name, bases, attrib): - attrib["_cache"] = {} - for key in ["get_type_signature", "get_constructor_signature"]: - func = attrib.get(key) - if func is None: - continue - if not must_return_type.return_type_is_ensured(func): - attrib[key] = must_return_type(Hashable)(func) - elif not issubclass(must_return_type.get_expect_type(func), Hashable): - raise RuntimeError( - "{}.{} must be ensured to return {}".format(name, key, Hashable) - ) - cls = type.__new__(mcls, name, bases, attrib) - return cls + def __new__(mcls, name, bases, attrib): + attrib["_cache"] = {} + for key in ["get_type_signature", "get_constructor_signature"]: + func = attrib.get(key) + if func is None: + continue + if not must_return_type.return_type_is_ensured(func): + attrib[key] = must_return_type(Hashable)(func) + elif not issubclass(must_return_type.get_expect_type(func), Hashable): + raise RuntimeError( + "{}.{} must be ensured to return {}".format(name, key, Hashable) + ) + cls = type.__new__(mcls, name, bases, attrib) + return cls class _Operator(with_metaclass(_OperatorMeta), object): - def __new__(cls, op_info): - type_signature = cls.get_type_signature(op_info) - construct_signature = cls.get_constructor_signature(op_info) - full_signature = (type_signature, construct_signature) - in_dtypes, out_dtypes = type_signature - if full_signature not in cls._cache: - self = object.__new__(cls) - self.in_dtypes = in_dtypes - self.out_dtypes = out_dtypes - self.construct_params = construct_signature - self.op_info = op_info - cls._cache[full_signature] = self - return cls._cache[full_signature] - - @classmethod - @must_return_type(Hashable) - def get_type_signature(cls, op_info): - in_dtypes = tuple(t.dtype for t in op_info.input_tensors) - out_dtypes = tuple(t.dtype for t in op_info.output_tensors) - return (in_dtypes, out_dtypes) - - @classmethod - @must_return_type(Hashable) - def get_constructor_signature(cls, op_info): - return tuple() - - def get_declare_snippet(self, op_var_name, tensor_var_map, **kwargs): - raise NotImplementedError( - "base get_declare_snippet invoked: {}".format(type(self)) - ) - def get_eval_snippet(self, op_var_name, op_info, tensor_var_map, **kwargs): - raise NotImplementedError( - "base get_eval_snippet invoked: {}".format(type(self)) - ) + def __new__(cls, op_info): + type_signature = cls.get_type_signature(op_info) + construct_signature = cls.get_constructor_signature(op_info) + full_signature = (type_signature, construct_signature) + in_dtypes, out_dtypes = type_signature + if full_signature not in cls._cache: + self = object.__new__(cls) + self.in_dtypes = in_dtypes + self.out_dtypes = out_dtypes + self.construct_params = construct_signature + self.op_info = op_info + cls._cache[full_signature] = self + return cls._cache[full_signature] + + @classmethod + @must_return_type(Hashable) + def get_type_signature(cls, op_info): + in_dtypes = tuple(t.dtype for t in op_info.input_tensors) + out_dtypes = tuple(t.dtype for t in op_info.output_tensors) + return (in_dtypes, out_dtypes) + + @classmethod + @must_return_type(Hashable) + def get_constructor_signature(cls, op_info): + return tuple() + + def get_declare_snippet(self, op_var_name, tensor_var_map, **kwargs): + raise NotImplementedError( + "base get_declare_snippet invoked: {}".format(type(self)) + ) + def get_eval_snippet(self, op_var_name, op_info, tensor_var_map, **kwargs): + raise NotImplementedError( + "base get_eval_snippet invoked: {}".format(type(self)) + ) diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py index 05ec2412..53f89759 100644 --- a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py +++ b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py @@ -116,6 +116,7 @@ def get_declare_snippet(self, op_var_name, tensor_var_map): op=self, templ_dtypes=[self.out_dtypes[0], self.in_dtypes[0]], op_var_name=op_var_name, + nested_namespaces=['TFLM'], ) def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): @@ -124,6 +125,7 @@ def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): templ_dtypes=[self.out_dtypes[0], self.in_dtypes[0]], op_name=op_var_name, tensor_var_map=tensor_var_map, + nested_namespaces=['TFLM'], ) @@ -136,6 +138,7 @@ def get_declare_snippet(self, op_var_name, tensor_var_map): op=self, templ_dtypes=[self.out_dtypes[0], self.in_dtypes[0]], op_var_name=op_var_name, + nested_namespaces=['TFLM'], ) def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): @@ -144,6 +147,7 @@ def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): templ_dtypes=[self.out_dtypes[0], self.in_dtypes[0]], op_name=op_var_name, tensor_var_map=tensor_var_map, + nested_namespaces=['TFLM'], ) @@ -226,6 +230,7 @@ def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): tensor_var_map=tensor_var_map, ) + class _PoolingOperatorMixin(object): @classmethod @@ -294,6 +299,28 @@ def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): ) +@OperatorFactory.register +class _DWSConvOperator(_Operator): + op_type = "DepthwiseSeparableConvOperator" + + def get_declare_snippet(self, op_var_name, tensor_var_map): + return DeclareOpSnippet( + op=self, + templ_dtypes=[self.out_dtypes[0]], + op_var_name=op_var_name, + nested_namespaces=['TFLM'], + ) + + def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): + return DepthwiseSeperateConvOpEvalSnippet( + op_info=op_info, + templ_dtypes=[self.out_dtypes[0]], + op_name=op_var_name, + tensor_var_map=tensor_var_map, + nested_namespaces=['TFLM'], + ) + + @OperatorFactory.register class _ConvOperator(_Operator): diff --git a/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py b/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py index 7d8ac321..f8d3dbcc 100644 --- a/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py +++ b/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py @@ -10,14 +10,20 @@ "DeclareRomTensorSnippet", "DeclareRamTensorSnippet", "DeclareOpSnippet", - "OpEvalSnippet", + "DepthwiseSeperateConvOpEvalSnippet", "AddOpEvalSnippet", "ReshahpeEvalSnippet", + "QuantizeEvalSnippet", "MatrixMultEvalSnippet", "ArgMaxEvalSnippet", "ArgMinEvalSnippet", - "QuantizeEvalSnippet", "DequantizeEvalSnippet", + "ReLUEvalSnippet", + "ReLU6EvalSnippet", + "MinEvalSnippet", + "MaxEvalSnippet", + "MinPoolEvalSnippet", + "MaxPoolEvalSnippet", "SimpleContainer", ] @@ -68,30 +74,32 @@ def __init__(self, tensor_info, tensor_var): class DeclareOpSnippet(_SnippetBase): __template_name__ = 'snippets/rearch/declare_op.cpp' - def __init__(self, op, templ_dtypes, op_var_name): + def __init__(self, op, templ_dtypes, op_var_name, nested_namespaces=None): _SnippetBase.__init__(self) + if nested_namespaces is None: + nested_namespaces = [] op_info = op.op_info if templ_dtypes: templ_params = ', '.join([NP_TYPES_MAP[dtype].tensor_type_str for dtype in templ_dtypes]) op_type = '{}<{}>'.format(op_info.op_type, templ_params) else: op_type = op_info.op_type + if nested_namespaces: + op_type = "::".join(nested_namespaces + [op_type]) self.template_vars['op_type'] = op_type self.template_vars['construct_params'] = op.construct_params self.template_vars['op_var_name'] = op_var_name -class DepthwiseSeperateConvOpDeclareSnippet(DeclareOpSnippet): - __template_name__ = "snippets/rearch/declare_dws_conv_op.cpp" - - class OpEvalSnippet(_SnippetBase): __template_name__ = 'snippets/rearch/eval_op.cpp' __inputs__ = [] __outputs__ = [] - def __init__(self, op_info, templ_dtypes, op_name, tensor_var_map): + def __init__(self, op_info, templ_dtypes, op_name, tensor_var_map, nested_namespaces=None): Snippet.__init__(self) + if nested_namespaces is None: + nested_namespaces = [] input_map = { name: tensor_var_map[tensor.name] for name, tensor in zip(self.__inputs__, op_info.input_tensors) @@ -111,6 +119,8 @@ def __init__(self, op_info, templ_dtypes, op_name, tensor_var_map): op_type = '{}<{}>'.format(op_info.op_type, templ_params) else: op_type = op_info.op_type + if nested_namespaces: + op_type = "::".join(nested_namespaces + [op_type]) self.template_vars['op_type'] = op_type self.template_vars['op_var_name'] = op_name self.template_vars['input_map'] = input_map @@ -129,22 +139,22 @@ class DepthwiseSeperateConvOpEvalSnippet(OpEvalSnippet): 2: "TFLM::TfLitePadding::kTfLitePaddingValid" } _ACTIVATION_MAP = { - 0: 'kTfLiteActNone', - 1: 'kTfLiteActRelu', - 2: 'kTfLiteActRelu1', - 3: 'kTfLiteActRelu6', - 4: 'kTfLiteActTanh', - 5: 'kTfLiteActSignBit', - 6: 'kTfLiteActSigmoid', + '0': 'kTfLiteActNone', + '1': 'kTfLiteActRelu', + '2': 'kTfLiteActRelu1', + '3': 'kTfLiteActRelu6', + '4': 'kTfLiteActTanh', + '5': 'kTfLiteActSignBit', + '6': 'kTfLiteActSigmoid', } _ACTIVATION_STR_PATTERN = re.compile(r'^(\d+) \(\w+\)$') - def __init__(self, op_info, templ_dtypes, op_name, tensor_var_map): - OpEvalSnippet.__init__(self, op_info, templ_dtypes, op_name, tensor_var_map) + def __init__(self, op_info, templ_dtypes, op_name, tensor_var_map, nested_namespaces=None): + OpEvalSnippet.__init__(self, op_info, templ_dtypes, op_name, tensor_var_map, nested_namespaces) cls = type(self) self.template_vars['padding'] = cls._PADDING_MAP[op_info.op_attr['Padding']] self.template_vars['stride_width'] = op_info.op_attr['StrideW'] - self.template_vars['stride+height'] = op_info.op_attr['StrideH'] + self.template_vars['stride_height'] = op_info.op_attr['StrideH'] self.template_vars['depth_multiplier'] = op_info.op_attr['DepthMultiplier'] activation_idx = cls._ACTIVATION_STR_PATTERN.match( op_info.op_attr['FusedActivationFunction'] @@ -153,6 +163,7 @@ def __init__(self, op_info, templ_dtypes, op_name, tensor_var_map): self.template_vars['dilation_width_factor'] = op_info.op_attr['DilationWFactor'] self.template_vars['dilation_height_factor'] = op_info.op_attr['DilationHFactor'] + class AddOpEvalSnippet(OpEvalSnippet): __inputs__ = ['a', 'b'] __outputs__ = ['c'] @@ -208,12 +219,12 @@ class MaxEvalSnippet(OpEvalSnippet): __outputs__ = ["out"] -class MaxPoolEvalSnippet(OpEvalSnippet): +class MinPoolEvalSnippet(OpEvalSnippet): __inputs__ = ["in"] __outputs__ = ["out"] -class MinPoolEvalSnippet(OpEvalSnippet): +class MaxPoolEvalSnippet(OpEvalSnippet): __inputs__ = ["in"] __outputs__ = ["out"] diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_dws_conv_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_dws_conv_op.cpp deleted file mode 100644 index 04ee01fb..00000000 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_dws_conv_op.cpp +++ /dev/null @@ -1,6 +0,0 @@ -{%if construct_params%} -{{op_type}} {{op_var_name}}({%for param in construct_params%}{{construct_params}}{%if not loop.last%}, {%endif%}{%endfor%}); -{%else%} -{{op_type}} {{op_var_name}}; -{%endif%} -TFLM::TfLiteDepthwiseConvParams {{op_var_name}}_params; \ No newline at end of file diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_dws_conv_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_dws_conv_op.cpp index 6b4c420b..1cd47738 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_dws_conv_op.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_dws_conv_op.cpp @@ -7,22 +7,25 @@ {%endfor%} {%endif%} - {{op_var_name}}_params.padding = {{padding}}; - {{op_var_name}}_params.stride_width = {{stride_width}}; - {{op_var_name}}_params.stride_height = {{stride_height}}; - {{op_var_name}}_params.depth_multiplier = {{depth_multiplier}}; - {{op_var_name}}_params.activation = {{activation}}; - {{op_var_name}}_params.dilation_width_factor = {{dilation_width_factor}}; - {{op_var_name}}_params.dilation_height_factor = {{dilation_height_factor}}; + { + dws_params.padding = {{padding}}; + dws_params.stride_width = {{stride_width}}; + dws_params.stride_height = {{stride_height}}; + dws_params.depth_multiplier = {{depth_multiplier}}; + dws_params.activation = {{activation}}; + dws_params.dilation_width_factor = {{dilation_width_factor}}; + dws_params.dilation_height_factor = {{dilation_height_factor}}; + {{op_var_name}}.set_params(dws_params); + } {{op_var_name}} - .set_inputs({ + ].set_inputs({ {%for name, tensor_var in input_map.items()%} - { {{op_type}}::{{name}}, {{tensor_var}} }, + { {{op_type}}::{{name}}, {{tensor_var}} }, {%endfor%} - }) - .set_outputs({ + }) + .set_outputs({ {%for name, tensor_var in output_map.items()%} - { {{op_type}}::{{name}}, {{tensor_var}}} + { {{op_type}}::{{name}}, {{tensor_var}}} {%endfor%} - }) - .eval(); \ No newline at end of file + }) + .eval(); diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_op.cpp index 71d5666a..556b56e8 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_op.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_op.cpp @@ -6,15 +6,15 @@ {%endif%} {%endfor%} {%endif%} - {{op_name}} - .set_inputs({ -{%for name, tensor_var in input_map.items()%} - { {{op_type}}::{{name}}, {{tensor_var}} }, -{%endfor%} - }) - .set_outputs({ -{%for name, tensor_var in output_map.items()%} - { {{op_type}}::{{name}}, {{tensor_var}}} -{%endfor%} - }) - .eval(); +{{op_var_name}} + .set_inputs({ + {%for name, tensor_var in input_map.items()%} + { {{op_type}}::{{name}}, {{tensor_var}} }, + {%endfor%} + }) + .set_outputs({ + {%for name, tensor_var in output_map.items()%} + { {{op_type}}::{{name}}, {{tensor_var}}} + {%endfor%} + }) + .eval(); diff --git a/utensor_cgen/frontend/tflite.py b/utensor_cgen/frontend/tflite.py index 2a6c4226..f293338c 100644 --- a/utensor_cgen/frontend/tflite.py +++ b/utensor_cgen/frontend/tflite.py @@ -2,6 +2,7 @@ import re import numpy as np + from utensor_cgen.frontend import FrontendSelector from utensor_cgen.frontend.base import Parser from utensor_cgen.ir.base import OperationInfo, TensorInfo, uTensorGraph @@ -201,7 +202,7 @@ def parse(self, tflite_file, output_nodes=None): ops_info={}, ) self._build_graph(fb_model, ugraph) - + _OpRenaming.apply(ugraph) return ugraph def _build_graph(self, fb_model, ugraph): @@ -387,3 +388,20 @@ def _format_tensor_name(self, name, node_name, offset): def _format_op_type(self, op_type): return ''.join(map(lambda s: s.capitalize(), op_type.split('_'))) + + +class _OpRenaming(object): + _OP_NAMES_MAP = { + "Quantize": "QuantizeOperator", + "DepthwiseConv2d": "DepthwiseSeparableConvOperator", + "MaxPool2d": "MaxPoolOperator", + "Dequantize": "DequantizeOperator", + "FullyConnected": "FullyConnectedOperator" + } + + @classmethod + def apply(cls, ugraph): + for op_info in ugraph.ops_info.values(): + op_info.op_type = cls._OP_NAMES_MAP.get( + op_info.op_type, op_info.op_type + ) From 090cd31a670a67d9f5fe4a6e392829eff6551774 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Thu, 30 Apr 2020 15:44:48 +0800 Subject: [PATCH 101/117] fix templates and type str --- .../code_generator/rearch/_code_generator.py | 4 +- .../backend/utensor/snippets/_types.py | 56 ++++++++++++------- .../templates/containers/rearch/simple.cpp | 5 +- .../snippets/rearch/eval_dws_conv_op.cpp | 13 ++--- 4 files changed, 48 insertions(+), 30 deletions(-) diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py b/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py index a4b3f7a7..205d973b 100644 --- a/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py +++ b/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py @@ -105,12 +105,12 @@ def _get_declare_snippets(self, ugraph, ops, tensor_var_map): op_info.op_attr['value'].value.np_array.ravel() ) ) - declare_global_snippets.append( + declare_local_snippets.append( DeclareRomTensorSnippet( tensor_info=tensor_info, tensor_var=tensor_var, buffer_var=buffer_name, - static=True, + # static=True, ) ) return ops_map, declare_global_snippets, declare_local_snippets, weight_snippets diff --git a/utensor_cgen/backend/utensor/snippets/_types.py b/utensor_cgen/backend/utensor/snippets/_types.py index c3e72d2c..d65f720e 100644 --- a/utensor_cgen/backend/utensor/snippets/_types.py +++ b/utensor_cgen/backend/utensor/snippets/_types.py @@ -39,24 +39,42 @@ def _init(cls): if not cls._inited: _TYPE_MAP_VALUE = namedtuple("_TYPE_MAP_VALUE", ["importer_type_str", "tensor_type_str"]) cls._NP_TYPES_MAP = { - np.dtype(tf.float32.as_numpy_dtype): _TYPE_MAP_VALUE(importer_type_str="float", - tensor_type_str="float"), - np.dtype(tf.qint8.as_numpy_dtype): _TYPE_MAP_VALUE(importer_type_str="byte", - tensor_type_str="uint8_t"), - np.dtype(tf.int32.as_numpy_dtype): _TYPE_MAP_VALUE(importer_type_str="int", - tensor_type_str="int"), - np.dtype(tf.int64.as_numpy_dtype): _TYPE_MAP_VALUE(importer_type_str="int", - tensor_type_str="int"), - np.dtype(tf.quint8.as_numpy_dtype): _TYPE_MAP_VALUE(importer_type_str="ubyte", - tensor_type_str="uint8_t"), - np.dtype(tf.qint32.as_numpy_dtype): _TYPE_MAP_VALUE(importer_type_str="int", - tensor_type_str="int"), - np.dtype('uint16'): _TYPE_MAP_VALUE(importer_type_str="ushort", - tensor_type_str="uint16_t"), - np.dtype('int8'): _TYPE_MAP_VALUE(importer_type_str="int8", - tensor_type_str="q7_t"), - np.dtype('uint8'): _TYPE_MAP_VALUE(importer_type_str="uint8", - tensor_type_str="uint8_t"), + np.dtype(tf.float32.as_numpy_dtype): _TYPE_MAP_VALUE( + importer_type_str="float", + tensor_type_str="float" + ), + np.dtype(tf.qint8.as_numpy_dtype): _TYPE_MAP_VALUE( + importer_type_str="byte", + tensor_type_str="uint8_t" + ), + np.dtype(tf.int32.as_numpy_dtype): _TYPE_MAP_VALUE( + importer_type_str="int", + tensor_type_str="int" + ), + np.dtype(tf.int64.as_numpy_dtype): _TYPE_MAP_VALUE( + importer_type_str="int", + tensor_type_str="int" + ), + np.dtype(tf.quint8.as_numpy_dtype): _TYPE_MAP_VALUE( + importer_type_str="ubyte", + tensor_type_str="uint8_t" + ), + np.dtype(tf.qint32.as_numpy_dtype): _TYPE_MAP_VALUE( + importer_type_str="int", + tensor_type_str="int" + ), + np.dtype('uint16'): _TYPE_MAP_VALUE( + importer_type_str="ushort", + tensor_type_str="uint16_t" + ), + np.dtype('int8'): _TYPE_MAP_VALUE( + importer_type_str="int8", + tensor_type_str="int8_t" #"q7_t" + ), + np.dtype('uint8'): _TYPE_MAP_VALUE( + importer_type_str="uint8", + tensor_type_str="uint8_t" + ), } cls._inited = True @@ -96,4 +114,4 @@ def _init(cls): } cls._inited = True -UTENSOR_TYPES_MAP = Numpy2uTensorTypesMap() \ No newline at end of file +UTENSOR_TYPES_MAP = Numpy2uTensorTypesMap() diff --git a/utensor_cgen/backend/utensor/snippets/templates/containers/rearch/simple.cpp b/utensor_cgen/backend/utensor/snippets/templates/containers/rearch/simple.cpp index 0b395fd6..28e2f197 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/containers/rearch/simple.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/containers/rearch/simple.cpp @@ -1,7 +1,7 @@ using namespace uTensor; -static localCircularArenaAllocator<{{meta_data_pool_size}}> meta_allocator; -static localCircularArenaAllocator<{{ram_data_pool_size}}> ram_allocator; +static const localCircularArenaAllocator<{{meta_data_pool_size}}> meta_allocator; +static const localCircularArenaAllocator<{{ram_data_pool_size}}> ram_allocator; {#ex: rom tensors, ops..etc#} // start rendering global declare snippets @@ -22,6 +22,7 @@ void compute_{{model_name}}({%for pl in placeholders%}Tensor& {{pl}}, {%endfor%} // start rendering eval snippets {%for snippet in eval_snippets%} {{snippet.render()}} + {%endfor%} // end of rendering eval snippets {%for out_var in out_tensor_var_names%} diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_dws_conv_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_dws_conv_op.cpp index 1cd47738..91da93d8 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_dws_conv_op.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_dws_conv_op.cpp @@ -6,8 +6,7 @@ {%endif%} {%endfor%} {%endif%} - - { +{ dws_params.padding = {{padding}}; dws_params.stride_width = {{stride_width}}; dws_params.stride_height = {{stride_height}}; @@ -18,14 +17,14 @@ {{op_var_name}}.set_params(dws_params); } {{op_var_name}} - ].set_inputs({ -{%for name, tensor_var in input_map.items()%} + .set_inputs({ + {%for name, tensor_var in input_map.items()%} { {{op_type}}::{{name}}, {{tensor_var}} }, -{%endfor%} + {%endfor%} }) .set_outputs({ -{%for name, tensor_var in output_map.items()%} + {%for name, tensor_var in output_map.items()%} { {{op_type}}::{{name}}, {{tensor_var}}} -{%endfor%} + {%endfor%} }) .eval(); From b4469a71513feb6cd97f1340d31f8325093758e9 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Fri, 1 May 2020 15:00:16 +0800 Subject: [PATCH 102/117] namespaces support (same optype, different namespaces) --- .../backend/utensor/_graph_lower/_op_lower.py | 21 +++++++++- .../code_generator/rearch/_code_generator.py | 3 ++ .../code_generator/rearch/_operators/_base.py | 19 +++++++--- .../rearch/_operators/_impls.py | 38 ++++++++++++++++++- .../utensor/snippets/rearch/_snippets.py | 12 +++++- .../templates/snippets/rearch/declare_op.cpp | 2 +- ...conv_op.cpp => eval_quant_dws_conv_op.cpp} | 0 utensor_cgen/ir/base.py | 29 ++++++-------- 8 files changed, 95 insertions(+), 29 deletions(-) rename utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/{eval_dws_conv_op.cpp => eval_quant_dws_conv_op.cpp} (100%) diff --git a/utensor_cgen/backend/utensor/_graph_lower/_op_lower.py b/utensor_cgen/backend/utensor/_graph_lower/_op_lower.py index 3c523099..7d0ed1e2 100644 --- a/utensor_cgen/backend/utensor/_graph_lower/_op_lower.py +++ b/utensor_cgen/backend/utensor/_graph_lower/_op_lower.py @@ -2,7 +2,7 @@ from utensor_cgen.backend.base import BackendPart from utensor_cgen.logger import logger -from utensor_cgen.utils import class_property +from utensor_cgen.utils import Configuration, class_property class uTensorGraphLowerBase(BackendPart): @@ -34,6 +34,10 @@ def default_config(cls): class uTensorRearchGraphLower(uTensorGraphLowerBase): PART = 'rearch_graph_lower' + def __init__(self, config): + final_config = Configuration(self.default_config, config) + self.tflite_use_quant_dws_conv = final_config['tflite_use_quant_dws_conv'] + class OptypeRenameManager(object): NAME_MAP = { 'Add': 'AddOperator', @@ -45,6 +49,13 @@ class OptypeRenameManager(object): def get_new_optype(cls, op_type): return cls.NAME_MAP.get(op_type, op_type) + class AddCodegenAttributes(object): + + @classmethod + def add_attributes(cls, ugraph): + for op_info in ugraph.get_ops_by_type('DepthwiseSeparableConvOperator'): + op_info.code_gen_attributes['namespaces'] = ('TFLM',) + @classmethod def add_name_map(cls, generic_name, target_specific_name): cls.OptypeRenameManager.NAME_MAP[generic_name] = target_specific_name @@ -52,7 +63,13 @@ def add_name_map(cls, generic_name, target_specific_name): def handle_tensorflow(self, ugraph): for op_info in ugraph.ops_info.values(): op_info.op_type = self.OptypeRenameManager.get_new_optype(op_info.op_type) + + def handle_tflite(self, ugraph): + if self.tflite_use_quant_dws_conv: + self.AddCodegenAttributes.add_attributes(ugraph) @class_property def default_config(cls): - return {} + return { + 'tflite_use_quant_dws_conv': True, + } diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py b/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py index 205d973b..3a122d7d 100644 --- a/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py +++ b/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py @@ -149,6 +149,7 @@ def _generate_files( weight_header_fname = None if weight_snippets: with (params_dir / 'params_{}.hpp'.format(ugraph.name)).open('w') as fid: + fid.write("/* Auto-generated by utensor cli */\n") weight_container = ContextGlobalArrayContainer( snippets=weight_snippets ) @@ -165,6 +166,7 @@ def _generate_files( container_snippet.template_vars.update(template_vars) (model_file_dir / ugraph.name).mkdir(parents=True, exist_ok=True) with (model_file_dir / ugraph.name / header_fname).open('w') as fid: + fid.write("/* Auto-generated by utensor cli */\n") template = env.get_template('snippets/rearch/simple.hpp') fid.write(template.render(**template_vars)) container_snippet.add_header(fid.name) @@ -173,6 +175,7 @@ def _generate_files( composer = Composer(snippets=[container_snippet]) src_fname = self.src_fname == 'None' and '{}.cpp'.format(ugraph.name) or self.src_fname with (model_file_dir / ugraph.name / src_fname ).open('w') as fid: + fid.write("/* Auto-generated by utensor cli */\n") fid.write(composer.compose()) @class_property diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_base.py b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_base.py index e3cfae7e..6e1eb68d 100644 --- a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_base.py +++ b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_base.py @@ -3,7 +3,7 @@ from six import with_metaclass -from utensor_cgen.utils import must_return_type +from utensor_cgen.utils import MUST_OVERWRITE, must_return_type __all__ = ["OperatorFactory", "OpNotSupportedError"] @@ -19,16 +19,19 @@ class OperatorFactory(object): @classmethod def get_opertor(cls, op_info): op_type = op_info.op_type - op_cls = cls._operators.get(op_type) + namespaces = op_info.code_gen_attributes.get('namespaces', tuple()) + op_cls = cls._operators.get((namespaces, op_type)) if op_cls is None: raise OpNotSupportedError( - "{} not supported in utensor_cgen".format(op_type) + "{}::{} not supported in utensor_cgen".format("::".join(namespaces), op_type) ) return op_cls(op_info) @classmethod def register(cls, op_cls): - cls._operators[op_cls.op_type] = op_cls + cls._operators[ + (op_cls.namespaces, op_cls.op_type) + ] = op_cls return op_cls @classmethod @@ -62,10 +65,16 @@ def __new__(mcls, name, bases, attrib): class _Operator(with_metaclass(_OperatorMeta), object): + namespaces = tuple() + op_type = MUST_OVERWRITE + def __new__(cls, op_info): + if cls.op_type is MUST_OVERWRITE: + raise ValueError('op_type must be overwritten: {}'.format(cls)) + type_signature = cls.get_type_signature(op_info) construct_signature = cls.get_constructor_signature(op_info) - full_signature = (type_signature, construct_signature) + full_signature = (cls.namespaces, type_signature, construct_signature) in_dtypes, out_dtypes = type_signature if full_signature not in cls._cache: self = object.__new__(cls) diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py index 53f89759..72219579 100644 --- a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py +++ b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py @@ -299,16 +299,51 @@ def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): ) +@OperatorFactory.register +class _QuantDWSConvOperator(_Operator): + namespaces = ('TFLM',) + op_type = "DepthwiseSeparableConvOperator" + + def get_declare_snippet(self, op_var_name, tensor_var_map): + return DeclareOpSnippet( + op=self, + templ_dtypes=[self.out_dtypes[0]], + op_var_name=op_var_name, + nested_namespaces=self.namespaces, + ) + + def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): + return QuantDepthwiseSeperateConvOpEvalSnippet( + op_info=op_info, + templ_dtypes=[self.out_dtypes[0]], + op_name=op_var_name, + tensor_var_map=tensor_var_map, + nested_namespaces=self.namespaces, + ) + + @OperatorFactory.register class _DWSConvOperator(_Operator): op_type = "DepthwiseSeparableConvOperator" + @classmethod + @must_return_type(Hashable) + def get_constructor_signature(cls, op_info): + strides = [ + 1, + op_info.op_attr['StrideW'], + op_info.op_attr['StrideH'], + 1, + ] + padding = op_info.op_attr['Padding'] == 1 and "VALID" or "SAME" + strides_str = ','.join(map(str, strides)) + return ("{{ {} }}".format(strides_str), padding) + def get_declare_snippet(self, op_var_name, tensor_var_map): return DeclareOpSnippet( op=self, templ_dtypes=[self.out_dtypes[0]], op_var_name=op_var_name, - nested_namespaces=['TFLM'], ) def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): @@ -317,7 +352,6 @@ def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): templ_dtypes=[self.out_dtypes[0]], op_name=op_var_name, tensor_var_map=tensor_var_map, - nested_namespaces=['TFLM'], ) diff --git a/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py b/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py index f8d3dbcc..17cca3c4 100644 --- a/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py +++ b/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py @@ -11,6 +11,7 @@ "DeclareRamTensorSnippet", "DeclareOpSnippet", "DepthwiseSeperateConvOpEvalSnippet", + "QuantDepthwiseSeperateConvOpEvalSnippet", "AddOpEvalSnippet", "ReshahpeEvalSnippet", "QuantizeEvalSnippet", @@ -78,6 +79,8 @@ def __init__(self, op, templ_dtypes, op_var_name, nested_namespaces=None): _SnippetBase.__init__(self) if nested_namespaces is None: nested_namespaces = [] + else: + nested_namespaces = list(nested_namespaces) op_info = op.op_info if templ_dtypes: templ_params = ', '.join([NP_TYPES_MAP[dtype].tensor_type_str for dtype in templ_dtypes]) @@ -100,6 +103,8 @@ def __init__(self, op_info, templ_dtypes, op_name, tensor_var_map, nested_namesp Snippet.__init__(self) if nested_namespaces is None: nested_namespaces = [] + else: + nested_namespaces = list(nested_namespaces) input_map = { name: tensor_var_map[tensor.name] for name, tensor in zip(self.__inputs__, op_info.input_tensors) @@ -129,10 +134,15 @@ def __init__(self, op_info, templ_dtypes, op_name, tensor_var_map, nested_namesp class DepthwiseSeperateConvOpEvalSnippet(OpEvalSnippet): - __template_name__ = 'snippets/rearch/eval_dws_conv_op.cpp' __inputs__ = ["in", "depthwise_filter", "pointwise_filter"] __outputs__ = ["out"] + +class QuantDepthwiseSeperateConvOpEvalSnippet(OpEvalSnippet): + __template_name__ = 'snippets/rearch/eval_quant_dws_conv_op.cpp' + __inputs__ = ["in", "filter", "bias"] + __outputs__ = ["out"] + _PADDING_MAP = { 0: "TFLM::TfLitePadding::kTfLitePaddingUnknown", 1: "TFLM::TfLitePadding::kTfLitePaddingSame", diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_op.cpp index 2145e7ff..0236957e 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_op.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/declare_op.cpp @@ -1,5 +1,5 @@ {%if construct_params%} -{{op_type}} {{op_var_name}}({%for param in construct_params%}{{construct_params}}{%if not loop.last%}, {%endif%}{%endfor%}); +{{op_type}} {{op_var_name}}({%for param in construct_params%}{{param}}{%if not loop.last%}, {%endif%}{%endfor%}); {%else%} {{op_type}} {{op_var_name}}; {%endif%} \ No newline at end of file diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_dws_conv_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_quant_dws_conv_op.cpp similarity index 100% rename from utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_dws_conv_op.cpp rename to utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_quant_dws_conv_op.cpp diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index 5dd70922..b2719c80 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -4,11 +4,10 @@ from functools import reduce import attr -import six -from attr.validators import instance_of - import numpy as np +import six import tensorflow as tf +from attr.validators import instance_of from tensorflow.core.framework.attr_value_pb2 import AttrValue as _AttrValue from tensorflow.core.framework.attr_value_pb2 import \ NameAttrList as _NameAttrList @@ -16,6 +15,7 @@ from tensorflow.core.framework.tensor_shape_pb2 import \ TensorShapeProto as _TensorShapeProto from tensorflow.core.framework.types_pb2 import DataType as _DataType + from utensor_cgen.logger import logger from utensor_cgen.utils import random_str, topologic_order_graph @@ -271,6 +271,9 @@ def check(self, attribute, value): op_attr = attr.ib(factory=dict, converter=dict) + # any codegen specific attributes goes to here + code_gen_attributes = attr.ib(factory=dict) + n_inputs = attr.ib() @n_inputs.default def default_n_inputs(self): @@ -474,7 +477,6 @@ class uTensorGraph(IRBase, _NoShallowCopyMixin, uTensorGraphBuilderMixin): ops_info = attr.ib(factory=dict) # non-init topo_order = attr.ib(factory=list, init=False) - _type_to_op_map = attr.ib(factory=dict, init=False, repr=False) attributes = attr.ib(factory=dict, init=False, repr=False) def __attrs_post_init__(self): @@ -496,17 +498,11 @@ def get_ops_by_type(self, given_op_type): :rtype: List[:class:`.OperationInfo`] """ - if not self._type_to_op_map: - for op_info in self.ops_info.values(): - op_type = op_info.op_type - ops = self._type_to_op_map.get( - op_type, - [] - ) + [op_info] - self._type_to_op_map.update( - [(op_type, ops),] - ) - return self._type_to_op_map.get(given_op_type, []) + ops = [] + for op_info in self.ops_info.values(): + if op_info.op_type == given_op_type: + ops.append(op_info) + return ops @property def output_ops(self): @@ -664,9 +660,6 @@ def unsafe_merge_into(self, other_ugraph): """ for op in self.ops_info.values(): op.move_into(other_ugraph) - if op.op_type not in self._type_to_op_map: - self._type_to_op_map[op.op_type] = [] - self._type_to_op_map[op.op_type].append(op) def __deepcopy__(self, memo): new_graph = uTensorGraph( From 5851a491fea80cb55e31e7c0a6900d4688cfce3b Mon Sep 17 00:00:00 2001 From: dboyliao Date: Fri, 1 May 2020 15:46:22 +0800 Subject: [PATCH 103/117] Fix bug and minor refactor (removing misleading op_info attributes for _Operator) --- .../utensor/code_generator/rearch/_operators/_base.py | 2 +- utensor_cgen/backend/utensor/snippets/rearch/_snippets.py | 6 ++---- .../templates/snippets/rearch/eval_quant_dws_conv_op.cpp | 1 + 3 files changed, 4 insertions(+), 5 deletions(-) diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_base.py b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_base.py index 6e1eb68d..d749a6a7 100644 --- a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_base.py +++ b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_base.py @@ -81,7 +81,7 @@ def __new__(cls, op_info): self.in_dtypes = in_dtypes self.out_dtypes = out_dtypes self.construct_params = construct_signature - self.op_info = op_info + self.op_type = op_info.op_type cls._cache[full_signature] = self return cls._cache[full_signature] diff --git a/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py b/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py index 17cca3c4..c5adc713 100644 --- a/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py +++ b/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py @@ -81,12 +81,10 @@ def __init__(self, op, templ_dtypes, op_var_name, nested_namespaces=None): nested_namespaces = [] else: nested_namespaces = list(nested_namespaces) - op_info = op.op_info + op_type = op.op_type if templ_dtypes: templ_params = ', '.join([NP_TYPES_MAP[dtype].tensor_type_str for dtype in templ_dtypes]) - op_type = '{}<{}>'.format(op_info.op_type, templ_params) - else: - op_type = op_info.op_type + op_type = '{}<{}>'.format(op_type, templ_params) if nested_namespaces: op_type = "::".join(nested_namespaces + [op_type]) self.template_vars['op_type'] = op_type diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_quant_dws_conv_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_quant_dws_conv_op.cpp index 91da93d8..336eae0e 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_quant_dws_conv_op.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_quant_dws_conv_op.cpp @@ -7,6 +7,7 @@ {%endfor%} {%endif%} { + TFLM::TfLiteDepthwiseConvParams dws_params; dws_params.padding = {{padding}}; dws_params.stride_width = {{stride_width}}; dws_params.stride_height = {{stride_height}}; From 5a0909472ff05fa18fcbfece2dc1848fa928d95f Mon Sep 17 00:00:00 2001 From: dboyliao Date: Fri, 1 May 2020 16:26:12 +0800 Subject: [PATCH 104/117] Fix templates and snippets bug --- .../utensor/code_generator/rearch/_operators/_impls.py | 4 ++-- utensor_cgen/backend/utensor/snippets/rearch/_snippets.py | 2 +- .../templates/snippets/rearch/eval_quant_dws_conv_op.cpp | 2 +- .../utensor/snippets/templates/snippets/rearch/simple.hpp | 2 +- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py index 72219579..c02b6ff6 100644 --- a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py +++ b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py @@ -260,7 +260,7 @@ def get_constructor_signature(cls, op_info): @OperatorFactory.register -class _MaxPoolOperator(_Operator, _PoolingOperatorMixin): +class _MaxPoolOperator(_PoolingOperatorMixin, _Operator): op_type = 'MaxPoolOperator' def get_declare_snippet(self, op_var_name, tensor_var_map): @@ -280,7 +280,7 @@ def get_eval_snippet(self, op_var_name, op_info, tensor_var_map): @OperatorFactory.register -class _MinPoolOperator(_Operator, _PoolingOperatorMixin): +class _MinPoolOperator(_PoolingOperatorMixin, _Operator): op_type = 'MinPoolOperator' def get_declare_snippet(self, op_var_name, tensor_var_map): diff --git a/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py b/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py index c5adc713..4fd295ab 100644 --- a/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py +++ b/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py @@ -29,7 +29,7 @@ ] class _SnippetBase(Snippet): - __headers__ = set(['"uTensor/uTensor.hpp"']) + __headers__ = set(['"uTensor/uTensor.h"']) @staticmethod def get_quant_param(tensor_info): diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_quant_dws_conv_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_quant_dws_conv_op.cpp index 336eae0e..3a7d3ea3 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_quant_dws_conv_op.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/eval_quant_dws_conv_op.cpp @@ -12,7 +12,7 @@ dws_params.stride_width = {{stride_width}}; dws_params.stride_height = {{stride_height}}; dws_params.depth_multiplier = {{depth_multiplier}}; - dws_params.activation = {{activation}}; + dws_params.activation = TFLM::{{activation}}; dws_params.dilation_width_factor = {{dilation_width_factor}}; dws_params.dilation_height_factor = {{dilation_height_factor}}; {{op_var_name}}.set_params(dws_params); diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/simple.hpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/simple.hpp index 90e379a8..cc24e474 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/simple.hpp +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/rearch/simple.hpp @@ -4,6 +4,6 @@ #include #include "uTensor/core/tensor.hpp" -void compute_{{model_name}}({%for pl in placeholders%}utensor::Tensor& {{pl}}, {%endfor%}std::vector& outputs); +void compute_{{model_name}}({%for pl in placeholders%}uTensor::Tensor& {{pl}}, {%endfor%}std::vector& outputs); #endif // __{{model_name.upper()}}_H \ No newline at end of file From fa6c7f445f758ac5026a102d82785b873a70506c Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 3 May 2020 11:29:43 +0800 Subject: [PATCH 105/117] method renaming to prevent misunderstanding --- .../utensor/code_generator/rearch/_operators/_base.py | 10 +++++----- .../utensor/code_generator/rearch/_operators/_impls.py | 4 ++-- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_base.py b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_base.py index d749a6a7..daf4c694 100644 --- a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_base.py +++ b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_base.py @@ -50,7 +50,7 @@ def is_supported(cls, op_type): class _OperatorMeta(type): def __new__(mcls, name, bases, attrib): attrib["_cache"] = {} - for key in ["get_type_signature", "get_constructor_signature"]: + for key in ["get_type_signature", "get_constructor_parameters"]: func = attrib.get(key) if func is None: continue @@ -73,14 +73,14 @@ def __new__(cls, op_info): raise ValueError('op_type must be overwritten: {}'.format(cls)) type_signature = cls.get_type_signature(op_info) - construct_signature = cls.get_constructor_signature(op_info) - full_signature = (cls.namespaces, type_signature, construct_signature) + construct_params = cls.get_constructor_parameters(op_info) + full_signature = (cls.namespaces, type_signature, construct_params) in_dtypes, out_dtypes = type_signature if full_signature not in cls._cache: self = object.__new__(cls) self.in_dtypes = in_dtypes self.out_dtypes = out_dtypes - self.construct_params = construct_signature + self.construct_params = construct_params self.op_type = op_info.op_type cls._cache[full_signature] = self return cls._cache[full_signature] @@ -94,7 +94,7 @@ def get_type_signature(cls, op_info): @classmethod @must_return_type(Hashable) - def get_constructor_signature(cls, op_info): + def get_constructor_parameters(cls, op_info): return tuple() def get_declare_snippet(self, op_var_name, tensor_var_map, **kwargs): diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py index c02b6ff6..60105ea8 100644 --- a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py +++ b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py @@ -235,7 +235,7 @@ class _PoolingOperatorMixin(object): @classmethod @must_return_type(Hashable) - def get_constructor_signature(cls, op_info): + def get_constructor_parameters(cls, op_info): if op_info.ugraph.lib_name == "tensorflow": strides = op_info.op_attr['strides'].value.ints_value ksize = op_info.op_attr['ksize'].value.ints_value[1:3] @@ -328,7 +328,7 @@ class _DWSConvOperator(_Operator): @classmethod @must_return_type(Hashable) - def get_constructor_signature(cls, op_info): + def get_constructor_parameters(cls, op_info): strides = [ 1, op_info.op_attr['StrideW'], From 249a11c124daa5b19c6a5ee2ffdd9674e13adde7 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Mon, 4 May 2020 16:07:21 +0800 Subject: [PATCH 106/117] Fix TF2.0 imports and bugs (v1 and v2 behavior inconsistent) --- utensor_cgen/__init__.py | 11 +++++++-- utensor_cgen/api.py | 18 ++++++++++++++ utensor_cgen/backend/transformer.py | 5 ++-- utensor_cgen/frontend/tensorflow.py | 7 +++--- utensor_cgen/ir/base.py | 14 +++++------ utensor_cgen/ir/converter.py | 28 +++++++++++++--------- utensor_cgen/legalizer/tensorflow.py | 12 ++++++++++ utensor_cgen/transformer/linear_reoder.py | 3 ++- utensor_cgen/transformer/ns_transformer.py | 3 ++- utensor_cgen/transformer/quantize.py | 13 +++++++++- utensor_cgen/utils.py | 4 +++- 11 files changed, 89 insertions(+), 29 deletions(-) create mode 100644 utensor_cgen/api.py diff --git a/utensor_cgen/__init__.py b/utensor_cgen/__init__.py index 87b59bd1..47c08995 100644 --- a/utensor_cgen/__init__.py +++ b/utensor_cgen/__init__.py @@ -4,8 +4,13 @@ # https://github.com/google/or-tools/issues/1830 # we need to import ortools before tensorflow -from ortools.sat.python import cp_model as _ -from utensor_cgen._extensions import _ExtensionsLoader +from ortools.sat.python import cp_model as _ # isort:skip +import tensorflow.compat.v1 as _tf # isort:skip + +_tf.disable_v2_behavior() # isort:skip +_tf.disable_v2_tensorshape() # isort:skip + +from utensor_cgen._extensions import _ExtensionsLoader # isort:skip __version__ = ( pkg_resources @@ -13,3 +18,5 @@ .version ) sys.modules['utensor_cgen.extensions'] = _ExtensionsLoader() + +del _tf diff --git a/utensor_cgen/api.py b/utensor_cgen/api.py new file mode 100644 index 00000000..e8625e05 --- /dev/null +++ b/utensor_cgen/api.py @@ -0,0 +1,18 @@ +import tensorflow as tf + + +def keras_export(model_or_path, converter_kwargs): + if isinstance(model_or_path, str): + converter = tf.lite.TFLiteConverter.from_saved_model( + model_or_path, **converter_kwargs + ) + elif isinstance(model_or_path, tf.keras.Model): + converter = tf.lite.TFLiteConverter.from_keras_model( + model_or_path, **converter_kwargs + ) + else: + raise RuntimeError( + "expecting a keras model or a path to saved model, get {}".format( + model_or_path + ) + ) diff --git a/utensor_cgen/backend/transformer.py b/utensor_cgen/backend/transformer.py index e77fdd31..92ae47f1 100644 --- a/utensor_cgen/backend/transformer.py +++ b/utensor_cgen/backend/transformer.py @@ -63,8 +63,9 @@ def default_config(cls): 'transform_methods': [ "dropout(name_pattern=r'(dropout[_\w\d]*)/.*')", "linear_reorder", - "quantize", - "conv_pool", + # these methods are deprecated + # "quantize", + # "conv_pool", "inline", "biasAdd", "remove_id_op", diff --git a/utensor_cgen/frontend/tensorflow.py b/utensor_cgen/frontend/tensorflow.py index bbb43bd2..27e7f749 100644 --- a/utensor_cgen/frontend/tensorflow.py +++ b/utensor_cgen/frontend/tensorflow.py @@ -2,11 +2,11 @@ import os -import six - import numpy as np -import tensorflow as tf +import six +import tensorflow.compat.v1 as tf from google.protobuf import text_format + from utensor_cgen.frontend import FrontendSelector from utensor_cgen.frontend.base import Parser from utensor_cgen.ir.base import OperationInfo, TensorInfo, uTensorGraph @@ -17,6 +17,7 @@ @FrontendSelector.register(target_exts=[".pb", ".pbtxt"]) class GraphDefParser(Parser): + def parse(self, pb_file, output_nodes=None): graph_def, graph_name = self._load_graph_def(pb_file) if not self._is_freeze_graph(graph_def): diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index b2719c80..4a2a8981 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -6,15 +6,15 @@ import attr import numpy as np import six -import tensorflow as tf +import tensorflow.compat.v1 as tf from attr.validators import instance_of -from tensorflow.core.framework.attr_value_pb2 import AttrValue as _AttrValue -from tensorflow.core.framework.attr_value_pb2 import \ - NameAttrList as _NameAttrList -from tensorflow.core.framework.tensor_pb2 import TensorProto as _TensorProto -from tensorflow.core.framework.tensor_shape_pb2 import \ +from tensorflow.compat.v1 import AttrValue as _AttrValue +from tensorflow.compat.v1 import NameAttrList as _NameAttrList +from tensorflow_core.core.framework.tensor_pb2 import \ + TensorProto as _TensorProto +from tensorflow_core.core.framework.tensor_shape_pb2 import \ TensorShapeProto as _TensorShapeProto -from tensorflow.core.framework.types_pb2 import DataType as _DataType +from tensorflow_core.core.framework.types_pb2 import DataType as _DataType from utensor_cgen.logger import logger from utensor_cgen.utils import random_str, topologic_order_graph diff --git a/utensor_cgen/ir/converter.py b/utensor_cgen/ir/converter.py index c916cba5..302872f0 100644 --- a/utensor_cgen/ir/converter.py +++ b/utensor_cgen/ir/converter.py @@ -17,7 +17,7 @@ from tensorflow.core.framework.tensor_shape_pb2 import \ TensorShapeProto as _TensorShapeProto from tensorflow.core.framework.types_pb2 import DataType as _DataType -from tensorflow.python.framework import tensor_shape +from tensorflow_core.python.framework import tensor_shape from .utils import is_list_of @@ -70,17 +70,17 @@ def get_tf_value(cls, generic): raise ValueError('__utensor_generic_type__ cannot be None: %s' % converter_cls) if converter_cls.__tfproto_type__ is None: raise ValueError('__tfproto_type__ cannot be None: %s' % converter_cls) - cls._TF2GENERIC_MAP[converter_cls.__tfproto_type__] = converter_cls - cls._GENERIC2TF_MAP[converter_cls.__utensor_generic_type__] = converter_cls + cls._TF2GENERIC_MAP[converter_cls.__tfproto_type__.__name__] = converter_cls + cls._GENERIC2TF_MAP[converter_cls.__utensor_generic_type__.__qualname__] = converter_cls return converter_cls @classmethod def get_generic_value(cls, tf_value): value_type = type(tf_value) - if value_type in cls._GENERIC2TF_MAP: + if value_type.__qualname__ in cls._GENERIC2TF_MAP: # already generic type return tf_value - cvt = cls._TF2GENERIC_MAP.get(value_type, None) + cvt = cls._TF2GENERIC_MAP.get(value_type.__name__, None) if not cvt: raise ValueError('Unknown tf value type: %s' % value_type) return cvt.get_generic_value(tf_value) @@ -88,10 +88,10 @@ def get_generic_value(cls, tf_value): @classmethod def get_tf_value(cls, generic): value_type = type(generic) - if value_type in cls._TF2GENERIC_MAP: + if value_type.__name__ in cls._TF2GENERIC_MAP: # already tf type return generic - cvt = cls._GENERIC2TF_MAP.get(value_type, None) + cvt = cls._GENERIC2TF_MAP.get(value_type.__qualname__, None) if not cvt: raise ValueError('Unknown generic type: %s' % value_type) return cvt.get_tf_value(generic) @@ -114,7 +114,7 @@ def all_generic_types(cls): def TF2GENERIC_MAP(cls): type_map = {} for converter in cls._TF2GENERIC_MAP.values(): - type_map[converter.__tfproto_type__] = converter.__utensor_generic_type__ + type_map[converter.__tfproto_type__.__name__] = converter.__utensor_generic_type__ return type_map @@ -218,8 +218,9 @@ def check(self, attrib, a_list): def _check_tf_type(conv_func): @wraps(conv_func) def wrap(cls, value): - assert isinstance(value, cls.__tfproto_type__), \ - "Expecting %s, get %s" % (cls.__tfproto_type__, type(value)) + value_type = type(value) + assert value_type.__name__ == cls.__tfproto_type__.__name__, \ + "Expecting %s, get %s" % (cls.__tfproto_type__, value_type) return conv_func(cls, value) return wrap @@ -249,7 +250,7 @@ def get_generic_value(cls, value): def get_tf_value(cls, value): return value -ConverterDispatcher._BUILTIN_MAP = dict((t, BuiltinConverter) for t in BuiltinConverter.__tfproto_type__) +ConverterDispatcher._BUILTIN_MAP = dict((t.__name__, BuiltinConverter) for t in BuiltinConverter.__tfproto_type__) ConverterDispatcher._GENERIC2TF_MAP.update(ConverterDispatcher._BUILTIN_MAP) ConverterDispatcher._TF2GENERIC_MAP.update(ConverterDispatcher._BUILTIN_MAP) @@ -327,6 +328,11 @@ def get_tf_value(cls, value): @_check_tf_type def get_generic_value(cls, value): try: + value_type = type(value) + if value_type.__name__ == cls.__tfproto_type__.__name__: + value = [ + value.dim[i].size if value.dim[i].size > 0 else None for i in range(len(value.dim)) + ] list_view = tensor_shape.TensorShape(value).as_list() except ValueError: # unknown shape diff --git a/utensor_cgen/legalizer/tensorflow.py b/utensor_cgen/legalizer/tensorflow.py index e2850c68..08f2a28f 100644 --- a/utensor_cgen/legalizer/tensorflow.py +++ b/utensor_cgen/legalizer/tensorflow.py @@ -4,6 +4,17 @@ class GraphDefLegalizer(LegalizerBase): TARGET = 'tensorflow' + class _OpTypeRenamePostProcessing(object): + _RENAME_MAP = { + 'BatchMatMulV2': 'MatMul' + } + + @classmethod + def apply(cls, ugraph): + for op_type, new_op_type in cls._RENAME_MAP.items(): + for op_info in ugraph.get_ops_by_type(op_type): + op_info.op_type = new_op_type + def legalize_ops(self, ugraph): '''Legalize ops to generic ops in given graph ''' @@ -11,6 +22,7 @@ def legalize_ops(self, ugraph): raise ValueError( 'expecting tensorflow graph, get {}'.format(ugraph.lib_name) ) + self._OpTypeRenamePostProcessing.apply(ugraph) return ugraph def legalize_dtype(self, ugraph): diff --git a/utensor_cgen/transformer/linear_reoder.py b/utensor_cgen/transformer/linear_reoder.py index 4af59b29..032f2b5a 100644 --- a/utensor_cgen/transformer/linear_reoder.py +++ b/utensor_cgen/transformer/linear_reoder.py @@ -4,7 +4,8 @@ Linear Operation Legalizations """ -import tensorflow as tf +import tensorflow.compat.v1 as tf + # FIXME: remove uTensorOpEqualityDelegate import after we have generic ops from utensor_cgen.backend.utensor.code_generator.legacy._operators import \ uTensorOpEqualityDelegate diff --git a/utensor_cgen/transformer/ns_transformer.py b/utensor_cgen/transformer/ns_transformer.py index 23a1690d..3463bafb 100644 --- a/utensor_cgen/transformer/ns_transformer.py +++ b/utensor_cgen/transformer/ns_transformer.py @@ -9,7 +9,8 @@ from copy import deepcopy import numpy as np -import tensorflow as tf +import tensorflow.compat.v1 as tf + # FIXME: remove uTensorOpEqualityDelegate import after we have generic ops from utensor_cgen.backend.utensor.code_generator.legacy._operators import \ uTensorOpEqualityDelegate diff --git a/utensor_cgen/transformer/quantize.py b/utensor_cgen/transformer/quantize.py index 3560c2c0..d2a91536 100644 --- a/utensor_cgen/transformer/quantize.py +++ b/utensor_cgen/transformer/quantize.py @@ -1,9 +1,18 @@ -from tensorflow.tools.graph_transforms import TransformGraph +"""Legacy, DON'T USE +""" from utensor_cgen.frontend.tensorflow import GraphDefParser +from utensor_cgen.logger import logger from .base import Transformer from .pipeline import TransformerPipeline +try: + from tensorflow.tools.graph_transforms import TransformGraph +except ImportError: + logger.warning("trying to import deprecated quantization transformer") + TransformGraph = None + + __all__ = ['QuantizeTransformer'] @TransformerPipeline.register_transformer @@ -16,6 +25,8 @@ def transform(self, ugraph): if ugraph.lib_name != 'tensorflow': raise ValueError('only support tensorflow graph') graph_def = ugraph.graph_def + if TransformGraph is None: + raise RuntimeError("quantization is temporary not supported") quant_graph_def = TransformGraph(input_graph_def=graph_def, inputs=[], outputs=ugraph.output_nodes, diff --git a/utensor_cgen/utils.py b/utensor_cgen/utils.py index 0c6c73ad..d4f2a666 100644 --- a/utensor_cgen/utils.py +++ b/utensor_cgen/utils.py @@ -66,7 +66,9 @@ def attrib(self): def log_graph(graph_or_graph_def, logdir): - if isinstance(graph_or_graph_def, tf.GraphDef): + from tensorflow.compat.v1 import GraphDef + + if isinstance(graph_or_graph_def, GraphDef): graph = tf.Graph() with graph.as_default(): tf.import_graph_def(graph_or_graph_def, name='') From 79491c34868a21dea6760581cf878ded85370965 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Mon, 4 May 2020 17:04:10 +0800 Subject: [PATCH 107/117] update tests and fix onnx parser --- .circleci/config.yml | 2 +- Pipfile.lock | 535 +++++++++++------- setup.py | 4 +- tests/test_frontend/test_selector.py | 9 +- tests/test_frontend/test_tensor_flow.py | 53 -- .../test_ops/conftest.py | 5 - .../test_ops/test_op_add.py | 3 +- .../test_ops/test_op_argmax.py | 3 +- .../test_ops/test_op_const.py | 3 +- .../test_ops/test_op_conv2d.py | 3 +- .../test_ops/test_op_matmul.py | 3 +- .../test_ops/test_op_max.py | 3 +- .../test_ops/test_op_maxpool.py | 3 +- .../test_ops/test_op_min.py | 3 +- .../test_ops/test_op_relu.py | 3 +- tests/test_ir/__init__.py | 3 + .../test_AttrValueConverter/conftest.py | 2 +- .../test_converter.py | 2 +- .../test_matcher/test_permutation/conftest.py | 2 +- tests/test_matcher/test_replace/conftest.py | 2 +- .../test_matcher/test_replace/test_replace.py | 3 +- tests/test_transformer/__init__.py | 3 + .../test_convpool/conftest.py | 2 +- .../test_convpool/test_vgg.py | 3 + .../test_transformer/test_dropout/conftest.py | 8 +- .../test_dropout/test_dropout_transormer.py | 3 +- .../test_linear_reorder/conftest.py | 2 +- .../test_pipeline/conftest.py | 3 +- utensor_cgen/frontend/onnx.py | 41 +- 29 files changed, 410 insertions(+), 304 deletions(-) delete mode 100644 tests/test_frontend/test_tensor_flow.py diff --git a/.circleci/config.yml b/.circleci/config.yml index 96c9de36..286fcda4 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -9,7 +9,7 @@ jobs: - run: pip3 install . - run: name: Test - command: LC_ALL=C.UTF-8 LANG=C.UTF-8 pytest -vv tests -m 'not slow_test' + command: LC_ALL=C.UTF-8 LANG=C.UTF-8 pytest -vv tests -m 'not slow_test and not deprecated' #- run: genhtml main_coverage.info --output-directory /tmp/coverage #- store_test_results: # path: /tmp/test_results diff --git a/Pipfile.lock b/Pipfile.lock index 86a93d24..1f0fe3a4 100644 --- a/Pipfile.lock +++ b/Pipfile.lock @@ -34,12 +34,33 @@ ], "version": "==19.3.0" }, + "cachetools": { + "hashes": [ + "sha256:1d057645db16ca7fe1f3bd953558897603d6f0b9c51ed9d11eb4d071ec4e2aab", + "sha256:de5d88f87781602201cde465d3afe837546663b168e8b39df67411b0bf10cefc" + ], + "version": "==4.1.0" + }, + "certifi": { + "hashes": [ + "sha256:1d987a998c75633c40847cc966fcf5904906c920a7f17ef374f5aa4282abd304", + "sha256:51fcb31174be6e6664c5f69e3e1691a2d72a1a12e90f872cbdb1567eb47b6519" + ], + "version": "==2020.4.5.1" + }, + "chardet": { + "hashes": [ + "sha256:84ab92ed1c4d4f16916e05906b6b75a6c0fb5db821cc65e70cbd64a3e2a5eaae", + "sha256:fc323ffcaeaed0e0a02bf4d117757b98aed530d9ed4531e3e15460124c106691" + ], + "version": "==3.0.4" + }, "click": { "hashes": [ - "sha256:8a18b4ea89d8820c5d0c7da8a64b2c324b4dabb695804dbfea19b9be9d88c0cc", - "sha256:e345d143d80bf5ee7534056164e5e112ea5e22716bbb1ce727941f4c8b471b9a" + "sha256:d2b5255c7c6349bc1bd1e59e08cd12acbbd63ce649f2588755783aa94dfb6b1a", + "sha256:dacca89f4bfadd5de3d7489b7c8a566eee0d3676333fbb50030263894c38c0dc" ], - "version": "==7.1.1" + "version": "==7.1.2" }, "cycler": { "hashes": [ @@ -59,19 +80,46 @@ ], "version": "==1.12" }, + "future": { + "hashes": [ + "sha256:b1bead90b70cf6ec3f0710ae53a525360fa360d306a86583adc6bf83a4db537d" + ], + "version": "==0.18.2" + }, "gast": { "hashes": [ - "sha256:8f46f5be57ae6889a4e16e2ca113b1703ef17f2b0abceb83793eaba9e1351a45", - "sha256:b881ef288a49aa81440d2c5eb8aeefd4c2bb8993d5f50edae7413a85bfdb3b57" + "sha256:fe939df4583692f0512161ec1c880e0a10e71e6a232da045ab8edd3756fbadf0" ], - "version": "==0.3.3" + "version": "==0.2.2" + }, + "google-auth": { + "hashes": [ + "sha256:0c41a453b9a8e77975bfa436b8daedac00aed1c545d84410daff8272fff40fbb", + "sha256:e63b2210e03c4ed829063b72c4af0c4b867c2788efb3210b6b9439b488bd3afd" + ], + "version": "==1.14.1" + }, + "google-auth-oauthlib": { + "hashes": [ + "sha256:88d2cd115e3391eb85e1243ac6902e76e77c5fe438b7276b297fbe68015458dd", + "sha256:a92a0f6f41a0fb6138454fbc02674e64f89d82a244ea32f98471733c8ef0e0e1" + ], + "version": "==0.4.1" + }, + "google-pasta": { + "hashes": [ + "sha256:4612951da876b1a10fe3960d7226f0c7682cf901e16ac06e473b267a5afa8954", + "sha256:b32482794a366b5366a32c92a9a9201b107821889935a02b3e51f6b432ea84ed", + "sha256:c9f2c8dfc8f96d0d5808299920721be30c9eec37f2389f28904f454565c8a16e" + ], + "version": "==0.2.0" }, "graphviz": { "hashes": [ - "sha256:241fb099e32b8e8c2acca747211c8237e40c0b89f24b1622860075d59f4c4b25", - "sha256:60acbeee346e8c14555821eab57dbf68a169e6c10bce40e83c1bf44f63a62a01" + "sha256:cb0e878f90378489f17aab140b68e64e44b79e4cb59a530c8863d84bf2e2e5f5", + "sha256:e104ba036c8aef84320ec80560e544cd3cad68c9f90394b4e2b87bc44ab09791" ], - "version": "==0.13.2" + "version": "==0.14" }, "grpcio": { "hashes": [ @@ -143,6 +191,13 @@ ], "version": "==2.10.0" }, + "idna": { + "hashes": [ + "sha256:7588d1c14ae4c77d74036e8c22ff447b26d0fde8f007354fd48a7814db15b7cb", + "sha256:a068a21ceac8a4d63dbfd964670474107f541babbd2250d61922f029858365fa" + ], + "version": "==2.9" + }, "idx2numpy": { "hashes": [ "sha256:c609d5b260e6e7b538d458ba266c55b5b478e43e581d24d7017351813e67fcae" @@ -244,38 +299,38 @@ ], "version": "==3.2.1" }, - 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"sha256:f46e0e024346e1474083c729d50de909974237c72daca05393ee32389dabe457", + "sha256:f54be399340aa602066adb63a86a6a5d4f395adfdd9da2b9a0162ea808c7b276", + "sha256:f784aad988f12c80aacfa5b381ec21fd3f38f851720f652b9f33facc5101cf4d" + ], + "version": "==7.1.2" }, "pluggy": { "hashes": [ @@ -816,17 +932,17 @@ }, "pycodestyle": { "hashes": [ - "sha256:95a2219d12372f05704562a14ec30bc76b05a5b297b21a5dfe3f6fac3491ae56", - "sha256:e40a936c9a450ad81df37f549d676d127b1b66000a6c500caa2b085bc0ca976c" + "sha256:933bfe8d45355fbb35f9017d81fc51df8cb7ce58b82aca2568b870bf7bea1611", + "sha256:c1362bf675a7c0171fa5f795917c570c2e405a97e5dc473b51f3656075d73acc" ], - "version": "==2.5.0" + "version": "==2.6.0a1" }, "pyflakes": { "hashes": [ - "sha256:17dbeb2e3f4d772725c777fabc446d5634d1038f234e77343108ce445ea69ce0", - "sha256:d976835886f8c5b31d47970ed689944a0262b5f3afa00a5a7b4dc81e5449f8a2" + "sha256:0d94e0e05a19e57a99444b6ddcf9a6eb2e5c68d3ca1e98e90707af8152c90a92", + "sha256:35b2d75ee967ea93b55750aa9edbbf72813e06a66ba54438df2cfac9e3c27fc8" ], - "version": "==2.1.1" + "version": "==2.2.0" }, "pygments": { "hashes": [ @@ -837,11 +953,11 @@ }, "pylint": { "hashes": [ - "sha256:3db5468ad013380e987410a8d6956226963aed94ecb5f9d3a28acca6d9ac36cd", - "sha256:886e6afc935ea2590b462664b161ca9a5e40168ea99e5300935f6591ad467df4" + "sha256:588e114e3f9a1630428c35b7dd1c82c1c93e1b0e78ee312ae4724c5e1a1e0245", + "sha256:bd556ba95a4cf55a1fc0004c00cf4560b1e70598a54a74c6904d933c8f3bd5a8" ], "index": "pypi", - "version": "==2.4.4" + "version": "==2.5.0" }, "pyparsing": { "hashes": [ @@ -867,10 +983,10 @@ }, "pytz": { "hashes": [ - "sha256:1c557d7d0e871de1f5ccd5833f60fb2550652da6be2693c1e02300743d21500d", - "sha256:b02c06db6cf09c12dd25137e563b31700d3b80fcc4ad23abb7a315f2789819be" + "sha256:a494d53b6d39c3c6e44c3bec237336e14305e4f29bbf800b599253057fbb79ed", + "sha256:c35965d010ce31b23eeb663ed3cc8c906275d6be1a34393a1d73a41febf4a048" ], - "version": "==2019.3" + "version": "==2020.1" }, "pyyaml": { "hashes": [ @@ -928,7 +1044,7 @@ "sha256:dc60bb302f48acf6da8ca4444cfa17d52c63c5415302a9ee77b3b21618090521", "sha256:dee1bbf3a6c8f73b6b218cb28eed8dd13347ea2f87d572ce19b289d6fd3fbc59" ], - "index": "pypi", + "markers": "python_version >= '3'", "version": "==1.4.1" }, "six": { @@ -947,11 +1063,11 @@ }, "sphinx": { "hashes": [ - "sha256:3145d87d0962366d4c5264c39094eae3f5788d01d4b1a12294051bfe4271d91b", - "sha256:d7c6e72c6aa229caf96af82f60a0d286a1521d42496c226fe37f5a75dcfe2941" + "sha256:62edfd92d955b868d6c124c0942eba966d54b5f3dcb4ded39e65f74abac3f572", + "sha256:f5505d74cf9592f3b997380f9bdb2d2d0320ed74dd69691e3ee0644b956b8d83" ], "index": "pypi", - "version": "==3.0.2" + "version": "==3.0.3" }, "sphinx-autoapi": { "hashes": [ @@ -1003,6 +1119,13 @@ ], "version": "==1.1.4" }, + "toml": { + "hashes": [ + "sha256:229f81c57791a41d65e399fc06bf0848bab550a9dfd5ed66df18ce5f05e73d5c", + "sha256:235682dd292d5899d361a811df37e04a8828a5b1da3115886b73cf81ebc9100e" + ], + "version": "==0.10.0" + }, "typed-ast": { "hashes": [ "sha256:0666aa36131496aed8f7be0410ff974562ab7eeac11ef351def9ea6fa28f6355", @@ -1053,9 +1176,9 @@ }, "wrapt": { "hashes": [ - "sha256:565a021fd19419476b9362b05eeaa094178de64f8361e44468f9e9d7843901e1" + "sha256:b62ffa81fb85f4332a4f609cab4ac40709470da05643a082ec1eb88e6d9b97d7" ], - "version": "==1.11.2" + "version": "==1.12.1" }, "zipp": { "hashes": [ diff --git a/setup.py b/setup.py index 1dac063d..a11f4f21 100644 --- a/setup.py +++ b/setup.py @@ -51,13 +51,13 @@ class _Develop(_CompileFlatbuffMixin, _develop): pass ]}, install_requires=[ 'Jinja2', - 'tensorflow==1.13.1', + 'tensorflow==2.1.0', + 'onnx', 'idx2numpy', 'attrs', 'click', 'torch', 'torchvision', - 'onnx-tf==1.2.1', 'graphviz', 'matplotlib', 'toml', diff --git a/tests/test_frontend/test_selector.py b/tests/test_frontend/test_selector.py index 5fa0f418..e070b365 100644 --- a/tests/test_frontend/test_selector.py +++ b/tests/test_frontend/test_selector.py @@ -1,8 +1,13 @@ -from utensor_cgen.frontend import FrontendSelector, Parser as _Parser +import pytest +from utensor_cgen.frontend import FrontendSelector +from utensor_cgen.frontend import Parser as _Parser -def test_select_parser(): + +def test_select_pb_parser(): FrontendSelector.select_parser('.pb') + +def test_select_onnx_parser(): FrontendSelector.select_parser('.onnx') def test_register(): diff --git a/tests/test_frontend/test_tensor_flow.py b/tests/test_frontend/test_tensor_flow.py deleted file mode 100644 index 5d4979db..00000000 --- a/tests/test_frontend/test_tensor_flow.py +++ /dev/null @@ -1,53 +0,0 @@ -import numpy as np -import tensorflow as tf - - -def test_scalar_shape(): - from utensor_cgen.frontend.tensorflow import GraphDefParser - - graph = tf.Graph() - with graph.as_default(): - tf.constant(1, dtype=tf.float32, name='x') - parser = GraphDefParser({}) - ugraph = parser.parse(graph.as_graph_def(), output_nodes=['x']) - # shape of scalar tensor should be empty list - out_tensor = ugraph.ops_info['x'].output_tensors[0] - assert out_tensor.shape == [] - assert out_tensor.dtype is np.dtype('float32') - -def test_placeholder_shape(): - from utensor_cgen.frontend.tensorflow import GraphDefParser - - graph = tf.Graph() - with graph.as_default(): - tf.placeholder(dtype=tf.float32, name='x') - parser = GraphDefParser({}) - ugraph = parser.parse(graph.as_graph_def(), output_nodes=['x']) - # nondeterministic shape, can be any shape - out_tensor = ugraph.ops_info['x'].output_tensors[0] - assert out_tensor.shape is None - assert out_tensor.dtype is np.dtype('float32') - - graph = tf.Graph() - with graph.as_default(): - tf.placeholder(dtype=tf.float32, name='x', shape=[None, 5]) - parser = GraphDefParser({}) - ugraph = parser.parse(graph.as_graph_def(), output_nodes=['x']) - # nondeterministic dimension - out_tensor = ugraph.ops_info['x'].output_tensors[0] - assert out_tensor.shape == [None, 5] - assert out_tensor.dtype is np.dtype('float32') - -def test_normal_tensor_shape(): - from utensor_cgen.frontend.tensorflow import GraphDefParser - shape = np.random.randint(1, 10, size=(10,)).tolist() - - graph = tf.Graph() - with graph.as_default(): - tf.constant(np.random.rand(*shape), dtype=tf.float32, name='x') - parser = GraphDefParser({}) - ugraph = parser.parse(graph.as_graph_def(), output_nodes=['x']) - # deterministic shape - out_tensor = ugraph.ops_info['x'].output_tensors[0] - assert out_tensor.shape == shape, 'expecting {}, get {}'.format(shape, out_tensor.shape) - assert out_tensor.dtype is np.dtype('float32') diff --git a/tests/test_graph_constructor/test_ops/conftest.py b/tests/test_graph_constructor/test_ops/conftest.py index 10ca60c4..0d5eead0 100644 --- a/tests/test_graph_constructor/test_ops/conftest.py +++ b/tests/test_graph_constructor/test_ops/conftest.py @@ -5,8 +5,3 @@ def _ugraph(): from utensor_cgen.ir import uTensorGraph return uTensorGraph(output_nodes=[]) - -@fixture(name='quant_trans') -def _quant_trans(): - from utensor_cgen.transformer.quantize import QuantizeTransformer - return QuantizeTransformer() diff --git a/tests/test_graph_constructor/test_ops/test_op_add.py b/tests/test_graph_constructor/test_ops/test_op_add.py index 970d0fe1..f69a29df 100644 --- a/tests/test_graph_constructor/test_ops/test_op_add.py +++ b/tests/test_graph_constructor/test_ops/test_op_add.py @@ -1,7 +1,7 @@ import numpy as np -def test_op_add(ugraph, quant_trans): +def test_op_add(ugraph): with ugraph.begin_construction(): tensor_x, = ugraph.add_op( op_type='Const', @@ -22,4 +22,3 @@ def test_op_add(ugraph, quant_trans): ) assert tensor_z.shape == [1, 3, 5] - quant_trans.transform(ugraph) diff --git a/tests/test_graph_constructor/test_ops/test_op_argmax.py b/tests/test_graph_constructor/test_ops/test_op_argmax.py index d31f4ad1..375b1895 100644 --- a/tests/test_graph_constructor/test_ops/test_op_argmax.py +++ b/tests/test_graph_constructor/test_ops/test_op_argmax.py @@ -1,7 +1,7 @@ import numpy as np -def test_op_argmax(ugraph, quant_trans): +def test_op_argmax(ugraph): with ugraph.begin_construction(): tensor_logits, = ugraph.add_op( np.random.rand(3, 5, 7).astype('float32'), @@ -28,4 +28,3 @@ def test_op_argmax(ugraph, quant_trans): assert tensor_out1.dtype == np.dtype('int32') assert tensor_out2.shape == [3, 5] assert tensor_out2.dtype == np.dtype('int64') - quant_trans.transform(ugraph) diff --git a/tests/test_graph_constructor/test_ops/test_op_const.py b/tests/test_graph_constructor/test_ops/test_op_const.py index 33499c93..4b1cab18 100644 --- a/tests/test_graph_constructor/test_ops/test_op_const.py +++ b/tests/test_graph_constructor/test_ops/test_op_const.py @@ -1,7 +1,7 @@ import numpy as np -def test_op_const(ugraph, quant_trans): +def test_op_const(ugraph): with ugraph.begin_construction(): out_tensor, = ugraph.add_op( op_type='Const', @@ -11,4 +11,3 @@ def test_op_const(ugraph, quant_trans): ) assert out_tensor.shape == [3, 3] - quant_trans.transform(ugraph) diff --git a/tests/test_graph_constructor/test_ops/test_op_conv2d.py b/tests/test_graph_constructor/test_ops/test_op_conv2d.py index 7af35bfd..b2ddd99b 100644 --- a/tests/test_graph_constructor/test_ops/test_op_conv2d.py +++ b/tests/test_graph_constructor/test_ops/test_op_conv2d.py @@ -1,7 +1,7 @@ import numpy as np -def test_op_conv2d(ugraph, quant_trans): +def test_op_conv2d(ugraph): with ugraph.begin_construction(): tensor_x, = ugraph.add_op( np.random.rand(10, 512, 512, 5), @@ -24,4 +24,3 @@ def test_op_conv2d(ugraph, quant_trans): is_output=True ) assert out.shape == [10, 256, 256, 10] - quant_trans.transform(ugraph) diff --git a/tests/test_graph_constructor/test_ops/test_op_matmul.py b/tests/test_graph_constructor/test_ops/test_op_matmul.py index bc309828..bfd5d442 100644 --- a/tests/test_graph_constructor/test_ops/test_op_matmul.py +++ b/tests/test_graph_constructor/test_ops/test_op_matmul.py @@ -1,7 +1,7 @@ import numpy as np -def test_op_matmul(ugraph, quant_trans): +def test_op_matmul(ugraph): with ugraph.begin_construction(): tensor_x, = ugraph.add_op( op_type='Const', @@ -21,4 +21,3 @@ def test_op_matmul(ugraph, quant_trans): ) assert tensor_z.shape == [3, 4] - quant_trans.transform(ugraph) diff --git a/tests/test_graph_constructor/test_ops/test_op_max.py b/tests/test_graph_constructor/test_ops/test_op_max.py index 4f6d562b..2311ae1c 100644 --- a/tests/test_graph_constructor/test_ops/test_op_max.py +++ b/tests/test_graph_constructor/test_ops/test_op_max.py @@ -1,7 +1,7 @@ import numpy as np -def test_op_max(ugraph, quant_trans): +def test_op_max(ugraph): with ugraph.begin_construction(): tensor_x, = ugraph.add_op( np.random.rand(3, 5, 9, 2).astype('float32'), @@ -35,4 +35,3 @@ def test_op_max(ugraph, quant_trans): assert tensor_out1.shape == [1, 5, 9, 2] assert tensor_out2.shape == [3, 1, 9, 2] assert tensor_out3.shape == [3, 5, 9] - quant_trans.transform(ugraph) diff --git a/tests/test_graph_constructor/test_ops/test_op_maxpool.py b/tests/test_graph_constructor/test_ops/test_op_maxpool.py index 10593ceb..0bf3964e 100644 --- a/tests/test_graph_constructor/test_ops/test_op_maxpool.py +++ b/tests/test_graph_constructor/test_ops/test_op_maxpool.py @@ -1,7 +1,7 @@ import numpy as np -def test_op_maxpool(ugraph, quant_trans): +def test_op_maxpool(ugraph): with ugraph.begin_construction(): tensor_x, = ugraph.add_op( np.random.rand(10, 256, 256, 5), @@ -20,4 +20,3 @@ def test_op_maxpool(ugraph, quant_trans): is_output=True ) assert tensor_out.shape == [10, 128, 128, 5] - quant_trans.transform(ugraph) diff --git a/tests/test_graph_constructor/test_ops/test_op_min.py b/tests/test_graph_constructor/test_ops/test_op_min.py index 82c47038..2b148d23 100644 --- a/tests/test_graph_constructor/test_ops/test_op_min.py +++ b/tests/test_graph_constructor/test_ops/test_op_min.py @@ -1,7 +1,7 @@ import numpy as np -def test_op_min(ugraph, quant_trans): +def test_op_min(ugraph): with ugraph.begin_construction(): tensor_x, = ugraph.add_op( np.random.rand(3, 5, 9, 2).astype('float32'), @@ -35,4 +35,3 @@ def test_op_min(ugraph, quant_trans): assert tensor_out1.shape == [1, 5, 9, 2] assert tensor_out2.shape == [3, 1, 9, 2] assert tensor_out3.shape == [3, 5, 9] - quant_trans.transform(ugraph) diff --git a/tests/test_graph_constructor/test_ops/test_op_relu.py b/tests/test_graph_constructor/test_ops/test_op_relu.py index 878cf758..8b838d5a 100644 --- a/tests/test_graph_constructor/test_ops/test_op_relu.py +++ b/tests/test_graph_constructor/test_ops/test_op_relu.py @@ -1,7 +1,7 @@ import numpy as np -def test_op_relu(ugraph, quant_trans): +def test_op_relu(ugraph): with ugraph.begin_construction(): tensor_x, = ugraph.add_op( op_type='Const', @@ -15,4 +15,3 @@ def test_op_relu(ugraph, quant_trans): is_output=True ) assert out.shape == [3, 5] - quant_trans.transform(ugraph) diff --git a/tests/test_ir/__init__.py b/tests/test_ir/__init__.py index 5b059508..b05419d3 100644 --- a/tests/test_ir/__init__.py +++ b/tests/test_ir/__init__.py @@ -1 +1,4 @@ import ortools.sat.python.cp_model as _ +import tensorflow.compat.v1 as _tf + +_tf.disable_v2_behavior() diff --git a/tests/test_ir/test_AttrValueConverter/conftest.py b/tests/test_ir/test_AttrValueConverter/conftest.py index ba22cf7c..c6cd7171 100644 --- a/tests/test_ir/test_AttrValueConverter/conftest.py +++ b/tests/test_ir/test_AttrValueConverter/conftest.py @@ -1,5 +1,5 @@ import pytest -from tensorflow import AttrValue, NameAttrList +from tensorflow.compat.v1 import AttrValue, NameAttrList @pytest.fixture(scope='session') diff --git a/tests/test_ir/test_TensorProtoConverter/test_converter.py b/tests/test_ir/test_TensorProtoConverter/test_converter.py index 867f61b6..49966646 100644 --- a/tests/test_ir/test_TensorProtoConverter/test_converter.py +++ b/tests/test_ir/test_TensorProtoConverter/test_converter.py @@ -5,7 +5,7 @@ def test_generic_array(generic_array): tf_value = TensorProtoConverter.get_tf_value(generic_array) - assert isinstance(tf_value, TensorProtoConverter.__tfproto_type__) + assert type(tf_value).__name__ == TensorProtoConverter.__tfproto_type__.__name__ generic = TensorProtoConverter.get_generic_value(tf_value) assert isinstance(generic, type(generic_array)) assert (generic.np_array == generic_array.np_array).all() diff --git a/tests/test_matcher/test_permutation/conftest.py b/tests/test_matcher/test_permutation/conftest.py index 71cb1713..51a89666 100644 --- a/tests/test_matcher/test_permutation/conftest.py +++ b/tests/test_matcher/test_permutation/conftest.py @@ -1,5 +1,5 @@ import pytest -import tensorflow as tf +import tensorflow.compat.v1 as tf from utensor_cgen.frontend.tensorflow import GraphDefParser diff --git a/tests/test_matcher/test_replace/conftest.py b/tests/test_matcher/test_replace/conftest.py index e212a332..4a510978 100644 --- a/tests/test_matcher/test_replace/conftest.py +++ b/tests/test_matcher/test_replace/conftest.py @@ -1,5 +1,5 @@ import numpy as np -import tensorflow as tf +import tensorflow.compat.v1 as tf from pytest import fixture from utensor_cgen.frontend.tensorflow import GraphDefParser diff --git a/tests/test_matcher/test_replace/test_replace.py b/tests/test_matcher/test_replace/test_replace.py index 4ca546d3..59410708 100644 --- a/tests/test_matcher/test_replace/test_replace.py +++ b/tests/test_matcher/test_replace/test_replace.py @@ -1,4 +1,5 @@ -import tensorflow as tf +import tensorflow.compat.v1 as tf + # FIXME: remove uTensorOpEqualityDelegate import after we have generic op_eq_deleate from utensor_cgen.backend.utensor.code_generator.legacy._operators import \ uTensorOpEqualityDelegate diff --git a/tests/test_transformer/__init__.py b/tests/test_transformer/__init__.py index 5b059508..b05419d3 100644 --- a/tests/test_transformer/__init__.py +++ b/tests/test_transformer/__init__.py @@ -1 +1,4 @@ import ortools.sat.python.cp_model as _ +import tensorflow.compat.v1 as _tf + +_tf.disable_v2_behavior() diff --git a/tests/test_transformer/test_convpool/conftest.py b/tests/test_transformer/test_convpool/conftest.py index dd3d7d68..0d18a7d3 100644 --- a/tests/test_transformer/test_convpool/conftest.py +++ b/tests/test_transformer/test_convpool/conftest.py @@ -1,7 +1,7 @@ from random import sample import numpy as np -import tensorflow as tf +import tensorflow.compat.v1 as tf from pytest import fixture from utensor_cgen.frontend.tensorflow import GraphDefParser diff --git a/tests/test_transformer/test_convpool/test_vgg.py b/tests/test_transformer/test_convpool/test_vgg.py index f9af19c1..9efecd63 100644 --- a/tests/test_transformer/test_convpool/test_vgg.py +++ b/tests/test_transformer/test_convpool/test_vgg.py @@ -1,7 +1,10 @@ +import pytest + from utensor_cgen.transformer.pipeline import TransformerPipeline def factory(): + @pytest.mark.deprecated def test(vgg_ugraph): trans = TransformerPipeline([ 'linear_reorder', diff --git a/tests/test_transformer/test_dropout/conftest.py b/tests/test_transformer/test_dropout/conftest.py index a0c685c7..32f5851f 100644 --- a/tests/test_transformer/test_dropout/conftest.py +++ b/tests/test_transformer/test_dropout/conftest.py @@ -2,10 +2,12 @@ import numpy as np import pytest -import tensorflow as tf -from utensor_cgen.frontend.tensorflow import GraphDefParser -from utensor_cgen.utils import random_str +import tensorflow.compat.v1 as tf # isort:skip +tf.disable_v2_behavior() # isort:skip + +from utensor_cgen.frontend.tensorflow import GraphDefParser # isort:skip +from utensor_cgen.utils import random_str # isort:skip @pytest.fixture(scope='session', name='droput_graph_tuple') diff --git a/tests/test_transformer/test_dropout/test_dropout_transormer.py b/tests/test_transformer/test_dropout/test_dropout_transormer.py index 3e9d554a..9601ee44 100644 --- a/tests/test_transformer/test_dropout/test_dropout_transormer.py +++ b/tests/test_transformer/test_dropout/test_dropout_transormer.py @@ -1,4 +1,5 @@ -import tensorflow as tf +import tensorflow.compat.v1 as tf + from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.transformer.ns_transformer import (DropoutTransformer, DropoutTransformerV2) diff --git a/tests/test_transformer/test_linear_reorder/conftest.py b/tests/test_transformer/test_linear_reorder/conftest.py index 7209afbe..bcdaae6f 100644 --- a/tests/test_transformer/test_linear_reorder/conftest.py +++ b/tests/test_transformer/test_linear_reorder/conftest.py @@ -1,4 +1,4 @@ -import tensorflow as tf +import tensorflow.compat.v1 as tf from pytest import fixture from utensor_cgen.frontend.tensorflow import GraphDefParser diff --git a/tests/test_transformer/test_pipeline/conftest.py b/tests/test_transformer/test_pipeline/conftest.py index a71cf887..1b3795e0 100644 --- a/tests/test_transformer/test_pipeline/conftest.py +++ b/tests/test_transformer/test_pipeline/conftest.py @@ -3,7 +3,7 @@ import pytest from utensor_cgen.transformer import (BatchNormTransformer, DropoutTransformer, - QuantizeTransformer, RefCntOptimizer) + RefCntOptimizer) @pytest.fixture(scope='function', name='methods') @@ -11,7 +11,6 @@ def pipeline_methods(): all_methods = [ BatchNormTransformer.METHOD_NAME, DropoutTransformer.METHOD_NAME, - QuantizeTransformer.METHOD_NAME, RefCntOptimizer.METHOD_NAME, ] shuffle(all_methods) diff --git a/utensor_cgen/frontend/onnx.py b/utensor_cgen/frontend/onnx.py index 92e735c7..c2691e50 100644 --- a/utensor_cgen/frontend/onnx.py +++ b/utensor_cgen/frontend/onnx.py @@ -1,12 +1,14 @@ import os import re from collections import Counter +from numbers import Number import numpy as np import onnx -import tensorflow as tf +import tensorflow.compat.v1 as tf +from onnx import mapping, numpy_helper from onnx.onnx_pb import TensorProto -from onnx_tf.backend import TensorflowBackend, prepare + from utensor_cgen.frontend import FrontendSelector from utensor_cgen.frontend.base import Parser from utensor_cgen.ir import OperationInfo, TensorInfo, uTensorGraph @@ -26,7 +28,38 @@ def _convert_op_attribute(attrib_pb): elif attrib_pb.HasField('s'): return attrib_pb.s else: - raise ValueError('Unknown attribute value: {}'.format(attrib_pb)) + raise ValueError('Unknown attribute value: {}'.format(attrib_pb)) + +# I stole these code snippets from [onnx-tf](https://github.com/onnx/onnx-tensorflow) +# I have to do so since TF2.0 fuck up onnx-tf's backend +def _onnx_dtype(dtype): + if isinstance(dtype, Number): + onnx_dype = dtype + elif isinstance(dtype, str): + onnx_dype = TensorProto.DataType.Value(dtype) + else: + raise RuntimeError("dtype should be number or str.") + return onnx_dype + +def onnx2tf(dtype): + return tf.as_dtype(mapping.TENSOR_TYPE_TO_NP_TYPE[_onnx_dtype(dtype)]) + +def _onnx_initializer_to_input_dict_items(initializer): + """ Convert ONNX graph initializer to input dict items. + + :param initializer: ONNX graph initializer, list of TensorProto. + :return: List of input dict items. + """ + def tensor2list(onnx_tensor): + # Use the onnx.numpy_helper because the data may be raw + return numpy_helper.to_array(onnx_tensor).flatten().tolist() + + return [(init.name, + tf.constant( + tensor2list(init), + shape=init.dims, + dtype=onnx2tf(init.data_type))) + for init in initializer] @FrontendSelector.register(target_exts=['.onnx']) @@ -81,7 +114,7 @@ def _build_param_ops(self, onnx_graph, ugraph, op_types_cnt, tensor_names_map): # find Const ops params_dict = {} # FIXME: avoid using internal api of other library - dict_items = TensorflowBackend._onnx_initializer_to_input_dict_items(onnx_graph.initializer) + dict_items = _onnx_initializer_to_input_dict_items(onnx_graph.initializer) for name, tf_tensor in dict_items: params_dict[name] = AttrValueConverter.GenericType( value_name='value', From 1d6e3c76150811be011dd90ba526224631b7afbb Mon Sep 17 00:00:00 2001 From: dboyliao Date: Mon, 4 May 2020 17:13:00 +0800 Subject: [PATCH 108/117] add tests --- tests/test_frontend/conftest.py | 9 ++++ tests/test_frontend/model_files/model.onnx | Bin 0 -> 1876050 bytes tests/test_frontend/test_onnx.py | 11 +++++ tests/test_frontend/test_tensorflow.py | 53 +++++++++++++++++++++ 4 files changed, 73 insertions(+) create mode 100644 tests/test_frontend/model_files/model.onnx create mode 100644 tests/test_frontend/test_onnx.py create mode 100644 tests/test_frontend/test_tensorflow.py diff --git a/tests/test_frontend/conftest.py b/tests/test_frontend/conftest.py index 909f17c1..840f530c 100644 --- a/tests/test_frontend/conftest.py +++ b/tests/test_frontend/conftest.py @@ -11,3 +11,12 @@ def tflm_mnist_path(): 'quant_mnist_cnn.tflite' ) return model_path + +@pytest.fixture(name='onnx_model_path', scope='session') +def onnx_model_path(): + model_path = os.path.join( + os.path.dirname(__file__), + 'model_files', + 'model.onnx' + ) + return model_path diff --git a/tests/test_frontend/model_files/model.onnx b/tests/test_frontend/model_files/model.onnx new file mode 100644 index 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z0u@s;$yJ7sZ2e;?4l+9?bN?W*h+0VvU)9O#m1$6MCmyRdpN8#cwYY6uPo%tAI9ug| zlRFQQ^x$EVIuwY58y|5gp?{LT!7%6xDZ~XPCh<1BPu#V&40brtl0h|Y;oNL zk=D(S)ri3x#^?r*IuuQekn&#^!P>w`DA0vq z*E||wt2}ul{dz)J$WI5Nh5{9&=u{Os50nQ~jo)C@` zRY}lX3$#gC0g{&*v~NCvKG#?qa4erw;fPExhyuH@UaE;_t=@h&s7%bj?ukyRX5=iD zY;>Y7b5prQz8&^0PlG)r#rRym7sfm+pw%`SPN0zvCvAee?^e#!h!wWe zjO**s#ZSeo&3}-_UoJ#9HtNs)){5JmPowqnY;+SFQB;9BNp{BoY^BKxicC z5#6MQ$GfSo#E8m{-6)p$V_;!4t9yMy6hFJuSmPq{q$LED*#RWFAs2(bsluWJ4RK_7 z>p_7SI^v2UMLfxwEYi^2w;5lk_hFdTQFMRF?3W#+VAW;~Ju5EBMrZ6MuZ|ah`l16` zd|H6(9R)KJl2dklFJ2;iOE8;1Lue!5@dW})zScTUB;YMsDzp{MVqyOOVYcMQv$OdR D$YHEs literal 0 HcmV?d00001 diff --git a/tests/test_frontend/test_onnx.py b/tests/test_frontend/test_onnx.py new file mode 100644 index 00000000..2efa454d --- /dev/null +++ b/tests/test_frontend/test_onnx.py @@ -0,0 +1,11 @@ +from utensor_cgen.frontend.onnx import OnnxParser + + +def test_onnx_parser(onnx_model_path): + parser = OnnxParser({}) + ugraph = parser.parse(onnx_model_path) + + assert ugraph.lib_name == 'onnx' + assert ugraph.output_nodes + assert ugraph.topo_order + assert ugraph.ops_info diff --git a/tests/test_frontend/test_tensorflow.py b/tests/test_frontend/test_tensorflow.py new file mode 100644 index 00000000..4e61541c --- /dev/null +++ b/tests/test_frontend/test_tensorflow.py @@ -0,0 +1,53 @@ +import numpy as np +import tensorflow.compat.v1 as tf + + +def test_scalar_shape(): + from utensor_cgen.frontend.tensorflow import GraphDefParser + + graph = tf.Graph() + with graph.as_default(): + tf.constant(1, dtype=tf.float32, name='x') + parser = GraphDefParser({}) + ugraph = parser.parse(graph.as_graph_def(), output_nodes=['x']) + # shape of scalar tensor should be empty list + out_tensor = ugraph.ops_info['x'].output_tensors[0] + assert out_tensor.shape == [] + assert out_tensor.dtype is np.dtype('float32') + +def test_placeholder_shape(): + from utensor_cgen.frontend.tensorflow import GraphDefParser + + graph = tf.Graph() + with graph.as_default(): + tf.placeholder(dtype=tf.float32, name='x') + parser = GraphDefParser({}) + ugraph = parser.parse(graph.as_graph_def(), output_nodes=['x']) + # nondeterministic shape, can be any shape + out_tensor = ugraph.ops_info['x'].output_tensors[0] + assert out_tensor.shape is None + assert out_tensor.dtype is np.dtype('float32') + + graph = tf.Graph() + with graph.as_default(): + tf.placeholder(dtype=tf.float32, name='x', shape=[None, 5]) + parser = GraphDefParser({}) + ugraph = parser.parse(graph.as_graph_def(), output_nodes=['x']) + # nondeterministic dimension + out_tensor = ugraph.ops_info['x'].output_tensors[0] + assert out_tensor.shape == [None, 5] + assert out_tensor.dtype is np.dtype('float32') + +def test_normal_tensor_shape(): + from utensor_cgen.frontend.tensorflow import GraphDefParser + shape = np.random.randint(1, 10, size=(10,)).tolist() + + graph = tf.Graph() + with graph.as_default(): + tf.constant(np.random.rand(*shape), dtype=tf.float32, name='x') + parser = GraphDefParser({}) + ugraph = parser.parse(graph.as_graph_def(), output_nodes=['x']) + # deterministic shape + out_tensor = ugraph.ops_info['x'].output_tensors[0] + assert out_tensor.shape == shape, 'expecting {}, get {}'.format(shape, out_tensor.shape) + assert out_tensor.dtype is np.dtype('float32') From d6352a329291221efafa9c838cbc7209b9b1c412 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 5 May 2020 00:01:21 +0800 Subject: [PATCH 109/117] TF 2.0 support - Fix bugs (dropout transformer) --- .../test_transformer/test_dropout/conftest.py | 7 +- .../test_dropout/test_dropout_transormer.py | 12 ++- utensor_cgen/transformer/ns_transformer.py | 100 ++++++------------ 3 files changed, 46 insertions(+), 73 deletions(-) diff --git a/tests/test_transformer/test_dropout/conftest.py b/tests/test_transformer/test_dropout/conftest.py index 32f5851f..dfe7a9bf 100644 --- a/tests/test_transformer/test_dropout/conftest.py +++ b/tests/test_transformer/test_dropout/conftest.py @@ -16,13 +16,12 @@ def dropout_graph_tuple(): with graph.as_default(): x = tf.constant(np.ones((5, 5)), name='x', dtype=tf.float32) - keep_prob = tf.placeholder(dtype=tf.float32, - name='keep_prob') - dropout_x = tf.nn.dropout(x, rate=1-keep_prob, name='dropout_x') + rate = tf.placeholder(dtype=tf.float32, name='rate') + dropout_x = tf.nn.dropout(x, rate=rate, name='dropout_x') bias = tf.constant(0.5, name='bias', dtype=tf.float32) y = tf.add(dropout_x, bias, name='y') return (graph.as_graph_def(), - [keep_prob.name, dropout_x.name], + [rate.name, dropout_x.name], [y.op.name]) @pytest.fixture(name='dropout_graph_tuple2') diff --git a/tests/test_transformer/test_dropout/test_dropout_transormer.py b/tests/test_transformer/test_dropout/test_dropout_transormer.py index 9601ee44..400e1b25 100644 --- a/tests/test_transformer/test_dropout/test_dropout_transormer.py +++ b/tests/test_transformer/test_dropout/test_dropout_transormer.py @@ -1,3 +1,4 @@ +import pytest import tensorflow.compat.v1 as tf from utensor_cgen.frontend.tensorflow import GraphDefParser @@ -7,7 +8,7 @@ def test_dropout_trans_1_1(droput_graph_tuple): (graph_def, - (keep_prob_name, dropout_output_name), + (rate_name, dropout_output_name), output_nodes) = droput_graph_tuple ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) transformer = DropoutTransformer() @@ -25,19 +26,20 @@ def test_dropout_trans_1_1(droput_graph_tuple): with graph_2.as_default(): tf.import_graph_def(new_ugraph.graph_def, name='') with tf.Session(graph=graph_1): - keep_prob = graph_1.get_tensor_by_name(keep_prob_name) + rate = graph_1.get_tensor_by_name(rate_name) dropout_output = graph_1.get_tensor_by_name(dropout_output_name) output = graph_1.get_tensor_by_name(output_nodes[0]+":0") # test the dropout ops are gone - assert keep_prob.op.name not in new_ugraph.ops_info + assert rate.op.name not in new_ugraph.ops_info assert dropout_output.op.name not in new_ugraph.ops_info - output_1 = output.eval({keep_prob:1.0}) + output_1 = output.eval({rate: 0.0}) with tf.Session(graph=graph_2): output = graph_2.get_tensor_by_name(output_nodes[0]+":0") output_2 = output.eval() # expecting the same outputs with keep_prob == 1.0 assert (output_1 == output_2).all() +@pytest.mark.deprecated def test_dropout_trans_1_2(droput_graph_tuple): (graph_def, (keep_prob_name, dropout_output_name), @@ -71,6 +73,7 @@ def test_dropout_trans_1_2(droput_graph_tuple): # expecting the same outputs with keep_prob == 1.0 assert (output_1 == output_2).all() +@pytest.mark.deprecated def test_dropout_trans_2(dropout_graph_tuple2): graph_def, output_nodes = dropout_graph_tuple2 ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) @@ -79,6 +82,7 @@ def test_dropout_trans_2(dropout_graph_tuple2): assert len(new_ugraph.ops_info) == 1 assert 'x' in new_ugraph.ops_info +@pytest.mark.deprecated def test_dropout_vgg(vgg_ugraph): trans = DropoutTransformerV2() new_ugraph = trans.transform(vgg_ugraph) diff --git a/utensor_cgen/transformer/ns_transformer.py b/utensor_cgen/transformer/ns_transformer.py index 3463bafb..e6c4cb07 100644 --- a/utensor_cgen/transformer/ns_transformer.py +++ b/utensor_cgen/transformer/ns_transformer.py @@ -92,75 +92,45 @@ def __init__(self, name_pattern=r'(dropout[_\w\d]*)/.*'): self._op_name_pattern = re.compile(name_pattern) def transform(self, ugraph): - new_graph = uTensorGraph(name=ugraph.name, output_nodes=ugraph.output_nodes) - dropout_input_map = self._find_input(ugraph) - new_ops_info = {} - for node_name in ugraph.ops_info: - match = self._op_name_pattern.match(node_name) - if match: - # ignore all dropout nodes - continue - # replace inputs with dropout inputs - op_info = ugraph.ops_info[node_name] - in_t_infos = [deepcopy(t_info, {'ugraph': new_graph}) - for t_info in op_info.input_tensors] - out_t_infos = [deepcopy(t_info, {'ugraph': new_graph}) - for t_info in op_info.output_tensors] - op_attr = deepcopy(op_info.op_attr) - for i, t_info in enumerate(in_t_infos): - op_name = parse_tensor_name(t_info.name)[0] - match = self._op_name_pattern.match(op_name) - if match: - name_scope = match.group(1) - # assume there should be only on input except keep_prob - dropout_in_tensor = dropout_input_map[name_scope] - in_t_infos.pop(i) - in_t_infos.insert(i, dropout_in_tensor) - new_op_info = OperationInfo(name=op_info.name, - input_tensors=in_t_infos, - n_inputs=len(in_t_infos), - output_tensors=out_t_infos, - n_outputs=len(out_t_infos), - op_type=op_info.op_type, - lib_name=op_info.lib_name, - op_attr=op_attr, - ugraph=new_graph) - new_ops_info[node_name] = new_op_info - new_graph.ops_info = new_ops_info - new_graph._lib_name = ugraph._lib_name - return new_graph + new_ugraph = deepcopy(ugraph) + ( + dropout_input_map, + dropout_output_map, + clusters + ) = self._find_dropout_clusters(ugraph) + for name_scope, cluster in clusters.items(): + input_tensor = list(dropout_input_map[name_scope])[0].output_tensors[0] + output_tensor = list(dropout_output_map[name_scope])[0].output_tensors[0] + for node in output_tensor.op.output_nodes: + new_input = new_ugraph.ops_info[input_tensor.op.name].output_tensors[0] + new_node = new_ugraph.ops_info[node.name] + for i, tensor in enumerate(new_node.input_tensors): + if tensor.name == output_tensor.name: + new_node.input_tensors[i] = new_input + for op_info in cluster: + if op_info.name in new_ugraph.ops_info: + del new_ugraph.ops_info[op_info.name] + return new_ugraph def _find_dropout_clusters(self, ugraph): - clusters = defaultdict(lambda: []) - for node_name in ugraph.topo_order: - match = self._op_name_pattern.match(node_name) - if match: - name_scope = match.group(1) - clusters[name_scope].append(node_name) - return dict(clusters) - - def _find_input(self, ugraph): - """dropout_name --> input_tensor_info - - input_tensor_info := the tensor info of a tensor which is not generated - in the dropout namescope but is consumed by ops in - dropout namescope with name not starts with 'keep_prob' - """ - clusters = self._find_dropout_clusters(ugraph) - input_map = {} - for node_name in ugraph.topo_order: - match = self._op_name_pattern.match(node_name) + clusters = defaultdict(set) + for op_info in ugraph.ops_info.values(): + match = self._op_name_pattern.match(op_info.name) if match: name_scope = match.group(1) - cluster = clusters[name_scope] - op_info = ugraph.ops_info[node_name] - for in_tensor_info in op_info.input_tensors: - in_op_name = in_tensor_info.op.name - if in_op_name not in cluster and not in_op_name.startswith('keep_prob'): - input_map[name_scope] = in_tensor_info - # assuming there is only one input for dropout - break - return input_map + clusters[name_scope].add(op_info) + clusters = dict(clusters) + input_map = defaultdict(set) + output_map = defaultdict(set) + for name_scope, cluster in clusters.items(): + for op_info in cluster: + for tensor in op_info.input_tensors: + if not re.match(r"^(rate|keep_prob).*", tensor.op.name) and tensor.op not in cluster: + input_map[name_scope].add(tensor.op) + for out_node in op_info.output_nodes: + if out_node not in cluster: + output_map[name_scope].add(op_info) + return dict(input_map), dict(output_map), clusters @TransformerPipeline.register_transformer From 139a890dba87588e909d983a0cd85d813c370b5e Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 5 May 2020 00:20:23 +0800 Subject: [PATCH 110/117] update requirements.txt --- requirements.txt | 193 +++++++++++++++++++++++++++-------------------- 1 file changed, 113 insertions(+), 80 deletions(-) diff --git a/requirements.txt b/requirements.txt index 77ff5802..d695ee6b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,102 +1,135 @@ -absl-py==0.7.1 +absl-py==0.9.0 alabaster==0.7.12 +appdirs==1.4.3 appnope==0.1.0 -astor==0.7.1 -astroid==2.2.5 -atomicwrites==1.3.0 -attrs==19.1.0 -Babel==2.7.0 +astor==0.8.1 +astroid==2.3.3 +attrs==19.3.0 +Babel==2.8.0 backcall==0.1.0 -certifi==2019.3.9 +better-setuptools-git-version==1.0.5 +black==19.10b0 +bleach==3.1.1 +cachetools==4.1.0 +certifi==2019.11.28 chardet==3.0.4 -Click==7.0 -decorator==4.4.0 -doc8==0.8.0 -docutils==0.14 +click==7.1.1 +cycler==0.10.0 +decorator==4.4.2 +defusedxml==0.6.0 +docutils==0.16 entrypoints==0.3 -flake8==3.7.7 +flake8==3.7.9 +flatbuffers==1.12 gast==0.2.2 -graphviz==0.11 -grpcio==1.19.0 -h5py==2.9.0 -idna==2.8 +google-auth==1.14.1 +google-auth-oauthlib==0.4.1 +google-pasta==0.2.0 +graphviz==0.13.2 +grpcio==1.28.1 +h5py==2.10.0 +idna==2.9 idx2numpy==1.2.2 -imagesize==1.1.0 -importlib-metadata==0.17 -ipdb==0.12 -ipykernel==5.1.0 -ipython==7.4.0 +imagesize==1.2.0 +importlib-metadata==1.5.0 +ipykernel==5.1.4 +ipython==7.13.0 ipython-genutils==0.2.0 -isort==4.3.15 -jedi==0.13.3 -Jinja2==2.10 -jupyter-client==5.2.4 -jupyter-core==4.4.0 -Keras-Applications==1.0.7 -Keras-Preprocessing==1.0.9 -lazy-object-proxy==1.3.1 -Markdown==3.0.1 -MarkupSafe==1.1.1 +ipywidgets==7.5.1 +isort==4.3.21 +jedi==0.16.0 +Jinja2==3.0.0a1 +jsonschema==3.2.0 +jupyter==1.0.0 +jupyter-client==6.0.0 +jupyter-console==6.1.0 +jupyter-contrib-core==0.3.3 +jupyter-core==4.6.3 +jupyter-nbextensions-configurator==0.4.1 +Keras-Applications==1.0.8 +Keras-Preprocessing==1.1.0 +kiwisolver==1.2.0 +lazy-object-proxy==1.4.3 +Markdown==3.2.1 +MarkupSafe==2.0.0a1 +matplotlib==3.2.1 mccabe==0.6.1 -mock==2.0.0 -more-itertools==6.0.0 -numpy==1.16.2 -onnx==1.4.1 +mistune==0.8.4 +mock==4.0.2 +more-itertools==8.2.0 +nbconvert==5.6.1 +nbformat==5.0.4 +notebook==6.0.3 +numpy==1.18.3 +oauthlib==3.1.0 +onnx==1.6.0 onnx-tf==1.2.1 -packaging==19.0 -parso==0.3.4 -pbr==5.1.3 -pexpect==4.6.0 +opt-einsum==3.2.1 +ortools==7.5.7466 +packaging==20.1 +pandocfilters==1.4.2 +parso==0.6.2 +pathspec==0.7.0 +pexpect==4.8.0 pickleshare==0.7.5 -Pillow==6.0.0 -pluggy==0.12.0 -prompt-toolkit==2.0.9 -protobuf==3.7.0 +Pillow==7.1.1 +pluggy==0.13.1 +prometheus-client==0.7.1 +prompt-toolkit==3.0.3 +protobuf==3.11.3 ptyprocess==0.6.0 -py==1.8.0 +py==1.8.1 +pyasn1==0.4.8 +pyasn1-modules==0.2.8 pycodestyle==2.5.0 pyflakes==2.1.1 -Pygments==2.3.1 -pylint==2.3.1 -pyparsing==2.4.0 -pytest==5.0.1 -python-dateutil==2.8.0 -pytz==2019.1 -PyYAML==5.1 -pyzmq==18.0.1 -requests==2.22.0 -restructuredtext-lint==1.3.0 -rope==0.14.0 -scipy==1.3.0 -six==1.12.0 -snowballstemmer==1.2.1 -Sphinx==2.1.2 -sphinx-autoapi==1.1.0 +Pygments==2.5.2 +pylint==2.4.4 +pyparsing==3.0.0a1 +pyrsistent==0.15.7 +pytest==5.4.1 +python-dateutil==2.8.1 +pytz==2019.3 +PyYAML==5.3.1 +pyzmq==19.0.0 +qtconsole==4.6.0 +regex==2020.2.20 +requests==2.23.0 +requests-oauthlib==1.3.0 +rope==0.16.0 +rsa==4.0 +rstcheck==3.3.1 +scipy==1.4.1 +Send2Trash==1.5.0 +six==1.14.0 +snowballstemmer==2.0.0 +Sphinx==2.4.3 +sphinx-autoapi==1.2.1 sphinxcontrib-applehelp==1.0.1 sphinxcontrib-devhelp==1.0.1 -sphinxcontrib-dotnetdomain==0.4 -sphinxcontrib-golangdomain==0.2.0.dev0 -sphinxcontrib-htmlhelp==1.0.2 +sphinxcontrib-htmlhelp==1.0.3 sphinxcontrib-jsmath==1.0.1 sphinxcontrib-qthelp==1.0.2 sphinxcontrib-serializinghtml==1.1.3 -stevedore==1.30.1 -tensorboard==1.13.1 -tensorflow==1.13.1 -tensorflow-estimator==1.13.0 +tensorboard==2.1.1 +tensorflow==2.1.0 +tensorflow-estimator==2.1.0 termcolor==1.1.0 +terminado==0.8.3 +testpath==0.4.4 toml==0.10.0 -torch==1.0.1.post2 -torchvision==0.2.1 -tornado==6.0.2 -traitlets==4.3.2 -typed-ast==1.3.1 -typing==3.6.6 -typing-extensions==3.7.2 +torch==1.4.0 +torchvision==0.5.0 +tornado==6.0.3 +traitlets==4.3.3 +typed-ast==1.4.1 +typing-extensions==3.7.4.2 Unidecode==1.1.1 -urllib3==1.25.3 --e git+https://github.com/uTensor/utensor_cgen.git@4cc432ad08900e453bbc3c0512e45950874dc02d#egg=utensor_cgen -wcwidth==0.1.7 -Werkzeug==0.14.1 -wrapt==1.11.1 -zipp==0.5.1 +urllib3==1.25.8 +-e git+https://dboyliao@github.com/uTensor/utensor_cgen.git@d6352a329291221efafa9c838cbc7209b9b1c412#egg=utensor_cgen +wcwidth==0.1.8 +webencodings==0.5.1 +Werkzeug==1.0.1 +widgetsnbextension==3.5.1 +wrapt==1.11.2 +zipp==3.0.0 From 2f84e090cc27a8ead77359ff2959f997eac7b552 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 5 May 2020 00:42:51 +0800 Subject: [PATCH 111/117] speed up loading time --- utensor_cgen/__init__.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/utensor_cgen/__init__.py b/utensor_cgen/__init__.py index 47c08995..b5eb3ef1 100644 --- a/utensor_cgen/__init__.py +++ b/utensor_cgen/__init__.py @@ -5,10 +5,6 @@ # https://github.com/google/or-tools/issues/1830 # we need to import ortools before tensorflow from ortools.sat.python import cp_model as _ # isort:skip -import tensorflow.compat.v1 as _tf # isort:skip - -_tf.disable_v2_behavior() # isort:skip -_tf.disable_v2_tensorshape() # isort:skip from utensor_cgen._extensions import _ExtensionsLoader # isort:skip @@ -18,5 +14,3 @@ .version ) sys.modules['utensor_cgen.extensions'] = _ExtensionsLoader() - -del _tf From 8418cc2c2818ad8cb20658911a53f935bc99ec6d Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 5 May 2020 00:52:37 +0800 Subject: [PATCH 112/117] prettify printing --- utensor_cgen/cli.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/utensor_cgen/cli.py b/utensor_cgen/cli.py index e0a18ca2..34dfb2f2 100644 --- a/utensor_cgen/cli.py +++ b/utensor_cgen/cli.py @@ -111,6 +111,7 @@ def convert_graph(model_file, output_nodes, config, target): @click.option('--verbose', is_flag=True) def list_trans_methods(verbose): from utensor_cgen.transformer import TransformerPipeline + from pprint import pformat if verbose: for name, trans_cls in TransformerPipeline.TRANSFORMER_MAP.items(): @@ -118,7 +119,7 @@ def list_trans_methods(verbose): click.secho(trans_cls.__doc__, fg='yellow', bold=True) else: click.secho( - str(TransformerPipeline.all_transform_methods()), + pformat(TransformerPipeline.all_transform_methods()), fg='white', bold=True ) return 0 From 32f78cd78f5efe3355891ad5c486d69ca8e5f950 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 5 May 2020 21:53:12 +0800 Subject: [PATCH 113/117] cli refactoring and exporting high-level api --- utensor_cgen/api.py | 18 -- utensor_cgen/api/__init__.py | 0 utensor_cgen/api/backend.py | 29 +++ utensor_cgen/api/convert.py | 23 ++ utensor_cgen/api/export.py | 39 +++ utensor_cgen/api/utils.py | 59 +++++ utensor_cgen/backend/utensor/_backend_impl.py | 2 +- utensor_cgen/cli.py | 223 ------------------ utensor_cgen/cli/__init__.py | 4 + utensor_cgen/cli/backend.py | 52 ++++ utensor_cgen/cli/convert.py | 37 +++ utensor_cgen/cli/main.py | 36 +++ utensor_cgen/cli/utils.py | 49 ++++ utensor_cgen/frontend/__init__.py | 4 +- utensor_cgen/frontend/base.py | 2 +- utensor_cgen/frontend/onnx.py | 9 +- utensor_cgen/frontend/tensorflow.py | 4 +- utensor_cgen/frontend/tflite.py | 9 +- utensor_cgen/frontend/utensor.py | 4 +- 19 files changed, 352 insertions(+), 251 deletions(-) delete mode 100644 utensor_cgen/api.py create mode 100644 utensor_cgen/api/__init__.py create mode 100644 utensor_cgen/api/backend.py create mode 100644 utensor_cgen/api/convert.py create mode 100644 utensor_cgen/api/export.py create mode 100644 utensor_cgen/api/utils.py delete mode 100644 utensor_cgen/cli.py create mode 100644 utensor_cgen/cli/__init__.py create mode 100644 utensor_cgen/cli/backend.py create mode 100644 utensor_cgen/cli/convert.py create mode 100644 utensor_cgen/cli/main.py create mode 100644 utensor_cgen/cli/utils.py diff --git a/utensor_cgen/api.py b/utensor_cgen/api.py deleted file mode 100644 index e8625e05..00000000 --- a/utensor_cgen/api.py +++ /dev/null @@ -1,18 +0,0 @@ -import tensorflow as tf - - -def keras_export(model_or_path, converter_kwargs): - if isinstance(model_or_path, str): - converter = tf.lite.TFLiteConverter.from_saved_model( - model_or_path, **converter_kwargs - ) - elif isinstance(model_or_path, tf.keras.Model): - converter = tf.lite.TFLiteConverter.from_keras_model( - model_or_path, **converter_kwargs - ) - else: - raise RuntimeError( - "expecting a keras model or a path to saved model, get {}".format( - model_or_path - ) - ) diff --git a/utensor_cgen/api/__init__.py b/utensor_cgen/api/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/utensor_cgen/api/backend.py b/utensor_cgen/api/backend.py new file mode 100644 index 00000000..72b79949 --- /dev/null +++ b/utensor_cgen/api/backend.py @@ -0,0 +1,29 @@ +from toml import dumps + +from utensor_cgen import __version__ +from utensor_cgen.backend.api import BackendManager + + +def get_backends(): + return BackendManager.backends + +def get_trans_methods(): + from utensor_cgen.transformer import TransformerPipeline + + return TransformerPipeline.TRANSFORMER_MAP + +def generate_config(target, output='utensor_cli.toml'): + backend_cls = BackendManager.get_backend(target) + config = backend_cls.default_config + + with open(output, 'w') as fid: + fid.write( + '# utensor-cli version {}\n'.format(__version__) + \ + '# https://github.com/toml-lang/toml\n' + \ + '# ..\n' + ) + fid.write( + '# we use string \'None\' to represent python None value\n' + '# you should convert the string to None if you try to write extension for utensor_cgen\n' + ) + fid.write(dumps(config)) diff --git a/utensor_cgen/api/convert.py b/utensor_cgen/api/convert.py new file mode 100644 index 00000000..38742c26 --- /dev/null +++ b/utensor_cgen/api/convert.py @@ -0,0 +1,23 @@ +import os + +from toml import loads + +from utensor_cgen.backend.api import BackendManager + + +def convert_graph(model_file, output_nodes=None, config='utensor_cli.toml', target='utensor', model_name=None): + from utensor_cgen.frontend import FrontendSelector + + if os.path.exists(config): + with open(config) as fid: + config = loads(fid.read()) + else: + config = {} + ugraph = FrontendSelector.parse( + model_file, output_nodes, + config=config, + model_name=model_name + ) + backend = BackendManager.get_backend(target)(config) + backend.apply(ugraph) + return ugraph diff --git a/utensor_cgen/api/export.py b/utensor_cgen/api/export.py new file mode 100644 index 00000000..2cef5831 --- /dev/null +++ b/utensor_cgen/api/export.py @@ -0,0 +1,39 @@ +import tempfile +from pathlib import Path + +import tensorflow as tf + +from .convert import convert_graph + + +def tflm_keras_export( + model_or_path, + representive_dataset, + model_name=None, + optimizations=None, + config_file='utensor_cli.toml' +): + with tempfile.TemporaryDirectory(prefix='utensor_') as tmp_dir: + dir_path = Path(tmp_dir) + if isinstance(model_or_path, str): + converter = tf.lite.TFLiteConverter.from_saved_model(model_or_path) + elif isinstance(model_or_path, tf.keras.Model): + model_path = str(dir_path / 'saved_model') + model_or_path.save(model_path) + converter = tf.lite.TFLiteConverter.from_saved_model(model_path) + else: + raise RuntimeError( + "expecting a keras model or a path to saved model, get {}".format( + model_or_path + ) + ) + if optimizations is None: + optimizations = [tf.lite.Optimize.DEFAULT] + converter.representative_dataset = representive_dataset + converter.optimizations = optimizations + tflm_model_content = converter.convert() + + with (dir_path / 'tflm_model.tflite').open('wb') as fid: + fid.write(tflm_model_content) + fid.flush() + convert_graph(fid.name, config=config_file, model_name=model_name) diff --git a/utensor_cgen/api/utils.py b/utensor_cgen/api/utils.py new file mode 100644 index 00000000..d90e7322 --- /dev/null +++ b/utensor_cgen/api/utils.py @@ -0,0 +1,59 @@ +import textwrap + +import click + + +def show_ugraph(ugraph, oneline=False, ignore_unknown_op=False): + from utensor_cgen.backend.utensor.code_generator.legacy._operators import OperatorFactory + + unknown_ops = set([]) + if oneline: + tmpl = click.style("{op_name} ", fg='yellow', bold=True) + \ + "op_type: {op_type}, inputs: {inputs}, outputs: {outputs}" + for op_name in ugraph.topo_order: + op_info = ugraph.ops_info[op_name] + msg = tmpl.format(op_name=op_name, op_type=op_info.op_type, + inputs=[tensor.name for tensor in op_info.input_tensors], + outputs=[tensor.name for tensor in op_info.output_tensors]) + click.echo(msg) + if not OperatorFactory.is_supported(op_info.op_type): + unknown_ops.add(op_info) + else: + tmpl = click.style('op_name: {op_name}\n', fg='yellow', bold=True) + \ + '''\ + op_type: {op_type} + input(s): + {inputs} + {input_shapes} + ouptut(s): + {outputs} + {output_shapes} + ''' + tmpl = textwrap.dedent(tmpl) + paragraphs = [] + for op_name in ugraph.topo_order: + op_info = ugraph.ops_info[op_name] + op_str = tmpl.format( + op_name=op_name, + op_type=op_info.op_type, + inputs=op_info.input_tensors, + outputs=op_info.output_tensors, + input_shapes=[tensor.shape for tensor in op_info.input_tensors], + output_shapes=[tensor.shape for tensor in op_info.output_tensors]) + paragraphs.append(op_str) + if not OperatorFactory.is_supported(op_info.op_type): + unknown_ops.add(op_info) + click.echo('\n'.join(paragraphs)) + click.secho( + 'topological ordered ops: {}'.format(ugraph.topo_order), + fg='white', bold=True, + ) + if unknown_ops and not ignore_unknown_op: + click.echo( + click.style('Unknown Ops Detected', fg='red', bold=True) + ) + for op_info in unknown_ops: + click.echo( + click.style(' {}: {}'.format(op_info.name, op_info.op_type), fg='red') + ) + return 0 diff --git a/utensor_cgen/backend/utensor/_backend_impl.py b/utensor_cgen/backend/utensor/_backend_impl.py index 790a5815..c37784e7 100644 --- a/utensor_cgen/backend/utensor/_backend_impl.py +++ b/utensor_cgen/backend/utensor/_backend_impl.py @@ -47,7 +47,7 @@ def default_config(cls): config = {} config[cls.TARGET] = {} config[cls.TARGET][cls.COMPONENT] = {} - config[cls.TARGET][cls.COMPONENT]['legacy-api'] = True + config[cls.TARGET][cls.COMPONENT]['legacy-api'] = False config[cls.TARGET][cls.COMPONENT][TensorAllocationPlanner.PART] = TensorAllocationPlanner.default_config config[cls.TARGET][cls.COMPONENT][uTensorLegacyCodeGenerator.PART] = uTensorLegacyCodeGenerator.default_config config[cls.TARGET][cls.COMPONENT][uTensorLegacyGraphLower.PART] = uTensorLegacyGraphLower.default_config diff --git a/utensor_cgen/cli.py b/utensor_cgen/cli.py deleted file mode 100644 index 34dfb2f2..00000000 --- a/utensor_cgen/cli.py +++ /dev/null @@ -1,223 +0,0 @@ -#-*- coding:utf8 -*- -import importlib -import os -import re -import sys -from pathlib import Path - -import click -from toml import dumps, loads - -from utensor_cgen import __version__ -from utensor_cgen.backend.api import BackendManager -from utensor_cgen.utils import NArgsParam - - -def _get_pb_model_name(path): - return os.path.basename(os.path.splitext(path)[0]) - -def _load_plugin(path): - path = Path(path) - if not path.exists(): - raise RuntimeError('{} does not exists'.format(path)) - sys.path.insert(0, str(path.parent)) - mod_name = re.sub(r'.py$', '', path.name.split()[0]) - importlib.import_module(mod_name) - sys.path.pop(0) - -@click.group(name='utensor-cli') -@click.help_option('-h', '--help') -@click.version_option( - __version__, - '-V', '--version' -) -@click.option( - "-p", - "--plugin", - multiple=True, - help="path of the python module which will be loaded as plugin (multiple)", - metavar="MODULE", -) -def cli(plugin): - for module_path in plugin: - _load_plugin(module_path) - -@cli.command(name='list-backends', help='list all available backends') -@click.help_option('-h', '--help') -def list_backends(): - click.secho('Available backends:', fg='green', bold=True) - for backend in BackendManager.backends: - click.secho( - ' - {}'.format(backend), fg='green' - ) - -@cli.command(name='generate-config', help='generate config toml file') -@click.help_option('-h', '--help') -@click.option('--target', required=True, help='target framework/platform') -@click.option('-o', '--output', default='utensor_cli.toml', metavar='CONFIG.toml', help='the output config file name') -def generate_config(target, output): - backend_cls = BackendManager.get_backend(target) - config = backend_cls.default_config - click.secho( - 'generating config file: {}'.format(output), - fg='white', - bold=True, - ) - with open(output, 'w') as fid: - fid.write( - '# utensor-cli version {}\n'.format(__version__) + \ - '# https://github.com/toml-lang/toml\n' + \ - '# ..\n' - ) - fid.write( - '# we use string \'None\' to represent python None value\n' - '# you should convert the string to None if you try to write extension for utensor_cgen\n' - ) - fid.write(dumps(config)) - -@cli.command(name='convert', help='convert graph to cpp/hpp files') -@click.help_option('-h', '--help') -@click.argument( - 'model_file', - required=True, - metavar='MODEL_FILE.{pb,onnx,pkl}', -) -@click.option( - '--output-nodes', - type=NArgsParam(), - metavar="NODE_NAME,NODE_NAME,...", - help="list of output nodes" -) -@click.option('--config', default='utensor_cli.toml', show_default=True, metavar='CONFIG.toml') -@click.option('--target', - default='utensor', - show_default=True, - help='target framework/platform' -) -def convert_graph(model_file, output_nodes, config, target): - from utensor_cgen.frontend import FrontendSelector - - if os.path.exists(config): - with open(config) as fid: - config = loads(fid.read()) - else: - config = {} - ugraph = FrontendSelector.parse(model_file, output_nodes, config) - backend = BackendManager.get_backend(target)(config) - backend.apply(ugraph) - -@cli.command(name='list-trans-methods', help='list all available graph transformation') -@click.help_option('-h', '--help') -@click.option('--verbose', is_flag=True) -def list_trans_methods(verbose): - from utensor_cgen.transformer import TransformerPipeline - from pprint import pformat - - if verbose: - for name, trans_cls in TransformerPipeline.TRANSFORMER_MAP.items(): - click.secho(name, fg='white', bold=True) - click.secho(trans_cls.__doc__, fg='yellow', bold=True) - else: - click.secho( - pformat(TransformerPipeline.all_transform_methods()), - fg='white', bold=True - ) - return 0 - -@cli.command(name='show', help='show node names in the pb file') -@click.help_option('-h', '--help') -@click.option('--oneline', is_flag=True, - help='show in oneline format (no detail information)') -@click.option('--ignore-unknown-op', is_flag=True, - help='ignore unknown/unsupported ops') -@click.option("--output-nodes", - type=NArgsParam(), - metavar="NODE_NAME,NODE_NAME,...", - help="list of output nodes") -@click.option('--config', default='utensor_cli.toml', show_default=True, metavar='CONFIG.toml') -@click.argument('model_file', required=True, metavar='MODEL.{pb,pkl}') -def show_graph(model_file, config, **kwargs): - import pickle - from utensor_cgen.frontend import FrontendSelector - - _, ext = os.path.splitext(model_file) - output_nodes = kwargs.pop('output_nodes') - - if ext == '.pkl': - with open(model_file, 'rb') as fid: - ugraph = pickle.load(fid) - _show_ugraph(ugraph, **kwargs) - return 0 - - if os.path.exists(config): - with open(config) as fid: - config = loads(fid.read()) - else: - config = {} - try: - ugraph = FrontendSelector.parse(model_file, output_nodes, config) - _show_ugraph(ugraph, **kwargs) - return 0 - except RuntimeError as err: - msg = err.args[0] - click.secho(msg, fg='red', bold=True) - return 1 - -def _show_ugraph(ugraph, oneline=False, ignore_unknown_op=False): - import textwrap - from utensor_cgen.backend.utensor.code_generator.legacy._operators import OperatorFactory - - unknown_ops = set([]) - if oneline: - tmpl = click.style("{op_name} ", fg='yellow', bold=True) + \ - "op_type: {op_type}, inputs: {inputs}, outputs: {outputs}" - for op_name in ugraph.topo_order: - op_info = ugraph.ops_info[op_name] - msg = tmpl.format(op_name=op_name, op_type=op_info.op_type, - inputs=[tensor.name for tensor in op_info.input_tensors], - outputs=[tensor.name for tensor in op_info.output_tensors]) - click.echo(msg) - if not OperatorFactory.is_supported(op_info.op_type): - unknown_ops.add(op_info) - else: - tmpl = click.style('op_name: {op_name}\n', fg='yellow', bold=True) + \ - '''\ - op_type: {op_type} - input(s): - {inputs} - {input_shapes} - ouptut(s): - {outputs} - {output_shapes} - ''' - tmpl = textwrap.dedent(tmpl) - paragraphs = [] - for op_name in ugraph.topo_order: - op_info = ugraph.ops_info[op_name] - op_str = tmpl.format( - op_name=op_name, - op_type=op_info.op_type, - inputs=op_info.input_tensors, - outputs=op_info.output_tensors, - input_shapes=[tensor.shape for tensor in op_info.input_tensors], - output_shapes=[tensor.shape for tensor in op_info.output_tensors]) - paragraphs.append(op_str) - if not OperatorFactory.is_supported(op_info.op_type): - unknown_ops.add(op_info) - click.echo('\n'.join(paragraphs)) - click.secho( - 'topological ordered ops: {}'.format(ugraph.topo_order), - fg='white', bold=True, - ) - if unknown_ops and not ignore_unknown_op: - click.echo( - click.style('Unknown Ops Detected', fg='red', bold=True) - ) - for op_info in unknown_ops: - click.echo( - click.style(' {}: {}'.format(op_info.name, op_info.op_type), fg='red') - ) - return 0 - -if __name__ == '__main__': - cli() diff --git a/utensor_cgen/cli/__init__.py b/utensor_cgen/cli/__init__.py new file mode 100644 index 00000000..f91c785f --- /dev/null +++ b/utensor_cgen/cli/__init__.py @@ -0,0 +1,4 @@ +from . import backend as _ +from . import convert as _ +from . import utils as _ +from .main import cli diff --git a/utensor_cgen/cli/backend.py b/utensor_cgen/cli/backend.py new file mode 100644 index 00000000..9f965dd5 --- /dev/null +++ b/utensor_cgen/cli/backend.py @@ -0,0 +1,52 @@ +import click + +from utensor_cgen import __version__ +from utensor_cgen.api.backend import generate_config as _generate_config +from utensor_cgen.api.backend import get_backends, get_trans_methods +from utensor_cgen.backend.api import BackendManager + +from .main import cli + + +@cli.command(name='list-backends', help='list all available backends') +@click.help_option('-h', '--help') +def list_backends(): + backends = get_backends() + click.secho('Available backends:', fg='green', bold=True) + for backend in backends: + click.secho( + ' - {}'.format(backend), fg='green' + ) + return 0 + +@cli.command(name='list-trans-methods', help='list all available graph transformation') +@click.help_option('-h', '--help') +@click.option('--verbose', is_flag=True) +def list_trans_methods(verbose): + from pprint import pformat + + trans_methods = get_trans_methods() + + if verbose: + for name, trans_cls in trans_methods.items(): + click.secho(name, fg='white', bold=True) + click.secho(trans_cls.__doc__, fg='yellow', bold=True) + else: + click.secho( + pformat(list(trans_methods.keys())), + fg='white', bold=True + ) + return 0 + +@cli.command(name='generate-config', help='generate config toml file') +@click.help_option('-h', '--help') +@click.option('--target', required=True, help='target framework/platform') +@click.option('-o', '--output', default='utensor_cli.toml', metavar='CONFIG.toml', help='the output config file name') +def generate_config(target, output): + _generate_config(target, output) + click.secho( + 'config file generated: {}'.format(output), + fg='white', + bold=True, + ) + return 0 diff --git a/utensor_cgen/cli/convert.py b/utensor_cgen/cli/convert.py new file mode 100644 index 00000000..7a00ec9c --- /dev/null +++ b/utensor_cgen/cli/convert.py @@ -0,0 +1,37 @@ +import os + +import click + +from utensor_cgen.api.convert import convert_graph as _convert_graph +from utensor_cgen.utils import NArgsParam + +from .main import cli + + +@cli.command(name='convert', help='convert graph to cpp/hpp files') +@click.help_option('-h', '--help') +@click.argument( + 'model_file', + required=True, + metavar='MODEL_FILE.{pb,onnx,pkl}', +) +@click.option( + '--output-nodes', + type=NArgsParam(), + metavar="NODE_NAME,NODE_NAME,...", + help="list of output nodes" +) +@click.option('--config', default='utensor_cli.toml', show_default=True, metavar='CONFIG.toml') +@click.option('--target', + default='utensor', + show_default=True, + help='target framework/platform' +) +def convert_graph(model_file, output_nodes, config, target): + _convert_graph( + model_file=model_file, + output_nodes=output_nodes, + config=config, + target=target + ) + return 0 diff --git a/utensor_cgen/cli/main.py b/utensor_cgen/cli/main.py new file mode 100644 index 00000000..9d27f342 --- /dev/null +++ b/utensor_cgen/cli/main.py @@ -0,0 +1,36 @@ +#-*- coding:utf8 -*- +import importlib +import re +import sys +from pathlib import Path + +import click + +from utensor_cgen import __version__ + + +def _load_plugin(path): + path = Path(path) + if not path.exists(): + raise RuntimeError('{} does not exists'.format(path)) + sys.path.insert(0, str(path.parent)) + mod_name = re.sub(r'.py$', '', path.name.split()[0]) + importlib.import_module(mod_name) + sys.path.pop(0) + +@click.group(name='utensor-cli') +@click.help_option('-h', '--help') +@click.version_option( + __version__, + '-V', '--version' +) +@click.option( + "-p", + "--plugin", + multiple=True, + help="path of the python module which will be loaded as plugin (multiple)", + metavar="MODULE", +) +def cli(plugin): + for module_path in plugin: + _load_plugin(module_path) diff --git a/utensor_cgen/cli/utils.py b/utensor_cgen/cli/utils.py new file mode 100644 index 00000000..d5aa78db --- /dev/null +++ b/utensor_cgen/cli/utils.py @@ -0,0 +1,49 @@ +import os + +import click +from toml import loads + +from utensor_cgen.api.utils import show_ugraph as _show_ugraph +from utensor_cgen.utils import NArgsParam + +from .main import cli + + +@cli.command(name='show', help='show node names in the pb file') +@click.help_option('-h', '--help') +@click.option('--oneline', is_flag=True, + help='show in oneline format (no detail information)') +@click.option('--ignore-unknown-op', is_flag=True, + help='ignore unknown/unsupported ops') +@click.option("--output-nodes", + type=NArgsParam(), + metavar="NODE_NAME,NODE_NAME,...", + help="list of output nodes") +@click.option('--config', default='utensor_cli.toml', show_default=True, metavar='CONFIG.toml') +@click.argument('model_file', required=True, metavar='MODEL.{pb,pkl}') +def show_graph(model_file, config, **kwargs): + import pickle + from utensor_cgen.frontend import FrontendSelector + + _, ext = os.path.splitext(model_file) + output_nodes = kwargs.pop('output_nodes') + + if ext == '.pkl': + with open(model_file, 'rb') as fid: + ugraph = pickle.load(fid) + _show_ugraph(ugraph, **kwargs) + return 0 + + if os.path.exists(config): + with open(config) as fid: + config = loads(fid.read()) + else: + config = {} + try: + ugraph = FrontendSelector.parse(model_file, output_nodes, config) + _show_ugraph(ugraph, **kwargs) + return 0 + except RuntimeError as err: + msg = err.args[0] + click.secho(msg, fg='red', bold=True) + return 1 diff --git a/utensor_cgen/frontend/__init__.py b/utensor_cgen/frontend/__init__.py index 32b82590..e11076ce 100644 --- a/utensor_cgen/frontend/__init__.py +++ b/utensor_cgen/frontend/__init__.py @@ -22,12 +22,12 @@ def _register(parser_cls): return _register @classmethod - def parse(cls, model_file, output_nodes=None, config=None): + def parse(cls, model_file, output_nodes=None, config=None, model_name=None): if config is None: config = {} _, ext = os.path.splitext(model_file) parser = cls.select_parser(ext)(config) - return parser.parse(model_file, output_nodes) + return parser.parse(model_file, output_nodes, model_name=model_name) @classmethod def select_parser(cls, file_ext): diff --git a/utensor_cgen/frontend/base.py b/utensor_cgen/frontend/base.py index e80792f7..52eb9a8f 100644 --- a/utensor_cgen/frontend/base.py +++ b/utensor_cgen/frontend/base.py @@ -17,5 +17,5 @@ def config(self): @classmethod @abstractmethod - def parse(cls, fname, outupt_nodes): + def parse(cls, fname, outupt_nodes, model_name=None): raise RuntimeError('abstract parse method involded') diff --git a/utensor_cgen/frontend/onnx.py b/utensor_cgen/frontend/onnx.py index c2691e50..b9e91da4 100644 --- a/utensor_cgen/frontend/onnx.py +++ b/utensor_cgen/frontend/onnx.py @@ -67,8 +67,13 @@ class OnnxParser(Parser): # https://github.com/onnx/onnx/blob/master/onnx/onnx.proto # https://pytorch.org/docs/stable/onnx.html - def parse(self, onnx_file, output_nodes=None): - graph_name, _ = os.path.splitext(onnx_file) + def parse(self, onnx_file, output_nodes=None, model_name=None): + if model_name: + graph_name = model_name + else: + graph_name, _ = os.path.splitext( + os.path.basename(onnx_file) + ) tf.reset_default_graph() model = onnx.load(onnx_file) onnx_graph = model.graph diff --git a/utensor_cgen/frontend/tensorflow.py b/utensor_cgen/frontend/tensorflow.py index 27e7f749..9082a333 100644 --- a/utensor_cgen/frontend/tensorflow.py +++ b/utensor_cgen/frontend/tensorflow.py @@ -18,8 +18,10 @@ @FrontendSelector.register(target_exts=[".pb", ".pbtxt"]) class GraphDefParser(Parser): - def parse(self, pb_file, output_nodes=None): + def parse(self, pb_file, output_nodes=None, model_name=None): graph_def, graph_name = self._load_graph_def(pb_file) + if model_name: + graph_name = model_name if not self._is_freeze_graph(graph_def): raise ValueError("Given graph_def is not freezed") if output_nodes is None: diff --git a/utensor_cgen/frontend/tflite.py b/utensor_cgen/frontend/tflite.py index f293338c..fc474f96 100644 --- a/utensor_cgen/frontend/tflite.py +++ b/utensor_cgen/frontend/tflite.py @@ -187,10 +187,15 @@ class TFLiteParser(Parser): } _BUILTIN_OPS = {v: k for k, v in BuiltinOperator.__dict__.items()} - def parse(self, tflite_file, output_nodes=None): + def parse(self, tflite_file, output_nodes=None, model_name=None): if output_nodes is None: output_nodes = [] - graph_name, _ = os.path.splitext(tflite_file) + if model_name: + graph_name = model_name + else: + graph_name, _ = os.path.splitext( + os.path.basename(tflite_file) + ) with open(tflite_file, "rb") as fid: buf = bytearray(fid.read()) fb_model = Model.GetRootAsModel(buf, 0) diff --git a/utensor_cgen/frontend/utensor.py b/utensor_cgen/frontend/utensor.py index 6c467b1c..5a5e0e6c 100644 --- a/utensor_cgen/frontend/utensor.py +++ b/utensor_cgen/frontend/utensor.py @@ -10,11 +10,13 @@ class PickleParser(Parser): @classmethod - def parse(cls, pkl_file, output_nodes=None): + def parse(cls, pkl_file, output_nodes=None, model_name=None): with open(pkl_file, 'rb') as fid: ugraph = pickle.load(fid) if not isinstance(ugraph, uTensorGraph): raise ValueError('expecting uTensorGraph object, get %s' % type(ugraph)) + if model_name: + ugraph.name = model_name if output_nodes is not None: ugraph.output_nodes = output_nodes topologic_order_graph(ugraph) From 357a212a2ed43de363cdd2639de080cfd15b4cf2 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Wed, 6 May 2020 00:46:38 +0800 Subject: [PATCH 114/117] Add isort:skip_file --- utensor_cgen/__init__.py | 7 +++++-- utensor_cgen/_extensions.py | 7 +++++-- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/utensor_cgen/__init__.py b/utensor_cgen/__init__.py index b5eb3ef1..bb0f8db3 100644 --- a/utensor_cgen/__init__.py +++ b/utensor_cgen/__init__.py @@ -1,12 +1,15 @@ +""" + isort:skip_file +""" import sys import pkg_resources # https://github.com/google/or-tools/issues/1830 # we need to import ortools before tensorflow -from ortools.sat.python import cp_model as _ # isort:skip +from ortools.sat.python import cp_model as _ -from utensor_cgen._extensions import _ExtensionsLoader # isort:skip +from utensor_cgen._extensions import _ExtensionsLoader __version__ = ( pkg_resources diff --git a/utensor_cgen/_extensions.py b/utensor_cgen/_extensions.py index 5a748c3b..c2db87bc 100644 --- a/utensor_cgen/_extensions.py +++ b/utensor_cgen/_extensions.py @@ -1,8 +1,11 @@ -import sys -import re +"""Experimental, DON'T USE +""" import importlib +import re +import sys from types import ModuleType + class _ExtensionsLoader(ModuleType): _ext_cache = {} _dunder_pattern = re.compile(r'__[A-Za-z0-9_]+__') From 18a396a11ae1a5f9e8129a1b1d252d48c18ae544 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Wed, 6 May 2020 10:51:37 +0800 Subject: [PATCH 115/117] Add target kwargs to api --- utensor_cgen/api/export.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/utensor_cgen/api/export.py b/utensor_cgen/api/export.py index 2cef5831..7d29f995 100644 --- a/utensor_cgen/api/export.py +++ b/utensor_cgen/api/export.py @@ -11,7 +11,8 @@ def tflm_keras_export( representive_dataset, model_name=None, optimizations=None, - config_file='utensor_cli.toml' + config_file='utensor_cli.toml', + target='utensor', ): with tempfile.TemporaryDirectory(prefix='utensor_') as tmp_dir: dir_path = Path(tmp_dir) @@ -36,4 +37,4 @@ def tflm_keras_export( with (dir_path / 'tflm_model.tflite').open('wb') as fid: fid.write(tflm_model_content) fid.flush() - convert_graph(fid.name, config=config_file, model_name=model_name) + convert_graph(fid.name, config=config_file, model_name=model_name, target=target) From cae78062ce567c051752833076cab0569f09f3a8 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Wed, 6 May 2020 16:31:20 +0800 Subject: [PATCH 116/117] remove frontend.tflite_flatbuffer module and update imports --- utensor_cgen/frontend/tflite.py | 29 ++-- .../frontend/tflite_flatbuffer/AbsOptions.py | 22 --- .../ActivationFunctionType.py | 12 -- .../frontend/tflite_flatbuffer/AddNOptions.py | 22 --- .../frontend/tflite_flatbuffer/AddOptions.py | 30 ---- .../tflite_flatbuffer/ArgMaxOptions.py | 30 ---- .../tflite_flatbuffer/ArgMinOptions.py | 30 ---- .../BatchToSpaceNDOptions.py | 22 --- .../BidirectionalSequenceLSTMOptions.py | 62 ------- .../BidirectionalSequenceRNNOptions.py | 46 ------ .../frontend/tflite_flatbuffer/Buffer.py | 46 ------ .../tflite_flatbuffer/BuiltinOperator.py | 121 -------------- .../tflite_flatbuffer/BuiltinOptions.py | 97 ----------- .../frontend/tflite_flatbuffer/CallOptions.py | 30 ---- .../frontend/tflite_flatbuffer/CastOptions.py | 38 ----- .../tflite_flatbuffer/CombinerType.py | 9 -- .../ConcatEmbeddingsOptions.py | 78 --------- .../tflite_flatbuffer/ConcatenationOptions.py | 38 ----- .../tflite_flatbuffer/Conv2DOptions.py | 70 -------- .../frontend/tflite_flatbuffer/CosOptions.py | 22 --- .../tflite_flatbuffer/CustomOptionsFormat.py | 7 - .../tflite_flatbuffer/CustomQuantization.py | 46 ------ .../DepthwiseConv2DOptions.py | 78 --------- .../tflite_flatbuffer/DequantizeOptions.py | 22 --- .../frontend/tflite_flatbuffer/DivOptions.py | 30 ---- .../EmbeddingLookupSparseOptions.py | 30 ---- .../tflite_flatbuffer/EqualOptions.py | 22 --- .../frontend/tflite_flatbuffer/ExpOptions.py | 22 --- .../tflite_flatbuffer/ExpandDimsOptions.py | 22 --- .../tflite_flatbuffer/FakeQuantOptions.py | 54 ------- .../frontend/tflite_flatbuffer/FillOptions.py | 22 --- .../tflite_flatbuffer/FloorDivOptions.py | 22 --- .../tflite_flatbuffer/FloorModOptions.py | 22 --- .../FullyConnectedOptions.py | 38 ----- .../FullyConnectedOptionsWeightsFormat.py | 8 - .../tflite_flatbuffer/GatherNdOptions.py | 22 --- .../tflite_flatbuffer/GatherOptions.py | 30 ---- .../tflite_flatbuffer/GreaterEqualOptions.py | 22 --- .../tflite_flatbuffer/GreaterOptions.py | 22 --- .../tflite_flatbuffer/L2NormOptions.py | 30 ---- .../tflite_flatbuffer/LSHProjectionOptions.py | 30 ---- .../tflite_flatbuffer/LSHProjectionType.py | 9 -- .../tflite_flatbuffer/LSTMKernelType.py | 8 - .../frontend/tflite_flatbuffer/LSTMOptions.py | 54 ------- .../tflite_flatbuffer/LeakyReluOptions.py | 30 ---- .../tflite_flatbuffer/LessEqualOptions.py | 22 --- .../frontend/tflite_flatbuffer/LessOptions.py | 22 --- .../LocalResponseNormalizationOptions.py | 54 ------- .../tflite_flatbuffer/LogSoftmaxOptions.py | 22 --- .../tflite_flatbuffer/LogicalAndOptions.py | 22 --- .../tflite_flatbuffer/LogicalNotOptions.py | 22 --- .../tflite_flatbuffer/LogicalOrOptions.py | 22 --- .../tflite_flatbuffer/MatrixDiagOptions.py | 22 --- .../tflite_flatbuffer/MatrixSetDiagOptions.py | 22 --- .../MaximumMinimumOptions.py | 22 --- .../tflite_flatbuffer/MirrorPadMode.py | 8 - .../tflite_flatbuffer/MirrorPadOptions.py | 30 ---- .../frontend/tflite_flatbuffer/Model.py | 128 --------------- .../frontend/tflite_flatbuffer/MulOptions.py | 30 ---- .../frontend/tflite_flatbuffer/NegOptions.py | 22 --- .../tflite_flatbuffer/NotEqualOptions.py | 22 --- .../tflite_flatbuffer/OneHotOptions.py | 30 ---- .../frontend/tflite_flatbuffer/Operator.py | 153 ------------------ .../tflite_flatbuffer/OperatorCode.py | 46 ------ .../frontend/tflite_flatbuffer/PackOptions.py | 38 ----- .../frontend/tflite_flatbuffer/PadOptions.py | 22 --- .../tflite_flatbuffer/PadV2Options.py | 22 --- .../frontend/tflite_flatbuffer/Padding.py | 8 - .../tflite_flatbuffer/Pool2DOptions.py | 70 -------- .../frontend/tflite_flatbuffer/PowOptions.py | 22 --- .../tflite_flatbuffer/QuantizationDetails.py | 8 - .../QuantizationParameters.py | 145 ----------------- .../tflite_flatbuffer/QuantizeOptions.py | 22 --- .../frontend/tflite_flatbuffer/RNNOptions.py | 30 ---- .../tflite_flatbuffer/RangeOptions.py | 22 --- .../frontend/tflite_flatbuffer/RankOptions.py | 22 --- .../tflite_flatbuffer/ReducerOptions.py | 30 ---- .../tflite_flatbuffer/ReshapeOptions.py | 46 ------ .../ResizeBilinearOptions.py | 30 ---- .../ResizeNearestNeighborOptions.py | 30 ---- .../ReverseSequenceOptions.py | 38 ----- .../tflite_flatbuffer/ReverseV2Options.py | 22 --- .../frontend/tflite_flatbuffer/SVDFOptions.py | 38 ----- .../tflite_flatbuffer/SelectOptions.py | 22 --- .../tflite_flatbuffer/SequenceRNNOptions.py | 38 ----- .../tflite_flatbuffer/ShapeOptions.py | 30 ---- .../tflite_flatbuffer/SkipGramOptions.py | 46 ------ .../tflite_flatbuffer/SliceOptions.py | 22 --- .../tflite_flatbuffer/SoftmaxOptions.py | 30 ---- .../SpaceToBatchNDOptions.py | 22 --- .../tflite_flatbuffer/SpaceToDepthOptions.py | 30 ---- .../tflite_flatbuffer/SparseToDenseOptions.py | 30 ---- .../tflite_flatbuffer/SplitOptions.py | 30 ---- .../tflite_flatbuffer/SplitVOptions.py | 30 ---- .../tflite_flatbuffer/SquareOptions.py | 22 --- .../SquaredDifferenceOptions.py | 22 --- .../tflite_flatbuffer/SqueezeOptions.py | 46 ------ .../tflite_flatbuffer/StridedSliceOptions.py | 62 ------- .../frontend/tflite_flatbuffer/SubGraph.py | 122 -------------- .../frontend/tflite_flatbuffer/SubOptions.py | 30 ---- .../frontend/tflite_flatbuffer/Tensor.py | 90 ----------- .../frontend/tflite_flatbuffer/TensorType.py | 16 -- .../frontend/tflite_flatbuffer/TileOptions.py | 22 --- .../tflite_flatbuffer/TopKV2Options.py | 22 --- .../tflite_flatbuffer/TransposeConvOptions.py | 46 ------ .../tflite_flatbuffer/TransposeOptions.py | 22 --- .../UnidirectionalSequenceLSTMOptions.py | 54 ------- .../tflite_flatbuffer/UniqueOptions.py | 30 ---- .../tflite_flatbuffer/UnpackOptions.py | 38 ----- .../tflite_flatbuffer/WhereOptions.py | 22 --- .../tflite_flatbuffer/ZerosLikeOptions.py | 22 --- .../__init__.py | 0 112 files changed, 15 insertions(+), 3945 deletions(-) delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/AbsOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ActivationFunctionType.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/AddNOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/AddOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ArgMaxOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ArgMinOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/BatchToSpaceNDOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/BidirectionalSequenceLSTMOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/BidirectionalSequenceRNNOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/Buffer.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/BuiltinOperator.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/BuiltinOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/CallOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/CastOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/CombinerType.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ConcatEmbeddingsOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ConcatenationOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/Conv2DOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/CosOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/CustomOptionsFormat.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/CustomQuantization.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/DepthwiseConv2DOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/DequantizeOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/DivOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/EmbeddingLookupSparseOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/EqualOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ExpOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ExpandDimsOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/FakeQuantOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/FillOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/FloorDivOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/FloorModOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/FullyConnectedOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/FullyConnectedOptionsWeightsFormat.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/GatherNdOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/GatherOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/GreaterEqualOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/GreaterOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/L2NormOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LSHProjectionOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LSHProjectionType.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LSTMKernelType.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LSTMOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LeakyReluOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LessEqualOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LessOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LocalResponseNormalizationOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LogSoftmaxOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LogicalAndOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LogicalNotOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/LogicalOrOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/MatrixDiagOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/MatrixSetDiagOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/MaximumMinimumOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/MirrorPadMode.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/MirrorPadOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/Model.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/MulOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/NegOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/NotEqualOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/OneHotOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/Operator.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/OperatorCode.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/PackOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/PadOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/PadV2Options.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/Padding.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/Pool2DOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/PowOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/QuantizationDetails.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/QuantizationParameters.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/QuantizeOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/RNNOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/RangeOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/RankOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ReducerOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ReshapeOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ResizeBilinearOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ResizeNearestNeighborOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ReverseSequenceOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ReverseV2Options.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SVDFOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SelectOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SequenceRNNOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ShapeOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SkipGramOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SliceOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SoftmaxOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SpaceToBatchNDOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SpaceToDepthOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SparseToDenseOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SplitOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SplitVOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SquareOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SquaredDifferenceOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SqueezeOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/StridedSliceOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SubGraph.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/SubOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/Tensor.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/TensorType.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/TileOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/TopKV2Options.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/TransposeConvOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/TransposeOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/UnidirectionalSequenceLSTMOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/UniqueOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/UnpackOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/WhereOptions.py delete mode 100644 utensor_cgen/frontend/tflite_flatbuffer/ZerosLikeOptions.py rename utensor_cgen/{frontend/tflite_flatbuffer => third_party}/__init__.py (100%) diff --git a/utensor_cgen/frontend/tflite.py b/utensor_cgen/frontend/tflite.py index fc474f96..cf169a1e 100644 --- a/utensor_cgen/frontend/tflite.py +++ b/utensor_cgen/frontend/tflite.py @@ -8,14 +8,15 @@ from utensor_cgen.ir.base import OperationInfo, TensorInfo, uTensorGraph from utensor_cgen.ir.converter import (AttrValueConverter, GenericTensorConverterMixin) -from utensor_cgen.utils import topologic_order_graph - -from .tflite_flatbuffer.ActivationFunctionType import ActivationFunctionType -from .tflite_flatbuffer.BuiltinOperator import BuiltinOperator -from .tflite_flatbuffer.CustomOptionsFormat import CustomOptionsFormat -from .tflite_flatbuffer.FullyConnectedOptionsWeightsFormat import \ +from utensor_cgen.third_party.tflite.ActivationFunctionType import \ + ActivationFunctionType +from utensor_cgen.third_party.tflite.BuiltinOperator import BuiltinOperator +from utensor_cgen.third_party.tflite.CustomOptionsFormat import \ + CustomOptionsFormat +from utensor_cgen.third_party.tflite.FullyConnectedOptionsWeightsFormat import \ FullyConnectedOptionsWeightsFormat -from .tflite_flatbuffer.Model import Model +from utensor_cgen.third_party.tflite.Model import Model +from utensor_cgen.utils import topologic_order_graph _CUSTOM_OPTION_FORMAT_MAP = {v: k for k, v in CustomOptionsFormat.__dict__.items()} @@ -27,7 +28,7 @@ def class_option2str(obj, idx): def fully_connected_op_data(op): option_dict = {} if op.CustomOptionsLength() < 1: - from .tflite_flatbuffer.FullyConnectedOptions import FullyConnectedOptions + from utensor_cgen.third_party.tflite.FullyConnectedOptions import FullyConnectedOptions option = FullyConnectedOptions() builtin_data = op.BuiltinOptions() @@ -47,7 +48,7 @@ def fully_connected_op_data(op): def depthwise_conv2d_op_data(op): option_dict = {} if op.CustomOptionsLength() < 1: - from .tflite_flatbuffer.DepthwiseConv2DOptions import DepthwiseConv2DOptions + from utensor_cgen.third_party.tflite.DepthwiseConv2DOptions import DepthwiseConv2DOptions option = DepthwiseConv2DOptions() builtin_data = op.BuiltinOptions() @@ -72,7 +73,7 @@ def depthwise_conv2d_op_data(op): def reshape_op_data(op): option_dict = {} if op.CustomOptionsLength() < 1: - from .tflite_flatbuffer.ReshapeOptions import ReshapeOptions + from utensor_cgen.third_party.tflite.ReshapeOptions import ReshapeOptions option = ReshapeOptions() builtin_data = op.BuiltinOptions() @@ -88,7 +89,7 @@ def reshape_op_data(op): def dequantize_op_data(op): option_dict = {} if op.CustomOptionsLength() < 1: - from .tflite_flatbuffer.DequantizeOptions import DequantizeOptions + from utensor_cgen.third_party.tflite.DequantizeOptions import DequantizeOptions option = DequantizeOptions() builtin_data = op.BuiltinOptions() @@ -106,7 +107,7 @@ def dequantize_op_data(op): def quantize_op_data(op): option_dict = {} if op.CustomOptionsLength() < 1: - from .tflite_flatbuffer.QuantizeOptions import QuantizeOptions + from utensor_cgen.third_party.tflite.QuantizeOptions import QuantizeOptions option = QuantizeOptions() builtin_data = op.BuiltinOptions() @@ -124,7 +125,7 @@ def quantize_op_data(op): def pool2d_op_data(op): option_dict = {} if op.CustomOptionsLength() < 1: - from .tflite_flatbuffer.Pool2DOptions import Pool2DOptions + from utensor_cgen.third_party.tflite.Pool2DOptions import Pool2DOptions option = Pool2DOptions() builtin_data = op.BuiltinOptions() @@ -147,7 +148,7 @@ def pool2d_op_data(op): def argmax_op_data(op): option_dict = {} if op.CustomOptionsLength() < 1: - from .tflite_flatbuffer.ArgMaxOptions import ArgMaxOptions + from utensor_cgen.third_party.tflite.ArgMaxOptions import ArgMaxOptions option = ArgMaxOptions() builtin_data = op.BuiltinOptions() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/AbsOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/AbsOptions.py deleted file mode 100644 index 0cbfb8c0..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/AbsOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class AbsOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsAbsOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = AbsOptions() - x.Init(buf, n + offset) - return x - - # AbsOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def AbsOptionsStart(builder): builder.StartObject(0) -def AbsOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ActivationFunctionType.py b/utensor_cgen/frontend/tflite_flatbuffer/ActivationFunctionType.py deleted file mode 100644 index dc177dee..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/ActivationFunctionType.py +++ /dev/null @@ -1,12 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -class ActivationFunctionType(object): - NONE = 0 - RELU = 1 - RELU_N1_TO_1 = 2 - RELU6 = 3 - TANH = 4 - SIGN_BIT = 5 - diff --git a/utensor_cgen/frontend/tflite_flatbuffer/AddNOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/AddNOptions.py deleted file mode 100644 index b5c2ddb7..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/AddNOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class AddNOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsAddNOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = AddNOptions() - x.Init(buf, n + offset) - return x - - # AddNOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def AddNOptionsStart(builder): builder.StartObject(0) -def AddNOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/AddOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/AddOptions.py deleted file mode 100644 index d6cbfcf5..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/AddOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class AddOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsAddOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = AddOptions() - x.Init(buf, n + offset) - return x - - # AddOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # AddOptions - def FusedActivationFunction(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - -def AddOptionsStart(builder): builder.StartObject(1) -def AddOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) -def AddOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ArgMaxOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ArgMaxOptions.py deleted file mode 100644 index fbf1415e..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/ArgMaxOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class ArgMaxOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsArgMaxOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ArgMaxOptions() - x.Init(buf, n + offset) - return x - - # ArgMaxOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ArgMaxOptions - def OutputType(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - -def ArgMaxOptionsStart(builder): builder.StartObject(1) -def ArgMaxOptionsAddOutputType(builder, outputType): builder.PrependInt8Slot(0, outputType, 0) -def ArgMaxOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ArgMinOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ArgMinOptions.py deleted file mode 100644 index 120fdca2..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/ArgMinOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class ArgMinOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsArgMinOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ArgMinOptions() - x.Init(buf, n + offset) - return x - - # ArgMinOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ArgMinOptions - def OutputType(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - -def ArgMinOptionsStart(builder): builder.StartObject(1) -def ArgMinOptionsAddOutputType(builder, outputType): builder.PrependInt8Slot(0, outputType, 0) -def ArgMinOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/BatchToSpaceNDOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/BatchToSpaceNDOptions.py deleted file mode 100644 index 3ddcfd3f..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/BatchToSpaceNDOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class BatchToSpaceNDOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsBatchToSpaceNDOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = BatchToSpaceNDOptions() - x.Init(buf, n + offset) - return x - - # BatchToSpaceNDOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def BatchToSpaceNDOptionsStart(builder): builder.StartObject(0) -def BatchToSpaceNDOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/BidirectionalSequenceLSTMOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/BidirectionalSequenceLSTMOptions.py deleted file mode 100644 index 8d8b7bea..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/BidirectionalSequenceLSTMOptions.py +++ /dev/null @@ -1,62 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class BidirectionalSequenceLSTMOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsBidirectionalSequenceLSTMOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = BidirectionalSequenceLSTMOptions() - x.Init(buf, n + offset) - return x - - # BidirectionalSequenceLSTMOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # BidirectionalSequenceLSTMOptions - def FusedActivationFunction(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - - # BidirectionalSequenceLSTMOptions - def CellClip(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) - return 0.0 - - # BidirectionalSequenceLSTMOptions - def ProjClip(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) - return 0.0 - - # BidirectionalSequenceLSTMOptions - def MergeOutputs(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - - # BidirectionalSequenceLSTMOptions - def TimeMajor(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return True - -def BidirectionalSequenceLSTMOptionsStart(builder): builder.StartObject(5) -def BidirectionalSequenceLSTMOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) -def BidirectionalSequenceLSTMOptionsAddCellClip(builder, cellClip): builder.PrependFloat32Slot(1, cellClip, 0.0) -def BidirectionalSequenceLSTMOptionsAddProjClip(builder, projClip): builder.PrependFloat32Slot(2, projClip, 0.0) -def BidirectionalSequenceLSTMOptionsAddMergeOutputs(builder, mergeOutputs): builder.PrependBoolSlot(3, mergeOutputs, 0) -def BidirectionalSequenceLSTMOptionsAddTimeMajor(builder, timeMajor): builder.PrependBoolSlot(4, timeMajor, 1) -def BidirectionalSequenceLSTMOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/BidirectionalSequenceRNNOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/BidirectionalSequenceRNNOptions.py deleted file mode 100644 index 673af6b9..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/BidirectionalSequenceRNNOptions.py +++ /dev/null @@ -1,46 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class BidirectionalSequenceRNNOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsBidirectionalSequenceRNNOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = BidirectionalSequenceRNNOptions() - x.Init(buf, n + offset) - return x - - # BidirectionalSequenceRNNOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # BidirectionalSequenceRNNOptions - def TimeMajor(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - - # BidirectionalSequenceRNNOptions - def FusedActivationFunction(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - - # BidirectionalSequenceRNNOptions - def MergeOutputs(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - -def BidirectionalSequenceRNNOptionsStart(builder): builder.StartObject(3) -def BidirectionalSequenceRNNOptionsAddTimeMajor(builder, timeMajor): builder.PrependBoolSlot(0, timeMajor, 0) -def BidirectionalSequenceRNNOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(1, fusedActivationFunction, 0) -def BidirectionalSequenceRNNOptionsAddMergeOutputs(builder, mergeOutputs): builder.PrependBoolSlot(2, mergeOutputs, 0) -def BidirectionalSequenceRNNOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/Buffer.py b/utensor_cgen/frontend/tflite_flatbuffer/Buffer.py deleted file mode 100644 index 754dee3b..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/Buffer.py +++ /dev/null @@ -1,46 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class Buffer(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsBuffer(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = Buffer() - x.Init(buf, n + offset) - return x - - # Buffer - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # Buffer - def Data(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Uint8Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1)) - return 0 - - # Buffer - def DataAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint8Flags, o) - return 0 - - # Buffer - def DataLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def BufferStart(builder): builder.StartObject(1) -def BufferAddData(builder, data): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(data), 0) -def BufferStartDataVector(builder, numElems): return builder.StartVector(1, numElems, 1) -def BufferEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/BuiltinOperator.py b/utensor_cgen/frontend/tflite_flatbuffer/BuiltinOperator.py deleted file mode 100644 index 229e1270..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/BuiltinOperator.py +++ /dev/null @@ -1,121 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -class BuiltinOperator(object): - ADD = 0 - AVERAGE_POOL_2D = 1 - CONCATENATION = 2 - CONV_2D = 3 - DEPTHWISE_CONV_2D = 4 - DEQUANTIZE = 6 - EMBEDDING_LOOKUP = 7 - FLOOR = 8 - FULLY_CONNECTED = 9 - HASHTABLE_LOOKUP = 10 - L2_NORMALIZATION = 11 - L2_POOL_2D = 12 - LOCAL_RESPONSE_NORMALIZATION = 13 - LOGISTIC = 14 - LSH_PROJECTION = 15 - LSTM = 16 - MAX_POOL_2D = 17 - MUL = 18 - RELU = 19 - RELU_N1_TO_1 = 20 - RELU6 = 21 - RESHAPE = 22 - RESIZE_BILINEAR = 23 - RNN = 24 - SOFTMAX = 25 - SPACE_TO_DEPTH = 26 - SVDF = 27 - TANH = 28 - CONCAT_EMBEDDINGS = 29 - SKIP_GRAM = 30 - CALL = 31 - CUSTOM = 32 - EMBEDDING_LOOKUP_SPARSE = 33 - PAD = 34 - UNIDIRECTIONAL_SEQUENCE_RNN = 35 - GATHER = 36 - BATCH_TO_SPACE_ND = 37 - SPACE_TO_BATCH_ND = 38 - TRANSPOSE = 39 - MEAN = 40 - SUB = 41 - DIV = 42 - SQUEEZE = 43 - UNIDIRECTIONAL_SEQUENCE_LSTM = 44 - STRIDED_SLICE = 45 - BIDIRECTIONAL_SEQUENCE_RNN = 46 - EXP = 47 - TOPK_V2 = 48 - SPLIT = 49 - LOG_SOFTMAX = 50 - DELEGATE = 51 - BIDIRECTIONAL_SEQUENCE_LSTM = 52 - CAST = 53 - PRELU = 54 - MAXIMUM = 55 - ARG_MAX = 56 - MINIMUM = 57 - LESS = 58 - NEG = 59 - PADV2 = 60 - GREATER = 61 - GREATER_EQUAL = 62 - LESS_EQUAL = 63 - SELECT = 64 - SLICE = 65 - SIN = 66 - TRANSPOSE_CONV = 67 - SPARSE_TO_DENSE = 68 - TILE = 69 - EXPAND_DIMS = 70 - EQUAL = 71 - NOT_EQUAL = 72 - LOG = 73 - SUM = 74 - SQRT = 75 - RSQRT = 76 - SHAPE = 77 - POW = 78 - ARG_MIN = 79 - FAKE_QUANT = 80 - REDUCE_PROD = 81 - REDUCE_MAX = 82 - PACK = 83 - LOGICAL_OR = 84 - ONE_HOT = 85 - LOGICAL_AND = 86 - LOGICAL_NOT = 87 - UNPACK = 88 - REDUCE_MIN = 89 - FLOOR_DIV = 90 - REDUCE_ANY = 91 - SQUARE = 92 - ZEROS_LIKE = 93 - FILL = 94 - FLOOR_MOD = 95 - RANGE = 96 - RESIZE_NEAREST_NEIGHBOR = 97 - LEAKY_RELU = 98 - SQUARED_DIFFERENCE = 99 - MIRROR_PAD = 100 - ABS = 101 - SPLIT_V = 102 - UNIQUE = 103 - CEIL = 104 - REVERSE_V2 = 105 - ADD_N = 106 - GATHER_ND = 107 - COS = 108 - WHERE = 109 - RANK = 110 - ELU = 111 - REVERSE_SEQUENCE = 112 - MATRIX_DIAG = 113 - QUANTIZE = 114 - MATRIX_SET_DIAG = 115 - diff --git a/utensor_cgen/frontend/tflite_flatbuffer/BuiltinOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/BuiltinOptions.py deleted file mode 100644 index 9830f648..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/BuiltinOptions.py +++ /dev/null @@ -1,97 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -class BuiltinOptions(object): - NONE = 0 - Conv2DOptions = 1 - DepthwiseConv2DOptions = 2 - ConcatEmbeddingsOptions = 3 - LSHProjectionOptions = 4 - Pool2DOptions = 5 - SVDFOptions = 6 - RNNOptions = 7 - FullyConnectedOptions = 8 - SoftmaxOptions = 9 - ConcatenationOptions = 10 - AddOptions = 11 - L2NormOptions = 12 - LocalResponseNormalizationOptions = 13 - LSTMOptions = 14 - ResizeBilinearOptions = 15 - CallOptions = 16 - ReshapeOptions = 17 - SkipGramOptions = 18 - SpaceToDepthOptions = 19 - EmbeddingLookupSparseOptions = 20 - MulOptions = 21 - PadOptions = 22 - GatherOptions = 23 - BatchToSpaceNDOptions = 24 - SpaceToBatchNDOptions = 25 - TransposeOptions = 26 - ReducerOptions = 27 - SubOptions = 28 - DivOptions = 29 - SqueezeOptions = 30 - SequenceRNNOptions = 31 - StridedSliceOptions = 32 - ExpOptions = 33 - TopKV2Options = 34 - SplitOptions = 35 - LogSoftmaxOptions = 36 - CastOptions = 37 - DequantizeOptions = 38 - MaximumMinimumOptions = 39 - ArgMaxOptions = 40 - LessOptions = 41 - NegOptions = 42 - PadV2Options = 43 - GreaterOptions = 44 - GreaterEqualOptions = 45 - LessEqualOptions = 46 - SelectOptions = 47 - SliceOptions = 48 - TransposeConvOptions = 49 - SparseToDenseOptions = 50 - TileOptions = 51 - ExpandDimsOptions = 52 - EqualOptions = 53 - NotEqualOptions = 54 - ShapeOptions = 55 - PowOptions = 56 - ArgMinOptions = 57 - FakeQuantOptions = 58 - PackOptions = 59 - LogicalOrOptions = 60 - OneHotOptions = 61 - LogicalAndOptions = 62 - LogicalNotOptions = 63 - UnpackOptions = 64 - FloorDivOptions = 65 - SquareOptions = 66 - ZerosLikeOptions = 67 - FillOptions = 68 - BidirectionalSequenceLSTMOptions = 69 - BidirectionalSequenceRNNOptions = 70 - UnidirectionalSequenceLSTMOptions = 71 - FloorModOptions = 72 - RangeOptions = 73 - ResizeNearestNeighborOptions = 74 - LeakyReluOptions = 75 - SquaredDifferenceOptions = 76 - MirrorPadOptions = 77 - AbsOptions = 78 - SplitVOptions = 79 - UniqueOptions = 80 - ReverseV2Options = 81 - AddNOptions = 82 - GatherNdOptions = 83 - CosOptions = 84 - WhereOptions = 85 - RankOptions = 86 - ReverseSequenceOptions = 87 - MatrixDiagOptions = 88 - QuantizeOptions = 89 - MatrixSetDiagOptions = 90 - diff --git a/utensor_cgen/frontend/tflite_flatbuffer/CallOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/CallOptions.py deleted file mode 100644 index 5ae2eeae..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/CallOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class CallOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsCallOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = CallOptions() - x.Init(buf, n + offset) - return x - - # CallOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # CallOptions - def Subgraph(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos) - return 0 - -def CallOptionsStart(builder): builder.StartObject(1) -def CallOptionsAddSubgraph(builder, subgraph): builder.PrependUint32Slot(0, subgraph, 0) -def CallOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/CastOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/CastOptions.py deleted file mode 100644 index 70ae2e37..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/CastOptions.py +++ /dev/null @@ -1,38 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class CastOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsCastOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = CastOptions() - x.Init(buf, n + offset) - return x - - # CastOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # CastOptions - def InDataType(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - - # CastOptions - def OutDataType(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - -def CastOptionsStart(builder): builder.StartObject(2) -def CastOptionsAddInDataType(builder, inDataType): builder.PrependInt8Slot(0, inDataType, 0) -def CastOptionsAddOutDataType(builder, outDataType): builder.PrependInt8Slot(1, outDataType, 0) -def CastOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/CombinerType.py b/utensor_cgen/frontend/tflite_flatbuffer/CombinerType.py deleted file mode 100644 index 17d91510..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/CombinerType.py +++ /dev/null @@ -1,9 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -class CombinerType(object): - SUM = 0 - MEAN = 1 - SQRTN = 2 - diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ConcatEmbeddingsOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ConcatEmbeddingsOptions.py deleted file mode 100644 index 9d26c510..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/ConcatEmbeddingsOptions.py +++ /dev/null @@ -1,78 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class ConcatEmbeddingsOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsConcatEmbeddingsOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ConcatEmbeddingsOptions() - x.Init(buf, n + offset) - return x - - # ConcatEmbeddingsOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ConcatEmbeddingsOptions - def NumChannels(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # ConcatEmbeddingsOptions - def NumColumnsPerChannel(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # ConcatEmbeddingsOptions - def NumColumnsPerChannelAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # ConcatEmbeddingsOptions - def NumColumnsPerChannelLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # ConcatEmbeddingsOptions - def EmbeddingDimPerChannel(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # ConcatEmbeddingsOptions - def EmbeddingDimPerChannelAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # ConcatEmbeddingsOptions - def EmbeddingDimPerChannelLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def ConcatEmbeddingsOptionsStart(builder): builder.StartObject(3) -def ConcatEmbeddingsOptionsAddNumChannels(builder, numChannels): builder.PrependInt32Slot(0, numChannels, 0) -def ConcatEmbeddingsOptionsAddNumColumnsPerChannel(builder, numColumnsPerChannel): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(numColumnsPerChannel), 0) -def ConcatEmbeddingsOptionsStartNumColumnsPerChannelVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def ConcatEmbeddingsOptionsAddEmbeddingDimPerChannel(builder, embeddingDimPerChannel): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(embeddingDimPerChannel), 0) -def ConcatEmbeddingsOptionsStartEmbeddingDimPerChannelVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def ConcatEmbeddingsOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ConcatenationOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ConcatenationOptions.py deleted file mode 100644 index c8e0b6ab..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/ConcatenationOptions.py +++ /dev/null @@ -1,38 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class ConcatenationOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsConcatenationOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ConcatenationOptions() - x.Init(buf, n + offset) - return x - - # ConcatenationOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ConcatenationOptions - def Axis(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # ConcatenationOptions - def FusedActivationFunction(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - -def ConcatenationOptionsStart(builder): builder.StartObject(2) -def ConcatenationOptionsAddAxis(builder, axis): builder.PrependInt32Slot(0, axis, 0) -def ConcatenationOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(1, fusedActivationFunction, 0) -def ConcatenationOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/Conv2DOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/Conv2DOptions.py deleted file mode 100644 index ef49f751..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/Conv2DOptions.py +++ /dev/null @@ -1,70 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class Conv2DOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsConv2DOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = Conv2DOptions() - x.Init(buf, n + offset) - return x - - # Conv2DOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # Conv2DOptions - def Padding(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - - # Conv2DOptions - def StrideW(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # Conv2DOptions - def StrideH(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # Conv2DOptions - def FusedActivationFunction(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - - # Conv2DOptions - def DilationWFactor(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 1 - - # Conv2DOptions - def DilationHFactor(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 1 - -def Conv2DOptionsStart(builder): builder.StartObject(6) -def Conv2DOptionsAddPadding(builder, padding): builder.PrependInt8Slot(0, padding, 0) -def Conv2DOptionsAddStrideW(builder, strideW): builder.PrependInt32Slot(1, strideW, 0) -def Conv2DOptionsAddStrideH(builder, strideH): builder.PrependInt32Slot(2, strideH, 0) -def Conv2DOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(3, fusedActivationFunction, 0) -def Conv2DOptionsAddDilationWFactor(builder, dilationWFactor): builder.PrependInt32Slot(4, dilationWFactor, 1) -def Conv2DOptionsAddDilationHFactor(builder, dilationHFactor): builder.PrependInt32Slot(5, dilationHFactor, 1) -def Conv2DOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/CosOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/CosOptions.py deleted file mode 100644 index 7fbf8487..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/CosOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class CosOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsCosOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = CosOptions() - x.Init(buf, n + offset) - return x - - # CosOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def CosOptionsStart(builder): builder.StartObject(0) -def CosOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/CustomOptionsFormat.py b/utensor_cgen/frontend/tflite_flatbuffer/CustomOptionsFormat.py deleted file mode 100644 index bef384cf..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/CustomOptionsFormat.py +++ /dev/null @@ -1,7 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -class CustomOptionsFormat(object): - FLEXBUFFERS = 0 - diff --git a/utensor_cgen/frontend/tflite_flatbuffer/CustomQuantization.py b/utensor_cgen/frontend/tflite_flatbuffer/CustomQuantization.py deleted file mode 100644 index 21ec0da4..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/CustomQuantization.py +++ /dev/null @@ -1,46 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class CustomQuantization(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsCustomQuantization(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = CustomQuantization() - x.Init(buf, n + offset) - return x - - # CustomQuantization - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # CustomQuantization - def Custom(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Uint8Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1)) - return 0 - - # CustomQuantization - def CustomAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint8Flags, o) - return 0 - - # CustomQuantization - def CustomLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def CustomQuantizationStart(builder): builder.StartObject(1) -def CustomQuantizationAddCustom(builder, custom): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(custom), 0) -def CustomQuantizationStartCustomVector(builder, numElems): return builder.StartVector(1, numElems, 1) -def CustomQuantizationEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/DepthwiseConv2DOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/DepthwiseConv2DOptions.py deleted file mode 100644 index 9689383b..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/DepthwiseConv2DOptions.py +++ /dev/null @@ -1,78 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class DepthwiseConv2DOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsDepthwiseConv2DOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = DepthwiseConv2DOptions() - x.Init(buf, n + offset) - return x - - # DepthwiseConv2DOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # DepthwiseConv2DOptions - def Padding(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - - # DepthwiseConv2DOptions - def StrideW(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # DepthwiseConv2DOptions - def StrideH(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # DepthwiseConv2DOptions - def DepthMultiplier(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # DepthwiseConv2DOptions - def FusedActivationFunction(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - - # DepthwiseConv2DOptions - def DilationWFactor(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 1 - - # DepthwiseConv2DOptions - def DilationHFactor(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 1 - -def DepthwiseConv2DOptionsStart(builder): builder.StartObject(7) -def DepthwiseConv2DOptionsAddPadding(builder, padding): builder.PrependInt8Slot(0, padding, 0) -def DepthwiseConv2DOptionsAddStrideW(builder, strideW): builder.PrependInt32Slot(1, strideW, 0) -def DepthwiseConv2DOptionsAddStrideH(builder, strideH): builder.PrependInt32Slot(2, strideH, 0) -def DepthwiseConv2DOptionsAddDepthMultiplier(builder, depthMultiplier): builder.PrependInt32Slot(3, depthMultiplier, 0) -def DepthwiseConv2DOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(4, fusedActivationFunction, 0) -def DepthwiseConv2DOptionsAddDilationWFactor(builder, dilationWFactor): builder.PrependInt32Slot(5, dilationWFactor, 1) -def DepthwiseConv2DOptionsAddDilationHFactor(builder, dilationHFactor): builder.PrependInt32Slot(6, dilationHFactor, 1) -def DepthwiseConv2DOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/DequantizeOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/DequantizeOptions.py deleted file mode 100644 index 5ef8b8dd..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/DequantizeOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class DequantizeOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsDequantizeOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = DequantizeOptions() - x.Init(buf, n + offset) - return x - - # DequantizeOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def DequantizeOptionsStart(builder): builder.StartObject(0) -def DequantizeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/DivOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/DivOptions.py deleted file mode 100644 index 905a3be0..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/DivOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class DivOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsDivOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = DivOptions() - x.Init(buf, n + offset) - return x - - # DivOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # DivOptions - def FusedActivationFunction(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - -def DivOptionsStart(builder): builder.StartObject(1) -def DivOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) -def DivOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/EmbeddingLookupSparseOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/EmbeddingLookupSparseOptions.py deleted file mode 100644 index 7d9c1442..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/EmbeddingLookupSparseOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class EmbeddingLookupSparseOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsEmbeddingLookupSparseOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = EmbeddingLookupSparseOptions() - x.Init(buf, n + offset) - return x - - # EmbeddingLookupSparseOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # EmbeddingLookupSparseOptions - def Combiner(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - -def EmbeddingLookupSparseOptionsStart(builder): builder.StartObject(1) -def EmbeddingLookupSparseOptionsAddCombiner(builder, combiner): builder.PrependInt8Slot(0, combiner, 0) -def EmbeddingLookupSparseOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/EqualOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/EqualOptions.py deleted file mode 100644 index f787ef85..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/EqualOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class EqualOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsEqualOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = EqualOptions() - x.Init(buf, n + offset) - return x - - # EqualOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def EqualOptionsStart(builder): builder.StartObject(0) -def EqualOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ExpOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ExpOptions.py deleted file mode 100644 index eac1456e..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/ExpOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class ExpOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsExpOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ExpOptions() - x.Init(buf, n + offset) - return x - - # ExpOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def ExpOptionsStart(builder): builder.StartObject(0) -def ExpOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ExpandDimsOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ExpandDimsOptions.py deleted file mode 100644 index 69d63665..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/ExpandDimsOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class ExpandDimsOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsExpandDimsOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ExpandDimsOptions() - x.Init(buf, n + offset) - return x - - # ExpandDimsOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def ExpandDimsOptionsStart(builder): builder.StartObject(0) -def ExpandDimsOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/FakeQuantOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/FakeQuantOptions.py deleted file mode 100644 index 46c371c3..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/FakeQuantOptions.py +++ /dev/null @@ -1,54 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class FakeQuantOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsFakeQuantOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = FakeQuantOptions() - x.Init(buf, n + offset) - return x - - # FakeQuantOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # FakeQuantOptions - def Min(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) - return 0.0 - - # FakeQuantOptions - def Max(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) - return 0.0 - - # FakeQuantOptions - def NumBits(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # FakeQuantOptions - def NarrowRange(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - -def FakeQuantOptionsStart(builder): builder.StartObject(4) -def FakeQuantOptionsAddMin(builder, min): builder.PrependFloat32Slot(0, min, 0.0) -def FakeQuantOptionsAddMax(builder, max): builder.PrependFloat32Slot(1, max, 0.0) -def FakeQuantOptionsAddNumBits(builder, numBits): builder.PrependInt32Slot(2, numBits, 0) -def FakeQuantOptionsAddNarrowRange(builder, narrowRange): builder.PrependBoolSlot(3, narrowRange, 0) -def FakeQuantOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/FillOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/FillOptions.py deleted file mode 100644 index 5a1e651a..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/FillOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class FillOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsFillOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = FillOptions() - x.Init(buf, n + offset) - return x - - # FillOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def FillOptionsStart(builder): builder.StartObject(0) -def FillOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/FloorDivOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/FloorDivOptions.py deleted file mode 100644 index 64b474fb..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/FloorDivOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class FloorDivOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsFloorDivOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = FloorDivOptions() - x.Init(buf, n + offset) - return x - - # FloorDivOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def FloorDivOptionsStart(builder): builder.StartObject(0) -def FloorDivOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/FloorModOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/FloorModOptions.py deleted file mode 100644 index 37c8e5a5..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/FloorModOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class FloorModOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsFloorModOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = FloorModOptions() - x.Init(buf, n + offset) - return x - - # FloorModOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def FloorModOptionsStart(builder): builder.StartObject(0) -def FloorModOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/FullyConnectedOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/FullyConnectedOptions.py deleted file mode 100644 index fb7ebc28..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/FullyConnectedOptions.py +++ /dev/null @@ -1,38 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class FullyConnectedOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsFullyConnectedOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = FullyConnectedOptions() - x.Init(buf, n + offset) - return x - - # FullyConnectedOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # FullyConnectedOptions - def FusedActivationFunction(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - - # FullyConnectedOptions - def WeightsFormat(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - -def FullyConnectedOptionsStart(builder): builder.StartObject(2) -def FullyConnectedOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) -def FullyConnectedOptionsAddWeightsFormat(builder, weightsFormat): builder.PrependInt8Slot(1, weightsFormat, 0) -def FullyConnectedOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/FullyConnectedOptionsWeightsFormat.py b/utensor_cgen/frontend/tflite_flatbuffer/FullyConnectedOptionsWeightsFormat.py deleted file mode 100644 index 6d81eca1..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/FullyConnectedOptionsWeightsFormat.py +++ /dev/null @@ -1,8 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -class FullyConnectedOptionsWeightsFormat(object): - DEFAULT = 0 - SHUFFLED4x16INT8 = 1 - diff --git a/utensor_cgen/frontend/tflite_flatbuffer/GatherNdOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/GatherNdOptions.py deleted file mode 100644 index f515eb5c..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/GatherNdOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class GatherNdOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsGatherNdOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = GatherNdOptions() - x.Init(buf, n + offset) - return x - - # GatherNdOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def GatherNdOptionsStart(builder): builder.StartObject(0) -def GatherNdOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/GatherOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/GatherOptions.py deleted file mode 100644 index 9fbc3e40..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/GatherOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class GatherOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsGatherOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = GatherOptions() - x.Init(buf, n + offset) - return x - - # GatherOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # GatherOptions - def Axis(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def GatherOptionsStart(builder): builder.StartObject(1) -def GatherOptionsAddAxis(builder, axis): builder.PrependInt32Slot(0, axis, 0) -def GatherOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/GreaterEqualOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/GreaterEqualOptions.py deleted file mode 100644 index a29e200a..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/GreaterEqualOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class GreaterEqualOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsGreaterEqualOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = GreaterEqualOptions() - x.Init(buf, n + offset) - return x - - # GreaterEqualOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def GreaterEqualOptionsStart(builder): builder.StartObject(0) -def GreaterEqualOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/GreaterOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/GreaterOptions.py deleted file mode 100644 index 59d63501..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/GreaterOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class GreaterOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsGreaterOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = GreaterOptions() - x.Init(buf, n + offset) - return x - - # GreaterOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def GreaterOptionsStart(builder): builder.StartObject(0) -def GreaterOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/L2NormOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/L2NormOptions.py deleted file mode 100644 index 38bdf573..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/L2NormOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class L2NormOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsL2NormOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = L2NormOptions() - x.Init(buf, n + offset) - return x - - # L2NormOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # L2NormOptions - def FusedActivationFunction(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - -def L2NormOptionsStart(builder): builder.StartObject(1) -def L2NormOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) -def L2NormOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LSHProjectionOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/LSHProjectionOptions.py deleted file mode 100644 index ad550be2..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/LSHProjectionOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class LSHProjectionOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsLSHProjectionOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = LSHProjectionOptions() - x.Init(buf, n + offset) - return x - - # LSHProjectionOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # LSHProjectionOptions - def Type(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - -def LSHProjectionOptionsStart(builder): builder.StartObject(1) -def LSHProjectionOptionsAddType(builder, type): builder.PrependInt8Slot(0, type, 0) -def LSHProjectionOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LSHProjectionType.py b/utensor_cgen/frontend/tflite_flatbuffer/LSHProjectionType.py deleted file mode 100644 index d6a136e8..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/LSHProjectionType.py +++ /dev/null @@ -1,9 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -class LSHProjectionType(object): - UNKNOWN = 0 - SPARSE = 1 - DENSE = 2 - diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LSTMKernelType.py b/utensor_cgen/frontend/tflite_flatbuffer/LSTMKernelType.py deleted file mode 100644 index 1ca4df0a..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/LSTMKernelType.py +++ /dev/null @@ -1,8 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -class LSTMKernelType(object): - FULL = 0 - BASIC = 1 - diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LSTMOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/LSTMOptions.py deleted file mode 100644 index 93a83093..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/LSTMOptions.py +++ /dev/null @@ -1,54 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class LSTMOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsLSTMOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = LSTMOptions() - x.Init(buf, n + offset) - return x - - # LSTMOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # LSTMOptions - def FusedActivationFunction(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - - # LSTMOptions - def CellClip(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) - return 0.0 - - # LSTMOptions - def ProjClip(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) - return 0.0 - - # LSTMOptions - def KernelType(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - -def LSTMOptionsStart(builder): builder.StartObject(4) -def LSTMOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) -def LSTMOptionsAddCellClip(builder, cellClip): builder.PrependFloat32Slot(1, cellClip, 0.0) -def LSTMOptionsAddProjClip(builder, projClip): builder.PrependFloat32Slot(2, projClip, 0.0) -def LSTMOptionsAddKernelType(builder, kernelType): builder.PrependInt8Slot(3, kernelType, 0) -def LSTMOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LeakyReluOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/LeakyReluOptions.py deleted file mode 100644 index b61b21d5..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/LeakyReluOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class LeakyReluOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsLeakyReluOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = LeakyReluOptions() - x.Init(buf, n + offset) - return x - - # LeakyReluOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # LeakyReluOptions - def Alpha(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) - return 0.0 - -def LeakyReluOptionsStart(builder): builder.StartObject(1) -def LeakyReluOptionsAddAlpha(builder, alpha): builder.PrependFloat32Slot(0, alpha, 0.0) -def LeakyReluOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LessEqualOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/LessEqualOptions.py deleted file mode 100644 index d49b7289..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/LessEqualOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class LessEqualOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsLessEqualOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = LessEqualOptions() - x.Init(buf, n + offset) - return x - - # LessEqualOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def LessEqualOptionsStart(builder): builder.StartObject(0) -def LessEqualOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LessOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/LessOptions.py deleted file mode 100644 index 469cb0b0..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/LessOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class LessOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsLessOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = LessOptions() - x.Init(buf, n + offset) - return x - - # LessOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def LessOptionsStart(builder): builder.StartObject(0) -def LessOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LocalResponseNormalizationOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/LocalResponseNormalizationOptions.py deleted file mode 100644 index db875603..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/LocalResponseNormalizationOptions.py +++ /dev/null @@ -1,54 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class LocalResponseNormalizationOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsLocalResponseNormalizationOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = LocalResponseNormalizationOptions() - x.Init(buf, n + offset) - return x - - # LocalResponseNormalizationOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # LocalResponseNormalizationOptions - def Radius(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # LocalResponseNormalizationOptions - def Bias(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) - return 0.0 - - # LocalResponseNormalizationOptions - def Alpha(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) - return 0.0 - - # LocalResponseNormalizationOptions - def Beta(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) - return 0.0 - -def LocalResponseNormalizationOptionsStart(builder): builder.StartObject(4) -def LocalResponseNormalizationOptionsAddRadius(builder, radius): builder.PrependInt32Slot(0, radius, 0) -def LocalResponseNormalizationOptionsAddBias(builder, bias): builder.PrependFloat32Slot(1, bias, 0.0) -def LocalResponseNormalizationOptionsAddAlpha(builder, alpha): builder.PrependFloat32Slot(2, alpha, 0.0) -def LocalResponseNormalizationOptionsAddBeta(builder, beta): builder.PrependFloat32Slot(3, beta, 0.0) -def LocalResponseNormalizationOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LogSoftmaxOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/LogSoftmaxOptions.py deleted file mode 100644 index 47893855..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/LogSoftmaxOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class LogSoftmaxOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsLogSoftmaxOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = LogSoftmaxOptions() - x.Init(buf, n + offset) - return x - - # LogSoftmaxOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def LogSoftmaxOptionsStart(builder): builder.StartObject(0) -def LogSoftmaxOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LogicalAndOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/LogicalAndOptions.py deleted file mode 100644 index cee1cdb4..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/LogicalAndOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class LogicalAndOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsLogicalAndOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = LogicalAndOptions() - x.Init(buf, n + offset) - return x - - # LogicalAndOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def LogicalAndOptionsStart(builder): builder.StartObject(0) -def LogicalAndOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LogicalNotOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/LogicalNotOptions.py deleted file mode 100644 index 9971450c..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/LogicalNotOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class LogicalNotOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsLogicalNotOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = LogicalNotOptions() - x.Init(buf, n + offset) - return x - - # LogicalNotOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def LogicalNotOptionsStart(builder): builder.StartObject(0) -def LogicalNotOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/LogicalOrOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/LogicalOrOptions.py deleted file mode 100644 index e94a5dec..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/LogicalOrOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class LogicalOrOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsLogicalOrOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = LogicalOrOptions() - x.Init(buf, n + offset) - return x - - # LogicalOrOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def LogicalOrOptionsStart(builder): builder.StartObject(0) -def LogicalOrOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/MatrixDiagOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/MatrixDiagOptions.py deleted file mode 100644 index 0f64e657..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/MatrixDiagOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class MatrixDiagOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsMatrixDiagOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = MatrixDiagOptions() - x.Init(buf, n + offset) - return x - - # MatrixDiagOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def MatrixDiagOptionsStart(builder): builder.StartObject(0) -def MatrixDiagOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/MatrixSetDiagOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/MatrixSetDiagOptions.py deleted file mode 100644 index 14178cf8..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/MatrixSetDiagOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class MatrixSetDiagOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsMatrixSetDiagOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = MatrixSetDiagOptions() - x.Init(buf, n + offset) - return x - - # MatrixSetDiagOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def MatrixSetDiagOptionsStart(builder): builder.StartObject(0) -def MatrixSetDiagOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/MaximumMinimumOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/MaximumMinimumOptions.py deleted file mode 100644 index f0806e2d..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/MaximumMinimumOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class MaximumMinimumOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsMaximumMinimumOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = MaximumMinimumOptions() - x.Init(buf, n + offset) - return x - - # MaximumMinimumOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def MaximumMinimumOptionsStart(builder): builder.StartObject(0) -def MaximumMinimumOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/MirrorPadMode.py b/utensor_cgen/frontend/tflite_flatbuffer/MirrorPadMode.py deleted file mode 100644 index ce7579d2..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/MirrorPadMode.py +++ /dev/null @@ -1,8 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -class MirrorPadMode(object): - REFLECT = 0 - SYMMETRIC = 1 - diff --git a/utensor_cgen/frontend/tflite_flatbuffer/MirrorPadOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/MirrorPadOptions.py deleted file mode 100644 index 254ae217..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/MirrorPadOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class MirrorPadOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsMirrorPadOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = MirrorPadOptions() - x.Init(buf, n + offset) - return x - - # MirrorPadOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # MirrorPadOptions - def Mode(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - -def MirrorPadOptionsStart(builder): builder.StartObject(1) -def MirrorPadOptionsAddMode(builder, mode): builder.PrependInt8Slot(0, mode, 0) -def MirrorPadOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/Model.py b/utensor_cgen/frontend/tflite_flatbuffer/Model.py deleted file mode 100644 index aab841ab..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/Model.py +++ /dev/null @@ -1,128 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class Model(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsModel(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = Model() - x.Init(buf, n + offset) - return x - - # Model - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # Model - def Version(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos) - return 0 - - # Model - def OperatorCodes(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - x = self._tab.Vector(o) - x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 - x = self._tab.Indirect(x) - from .OperatorCode import OperatorCode - obj = OperatorCode() - obj.Init(self._tab.Bytes, x) - return obj - return None - - # Model - def OperatorCodesLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # Model - def Subgraphs(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - x = self._tab.Vector(o) - x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 - x = self._tab.Indirect(x) - from .SubGraph import SubGraph - obj = SubGraph() - obj.Init(self._tab.Bytes, x) - return obj - return None - - # Model - def SubgraphsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # Model - def Description(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.String(o + self._tab.Pos) - return None - - # Model - def Buffers(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - x = self._tab.Vector(o) - x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 - x = self._tab.Indirect(x) - from .Buffer import Buffer - obj = Buffer() - obj.Init(self._tab.Bytes, x) - return obj - return None - - # Model - def BuffersLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # Model - def MetadataBuffer(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # Model - def MetadataBufferAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # Model - def MetadataBufferLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def ModelStart(builder): builder.StartObject(6) -def ModelAddVersion(builder, version): builder.PrependUint32Slot(0, version, 0) -def ModelAddOperatorCodes(builder, operatorCodes): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(operatorCodes), 0) -def ModelStartOperatorCodesVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def ModelAddSubgraphs(builder, subgraphs): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(subgraphs), 0) -def ModelStartSubgraphsVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def ModelAddDescription(builder, description): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(description), 0) -def ModelAddBuffers(builder, buffers): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(buffers), 0) -def ModelStartBuffersVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def ModelAddMetadataBuffer(builder, metadataBuffer): builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(metadataBuffer), 0) -def ModelStartMetadataBufferVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def ModelEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/MulOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/MulOptions.py deleted file mode 100644 index 55b9506f..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/MulOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class MulOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsMulOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = MulOptions() - x.Init(buf, n + offset) - return x - - # MulOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # MulOptions - def FusedActivationFunction(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - -def MulOptionsStart(builder): builder.StartObject(1) -def MulOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) -def MulOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/NegOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/NegOptions.py deleted file mode 100644 index 05d55c26..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/NegOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class NegOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsNegOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = NegOptions() - x.Init(buf, n + offset) - return x - - # NegOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def NegOptionsStart(builder): builder.StartObject(0) -def NegOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/NotEqualOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/NotEqualOptions.py deleted file mode 100644 index 4c511e93..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/NotEqualOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class NotEqualOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsNotEqualOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = NotEqualOptions() - x.Init(buf, n + offset) - return x - - # NotEqualOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def NotEqualOptionsStart(builder): builder.StartObject(0) -def NotEqualOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/OneHotOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/OneHotOptions.py deleted file mode 100644 index 793a3e75..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/OneHotOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class OneHotOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsOneHotOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = OneHotOptions() - x.Init(buf, n + offset) - return x - - # OneHotOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # OneHotOptions - def Axis(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def OneHotOptionsStart(builder): builder.StartObject(1) -def OneHotOptionsAddAxis(builder, axis): builder.PrependInt32Slot(0, axis, 0) -def OneHotOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/Operator.py b/utensor_cgen/frontend/tflite_flatbuffer/Operator.py deleted file mode 100644 index fd237845..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/Operator.py +++ /dev/null @@ -1,153 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class Operator(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsOperator(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = Operator() - x.Init(buf, n + offset) - return x - - # Operator - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # Operator - def OpcodeIndex(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos) - return 0 - - # Operator - def Inputs(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # Operator - def InputsAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # Operator - def InputsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # Operator - def Outputs(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # Operator - def OutputsAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # Operator - def OutputsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # Operator - def BuiltinOptionsType(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Uint8Flags, o + self._tab.Pos) - return 0 - - # Operator - def BuiltinOptions(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - from flatbuffers.table import Table - obj = Table(bytearray(), 0) - self._tab.Union(obj, o) - return obj - return None - - # Operator - def CustomOptions(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Uint8Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1)) - return 0 - - # Operator - def CustomOptionsAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint8Flags, o) - return 0 - - # Operator - def CustomOptionsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # Operator - def CustomOptionsFormat(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - - # Operator - def MutatingVariableInputs(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.BoolFlags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1)) - return 0 - - # Operator - def MutatingVariableInputsAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.BoolFlags, o) - return 0 - - # Operator - def MutatingVariableInputsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def OperatorStart(builder): builder.StartObject(8) -def OperatorAddOpcodeIndex(builder, opcodeIndex): builder.PrependUint32Slot(0, opcodeIndex, 0) -def OperatorAddInputs(builder, inputs): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(inputs), 0) -def OperatorStartInputsVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def OperatorAddOutputs(builder, outputs): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(outputs), 0) -def OperatorStartOutputsVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def OperatorAddBuiltinOptionsType(builder, builtinOptionsType): builder.PrependUint8Slot(3, builtinOptionsType, 0) -def OperatorAddBuiltinOptions(builder, builtinOptions): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(builtinOptions), 0) -def OperatorAddCustomOptions(builder, customOptions): builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(customOptions), 0) -def OperatorStartCustomOptionsVector(builder, numElems): return builder.StartVector(1, numElems, 1) -def OperatorAddCustomOptionsFormat(builder, customOptionsFormat): builder.PrependInt8Slot(6, customOptionsFormat, 0) -def OperatorAddMutatingVariableInputs(builder, mutatingVariableInputs): builder.PrependUOffsetTRelativeSlot(7, flatbuffers.number_types.UOffsetTFlags.py_type(mutatingVariableInputs), 0) -def OperatorStartMutatingVariableInputsVector(builder, numElems): return builder.StartVector(1, numElems, 1) -def OperatorEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/OperatorCode.py b/utensor_cgen/frontend/tflite_flatbuffer/OperatorCode.py deleted file mode 100644 index dd525f53..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/OperatorCode.py +++ /dev/null @@ -1,46 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class OperatorCode(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsOperatorCode(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = OperatorCode() - x.Init(buf, n + offset) - return x - - # OperatorCode - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # OperatorCode - def BuiltinCode(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - - # OperatorCode - def CustomCode(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.String(o + self._tab.Pos) - return None - - # OperatorCode - def Version(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 1 - -def OperatorCodeStart(builder): builder.StartObject(3) -def OperatorCodeAddBuiltinCode(builder, builtinCode): builder.PrependInt8Slot(0, builtinCode, 0) -def OperatorCodeAddCustomCode(builder, customCode): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(customCode), 0) -def OperatorCodeAddVersion(builder, version): builder.PrependInt32Slot(2, version, 1) -def OperatorCodeEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/PackOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/PackOptions.py deleted file mode 100644 index 6a8ee2bb..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/PackOptions.py +++ /dev/null @@ -1,38 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class PackOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsPackOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = PackOptions() - x.Init(buf, n + offset) - return x - - # PackOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # PackOptions - def ValuesCount(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # PackOptions - def Axis(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def PackOptionsStart(builder): builder.StartObject(2) -def PackOptionsAddValuesCount(builder, valuesCount): builder.PrependInt32Slot(0, valuesCount, 0) -def PackOptionsAddAxis(builder, axis): builder.PrependInt32Slot(1, axis, 0) -def PackOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/PadOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/PadOptions.py deleted file mode 100644 index d0833c68..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/PadOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class PadOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsPadOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = PadOptions() - x.Init(buf, n + offset) - return x - - # PadOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def PadOptionsStart(builder): builder.StartObject(0) -def PadOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/PadV2Options.py b/utensor_cgen/frontend/tflite_flatbuffer/PadV2Options.py deleted file mode 100644 index 5ea0d70c..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/PadV2Options.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class PadV2Options(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsPadV2Options(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = PadV2Options() - x.Init(buf, n + offset) - return x - - # PadV2Options - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def PadV2OptionsStart(builder): builder.StartObject(0) -def PadV2OptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/Padding.py b/utensor_cgen/frontend/tflite_flatbuffer/Padding.py deleted file mode 100644 index 6027f4b5..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/Padding.py +++ /dev/null @@ -1,8 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -class Padding(object): - SAME = 0 - VALID = 1 - diff --git a/utensor_cgen/frontend/tflite_flatbuffer/Pool2DOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/Pool2DOptions.py deleted file mode 100644 index b8b9f178..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/Pool2DOptions.py +++ /dev/null @@ -1,70 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class Pool2DOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsPool2DOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = Pool2DOptions() - x.Init(buf, n + offset) - return x - - # Pool2DOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # Pool2DOptions - def Padding(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - - # Pool2DOptions - def StrideW(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # Pool2DOptions - def StrideH(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # Pool2DOptions - def FilterWidth(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # Pool2DOptions - def FilterHeight(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # Pool2DOptions - def FusedActivationFunction(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - -def Pool2DOptionsStart(builder): builder.StartObject(6) -def Pool2DOptionsAddPadding(builder, padding): builder.PrependInt8Slot(0, padding, 0) -def Pool2DOptionsAddStrideW(builder, strideW): builder.PrependInt32Slot(1, strideW, 0) -def Pool2DOptionsAddStrideH(builder, strideH): builder.PrependInt32Slot(2, strideH, 0) -def Pool2DOptionsAddFilterWidth(builder, filterWidth): builder.PrependInt32Slot(3, filterWidth, 0) -def Pool2DOptionsAddFilterHeight(builder, filterHeight): builder.PrependInt32Slot(4, filterHeight, 0) -def Pool2DOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(5, fusedActivationFunction, 0) -def Pool2DOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/PowOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/PowOptions.py deleted file mode 100644 index 666ca488..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/PowOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class PowOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsPowOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = PowOptions() - x.Init(buf, n + offset) - return x - - # PowOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def PowOptionsStart(builder): builder.StartObject(0) -def PowOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/QuantizationDetails.py b/utensor_cgen/frontend/tflite_flatbuffer/QuantizationDetails.py deleted file mode 100644 index 7159efbd..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/QuantizationDetails.py +++ /dev/null @@ -1,8 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -class QuantizationDetails(object): - NONE = 0 - CustomQuantization = 1 - diff --git a/utensor_cgen/frontend/tflite_flatbuffer/QuantizationParameters.py b/utensor_cgen/frontend/tflite_flatbuffer/QuantizationParameters.py deleted file mode 100644 index fcd686cf..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/QuantizationParameters.py +++ /dev/null @@ -1,145 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class QuantizationParameters(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsQuantizationParameters(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = QuantizationParameters() - x.Init(buf, n + offset) - return x - - # QuantizationParameters - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # QuantizationParameters - def Min(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # QuantizationParameters - def MinAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o) - return 0 - - # QuantizationParameters - def MinLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # QuantizationParameters - def Max(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # QuantizationParameters - def MaxAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o) - return 0 - - # QuantizationParameters - def MaxLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # QuantizationParameters - def Scale(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # QuantizationParameters - def ScaleAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o) - return 0 - - # QuantizationParameters - def ScaleLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # QuantizationParameters - def ZeroPoint(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8)) - return 0 - - # QuantizationParameters - def ZeroPointAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o) - return 0 - - # QuantizationParameters - def ZeroPointLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # QuantizationParameters - def DetailsType(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Uint8Flags, o + self._tab.Pos) - return 0 - - # QuantizationParameters - def Details(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - from flatbuffers.table import Table - obj = Table(bytearray(), 0) - self._tab.Union(obj, o) - return obj - return None - - # QuantizationParameters - def QuantizedDimension(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def QuantizationParametersStart(builder): builder.StartObject(7) -def QuantizationParametersAddMin(builder, min): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(min), 0) -def QuantizationParametersStartMinVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def QuantizationParametersAddMax(builder, max): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(max), 0) -def QuantizationParametersStartMaxVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def QuantizationParametersAddScale(builder, scale): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(scale), 0) -def QuantizationParametersStartScaleVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def QuantizationParametersAddZeroPoint(builder, zeroPoint): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(zeroPoint), 0) -def QuantizationParametersStartZeroPointVector(builder, numElems): return builder.StartVector(8, numElems, 8) -def QuantizationParametersAddDetailsType(builder, detailsType): builder.PrependUint8Slot(4, detailsType, 0) -def QuantizationParametersAddDetails(builder, details): builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(details), 0) -def QuantizationParametersAddQuantizedDimension(builder, quantizedDimension): builder.PrependInt32Slot(6, quantizedDimension, 0) -def QuantizationParametersEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/QuantizeOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/QuantizeOptions.py deleted file mode 100644 index 28af8cc9..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/QuantizeOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class QuantizeOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsQuantizeOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = QuantizeOptions() - x.Init(buf, n + offset) - return x - - # QuantizeOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def QuantizeOptionsStart(builder): builder.StartObject(0) -def QuantizeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/RNNOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/RNNOptions.py deleted file mode 100644 index 3cfdb6af..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/RNNOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class RNNOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsRNNOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = RNNOptions() - x.Init(buf, n + offset) - return x - - # RNNOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # RNNOptions - def FusedActivationFunction(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - -def RNNOptionsStart(builder): builder.StartObject(1) -def RNNOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) -def RNNOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/RangeOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/RangeOptions.py deleted file mode 100644 index cb705b57..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/RangeOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class RangeOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsRangeOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = RangeOptions() - x.Init(buf, n + offset) - return x - - # RangeOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def RangeOptionsStart(builder): builder.StartObject(0) -def RangeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/RankOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/RankOptions.py deleted file mode 100644 index 4e4a5ecd..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/RankOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class RankOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsRankOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = RankOptions() - x.Init(buf, n + offset) - return x - - # RankOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def RankOptionsStart(builder): builder.StartObject(0) -def RankOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ReducerOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ReducerOptions.py deleted file mode 100644 index 93bbde17..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/ReducerOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class ReducerOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsReducerOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ReducerOptions() - x.Init(buf, n + offset) - return x - - # ReducerOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ReducerOptions - def KeepDims(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - -def ReducerOptionsStart(builder): builder.StartObject(1) -def ReducerOptionsAddKeepDims(builder, keepDims): builder.PrependBoolSlot(0, keepDims, 0) -def ReducerOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ReshapeOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ReshapeOptions.py deleted file mode 100644 index 157d45d9..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/ReshapeOptions.py +++ /dev/null @@ -1,46 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class ReshapeOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsReshapeOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ReshapeOptions() - x.Init(buf, n + offset) - return x - - # ReshapeOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ReshapeOptions - def NewShape(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # ReshapeOptions - def NewShapeAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # ReshapeOptions - def NewShapeLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def ReshapeOptionsStart(builder): builder.StartObject(1) -def ReshapeOptionsAddNewShape(builder, newShape): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(newShape), 0) -def ReshapeOptionsStartNewShapeVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def ReshapeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ResizeBilinearOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ResizeBilinearOptions.py deleted file mode 100644 index 0d1f9624..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/ResizeBilinearOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class ResizeBilinearOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsResizeBilinearOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ResizeBilinearOptions() - x.Init(buf, n + offset) - return x - - # ResizeBilinearOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ResizeBilinearOptions - def AlignCorners(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - -def ResizeBilinearOptionsStart(builder): builder.StartObject(3) -def ResizeBilinearOptionsAddAlignCorners(builder, alignCorners): builder.PrependBoolSlot(2, alignCorners, 0) -def ResizeBilinearOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ResizeNearestNeighborOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ResizeNearestNeighborOptions.py deleted file mode 100644 index 4b166e95..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/ResizeNearestNeighborOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class ResizeNearestNeighborOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsResizeNearestNeighborOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ResizeNearestNeighborOptions() - x.Init(buf, n + offset) - return x - - # ResizeNearestNeighborOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ResizeNearestNeighborOptions - def AlignCorners(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - -def ResizeNearestNeighborOptionsStart(builder): builder.StartObject(1) -def ResizeNearestNeighborOptionsAddAlignCorners(builder, alignCorners): builder.PrependBoolSlot(0, alignCorners, 0) -def ResizeNearestNeighborOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ReverseSequenceOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ReverseSequenceOptions.py deleted file mode 100644 index cbaf96db..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/ReverseSequenceOptions.py +++ /dev/null @@ -1,38 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class ReverseSequenceOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsReverseSequenceOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ReverseSequenceOptions() - x.Init(buf, n + offset) - return x - - # ReverseSequenceOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ReverseSequenceOptions - def SeqDim(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # ReverseSequenceOptions - def BatchDim(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def ReverseSequenceOptionsStart(builder): builder.StartObject(2) -def ReverseSequenceOptionsAddSeqDim(builder, seqDim): builder.PrependInt32Slot(0, seqDim, 0) -def ReverseSequenceOptionsAddBatchDim(builder, batchDim): builder.PrependInt32Slot(1, batchDim, 0) -def ReverseSequenceOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ReverseV2Options.py b/utensor_cgen/frontend/tflite_flatbuffer/ReverseV2Options.py deleted file mode 100644 index dbac9362..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/ReverseV2Options.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class ReverseV2Options(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsReverseV2Options(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ReverseV2Options() - x.Init(buf, n + offset) - return x - - # ReverseV2Options - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def ReverseV2OptionsStart(builder): builder.StartObject(0) -def ReverseV2OptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SVDFOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SVDFOptions.py deleted file mode 100644 index 6f391db1..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/SVDFOptions.py +++ /dev/null @@ -1,38 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class SVDFOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsSVDFOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = SVDFOptions() - x.Init(buf, n + offset) - return x - - # SVDFOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # SVDFOptions - def Rank(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # SVDFOptions - def FusedActivationFunction(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - -def SVDFOptionsStart(builder): builder.StartObject(2) -def SVDFOptionsAddRank(builder, rank): builder.PrependInt32Slot(0, rank, 0) -def SVDFOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(1, fusedActivationFunction, 0) -def SVDFOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SelectOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SelectOptions.py deleted file mode 100644 index d67daf36..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/SelectOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class SelectOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsSelectOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = SelectOptions() - x.Init(buf, n + offset) - return x - - # SelectOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def SelectOptionsStart(builder): builder.StartObject(0) -def SelectOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SequenceRNNOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SequenceRNNOptions.py deleted file mode 100644 index 74a4954a..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/SequenceRNNOptions.py +++ /dev/null @@ -1,38 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class SequenceRNNOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsSequenceRNNOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = SequenceRNNOptions() - x.Init(buf, n + offset) - return x - - # SequenceRNNOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # SequenceRNNOptions - def TimeMajor(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - - # SequenceRNNOptions - def FusedActivationFunction(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - -def SequenceRNNOptionsStart(builder): builder.StartObject(2) -def SequenceRNNOptionsAddTimeMajor(builder, timeMajor): builder.PrependBoolSlot(0, timeMajor, 0) -def SequenceRNNOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(1, fusedActivationFunction, 0) -def SequenceRNNOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ShapeOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ShapeOptions.py deleted file mode 100644 index 2d24c05f..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/ShapeOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class ShapeOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsShapeOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ShapeOptions() - x.Init(buf, n + offset) - return x - - # ShapeOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ShapeOptions - def OutType(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - -def ShapeOptionsStart(builder): builder.StartObject(1) -def ShapeOptionsAddOutType(builder, outType): builder.PrependInt8Slot(0, outType, 0) -def ShapeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SkipGramOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SkipGramOptions.py deleted file mode 100644 index 0e8bdc1d..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/SkipGramOptions.py +++ /dev/null @@ -1,46 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class SkipGramOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsSkipGramOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = SkipGramOptions() - x.Init(buf, n + offset) - return x - - # SkipGramOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # SkipGramOptions - def NgramSize(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # SkipGramOptions - def MaxSkipSize(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # SkipGramOptions - def IncludeAllNgrams(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - -def SkipGramOptionsStart(builder): builder.StartObject(3) -def SkipGramOptionsAddNgramSize(builder, ngramSize): builder.PrependInt32Slot(0, ngramSize, 0) -def SkipGramOptionsAddMaxSkipSize(builder, maxSkipSize): builder.PrependInt32Slot(1, maxSkipSize, 0) -def SkipGramOptionsAddIncludeAllNgrams(builder, includeAllNgrams): builder.PrependBoolSlot(2, includeAllNgrams, 0) -def SkipGramOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SliceOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SliceOptions.py deleted file mode 100644 index 4b41568d..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/SliceOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class SliceOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsSliceOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = SliceOptions() - x.Init(buf, n + offset) - return x - - # SliceOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def SliceOptionsStart(builder): builder.StartObject(0) -def SliceOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SoftmaxOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SoftmaxOptions.py deleted file mode 100644 index a7168534..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/SoftmaxOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class SoftmaxOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsSoftmaxOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = SoftmaxOptions() - x.Init(buf, n + offset) - return x - - # SoftmaxOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # SoftmaxOptions - def Beta(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) - return 0.0 - -def SoftmaxOptionsStart(builder): builder.StartObject(1) -def SoftmaxOptionsAddBeta(builder, beta): builder.PrependFloat32Slot(0, beta, 0.0) -def SoftmaxOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SpaceToBatchNDOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SpaceToBatchNDOptions.py deleted file mode 100644 index b61ef96f..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/SpaceToBatchNDOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class SpaceToBatchNDOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsSpaceToBatchNDOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = SpaceToBatchNDOptions() - x.Init(buf, n + offset) - return x - - # SpaceToBatchNDOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def SpaceToBatchNDOptionsStart(builder): builder.StartObject(0) -def SpaceToBatchNDOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SpaceToDepthOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SpaceToDepthOptions.py deleted file mode 100644 index d571174a..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/SpaceToDepthOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class SpaceToDepthOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsSpaceToDepthOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = SpaceToDepthOptions() - x.Init(buf, n + offset) - return x - - # SpaceToDepthOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # SpaceToDepthOptions - def BlockSize(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def SpaceToDepthOptionsStart(builder): builder.StartObject(1) -def SpaceToDepthOptionsAddBlockSize(builder, blockSize): builder.PrependInt32Slot(0, blockSize, 0) -def SpaceToDepthOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SparseToDenseOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SparseToDenseOptions.py deleted file mode 100644 index 826eee08..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/SparseToDenseOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class SparseToDenseOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsSparseToDenseOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = SparseToDenseOptions() - x.Init(buf, n + offset) - return x - - # SparseToDenseOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # SparseToDenseOptions - def ValidateIndices(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - -def SparseToDenseOptionsStart(builder): builder.StartObject(1) -def SparseToDenseOptionsAddValidateIndices(builder, validateIndices): builder.PrependBoolSlot(0, validateIndices, 0) -def SparseToDenseOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SplitOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SplitOptions.py deleted file mode 100644 index 3207525b..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/SplitOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class SplitOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsSplitOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = SplitOptions() - x.Init(buf, n + offset) - return x - - # SplitOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # SplitOptions - def NumSplits(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def SplitOptionsStart(builder): builder.StartObject(1) -def SplitOptionsAddNumSplits(builder, numSplits): builder.PrependInt32Slot(0, numSplits, 0) -def SplitOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SplitVOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SplitVOptions.py deleted file mode 100644 index 418959de..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/SplitVOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class SplitVOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsSplitVOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = SplitVOptions() - x.Init(buf, n + offset) - return x - - # SplitVOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # SplitVOptions - def NumSplits(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def SplitVOptionsStart(builder): builder.StartObject(1) -def SplitVOptionsAddNumSplits(builder, numSplits): builder.PrependInt32Slot(0, numSplits, 0) -def SplitVOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SquareOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SquareOptions.py deleted file mode 100644 index 56633f6a..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/SquareOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class SquareOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsSquareOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = SquareOptions() - x.Init(buf, n + offset) - return x - - # SquareOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def SquareOptionsStart(builder): builder.StartObject(0) -def SquareOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SquaredDifferenceOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SquaredDifferenceOptions.py deleted file mode 100644 index 906855d1..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/SquaredDifferenceOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class SquaredDifferenceOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsSquaredDifferenceOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = SquaredDifferenceOptions() - x.Init(buf, n + offset) - return x - - # SquaredDifferenceOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def SquaredDifferenceOptionsStart(builder): builder.StartObject(0) -def SquaredDifferenceOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SqueezeOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SqueezeOptions.py deleted file mode 100644 index 25b294dc..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/SqueezeOptions.py +++ /dev/null @@ -1,46 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class SqueezeOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsSqueezeOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = SqueezeOptions() - x.Init(buf, n + offset) - return x - - # SqueezeOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # SqueezeOptions - def SqueezeDims(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # SqueezeOptions - def SqueezeDimsAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # SqueezeOptions - def SqueezeDimsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def SqueezeOptionsStart(builder): builder.StartObject(1) -def SqueezeOptionsAddSqueezeDims(builder, squeezeDims): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(squeezeDims), 0) -def SqueezeOptionsStartSqueezeDimsVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def SqueezeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/StridedSliceOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/StridedSliceOptions.py deleted file mode 100644 index 3bbb36b8..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/StridedSliceOptions.py +++ /dev/null @@ -1,62 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class StridedSliceOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsStridedSliceOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = StridedSliceOptions() - x.Init(buf, n + offset) - return x - - # StridedSliceOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # StridedSliceOptions - def BeginMask(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # StridedSliceOptions - def EndMask(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # StridedSliceOptions - def EllipsisMask(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # StridedSliceOptions - def NewAxisMask(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # StridedSliceOptions - def ShrinkAxisMask(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def StridedSliceOptionsStart(builder): builder.StartObject(5) -def StridedSliceOptionsAddBeginMask(builder, beginMask): builder.PrependInt32Slot(0, beginMask, 0) -def StridedSliceOptionsAddEndMask(builder, endMask): builder.PrependInt32Slot(1, endMask, 0) -def StridedSliceOptionsAddEllipsisMask(builder, ellipsisMask): builder.PrependInt32Slot(2, ellipsisMask, 0) -def StridedSliceOptionsAddNewAxisMask(builder, newAxisMask): builder.PrependInt32Slot(3, newAxisMask, 0) -def StridedSliceOptionsAddShrinkAxisMask(builder, shrinkAxisMask): builder.PrependInt32Slot(4, shrinkAxisMask, 0) -def StridedSliceOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SubGraph.py b/utensor_cgen/frontend/tflite_flatbuffer/SubGraph.py deleted file mode 100644 index eaa42fac..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/SubGraph.py +++ /dev/null @@ -1,122 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class SubGraph(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsSubGraph(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = SubGraph() - x.Init(buf, n + offset) - return x - - # SubGraph - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # SubGraph - def Tensors(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - x = self._tab.Vector(o) - x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 - x = self._tab.Indirect(x) - from .Tensor import Tensor - obj = Tensor() - obj.Init(self._tab.Bytes, x) - return obj - return None - - # SubGraph - def TensorsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # SubGraph - def Inputs(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # SubGraph - def InputsAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # SubGraph - def InputsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # SubGraph - def Outputs(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # SubGraph - def OutputsAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # SubGraph - def OutputsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # SubGraph - def Operators(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - x = self._tab.Vector(o) - x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 - x = self._tab.Indirect(x) - from .Operator import Operator - obj = Operator() - obj.Init(self._tab.Bytes, x) - return obj - return None - - # SubGraph - def OperatorsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # SubGraph - def Name(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return self._tab.String(o + self._tab.Pos) - return None - -def SubGraphStart(builder): builder.StartObject(5) -def SubGraphAddTensors(builder, tensors): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(tensors), 0) -def SubGraphStartTensorsVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def SubGraphAddInputs(builder, inputs): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(inputs), 0) -def SubGraphStartInputsVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def SubGraphAddOutputs(builder, outputs): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(outputs), 0) -def SubGraphStartOutputsVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def SubGraphAddOperators(builder, operators): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(operators), 0) -def SubGraphStartOperatorsVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def SubGraphAddName(builder, name): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0) -def SubGraphEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/SubOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/SubOptions.py deleted file mode 100644 index eccd7aba..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/SubOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class SubOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsSubOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = SubOptions() - x.Init(buf, n + offset) - return x - - # SubOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # SubOptions - def FusedActivationFunction(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - -def SubOptionsStart(builder): builder.StartObject(1) -def SubOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) -def SubOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/Tensor.py b/utensor_cgen/frontend/tflite_flatbuffer/Tensor.py deleted file mode 100644 index 9b5b8f87..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/Tensor.py +++ /dev/null @@ -1,90 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class Tensor(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsTensor(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = Tensor() - x.Init(buf, n + offset) - return x - - # Tensor - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # Tensor - def Shape(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # Tensor - def ShapeAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # Tensor - def ShapeLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # Tensor - def Type(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - - # Tensor - def Buffer(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos) - return 0 - - # Tensor - def Name(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.String(o + self._tab.Pos) - return None - - # Tensor - def Quantization(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - x = self._tab.Indirect(o + self._tab.Pos) - from .QuantizationParameters import QuantizationParameters - obj = QuantizationParameters() - obj.Init(self._tab.Bytes, x) - return obj - return None - - # Tensor - def IsVariable(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - -def TensorStart(builder): builder.StartObject(6) -def TensorAddShape(builder, shape): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(shape), 0) -def TensorStartShapeVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TensorAddType(builder, type): builder.PrependInt8Slot(1, type, 0) -def TensorAddBuffer(builder, buffer): builder.PrependUint32Slot(2, buffer, 0) -def TensorAddName(builder, name): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0) -def TensorAddQuantization(builder, quantization): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(quantization), 0) -def TensorAddIsVariable(builder, isVariable): builder.PrependBoolSlot(5, isVariable, 0) -def TensorEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/TensorType.py b/utensor_cgen/frontend/tflite_flatbuffer/TensorType.py deleted file mode 100644 index 2455835a..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/TensorType.py +++ /dev/null @@ -1,16 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -class TensorType(object): - FLOAT32 = 0 - FLOAT16 = 1 - INT32 = 2 - UINT8 = 3 - INT64 = 4 - STRING = 5 - BOOL = 6 - INT16 = 7 - COMPLEX64 = 8 - INT8 = 9 - diff --git a/utensor_cgen/frontend/tflite_flatbuffer/TileOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/TileOptions.py deleted file mode 100644 index ec8396dc..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/TileOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class TileOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsTileOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = TileOptions() - x.Init(buf, n + offset) - return x - - # TileOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def TileOptionsStart(builder): builder.StartObject(0) -def TileOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/TopKV2Options.py b/utensor_cgen/frontend/tflite_flatbuffer/TopKV2Options.py deleted file mode 100644 index ccd51033..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/TopKV2Options.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class TopKV2Options(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsTopKV2Options(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = TopKV2Options() - x.Init(buf, n + offset) - return x - - # TopKV2Options - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def TopKV2OptionsStart(builder): builder.StartObject(0) -def TopKV2OptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/TransposeConvOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/TransposeConvOptions.py deleted file mode 100644 index 423571c8..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/TransposeConvOptions.py +++ /dev/null @@ -1,46 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class TransposeConvOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsTransposeConvOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = TransposeConvOptions() - x.Init(buf, n + offset) - return x - - # TransposeConvOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # TransposeConvOptions - def Padding(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - - # TransposeConvOptions - def StrideW(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # TransposeConvOptions - def StrideH(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def TransposeConvOptionsStart(builder): builder.StartObject(3) -def TransposeConvOptionsAddPadding(builder, padding): builder.PrependInt8Slot(0, padding, 0) -def TransposeConvOptionsAddStrideW(builder, strideW): builder.PrependInt32Slot(1, strideW, 0) -def TransposeConvOptionsAddStrideH(builder, strideH): builder.PrependInt32Slot(2, strideH, 0) -def TransposeConvOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/TransposeOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/TransposeOptions.py deleted file mode 100644 index 42c596d9..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/TransposeOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class TransposeOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsTransposeOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = TransposeOptions() - x.Init(buf, n + offset) - return x - - # TransposeOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def TransposeOptionsStart(builder): builder.StartObject(0) -def TransposeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/UnidirectionalSequenceLSTMOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/UnidirectionalSequenceLSTMOptions.py deleted file mode 100644 index 1b0c112c..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/UnidirectionalSequenceLSTMOptions.py +++ /dev/null @@ -1,54 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class UnidirectionalSequenceLSTMOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsUnidirectionalSequenceLSTMOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = UnidirectionalSequenceLSTMOptions() - x.Init(buf, n + offset) - return x - - # UnidirectionalSequenceLSTMOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # UnidirectionalSequenceLSTMOptions - def FusedActivationFunction(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 0 - - # UnidirectionalSequenceLSTMOptions - def CellClip(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) - return 0.0 - - # UnidirectionalSequenceLSTMOptions - def ProjClip(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) - return 0.0 - - # UnidirectionalSequenceLSTMOptions - def TimeMajor(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - -def UnidirectionalSequenceLSTMOptionsStart(builder): builder.StartObject(4) -def UnidirectionalSequenceLSTMOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0) -def UnidirectionalSequenceLSTMOptionsAddCellClip(builder, cellClip): builder.PrependFloat32Slot(1, cellClip, 0.0) -def UnidirectionalSequenceLSTMOptionsAddProjClip(builder, projClip): builder.PrependFloat32Slot(2, projClip, 0.0) -def UnidirectionalSequenceLSTMOptionsAddTimeMajor(builder, timeMajor): builder.PrependBoolSlot(3, timeMajor, 0) -def UnidirectionalSequenceLSTMOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/UniqueOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/UniqueOptions.py deleted file mode 100644 index 841c6977..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/UniqueOptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class UniqueOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsUniqueOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = UniqueOptions() - x.Init(buf, n + offset) - return x - - # UniqueOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # UniqueOptions - def IdxOutType(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) - return 2 - -def UniqueOptionsStart(builder): builder.StartObject(1) -def UniqueOptionsAddIdxOutType(builder, idxOutType): builder.PrependInt8Slot(0, idxOutType, 2) -def UniqueOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/UnpackOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/UnpackOptions.py deleted file mode 100644 index eed40193..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/UnpackOptions.py +++ /dev/null @@ -1,38 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class UnpackOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsUnpackOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = UnpackOptions() - x.Init(buf, n + offset) - return x - - # UnpackOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # UnpackOptions - def Num(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # UnpackOptions - def Axis(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def UnpackOptionsStart(builder): builder.StartObject(2) -def UnpackOptionsAddNum(builder, num): builder.PrependInt32Slot(0, num, 0) -def UnpackOptionsAddAxis(builder, axis): builder.PrependInt32Slot(1, axis, 0) -def UnpackOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/WhereOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/WhereOptions.py deleted file mode 100644 index ab69f6aa..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/WhereOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class WhereOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsWhereOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = WhereOptions() - x.Init(buf, n + offset) - return x - - # WhereOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def WhereOptionsStart(builder): builder.StartObject(0) -def WhereOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/ZerosLikeOptions.py b/utensor_cgen/frontend/tflite_flatbuffer/ZerosLikeOptions.py deleted file mode 100644 index e6aa9639..00000000 --- a/utensor_cgen/frontend/tflite_flatbuffer/ZerosLikeOptions.py +++ /dev/null @@ -1,22 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# namespace: tflite - -import flatbuffers - -class ZerosLikeOptions(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsZerosLikeOptions(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ZerosLikeOptions() - x.Init(buf, n + offset) - return x - - # ZerosLikeOptions - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - -def ZerosLikeOptionsStart(builder): builder.StartObject(0) -def ZerosLikeOptionsEnd(builder): return builder.EndObject() diff --git a/utensor_cgen/frontend/tflite_flatbuffer/__init__.py b/utensor_cgen/third_party/__init__.py similarity index 100% rename from utensor_cgen/frontend/tflite_flatbuffer/__init__.py rename to utensor_cgen/third_party/__init__.py From 31b9f50b6d7eda4e8fd8f71a5c795e0d0c82de79 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Wed, 6 May 2020 16:43:01 +0800 Subject: [PATCH 117/117] update tests --- .../test_tflm/test_export}/__init__.py | 0 .../test_tflm/test_export}/conftest.py | 17 +++++++---------- .../test_tflm/test_export}/test_write.py | 0 3 files changed, 7 insertions(+), 10 deletions(-) rename tests/{tflm/tflite_export => test_transformer/test_tflm/test_export}/__init__.py (100%) rename tests/{tflm/tflite_export => test_transformer/test_tflm/test_export}/conftest.py (87%) rename tests/{tflm/tflite_export => test_transformer/test_tflm/test_export}/test_write.py (100%) diff --git a/tests/tflm/tflite_export/__init__.py b/tests/test_transformer/test_tflm/test_export/__init__.py similarity index 100% rename from tests/tflm/tflite_export/__init__.py rename to tests/test_transformer/test_tflm/test_export/__init__.py diff --git a/tests/tflm/tflite_export/conftest.py b/tests/test_transformer/test_tflm/test_export/conftest.py similarity index 87% rename from tests/tflm/tflite_export/conftest.py rename to tests/test_transformer/test_tflm/test_export/conftest.py index dd5bc005..54b11e71 100644 --- a/tests/tflm/tflite_export/conftest.py +++ b/tests/test_transformer/test_tflm/test_export/conftest.py @@ -1,17 +1,14 @@ import numpy as np from pytest import fixture -import tensorflow as tf -from utensor_cgen.ir import TensorInfo, OperationInfo, uTensorGraph -from utensor_cgen.ir.converter import (AttrValueConverter, DataTypeConverter, - GenericTensorConverterMixin) -from utensor_cgen.utils import prune_graph, topologic_order_graph -from utensor_cgen.backend.operators import OperatorFactory, _Operator -from utensor_cgen.matcher import OpEqualityDelegate, _morphism +from utensor_cgen.backend.utensor.code_generator.legacy._operators import ( + OperatorFactory, _Operator, uTensorOpEqualityDelegate) +from utensor_cgen.ir import OperationInfo, TensorInfo, uTensorGraph +from utensor_cgen.ir.converter import AttrValueConverter, DataTypeConverter @OperatorFactory.register -@OpEqualityDelegate.is_associative( +@uTensorOpEqualityDelegate.is_associative( permutations=((0, 1), (1, 0)) ) class _TFLM_AddOperator(_Operator): @@ -51,7 +48,7 @@ def build_op_info(cls, ugraph, name, tensor_x, tensor_y, **kwargs): ) }, ugraph=ugraph, - backend=kwargs.get('backend', 'TFLM') + lib_name=kwargs.get('lib_name', 'tflite') ) @@ -93,7 +90,7 @@ def build_op_info(cls, ugraph, name, tensor_x, tensor_w, tensor_b, **kwargs): ) }, ugraph=ugraph, - backend=kwargs.get('backend', 'TFLM') + lib_name=kwargs.get('lib_name', 'tflite') ) @fixture(name='hybrid_quant_output') diff --git a/tests/tflm/tflite_export/test_write.py b/tests/test_transformer/test_tflm/test_export/test_write.py similarity index 100% rename from tests/tflm/tflite_export/test_write.py rename to tests/test_transformer/test_tflm/test_export/test_write.py

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/_images/convert_example.png :alt: convert-example +.. |mlp-alloc| image:: /_images/mlp_alloc.png + :alt: mlp-alloc +.. |mlp-alloc-graph| image:: /_images/mlp_alloc_graph.png + :alt: mlp-alloc-graph + :width: 300 + :height: 450 diff --git a/tests/deep_cnn/.gitignore b/tests/deep_cnn/.gitignore index 1269488f..9b64c748 100644 --- a/tests/deep_cnn/.gitignore +++ b/tests/deep_cnn/.gitignore @@ -1 +1,2 @@ data +*.toml \ No newline at end of file From 9fb54f74d7254c0b70a4789ee7c2ff56c73fbdef Mon Sep 17 00:00:00 2001 From: kazami Date: Wed, 19 Feb 2020 16:54:01 +0800 Subject: [PATCH 043/117] use backendpart to refactor the offline memory planner --- tests/test_backend/test_utensor.py | 11 + utensor_cgen/backend/utensor/__init__.py | 1 + utensor_cgen/backend/utensor/_graph_lower.py | 212 ++++++++++++++++++- 3 files changed, 222 insertions(+), 2 deletions(-) diff --git a/tests/test_backend/test_utensor.py b/tests/test_backend/test_utensor.py index aa41b5d8..7510a8bc 100644 --- a/tests/test_backend/test_utensor.py +++ b/tests/test_backend/test_utensor.py @@ -17,3 +17,14 @@ def test_legacy_utensor(mlp_ugraph): }, } }).apply(mlp_ugraph) + + +def test_offlinememory(mlp_ugraph): + from utensor_cgen.backend.utensor import uTensorOfflineMemoryPlanner + + uTensorOfflineMemoryPlanner(config={ + 'size_float': 4, + 'size_int': 4, + 'size_uint8_t': 1 + } + ).apply(mlp_ugraph) \ No newline at end of file diff --git a/utensor_cgen/backend/utensor/__init__.py b/utensor_cgen/backend/utensor/__init__.py index ed85033c..12a91fb5 100644 --- a/utensor_cgen/backend/utensor/__init__.py +++ b/utensor_cgen/backend/utensor/__init__.py @@ -1 +1,2 @@ from ._backend_impl import uTensorBackend +from ._graph_lower import uTensorOfflineMemoryPlanner diff --git a/utensor_cgen/backend/utensor/_graph_lower.py b/utensor_cgen/backend/utensor/_graph_lower.py index 95f51b4e..57a90b92 100644 --- a/utensor_cgen/backend/utensor/_graph_lower.py +++ b/utensor_cgen/backend/utensor/_graph_lower.py @@ -1,10 +1,10 @@ -import logging from copy import deepcopy from utensor_cgen.backend.base import BackendPart +from utensor_cgen.backend.utensor.snippets._types import NP_TYPES_MAP +from utensor_cgen.logger import logger from utensor_cgen.utils import class_property -logger = logging.getLogger(__name__) class uTensorGraphLowerBase(BackendPart): TARGET = 'utensor' @@ -55,3 +55,211 @@ def handle_tensorflow(self, ugraph): @class_property def default_config(cls): return {} + + +class uTensorOfflineMemoryPlanner(BackendPart): + + TARGET = 'utensor' + PART = 'offlinememory' + + + def __init__( + self, + buff_size=100000, #1k bytes + unit_size=4, + config + ): + self.buff_size = buff_size + self.unit_size = unit_size + final_config = Configuration(self.default_config, config) + self._type = {} + self._type[np.dtype(tf.float32.as_numpy_dtype)] = final_config['size_float'] + self._type[np.dtype(tf.int32.as_numpy_dtype)] = final_config['size_int'] + self._type[np.dtype(tf.quint8.as_numpy_dtype)] = final_config['size_uint8_t'] + self._type[np.dtype(tf.qint8.as_numpy_dtype)] = final_config['size_uint8_t'] + self._type[np.dtype(tf.qint32.as_numpy_dtype)] = final_config['size_int'] + + def apply(self, ugraph): + new_ugraph = deepcopy(ugraph) + new_ugraph.setup_data_manager({self.DATA_NAME: " "}) + # use_def_table: dict, tensor_name -> {'start': op_idx, 'end': op_idx} + use_def_table = self._create_resource_table(new_ugraph) + allocate_table = dict() + allocate_success = self.allocate_graph(new_ugraph, allocate_table, use_def_table, self.buff_size, self.unit_size) + + if allocate_success: + for node_name in new_ugraph.topo_order: + in_t_infos = new_ugraph.ops_info[node_name].input_tensors + for in_o in in_t_infos: + if in_o.name in allocate_table: + new_ugraph.data_manager.address = (in_o.name, allocate_table[in_o.name]['offsetstart']) + out_t_infos = new_ugraph.ops_info[node_name].output_tensors + for out_o in out_t_infos: + if out_o.name in allocate_table: + new_ugraph.data_manager.address = (out_o.name, allocate_table[out_o.name]['offsetstart']) + return new_ugraph + return ugraph + + def _query_offset_fromallocate_table(self, allocate_table, start, end): + new_start = start + new_end = end + for key in allocate_table: + if allocate_table[key]['offsetstart'] >= start and allocate_table[key]['offsetend'] <= end: + continue + elif allocate_table[key]['offsetstart'] <= start and allocate_table[key]['offsetend'] >= start: + new_start = allocate_table[key]['offsetstart'] + if allocate_table[key]['offsetend'] >= end: + new_end = max(new_end, allocate_table[key]['offsetend']) + else: + new_end = max(end, new_end) + elif allocate_table[key]['offsetstart'] >= start and allocate_table[key]['offsetend'] >= start: + if allocate_table[key]['offsetend'] >= end: + new_end = max(new_end, allocate_table[key]['offsetend']) + else: + new_end = max(end, new_end) + return new_start, new_end + + def _query_time_fromallocate_table(self, allocate_table, start, end): + time_start = start + time_end = end + for key in allocate_table: + if allocate_table[key]['start'] >= start and allocate_table[key]['end'] <= end: + continue + elif allocate_table[key]['start'] <= start and allocate_table[key]['end'] >= start: + if allocate_table[key]['end'] >= end: + time_end = max(time_end, allocate_table[key]['end']) + else: + time_end = max(end, time_end) + elif allocate_table[key]['start'] >= start and allocate_table[key]['end'] >= start: + if allocate_table[key]['end'] >= end: + time_end = max(time_end, allocate_table[key]['end']) + else: + time_end = max(end, time_end) + return time_start, time_end + + def _query_result(self, allocate_table, offset, length, timestart, timeend): + for key in allocate_table: + mem_occupied = ( + (allocate_table[key]['offsetstart'] >= offset and allocate_table[key]['offsetstart'] <= offset + length) or + (allocate_table[key]['offsetstart'] <= offset and allocate_table[key]['offsetend'] >= offset) + ) + life_span_occupied = ( + (allocate_table[key]['start'] >= timestart and allocate_table[key]['start'] <= timeend) or + (allocate_table[key]['start'] <= timestart and allocate_table[key]['end'] >= timestart) + ) + if mem_occupied and life_span_occupied: + return True + return False + + def allocate_tensor(self, tensors, tensor_index, allocate_table, use_def_table, buffer_size, unit_size): + if tensor_index == len(tensors): + return True + if tensors[tensor_index].name in allocate_table: + return self.allocate_tensor(tensors, tensor_index + 1, allocate_table, use_def_table, buffer_size, unit_size) + + tensor = tensors[tensor_index] + candidates = self._get_candidates(allocate_table, use_def_table, buffer_size, unit_size, tensor) + if not candidates: + return False + success = False + for candidate in candidates: + self._update_allocation_table(allocate_table, use_def_table, tensor, candidate, candidate + tensor.size) + success = self.allocate_tensor(tensors, tensor_index + 1, allocate_table, use_def_table, buffer_size, unit_size) + if success: + break + else: + self._remove_allocate_table(allocate_table, tensor) + return success + + def allocate_graph(self, ugraph, allocate_table, use_def_table, buffer_size, unit_size): + tensors = [] + + for node_name in ugraph.topo_order: + in_t_infos = [ + tensor + for tensor in ugraph.ops_info[node_name].input_tensors + if tensor.op.op_type != 'Inline' + ] + out_t_infos = [ + tensor + for tensor in ugraph.ops_info[node_name].output_tensors + if tensor.op.op_type != 'Inline' + ] + tensors.extend(in_t_infos) + tensors.extend(out_t_infos) + + succ = self.allocate_tensor(tensors, 0, allocate_table, use_def_table, buffer_size, unit_size) + return succ + + def _check(self, allocate_table, use_def_table, tensor, tensor_offset_start, tensor_offset_end): + valid = False + timestart = use_def_table[tensor.name]['start'] + timeend = use_def_table[tensor.name]['end'] + offset, length = self._query_offset_fromallocate_table(allocate_table, tensor_offset_start, tensor_offset_end) + timestart, timeend = self._query_time_fromallocate_table(allocate_table, timestart, timeend) + occupied = self._query_result(allocate_table, offset, length, timestart, timeend) + if not occupied: + valid = True + return valid + + def _get_candidates(self, allocate_table, use_def_table, buffer_size, unit_size, in_o): + ret = [] + for i in range(0, buffer_size, unit_size): + if self._check(allocate_table, use_def_table, in_o, i, i + in_o.size * self._type[in_o.dtype]): + ret.append(i) + return ret + + def _update_allocation_table( + self, + allocate_table, + use_def_table, + tensor, + offset_start, + offset_end + ): + time_start = use_def_table[tensor.name]['start'] + time_end = use_def_table[tensor.name]['end'] + attribute = dict() + attribute['start'] = time_start + attribute['end'] = time_end + attribute['offsetstart'] = offset_start + attribute['offsetend'] = offset_end + allocate_table[tensor.name] = attribute + return allocate_table + + def _remove_allocate_table(self, allocate_table, tensor): + del allocate_table[tensor.name] + + def _create_resource_table(self, ugraph): + resource_table = dict() + len_map = { + op_name: idx + for idx, op_name in enumerate(ugraph.topo_order) + } + for node_name in ugraph.topo_order: + for tensor_info in ugraph.ops_info[node_name].input_tensors: + if tensor_info.name not in resource_table: + lifetime = dict() + lifetime['start'] = len_map[node_name] + lifetime['end'] = len_map[node_name] + resource_table[tensor_info.name] = lifetime + resource_table[tensor_info.name]['end']= len_map[node_name] + + for outtensor in ugraph.ops_info[node_name].output_tensors: + if outtensor.name not in resource_table: + lifetime = dict() + lifetime['start'] = len_map[node_name] + lifetime['end'] = len_map[node_name] + resource_table[outtensor.name] = lifetime + + return resource_table + + + @class_property + def default_config(cls): + config = {} + config['size_float'] = 4 + config['size_int'] = 4 + config['size_uint8_t'] = 1 + + return config From 416f21aec279e649c57ecc8dd077a5f0b5caa69a Mon Sep 17 00:00:00 2001 From: dboyliao Date: Wed, 4 Mar 2020 22:37:41 +0800 Subject: [PATCH 044/117] fix bugs and update tests (exclude slow test) --- .circleci/config.yml | 2 +- tests/test_backend/test_utensor.py | 6 +- utensor_cgen/backend/base.py | 8 +-- utensor_cgen/backend/utensor/_graph_lower.py | 26 ++++----- utensor_cgen/ir/base.py | 5 ++ utensor_cgen/ir/instr.py | 59 ++++++++++++++++++++ 6 files changed, 86 insertions(+), 20 deletions(-) create mode 100644 utensor_cgen/ir/instr.py diff --git a/.circleci/config.yml b/.circleci/config.yml index dcb2f03f..db736b73 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -9,7 +9,7 @@ jobs: - run: pip3 install . - run: name: Test - command: pytest -vv tests + command: pytest -vv tests -m 'not slow_test' #- run: genhtml main_coverage.info --output-directory /tmp/coverage #- store_test_results: # path: /tmp/test_results diff --git a/tests/test_backend/test_utensor.py b/tests/test_backend/test_utensor.py index 7510a8bc..d9d0bcc1 100644 --- a/tests/test_backend/test_utensor.py +++ b/tests/test_backend/test_utensor.py @@ -1,5 +1,8 @@ import os +import pytest + + def test_legacy_utensor(mlp_ugraph): from utensor_cgen.backend.utensor import uTensorBackend @@ -19,6 +22,7 @@ def test_legacy_utensor(mlp_ugraph): }).apply(mlp_ugraph) +@pytest.mark.slow_test def test_offlinememory(mlp_ugraph): from utensor_cgen.backend.utensor import uTensorOfflineMemoryPlanner @@ -27,4 +31,4 @@ def test_offlinememory(mlp_ugraph): 'size_int': 4, 'size_uint8_t': 1 } - ).apply(mlp_ugraph) \ No newline at end of file + ).apply(mlp_ugraph) diff --git a/utensor_cgen/backend/base.py b/utensor_cgen/backend/base.py index ed74618a..cf5ac0ce 100644 --- a/utensor_cgen/backend/base.py +++ b/utensor_cgen/backend/base.py @@ -1,7 +1,5 @@ -from utensor_cgen.utils import ( - MUST_OVERWRITEN, class_property, - parse_toml, Configuration, -) +from utensor_cgen.utils import (MUST_OVERWRITEN, Configuration, class_property, + parse_toml) class _BackendBase(object): @@ -24,7 +22,7 @@ def apply(self, ugraph): @class_property def default_config(cls): - return NotImplementedError('All backends should overwrite default config') + raise NotImplementedError('All backends should overwrite default config') def __call__(self, *args, **kwargs): return self.apply(*args, **kwargs) diff --git a/utensor_cgen/backend/utensor/_graph_lower.py b/utensor_cgen/backend/utensor/_graph_lower.py index 57a90b92..1447c59a 100644 --- a/utensor_cgen/backend/utensor/_graph_lower.py +++ b/utensor_cgen/backend/utensor/_graph_lower.py @@ -1,9 +1,11 @@ from copy import deepcopy +import numpy as np +import tensorflow as tf from utensor_cgen.backend.base import BackendPart from utensor_cgen.backend.utensor.snippets._types import NP_TYPES_MAP from utensor_cgen.logger import logger -from utensor_cgen.utils import class_property +from utensor_cgen.utils import Configuration, class_property class uTensorGraphLowerBase(BackendPart): @@ -58,16 +60,15 @@ def default_config(cls): class uTensorOfflineMemoryPlanner(BackendPart): - TARGET = 'utensor' PART = 'offlinememory' - + DATA_NAME = 'address' def __init__( self, + config, buff_size=100000, #1k bytes unit_size=4, - config ): self.buff_size = buff_size self.unit_size = unit_size @@ -254,12 +255,11 @@ def _create_resource_table(self, ugraph): return resource_table - - @class_property - def default_config(cls): - config = {} - config['size_float'] = 4 - config['size_int'] = 4 - config['size_uint8_t'] = 1 - - return config + @class_property + def default_config(cls): + config = {} + config['size_float'] = 4 + config['size_int'] = 4 + config['size_uint8_t'] = 1 + + return config diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index 1c674de0..638f0328 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -21,6 +21,7 @@ from .converter import AttrValueConverter, ConverterDispatcher from .graph_builder import uTensorGraphBuilderMixin +from .instr import DataManager __all__ = [ 'TensorInfo', 'OperationInfo', @@ -616,6 +617,10 @@ def ops(self): topologic_order_graph(self) return [self.ops_info[name] for name in self.topo_order] + def setup_data_manager(self, datas): + manager = DataManager(datas) + self.data_manager = manager + def unsafe_merge_into(self, other_ugraph): """ Merge this graph with other given graph (unsafe) diff --git a/utensor_cgen/ir/instr.py b/utensor_cgen/ir/instr.py new file mode 100644 index 00000000..b452cd4c --- /dev/null +++ b/utensor_cgen/ir/instr.py @@ -0,0 +1,59 @@ +class MemoryAllocation(object): + _store_name = "address" + + def __init__(self): + self._address_map = {} + + def __setattr__(self, attr, value): + if attr == '_address_map': + return super(MemoryAllocation, self).__setattr__(attr, value) + self._address_map[attr] = value + + def __getattr__(self, att): + return self._address_map[att] + + def __deepcopy__(self, memo): + new_obj = MemoryAllocation() + new_obj._address_map = {k: v for k, v in self._address_map.items()} + return new_obj + +class DataManager(object): + + InfoCenterMap = { + MemoryAllocation._store_name: MemoryAllocation + } + + def __init__(self, datas): + self.StorageCenter = {} + for data_name in datas: + data_cls = self.InfoCenterMap.get(data_name, None) + if data_cls is None: + raise ValueError("Unknown transformation method: {}".format(data_name)) + datastorage = data_cls() + self.StorageCenter.update({data_name: datastorage}) + + @classmethod + def register_datamap(cls, data_cls, overwrite=False): + cls.InfoCenterMap[data_cls.name] = data_cls + + def __getattr__(self, attr): + if attr == 'StorageCenter': + raise AttributeError('StorageCenter') + elif attr.startswith('__'): + return super(DataManager, self).__getattr__(attr) + cls_instance = self.StorageCenter[attr] + return cls_instance + + def __setattr__(self, attr, value): + if attr == 'StorageCenter': + return super(DataManager, self).__setattr__(attr, value) + cls_instance = self.StorageCenter[attr] + k, v = value + cls_instance.__setattr__(k, v) + + def group(self, tensor): + ret = {} + for cls_object in self.StorageCenter.values(): + ans = cls_object.__getattr__(tensor) + ret.update({cls_object._store_name: ans}) + return ret From b0ecd725027dd579c6b9bab20acb5f93712c6ae2 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Thu, 5 Mar 2020 14:45:18 +0800 Subject: [PATCH 045/117] make graph_lower a submodule --- .../backend/utensor/_graph_lower/__init__.py | 2 + .../_mem_alloc.py} | 51 ----------------- .../backend/utensor/_graph_lower/_op_lower.py | 56 +++++++++++++++++++ 3 files changed, 58 insertions(+), 51 deletions(-) create mode 100644 utensor_cgen/backend/utensor/_graph_lower/__init__.py rename utensor_cgen/backend/utensor/{_graph_lower.py => _graph_lower/_mem_alloc.py} (86%) create mode 100644 utensor_cgen/backend/utensor/_graph_lower/_op_lower.py diff --git a/utensor_cgen/backend/utensor/_graph_lower/__init__.py b/utensor_cgen/backend/utensor/_graph_lower/__init__.py new file mode 100644 index 00000000..47767c38 --- /dev/null +++ b/utensor_cgen/backend/utensor/_graph_lower/__init__.py @@ -0,0 +1,2 @@ +from ._mem_alloc import uTensorOfflineMemoryPlanner +from ._op_lower import uTensorLegacyGraphLower, uTensorRearchGraphLower diff --git a/utensor_cgen/backend/utensor/_graph_lower.py b/utensor_cgen/backend/utensor/_graph_lower/_mem_alloc.py similarity index 86% rename from utensor_cgen/backend/utensor/_graph_lower.py rename to utensor_cgen/backend/utensor/_graph_lower/_mem_alloc.py index 1447c59a..ba99431c 100644 --- a/utensor_cgen/backend/utensor/_graph_lower.py +++ b/utensor_cgen/backend/utensor/_graph_lower/_mem_alloc.py @@ -8,57 +8,6 @@ from utensor_cgen.utils import Configuration, class_property -class uTensorGraphLowerBase(BackendPart): - TARGET = 'utensor' - - def handle_default(self, ugraph): - logger.warning('fall back to default graph lowering (do nothing)') - return ugraph - - def get_handler(self, ugraph): - handler = getattr(self, 'handle_{}'.format(ugraph.lib_name), self.handle_default) - return handler - - def apply(self, ugraph): - handler = self.get_handler(ugraph) - return handler(ugraph) - -class uTensorLegacyGraphLower(uTensorGraphLowerBase): - PART = 'legacy_graph_lower' - - def handle_tensorflow(self, ugraph): - return ugraph - - @class_property - def default_config(cls): - return {} - - -class uTensorRearchGraphLower(uTensorGraphLowerBase): - PART = 'rearch_graph_lower' - - class OptypeRenameManager(object): - NAME_MAP = { - 'Add': 'AddOperator', - 'Conv2D': 'ConvOperator', - 'MatMul': 'MatrixMultOperator' - } - - @classmethod - def get_new_optype(cls, op_type): - return cls.NAME_MAP.get(op_type, op_type) - - def handle_tensorflow(self, ugraph): - new_ugraph = deepcopy(ugraph) - for op_info in new_ugraph.ops_info.values(): - op_info.op_type = self.OptypeRenameManager.get_new_optype(op_info.op_type) - return new_ugraph - - @class_property - def default_config(cls): - return {} - - class uTensorOfflineMemoryPlanner(BackendPart): TARGET = 'utensor' PART = 'offlinememory' diff --git a/utensor_cgen/backend/utensor/_graph_lower/_op_lower.py b/utensor_cgen/backend/utensor/_graph_lower/_op_lower.py new file mode 100644 index 00000000..54ef5e2d --- /dev/null +++ b/utensor_cgen/backend/utensor/_graph_lower/_op_lower.py @@ -0,0 +1,56 @@ +from copy import deepcopy + +from utensor_cgen.backend.base import BackendPart +from utensor_cgen.logger import logger +from utensor_cgen.utils import class_property + + +class uTensorGraphLowerBase(BackendPart): + TARGET = 'utensor' + + def handle_default(self, ugraph): + logger.warning('fall back to default graph lowering (do nothing)') + return ugraph + + def get_handler(self, ugraph): + handler = getattr(self, 'handle_{}'.format(ugraph.lib_name), self.handle_default) + return handler + + def apply(self, ugraph): + handler = self.get_handler(ugraph) + return handler(ugraph) + +class uTensorLegacyGraphLower(uTensorGraphLowerBase): + PART = 'legacy_graph_lower' + + def handle_tensorflow(self, ugraph): + return ugraph + + @class_property + def default_config(cls): + return {} + + +class uTensorRearchGraphLower(uTensorGraphLowerBase): + PART = 'rearch_graph_lower' + + class OptypeRenameManager(object): + NAME_MAP = { + 'Add': 'AddOperator', + 'Conv2D': 'ConvOperator', + 'MatMul': 'MatrixMultOperator' + } + + @classmethod + def get_new_optype(cls, op_type): + return cls.NAME_MAP.get(op_type, op_type) + + def handle_tensorflow(self, ugraph): + new_ugraph = deepcopy(ugraph) + for op_info in new_ugraph.ops_info.values(): + op_info.op_type = self.OptypeRenameManager.get_new_optype(op_info.op_type) + return new_ugraph + + @class_property + def default_config(cls): + return {} From facaa5d12605b75bc23695c7ecdfd90e3978e75a Mon Sep 17 00:00:00 2001 From: dboyliao Date: Thu, 5 Mar 2020 15:07:45 +0800 Subject: [PATCH 046/117] Decouple graph transformation and code generator Offline memalloc planner backend part --- utensor_cgen/backend/base.py | 20 +- utensor_cgen/backend/graph_lower/__init__.py | 1 + .../backend/graph_lower/alloc_plan.py | 112 +++++ .../graph_lower/generic_graph_lower.py | 456 ++++++++++++++++++ utensor_cgen/backend/transformer.py | 74 +++ utensor_cgen/backend/utensor/__init__.py | 1 - utensor_cgen/backend/utensor/_backend_impl.py | 50 +- .../backend/utensor/_graph_lower/__init__.py | 1 - .../utensor/_graph_lower/_mem_alloc.py | 214 -------- .../code_generator/legacy/_code_generator.py | 77 +-- .../code_generator/legacy/_operators.py | 46 +- .../code_generator/rearch/_code_generator.py | 35 +- .../backend/utensor/snippets/_base.py | 4 + utensor_cgen/ir/misc/graph_viz.py | 57 +-- utensor_cgen/transformer/__init__.py | 1 - utensor_cgen/transformer/mem_alloc.py | 156 ------ utensor_cgen/utils.py | 13 +- 17 files changed, 764 insertions(+), 554 deletions(-) create mode 100644 utensor_cgen/backend/graph_lower/__init__.py create mode 100644 utensor_cgen/backend/graph_lower/alloc_plan.py create mode 100644 utensor_cgen/backend/graph_lower/generic_graph_lower.py create mode 100644 utensor_cgen/backend/transformer.py delete mode 100644 utensor_cgen/backend/utensor/_graph_lower/_mem_alloc.py delete mode 100644 utensor_cgen/transformer/mem_alloc.py diff --git a/utensor_cgen/backend/base.py b/utensor_cgen/backend/base.py index cf5ac0ce..cd764f6b 100644 --- a/utensor_cgen/backend/base.py +++ b/utensor_cgen/backend/base.py @@ -14,11 +14,27 @@ def __new__(cls, config, *args, **kwargs): validator(config, *args, **kwargs) if isinstance(config, dict): config = Configuration(cls.default_config, config) + elif config is None: + config = Configuration(cls.default_config, {}) self._config = config return self def apply(self, ugraph): - raise NotImplementedError('all backend object must overwrite apply method') + """Applying side-effect to ugraph + + Any backend part that implement apply method can create side-effect on given graph, + such as adding attribute or creating files. + """ + raise NotImplementedError('base apply method invoked: %s' % self) + + def transform(self, ugraph): + """Transform Graph + + transform should not create side-effect on the given graph and should + return a new ugraph that is the result of transformation applied to the + given ugraph. + """ + raise NotImplementedError('base transform method invoked: %s' % self) @class_property def default_config(cls): @@ -32,7 +48,7 @@ def config(self): return self._config def _validate_config(self, config): - assert isinstance(config, (dict, Configuration)), \ + assert isinstance(config, (dict, Configuration, type(None))), \ 'expecting {}, get {}'.format(dict, type(config)) diff --git a/utensor_cgen/backend/graph_lower/__init__.py b/utensor_cgen/backend/graph_lower/__init__.py new file mode 100644 index 00000000..47024bed --- /dev/null +++ b/utensor_cgen/backend/graph_lower/__init__.py @@ -0,0 +1 @@ +from .generic_graph_lower import TensorAllocationPlanner diff --git a/utensor_cgen/backend/graph_lower/alloc_plan.py b/utensor_cgen/backend/graph_lower/alloc_plan.py new file mode 100644 index 00000000..1f597ba1 --- /dev/null +++ b/utensor_cgen/backend/graph_lower/alloc_plan.py @@ -0,0 +1,112 @@ +import attr +import six +from attr.validators import instance_of + +from utensor_cgen.logger import logger + +__all__ = ['TimeslotAllocation', 'SpaceAllocation', 'TimeSpaceAllocation', 'AllocationPlan'] + +@attr.s +class TimeslotAllocation(object): + time_slot_start = attr.ib(validator=instance_of(int)) + # if time_slot_end is None, it's a time sapn with no end + time_slot_end = attr.ib(validator=instance_of((int, type(None)))) + + def __attrs_post_init__(self): + assert self.time_slot_start >= 0, \ + 'invalid time_slot_start: %s' % self.time_slot_start + if self.time_slot_end is not None: + assert self.time_slot_end >= self.time_slot_start, \ + 'invalid time_slot_end: %s ~ %s' % (self.time_slot_start, self.time_slot_end) + + def __contains__(self, slot): + assert isinstance(slot, int), 'incorrect slot type: %s' % type(slot) + is_in = self.time_slot_start <= slot + if self.time_slot_end is not None: + is_in = is_in and slot <= self.time_slot_end + return is_in + + +@attr.s +class SpaceAllocation(object): + offset_start = attr.ib(validator=instance_of(int)) + size = attr.ib(validator=instance_of(int)) + offset_end = attr.ib(init=False) + + def __attrs_post_init__(self): + assert self.offset_start >= 0, \ + 'invalid offset_start: %s' % self.offset_start + assert self.size > 0, \ + 'invalid size: %s' % self.size + self.offset_end = self.offset_start + self.size - 1 + + def __contains__(self, offset): + return self.offset_start <= offset <= self.offset_end + + +@attr.s +class TimeSpaceAllocation(object): + entity_name = attr.ib(validator=instance_of(six.string_types)) + _time_alloc = attr.ib(validator=instance_of(TimeslotAllocation), repr=False) + _space_alloc = attr.ib(validator=instance_of(SpaceAllocation), repr=False) + time_slot_start = attr.ib(init=False) + time_slot_end = attr.ib(init=False) + offset_start = attr.ib(init=False) + offset_end = attr.ib(init=False) + size = attr.ib(init=False) + + def __attrs_post_init__(self): + self.time_slot_start = self._time_alloc.time_slot_start + self.time_slot_end = self._time_alloc.time_slot_end + self.offset_start = self._space_alloc.offset_start + self.offset_end = self._space_alloc.offset_end + self.size = self._space_alloc.size + + @classmethod + def init(cls, entity_name, time_slot_start, time_slot_end, offset_start, size): + time_alloc = TimeslotAllocation(time_slot_start, time_slot_end) + space_alloc = SpaceAllocation(offset_start, size) + return cls( + entity_name=entity_name, + time_alloc=time_alloc, + space_alloc=space_alloc + ) + + def is_alive_in_timeslot(self, time_slot): + return time_slot in self._time_alloc + + def is_occupied(self, offset): + return offset in self._space_alloc + + +class AllocationPlan(object): + + def __init__(self, allocs, total_size): + for alloc in allocs: + if not isinstance(alloc, TimeSpaceAllocation): + raise ValueError( + 'expecting value of {} of type {}, get {}'.format(k, TimeSpaceAllocation, type(v)) + ) + self.plan = {alloc.entity_name: alloc for alloc in allocs} + self.total_size = total_size + + def __setitem__(self, entity_name, alloc): + if not isinstance(alloc, TimeSpaceAllocation): + raise ValueError( + 'the value should be of type {}, get {}'.format(TimeSpaceAllocation, type(alloc)) + ) + if entity_name in self._plan: + logger.warning( + 'duplicate entity_name detected: {}'.format(entity_name) + ) + self._plan[entity_name] = alloc + + def __getitem__(self, entity_name): + if entity_name not in self._plan: + raise KeyError('%s not found' % entity_name) + + def __delitem__(self, entity_name): + del self._plan[entity_name] + + def __getattr__(self, attr_name): + return getattr(self._plan, attr_name) diff --git a/utensor_cgen/backend/graph_lower/generic_graph_lower.py b/utensor_cgen/backend/graph_lower/generic_graph_lower.py new file mode 100644 index 00000000..b8c00172 --- /dev/null +++ b/utensor_cgen/backend/graph_lower/generic_graph_lower.py @@ -0,0 +1,456 @@ +r"""Generic Graph Lowering + +All graph lower in this module should be generic. +That is, it should be able to apply to any graph (i.e target independent) +""" +import os +import pickle +from collections import Counter, defaultdict, namedtuple +from copy import deepcopy +from itertools import chain, combinations, product +from math import ceil, log10 + +import numpy as np +import tensorflow as tf +from ortools.sat.python import cp_model +from utensor_cgen.backend.base import BackendPart +from utensor_cgen.backend.utensor.snippets._types import NP_TYPES_MAP +from utensor_cgen.logger import logger +from utensor_cgen.utils import Configuration, class_property, timed + +from .alloc_plan import (AllocationPlan, SpaceAllocation, TimeslotAllocation, + TimeSpaceAllocation) + +__all__ = ['TensorAllocationPlanner', 'BrutalForceMemoryPlanner'] + +_VarMemorySpan = namedtuple('_VarMemorySpan', ['start', 'end', 'size']) + +class TopoOrderTensorTimeslotPlanner(BackendPart): + TARGET = 'generic' + PART = 'tensor_timeslot_planner' + KWARGS_NAMESCOPE = '_tensor_timeslot' + + @timed + def apply(self, ugraph): + ref_cnts = Counter() + life_span = defaultdict(lambda: [None, None]) + for op_info in ugraph.ops_info.values(): + for tensor in op_info.input_tensors: + ref_cnts[tensor.name] += 1 + for time_slot, op_name in enumerate(ugraph.topo_order): + op_info = ugraph.ops_info[op_name] + for tensor in op_info.output_tensors: + life_span[tensor.name][0] = time_slot + for tensor in op_info.input_tensors: + ref_cnts[tensor.name] -= 1 + if ref_cnts[tensor.name] == 0: + life_span[tensor.name][1] = time_slot + time_alloc_plan = {} + for tensor_name, (start, end) in life_span.items(): + time_alloc = TimeslotAllocation( + time_slot_start=start, + time_slot_end=end + ) + time_alloc_plan[tensor_name] = time_alloc + logger.info('topo ordered tensor life span analysis done') + ugraph.attributes[self.KWARGS_NAMESCOPE] = time_alloc_plan + + @class_property + def default_config(cls): + return {} + + +class TensorAllocationPlanner(BackendPart): + """ + Offline Tensor Allocation Optimizer + + analyse tensor lifetime and find the optimal allocation offset in the managed memory pool + + :param max_pool_size: the size of the memory pool (default: 1 KB) + :type max_pool_size: int + + :param include_inputs: include the input tensors (Placeholder) in the allocation plan + :type include_inputs: bool + + :param out_fname: the file name of memory allocation visualization (will NOT generate if not given) + :type out_fname: str + + :param aesthetic_kwargs: the keyword arguments controlling the aesthetic of the visualization of allocation plan + :type aesthetic_kwargs: dict + """ + TARGET = 'generic' + PART = 'tensor_alloc_planner' + KWARGS_NAMESCOPE = "_tensor_alloc" + + def __init__(self, config): + self.max_pool_size = self.config['max_pool_size'] + self.include_inputs = self.config['include_inputs'] + self.out_fname = self.config['out_fname'] + if self.out_fname == 'None': + self.out_fname = None + aesthetic_kwargs = self.config['aesthetic_kwargs'] + if aesthetic_kwargs['figsize'] == 'None': + aesthetic_kwargs['figsize'] = None + self.aesthetic_kwargs = aesthetic_kwargs.to_dict() + self.dtype_size_map = self._parse_dtype_size_map(self.config) + + def apply(self, ugraph): + time_alloc_plan = ugraph.attributes.get( + TopoOrderTensorTimeslotPlanner.KWARGS_NAMESCOPE + ) + if time_alloc_plan is None: + TopoOrderTensorTimeslotPlanner(config={}).apply(ugraph) + time_alloc_plan = ugraph.attributes[TopoOrderTensorTimeslotPlanner.KWARGS_NAMESCOPE] + tensors_to_ignore = set( + chain(*[ + op.output_tensors + for op in ugraph.get_ops_by_type('Inline') + ]) + ) + if not self.include_inputs: + tensors_to_ignore.update( + chain(*[ + op.output_tensors + for op in ugraph.get_ops_by_type('Placeholder') + ]) + ) + tensors_to_schedule = set() + nonoverlap_map = defaultdict(set) + for time_slot, op_name in enumerate(ugraph.topo_order): + op_info = ugraph.ops_info[op_name] + # ignore inline ops + if op_info.op_type == 'Inline': + continue + # ignore placeholders if not included + if not self.include_inputs and op_info.op_type == 'Placeholder': + continue + # all output tensor should not overlap with tensors that's still alive + for out_tensor, known_tensor in product(op_info.output_tensors, tensors_to_schedule): + time_alloc = time_alloc_plan[known_tensor.name] + if time_slot in time_alloc: + nonoverlap_map[out_tensor].add(known_tensor) + # all output tensors should not overlap with each other + for out_tensor1, out_tensor2 in combinations(op_info.output_tensors, 2): + nonoverlap_map[out_tensor1].add(out_tensor2) + # update tensors to be scheduled + tensors_to_schedule.update(op_info.output_tensors) + space_alloc_plan, opt_mempool_size = self._solve_space_alloc(tensors_to_schedule, nonoverlap_map) + time_space_allocs = [] + if space_alloc_plan: + for tensor_name in space_alloc_plan: + space_alloc = space_alloc_plan[tensor_name] + time_alloc = time_alloc_plan[tensor_name] + time_space_allocs.append( + TimeSpaceAllocation( + tensor_name, + time_alloc=time_alloc, + space_alloc=space_alloc + ) + ) + ugraph.attributes[self.KWARGS_NAMESCOPE] = AllocationPlan( + allocs=time_space_allocs, + total_size=opt_mempool_size + ) + if self.out_fname: + figs = viz_memalloc(ugraph=ugraph, **self.aesthetic_kwargs) + if len(figs) == 1: + logger.info('saving tensor mem allocation to %s', self.out_fname) + figs[0].savefig(self.out_fname) + else: + num_digits = ceil(log10(len(figs))) + file_format = '{{}}_{{:0{}d}}{{}}'.format(num_digits) + for i, fig in enumerate(figs, 1): + fname, ext = os.path.splitext(self.out_fname) + fname = file_format.format(fname, i, ext) + logger.info('saving tensor mem allocation to %s', fname) + fig.savefig(fname) + with open('{}.pkl'.format(self.out_fname), 'wb') as fid: + pickle.dump(figs, fid) + logger.info('matplotlib figure object dumped (pickle): %s', fid.name) + + @timed + def _solve_space_alloc(self, tensors_to_schedule, nonoverlap_map): + model = cp_model.CpModel() + inter_vars = {} + tensor_allocs = {} + for tensor in tensors_to_schedule: + var_start = model.NewIntVar(0, self.max_pool_size, '{}_start'.format(tensor.name)) + var_end = model.NewIntVar(0, self.max_pool_size, '{}_end'.format(tensor.name)) + size = self._compute_tensor_bytes_size(tensor) + intv_var = model.NewIntervalVar(var_start, size, var_end, '{}_alloc'.format(tensor.name)) + inter_vars[tensor.name] = intv_var + tensor_allocs[tensor.name] = _VarMemorySpan(var_start, var_end, size) + for tensor in tensors_to_schedule: + inter_var = inter_vars[tensor.name] + nonoverlap_vars = [inter_vars[t.name] for t in nonoverlap_map[tensor]] + for other in nonoverlap_vars: + model.AddNoOverlap([inter_var, other]) + var_mempool_size = model.NewIntVar(0, self.max_pool_size, 'mempool_size') + model.AddMaxEquality(var_mempool_size, [alloc.end for alloc in tensor_allocs.values()]) + model.Minimize(var_mempool_size) + + solver = cp_model.CpSolver() + status = solver.Solve(model) + alloc_plan = {} + opt_mempool_size = None + if solver.StatusName(status) == 'OPTIMAL': + opt_mempool_size = solver.Value(var_mempool_size) + for name, alloc in tensor_allocs.items(): + alloc_plan[name] = SpaceAllocation( + offset_start=solver.Value(alloc.start), + size=alloc.size + ) + logger.info('optimal tensor allocation plan solved, memory span: %i bytes', opt_mempool_size) + else: + logger.info('tensor allocation plan not found, status: %s', solver.StatusName(status)) + return alloc_plan, opt_mempool_size + + def _compute_tensor_bytes_size(self, tensor_info): + size = tensor_info.size + elem_size = self.dtype_size_map.get(tensor_info.dtype, tensor_info.dtype.itemsize) + return elem_size * size + + @classmethod + def _parse_dtype_size_map(cls, config): + dtype_size_map = {} + for dtype_str, size in config['dtype_size_map'].items(): + if dtype_str == 'float': + dtype_size_map[np.dtype('float32')] = size + elif dtype_str == 'uint8_t': + dtype_size_map[np.dtype('uint8')] = size + else: + # TODO: user-defined data types + # https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html + dtype_size_map[np.dtype(dtype_str)] = size + return dtype_size_map + + @class_property + def default_config(cls): + return { + 'max_pool_size': 1024*1024, # 1k bytes + 'include_inputs': False, + 'out_fname': 'None', + 'aesthetic_kwargs': { + 'split_on_large_graph': True, + 'num_tensors_per_split': 20, + 'figsize': 'None', + 'fontsize': 12, + 'lw': 12, + 'rand_seed': 1111 + }, + 'dtype_size_map': { + 'float': 4, + 'double': 8, + 'uint8': 1, + 'int': 4, + 'long': 8, + }, + } + +class BrutalForceMemoryPlanner(BackendPart): + TARGET = 'utensor' + PART = 'offlinememory' + DATA_NAME = 'address' + + def __init__( + self, + config, + buff_size=100000, #1k bytes + unit_size=4, + ): + self.buff_size = buff_size + self.unit_size = unit_size + final_config = Configuration(self.default_config, config) + self._type = {} + self._type[np.dtype(tf.float32.as_numpy_dtype)] = final_config['size_float'] + self._type[np.dtype(tf.int32.as_numpy_dtype)] = final_config['size_int'] + self._type[np.dtype(tf.quint8.as_numpy_dtype)] = final_config['size_uint8_t'] + self._type[np.dtype(tf.qint8.as_numpy_dtype)] = final_config['size_uint8_t'] + self._type[np.dtype(tf.qint32.as_numpy_dtype)] = final_config['size_int'] + + def apply(self, ugraph): + new_ugraph = deepcopy(ugraph) + new_ugraph.setup_data_manager({self.DATA_NAME: " "}) + # use_def_table: dict, tensor_name -> {'start': op_idx, 'end': op_idx} + use_def_table = self._create_resource_table(new_ugraph) + allocate_table = dict() + allocate_success = self.allocate_graph(new_ugraph, allocate_table, use_def_table, self.buff_size, self.unit_size) + + if allocate_success: + for node_name in new_ugraph.topo_order: + in_t_infos = new_ugraph.ops_info[node_name].input_tensors + for in_o in in_t_infos: + if in_o.name in allocate_table: + new_ugraph.data_manager.address = (in_o.name, allocate_table[in_o.name]['offsetstart']) + out_t_infos = new_ugraph.ops_info[node_name].output_tensors + for out_o in out_t_infos: + if out_o.name in allocate_table: + new_ugraph.data_manager.address = (out_o.name, allocate_table[out_o.name]['offsetstart']) + return new_ugraph + return ugraph + + def _query_offset_fromallocate_table(self, allocate_table, start, end): + new_start = start + new_end = end + for key in allocate_table: + if allocate_table[key]['offsetstart'] >= start and allocate_table[key]['offsetend'] <= end: + continue + elif allocate_table[key]['offsetstart'] <= start and allocate_table[key]['offsetend'] >= start: + new_start = allocate_table[key]['offsetstart'] + if allocate_table[key]['offsetend'] >= end: + new_end = max(new_end, allocate_table[key]['offsetend']) + else: + new_end = max(end, new_end) + elif allocate_table[key]['offsetstart'] >= start and allocate_table[key]['offsetend'] >= start: + if allocate_table[key]['offsetend'] >= end: + new_end = max(new_end, allocate_table[key]['offsetend']) + else: + new_end = max(end, new_end) + return new_start, new_end + + def _query_time_fromallocate_table(self, allocate_table, start, end): + time_start = start + time_end = end + for key in allocate_table: + if allocate_table[key]['start'] >= start and allocate_table[key]['end'] <= end: + continue + elif allocate_table[key]['start'] <= start and allocate_table[key]['end'] >= start: + if allocate_table[key]['end'] >= end: + time_end = max(time_end, allocate_table[key]['end']) + else: + time_end = max(end, time_end) + elif allocate_table[key]['start'] >= start and allocate_table[key]['end'] >= start: + if allocate_table[key]['end'] >= end: + time_end = max(time_end, allocate_table[key]['end']) + else: + time_end = max(end, time_end) + return time_start, time_end + + def _query_result(self, allocate_table, offset, length, timestart, timeend): + for key in allocate_table: + mem_occupied = ( + (allocate_table[key]['offsetstart'] >= offset and allocate_table[key]['offsetstart'] <= offset + length) or + (allocate_table[key]['offsetstart'] <= offset and allocate_table[key]['offsetend'] >= offset) + ) + life_span_occupied = ( + (allocate_table[key]['start'] >= timestart and allocate_table[key]['start'] <= timeend) or + (allocate_table[key]['start'] <= timestart and allocate_table[key]['end'] >= timestart) + ) + if mem_occupied and life_span_occupied: + return True + return False + + def allocate_tensor(self, tensors, tensor_index, allocate_table, use_def_table, buffer_size, unit_size): + if tensor_index == len(tensors): + return True + if tensors[tensor_index].name in allocate_table: + return self.allocate_tensor(tensors, tensor_index + 1, allocate_table, use_def_table, buffer_size, unit_size) + + tensor = tensors[tensor_index] + candidates = self._get_candidates(allocate_table, use_def_table, buffer_size, unit_size, tensor) + if not candidates: + return False + success = False + for candidate in candidates: + self._update_allocation_table(allocate_table, use_def_table, tensor, candidate, candidate + tensor.size) + success = self.allocate_tensor(tensors, tensor_index + 1, allocate_table, use_def_table, buffer_size, unit_size) + if success: + break + else: + self._remove_allocate_table(allocate_table, tensor) + return success + + def allocate_graph(self, ugraph, allocate_table, use_def_table, buffer_size, unit_size): + tensors = [] + + for node_name in ugraph.topo_order: + in_t_infos = [ + tensor + for tensor in ugraph.ops_info[node_name].input_tensors + if tensor.op.op_type != 'Inline' + ] + out_t_infos = [ + tensor + for tensor in ugraph.ops_info[node_name].output_tensors + if tensor.op.op_type != 'Inline' + ] + tensors.extend(in_t_infos) + tensors.extend(out_t_infos) + + succ = self.allocate_tensor(tensors, 0, allocate_table, use_def_table, buffer_size, unit_size) + return succ + + def _check(self, allocate_table, use_def_table, tensor, tensor_offset_start, tensor_offset_end): + valid = False + timestart = use_def_table[tensor.name]['start'] + timeend = use_def_table[tensor.name]['end'] + offset, length = self._query_offset_fromallocate_table(allocate_table, tensor_offset_start, tensor_offset_end) + timestart, timeend = self._query_time_fromallocate_table(allocate_table, timestart, timeend) + occupied = self._query_result(allocate_table, offset, length, timestart, timeend) + if not occupied: + valid = True + return valid + + def _get_candidates(self, allocate_table, use_def_table, buffer_size, unit_size, in_o): + ret = [] + for i in range(0, buffer_size, unit_size): + if self._check(allocate_table, use_def_table, in_o, i, i + in_o.size * self._type[in_o.dtype]): + ret.append(i) + return ret + + def _update_allocation_table( + self, + allocate_table, + use_def_table, + tensor, + offset_start, + offset_end + ): + time_start = use_def_table[tensor.name]['start'] + time_end = use_def_table[tensor.name]['end'] + attribute = dict() + attribute['start'] = time_start + attribute['end'] = time_end + attribute['offsetstart'] = offset_start + attribute['offsetend'] = offset_end + allocate_table[tensor.name] = attribute + return allocate_table + + def _remove_allocate_table(self, allocate_table, tensor): + del allocate_table[tensor.name] + + def _create_resource_table(self, ugraph): + resource_table = dict() + len_map = { + op_name: idx + for idx, op_name in enumerate(ugraph.topo_order) + } + for node_name in ugraph.topo_order: + for tensor_info in ugraph.ops_info[node_name].input_tensors: + if tensor_info.name not in resource_table: + lifetime = dict() + lifetime['start'] = len_map[node_name] + lifetime['end'] = len_map[node_name] + resource_table[tensor_info.name] = lifetime + resource_table[tensor_info.name]['end']= len_map[node_name] + + for outtensor in ugraph.ops_info[node_name].output_tensors: + if outtensor.name not in resource_table: + lifetime = dict() + lifetime['start'] = len_map[node_name] + lifetime['end'] = len_map[node_name] + resource_table[outtensor.name] = lifetime + + return resource_table + + @class_property + def default_config(cls): + config = {} + config['size_float'] = 4 + config['size_int'] = 4 + config['size_uint8_t'] = 1 + + return config + +# FIXME: cyclic import +from utensor_cgen.ir.misc.graph_viz import viz_memalloc # isort:skip diff --git a/utensor_cgen/backend/transformer.py b/utensor_cgen/backend/transformer.py new file mode 100644 index 00000000..e77fdd31 --- /dev/null +++ b/utensor_cgen/backend/transformer.py @@ -0,0 +1,74 @@ +import pickle + +from utensor_cgen.logger import logger +from utensor_cgen.transformer.pipeline import TransformerPipeline +from utensor_cgen.utils import class_property + +from .base import BackendPart + +__all__ = ['PipelineTransformer'] + +class PipelineTransformer(BackendPart): + TARGET = 'generic' + PART = 'pipeline_transformer' + + def __init__(self, config): + self.transformer = TransformerPipeline( + methods=self.config['transform_methods'] + ) + self.trans_methods = self.config['transform_methods'] + self.save_graph = self.config['save_graph'] + + def transform(self, ugraph): + logger.info("Transforming graph: %s", ugraph.name) + logger.info("Transform pipeline: %s", ' -> '.join(self.trans_methods)) + self._check_non_quantized(ugraph) + new_ugraph = self.transformer.transform(ugraph) + new_ugraph.name = ugraph.name + logger.info('Graph transormation done') + if self.save_graph: + logger.info('Saving transformed graph') + pkl_fname = "{}_transformed.pkl".format(ugraph.name) + with open(pkl_fname, 'wb') as fid: + pickle.dump(new_ugraph, fid) + logger.info('{} saved'.format(pkl_fname)) + return new_ugraph + + @classmethod + def _check_non_quantized(cls, ugraph): + is_quantized = False + quant_ops = set([ + "Dequantize", "QuantizedMaxPool", + "QuantizeV2", "QuantizedMatMul", + "QuantizedRelu", "QuantizedAdd", + "RequantizationRange", + "Requantize", + "QuantizedReshape", + "QuantizedConv2D" + ]) + for op_info in ugraph.ops_info.values(): + if op_info.op_type in quant_ops: + is_quantized = True + break + if is_quantized: + logger.warning(( + "Expecting non-quantized graph, " + "graph transformation/optimization might not work properly" + )) + + @class_property + def default_config(cls): + return { + 'save_graph': False, + 'transform_methods': [ + "dropout(name_pattern=r'(dropout[_\w\d]*)/.*')", + "linear_reorder", + "quantize", + "conv_pool", + "inline", + "biasAdd", + "remove_id_op", + "fake_gather_v2", + "refcnt", + ] + } diff --git a/utensor_cgen/backend/utensor/__init__.py b/utensor_cgen/backend/utensor/__init__.py index 12a91fb5..ed85033c 100644 --- a/utensor_cgen/backend/utensor/__init__.py +++ b/utensor_cgen/backend/utensor/__init__.py @@ -1,2 +1 @@ from ._backend_impl import uTensorBackend -from ._graph_lower import uTensorOfflineMemoryPlanner diff --git a/utensor_cgen/backend/utensor/_backend_impl.py b/utensor_cgen/backend/utensor/_backend_impl.py index cc5db5b7..2f40d734 100644 --- a/utensor_cgen/backend/utensor/_backend_impl.py +++ b/utensor_cgen/backend/utensor/_backend_impl.py @@ -1,24 +1,31 @@ from utensor_cgen.backend.base import Backend -from utensor_cgen.utils import ( - class_property, parse_toml, - LazyLoader, LazyAttrib, - Configuration, -) +from utensor_cgen.utils import (Configuration, LazyAttrib, LazyLoader, + class_property, parse_toml) code_generator = LazyLoader(submod_name='backend.utensor.code_generator') -_graph_lower = LazyLoader(submod_name='backend.utensor._graph_lower') +transformer = LazyLoader(submod_name='backend.transformer') +_op_lower = LazyLoader(submod_name='backend.utensor._graph_lower._op_lower') +generic_graph_lower = LazyLoader(submod_name='backend.graph_lower') uTensorLegacyCodeGenerator = LazyAttrib(code_generator, 'uTensorLegacyCodeGenerator') uTensorRearchCodeGenerator = LazyAttrib(code_generator, 'uTensorRearchCodeGenerator') -uTensorLegacyGraphLower = LazyAttrib(_graph_lower, 'uTensorLegacyGraphLower') -uTensorRearchGraphLower = LazyAttrib(_graph_lower, 'uTensorRearchGraphLower') +uTensorLegacyGraphLower = LazyAttrib(_op_lower, 'uTensorLegacyGraphLower') +uTensorRearchGraphLower = LazyAttrib(_op_lower, 'uTensorRearchGraphLower') +TensorAllocationPlanner = LazyAttrib(generic_graph_lower, 'TensorAllocationPlanner') +PipelineTransformer = LazyAttrib(transformer, 'PipelineTransformer') -del code_generator, _graph_lower +del code_generator, _op_lower, generic_graph_lower class uTensorBackend(Backend): TARGET = 'utensor' - def __init__(self, config, code_generator=None, graph_lower=None): + def __init__(self, + config, + code_generator=None, + graph_transformer=None, + graph_op_lower=None, + graph_alloc_lower=None, + ): default_config = self.default_config[self.TARGET][self.COMPONENT] config = Configuration(default_config, config.get( self.TARGET, @@ -29,11 +36,16 @@ def __init__(self, config, code_generator=None, graph_lower=None): ) if config['legacy-api']: code_generator = code_generator or uTensorLegacyCodeGenerator(config=config[uTensorLegacyCodeGenerator.PART]) - graph_lower = graph_lower or uTensorLegacyGraphLower(config=config[uTensorLegacyGraphLower.PART]) + graph_op_lower = graph_op_lower or uTensorLegacyGraphLower(config=config[uTensorLegacyGraphLower.PART]) + graph_alloc_lower = graph_alloc_lower or TensorAllocationPlanner(config=config[TensorAllocationPlanner.PART]) else: code_generator = code_generator or uTensorRearchCodeGenerator(config=config[uTensorRearchCodeGenerator.PART]) - graph_lower = graph_lower or uTensorRearchGraphLower(config=config[uTensorRearchGraphLower.PART]) - self._graph_lower = graph_lower + graph_op_lower = graph_op_lower or uTensorRearchGraphLower(config=config[uTensorRearchGraphLower.PART]) + graph_alloc_lower = TensorAllocationPlanner(config=config[TensorAllocationPlanner.PART]) + graph_transformer = graph_transformer or PipelineTransformer(config=config[PipelineTransformer.PART]) + self._graph_op_lower = graph_op_lower + self._graph_transformer = graph_transformer + self._graph_alloc_lower = graph_alloc_lower self._code_generator = code_generator @class_property @@ -42,16 +54,20 @@ def default_config(cls): config[cls.TARGET] = {} config[cls.TARGET][cls.COMPONENT] = {} config[cls.TARGET][cls.COMPONENT]['legacy-api'] = True + config[cls.TARGET][cls.COMPONENT][TensorAllocationPlanner.PART] = TensorAllocationPlanner.default_config config[cls.TARGET][cls.COMPONENT][uTensorLegacyCodeGenerator.PART] = uTensorLegacyCodeGenerator.default_config - config[cls.TARGET][cls.COMPONENT][uTensorRearchCodeGenerator.PART] = uTensorRearchCodeGenerator.default_config config[cls.TARGET][cls.COMPONENT][uTensorLegacyGraphLower.PART] = uTensorLegacyGraphLower.default_config + config[cls.TARGET][cls.COMPONENT][uTensorRearchCodeGenerator.PART] = uTensorRearchCodeGenerator.default_config config[cls.TARGET][cls.COMPONENT][uTensorRearchGraphLower.PART] = uTensorRearchGraphLower.default_config + config[cls.TARGET][cls.COMPONENT][PipelineTransformer.PART] = PipelineTransformer.default_config return config def apply(self, ugraph): - lower_ugraph = self._graph_lower.apply(ugraph) - self._code_generator.apply(lower_ugraph) - return lower_ugraph + self._graph_op_lower.apply(ugraph) + new_ugraph = self._graph_transformer.transform(ugraph) + self._graph_alloc_lower.apply(new_ugraph) + self._code_generator.apply(new_ugraph) + return new_ugraph def __call__(self, ugraph): return self.apply(ugraph) diff --git a/utensor_cgen/backend/utensor/_graph_lower/__init__.py b/utensor_cgen/backend/utensor/_graph_lower/__init__.py index 47767c38..84bb477d 100644 --- a/utensor_cgen/backend/utensor/_graph_lower/__init__.py +++ b/utensor_cgen/backend/utensor/_graph_lower/__init__.py @@ -1,2 +1 @@ -from ._mem_alloc import uTensorOfflineMemoryPlanner from ._op_lower import uTensorLegacyGraphLower, uTensorRearchGraphLower diff --git a/utensor_cgen/backend/utensor/_graph_lower/_mem_alloc.py b/utensor_cgen/backend/utensor/_graph_lower/_mem_alloc.py deleted file mode 100644 index ba99431c..00000000 --- a/utensor_cgen/backend/utensor/_graph_lower/_mem_alloc.py +++ /dev/null @@ -1,214 +0,0 @@ -from copy import deepcopy - -import numpy as np -import tensorflow as tf -from utensor_cgen.backend.base import BackendPart -from utensor_cgen.backend.utensor.snippets._types import NP_TYPES_MAP -from utensor_cgen.logger import logger -from utensor_cgen.utils import Configuration, class_property - - -class uTensorOfflineMemoryPlanner(BackendPart): - TARGET = 'utensor' - PART = 'offlinememory' - DATA_NAME = 'address' - - def __init__( - self, - config, - buff_size=100000, #1k bytes - unit_size=4, - ): - self.buff_size = buff_size - self.unit_size = unit_size - final_config = Configuration(self.default_config, config) - self._type = {} - self._type[np.dtype(tf.float32.as_numpy_dtype)] = final_config['size_float'] - self._type[np.dtype(tf.int32.as_numpy_dtype)] = final_config['size_int'] - self._type[np.dtype(tf.quint8.as_numpy_dtype)] = final_config['size_uint8_t'] - self._type[np.dtype(tf.qint8.as_numpy_dtype)] = final_config['size_uint8_t'] - self._type[np.dtype(tf.qint32.as_numpy_dtype)] = final_config['size_int'] - - def apply(self, ugraph): - new_ugraph = deepcopy(ugraph) - new_ugraph.setup_data_manager({self.DATA_NAME: " "}) - # use_def_table: dict, tensor_name -> {'start': op_idx, 'end': op_idx} - use_def_table = self._create_resource_table(new_ugraph) - allocate_table = dict() - allocate_success = self.allocate_graph(new_ugraph, allocate_table, use_def_table, self.buff_size, self.unit_size) - - if allocate_success: - for node_name in new_ugraph.topo_order: - in_t_infos = new_ugraph.ops_info[node_name].input_tensors - for in_o in in_t_infos: - if in_o.name in allocate_table: - new_ugraph.data_manager.address = (in_o.name, allocate_table[in_o.name]['offsetstart']) - out_t_infos = new_ugraph.ops_info[node_name].output_tensors - for out_o in out_t_infos: - if out_o.name in allocate_table: - new_ugraph.data_manager.address = (out_o.name, allocate_table[out_o.name]['offsetstart']) - return new_ugraph - return ugraph - - def _query_offset_fromallocate_table(self, allocate_table, start, end): - new_start = start - new_end = end - for key in allocate_table: - if allocate_table[key]['offsetstart'] >= start and allocate_table[key]['offsetend'] <= end: - continue - elif allocate_table[key]['offsetstart'] <= start and allocate_table[key]['offsetend'] >= start: - new_start = allocate_table[key]['offsetstart'] - if allocate_table[key]['offsetend'] >= end: - new_end = max(new_end, allocate_table[key]['offsetend']) - else: - new_end = max(end, new_end) - elif allocate_table[key]['offsetstart'] >= start and allocate_table[key]['offsetend'] >= start: - if allocate_table[key]['offsetend'] >= end: - new_end = max(new_end, allocate_table[key]['offsetend']) - else: - new_end = max(end, new_end) - return new_start, new_end - - def _query_time_fromallocate_table(self, allocate_table, start, end): - time_start = start - time_end = end - for key in allocate_table: - if allocate_table[key]['start'] >= start and allocate_table[key]['end'] <= end: - continue - elif allocate_table[key]['start'] <= start and allocate_table[key]['end'] >= start: - if allocate_table[key]['end'] >= end: - time_end = max(time_end, allocate_table[key]['end']) - else: - time_end = max(end, time_end) - elif allocate_table[key]['start'] >= start and allocate_table[key]['end'] >= start: - if allocate_table[key]['end'] >= end: - time_end = max(time_end, allocate_table[key]['end']) - else: - time_end = max(end, time_end) - return time_start, time_end - - def _query_result(self, allocate_table, offset, length, timestart, timeend): - for key in allocate_table: - mem_occupied = ( - (allocate_table[key]['offsetstart'] >= offset and allocate_table[key]['offsetstart'] <= offset + length) or - (allocate_table[key]['offsetstart'] <= offset and allocate_table[key]['offsetend'] >= offset) - ) - life_span_occupied = ( - (allocate_table[key]['start'] >= timestart and allocate_table[key]['start'] <= timeend) or - (allocate_table[key]['start'] <= timestart and allocate_table[key]['end'] >= timestart) - ) - if mem_occupied and life_span_occupied: - return True - return False - - def allocate_tensor(self, tensors, tensor_index, allocate_table, use_def_table, buffer_size, unit_size): - if tensor_index == len(tensors): - return True - if tensors[tensor_index].name in allocate_table: - return self.allocate_tensor(tensors, tensor_index + 1, allocate_table, use_def_table, buffer_size, unit_size) - - tensor = tensors[tensor_index] - candidates = self._get_candidates(allocate_table, use_def_table, buffer_size, unit_size, tensor) - if not candidates: - return False - success = False - for candidate in candidates: - self._update_allocation_table(allocate_table, use_def_table, tensor, candidate, candidate + tensor.size) - success = self.allocate_tensor(tensors, tensor_index + 1, allocate_table, use_def_table, buffer_size, unit_size) - if success: - break - else: - self._remove_allocate_table(allocate_table, tensor) - return success - - def allocate_graph(self, ugraph, allocate_table, use_def_table, buffer_size, unit_size): - tensors = [] - - for node_name in ugraph.topo_order: - in_t_infos = [ - tensor - for tensor in ugraph.ops_info[node_name].input_tensors - if tensor.op.op_type != 'Inline' - ] - out_t_infos = [ - tensor - for tensor in ugraph.ops_info[node_name].output_tensors - if tensor.op.op_type != 'Inline' - ] - tensors.extend(in_t_infos) - tensors.extend(out_t_infos) - - succ = self.allocate_tensor(tensors, 0, allocate_table, use_def_table, buffer_size, unit_size) - return succ - - def _check(self, allocate_table, use_def_table, tensor, tensor_offset_start, tensor_offset_end): - valid = False - timestart = use_def_table[tensor.name]['start'] - timeend = use_def_table[tensor.name]['end'] - offset, length = self._query_offset_fromallocate_table(allocate_table, tensor_offset_start, tensor_offset_end) - timestart, timeend = self._query_time_fromallocate_table(allocate_table, timestart, timeend) - occupied = self._query_result(allocate_table, offset, length, timestart, timeend) - if not occupied: - valid = True - return valid - - def _get_candidates(self, allocate_table, use_def_table, buffer_size, unit_size, in_o): - ret = [] - for i in range(0, buffer_size, unit_size): - if self._check(allocate_table, use_def_table, in_o, i, i + in_o.size * self._type[in_o.dtype]): - ret.append(i) - return ret - - def _update_allocation_table( - self, - allocate_table, - use_def_table, - tensor, - offset_start, - offset_end - ): - time_start = use_def_table[tensor.name]['start'] - time_end = use_def_table[tensor.name]['end'] - attribute = dict() - attribute['start'] = time_start - attribute['end'] = time_end - attribute['offsetstart'] = offset_start - attribute['offsetend'] = offset_end - allocate_table[tensor.name] = attribute - return allocate_table - - def _remove_allocate_table(self, allocate_table, tensor): - del allocate_table[tensor.name] - - def _create_resource_table(self, ugraph): - resource_table = dict() - len_map = { - op_name: idx - for idx, op_name in enumerate(ugraph.topo_order) - } - for node_name in ugraph.topo_order: - for tensor_info in ugraph.ops_info[node_name].input_tensors: - if tensor_info.name not in resource_table: - lifetime = dict() - lifetime['start'] = len_map[node_name] - lifetime['end'] = len_map[node_name] - resource_table[tensor_info.name] = lifetime - resource_table[tensor_info.name]['end']= len_map[node_name] - - for outtensor in ugraph.ops_info[node_name].output_tensors: - if outtensor.name not in resource_table: - lifetime = dict() - lifetime['start'] = len_map[node_name] - lifetime['end'] = len_map[node_name] - resource_table[outtensor.name] = lifetime - - return resource_table - - @class_property - def default_config(cls): - config = {} - config['size_float'] = 4 - config['size_int'] = 4 - config['size_uint8_t'] = 1 - - return config diff --git a/utensor_cgen/backend/utensor/code_generator/legacy/_code_generator.py b/utensor_cgen/backend/utensor/code_generator/legacy/_code_generator.py index 84892809..80650c36 100644 --- a/utensor_cgen/backend/utensor/code_generator/legacy/_code_generator.py +++ b/utensor_cgen/backend/utensor/code_generator/legacy/_code_generator.py @@ -1,23 +1,15 @@ # -*- coding:utf8 -*- import logging import os -import pickle -from tempfile import NamedTemporaryFile -import numpy as np from utensor_cgen.backend.base import BackendPart from utensor_cgen.backend.utensor.snippets.composer import Composer from utensor_cgen.backend.utensor.snippets.legacy import ( CommentSnippet, ContextGlobalArrayContainer, ContextHeaderSnippet, - ContextSnippetsContainer, CreateTensorBinarySnippet, - CreateTensorIdxSnippet) -from utensor_cgen.frontend import FrontendSelector -from utensor_cgen.ir import uTensorGraph + ContextSnippetsContainer) from utensor_cgen.transformer.optimizer import RefCntOptimizer -from utensor_cgen.transformer.pipeline import TransformerPipeline from utensor_cgen.utils import (Configuration, LazyLoader, - NamescopedKWArgsParser, class_property, - parse_toml) + NamescopedKWArgsParser, class_property) from ._operators import OperatorFactory @@ -25,7 +17,7 @@ _logger = logging.getLogger('utensor-cli') tf = LazyLoader('tensorflow') -class uTensorLegacyCodeGenerator(BackendPart, object): +class uTensorLegacyCodeGenerator(BackendPart): TARGET = 'utensor' PART = 'legacy_code_generator' @@ -38,8 +30,6 @@ def __init__(self, config): os.makedirs(self.params_dir) self.embed_data_dir = final_config['embed_params_dir'].rstrip('/') self.model_dir = final_config['model_dir'].rstrip('/') - self.trans_methods = final_config['transform_methods'] - self.save_graph = final_config['save_graph'] self.debug_cmt = final_config['debug_cmt'] @classmethod @@ -64,24 +54,10 @@ def apply(self, ugraph): opFactory = OperatorFactory() - self._check_non_quantized(ugraph) - _logger.info("Transforming graph: %s", ugraph.name) - _logger.info("Transform pipeline: %s", ' -> '.join(self.trans_methods)) - quant_ugraph = self._transform_graph(ugraph, - self.trans_methods) - _logger.info('Graph transormation done') - - if self.save_graph: - _logger.info('Saving transformed graph') - pkl_fname = "quant_{}.pkl".format(ugraph.name) - with open(pkl_fname, 'wb') as fid: - pickle.dump(quant_ugraph, fid) - _logger.info('{} saved'.format(pkl_fname)) - if not os.path.exists(os.path.join(self.params_dir, ugraph.name)): os.makedirs(os.path.join(self.params_dir, ugraph.name)) - for op_id, op_name in enumerate(quant_ugraph.topo_order): - op_info = quant_ugraph.ops_info[op_name] + for op_id, op_name in enumerate(ugraph.topo_order): + op_info = ugraph.ops_info[op_name] op_type = op_info.op_type # TODO: better abstraction for snippet if op_type == "Placeholder": @@ -113,7 +89,7 @@ def apply(self, ugraph): # generate cpp/hpp files if not os.path.exists(self.model_dir): os.makedirs(self.model_dir) - if any([method == 'inline' for method in self.trans_methods]): + if weight_container.snippets: _logger.info("Generate weight file: %s", weight_header_fname) with open(os.path.join(self.model_dir, weight_header_fname), "w") as wf: wf.write('// Auto generated by utensor-cli\n\n') @@ -137,46 +113,5 @@ def default_config(cls): config['params_dir'] = 'data' config['embed_params_dir'] = '/fs/data' config['model_dir'] = 'models' - config['transform_methods'] = [ - "dropout(name_pattern=r'(dropout[_\w\d]*)/.*')", - "linear_reorder", - "quantize", - "conv_pool", - "inline", - "biasAdd", - "remove_id_op", - "fake_gather_v2", - "refcnt", - ] - config['save_graph'] = False config['debug_cmt'] = False return config - - @classmethod - def _check_non_quantized(cls, ugraph): - is_quantized = False - for op_info in ugraph.ops_info.values(): - if op_info.op_type in [ - "Dequantize", "QuantizedMaxPool", - "QuantizeV2", "QuantizedMatMul", - "QuantizedRelu", "QuantizedAdd", - "RequantizationRange", - "Requantize", - "QuantizedReshape", - "QuantizedConv2D" - ]: - is_quantized = True - break - if is_quantized: - _logger.warning(("Expecting non-quantized graph, " - "graph transformation/optimization might not work properly")) - - def _transform_graph(self, ugraph, methods): - pipeline = TransformerPipeline(methods) - return pipeline.transform(ugraph) - - def _tf_load_graph_def(self, pb_fname): - with tf.gfile.FastGFile(pb_fname, 'rb') as fid: - graph_def = tf.GraphDef() - graph_def.ParseFromString(fid.read()) - return graph_def diff --git a/utensor_cgen/backend/utensor/code_generator/legacy/_operators.py b/utensor_cgen/backend/utensor/code_generator/legacy/_operators.py index 3a31791f..e43980e6 100644 --- a/utensor_cgen/backend/utensor/code_generator/legacy/_operators.py +++ b/utensor_cgen/backend/utensor/code_generator/legacy/_operators.py @@ -6,13 +6,13 @@ import idx2numpy as idx2np import numpy as np +from utensor_cgen.backend.graph_lower import TensorAllocationPlanner from utensor_cgen.backend.utensor.snippets.legacy import * # pylint: disable=W0401,W0614 from utensor_cgen.ir import OperationInfo, TensorInfo from utensor_cgen.ir.converter import (AttrValueConverter, DataTypeConverter, GenericTensorConverterMixin) from utensor_cgen.logger import logger from utensor_cgen.matcher import OpEqualityDelegate, _morphism -from utensor_cgen.transformer.mem_alloc import TensorAllocationTransformer from utensor_cgen.transformer.optimizer import RefCntOptimizer from utensor_cgen.utils import LazyLoader, NamescopedKWArgsParser @@ -223,10 +223,10 @@ def __init__(self, op_info, **kwargs): ) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationTransformer.KWARGS_NAMESCOPE) + alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) if alloc_plan is not None: address = [ - alloc_plan.plan[tensor.name].start for tensor in op_info.output_tensors + alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors ] else: address = [] @@ -290,10 +290,10 @@ def __init__(self, op_info, **kwargs): ) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationTransformer.KWARGS_NAMESCOPE) + alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) if alloc_plan is not None: address = [ - alloc_plan.plan[tensor.name].start for tensor in op_info.output_tensors + alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors ] else: address = [] @@ -321,10 +321,10 @@ def __init__(self, op_info, **kwargs): ) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationTransformer.KWARGS_NAMESCOPE) + alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) if alloc_plan is not None: address = [ - alloc_plan.plan[tensor.name].start for tensor in op_info.output_tensors + alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors ] else: address = [] @@ -543,10 +543,10 @@ def __init__(self, op_info, **kwargs): ) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationTransformer.KWARGS_NAMESCOPE) + alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) if alloc_plan is not None: address = [ - alloc_plan.plan[tensor.name].start for tensor in op_info.output_tensors + alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors ] else: address = [] @@ -609,10 +609,10 @@ def __init__(self, op_info, **kwargs): ) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationTransformer.KWARGS_NAMESCOPE) + alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) if alloc_plan is not None: address = [ - alloc_plan.plan[tensor.name].start for tensor in op_info.output_tensors + alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors ] else: address = [] @@ -702,10 +702,10 @@ def __init__(self, op_info, **kwargs): ) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationTransformer.KWARGS_NAMESCOPE) + alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) if alloc_plan is not None: address = [ - alloc_plan.plan[tensor.name].start for tensor in op_info.output_tensors + alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors ] else: address = [] @@ -779,10 +779,10 @@ def __init__(self, op_info, **kwargs): ) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationTransformer.KWARGS_NAMESCOPE) + alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) if alloc_plan is not None: address = [ - alloc_plan.plan[tensor.name].start for tensor in op_info.output_tensors + alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors ] else: address = [] @@ -809,10 +809,10 @@ def __init__(self, op_info, **kwargs): ) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationTransformer.KWARGS_NAMESCOPE) + alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) if alloc_plan is not None: address = [ - alloc_plan.plan[tensor.name].start for tensor in op_info.output_tensors + alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors ] else: address = [] @@ -856,9 +856,9 @@ def __init__(self, op_info, **kwargs): op_info.op_attr) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationTransformer.KWARGS_NAMESCOPE) + alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) if alloc_plan is not None: - address = [alloc_plan.plan[tensor.name].start for tensor in op_info.output_tensors] + address = [alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors] else: address = [] self._snippet = RequantizationRangeOpSnippet(inputs, outputs, out_dtype, @@ -881,10 +881,10 @@ def __init__(self, op_info, **kwargs): ) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationTransformer.KWARGS_NAMESCOPE) + alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) if alloc_plan is not None: address = [ - alloc_plan.plan[tensor.name].start for tensor in op_info.output_tensors + alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors ] else: address = [] @@ -909,10 +909,10 @@ def __init__(self, op_info, **kwargs): ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) dtype = op_info.input_tensors[0].dtype - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationTransformer.KWARGS_NAMESCOPE) + alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) if alloc_plan is not None: address = [ - alloc_plan.plan[tensor.name].start for tensor in op_info.output_tensors + alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors ] else: address = [] diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py b/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py index 65c53e1d..90469b8a 100644 --- a/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py +++ b/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py @@ -26,26 +26,19 @@ def __init__(self, config): self.src_fname = final_config['src_fname'] self.header_fname = final_config['header_fname'] self.params_dir = final_config['params_dir'].rstrip('/') - self.trans_methods = final_config['transform_methods'] self.meta_data_pool_size = final_config['meta_data_pool_size'] self.ram_data_pool_size = final_config['ram_data_pool_size'] self.model_dir = final_config['model_dir'].rstrip('/') - self.save_graph = final_config['save_graph'] def apply(self, ugraph): src_fname = self.src_fname if src_fname == 'None': src_fname = '{}.cpp'.format(ugraph.name) - pipeline = TransformerPipeline(self.trans_methods) - new_ugraph = pipeline.transform(ugraph) - if self.save_graph: - with open('transformed_{}.pkl'.format(ugraph.name), 'wb') as fid: - pickle.dump(new_ugraph, fid) # 1. find all ops required ops = set() placeholders = set() tensor_var_map = {} # tensor name -> var name - for op_info in new_ugraph.ops_info.values(): + for op_info in ugraph.ops_info.values(): for tensor in op_info.output_tensors: tensor_var_name = re.sub(r'[:/]', '', tensor.name) tensor_var_map[tensor.name] = tensor_var_name @@ -63,7 +56,7 @@ def apply(self, ugraph): ops_map[op] = op_var_name declare_snippets.append(op.get_declare_snippet(op_var_name)) weight_snippets = [] - for op_info in filter(lambda op_info: op_info.op_type == 'Inline', new_ugraph.ops_info.values()): + for op_info in filter(lambda op_info: op_info.op_type == 'Inline', ugraph.ops_info.values()): tensor = op_info.output_tensors[0] buffer_name = 'data_{}'.format(tensor.name.replace(':', '_').replace('/', '_')) weight_snippets.append( @@ -83,8 +76,8 @@ def apply(self, ugraph): ) # 3. evaluation snippets eval_snippets = [] - for op_name in new_ugraph.topo_order: - op_info = new_ugraph.ops_info[op_name] + for op_name in ugraph.topo_order: + op_info = ugraph.ops_info[op_name] if op_info.op_type in ['Placeholder', 'Inline']: continue op = OperatorFactory.get_opertor(op_info) @@ -94,13 +87,13 @@ def apply(self, ugraph): ) template_vars = {} template_vars['model_name'] = ugraph.name - template_vars['meta_data_pool_size'] = self._compute_meta_data_size(new_ugraph) - template_vars['ram_data_pool_size'] = self._compute_ram_data_size(new_ugraph) + template_vars['meta_data_pool_size'] = self._compute_meta_data_size(ugraph) + template_vars['ram_data_pool_size'] = self._compute_ram_data_size(ugraph) template_vars['placeholders'] = placeholders template_vars['out_tensor_var_names'] = [ tensor_var_map[tensor.name] for tensor in chain(*[ - new_ugraph.ops_info[op_name].output_tensors - for op_name in new_ugraph.output_nodes + ugraph.ops_info[op_name].output_tensors + for op_name in ugraph.output_nodes ]) ] # 4. write files @@ -137,20 +130,8 @@ def default_config(cls): config['header_fname'] = 'None' config['params_dir'] = 'data' config['model_dir'] = 'models' - config['transform_methods'] = [ - 'dropout(name_pattern=r"(dropout[_\w\d]*)/.*")', - # 'linear_reorder', - # 'quantize', - # 'conv_pool', - 'inline', - 'biasAdd', - 'remove_id_op', - 'fake_gather_v2', - # 'refcnt' - ] config['meta_data_pool_size'] = 'auto' config['ram_data_pool_size'] = 'auto' - config['save_graph'] = False return config def _compute_meta_data_size(self, ugraph): diff --git a/utensor_cgen/backend/utensor/snippets/_base.py b/utensor_cgen/backend/utensor/snippets/_base.py index 0d03add4..c21cb721 100644 --- a/utensor_cgen/backend/utensor/snippets/_base.py +++ b/utensor_cgen/backend/utensor/snippets/_base.py @@ -62,6 +62,10 @@ def __init__(self, snippets=None): self._snippets = snippets for snp in self._snippets: self.__headers__.update(snp.headers) + + @property + def snippets(self): + return self._snippets def add_snippet(self, snippet): """Add snippet into containers diff --git a/utensor_cgen/ir/misc/graph_viz.py b/utensor_cgen/ir/misc/graph_viz.py index f4f7efc5..7b18578f 100644 --- a/utensor_cgen/ir/misc/graph_viz.py +++ b/utensor_cgen/ir/misc/graph_viz.py @@ -1,5 +1,3 @@ -import os -from math import ceil, log10 from random import random, seed import matplotlib.pyplot as plt @@ -80,53 +78,32 @@ class _MemallcVisualizer(object): def viz_memalloc( cls, ugraph, - out_fname=None, split_on_large_graph=True, num_tensors_per_split=20, - figsize=None, + figsize=None, fontsize=12, - lw=15, + lw=12, cmap=_cm.BrBG_r, rand_seed=1111 ): seed(rand_seed) - if TensorAllocationTransformer.KWARGS_NAMESCOPE not in ugraph.attributes: + if TensorAllocationPlanner.KWARGS_NAMESCOPE not in ugraph.attributes: logger.info('No tensor allocation plan to visualize: %s', ugraph.name) return plt.Figure() - alloc_plan = ugraph.attributes[TensorAllocationTransformer.KWARGS_NAMESCOPE] - topo_tensors = [] - visited_tensors = set() - for op_name in ugraph.topo_order: - op_info = ugraph.ops_info[op_name] - for tensor in op_info.input_tensors: - if tensor.name in alloc_plan.plan and tensor not in visited_tensors: - topo_tensors.append(tensor) - visited_tensors.add(tensor) - for tensor in ugraph.output_tensors: - if tensor.name in alloc_plan.plan and tensor not in visited_tensors: - topo_tensors.append(tensor) - visited_tensors.add(tensor) - if out_fname: - figs = cls._draw_figs(topo_tensors, alloc_plan, cmap, figsize, fontsize, lw, split_on_large_graph, num_tensors_per_split) - if len(figs) == 1: - logger.info('saving tensor mem allocation to %s', out_fname) - figs[0].savefig(out_fname) - else: - num_digits = ceil(log10(len(figs))) - file_format = '{{}}_{{:0{}d}}{{}}'.format(num_digits) - for i, fig in enumerate(figs, 1): - fname, ext = os.path.splitext(out_fname) - fname = file_format.format(fname, i, ext) - logger.info('saving tensor mem allocation to %s', fname) - fig.savefig(fname) + alloc_plan = ugraph.attributes[TensorAllocationPlanner.KWARGS_NAMESCOPE] + topo_tensors = sorted( + [tensor_name for tensor_name in alloc_plan.plan], + key=lambda tensor_name: alloc_plan.plan[tensor_name].time_slot_start + ) + return cls._draw_figs(topo_tensors, alloc_plan, cmap, figsize, fontsize, lw, split_on_large_graph, num_tensors_per_split) @classmethod def _draw_figs(cls, topo_tensors, alloc_plan, cmap, figsize, fontsize, lw, split_on_large_graph, num_tensors_per_split): - xmins = [alloc_plan.plan[tensor.name].start for tensor in topo_tensors] - xmaxs = [alloc_plan.plan[tensor.name].end for tensor in topo_tensors] + xmins = [alloc_plan.plan[tensor_name].offset_start for tensor_name in topo_tensors] + xmaxs = [alloc_plan.plan[tensor_name].offset_end for tensor_name in topo_tensors] colors = [cmap(random()) for _ in alloc_plan.plan] - labels = [tensor.name for tensor in topo_tensors] - sizes = [alloc_plan.plan[tensor.name].size for tensor in topo_tensors] + labels = topo_tensors[:] + sizes = [alloc_plan.plan[tensor_name].size for tensor_name in topo_tensors] if split_on_large_graph: xmin_chunks = [xmins[i:i+num_tensors_per_split] for i in range(0, len(xmins), num_tensors_per_split)] xmax_chunks = [xmaxs[i:i+num_tensors_per_split] for i in range(0, len(xmaxs), num_tensors_per_split)] @@ -137,15 +114,15 @@ def _draw_figs(cls, topo_tensors, alloc_plan, cmap, figsize, fontsize, lw, split xmin_chunks = [xmins] xmax_chunks = [xmaxs] color_chunks = [colors] - label_chunks = [labe] + label_chunks = [labels] size_chunks = [sizes] figs = [] - for i, (xmins, xmaxs, colors, sizes) in enumerate(zip(xmin_chunks, xmax_chunks, color_chunks, size_chunks)): + for i, (xmins, xmaxs, colors, labels, sizes) in enumerate(zip(xmin_chunks, xmax_chunks, color_chunks, label_chunks, size_chunks)): fig, _ = plt.subplots(1, 1) ys = [len(xmins)-i for i in range(len(xmins))] for y, xmin, xmax, color, size in zip(ys, xmins, xmaxs, colors, sizes): plt.hlines(y, xmin, xmax, lw=lw, colors=color) - plt.text(xmax+lw*5, y-0.2*lw/3, '{} bytes'.format(size), fontdict={'fontsize': fontsize}) + plt.text(xmax+lw*10, y-0.01*lw, '{} bytes'.format(size), fontdict={'fontsize': fontsize}) plt.xlabel('Offset (bytes)', fontdict={'fontsize': fontsize}) plt.yticks(ys, labels, fontsize=fontsize) plt.xticks(fontsize=fontsize) @@ -171,4 +148,4 @@ def _draw_figs(cls, topo_tensors, alloc_plan, cmap, figsize, fontsize, lw, split viz_memalloc = _MemallcVisualizer.viz_memalloc # FIXME: cyclic import -from utensor_cgen.transformer.mem_alloc import TensorAllocationTransformer # isort:skip +from utensor_cgen.backend.graph_lower.generic_graph_lower import TensorAllocationPlanner # isort:skip diff --git a/utensor_cgen/transformer/__init__.py b/utensor_cgen/transformer/__init__.py index 74f846c6..1e683fd6 100644 --- a/utensor_cgen/transformer/__init__.py +++ b/utensor_cgen/transformer/__init__.py @@ -4,7 +4,6 @@ from .conv_pool import * from .graph_viz import * from .linear_reoder import * -from .mem_alloc import * from .ns_transformer import * from .optimizer import * from .quantize import * diff --git a/utensor_cgen/transformer/mem_alloc.py b/utensor_cgen/transformer/mem_alloc.py deleted file mode 100644 index 1e5bb8d3..00000000 --- a/utensor_cgen/transformer/mem_alloc.py +++ /dev/null @@ -1,156 +0,0 @@ -import pickle -from collections import Counter, defaultdict, namedtuple -from itertools import chain, combinations, product - -from ortools.sat.python import cp_model -from utensor_cgen.logger import logger -from utensor_cgen.utils import timed - -from .base import Transformer -from .pipeline import TransformerPipeline - -__all__ = ['TensorAllocationTransformer'] - -MemorySpan = namedtuple('MemorySpan', ['start', 'end', 'size']) -AllocationPlan = namedtuple('AllocationPlan', ['plan', 'total_size']) - -@TransformerPipeline.register_transformer -class TensorAllocationTransformer(Transformer): - """ - Offline Tensor Allocation Optimizer - - analyse tensor lifetime and find the optimal allocation offset in the managed memory pool - - :param max_pool_size: the size of the memory pool (default: 1 KB) - :type max_pool_size: int - - :param include_inputs: include the input tensors (Placeholder) in the allocation plan - :type include_inputs: bool - - :param out_fname: the file name of memory allocation visualization (will NOT generate if not given) - :type out_fname: str - - :param aesthetic_kwargs: the keyword arguments controlling the aesthetic of the visualization of allocation plan - :type aesthetic_kwargs: dict - """ - METHOD_NAME = "tensor_alloc" - KWARGS_NAMESCOPE = "_tensor_alloc" - - def __init__( - self, - prune_graph=False, - max_pool_size=1024*1024, # 1k bytes - include_inputs=False, - out_fname=None, - **aesthetic_kwargs - ): - super(TensorAllocationTransformer, self).__init__(prune_graph=prune_graph) - self.max_pool_size = max_pool_size - self.include_inputs = include_inputs - self.out_fname = out_fname - self.aesthetic_kwargs = aesthetic_kwargs - - def transform(self, ugraph): - ref_cnts = Counter() - for op_name in ugraph.topo_order: - op_info = ugraph.ops_info[op_name] - for tensor in op_info.input_tensors: - ref_cnts[tensor.name] += 1 - tensors_to_schedule = set() - tensors_to_ignore = set( - chain(*[ - op.output_tensors - for op in ugraph.get_ops_by_type('Inline') - ]) - ) - if not self.include_inputs: - tensors_to_ignore.update( - chain(*[ - op.output_tensors - for op in ugraph.get_ops_by_type('Placeholder') - ]) - ) - nonoverlap_map = defaultdict(set) - for op_name in ugraph.topo_order: - op_info = ugraph.ops_info[op_name] - # ignore inline ops - if op_info.op_type == 'Inline': - continue - # ignore placeholders if not included - if not self.include_inputs and op_info.op_type == 'Placeholder': - continue - # no overlapping between input/output tensors - for in_tensor, out_tensor in product(op_info.input_tensors, op_info.output_tensors): - if not in_tensor in tensors_to_ignore: - nonoverlap_map[out_tensor].add(in_tensor) - tensors_to_schedule.add(in_tensor) - # no overlapping among input tensors - for in_tensor1, in_tensor2 in combinations( - filter(lambda tensor: tensor not in tensors_to_ignore, op_info.input_tensors), - 2 - ): - nonoverlap_map[in_tensor1].add(in_tensor2) - # no overlapping between output tensors and all evaluated tensors - for known_tensor, out_tensor in product(tensors_to_schedule, op_info.output_tensors): - if ref_cnts[known_tensor.name] > 0: - nonoverlap_map[out_tensor].add(known_tensor) - tensors_to_schedule.update(op_info.output_tensors) - for in_tensor in op_info.input_tensors: - ref_cnts[in_tensor.name] -= 1 - for out_tensor1, out_tensor2 in combinations(ugraph.output_tensors, 2): - nonoverlap_map[out_tensor1].add(out_tensor2) - tensors_to_schedule.update(ugraph.output_tensors) - alloc_plan = self._solve_opt_plan(tensors_to_schedule, nonoverlap_map) - if alloc_plan is not None: - ugraph.attributes[self.KWARGS_NAMESCOPE] = alloc_plan - if self.out_fname: - fig = viz_memalloc(ugraph=ugraph, out_fname=self.out_fname, **self.aesthetic_kwargs) - with open('{}.pkl'.format(self.out_fname), 'wb') as fid: - pickle.dump(fig, fid) - logger.info('matplotlib figure object dumped (pickle): %s', fid.name) - return ugraph - - @timed - def _solve_opt_plan(self, tensors_to_schedule, nonoverlap_map): - model = cp_model.CpModel() - inter_vars = {} - tensor_allocs = {} - for tensor in tensors_to_schedule: - var_start = model.NewIntVar(0, self.max_pool_size, '{}_start'.format(tensor.name)) - var_end = model.NewIntVar(0, self.max_pool_size, '{}_end'.format(tensor.name)) - size = tensor.size * tensor.dtype.itemsize - intv_var = model.NewIntervalVar(var_start, size, var_end, '{}_alloc'.format(tensor.name)) - inter_vars[tensor.name] = intv_var - tensor_allocs[tensor.name] = MemorySpan(var_start, var_end, size) - for tensor in tensors_to_schedule: - inter_var = inter_vars[tensor.name] - nonoverlap_vars = [inter_vars[t.name] for t in nonoverlap_map[tensor]] - for other in nonoverlap_vars: - model.AddNoOverlap([inter_var, other]) - var_mempool_size = model.NewIntVar(0, self.max_pool_size, 'mempool_size') - model.AddMaxEquality(var_mempool_size, [alloc.end for alloc in tensor_allocs.values()]) - model.Minimize(var_mempool_size) - - solver = cp_model.CpSolver() - status = solver.Solve(model) - alloc_plan = None - if solver.StatusName(status) == 'OPTIMAL': - opt_mempool_size = solver.Value(var_mempool_size) - plan = {} - for name, alloc in tensor_allocs.items(): - plan[name] = MemorySpan( - start=solver.Value(alloc.start), - end=solver.Value(alloc.end), - size=alloc.size - ) - logger.info('optimal tensor allocation plan solved, memory span: %i bytes', opt_mempool_size) - alloc_plan = AllocationPlan( - plan=plan, - total_size=opt_mempool_size - ) - else: - logger.info('tensor allocation plan not found, status: %s', solver.StatusName(status)) - return alloc_plan - -# FIXME: cyclic import -from utensor_cgen.ir.misc.graph_viz import viz_memalloc # isort:skip diff --git a/utensor_cgen/utils.py b/utensor_cgen/utils.py index 095c53c0..e612be1b 100644 --- a/utensor_cgen/utils.py +++ b/utensor_cgen/utils.py @@ -509,7 +509,18 @@ def get(self, key, default=None): elif key in self._defaults: value = self._defaults[key] return value - + + def items(self): + config = self.to_dict() + return config.items() + + def to_dict(self): + config = deepcopy(self._defaults) + config.update(self._user_config) + return config + + def __setitem__(self, key, value): + self._user_config[key] = value def __getitem__(self, key): if key not in self: From 444ddbe04a5aeeef248c7f04298e4629d5e68fb1 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sat, 7 Mar 2020 15:02:33 +0800 Subject: [PATCH 047/117] update tests --- tests/test_backend/test_utensor.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_backend/test_utensor.py b/tests/test_backend/test_utensor.py index d9d0bcc1..f6ef81e5 100644 --- a/tests/test_backend/test_utensor.py +++ b/tests/test_backend/test_utensor.py @@ -24,9 +24,9 @@ def test_legacy_utensor(mlp_ugraph): @pytest.mark.slow_test def test_offlinememory(mlp_ugraph): - from utensor_cgen.backend.utensor import uTensorOfflineMemoryPlanner + from utensor_cgen.backend.graph_lower.generic_graph_lower import BrutalForceMemoryPlanner - uTensorOfflineMemoryPlanner(config={ + BrutalForceMemoryPlanner(config={ 'size_float': 4, 'size_int': 4, 'size_uint8_t': 1 From 11bc08bbcc6cb08b884b62118558aa9b25e1d7d9 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sat, 7 Mar 2020 16:28:53 +0800 Subject: [PATCH 048/117] Add test for cli speed --- tests/test_cli.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 tests/test_cli.py diff --git a/tests/test_cli.py b/tests/test_cli.py new file mode 100644 index 00000000..95f3007d --- /dev/null +++ b/tests/test_cli.py @@ -0,0 +1,14 @@ +import time + + +def test_cli_load_speed(): + from utensor_cgen.cli import cli + + durations = [] + for _ in range(1000): + start_time = time.time() + cli.main(args=['-h'], standalone_mode=False, ) + end_time = time.time() + durations.append(end_time - start_time) + mean_duration = sum(durations) / len(durations) + assert mean_duration <= 0.005, 'cli is too slow: {:0.5f}'.format(mean_duration) From fd749f4e0b85205be8f9ab6f2e605610fbb31034 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sat, 7 Mar 2020 16:35:08 +0800 Subject: [PATCH 049/117] circle-ci: utf8 local --- .circleci/config.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index db736b73..96c9de36 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -9,7 +9,7 @@ jobs: - run: pip3 install . - run: name: Test - command: pytest -vv tests -m 'not slow_test' + command: LC_ALL=C.UTF-8 LANG=C.UTF-8 pytest -vv tests -m 'not slow_test' #- run: genhtml main_coverage.info --output-directory /tmp/coverage #- store_test_results: # path: /tmp/test_results From 1ccc721854e62a6e8437dce45ea0b34f6a006cda Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 8 Mar 2020 13:37:20 +0800 Subject: [PATCH 050/117] making tensor alloc lowering optional --- utensor_cgen/backend/graph_lower/generic_graph_lower.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/utensor_cgen/backend/graph_lower/generic_graph_lower.py b/utensor_cgen/backend/graph_lower/generic_graph_lower.py index b8c00172..7939fe8f 100644 --- a/utensor_cgen/backend/graph_lower/generic_graph_lower.py +++ b/utensor_cgen/backend/graph_lower/generic_graph_lower.py @@ -13,6 +13,7 @@ import numpy as np import tensorflow as tf from ortools.sat.python import cp_model + from utensor_cgen.backend.base import BackendPart from utensor_cgen.backend.utensor.snippets._types import NP_TYPES_MAP from utensor_cgen.logger import logger @@ -92,9 +93,13 @@ def __init__(self, config): if aesthetic_kwargs['figsize'] == 'None': aesthetic_kwargs['figsize'] = None self.aesthetic_kwargs = aesthetic_kwargs.to_dict() + self.enabled = self.config['enabled'] self.dtype_size_map = self._parse_dtype_size_map(self.config) def apply(self, ugraph): + if not self.enabled: + # not enabled, do nothing + return time_alloc_plan = ugraph.attributes.get( TopoOrderTensorTimeslotPlanner.KWARGS_NAMESCOPE ) @@ -238,6 +243,7 @@ def default_config(cls): 'lw': 12, 'rand_seed': 1111 }, + 'enabled': True, 'dtype_size_map': { 'float': 4, 'double': 8, From d8a17cfd41a9e954f4a0734d58a24e08282079dc Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 8 Mar 2020 15:21:58 +0800 Subject: [PATCH 051/117] update tests and add comments/docstrings --- tests/test_utils/conftest.py | 37 +++++++++++++++++++ tests/test_utils/test_config.py | 8 ++++ utensor_cgen/backend/graph_lower/__init__.py | 5 +++ utensor_cgen/backend/utensor/_backend_impl.py | 16 +++----- utensor_cgen/utils.py | 6 +++ 5 files changed, 61 insertions(+), 11 deletions(-) create mode 100644 tests/test_utils/test_config.py diff --git a/tests/test_utils/conftest.py b/tests/test_utils/conftest.py index e69de29b..15b15f0d 100644 --- a/tests/test_utils/conftest.py +++ b/tests/test_utils/conftest.py @@ -0,0 +1,37 @@ +import pytest + + +@pytest.fixture(name='config_user_values') +def config_user_values(): + from utensor_cgen.utils import Configuration + + return Configuration( + defaults={ + 'x': 1, + 'y': 2 + }, + user_config={ + 'x': 2 + } + ) + +@pytest.fixture(name='config_nested') +def config_nested(): + from utensor_cgen.utils import Configuration + + return Configuration( + defaults={ + 'dict1': { + 'inner': { + 'x': 3 + } + } + }, + user_config={ + 'dict1': { + 'inner': { + 'x': 2 + } + } + } + ) diff --git a/tests/test_utils/test_config.py b/tests/test_utils/test_config.py new file mode 100644 index 00000000..1b2de543 --- /dev/null +++ b/tests/test_utils/test_config.py @@ -0,0 +1,8 @@ +def test_user_values(config_user_values): + assert config_user_values['x'] == 2 + assert config_user_values['y'] == 2 + +def test_config_nested(config_nested): + assert isinstance(config_nested['dict1'], type(config_nested)) + assert isinstance(config_nested['dict1']['inner'], type(config_nested)) + assert config_nested['dict1']['inner']['x'] == 2 diff --git a/utensor_cgen/backend/graph_lower/__init__.py b/utensor_cgen/backend/graph_lower/__init__.py index 47024bed..403c0958 100644 --- a/utensor_cgen/backend/graph_lower/__init__.py +++ b/utensor_cgen/backend/graph_lower/__init__.py @@ -1 +1,6 @@ +r"""Generic Graph Lowering + +Any graph lower exported within this module should be generic. +That is, it should work with any uTensorGraph for any backend. +""" from .generic_graph_lower import TensorAllocationPlanner diff --git a/utensor_cgen/backend/utensor/_backend_impl.py b/utensor_cgen/backend/utensor/_backend_impl.py index 2f40d734..129d831a 100644 --- a/utensor_cgen/backend/utensor/_backend_impl.py +++ b/utensor_cgen/backend/utensor/_backend_impl.py @@ -1,6 +1,6 @@ from utensor_cgen.backend.base import Backend -from utensor_cgen.utils import (Configuration, LazyAttrib, LazyLoader, - class_property, parse_toml) +from utensor_cgen.utils import (LazyAttrib, LazyLoader, class_property, + parse_toml) code_generator = LazyLoader(submod_name='backend.utensor.code_generator') transformer = LazyLoader(submod_name='backend.transformer') @@ -19,21 +19,15 @@ class uTensorBackend(Backend): TARGET = 'utensor' - def __init__(self, + def __init__( + self, config, code_generator=None, graph_transformer=None, graph_op_lower=None, graph_alloc_lower=None, ): - default_config = self.default_config[self.TARGET][self.COMPONENT] - config = Configuration(default_config, config.get( - self.TARGET, - {self.COMPONENT: {}} - ).get( - self.COMPONENT, {} - ) - ) + config = self.config[self.TARGET][self.COMPONENT] if config['legacy-api']: code_generator = code_generator or uTensorLegacyCodeGenerator(config=config[uTensorLegacyCodeGenerator.PART]) graph_op_lower = graph_op_lower or uTensorLegacyGraphLower(config=config[uTensorLegacyGraphLower.PART]) diff --git a/utensor_cgen/utils.py b/utensor_cgen/utils.py index e612be1b..b6c12178 100644 --- a/utensor_cgen/utils.py +++ b/utensor_cgen/utils.py @@ -509,7 +509,13 @@ def get(self, key, default=None): elif key in self._defaults: value = self._defaults[key] return value + + def keys(self): + return self.to_dict().keys() + def values(self): + return self.to_dict().values() + def items(self): config = self.to_dict() return config.items() From af52ec841227ee3d8d1e994a6ae95d211e226239 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 8 Mar 2020 15:59:59 +0800 Subject: [PATCH 052/117] fix bug and update tests --- tests/test_backend/test_utensor.py | 8 ++++---- tests/test_utils/conftest.py | 5 +++-- tests/test_utils/test_config.py | 1 + .../backend/graph_lower/generic_graph_lower.py | 2 +- utensor_cgen/backend/utensor/_backend_impl.py | 14 +++++++------- utensor_cgen/utils.py | 10 ++++++++-- 6 files changed, 24 insertions(+), 16 deletions(-) diff --git a/tests/test_backend/test_utensor.py b/tests/test_backend/test_utensor.py index f6ef81e5..89770c22 100644 --- a/tests/test_backend/test_utensor.py +++ b/tests/test_backend/test_utensor.py @@ -8,18 +8,18 @@ def test_legacy_utensor(mlp_ugraph): this_dir = os.path.dirname(__file__) - uTensorBackend(config={ + backend = uTensorBackend(config={ 'utensor': { 'backend': { 'legacy-api': True, - 'code_generator': { + 'legacy_code_generator': { 'model_dir': os.path.join(this_dir, 'models'), 'params_dir': os.path.join(this_dir, 'data'), }, - 'graph_lower': {} }, } - }).apply(mlp_ugraph) + }) + backend.apply(mlp_ugraph) @pytest.mark.slow_test diff --git a/tests/test_utils/conftest.py b/tests/test_utils/conftest.py index 15b15f0d..a310b8f1 100644 --- a/tests/test_utils/conftest.py +++ b/tests/test_utils/conftest.py @@ -23,14 +23,15 @@ def config_nested(): defaults={ 'dict1': { 'inner': { - 'x': 3 + 'x': 3, + 'y': 4 } } }, user_config={ 'dict1': { 'inner': { - 'x': 2 + 'x': 2, } } } diff --git a/tests/test_utils/test_config.py b/tests/test_utils/test_config.py index 1b2de543..1a054fa7 100644 --- a/tests/test_utils/test_config.py +++ b/tests/test_utils/test_config.py @@ -6,3 +6,4 @@ def test_config_nested(config_nested): assert isinstance(config_nested['dict1'], type(config_nested)) assert isinstance(config_nested['dict1']['inner'], type(config_nested)) assert config_nested['dict1']['inner']['x'] == 2 + assert config_nested['dict1']['inner']['y'] == 4 diff --git a/utensor_cgen/backend/graph_lower/generic_graph_lower.py b/utensor_cgen/backend/graph_lower/generic_graph_lower.py index 7939fe8f..1c359fd4 100644 --- a/utensor_cgen/backend/graph_lower/generic_graph_lower.py +++ b/utensor_cgen/backend/graph_lower/generic_graph_lower.py @@ -13,7 +13,6 @@ import numpy as np import tensorflow as tf from ortools.sat.python import cp_model - from utensor_cgen.backend.base import BackendPart from utensor_cgen.backend.utensor.snippets._types import NP_TYPES_MAP from utensor_cgen.logger import logger @@ -253,6 +252,7 @@ def default_config(cls): }, } + class BrutalForceMemoryPlanner(BackendPart): TARGET = 'utensor' PART = 'offlinememory' diff --git a/utensor_cgen/backend/utensor/_backend_impl.py b/utensor_cgen/backend/utensor/_backend_impl.py index 129d831a..ee0ac712 100644 --- a/utensor_cgen/backend/utensor/_backend_impl.py +++ b/utensor_cgen/backend/utensor/_backend_impl.py @@ -29,14 +29,14 @@ def __init__( ): config = self.config[self.TARGET][self.COMPONENT] if config['legacy-api']: - code_generator = code_generator or uTensorLegacyCodeGenerator(config=config[uTensorLegacyCodeGenerator.PART]) - graph_op_lower = graph_op_lower or uTensorLegacyGraphLower(config=config[uTensorLegacyGraphLower.PART]) - graph_alloc_lower = graph_alloc_lower or TensorAllocationPlanner(config=config[TensorAllocationPlanner.PART]) + code_generator = code_generator or uTensorLegacyCodeGenerator(config=config[uTensorLegacyCodeGenerator.PART].to_dict()) + graph_op_lower = graph_op_lower or uTensorLegacyGraphLower(config=config[uTensorLegacyGraphLower.PART].to_dict()) + graph_alloc_lower = graph_alloc_lower or TensorAllocationPlanner(config=config[TensorAllocationPlanner.PART].to_dict()) else: - code_generator = code_generator or uTensorRearchCodeGenerator(config=config[uTensorRearchCodeGenerator.PART]) - graph_op_lower = graph_op_lower or uTensorRearchGraphLower(config=config[uTensorRearchGraphLower.PART]) - graph_alloc_lower = TensorAllocationPlanner(config=config[TensorAllocationPlanner.PART]) - graph_transformer = graph_transformer or PipelineTransformer(config=config[PipelineTransformer.PART]) + code_generator = code_generator or uTensorRearchCodeGenerator(config=config[uTensorRearchCodeGenerator.PART].to_dict()) + graph_op_lower = graph_op_lower or uTensorRearchGraphLower(config=config[uTensorRearchGraphLower.PART].to_dict()) + graph_alloc_lower = TensorAllocationPlanner(config=config[TensorAllocationPlanner.PART].to_dict()) + graph_transformer = graph_transformer or PipelineTransformer(config=config[PipelineTransformer.PART].to_dict()) self._graph_op_lower = graph_op_lower self._graph_transformer = graph_transformer self._graph_alloc_lower = graph_alloc_lower diff --git a/utensor_cgen/utils.py b/utensor_cgen/utils.py index b6c12178..2a85bd52 100644 --- a/utensor_cgen/utils.py +++ b/utensor_cgen/utils.py @@ -487,6 +487,12 @@ def parse_toml(file_or_path): class Configuration(object): def __init__(self, defaults=None, user_config=None): + """ + Note + ---- + - any value that is in user_config should be in defaults + - any value that is not in defaults should not be in user_config + """ if defaults is None: defaults = {} if user_config is None: @@ -533,9 +539,9 @@ def __getitem__(self, key): raise KeyError('invalid key: {}'.format(key)) value = self._user_config.get( key, self._defaults[key] - ) + ) if isinstance(value, dict): - value = type(self)(value, {}) + value = type(self)(self._defaults[key], value) return value def __contains__(self, key): From a3f036855fbc1d91d333cdb553bcd2a852b27cc6 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 8 Mar 2020 16:03:08 +0800 Subject: [PATCH 053/117] make Configuration nearly unmutable object --- utensor_cgen/backend/graph_lower/generic_graph_lower.py | 7 +++---- utensor_cgen/utils.py | 4 +--- 2 files changed, 4 insertions(+), 7 deletions(-) diff --git a/utensor_cgen/backend/graph_lower/generic_graph_lower.py b/utensor_cgen/backend/graph_lower/generic_graph_lower.py index 1c359fd4..482955a1 100644 --- a/utensor_cgen/backend/graph_lower/generic_graph_lower.py +++ b/utensor_cgen/backend/graph_lower/generic_graph_lower.py @@ -88,10 +88,9 @@ def __init__(self, config): self.out_fname = self.config['out_fname'] if self.out_fname == 'None': self.out_fname = None - aesthetic_kwargs = self.config['aesthetic_kwargs'] - if aesthetic_kwargs['figsize'] == 'None': - aesthetic_kwargs['figsize'] = None - self.aesthetic_kwargs = aesthetic_kwargs.to_dict() + self.aesthetic_kwargs = self.config['aesthetic_kwargs'].to_dict() + if self.aesthetic_kwargs['figsize'] == 'None': + self.aesthetic_kwargs['figsize'] = None self.enabled = self.config['enabled'] self.dtype_size_map = self._parse_dtype_size_map(self.config) diff --git a/utensor_cgen/utils.py b/utensor_cgen/utils.py index 2a85bd52..dbd9f74c 100644 --- a/utensor_cgen/utils.py +++ b/utensor_cgen/utils.py @@ -493,6 +493,7 @@ def __init__(self, defaults=None, user_config=None): - any value that is in user_config should be in defaults - any value that is not in defaults should not be in user_config """ + # TODO: write a check on the inputs? if defaults is None: defaults = {} if user_config is None: @@ -531,9 +532,6 @@ def to_dict(self): config.update(self._user_config) return config - def __setitem__(self, key, value): - self._user_config[key] = value - def __getitem__(self, key): if key not in self: raise KeyError('invalid key: {}'.format(key)) From f07f5dda6df89d02506ccad22a5afd5fee53d102 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 8 Mar 2020 17:06:39 +0800 Subject: [PATCH 054/117] Add notes to generated config file Update example_config.toml --- example_config.toml | 41 +++++++++++++++++++++++++++++++++--- utensor_cgen/backend/base.py | 1 + utensor_cgen/cli.py | 4 ++++ 3 files changed, 43 insertions(+), 3 deletions(-) diff --git a/example_config.toml b/example_config.toml index 6cc4500a..e3262077 100644 --- a/example_config.toml +++ b/example_config.toml @@ -1,15 +1,50 @@ # https://github.com/toml-lang/toml # .. +# we use string 'None' to represent python None value +# you should convert the string to None if you try to write extension for utensor_cgen [utensor.backend] legacy-api = true +[utensor.backend.tensor_alloc_planner] +max_pool_size = 1048576 +include_inputs = false +out_fname = "None" +enabled = true + [utensor.backend.legacy_code_generator] src_fname = "None" params_dir = "data" embed_params_dir = "/fs/data" model_dir = "models" -transform_methods = [ "dropout(name_pattern=r\"(dropout[_\\w\\d]*)/.*\")", "linear_reorder", "quantize", "conv_pool", "inline", "biasAdd", "remove_id_op", "fake_gather_v2", "refcnt",] -save_graph = false debug_cmt = false -[utensor.backend.graph_lower] +[utensor.backend.legacy_graph_lower] + +[utensor.backend.rearch_code_generator] +src_fname = "None" +header_fname = "None" +params_dir = "data" +model_dir = "models" +meta_data_pool_size = "auto" +ram_data_pool_size = "auto" + +[utensor.backend.rearch_graph_lower] + +[utensor.backend.pipeline_transformer] +save_graph = false +transform_methods = [ "dropout(name_pattern=r'(dropout[_\\w\\d]*)/.*')", "linear_reorder", "quantize", "conv_pool", "inline", "biasAdd", "remove_id_op", "fake_gather_v2", "refcnt",] + +[utensor.backend.tensor_alloc_planner.aesthetic_kwargs] +split_on_large_graph = true +num_tensors_per_split = 20 +figsize = "None" +fontsize = 12 +lw = 12 +rand_seed = 1111 + +[utensor.backend.tensor_alloc_planner.dtype_size_map] +float = 4 +double = 8 +uint8 = 1 +int = 4 +long = 8 diff --git a/utensor_cgen/backend/base.py b/utensor_cgen/backend/base.py index cd764f6b..82eb7a0f 100644 --- a/utensor_cgen/backend/base.py +++ b/utensor_cgen/backend/base.py @@ -72,6 +72,7 @@ def from_file(cls, file_or_path, *args, **kwargs): def from_config(cls, config, *args, **kwargs): return cls(config, *args, **kwargs) + class BackendPart(Backend): PART = MUST_OVERWRITEN diff --git a/utensor_cgen/cli.py b/utensor_cgen/cli.py index e7ed0394..3c89bd4d 100644 --- a/utensor_cgen/cli.py +++ b/utensor_cgen/cli.py @@ -68,6 +68,10 @@ def generate_config(target, output): '# https://github.com/toml-lang/toml\n' '# ..\n' ) + fid.write( + '# we use string \'None\' to represent python None value\n' + '# you should convert the string to None if you try to write extension for utensor_cgen\n' + ) fid.write(dumps(config)) @cli.command(name='convert', help='convert graph to cpp/hpp files') From aa7a2fed6b86b4811baaeda88417a39e437cbe3f Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 8 Mar 2020 22:47:34 +0800 Subject: [PATCH 055/117] Fix transformer bug and update tests --- plugins/dummy_backend/__init__.py | 3 ++- .../test_dropout/test_dropout_transormer.py | 3 ++- .../test_id_remove/test_transformer.py | 4 ++++ .../test_inline/test_inline_optimizer.py | 1 + .../test_linear_reorder/test_transformer.py | 1 + .../test_refcnt/test_refcnt_optimizer.py | 3 ++- utensor_cgen/backend/utensor/_backend_impl.py | 1 - utensor_cgen/cli.py | 6 +++--- utensor_cgen/transformer/__init__.py | 3 ++- utensor_cgen/transformer/base.py | 15 ++++----------- utensor_cgen/transformer/graph_viz.py | 2 +- utensor_cgen/transformer/linear_reoder.py | 3 +-- utensor_cgen/transformer/ns_transformer.py | 4 ++++ utensor_cgen/transformer/optimizer.py | 7 ++----- utensor_cgen/transformer/quantize.py | 1 - 15 files changed, 29 insertions(+), 28 deletions(-) diff --git a/plugins/dummy_backend/__init__.py b/plugins/dummy_backend/__init__.py index 2e422f1e..7424b1a5 100644 --- a/plugins/dummy_backend/__init__.py +++ b/plugins/dummy_backend/__init__.py @@ -1,9 +1,10 @@ from textwrap import wrap -from utensor_cgen.backend.base import Backend from utensor_cgen.backend import BackendManager +from utensor_cgen.backend.base import Backend from utensor_cgen.utils import class_property + @BackendManager.register class DummyBackend(Backend): TARGET = 'dummy-backend' diff --git a/tests/test_transformer/test_dropout/test_dropout_transormer.py b/tests/test_transformer/test_dropout/test_dropout_transormer.py index a6085879..3e9d554a 100644 --- a/tests/test_transformer/test_dropout/test_dropout_transormer.py +++ b/tests/test_transformer/test_dropout/test_dropout_transormer.py @@ -1,5 +1,4 @@ import tensorflow as tf - from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.transformer.ns_transformer import (DropoutTransformer, DropoutTransformerV2) @@ -11,6 +10,7 @@ def test_dropout_trans_1_1(droput_graph_tuple): output_nodes) = droput_graph_tuple ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) transformer = DropoutTransformer() + assert transformer.prune_graph new_ugraph = transformer.transform(ugraph) for op in new_ugraph.ops_info.values(): assert op.ugraph @@ -43,6 +43,7 @@ def test_dropout_trans_1_2(droput_graph_tuple): output_nodes) = droput_graph_tuple ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) transformer = DropoutTransformerV2() + assert transformer.prune_graph new_ugraph = transformer.transform(ugraph) for op in new_ugraph.ops_info.values(): assert op.ugraph diff --git a/tests/test_transformer/test_id_remove/test_transformer.py b/tests/test_transformer/test_id_remove/test_transformer.py index edd1b440..2726a65a 100644 --- a/tests/test_transformer/test_id_remove/test_transformer.py +++ b/tests/test_transformer/test_id_remove/test_transformer.py @@ -2,6 +2,10 @@ from utensor_cgen.transformer.optimizer import IdOpRemoveOptimizer +def test_id_rm_prune_graph(): + optimizer = IdOpRemoveOptimizer() + assert optimizer.prune_graph + def test_id_rm_transform_1(id_graph_def_1): ugraph = GraphDefParser(config={}).parse(id_graph_def_1, output_nodes=['z']) optimizer = IdOpRemoveOptimizer() diff --git a/tests/test_transformer/test_inline/test_inline_optimizer.py b/tests/test_transformer/test_inline/test_inline_optimizer.py index f416d9b3..79a449d0 100644 --- a/tests/test_transformer/test_inline/test_inline_optimizer.py +++ b/tests/test_transformer/test_inline/test_inline_optimizer.py @@ -6,6 +6,7 @@ def test_inline_optimizer(inlinegraph_tuple): (graph_def, inline_ans, output_nodes)= inlinegraph_tuple ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes) transformer = InlineTransformer() + assert not transformer.prune_graph ugraph = transformer.transform(ugraph) for node_name in ugraph.topo_order: if node_name in inline_ans: diff --git a/tests/test_transformer/test_linear_reorder/test_transformer.py b/tests/test_transformer/test_linear_reorder/test_transformer.py index 405b3c94..4ec42347 100644 --- a/tests/test_transformer/test_linear_reorder/test_transformer.py +++ b/tests/test_transformer/test_linear_reorder/test_transformer.py @@ -2,6 +2,7 @@ def test_linear_reorder_1(subj_ugraph_1): from utensor_cgen.transformer.linear_reoder import LinearReorderTransformerV2 transformer = LinearReorderTransformerV2() + assert not transformer.prune_graph new_ugraph = transformer.transform(subj_ugraph_1) for op in new_ugraph['output'].input_nodes: assert op.op_type == 'Relu', 'expecting Relu, get {}'.format(op.op_type) diff --git a/tests/test_transformer/test_refcnt/test_refcnt_optimizer.py b/tests/test_transformer/test_refcnt/test_refcnt_optimizer.py index ea696d7a..47244a80 100644 --- a/tests/test_transformer/test_refcnt/test_refcnt_optimizer.py +++ b/tests/test_transformer/test_refcnt/test_refcnt_optimizer.py @@ -3,9 +3,10 @@ def test_refcnt_optimizer(refgraph_tuple): - (graph_def, refcnt_ans, output_nodes)= refgraph_tuple + (graph_def, refcnt_ans, output_nodes) = refgraph_tuple ugraph = GraphDefParser(config={}).parse(graph_def, output_nodes=output_nodes) transformer = RefCntOptimizer() + assert not transformer.prune_graph ugraph = transformer.transform(ugraph) for node_name in ugraph.topo_order: if node_name in refcnt_ans: diff --git a/utensor_cgen/backend/utensor/_backend_impl.py b/utensor_cgen/backend/utensor/_backend_impl.py index ee0ac712..03b918a7 100644 --- a/utensor_cgen/backend/utensor/_backend_impl.py +++ b/utensor_cgen/backend/utensor/_backend_impl.py @@ -61,7 +61,6 @@ def apply(self, ugraph): new_ugraph = self._graph_transformer.transform(ugraph) self._graph_alloc_lower.apply(new_ugraph) self._code_generator.apply(new_ugraph) - return new_ugraph def __call__(self, ugraph): return self.apply(ugraph) diff --git a/utensor_cgen/cli.py b/utensor_cgen/cli.py index 3c89bd4d..47dc149b 100644 --- a/utensor_cgen/cli.py +++ b/utensor_cgen/cli.py @@ -35,12 +35,12 @@ def _load_plugin(path): "-p", "--plugin", multiple=True, - help="path of the python module which will be loaded as plugin", + help="path of the python module which will be loaded as plugin (multiple)", metavar="MODULE", ) def cli(plugin): - for module in plugin: - _load_plugin(module) + for module_path in plugin: + _load_plugin(module_path) @cli.command(name='list-backends', help='list all available backends') @click.help_option('-h', '--help') diff --git a/utensor_cgen/transformer/__init__.py b/utensor_cgen/transformer/__init__.py index 1e683fd6..9c64a9e9 100644 --- a/utensor_cgen/transformer/__init__.py +++ b/utensor_cgen/transformer/__init__.py @@ -1,5 +1,4 @@ # -*- coding:utf8 -*- -from .pipeline import TransformerPipeline # isort:skip from .base import Transformer from .conv_pool import * from .graph_viz import * @@ -7,3 +6,5 @@ from .ns_transformer import * from .optimizer import * from .quantize import * + +from .pipeline import TransformerPipeline # isort:skip diff --git a/utensor_cgen/transformer/base.py b/utensor_cgen/transformer/base.py index 145a2fb0..30613528 100644 --- a/utensor_cgen/transformer/base.py +++ b/utensor_cgen/transformer/base.py @@ -7,21 +7,19 @@ class Transformer(object): """ - Any subclass of Transformer should follow the + Any subclass of Transformer should follow the signature of __new__ and __init__ defined here - if the overwrite these two methods in order to + if the overwrite these two methods in order to work properly """ __metaclass__ = ABCMeta KWARGS_NAMESCOPE = None METHOD_NAME = None - def __new__(cls, - prune_graph=True, - **kwargs): + def __init__(self, prune_graph=True, **kwargs): + cls = type(self) if cls.KWARGS_NAMESCOPE is None: raise ValueError('kwargs namescope not found for %s' % cls) - self = object.__new__(cls) self.prune_graph = prune_graph ori_transform = self.transform @@ -34,11 +32,6 @@ def transform(ugraph): return new_ugraph self.transform = transform - return self - - def __init__(self, prune_graph=True, **kwargs): - # just for defining the __init__ signature - pass @abstractmethod def transform(self, ugraph): diff --git a/utensor_cgen/transformer/graph_viz.py b/utensor_cgen/transformer/graph_viz.py index c646a15f..43208318 100644 --- a/utensor_cgen/transformer/graph_viz.py +++ b/utensor_cgen/transformer/graph_viz.py @@ -17,9 +17,9 @@ class GraphVizTransformer(Transformer): KWARGS_NAMESCOPE = '_utensor_graph_viz' def __init__(self, out_fname="graph.gv", view=False, cleanup=True): + super(GraphVizTransformer, self).__init__(prune_graph=False) self.out_fname = out_fname self.view = view - self.prune_graph = False self.cleanup = cleanup def transform(self, ugraph): diff --git a/utensor_cgen/transformer/linear_reoder.py b/utensor_cgen/transformer/linear_reoder.py index 491f9f11..68f8a204 100644 --- a/utensor_cgen/transformer/linear_reoder.py +++ b/utensor_cgen/transformer/linear_reoder.py @@ -5,7 +5,6 @@ """ import tensorflow as tf - from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.matcher import uTensorGraphMatcher from utensor_cgen.utils import prune_graph, topologic_order_graph @@ -22,7 +21,7 @@ class LinearReorderTransformerV2(Transformer): KWARGS_NAMESCOPE = '_linear_reorder' def __init__(self): - self.prune_graph = False + super(LinearReorderTransformerV2, self).__init__(prune_graph=False) @property def pattern_ugraph(self): diff --git a/utensor_cgen/transformer/ns_transformer.py b/utensor_cgen/transformer/ns_transformer.py index 31acd247..d8e6bc19 100644 --- a/utensor_cgen/transformer/ns_transformer.py +++ b/utensor_cgen/transformer/ns_transformer.py @@ -49,6 +49,9 @@ class InlineTransformer(Transformer): METHOD_NAME = 'inline' KWARGS_NAMESCOPE = '_utensor_inline' + def __init__(self): + super(InlineTransformer, self).__init__(prune_graph=False) + def transform(self, ugraph): for node_name in ugraph.topo_order: op_type = ugraph.ops_info[node_name].op_type @@ -79,6 +82,7 @@ class DropoutTransformer(Transformer): TARGET_NODENAME_PATTERN = re.compile(r'(dropout[_\w\d]*)/.*') def __init__(self, name_pattern=r'(dropout[_\w\d]*)/.*'): + super(DropoutTransformer, self).__init__(prune_graph=True) self._op_name_pattern = re.compile(name_pattern) def transform(self, ugraph): diff --git a/utensor_cgen/transformer/optimizer.py b/utensor_cgen/transformer/optimizer.py index f10c9cfc..f3c9b873 100644 --- a/utensor_cgen/transformer/optimizer.py +++ b/utensor_cgen/transformer/optimizer.py @@ -13,8 +13,8 @@ class RefCntOptimizer(Transformer): METHOD_NAME = 'refcnt' KWARGS_NAMESCOPE = '_utensor_refcnt' - def __init__(self, **kwargs): - self.prune_graph = False + def __init__(self): + super(RefCntOptimizer, self).__init__(prune_graph=False) def transform(self, ugraph): """Optimization with reference count @@ -50,9 +50,6 @@ class IdOpRemoveOptimizer(Transformer): METHOD_NAME = 'remove_id_op' KWARGS_NAMESCOPE = '_utensor_remove_id_op' - def __init__(self, **kwargs): - self.prune_graph = True - def transform(self, ugraph): return self._transform(ugraph) diff --git a/utensor_cgen/transformer/quantize.py b/utensor_cgen/transformer/quantize.py index 8f030565..86778e39 100644 --- a/utensor_cgen/transformer/quantize.py +++ b/utensor_cgen/transformer/quantize.py @@ -8,7 +8,6 @@ @TransformerPipeline.register_transformer class QuantizeTransformer(Transformer): - METHOD_NAME = 'quantize' KWARGS_NAMESCOPE = '_quantize' From e11cc0d71f880a9d6b554d01e269bbdeaa63cb0c Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 8 Mar 2020 23:04:13 +0800 Subject: [PATCH 056/117] update plugin example --- plugins/dummy_backend/__init__.py | 42 +++++++++---------- .../graph_lower/generic_graph_lower.py | 4 +- 2 files changed, 22 insertions(+), 24 deletions(-) diff --git a/plugins/dummy_backend/__init__.py b/plugins/dummy_backend/__init__.py index 7424b1a5..e03300ae 100644 --- a/plugins/dummy_backend/__init__.py +++ b/plugins/dummy_backend/__init__.py @@ -7,29 +7,27 @@ @BackendManager.register class DummyBackend(Backend): - TARGET = 'dummy-backend' + TARGET = 'dummy-backend' - def __init__(self, config): - if not config: - config = self.default_config - self.output_file = config[self.TARGET][self.COMPONENT]['output-file'] + def __init__(self, config): + self.output_file = self.config[self.TARGET][self.COMPONENT]['output-file'] - def apply(self, ugraph): - with open(self.output_file, 'w') as fid: - fid.write('#include \n\n') - fid.write('int main(int argc, char* argv[]) {\n') - fid.write(' printf("graph name: {}\\n");\n'.format(ugraph.name)) - fid.write(' printf("ops in topological sorted order:\\n");\n') - for op_name in ugraph.topo_order: - fid.write(' printf(" {}\\n");\n'.format(op_name)) - fid.write(' return 0;\n}') + def apply(self, ugraph): + with open(self.output_file, 'w') as fid: + fid.write('#include \n\n') + fid.write('int main(int argc, char* argv[]) {\n') + fid.write(' printf("graph name: {}\\n");\n'.format(ugraph.name)) + fid.write(' printf("ops in topological sorted order:\\n");\n') + for op_name in ugraph.topo_order: + fid.write(' printf(" {}\\n");\n'.format(op_name)) + fid.write(' return 0;\n}') - @class_property - def default_config(cls): - return { - cls.TARGET: { - cls.COMPONENT: { - 'output-file': 'list_op.c' - } - } + @class_property + def default_config(cls): + return { + cls.TARGET: { + cls.COMPONENT: { + 'output-file': 'list_op.c' } + } + } diff --git a/utensor_cgen/backend/graph_lower/generic_graph_lower.py b/utensor_cgen/backend/graph_lower/generic_graph_lower.py index 482955a1..a2eb5fa7 100644 --- a/utensor_cgen/backend/graph_lower/generic_graph_lower.py +++ b/utensor_cgen/backend/graph_lower/generic_graph_lower.py @@ -254,8 +254,8 @@ def default_config(cls): class BrutalForceMemoryPlanner(BackendPart): TARGET = 'utensor' - PART = 'offlinememory' - DATA_NAME = 'address' + PART = 'brutal_force_mem_alloc' + DATA_NAME = '_brutal_force_mem_alloc' def __init__( self, From 2ad07b9666fd841447f0559c9a12ec8bd82f6d09 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Mon, 9 Mar 2020 12:43:53 +0800 Subject: [PATCH 057/117] unify alloc plan format --- .../graph_lower/generic_graph_lower.py | 57 +++++++++++++++---- 1 file changed, 45 insertions(+), 12 deletions(-) diff --git a/utensor_cgen/backend/graph_lower/generic_graph_lower.py b/utensor_cgen/backend/graph_lower/generic_graph_lower.py index a2eb5fa7..eb735789 100644 --- a/utensor_cgen/backend/graph_lower/generic_graph_lower.py +++ b/utensor_cgen/backend/graph_lower/generic_graph_lower.py @@ -255,7 +255,7 @@ def default_config(cls): class BrutalForceMemoryPlanner(BackendPart): TARGET = 'utensor' PART = 'brutal_force_mem_alloc' - DATA_NAME = '_brutal_force_mem_alloc' + KWARGS_NAMESCOPE = '_brutal_force_mem_alloc' def __init__( self, @@ -274,25 +274,58 @@ def __init__( self._type[np.dtype(tf.qint32.as_numpy_dtype)] = final_config['size_int'] def apply(self, ugraph): - new_ugraph = deepcopy(ugraph) - new_ugraph.setup_data_manager({self.DATA_NAME: " "}) + # ugraph.setup_data_manager(self.KWARGS_NAMESCOPE, {}) # use_def_table: dict, tensor_name -> {'start': op_idx, 'end': op_idx} - use_def_table = self._create_resource_table(new_ugraph) + use_def_table = self._create_resource_table(ugraph) allocate_table = dict() - allocate_success = self.allocate_graph(new_ugraph, allocate_table, use_def_table, self.buff_size, self.unit_size) - + allocate_success = self.allocate_graph(ugraph, allocate_table, use_def_table, self.buff_size, self.unit_size) if allocate_success: - for node_name in new_ugraph.topo_order: - in_t_infos = new_ugraph.ops_info[node_name].input_tensors + allocs = [] + max_offset_end = 0 + for node_name in ugraph.topo_order: + in_t_infos = ugraph.ops_info[node_name].input_tensors for in_o in in_t_infos: if in_o.name in allocate_table: - new_ugraph.data_manager.address = (in_o.name, allocate_table[in_o.name]['offsetstart']) + time_alloc = TimeslotAllocation( + time_slot_start=use_def_table[in_o.name]['start'], + time_slot_end=use_def_table[in_o.name]['end'] + ) + space_alloc = SpaceAllocation( + offset_start=allocate_table[in_o.name]['offsetstart'], + offset_end=allocate_table[in_o.name]['offsetend'], + ) + if allocate_table[in_o.name]['offsetend'] >= max_offset_end: + max_offset_end = allocate_table[in_o.name]['offsetend'] + allocs.append( + TimeSpaceAllocation( + entity_name=in_o.name, + time_alloc=time_alloc, + space_alloc=space_alloc, + ) + ) + # new_ugraph.data_manager.address = (in_o.name, allocate_table[in_o.name]['offsetstart']) out_t_infos = new_ugraph.ops_info[node_name].output_tensors for out_o in out_t_infos: if out_o.name in allocate_table: - new_ugraph.data_manager.address = (out_o.name, allocate_table[out_o.name]['offsetstart']) - return new_ugraph - return ugraph + time_alloc = TimeslotAllocation( + time_slot_start=use_def_table[out_o.name]['start'], + time_slot_end=use_def_table[out_o.name]['end'] + ) + space_alloc = SpaceAllocation( + offset_start=allocate_table[out_o.name]['offsetstart'], + offset_end=allocate_table[out_o.name]['offsetend'], + ) + if allocate_table[out_o.name]['offsetend'] >= max_offset_end: + max_offset_end = allocate_table[out_o.name]['offsetend'] + allocs.append( + TimeSpaceAllocation( + entity_name=out_o.name, + time_alloc=time_alloc, + space_alloc=space_alloc, + ) + ) + # new_ugraph.data_manager.address = (out_o.name, allocate_table[out_o.name]['offsetstart']) + ugraph.attributes[self.KWARGS_NAMESCOPE] = AllocationPlan(allocs=allocs, total_size=max_offset_end) def _query_offset_fromallocate_table(self, allocate_table, start, end): new_start = start From 1154aec3e709ee55e8b8d7b802d30be060e52592 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Mon, 9 Mar 2020 12:53:00 +0800 Subject: [PATCH 058/117] Fix init signature and add comments --- utensor_cgen/backend/base.py | 7 ++++++ .../graph_lower/generic_graph_lower.py | 24 ++++++++----------- 2 files changed, 17 insertions(+), 14 deletions(-) diff --git a/utensor_cgen/backend/base.py b/utensor_cgen/backend/base.py index 82eb7a0f..075fbedf 100644 --- a/utensor_cgen/backend/base.py +++ b/utensor_cgen/backend/base.py @@ -53,6 +53,13 @@ def _validate_config(self, config): class Backend(_BackendBase): + """ + - Constrcutor signature must be ``__init__(self, config, *args, **kwargs)`` + - ``config`` should be a dictionay + - It will run through various check in ``__new__``, so it's better to access the value + of config via ``self.config``, which is an instance of ``Configuration`` + - It will make sure if users do not provide the value required, default one will be used + """ TARGET = MUST_OVERWRITEN diff --git a/utensor_cgen/backend/graph_lower/generic_graph_lower.py b/utensor_cgen/backend/graph_lower/generic_graph_lower.py index eb735789..05f12d4b 100644 --- a/utensor_cgen/backend/graph_lower/generic_graph_lower.py +++ b/utensor_cgen/backend/graph_lower/generic_graph_lower.py @@ -257,21 +257,15 @@ class BrutalForceMemoryPlanner(BackendPart): PART = 'brutal_force_mem_alloc' KWARGS_NAMESCOPE = '_brutal_force_mem_alloc' - def __init__( - self, - config, - buff_size=100000, #1k bytes - unit_size=4, - ): - self.buff_size = buff_size - self.unit_size = unit_size - final_config = Configuration(self.default_config, config) + def __init__(self, config): + self.buff_size = self.config['buff_size'] + self.unit_size = self.config['unit_size'] self._type = {} - self._type[np.dtype(tf.float32.as_numpy_dtype)] = final_config['size_float'] - self._type[np.dtype(tf.int32.as_numpy_dtype)] = final_config['size_int'] - self._type[np.dtype(tf.quint8.as_numpy_dtype)] = final_config['size_uint8_t'] - self._type[np.dtype(tf.qint8.as_numpy_dtype)] = final_config['size_uint8_t'] - self._type[np.dtype(tf.qint32.as_numpy_dtype)] = final_config['size_int'] + self._type[np.dtype(tf.float32.as_numpy_dtype)] = self.config['size_float'] + self._type[np.dtype(tf.int32.as_numpy_dtype)] = self.config['size_int'] + self._type[np.dtype(tf.quint8.as_numpy_dtype)] = self.config['size_uint8_t'] + self._type[np.dtype(tf.qint8.as_numpy_dtype)] = self.config['size_uint8_t'] + self._type[np.dtype(tf.qint32.as_numpy_dtype)] = self.config['size_int'] def apply(self, ugraph): # ugraph.setup_data_manager(self.KWARGS_NAMESCOPE, {}) @@ -487,6 +481,8 @@ def default_config(cls): config['size_float'] = 4 config['size_int'] = 4 config['size_uint8_t'] = 1 + config['buff_size'] = 100000 #1k bytes + config['unit_size'] = 4 return config From f8a81d623d183186580ae3e95a6aebb52fa1f8d1 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 10 Mar 2020 11:07:38 +0800 Subject: [PATCH 059/117] Fix indentation --- utensor_cgen/frontend/tensorflow.py | 166 ++++++++++++++-------------- 1 file changed, 83 insertions(+), 83 deletions(-) diff --git a/utensor_cgen/frontend/tensorflow.py b/utensor_cgen/frontend/tensorflow.py index 64a42b8b..1a5be404 100644 --- a/utensor_cgen/frontend/tensorflow.py +++ b/utensor_cgen/frontend/tensorflow.py @@ -2,11 +2,11 @@ import os -import numpy as np import six + +import numpy as np import tensorflow as tf from google.protobuf import text_format - from utensor_cgen.frontend import FrontendSelector from utensor_cgen.frontend.base import Parser from utensor_cgen.ir.base import OperationInfo, TensorInfo, uTensorGraph @@ -17,89 +17,89 @@ @FrontendSelector.register(target_exts=[".pb", ".pbtxt"]) class GraphDefParser(Parser): - def parse(self, pb_file, output_nodes=None): - graph_def, graph_name = self._load_graph_def(pb_file) - if not self._is_freeze_graph(graph_def): - raise ValueError("Given graph_def is not freezed") - if output_nodes is None: - output_nodes = [node.name for node in graph_def.node] - logger.warning( - 'output_nodes is not given, use all nodes instead (may cause unexpected behaviour)' - ) + def parse(self, pb_file, output_nodes=None): + graph_def, graph_name = self._load_graph_def(pb_file) + if not self._is_freeze_graph(graph_def): + raise ValueError("Given graph_def is not freezed") + if output_nodes is None: + output_nodes = [node.name for node in graph_def.node] + logger.warning( + 'output_nodes is not given, use all nodes instead (may cause unexpected behaviour)' + ) - graph = tf.Graph() - with graph.as_default(): - tf.import_graph_def(graph_def, name="") - ugraph = uTensorGraph( - name=graph_name, output_nodes=output_nodes, lib_name="tensorflow", + graph = tf.Graph() + with graph.as_default(): + tf.import_graph_def(graph_def, name="") + ugraph = uTensorGraph( + name=graph_name, output_nodes=output_nodes, lib_name="tensorflow", + ) + for node in graph_def.node: + op = graph.get_operation_by_name(node.name) + in_tensors = [ + TensorInfo( + name=tensor.name, + ugraph=ugraph, + op_name=tensor.op.name, + dtype=np.dtype(tensor.dtype.as_numpy_dtype), + shape=self._tf_parse_tshape(tensor.shape), + ) + for tensor in op.inputs + ] + out_tensors = [ + TensorInfo( + name=tensor.name, + ugraph=ugraph, + op_name=op.name, + dtype=np.dtype(tensor.dtype.as_numpy_dtype), + shape=self._tf_parse_tshape(tensor.shape), ) - for node in graph_def.node: - op = graph.get_operation_by_name(node.name) - in_tensors = [ - TensorInfo( - name=tensor.name, - ugraph=ugraph, - op_name=tensor.op.name, - dtype=np.dtype(tensor.dtype.as_numpy_dtype), - shape=self._tf_parse_tshape(tensor.shape), - ) - for tensor in op.inputs - ] - out_tensors = [ - TensorInfo( - name=tensor.name, - ugraph=ugraph, - op_name=op.name, - dtype=np.dtype(tensor.dtype.as_numpy_dtype), - shape=self._tf_parse_tshape(tensor.shape), - ) - for tensor in op.outputs - ] - op_type = node.op - op_attr = node.attr - op_info = OperationInfo( - name=node.name, - input_tensors=in_tensors, - n_inputs=len(in_tensors), - output_tensors=out_tensors, - n_outputs=len(out_tensors), - op_type=op_type, - lib_name="tensorflow", - op_attr=op_attr, - ugraph=ugraph, - ) - op_info.op_attr["tensorflow__device"] = node.device - ugraph.ops_info[node.name] = op_info - topologic_order_graph(ugraph) - ugraph = Legalizer.legalize(ugraph, {}) - return ugraph + for tensor in op.outputs + ] + op_type = node.op + op_attr = node.attr + op_info = OperationInfo( + name=node.name, + input_tensors=in_tensors, + n_inputs=len(in_tensors), + output_tensors=out_tensors, + n_outputs=len(out_tensors), + op_type=op_type, + lib_name="tensorflow", + op_attr=op_attr, + ugraph=ugraph, + ) + op_info.op_attr["tensorflow__device"] = node.device + ugraph.ops_info[node.name] = op_info + topologic_order_graph(ugraph) + ugraph = Legalizer.legalize(ugraph, {}) + return ugraph - @staticmethod - def _load_graph_def(pb_file): - if isinstance(pb_file, tf.GraphDef): - return pb_file, "tf_graph_{}".format(random_str(6)) - assert isinstance(pb_file, six.string_types) - graph_name, _ = os.path.splitext(os.path.basename(pb_file)) - graph_def = tf.GraphDef() - if pb_file[-3:] == ".pb": - with open(pb_file, "rb") as fid: - graph_def.ParseFromString(fid.read()) - elif pb_file[-6:] == ".pbtxt": - with open(pb_file, "r") as fid: - text_format.Parse(fid.read(), graph_def) - else: - raise ValueError("unknown file format: %s" % pb_file) - return graph_def, graph_name + @staticmethod + def _load_graph_def(pb_file): + if isinstance(pb_file, tf.GraphDef): + return pb_file, "tf_graph_{}".format(random_str(6)) + assert isinstance(pb_file, six.string_types) + graph_name, _ = os.path.splitext(os.path.basename(pb_file)) + graph_def = tf.GraphDef() + if pb_file[-3:] == ".pb": + with open(pb_file, "rb") as fid: + graph_def.ParseFromString(fid.read()) + elif pb_file[-6:] == ".pbtxt": + with open(pb_file, "r") as fid: + text_format.Parse(fid.read(), graph_def) + else: + raise ValueError("unknown file format: %s" % pb_file) + return graph_def, graph_name - @staticmethod - def _tf_parse_tshape(t_shape): - try: - shape = t_shape.as_list() - except ValueError: - shape = None - return shape + @staticmethod + def _tf_parse_tshape(t_shape): + try: + shape = t_shape.as_list() + except ValueError: + shape = None + return shape - @staticmethod - def _is_freeze_graph(graph_def): - is_frozen = all(node.op not in ["VariableV2"] for node in graph_def.node) - return is_frozen + @staticmethod + def _is_freeze_graph(graph_def): + is_frozen = all(node.op not in ["VariableV2"] for node in graph_def.node) + return is_frozen From 78e967859107f26327a6fc363236c91d421e46be Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 10 Mar 2020 14:16:24 +0800 Subject: [PATCH 060/117] making bakend part apply and transform abstract and put checks on them --- utensor_cgen/backend/base.py | 17 +++++++++--- utensor_cgen/utils.py | 50 +++++++++++++++++------------------- 2 files changed, 38 insertions(+), 29 deletions(-) diff --git a/utensor_cgen/backend/base.py b/utensor_cgen/backend/base.py index 075fbedf..a316b05b 100644 --- a/utensor_cgen/backend/base.py +++ b/utensor_cgen/backend/base.py @@ -1,5 +1,7 @@ +from abc import abstractmethod + from utensor_cgen.utils import (MUST_OVERWRITEN, Configuration, class_property, - parse_toml) + is_abstract, parse_toml) class _BackendBase(object): @@ -19,6 +21,7 @@ def __new__(cls, config, *args, **kwargs): self._config = config return self + @abstractmethod def apply(self, ugraph): """Applying side-effect to ugraph @@ -26,7 +29,8 @@ def apply(self, ugraph): such as adding attribute or creating files. """ raise NotImplementedError('base apply method invoked: %s' % self) - + + @abstractmethod def transform(self, ugraph): """Transform Graph @@ -47,10 +51,14 @@ def __call__(self, *args, **kwargs): def config(self): return self._config - def _validate_config(self, config): + def _validate_config(self, config, *args, **kwargs): assert isinstance(config, (dict, Configuration, type(None))), \ 'expecting {}, get {}'.format(dict, type(config)) + def _validate_abstracts(self, config, *args, **kwargs): + if is_abstract(self.apply) and is_abstract(self.transform): + raise ValueError('must overwrite at least one of apply or transorm: %s' % self) + class Backend(_BackendBase): """ @@ -59,6 +67,9 @@ class Backend(_BackendBase): - It will run through various check in ``__new__``, so it's better to access the value of config via ``self.config``, which is an instance of ``Configuration`` - It will make sure if users do not provide the value required, default one will be used + - You must at least implement one of ``apply`` or ``transform`` method + - ``apply`` will introduce side-effect on given ugraph **in place** and return nothing + - ``transform`` will create a new ugraph, applying side-effect on new ugraph and return it """ TARGET = MUST_OVERWRITEN diff --git a/utensor_cgen/utils.py b/utensor_cgen/utils.py index dbd9f74c..b51790b5 100644 --- a/utensor_cgen/utils.py +++ b/utensor_cgen/utils.py @@ -329,7 +329,6 @@ def topologic_order_graph(ugraph): """ ugraph.topo_order = get_topologic_order(ugraph, ugraph.output_nodes)[::-1] - def get_topologic_order(ugraph, init_nodes=None): """ return list of op names in topological order @@ -376,7 +375,6 @@ def visit(node_name): visit(node_name) return ops_torder - def ops_bfs_queue(ugraph, init_nodes=None): if init_nodes is None: init_nodes = [ @@ -397,7 +395,6 @@ def ops_bfs_queue(ugraph, init_nodes=None): bfs_deck.append(op) return bfs_deck - def prune_graph(ugraph): """ Remove nodes that is no longer needed *in-place* @@ -441,12 +438,35 @@ def prune_graph(ugraph): new_ugraph.ops_info.pop(op_name) return new_ugraph - def random_str(length=8): letters = ascii_letters+digits chars = [choice(letters) for _ in range(length)] return ''.join(chars) +def parse_toml(file_or_path): + if isinstance(file_or_path, str): + fid = open(file_or_path, 'r') + doc = _parse(fid.read()) + fid.close() + return doc + +def timed(func): + + @wraps(func) + def wrapped(*args, **kwargs): + start_time = time() + ret = func(*args, **kwargs) + end_time = time() + logger.info('collapsed time of calling %s: %0.4f seconds', func.__name__, end_time - start_time) + return ret + + return wrapped + +def is_abstract(func): + if isinstance(func, types.MethodType): + func = func.__func__ + return getattr(func, '__isabstractmethod__', False) + class class_property(object): @@ -476,15 +496,6 @@ def __getitem__(self, slice_obj): cls = type(self) return cls(funcs=self._funcs[slice_obj]) - -def parse_toml(file_or_path): - if isinstance(file_or_path, str): - fid = open(file_or_path, 'r') - doc = _parse(fid.read()) - fid.close() - return doc - - class Configuration(object): def __init__(self, defaults=None, user_config=None): """ @@ -552,16 +563,3 @@ def __repr__(self): ' user_config={} \n' ')' ).format(self._defaults, self._user_config) - - -def timed(func): - - @wraps(func) - def wrapped(*args, **kwargs): - start_time = time() - ret = func(*args, **kwargs) - end_time = time() - logger.info('collapsed time of calling %s: %0.4f seconds', func.__name__, end_time - start_time) - return ret - - return wrapped From ef50c059824432ee9cca63f79fe985e9e37a0cf2 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 10 Mar 2020 15:00:48 +0800 Subject: [PATCH 061/117] update templates - old mem alloc scheme is deprecated - update template files in favor of rearch --- .../snippets/templates/snippets/legacy/argmax_op.cpp | 4 ++-- .../templates/snippets/legacy/dequantize_op.cpp | 4 ++-- .../snippets/templates/snippets/legacy/max_op.cpp | 4 ++-- .../snippets/templates/snippets/legacy/min_op.cpp | 4 ++-- .../snippets/templates/snippets/legacy/qadd_op.cpp | 12 ++++++------ .../templates/snippets/legacy/qmatmul_op.cpp | 12 ++++++------ .../snippets/templates/snippets/legacy/qrelu_op.cpp | 12 ++++++------ .../templates/snippets/legacy/quantV2_op.cpp | 12 ++++++------ .../templates/snippets/legacy/requant_op.cpp | 12 ++++++------ .../templates/snippets/legacy/requant_range_op.cpp | 8 ++++---- .../templates/snippets/legacy/reshape_op.cpp | 4 ++-- 11 files changed, 44 insertions(+), 44 deletions(-) diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/argmax_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/argmax_op.cpp index 07073749..0e4ebe0f 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/argmax_op.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/argmax_op.cpp @@ -3,9 +3,9 @@ S_TENSOR {{sptr_name}}; {% endif %} { {% if ref_count %} - ctx.add(new RamTensor<{{out_dtype}}>({%if address %}{{address[0]}}{%endif%}), "{{output}}", {{ref_count}}); + ctx.add(new RamTensor<{{out_dtype}}>(), "{{output}}", {{ref_count}}); {% else %} - ctx.add(new RamTensor<{{out_dtype}}>({%if address %}{{address[0]}}{%endif%}), "{{output}}"); + ctx.add(new RamTensor<{{out_dtype}}>(), "{{output}}"); {% endif %} ctx.push(new ArgMaxOp<{{in_dtype}}, {{out_dtype}}>(), { {% for tname in inputs[:-1]%}"{{tname}}", {%endfor%}"{{inputs[-1]}}" }, diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/dequantize_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/dequantize_op.cpp index 9ee9f8e4..58882518 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/dequantize_op.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/dequantize_op.cpp @@ -3,9 +3,9 @@ S_TENSOR {{sptr_name}}; {% endif %} { {% if ref_count %} - ctx.add(new RamTensor<{{out_dtype}}>({%if address %}{{address[0]}}{%endif%}), "{{output}}", {{ref_count}}); + ctx.add(new RamTensor<{{out_dtype}}>(), "{{output}}", {{ref_count}}); {% else %} - ctx.add(new RamTensor<{{out_dtype}}>({%if address %}{{address[0]}}{%endif%}), "{{output}}"); + ctx.add(new RamTensor<{{out_dtype}}>(), "{{output}}"); {% endif %} ctx.push(new DequantizeOp(), { {% for tname in inputs[:-1]%}"{{tname}}", {%endfor%}"{{inputs[-1]}}" }, diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/max_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/max_op.cpp index 5ebb3fd0..356a25cd 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/max_op.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/max_op.cpp @@ -4,9 +4,9 @@ S_TENSOR {{sptr_name}}; { RamTensor<{{out_dtype}}>* out_tensor; {%if out_shape %} - out_tensor = new RamTensor<{{out_dtype}}>({ {%for dim in out_shape[:-1]%}{{dim}}, {%endfor%}{{out_shape[-1]}} }{%if address %}, {{address[0]}}{%endif%}); + out_tensor = new RamTensor<{{out_dtype}}>({ {%for dim in out_shape[:-1]%}{{dim}}, {%endfor%}{{out_shape[-1]}} }); {%else%} - out_tensor = new RamTensor<{{out_dtype}}>({%if address %}{{address[0]}}{%endif%}); + out_tensor = new RamTensor<{{out_dtype}}>(); {%endif%} {%if ref_count %} ctx.add(out_tensor, "{{output}}", {{ref_count}}); diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/min_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/min_op.cpp index c6272255..4bc2aab4 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/min_op.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/min_op.cpp @@ -4,9 +4,9 @@ S_TENSOR {{sptr_name}}; { RamTensor<{{out_dtype}}>* out_tensor; {% if out_shape %} - out_tensor = new RamTensor<{{out_dtype}}>({ {%for shape in out_shape[:-1]%}{{shape}}, {%endfor%}{{out_shape[-1]}} }{%if address %}, {{address[0]}}{%endif%}); + out_tensor = new RamTensor<{{out_dtype}}>({ {%for shape in out_shape[:-1]%}{{shape}}, {%endfor%}{{out_shape[-1]}} }); {% else %} - out_tensor = new RamTensor<{{out_dtype}}>({%if address %}{{address[0]}}{%endif%}); + out_tensor = new RamTensor<{{out_dtype}}>(); {% endif %} {% if ref_count%} ctx.add(out_tensor, "{{output}}", {{ref_count}}); diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/qadd_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/qadd_op.cpp index 47a3aadc..9a33c897 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/qadd_op.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/qadd_op.cpp @@ -3,13 +3,13 @@ S_TENSOR {%for sptr_name in sptr_names[:-1]%}{{sptr_name}}, {%endfor%} {{sptr_na {% endif %} { {% if ref_counts %} - ctx.add(new RamTensor<{{out_dtype}}>({%if address %}{{address[0]}}{%endif%}), "{{outputs[0]}}", {{ref_counts[0]}}); - ctx.add(new RamTensor({1}{%if address %}, {{address[1]}}{%endif%}), "{{outputs[1]}}", {{ref_counts[1]}}); - ctx.add(new RamTensor({1}{%if address %}, {{address[2]}}{%endif%}), "{{outputs[2]}}", {{ref_counts[2]}}); + ctx.add(new RamTensor<{{out_dtype}}>(), "{{outputs[0]}}", {{ref_counts[0]}}); + ctx.add(new RamTensor({1}), "{{outputs[1]}}", {{ref_counts[1]}}); + ctx.add(new RamTensor({1}), "{{outputs[2]}}", {{ref_counts[2]}}); {% else %} - ctx.add(new RamTensor<{{out_dtype}}>({%if address %}{{address[0]}}{%endif%}), "{{outputs[0]}}"); - ctx.add(new RamTensor({1}{%if address %}, {{address[1]}}{%endif%}), "{{outputs[1]}}"); - ctx.add(new RamTensor({1}{%if address %}, {{address[2]}}{%endif%}), "{{outputs[2]}}"); + ctx.add(new RamTensor<{{out_dtype}}>(), "{{outputs[0]}}"); + ctx.add(new RamTensor({1}), "{{outputs[1]}}"); + ctx.add(new RamTensor({1}), "{{outputs[2]}}"); {% endif %} ctx.push(new QuantizedAddOp<{{x_dtype}}, {{w_dtype}}, {{out_dtype}}>(), { {%for tname in inputs[:-1] %}"{{tname}}", {% endfor %} "{{inputs[-1]}}" }, diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/qmatmul_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/qmatmul_op.cpp index b09788cb..bf8320d1 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/qmatmul_op.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/qmatmul_op.cpp @@ -3,13 +3,13 @@ S_TENSOR {%for sptr_name in sptr_names[:-1]%}{{sptr_name}}, {%endfor%} {{sptr_na {% endif %} { {% if ref_counts %} - ctx.add(new RamTensor<{{out_dtype}}>({%if address %}{{address[0]}}{%endif%}), "{{outputs[0]}}", {{ref_counts[0]}}); - ctx.add(new RamTensor({1}{%if address %}, {{address[1]}}{%endif%}), "{{outputs[1]}}", {{ref_counts[1]}}); - ctx.add(new RamTensor({1}{%if address %}, {{address[2]}}{%endif%}), "{{outputs[2]}}", {{ref_counts[2]}}); + ctx.add(new RamTensor<{{out_dtype}}>(), "{{outputs[0]}}", {{ref_counts[0]}}); + ctx.add(new RamTensor({1}), "{{outputs[1]}}", {{ref_counts[1]}}); + ctx.add(new RamTensor({1}), "{{outputs[2]}}", {{ref_counts[2]}}); {% else %} - ctx.add(new RamTensor<{{out_dtype}}>({%if address %}{{address[0]}}{%endif%}), "{{outputs[0]}}"); - ctx.add(new RamTensor({1}{%if address %}, {{address[1]}}{%endif%}), "{{outputs[1]}}"); - ctx.add(new RamTensor({1}{%if address %}, {{address[2]}}{%endif%}), "{{outputs[2]}}"); + ctx.add(new RamTensor<{{out_dtype}}>(), "{{outputs[0]}}"); + ctx.add(new RamTensor({1}), "{{outputs[1]}}"); + ctx.add(new RamTensor({1}), "{{outputs[2]}}"); {% endif %} ctx.push(new QntMatMulOp<{{x_dtype}}, {{w_dtype}}, {{out_dtype}}>(), { {%for tname in inputs[:-1] %}"{{tname}}", {% endfor %} "{{inputs[-1]}}" }, diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/qrelu_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/qrelu_op.cpp index b4516e43..cac38f62 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/qrelu_op.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/qrelu_op.cpp @@ -3,13 +3,13 @@ S_TENSOR {%for sptr_name in sptr_names[:-1]%}{{sptr_name}}, {%endfor%} {{sptr_na {% endif %} { {%if ref_counts%} - ctx.add(new RamTensor<{{qout_dtype}}>({%if address %}{{address[0]}}{%endif%}), "{{outputs[0]}}", {{ref_counts[0]}}); - ctx.add(new RamTensor<{{out_dtypes[0]}}>({1}{%if address %}, {{address[1]}}{%endif%}), "{{outputs[1]}}", {{ref_counts[1]}}); - ctx.add(new RamTensor<{{out_dtypes[1]}}>({1}{%if address %}, {{address[2]}}{%endif%}), "{{outputs[2]}}", {{ref_counts[2]}}); + ctx.add(new RamTensor<{{qout_dtype}}>(), "{{outputs[0]}}", {{ref_counts[0]}}); + ctx.add(new RamTensor<{{out_dtypes[0]}}>({1}), "{{outputs[1]}}", {{ref_counts[1]}}); + ctx.add(new RamTensor<{{out_dtypes[1]}}>({1}), "{{outputs[2]}}", {{ref_counts[2]}}); {%else%} - ctx.add(new RamTensor<{{qout_dtype}}>({%if address %}{{address[0]}}{%endif%}), "{{outputs[0]}}"); - ctx.add(new RamTensor<{{out_dtypes[0]}}>({1}{%if address %}, {{address[1]}}{%endif%}), "{{outputs[1]}}"); - ctx.add(new RamTensor<{{out_dtypes[1]}}>({1}{%if address %}, {{address[2]}}{%endif%}), "{{outputs[2]}}"); + ctx.add(new RamTensor<{{qout_dtype}}>(), "{{outputs[0]}}"); + ctx.add(new RamTensor<{{out_dtypes[0]}}>({1}), "{{outputs[1]}}"); + ctx.add(new RamTensor<{{out_dtypes[1]}}>({1}), "{{outputs[2]}}"); {%endif%} ctx.push(new QuantizedReluOp<{{in_dtype}}, {{out_dtypes[0]}}, {{qout_dtype}}>(), { {% for tname in inputs[:-1]%}"{{tname}}", {% endfor %}"{{inputs[-1]}}" }, diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/quantV2_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/quantV2_op.cpp index e5ca1067..6f55c1a4 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/quantV2_op.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/quantV2_op.cpp @@ -3,13 +3,13 @@ S_TENSOR {%for sptr_name in sptr_names[:-1]%}{{sptr_name}}, {%endfor%} {{sptr_na {% endif %} { {% if ref_counts%} - ctx.add(new RamTensor<{{out_dtype}}>({%if address %}{{address[0]}}{%endif%}), "{{outputs[0]}}", {{ref_counts[0]}}); - ctx.add(new RamTensor({1}{%if address %}, {{address[1]}}{%endif%}), "{{outputs[1]}}", {{ref_counts[1]}}); - ctx.add(new RamTensor({1}{%if address %}, {{address[2]}}{%endif%}), "{{outputs[2]}}", {{ref_counts[2]}}); + ctx.add(new RamTensor<{{out_dtype}}>(), "{{outputs[0]}}", {{ref_counts[0]}}); + ctx.add(new RamTensor({1}), "{{outputs[1]}}", {{ref_counts[1]}}); + ctx.add(new RamTensor({1}), "{{outputs[2]}}", {{ref_counts[2]}}); {% else %} - ctx.add(new RamTensor<{{out_dtype}}>({%if address %}{{address[0]}}{%endif%}), "{{outputs[0]}}"); - ctx.add(new RamTensor({1}{%if address %}, {{address[1]}}{%endif%}), "{{outputs[1]}}"); - ctx.add(new RamTensor({1}{%if address %}, {{address[2]}}{%endif%}), "{{outputs[2]}}"); + ctx.add(new RamTensor<{{out_dtype}}>(), "{{outputs[0]}}"); + ctx.add(new RamTensor({1}), "{{outputs[1]}}"); + ctx.add(new RamTensor({1}), "{{outputs[2]}}"); {% endif %} ctx.push(new QuantizeV2Op(), { {% for tname in inputs[:-1]%} "{{tname}}", {% endfor %}"{{inputs[-1]}}" }, diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/requant_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/requant_op.cpp index e2072e93..4bac1905 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/requant_op.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/requant_op.cpp @@ -3,13 +3,13 @@ S_TENSOR {%for sptr_name in sptr_names[:-1]%}{{sptr_name}}, {%endfor%} {{sptr_na {% endif %} { {%if ref_counts%} - ctx.add(new RamTensor<{{qout_dtype}}>({%if address %}{{address[0]}}{%endif%}), "{{outputs[0]}}", {{ref_counts[0]}}); - ctx.add(new RamTensor<{{range_dtype}}>({1}{%if address %}, {{address[1]}}{%endif%}), "{{outputs[1]}}", {{ref_counts[1]}}); - ctx.add(new RamTensor<{{range_dtype}}>({1}{%if address %}, {{address[2]}}{%endif%}), "{{outputs[2]}}", {{ref_counts[2]}}); + ctx.add(new RamTensor<{{qout_dtype}}>(), "{{outputs[0]}}", {{ref_counts[0]}}); + ctx.add(new RamTensor<{{range_dtype}}>({1}), "{{outputs[1]}}", {{ref_counts[1]}}); + ctx.add(new RamTensor<{{range_dtype}}>({1}), "{{outputs[2]}}", {{ref_counts[2]}}); {%else%} - ctx.add(new RamTensor<{{qout_dtype}}>({%if address %}{{address[0]}}{%endif%}), "{{outputs[0]}}"); - ctx.add(new RamTensor<{{range_dtype}}>({1}{%if address %}, {{address[1]}}{%endif%}), "{{outputs[1]}}"); - ctx.add(new RamTensor<{{range_dtype}}>({1}{%if address %}, {{address[2]}}{%endif%}), "{{outputs[2]}}"); + ctx.add(new RamTensor<{{qout_dtype}}>(), "{{outputs[0]}}"); + ctx.add(new RamTensor<{{range_dtype}}>({1}), "{{outputs[1]}}"); + ctx.add(new RamTensor<{{range_dtype}}>({1}), "{{outputs[2]}}"); {%endif%} ctx.push(new RequantizeOp(), { {% for tname in inputs[:-1]%}"{{tname}}", {% endfor %}"{{inputs[-1]}}" }, diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/requant_range_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/requant_range_op.cpp index 422a59ad..97e126cf 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/requant_range_op.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/requant_range_op.cpp @@ -3,11 +3,11 @@ S_TENSOR {%for sptr_name in sptr_names[:-1]%}{{sptr_name}}, {%endfor%} {{sptr_na {% endif %} { {%if ref_counts%} - ctx.add(new RamTensor<{{out_dtype}}>({1}{%if address %}, {{address[0]}}{%endif%}), "{{outputs[0]}}", {{ref_counts[0]}}); - ctx.add(new RamTensor<{{out_dtype}}>({1}{%if address %}, {{address[1]}}{%endif%}), "{{outputs[1]}}", {{ref_counts[1]}}); + ctx.add(new RamTensor<{{out_dtype}}>({1}), "{{outputs[0]}}", {{ref_counts[0]}}); + ctx.add(new RamTensor<{{out_dtype}}>({1}), "{{outputs[1]}}", {{ref_counts[1]}}); {%else%} - ctx.add(new RamTensor<{{out_dtype}}>({1}{%if address %}, {{address[0]}}{%endif%}), "{{outputs[0]}}"); - ctx.add(new RamTensor<{{out_dtype}}>({1}{%if address %}, {{address[1]}}{%endif%}), "{{outputs[1]}}"); + ctx.add(new RamTensor<{{out_dtype}}>({1}), "{{outputs[0]}}"); + ctx.add(new RamTensor<{{out_dtype}}>({1}), "{{outputs[1]}}"); {%endif%} ctx.push(new Requantization_RangeOp(), { {%for tname in inputs[:-1]%}"{{tname}}", {% endfor %}"{{inputs[-1]}}" }, diff --git a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/reshape_op.cpp b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/reshape_op.cpp index 3cf06fd9..092a5f3e 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/reshape_op.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/snippets/legacy/reshape_op.cpp @@ -3,9 +3,9 @@ S_TENSOR {{sptr_name}}; {% endif %} { {% if ref_count %} - ctx.add(new RamTensor<{{dtype}}>({%if address %}{{address[0]}}{%endif%}), "{{output}}", {{ref_count}}); + ctx.add(new RamTensor<{{dtype}}>(), "{{output}}", {{ref_count}}); {% else %} - ctx.add(new RamTensor<{{dtype}}>({%if address %}{{address[0]}}{%endif%}), "{{output}}"); + ctx.add(new RamTensor<{{dtype}}>(), "{{output}}"); {% endif %} ctx.push(new ReshapeOp(), { {% for tname in inputs[:-1]%}"{{tname}}", {%endfor%}"{{inputs[-1]}}" }, From 7670a0a27422c9dad70b9d1a17530d75ec21c6b7 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Wed, 11 Mar 2020 15:32:55 +0800 Subject: [PATCH 062/117] add tests --- tests/test_frontend/test_tensor_flow.py | 53 +++++++++++++++++++++++++ utensor_cgen/frontend/base.py | 4 ++ 2 files changed, 57 insertions(+) create mode 100644 tests/test_frontend/test_tensor_flow.py diff --git a/tests/test_frontend/test_tensor_flow.py b/tests/test_frontend/test_tensor_flow.py new file mode 100644 index 00000000..5d4979db --- /dev/null +++ b/tests/test_frontend/test_tensor_flow.py @@ -0,0 +1,53 @@ +import numpy as np +import tensorflow as tf + + +def test_scalar_shape(): + from utensor_cgen.frontend.tensorflow import GraphDefParser + + graph = tf.Graph() + with graph.as_default(): + tf.constant(1, dtype=tf.float32, name='x') + parser = GraphDefParser({}) + ugraph = parser.parse(graph.as_graph_def(), output_nodes=['x']) + # shape of scalar tensor should be empty list + out_tensor = ugraph.ops_info['x'].output_tensors[0] + assert out_tensor.shape == [] + assert out_tensor.dtype is np.dtype('float32') + +def test_placeholder_shape(): + from utensor_cgen.frontend.tensorflow import GraphDefParser + + graph = tf.Graph() + with graph.as_default(): + tf.placeholder(dtype=tf.float32, name='x') + parser = GraphDefParser({}) + ugraph = parser.parse(graph.as_graph_def(), output_nodes=['x']) + # nondeterministic shape, can be any shape + out_tensor = ugraph.ops_info['x'].output_tensors[0] + assert out_tensor.shape is None + assert out_tensor.dtype is np.dtype('float32') + + graph = tf.Graph() + with graph.as_default(): + tf.placeholder(dtype=tf.float32, name='x', shape=[None, 5]) + parser = GraphDefParser({}) + ugraph = parser.parse(graph.as_graph_def(), output_nodes=['x']) + # nondeterministic dimension + out_tensor = ugraph.ops_info['x'].output_tensors[0] + assert out_tensor.shape == [None, 5] + assert out_tensor.dtype is np.dtype('float32') + +def test_normal_tensor_shape(): + from utensor_cgen.frontend.tensorflow import GraphDefParser + shape = np.random.randint(1, 10, size=(10,)).tolist() + + graph = tf.Graph() + with graph.as_default(): + tf.constant(np.random.rand(*shape), dtype=tf.float32, name='x') + parser = GraphDefParser({}) + ugraph = parser.parse(graph.as_graph_def(), output_nodes=['x']) + # deterministic shape + out_tensor = ugraph.ops_info['x'].output_tensors[0] + assert out_tensor.shape == shape, 'expecting {}, get {}'.format(shape, out_tensor.shape) + assert out_tensor.dtype is np.dtype('float32') diff --git a/utensor_cgen/frontend/base.py b/utensor_cgen/frontend/base.py index 86131f65..e80792f7 100644 --- a/utensor_cgen/frontend/base.py +++ b/utensor_cgen/frontend/base.py @@ -11,6 +11,10 @@ def __new__(cls, config): self._config = config return self + @property + def config(self): + return self._config + @classmethod @abstractmethod def parse(cls, fname, outupt_nodes): From d17e873745bdcc453d6a9b95e96f33cfb6a57007 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Wed, 11 Mar 2020 16:03:12 +0800 Subject: [PATCH 063/117] Extensible OpEqualityDelegate - OpEqualityDelegate is designed to isolate the logic of "what is the equivalent operations of given op", which is the core of the isomorphic subgraph matching - Before this commit, such delegate is not extensible, which means it's exclusive to utensor backend (target dependent) - In this commit, I defines a base op eqality deleate class, OpEqualityDelegateBase, so that users can define target dependent delegate by subclassing this base class. --- .../test_permutation/test_match_patrn1.py | 11 +++-- .../test_matcher/test_replace/test_replace.py | 7 +-- .../code_generator/legacy/_operators.py | 10 ++-- utensor_cgen/matcher/__init__.py | 2 +- utensor_cgen/matcher/_matcher_impl.py | 47 ++++++++++++++----- utensor_cgen/transformer/conv_pool.py | 9 +++- utensor_cgen/transformer/linear_reoder.py | 9 +++- utensor_cgen/transformer/ns_transformer.py | 9 +++- 8 files changed, 76 insertions(+), 28 deletions(-) diff --git a/tests/test_matcher/test_permutation/test_match_patrn1.py b/tests/test_matcher/test_permutation/test_match_patrn1.py index 9c2664b4..d4c65b07 100644 --- a/tests/test_matcher/test_permutation/test_match_patrn1.py +++ b/tests/test_matcher/test_permutation/test_match_patrn1.py @@ -1,8 +1,11 @@ +# FIXME: remove uTensorOpEqualityDelegate import after we have generic op_eq_deleate +from utensor_cgen.backend.utensor.code_generator.legacy._operators import \ + uTensorOpEqualityDelegate from utensor_cgen.matcher import uTensorGraphMatcher def test_id_match(patrn_ugraph): - matcher = uTensorGraphMatcher(patrn_ugraph) + matcher = uTensorGraphMatcher(patrn_ugraph, op_equality_delegate=uTensorOpEqualityDelegate) matches = matcher.match(patrn_ugraph) assert matches, 'expecting matches, get {} matches'.format(len(matches)) match = matches[0] @@ -27,7 +30,7 @@ def test_id_match(patrn_ugraph): '{} is missing'.format(tensor.name) def test_match_sub1(patrn_ugraph, subject_ugraph1): - matcher = uTensorGraphMatcher(patrn_ugraph) + matcher = uTensorGraphMatcher(patrn_ugraph, op_equality_delegate=uTensorOpEqualityDelegate) matches = matcher.match_all(subject_ugraph1) assert matches, 'expecting matches, get {} matches'.format(len(matches)) match = matches[0] @@ -39,7 +42,7 @@ def test_match_sub1(patrn_ugraph, subject_ugraph1): assert match.patrn2subj_op_map['output'].name == 'sub_add1', match def test_match_sub1_1(patrn_ugraph, subject_ugraph1_1): - matcher = uTensorGraphMatcher(patrn_ugraph) + matcher = uTensorGraphMatcher(patrn_ugraph, op_equality_delegate=uTensorOpEqualityDelegate) matches = matcher.match(subject_ugraph1_1) assert matches, 'expecting matches, get {} matches'.format(len(matches)) match = matches[0] @@ -49,7 +52,7 @@ def test_match_sub1_1(patrn_ugraph, subject_ugraph1_1): assert match.patrn2subj_op_map['output'].name == 'sub_add1' def test_match_sub1_2(patrn_ugraph, subject_ugraph1_2): - matcher = uTensorGraphMatcher(patrn_ugraph) + matcher = uTensorGraphMatcher(patrn_ugraph, op_equality_delegate=uTensorOpEqualityDelegate) matches = matcher.match(subject_ugraph1_2) assert matches, 'expecting matches, get {} matches'.format(len(matches)) match = matches[0] diff --git a/tests/test_matcher/test_replace/test_replace.py b/tests/test_matcher/test_replace/test_replace.py index 0bfb96d4..4ca546d3 100644 --- a/tests/test_matcher/test_replace/test_replace.py +++ b/tests/test_matcher/test_replace/test_replace.py @@ -1,6 +1,7 @@ -import numpy as np import tensorflow as tf - +# FIXME: remove uTensorOpEqualityDelegate import after we have generic op_eq_deleate +from utensor_cgen.backend.utensor.code_generator.legacy._operators import \ + uTensorOpEqualityDelegate from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.matcher import uTensorGraphMatcher from utensor_cgen.utils import prune_graph, topologic_order_graph @@ -28,7 +29,7 @@ def callback(match): patrn_ugraph.ops_info['r_prime'].output_tensors[0]: ugraph.ops_info['fused_node'].output_tensors[0] } return ugraph, input_map, output_map - matcher = uTensorGraphMatcher(patrn_fc_1) + matcher = uTensorGraphMatcher(patrn_fc_1, op_equality_delegate=uTensorOpEqualityDelegate) matches = matcher.match(subj_graph_1) assert matches, 'no match found' match = matches[0] diff --git a/utensor_cgen/backend/utensor/code_generator/legacy/_operators.py b/utensor_cgen/backend/utensor/code_generator/legacy/_operators.py index e43980e6..fd1d9ac3 100644 --- a/utensor_cgen/backend/utensor/code_generator/legacy/_operators.py +++ b/utensor_cgen/backend/utensor/code_generator/legacy/_operators.py @@ -12,13 +12,15 @@ from utensor_cgen.ir.converter import (AttrValueConverter, DataTypeConverter, GenericTensorConverterMixin) from utensor_cgen.logger import logger -from utensor_cgen.matcher import OpEqualityDelegate, _morphism +from utensor_cgen.matcher import OpEqualityDelegateBase, _morphism from utensor_cgen.transformer.optimizer import RefCntOptimizer from utensor_cgen.utils import LazyLoader, NamescopedKWArgsParser __all__ = ['OperatorFactory', 'OpNotSupportedError'] tf = LazyLoader('tensorflow') +class uTensorOpEqualityDelegate(OpEqualityDelegateBase): pass + class OpNotSupportedError(Exception): pass @@ -86,7 +88,7 @@ def build_op_info(cls, ugraph, name, *args, **kwargs): @OperatorFactory.register -@OpEqualityDelegate.is_compatible_with("Inline", _morphism.Const2InlineMorphism) +@uTensorOpEqualityDelegate.is_compatible_with("Inline", _morphism.Const2InlineMorphism) class _ConstOperator(_Operator): op_type = "Const" @@ -157,7 +159,7 @@ def _tf_save_data(self, path, value): @OperatorFactory.register -@OpEqualityDelegate.is_associative( +@uTensorOpEqualityDelegate.is_associative( permutations=((0, 1), (1, 0)) ) class _AddOperator(_Operator): @@ -1137,7 +1139,7 @@ def __init__(self, op_info, **kwargs): @OperatorFactory.register -@OpEqualityDelegate.is_compatible_with("Const", _morphism.Inline2ConstMorphism) +@uTensorOpEqualityDelegate.is_compatible_with("Const", _morphism.Inline2ConstMorphism) class _InlineOperator(_Operator): op_type = "Inline" diff --git a/utensor_cgen/matcher/__init__.py b/utensor_cgen/matcher/__init__.py index 2522277b..58e0737b 100644 --- a/utensor_cgen/matcher/__init__.py +++ b/utensor_cgen/matcher/__init__.py @@ -1,2 +1,2 @@ -from ._matcher_impl import (OpEqualityDelegate, uTensorGraphMatch, +from ._matcher_impl import (OpEqualityDelegateBase, uTensorGraphMatch, uTensorGraphMatcher) diff --git a/utensor_cgen/matcher/_matcher_impl.py b/utensor_cgen/matcher/_matcher_impl.py index 4eca39e8..dc37f27c 100644 --- a/utensor_cgen/matcher/_matcher_impl.py +++ b/utensor_cgen/matcher/_matcher_impl.py @@ -5,6 +5,7 @@ import attr from attr.validators import instance_of from click import style +from six import with_metaclass from utensor_cgen.ir import (MetaOperationInfo, OperationInfo, uTensorGraph, uTensorGraphView) @@ -15,16 +16,30 @@ __all__ = ["uTensorGraphMatcher", "uTensorGraphMatch"] +class _OpEqualityDelegateMeta(type): + def __new__(mcls, name, bases, attribs): + if name != 'OpEqualityDelegateBase': + # op_type -> list[tuple] (permutations) + attribs['_association_map'] = {} + # op_type -> dict[op_type] -> morphism + attribs['_compatibility_map'] = {} + + cls = type.__new__(mcls, name, bases, attribs) + return cls + @attr.s(frozen=True, slots=True) -class OpEqualityDelegate(object): +class OpEqualityDelegateBase(with_metaclass(_OpEqualityDelegateMeta)): - # op_type -> list[tuple] (permutations) - _association_map = {} - # op_type -> dict[op_type] -> morphism - _compatibility_map = {} + def __new__(cls, *args, **kwargs): + if cls is OpEqualityDelegateBase: + raise RuntimeError('OpEqualityDelegateBase is abstract base class and should not be instantiated') + self = object.__new__(cls) + return self @classmethod def is_associative(cls, permutations): + if cls is OpEqualityDelegateBase: + raise RuntimeError('You should use OpEqualityDelegateBase directly, please create a subclass') def deco(op): if op.op_type in cls._association_map: raise ValueError( @@ -40,6 +55,8 @@ def deco(op): @classmethod def is_compatible_with(cls, other_op_type, morphism_type, **kwargs): + if cls is OpEqualityDelegateBase: + raise RuntimeError('You should use OpEqualityDelegateBase directly, please create a subclass') if not issubclass(morphism_type, Morphism): raise ValueError( 'expecting Morphism for `morphism`, get {}'.format(morphism_type) @@ -73,7 +90,8 @@ def query(cls, sub_op, patrn_op): equivalent_ops : List[OperationInfo] a list of equivalent ops derieved from `sub_op` """ - cls._setup() + if cls is OpEqualityDelegateBase: + raise RuntimeError('You should use OpEqualityDelegateBase directly, please create a subclass') is_eq = False equivalent_ops = [] if sub_op is None or patrn_op is None: @@ -105,11 +123,6 @@ def query(cls, sub_op, patrn_op): equivalent_ops = [MetaOperationInfo(op_info=sub_op, morphism=morphism)] return is_eq, equivalent_ops - - @classmethod - def _setup(cls): - # to activate all configurations - import utensor_cgen.backend.utensor.code_generator.legacy._operators @attr.s class uTensorGraphMatcher(object): @@ -126,7 +139,10 @@ class uTensorGraphMatcher(object): patrn_ugraph = ... # load the pattern uTensorGraph # create a matcher - matcher = uTensorGraphMatcher(pattern_ugraph=patrn_ugraph) + matcher = uTensorGraphMatcher( + pattern_ugraph=patrn_ugraph, + op_equality_delegate=delegate, # a subclass of :py:class:`OpEqualityDelegateBase` + ) # define a callback def callback(match): @@ -152,6 +168,11 @@ def callback(match): """ pattern_ugraph = attr.ib(validator=instance_of(uTensorGraph)) + _op_equality_delegate = attr.ib() + + def __attrs_post_init__(self): + assert issubclass(self._op_equality_delegate, OpEqualityDelegateBase), \ + 'expecting subclass of %s, get %s' % (OpEqualityDelegateBase, self._op_equality_delegate) def _match(self, other_ugraph): outputs_pool = [] @@ -227,7 +248,7 @@ def _visit(self, states): match = state.match sub_op = state.sub_bfs_queue.popleft() patrn_op = state.patrn_bfs_queue.popleft() - is_eq, eq_ops = OpEqualityDelegate.query(sub_op, patrn_op) + is_eq, eq_ops = self._op_equality_delegate.query(sub_op, patrn_op) if is_eq: for eq_op in eq_ops: new_sub_bfs_queue = deque(state.sub_bfs_queue) diff --git a/utensor_cgen/transformer/conv_pool.py b/utensor_cgen/transformer/conv_pool.py index d3de708a..4bae5ae5 100644 --- a/utensor_cgen/transformer/conv_pool.py +++ b/utensor_cgen/transformer/conv_pool.py @@ -7,6 +7,9 @@ from copy import deepcopy import tensorflow as tf +# FIXME: remove uTensorOpEqualityDelegate import after we have generic ops +from utensor_cgen.backend.utensor.code_generator.legacy._operators import \ + uTensorOpEqualityDelegate from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.ir import OperationInfo, TensorInfo, uTensorGraph from utensor_cgen.matcher import uTensorGraphMatcher @@ -49,7 +52,11 @@ def pattern_ugraph(self): def transform(self, ugraph): if ugraph.lib_name != 'tensorflow': raise ValueError('only support tensorflow graph') - matcher = uTensorGraphMatcher(pattern_ugraph=self.pattern_ugraph) + # FIXME: should use a generic op_equality_delegate + matcher = uTensorGraphMatcher( + pattern_ugraph=self.pattern_ugraph, + op_equality_delegate=uTensorOpEqualityDelegate + ) matches = matcher.match(ugraph, n=1) while matches: match = matches[0] diff --git a/utensor_cgen/transformer/linear_reoder.py b/utensor_cgen/transformer/linear_reoder.py index 68f8a204..2d421e3c 100644 --- a/utensor_cgen/transformer/linear_reoder.py +++ b/utensor_cgen/transformer/linear_reoder.py @@ -5,6 +5,9 @@ """ import tensorflow as tf +# FIXME: remove uTensorOpEqualityDelegate import after we have generic ops +from utensor_cgen.backend.utensor.code_generator.legacy._operators import \ + uTensorOpEqualityDelegate from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.matcher import uTensorGraphMatcher from utensor_cgen.utils import prune_graph, topologic_order_graph @@ -37,7 +40,11 @@ def pattern_ugraph(self): return pattern_ugraph def transform(self, ugraph): - matcher = uTensorGraphMatcher(pattern_ugraph=self.pattern_ugraph) + # FIXME: should use a generic op_equality_delegate + matcher = uTensorGraphMatcher( + pattern_ugraph=self.pattern_ugraph, + op_equality_delegate=uTensorOpEqualityDelegate + ) matches = matcher.match(ugraph, 1) while matches: match = matches[0] diff --git a/utensor_cgen/transformer/ns_transformer.py b/utensor_cgen/transformer/ns_transformer.py index d8e6bc19..d3e68a32 100644 --- a/utensor_cgen/transformer/ns_transformer.py +++ b/utensor_cgen/transformer/ns_transformer.py @@ -10,6 +10,9 @@ import numpy as np import tensorflow as tf +# FIXME: remove uTensorOpEqualityDelegate import after we have generic ops +from utensor_cgen.backend.utensor.code_generator.legacy._operators import \ + uTensorOpEqualityDelegate from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.ir import OperationInfo, uTensorGraph from utensor_cgen.logger import logger @@ -206,7 +209,11 @@ def transform(self, ugraph): return new_ugraph def _transform_tf(self, ugraph): - matcher = uTensorGraphMatcher(pattern_ugraph=self.pattern_ugraph) + # FIXME: should use a generic op_equality_delegate + matcher = uTensorGraphMatcher( + pattern_ugraph=self.pattern_ugraph, + op_equality_delegate=uTensorOpEqualityDelegate + ) matches = matcher.match(ugraph, n=1) while matches: match = matches[0] From 6feccbef345f920e06cbf3092952b6d85428c7a1 Mon Sep 17 00:00:00 2001 From: kazami Date: Wed, 11 Mar 2020 18:38:49 +0800 Subject: [PATCH 064/117] modify for brutal force version of memory planner --- utensor_cgen/backend/graph_lower/__init__.py | 1 + utensor_cgen/backend/graph_lower/generic_graph_lower.py | 8 +++++--- utensor_cgen/backend/utensor/_backend_impl.py | 5 +++-- 3 files changed, 9 insertions(+), 5 deletions(-) diff --git a/utensor_cgen/backend/graph_lower/__init__.py b/utensor_cgen/backend/graph_lower/__init__.py index 403c0958..7e9dcad6 100644 --- a/utensor_cgen/backend/graph_lower/__init__.py +++ b/utensor_cgen/backend/graph_lower/__init__.py @@ -4,3 +4,4 @@ That is, it should work with any uTensorGraph for any backend. """ from .generic_graph_lower import TensorAllocationPlanner +from .generic_graph_lower import BrutalForceMemoryPlanner diff --git a/utensor_cgen/backend/graph_lower/generic_graph_lower.py b/utensor_cgen/backend/graph_lower/generic_graph_lower.py index 05f12d4b..d2f1bdc7 100644 --- a/utensor_cgen/backend/graph_lower/generic_graph_lower.py +++ b/utensor_cgen/backend/graph_lower/generic_graph_lower.py @@ -9,6 +9,7 @@ from copy import deepcopy from itertools import chain, combinations, product from math import ceil, log10 +import pdb import numpy as np import tensorflow as tf @@ -266,6 +267,7 @@ def __init__(self, config): self._type[np.dtype(tf.quint8.as_numpy_dtype)] = self.config['size_uint8_t'] self._type[np.dtype(tf.qint8.as_numpy_dtype)] = self.config['size_uint8_t'] self._type[np.dtype(tf.qint32.as_numpy_dtype)] = self.config['size_int'] + self._type[np.dtype(tf.int64.as_numpy_dtype)] = self.config['size_int'] def apply(self, ugraph): # ugraph.setup_data_manager(self.KWARGS_NAMESCOPE, {}) @@ -286,7 +288,7 @@ def apply(self, ugraph): ) space_alloc = SpaceAllocation( offset_start=allocate_table[in_o.name]['offsetstart'], - offset_end=allocate_table[in_o.name]['offsetend'], + size=allocate_table[in_o.name]['offsetend'] - allocate_table[in_o.name]['offsetstart'], ) if allocate_table[in_o.name]['offsetend'] >= max_offset_end: max_offset_end = allocate_table[in_o.name]['offsetend'] @@ -298,7 +300,7 @@ def apply(self, ugraph): ) ) # new_ugraph.data_manager.address = (in_o.name, allocate_table[in_o.name]['offsetstart']) - out_t_infos = new_ugraph.ops_info[node_name].output_tensors + out_t_infos = ugraph.ops_info[node_name].output_tensors for out_o in out_t_infos: if out_o.name in allocate_table: time_alloc = TimeslotAllocation( @@ -307,7 +309,7 @@ def apply(self, ugraph): ) space_alloc = SpaceAllocation( offset_start=allocate_table[out_o.name]['offsetstart'], - offset_end=allocate_table[out_o.name]['offsetend'], + size=allocate_table[out_o.name]['offsetend'] - allocate_table[out_o.name]['offsetstart'], ) if allocate_table[out_o.name]['offsetend'] >= max_offset_end: max_offset_end = allocate_table[out_o.name]['offsetend'] diff --git a/utensor_cgen/backend/utensor/_backend_impl.py b/utensor_cgen/backend/utensor/_backend_impl.py index 03b918a7..f245c60e 100644 --- a/utensor_cgen/backend/utensor/_backend_impl.py +++ b/utensor_cgen/backend/utensor/_backend_impl.py @@ -11,6 +11,7 @@ uTensorLegacyGraphLower = LazyAttrib(_op_lower, 'uTensorLegacyGraphLower') uTensorRearchGraphLower = LazyAttrib(_op_lower, 'uTensorRearchGraphLower') TensorAllocationPlanner = LazyAttrib(generic_graph_lower, 'TensorAllocationPlanner') +BrutalForceMemoryPlanner = LazyAttrib(generic_graph_lower, 'BrutalForceMemoryPlanner') PipelineTransformer = LazyAttrib(transformer, 'PipelineTransformer') del code_generator, _op_lower, generic_graph_lower @@ -31,7 +32,7 @@ def __init__( if config['legacy-api']: code_generator = code_generator or uTensorLegacyCodeGenerator(config=config[uTensorLegacyCodeGenerator.PART].to_dict()) graph_op_lower = graph_op_lower or uTensorLegacyGraphLower(config=config[uTensorLegacyGraphLower.PART].to_dict()) - graph_alloc_lower = graph_alloc_lower or TensorAllocationPlanner(config=config[TensorAllocationPlanner.PART].to_dict()) + graph_alloc_lower = graph_alloc_lower or BrutalForceMemoryPlanner(config=config[BrutalForceMemoryPlanner.PART].to_dict()) else: code_generator = code_generator or uTensorRearchCodeGenerator(config=config[uTensorRearchCodeGenerator.PART].to_dict()) graph_op_lower = graph_op_lower or uTensorRearchGraphLower(config=config[uTensorRearchGraphLower.PART].to_dict()) @@ -48,7 +49,7 @@ def default_config(cls): config[cls.TARGET] = {} config[cls.TARGET][cls.COMPONENT] = {} config[cls.TARGET][cls.COMPONENT]['legacy-api'] = True - config[cls.TARGET][cls.COMPONENT][TensorAllocationPlanner.PART] = TensorAllocationPlanner.default_config + config[cls.TARGET][cls.COMPONENT][BrutalForceMemoryPlanner.PART] = BrutalForceMemoryPlanner.default_config config[cls.TARGET][cls.COMPONENT][uTensorLegacyCodeGenerator.PART] = uTensorLegacyCodeGenerator.default_config config[cls.TARGET][cls.COMPONENT][uTensorLegacyGraphLower.PART] = uTensorLegacyGraphLower.default_config config[cls.TARGET][cls.COMPONENT][uTensorRearchCodeGenerator.PART] = uTensorRearchCodeGenerator.default_config From 4b5f8fd36d4192082ea3c8287e0f882da9a1dbe9 Mon Sep 17 00:00:00 2001 From: kazami Date: Wed, 11 Mar 2020 18:40:57 +0800 Subject: [PATCH 065/117] remove the unnecssary lib --- utensor_cgen/backend/graph_lower/generic_graph_lower.py | 1 - 1 file changed, 1 deletion(-) diff --git a/utensor_cgen/backend/graph_lower/generic_graph_lower.py b/utensor_cgen/backend/graph_lower/generic_graph_lower.py index d2f1bdc7..864c7f41 100644 --- a/utensor_cgen/backend/graph_lower/generic_graph_lower.py +++ b/utensor_cgen/backend/graph_lower/generic_graph_lower.py @@ -9,7 +9,6 @@ from copy import deepcopy from itertools import chain, combinations, product from math import ceil, log10 -import pdb import numpy as np import tensorflow as tf From 3dc3f031300fe0cef144839f620ece499f8a7b51 Mon Sep 17 00:00:00 2001 From: kazami Date: Wed, 11 Mar 2020 18:53:33 +0800 Subject: [PATCH 066/117] change the default setting of memory planner --- utensor_cgen/backend/utensor/_backend_impl.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/utensor_cgen/backend/utensor/_backend_impl.py b/utensor_cgen/backend/utensor/_backend_impl.py index f245c60e..826c1b4e 100644 --- a/utensor_cgen/backend/utensor/_backend_impl.py +++ b/utensor_cgen/backend/utensor/_backend_impl.py @@ -32,7 +32,7 @@ def __init__( if config['legacy-api']: code_generator = code_generator or uTensorLegacyCodeGenerator(config=config[uTensorLegacyCodeGenerator.PART].to_dict()) graph_op_lower = graph_op_lower or uTensorLegacyGraphLower(config=config[uTensorLegacyGraphLower.PART].to_dict()) - graph_alloc_lower = graph_alloc_lower or BrutalForceMemoryPlanner(config=config[BrutalForceMemoryPlanner.PART].to_dict()) + graph_alloc_lower = graph_alloc_lower or TensorAllocationPlanner(config=config[TensorAllocationPlanner.PART].to_dict()) else: code_generator = code_generator or uTensorRearchCodeGenerator(config=config[uTensorRearchCodeGenerator.PART].to_dict()) graph_op_lower = graph_op_lower or uTensorRearchGraphLower(config=config[uTensorRearchGraphLower.PART].to_dict()) @@ -49,7 +49,7 @@ def default_config(cls): config[cls.TARGET] = {} config[cls.TARGET][cls.COMPONENT] = {} config[cls.TARGET][cls.COMPONENT]['legacy-api'] = True - config[cls.TARGET][cls.COMPONENT][BrutalForceMemoryPlanner.PART] = BrutalForceMemoryPlanner.default_config + config[cls.TARGET][cls.COMPONENT][TensorAllocationPlanner.PART] = TensorAllocationPlanner.default_config config[cls.TARGET][cls.COMPONENT][uTensorLegacyCodeGenerator.PART] = uTensorLegacyCodeGenerator.default_config config[cls.TARGET][cls.COMPONENT][uTensorLegacyGraphLower.PART] = uTensorLegacyGraphLower.default_config config[cls.TARGET][cls.COMPONENT][uTensorRearchCodeGenerator.PART] = uTensorRearchCodeGenerator.default_config From 26e38f6a3e79837b8e1ab782b38e486f248ea115 Mon Sep 17 00:00:00 2001 From: kazami Date: Wed, 11 Mar 2020 18:54:47 +0800 Subject: [PATCH 067/117] remove the brutal force memory planner --- utensor_cgen/backend/utensor/_backend_impl.py | 1 - 1 file changed, 1 deletion(-) diff --git a/utensor_cgen/backend/utensor/_backend_impl.py b/utensor_cgen/backend/utensor/_backend_impl.py index 826c1b4e..03b918a7 100644 --- a/utensor_cgen/backend/utensor/_backend_impl.py +++ b/utensor_cgen/backend/utensor/_backend_impl.py @@ -11,7 +11,6 @@ uTensorLegacyGraphLower = LazyAttrib(_op_lower, 'uTensorLegacyGraphLower') uTensorRearchGraphLower = LazyAttrib(_op_lower, 'uTensorRearchGraphLower') TensorAllocationPlanner = LazyAttrib(generic_graph_lower, 'TensorAllocationPlanner') -BrutalForceMemoryPlanner = LazyAttrib(generic_graph_lower, 'BrutalForceMemoryPlanner') PipelineTransformer = LazyAttrib(transformer, 'PipelineTransformer') del code_generator, _op_lower, generic_graph_lower From ac08440b0f7cbabaa68dd01459c6cbfe13820d1a Mon Sep 17 00:00:00 2001 From: dboyliao Date: Fri, 13 Mar 2020 13:56:20 +0800 Subject: [PATCH 068/117] resolve review issues - making data alignment for offline memory planner configurable - fix typos (REAME.rst and code base) --- README.rst | 7 +++---- utensor_cgen/backend/base.py | 10 +++++----- .../backend/graph_lower/generic_graph_lower.py | 12 ++++++++++-- utensor_cgen/backend/utensor/snippets/_base.py | 10 +++++----- utensor_cgen/legalizer/base.py | 6 +++--- utensor_cgen/matcher/_morphism.py | 6 +++--- utensor_cgen/utils.py | 4 ++-- 7 files changed, 31 insertions(+), 24 deletions(-) diff --git a/README.rst b/README.rst index 000be69e..5cabcb70 100644 --- a/README.rst +++ b/README.rst @@ -241,14 +241,13 @@ Considering following simple multi layers perceptron (`simple_mnist.pb`_): \ |mlp-alloc-graph| -Once enabled the optimization transformer, ``tensor_alloc``, -which is an offline tensor memory allocation planner, +Once enabled the optimization transformer, ``tensor_alloc``, an offline tensor memory allocation planner, ``utensor-cli`` will generate ``uTensor`` runtime codes that use following optimized allocation plan: \ |mlp-alloc| -- On the y-axis, these are tensor names ordered by topological sorting -- On the x-axis, these are the memory span occupied by each tensor +- y-axis: tensor names ordered by topological sorting +- x-axis: these are the memory span occupied by each tensor .. offline-tensor-alloc-end diff --git a/utensor_cgen/backend/base.py b/utensor_cgen/backend/base.py index a316b05b..7b586937 100644 --- a/utensor_cgen/backend/base.py +++ b/utensor_cgen/backend/base.py @@ -1,6 +1,6 @@ from abc import abstractmethod -from utensor_cgen.utils import (MUST_OVERWRITEN, Configuration, class_property, +from utensor_cgen.utils import (MUST_OVERWRITE, Configuration, class_property, is_abstract, parse_toml) @@ -72,11 +72,11 @@ class Backend(_BackendBase): - ``transform`` will create a new ugraph, applying side-effect on new ugraph and return it """ - TARGET = MUST_OVERWRITEN + TARGET = MUST_OVERWRITE @classmethod def _validate_target(cls, config, *args, **kwargs): - if cls.TARGET is MUST_OVERWRITEN: + if cls.TARGET is MUST_OVERWRITE: raise ValueError( 'Every Backend must overwrite TARGET attribute: {}'.format(cls) ) @@ -93,11 +93,11 @@ def from_config(cls, config, *args, **kwargs): class BackendPart(Backend): - PART = MUST_OVERWRITEN + PART = MUST_OVERWRITE @classmethod def _validate_part(cls, config, *args, **kwargs): - if cls.PART is MUST_OVERWRITEN: + if cls.PART is MUST_OVERWRITE: raise ValueError( 'Every BackendPart must overwrite PART attribute: {}'.format(cls) ) diff --git a/utensor_cgen/backend/graph_lower/generic_graph_lower.py b/utensor_cgen/backend/graph_lower/generic_graph_lower.py index 864c7f41..6c20b773 100644 --- a/utensor_cgen/backend/graph_lower/generic_graph_lower.py +++ b/utensor_cgen/backend/graph_lower/generic_graph_lower.py @@ -92,6 +92,7 @@ def __init__(self, config): if self.aesthetic_kwargs['figsize'] == 'None': self.aesthetic_kwargs['figsize'] = None self.enabled = self.config['enabled'] + self.data_alignment = self.config['data_alignment'] self.dtype_size_map = self._parse_dtype_size_map(self.config) def apply(self, ugraph): @@ -209,9 +210,15 @@ def _solve_space_alloc(self, tensors_to_schedule, nonoverlap_map): return alloc_plan, opt_mempool_size def _compute_tensor_bytes_size(self, tensor_info): + """ + compute aligned memory size (in bytes) + """ size = tensor_info.size elem_size = self.dtype_size_map.get(tensor_info.dtype, tensor_info.dtype.itemsize) - return elem_size * size + total_size = elem_size * size + if total_size % self.data_alignment: + total_size = ceil(total_size / self.data_alignment) * self.data_alignment + return total_size @classmethod def _parse_dtype_size_map(cls, config): @@ -230,7 +237,7 @@ def _parse_dtype_size_map(cls, config): @class_property def default_config(cls): return { - 'max_pool_size': 1024*1024, # 1k bytes + 'max_pool_size': 1024*1024, # 1G bytes 'include_inputs': False, 'out_fname': 'None', 'aesthetic_kwargs': { @@ -242,6 +249,7 @@ def default_config(cls): 'rand_seed': 1111 }, 'enabled': True, + 'data_alignment': 2, # 2-bytes alignment 'dtype_size_map': { 'float': 4, 'double': 8, diff --git a/utensor_cgen/backend/utensor/snippets/_base.py b/utensor_cgen/backend/utensor/snippets/_base.py index c21cb721..49daf799 100644 --- a/utensor_cgen/backend/utensor/snippets/_base.py +++ b/utensor_cgen/backend/utensor/snippets/_base.py @@ -2,7 +2,7 @@ from abc import ABCMeta from copy import deepcopy -from utensor_cgen.utils import MUST_OVERWRITEN +from utensor_cgen.utils import MUST_OVERWRITE from .template_env import env as _env @@ -11,13 +11,13 @@ class SnippetBase(object): __metaclass__ = ABCMeta - __template_name__ = MUST_OVERWRITEN - __headers__ = MUST_OVERWRITEN + __template_name__ = MUST_OVERWRITE + __headers__ = MUST_OVERWRITE def __init__(self): - if self.__template_name__ is MUST_OVERWRITEN: + if self.__template_name__ is MUST_OVERWRITE: raise ValueError('must overwrite {}.__template_name__'.format(type(self))) - if self.__headers__ is MUST_OVERWRITEN: + if self.__headers__ is MUST_OVERWRITE: raise ValueError('must overwrite {}.__headers__'.format(type(self))) if not isinstance(self.__headers__, set): raise ValueError('__headers__ should be of type set, get {}'.format(type(self.__headers__))) diff --git a/utensor_cgen/legalizer/base.py b/utensor_cgen/legalizer/base.py index e401eb8b..10903ed3 100644 --- a/utensor_cgen/legalizer/base.py +++ b/utensor_cgen/legalizer/base.py @@ -1,9 +1,9 @@ -from utensor_cgen.utils import MUST_OVERWRITEN +from utensor_cgen.utils import MUST_OVERWRITE class LegalizerBase(object): - TARGET = MUST_OVERWRITEN + TARGET = MUST_OVERWRITE COMPONET = 'legalizer' def __new__(cls, config): @@ -11,7 +11,7 @@ def __new__(cls, config): raise TypeError( 'expecting dict as config, get {}'.format(type(config)) ) - if cls.TARGET is MUST_OVERWRITEN: + if cls.TARGET is MUST_OVERWRITE: raise ValueError('cls.TARGET must be overwriten') self = object.__new__(cls) self._config = config diff --git a/utensor_cgen/matcher/_morphism.py b/utensor_cgen/matcher/_morphism.py index 4013e7f1..9b537fcc 100644 --- a/utensor_cgen/matcher/_morphism.py +++ b/utensor_cgen/matcher/_morphism.py @@ -3,7 +3,7 @@ import attr -from utensor_cgen.utils import MUST_OVERWRITEN +from utensor_cgen.utils import MUST_OVERWRITE class Morphism(object): @@ -16,10 +16,10 @@ def apply(self, from_op): @attr.s class TypedMorphism(Morphism): - from_op_type = MUST_OVERWRITEN + from_op_type = MUST_OVERWRITE def __attrs_post_init__(self): - if self.from_op_type is MUST_OVERWRITEN: + if self.from_op_type is MUST_OVERWRITE: raise ValueError( "must overwrite {}.from_op_type".format(type(self).__name__) ) diff --git a/utensor_cgen/utils.py b/utensor_cgen/utils.py index b51790b5..3f47219f 100644 --- a/utensor_cgen/utils.py +++ b/utensor_cgen/utils.py @@ -20,7 +20,7 @@ from utensor_cgen.logger import logger __all__ = ["save_idx", "save_consts", "save_graph", "log_graph", - "NamescopedKWArgsParser", "NArgsParam", "MUST_OVERWRITEN"] + "NamescopedKWArgsParser", "NArgsParam", "MUST_OVERWRITE"] class LazyLoader(types.ModuleType): @@ -315,7 +315,7 @@ def __new__(cls, *args, **kwargs): return cls._obj -MUST_OVERWRITEN = _MustOverwrite() +MUST_OVERWRITE = _MustOverwrite() def topologic_order_graph(ugraph): From ef4f1cb404babcb88b4302fc9dfbe79612edf467 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Fri, 13 Mar 2020 14:08:47 +0800 Subject: [PATCH 069/117] Add data alignment attribute for SpaceAllocation --- utensor_cgen/backend/graph_lower/alloc_plan.py | 7 +++++++ utensor_cgen/backend/graph_lower/generic_graph_lower.py | 3 ++- 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/utensor_cgen/backend/graph_lower/alloc_plan.py b/utensor_cgen/backend/graph_lower/alloc_plan.py index 1f597ba1..e6416ee3 100644 --- a/utensor_cgen/backend/graph_lower/alloc_plan.py +++ b/utensor_cgen/backend/graph_lower/alloc_plan.py @@ -31,6 +31,7 @@ def __contains__(self, slot): class SpaceAllocation(object): offset_start = attr.ib(validator=instance_of(int)) size = attr.ib(validator=instance_of(int)) + data_alignment = attr.ib(validator=instance_of(int)) offset_end = attr.ib(init=False) def __attrs_post_init__(self): @@ -38,6 +39,12 @@ def __attrs_post_init__(self): 'invalid offset_start: %s' % self.offset_start assert self.size > 0, \ 'invalid size: %s' % self.size + errmsg = ( self.data_alignment > 1 and + 'the memory offset is not aligned: %s (not %ss aligned)' or + 'the memory offset is not aligned: %s (not %s aligned)' + ) + assert self.size % self.data_alignment == 0, \ + errmsg % (self.size, self.data_alignment) self.offset_end = self.offset_start + self.size - 1 def __contains__(self, offset): diff --git a/utensor_cgen/backend/graph_lower/generic_graph_lower.py b/utensor_cgen/backend/graph_lower/generic_graph_lower.py index 6c20b773..bf3a6048 100644 --- a/utensor_cgen/backend/graph_lower/generic_graph_lower.py +++ b/utensor_cgen/backend/graph_lower/generic_graph_lower.py @@ -202,7 +202,8 @@ def _solve_space_alloc(self, tensors_to_schedule, nonoverlap_map): for name, alloc in tensor_allocs.items(): alloc_plan[name] = SpaceAllocation( offset_start=solver.Value(alloc.start), - size=alloc.size + size=alloc.size, + data_alignment=self.data_alignment, ) logger.info('optimal tensor allocation plan solved, memory span: %i bytes', opt_mempool_size) else: From 2c2c9e6ac07a07d556a4edec18f979b95436f7de Mon Sep 17 00:00:00 2001 From: dboyliao Date: Fri, 13 Mar 2020 14:35:28 +0800 Subject: [PATCH 070/117] better logging messages --- .../backend/graph_lower/generic_graph_lower.py | 8 ++++++-- utensor_cgen/ir/base.py | 16 ++++++++++------ 2 files changed, 16 insertions(+), 8 deletions(-) diff --git a/utensor_cgen/backend/graph_lower/generic_graph_lower.py b/utensor_cgen/backend/graph_lower/generic_graph_lower.py index bf3a6048..22fbc484 100644 --- a/utensor_cgen/backend/graph_lower/generic_graph_lower.py +++ b/utensor_cgen/backend/graph_lower/generic_graph_lower.py @@ -197,7 +197,7 @@ def _solve_space_alloc(self, tensors_to_schedule, nonoverlap_map): status = solver.Solve(model) alloc_plan = {} opt_mempool_size = None - if solver.StatusName(status) == 'OPTIMAL': + if status == cp_model.OPTIMAL: opt_mempool_size = solver.Value(var_mempool_size) for name, alloc in tensor_allocs.items(): alloc_plan[name] = SpaceAllocation( @@ -205,9 +205,13 @@ def _solve_space_alloc(self, tensors_to_schedule, nonoverlap_map): size=alloc.size, data_alignment=self.data_alignment, ) - logger.info('optimal tensor allocation plan solved, memory span: %i bytes', opt_mempool_size) + logger.info('optimal tensor allocation plan solved, total memory required: %i bytes', opt_mempool_size) else: logger.info('tensor allocation plan not found, status: %s', solver.StatusName(status)) + if status == cp_model.INFEASIBLE: + logger.info( + 'the optimal plan is infeasible, please set `max_pool_size` a larger value: %s' % self.max_pool_size + ) return alloc_plan, opt_mempool_size def _compute_tensor_bytes_size(self, tensor_info): diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index 638f0328..6b336878 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -174,20 +174,24 @@ def is_null_tensor(self): @property def size(self): + if self.shape is None: + raise RuntimeError('nondeterministic shape has no size') if None in self.shape: logger.warning( - 'nondeterministic shape, implicit converting None to 1: %s, %s', + 'nondeterministic dimension detected, implicitly converting None to 1: %s, %s', self.name, self.shape, ) return reduce(lambda i, j: i*(j is None and 1 or j), self.shape, 1) def __deepcopy__(self, memo): - new_tensor = TensorInfo(name=self.name, - ugraph=memo['ugraph'], - op_name=self.op_name, - dtype=self.dtype, - shape=deepcopy(self.shape, memo)) + new_tensor = TensorInfo( + name=self.name, + ugraph=memo['ugraph'], + op_name=self.op_name, + dtype=self.dtype, + shape=deepcopy(self.shape, memo) + ) return new_tensor def __hash__(self): From 7d9d6eaeb9e380d233580612cb173e2ea7085e77 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sat, 14 Mar 2020 14:44:23 +0800 Subject: [PATCH 071/117] add comments --- utensor_cgen/backend/graph_lower/generic_graph_lower.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/utensor_cgen/backend/graph_lower/generic_graph_lower.py b/utensor_cgen/backend/graph_lower/generic_graph_lower.py index 22fbc484..5d87f9f6 100644 --- a/utensor_cgen/backend/graph_lower/generic_graph_lower.py +++ b/utensor_cgen/backend/graph_lower/generic_graph_lower.py @@ -219,9 +219,12 @@ def _compute_tensor_bytes_size(self, tensor_info): compute aligned memory size (in bytes) """ size = tensor_info.size + # use user-defined element size or fall back to numpy size elem_size = self.dtype_size_map.get(tensor_info.dtype, tensor_info.dtype.itemsize) total_size = elem_size * size - if total_size % self.data_alignment: + if total_size % self.data_alignment != 0: + # if not aligned, compute the smallest aligned size + # TODO: do we need to take care how data is zero-padded? total_size = ceil(total_size / self.data_alignment) * self.data_alignment return total_size From 6796df585004a548adef711a1235b433d5fd83c7 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Wed, 18 Mar 2020 17:21:23 +0800 Subject: [PATCH 072/117] resolve code review issues, including renaming and minor code refactoring. --- .../code_generator/rearch/_code_generator.py | 32 +++++-- utensor_cgen/frontend/onnx.py | 95 ++++++++++--------- utensor_cgen/legalizer/onnx.py | 4 +- 3 files changed, 77 insertions(+), 54 deletions(-) diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py b/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py index 90469b8a..3770f730 100644 --- a/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py +++ b/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py @@ -29,12 +29,9 @@ def __init__(self, config): self.meta_data_pool_size = final_config['meta_data_pool_size'] self.ram_data_pool_size = final_config['ram_data_pool_size'] self.model_dir = final_config['model_dir'].rstrip('/') - - def apply(self, ugraph): - src_fname = self.src_fname - if src_fname == 'None': - src_fname = '{}.cpp'.format(ugraph.name) - # 1. find all ops required + + def _find_required_ops(self, ugraph): + # find all ops required ops = set() placeholders = set() tensor_var_map = {} # tensor name -> var name @@ -48,7 +45,10 @@ def apply(self, ugraph): ops.add( OperatorFactory.get_opertor(op_info) ) - # 2. ops/tensors declaration + return ops, placeholders, tensor_var_map + + def _get_declare_snippets(self, ugraph, ops, tensor_var_map): + # get ops/tensors declaration snippets declare_snippets = [] ops_map = {} # op -> op variable name for i, op in enumerate(ops): @@ -74,6 +74,18 @@ def apply(self, ugraph): tensor=tensor ) ) + return ops_map, declare_snippets + + def apply(self, ugraph): + src_fname = self.src_fname + if src_fname == 'None': + src_fname = '{}.cpp'.format(ugraph.name) + ( + ops, # Set[OperationInfo] + placeholders, # Set[OperationInfo] + tensor_var_map, # dict, tensor name -> var name + ) = self._find_required_ops(ugraph) + ops_map, declare_snippets = self._get_declare_snippets(ugraph, ops, tensor_var_map) # 3. evaluation snippets eval_snippets = [] for op_name in ugraph.topo_order: @@ -134,7 +146,8 @@ def default_config(cls): config['ram_data_pool_size'] = 'auto' return config - def _compute_meta_data_size(self, ugraph): + def _compute_meta_data_size(self, ugraph, mem_optimizer=None): + # TODO: if mem_optimizer is None, use a default mem optimizer if self.meta_data_pool_size == 'auto': # TODO: compute actual meta data size with ugraph size = 256 @@ -142,7 +155,8 @@ def _compute_meta_data_size(self, ugraph): size = self.meta_data_pool_size return size - def _compute_ram_data_size(self, ugraph): + def _compute_ram_data_size(self, ugraph, mem_optimizer=None): + # TODO: if mem_optimizer is None, use a default mem optimizer if self.ram_data_pool_size == 'auto': # TODO: compute actual ram data size with ugraph size = 256 diff --git a/utensor_cgen/frontend/onnx.py b/utensor_cgen/frontend/onnx.py index c7767eda..92e735c7 100644 --- a/utensor_cgen/frontend/onnx.py +++ b/utensor_cgen/frontend/onnx.py @@ -7,7 +7,6 @@ import tensorflow as tf from onnx.onnx_pb import TensorProto from onnx_tf.backend import TensorflowBackend, prepare - from utensor_cgen.frontend import FrontendSelector from utensor_cgen.frontend.base import Parser from utensor_cgen.ir import OperationInfo, TensorInfo, uTensorGraph @@ -50,12 +49,47 @@ def parse(self, onnx_file, output_nodes=None): ugraph = Legalizer.legalize(ugraph) tf.reset_default_graph() return ugraph - + def _build_graph(self, onnx_graph, ugraph): - op_types_cnt = Counter() - tensor_names_map = {} - # add const ops - params_dict = self._get_params_dict(onnx_graph) + op_types_cnt = Counter() # op_type (str) -> count (int) + tensor_names_map = {} # tensor_name (str) -> tensor_info (TensorInfo) + # these methods will update inputs **inplace** + self._build_param_ops( + onnx_graph, ugraph, op_types_cnt, tensor_names_map + ) + self._build_input_ops( + onnx_graph, ugraph, op_types_cnt, tensor_names_map + ) + self._build_intermediate_ops( + onnx_graph, ugraph, op_types_cnt, tensor_names_map, + ) + # find outupt nodes + distinct_out_ops = set() + graph_output = set([v.name for v in onnx_graph.output]) + for name, tensor_info in tensor_names_map.items(): + if name in graph_output: + distinct_out_ops.add(tensor_info.op_name) + ugraph.output_nodes = list(distinct_out_ops) + topologic_order_graph(ugraph) + _PostProcessing.post_process(ugraph) + + def _build_param_ops(self, onnx_graph, ugraph, op_types_cnt, tensor_names_map): + """Find all tensors in initialization list in onnx_graph, normally constants + + Note that this method will update op_types_cnt and tensor_names_map **inplace** + """ + # find Const ops + params_dict = {} + # FIXME: avoid using internal api of other library + dict_items = TensorflowBackend._onnx_initializer_to_input_dict_items(onnx_graph.initializer) + for name, tf_tensor in dict_items: + params_dict[name] = AttrValueConverter.GenericType( + value_name='value', + value=TensorProtoConverter.get_generic_value( + tf_tensor.op.get_attr('value') + ) + ) + # build Const ops for tensor_name, tensor_value in params_dict.items(): cnt = op_types_cnt['Const'] node_name = self._format_node_name(tensor_name, 'Const', cnt) @@ -78,6 +112,13 @@ def _build_graph(self, onnx_graph, ugraph): 'value': tensor_value } ) + + def _build_input_ops(self, onnx_graph, ugraph, op_types_cnt, tensor_names_map): + """Find placeholders + That is, those ops in the input list but not in initialization list + + Note this method will update inputs **inplace** + """ # placeholders for value in onnx_graph.input: # value: ValueInfoProto @@ -113,18 +154,10 @@ def _build_graph(self, onnx_graph, ugraph): lib_name='onnx', op_attr={} ) - for node in onnx_graph.node: - cnt = op_types_cnt[node.op_type] - node_name = self._format_node_name(node.name, node.op_type, cnt) - for i, tensor_name in enumerate(node.output): - tensor_names_map[tensor_name] = TensorInfo( - name=self._format_tensor_name(tensor_name, node_name, i), - op_name=node_name, - dtype=None, - shape=None, - ugraph=ugraph, - ) - node_names_map = {} + + def _build_intermediate_ops(self, onnx_graph, ugraph, op_types_cnt, tensor_names_map): + """Build all intermediate nodes, the nodes that is not in neither initialization list nor input list + """ # create all outupt tensors for node in onnx_graph.node: cnt = op_types_cnt[node.op_type] @@ -138,8 +171,6 @@ def _build_graph(self, onnx_graph, ugraph): shape=None, ugraph=ugraph ) - if node.name: - node_names_map[node.name] = node_name # create ops for node in onnx_graph.node: input_tensors = [ @@ -148,7 +179,6 @@ def _build_graph(self, onnx_graph, ugraph): output_tensors = [ tensor_names_map[name] for name in node.output ] - op_attr = { attrib_pb.name: _convert_op_attribute(attrib_pb) for attrib_pb in node.attribute @@ -163,16 +193,7 @@ def _build_graph(self, onnx_graph, ugraph): ugraph=ugraph, op_attr=op_attr ) - # find outupt nodes - distinct_out_tensors = set() - graph_output = set([v.name for v in onnx_graph.output]) - for name, tensor_info in tensor_names_map.items(): - if name in graph_output: - distinct_out_tensors.add(tensor_info.op_name) - ugraph.output_nodes = list(distinct_out_tensors) - topologic_order_graph(ugraph) - _PostProcessing.post_process(ugraph) - + def _format_node_name(self, node_name, op_type, op_cnt): if node_name == '': node_name = '{}_{}'.format(op_type, op_cnt) @@ -183,18 +204,6 @@ def _format_tensor_name(self, name, node_name, offset): return name return '{}:{}'.format(node_name, offset) - def _get_params_dict(self, onnx_graph): - dict_items = TensorflowBackend._onnx_initializer_to_input_dict_items(onnx_graph.initializer) - params_dict = {} - for name, tf_tensor in dict_items: - params_dict[name] = AttrValueConverter.GenericType( - value_name='value', - value=TensorProtoConverter.get_generic_value( - tf_tensor.op.get_attr('value') - ) - ) - return params_dict - class _PostProcessing(object): diff --git a/utensor_cgen/legalizer/onnx.py b/utensor_cgen/legalizer/onnx.py index 2a8fceba..d9459252 100644 --- a/utensor_cgen/legalizer/onnx.py +++ b/utensor_cgen/legalizer/onnx.py @@ -8,10 +8,10 @@ class OnnxLegalizer(LegalizerBase): TARGET = 'onnx' def legalize(self, ugraph): - self._visit(ugraph) + self._visit_all(ugraph) return ugraph - def _visit(self, ugraph): + def _visit_all(self, ugraph): for op_info in list(ugraph.ops_info.values()): visitor = getattr( self, From 319f6356c4b1857d0a00f27f986a43b4fe0cb9e8 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Fri, 20 Mar 2020 22:20:47 +0800 Subject: [PATCH 073/117] add new cnn pb file --- tests/deep_cnn/buggy.pb | Bin 0 -> 243447 bytes tests/deep_cnn/cifar10_cnn.pb | Bin 243254 -> 241272 bytes utensor_cgen/frontend/tensorflow.py | 6 +++--- 3 files changed, 3 insertions(+), 3 deletions(-) create mode 100644 tests/deep_cnn/buggy.pb diff --git a/tests/deep_cnn/buggy.pb b/tests/deep_cnn/buggy.pb new file mode 100644 index 0000000000000000000000000000000000000000..9d723332e18522bb284f7a7c6168181c11ef9f95 GIT binary patch literal 243447 zcmaI72~e=^}WEN3K znKOh$nIpdE|6A|-eeb*eYkg;}v(7r_tmh2-xv%To_jO-eey+TN%Ol*!0T2YYSW 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Subject: [PATCH 074/117] update README.rst --- README.rst | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 5cabcb70..eff75d7b 100644 --- a/README.rst +++ b/README.rst @@ -247,7 +247,8 @@ Once enabled the optimization transformer, ``tensor_alloc``, an offline tensor m \ |mlp-alloc| - y-axis: tensor names ordered by topological sorting -- x-axis: these are the memory span occupied by each tensor +- x-axis: these are the memory span occupied by each tensor, that is, the memory address offset and +the size of the tensor .. offline-tensor-alloc-end From 982bffd6450bb1e5ad6d8aea0cd507c6ae69a07b Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sat, 21 Mar 2020 21:54:38 +0800 Subject: [PATCH 075/117] resolve review issue: subroutines for rearch code generator and tests --- tests/simple_graph.pb | Bin 0 -> 198 bytes tests/test_backend/conftest.py | 14 ++++ .../data/simple_graph/params_simple_graph.hpp | 5 ++ tests/test_backend/test_utensor.py | 21 ++++++ .../backend/utensor/_graph_lower/_op_lower.py | 4 +- .../code_generator/rearch/_code_generator.py | 64 +++++++++++++----- .../templates/containers/rearch/simple.cpp | 6 +- 7 files changed, 91 insertions(+), 23 deletions(-) create mode 100644 tests/simple_graph.pb create mode 100644 tests/test_backend/data/simple_graph/params_simple_graph.hpp diff --git a/tests/simple_graph.pb b/tests/simple_graph.pb new file mode 100644 index 0000000000000000000000000000000000000000..895b9d7f8093298d61a28c46cfd2bf52fb4708a2 GIT binary patch literal 198 zcmd + +const float data_x_0 [ 1 ] = { 1.0, }; +const float data_y_0 [ 1 ] = { 2.0, }; +const float data_w_0 [ 1 ] = { 3.0, }; diff --git a/tests/test_backend/test_utensor.py b/tests/test_backend/test_utensor.py index 89770c22..20597a10 100644 --- a/tests/test_backend/test_utensor.py +++ b/tests/test_backend/test_utensor.py @@ -21,6 +21,27 @@ def test_legacy_utensor(mlp_ugraph): }) backend.apply(mlp_ugraph) +def test_rearch_utensor(simple_ugraph): + from utensor_cgen.backend.utensor import uTensorBackend + + this_dir = os.path.dirname(__file__) + + backend = uTensorBackend(config={ + 'utensor': { + 'backend': { + 'legacy-api': False, + 'rearch_code_generator': { + 'model_dir': os.path.join(this_dir, 'models'), + 'params_dir': os.path.join(this_dir, 'data'), + }, + 'pipeline_transformer': { + 'transform_methods': ['inline'] + } + }, + } + }) + backend.apply(simple_ugraph) + @pytest.mark.slow_test def test_offlinememory(mlp_ugraph): diff --git a/utensor_cgen/backend/utensor/_graph_lower/_op_lower.py b/utensor_cgen/backend/utensor/_graph_lower/_op_lower.py index 54ef5e2d..613a69cc 100644 --- a/utensor_cgen/backend/utensor/_graph_lower/_op_lower.py +++ b/utensor_cgen/backend/utensor/_graph_lower/_op_lower.py @@ -46,10 +46,8 @@ def get_new_optype(cls, op_type): return cls.NAME_MAP.get(op_type, op_type) def handle_tensorflow(self, ugraph): - new_ugraph = deepcopy(ugraph) - for op_info in new_ugraph.ops_info.values(): + for op_info in ugraph.ops_info.values(): op_info.op_type = self.OptypeRenameManager.get_new_optype(op_info.op_type) - return new_ugraph @class_property def default_config(cls): diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py b/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py index 3770f730..4949add9 100644 --- a/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py +++ b/utensor_cgen/backend/utensor/code_generator/rearch/_code_generator.py @@ -29,7 +29,38 @@ def __init__(self, config): self.meta_data_pool_size = final_config['meta_data_pool_size'] self.ram_data_pool_size = final_config['ram_data_pool_size'] self.model_dir = final_config['model_dir'].rstrip('/') - + + def apply(self, ugraph): + # take a look of template file "simple.cpp", which is under templates/container/rearch/ directory + # in submodule utensor_cgen.backend.utensor.snippets + # you will see how declare_snippets and eval_snippets works and have better understanding of + # the rearch code generator + src_fname = self.src_fname + if src_fname == 'None': + src_fname = '{}.cpp'.format(ugraph.name) + # find all required ops and the variable names for the tensors in the generate files + ( + ops, # Set[OperationInfo] + placeholders, # Set[OperationInfo] + tensor_var_map, # dict, tensor name -> var name + ) = self._find_required_ops(ugraph) + ( + ops_map, # dict, op_info -> variable name of op in the output files + declare_snippets, # list of Snippet objects, which will render code snippets for tensor declaration + weight_snippets, # snippets for generating weights header file + ) = self._get_declare_snippets(ugraph, ops, tensor_var_map) + # eval_snippets: List of snippet objects, which will render code snippets for tensor evaluation + eval_snippets = self._get_evaluation_snippets(ugraph, ops_map, tensor_var_map) + # generate files + self._generate_files( + ugraph, + placeholders, + tensor_var_map, + weight_snippets, + declare_snippets, + eval_snippets + ) + def _find_required_ops(self, ugraph): # find all ops required ops = set() @@ -51,11 +82,11 @@ def _get_declare_snippets(self, ugraph, ops, tensor_var_map): # get ops/tensors declaration snippets declare_snippets = [] ops_map = {} # op -> op variable name + weight_snippets = [] for i, op in enumerate(ops): op_var_name = 'op_{:03d}'.format(i) ops_map[op] = op_var_name declare_snippets.append(op.get_declare_snippet(op_var_name)) - weight_snippets = [] for op_info in filter(lambda op_info: op_info.op_type == 'Inline', ugraph.ops_info.values()): tensor = op_info.output_tensors[0] buffer_name = 'data_{}'.format(tensor.name.replace(':', '_').replace('/', '_')) @@ -74,19 +105,9 @@ def _get_declare_snippets(self, ugraph, ops, tensor_var_map): tensor=tensor ) ) - return ops_map, declare_snippets + return ops_map, declare_snippets, weight_snippets - def apply(self, ugraph): - src_fname = self.src_fname - if src_fname == 'None': - src_fname = '{}.cpp'.format(ugraph.name) - ( - ops, # Set[OperationInfo] - placeholders, # Set[OperationInfo] - tensor_var_map, # dict, tensor name -> var name - ) = self._find_required_ops(ugraph) - ops_map, declare_snippets = self._get_declare_snippets(ugraph, ops, tensor_var_map) - # 3. evaluation snippets + def _get_evaluation_snippets(self, ugraph, ops_map, tensor_var_map): eval_snippets = [] for op_name in ugraph.topo_order: op_info = ugraph.ops_info[op_name] @@ -97,6 +118,9 @@ def apply(self, ugraph): eval_snippets.append( op.get_eval_snippet(op_info, op_name, tensor_var_map) ) + return eval_snippets + + def _generate_files(self, ugraph, placeholders, tensor_var_map, weight_snippets, declare_snippets, eval_snippets): template_vars = {} template_vars['model_name'] = ugraph.name template_vars['meta_data_pool_size'] = self._compute_meta_data_size(ugraph) @@ -108,20 +132,24 @@ def apply(self, ugraph): for op_name in ugraph.output_nodes ]) ] - # 4. write files params_dir = Path(self.params_dir) / ugraph.name params_dir.mkdir(parents=True, exist_ok=True) weight_header_fname = None if weight_snippets: with (params_dir / 'params_{}.hpp'.format(ugraph.name)).open('w') as fid: - weight_container = ContextGlobalArrayContainer(snippets=weight_snippets) + weight_container = ContextGlobalArrayContainer( + snippets=weight_snippets + ) fid.write(weight_container.render()) weight_header_fname = fid.name - # # generate the computation function + # generate the compute function model_file_dir = Path(self.model_dir) header_fname = self.header_fname == 'None' and '{}.hpp'.format(ugraph.name) or self.header_fname - container_snippet = SimpleContainer(declare_snippets=declare_snippets, eval_snippests=eval_snippets) + container_snippet = SimpleContainer( + declare_snippets=declare_snippets, + eval_snippests=eval_snippets + ) container_snippet.template_vars.update(template_vars) (model_file_dir / ugraph.name).mkdir(parents=True, exist_ok=True) with (model_file_dir / ugraph.name / header_fname).open('w') as fid: diff --git a/utensor_cgen/backend/utensor/snippets/templates/containers/rearch/simple.cpp b/utensor_cgen/backend/utensor/snippets/templates/containers/rearch/simple.cpp index fb1c60c3..76571151 100644 --- a/utensor_cgen/backend/utensor/snippets/templates/containers/rearch/simple.cpp +++ b/utensor_cgen/backend/utensor/snippets/templates/containers/rearch/simple.cpp @@ -1,19 +1,21 @@ using namespace uTensor; +// start rendering declare snippets {%for snippet in declare_snippets%} {{snippet.render()}} {%endfor%} +// end of rendering declare snippets localCircularArenaAllocator<{{meta_data_pool_size}}> meta_allocator; localCircularArenaAllocator<{{ram_data_pool_size}}> ram_allocator; void compute_{{model_name}}({%for pl in placeholders%}Tensor& {{pl}}, {%endfor%}std::vector& outputs){ Context::get_default_context()->set_metadata_allocator(&meta_allocator); Context::get_default_context()->set_ram_data_allocator(&ram_allocator); - + // start rendering eval snippets {%for snippet in eval_snippets%} {{snippet.render()}} - {%endfor%} + // end of rendering eval snippets {%for out_var in out_tensor_var_names%} outputs.push_back({{out_var}}); {%endfor%} From f31f012d231a848ca284c1c2fc22def6f371e0d4 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sat, 21 Mar 2020 21:57:48 +0800 Subject: [PATCH 076/117] minor update for tests --- tests/test_backend/conftest.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/tests/test_backend/conftest.py b/tests/test_backend/conftest.py index b799f221..f33815f2 100644 --- a/tests/test_backend/conftest.py +++ b/tests/test_backend/conftest.py @@ -2,16 +2,19 @@ from pytest import fixture +test_dir = os.path.abspath( + os.path.join( + os.path.dirname(__file__), + '..' + ) +) @fixture(scope='session', name='mlp_ugraph') def mlp_ugraph(): from utensor_cgen.frontend import FrontendSelector model_file = os.path.join( - os.path.abspath( - os.path.join( - os.path.dirname(__file__), '..') - ), - 'deep_mlp/simple_mnist.pb' + test_dir, + 'deep_mlp/simple_mnist.pb' ) return FrontendSelector.parse(model_file, output_nodes=['y_pred']) @@ -19,10 +22,7 @@ def mlp_ugraph(): def simple_ugraph(): from utensor_cgen.frontend import FrontendSelector model_file = os.path.join( - os.path.abspath( - os.path.join( - os.path.dirname(__file__), '..') - ), - 'simple_graph.pb' + test_dir, + 'simple_graph.pb' ) return FrontendSelector.parse(model_file, output_nodes=['u']) From 97efefdbdc9698b57c553b850b201fd4997fcfe5 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 24 Mar 2020 00:49:55 +0800 Subject: [PATCH 077/117] Add attributes to _Operator (rearch) --- tests/test_backend/data/simple_graph/params_simple_graph.hpp | 5 ----- .../backend/utensor/code_generator/rearch/_operators.py | 3 +++ 2 files changed, 3 insertions(+), 5 deletions(-) delete mode 100644 tests/test_backend/data/simple_graph/params_simple_graph.hpp diff --git a/tests/test_backend/data/simple_graph/params_simple_graph.hpp b/tests/test_backend/data/simple_graph/params_simple_graph.hpp deleted file mode 100644 index 37a60559..00000000 --- a/tests/test_backend/data/simple_graph/params_simple_graph.hpp +++ /dev/null @@ -1,5 +0,0 @@ -#include - -const float data_x_0 [ 1 ] = { 1.0, }; -const float data_y_0 [ 1 ] = { 2.0, }; -const float data_w_0 [ 1 ] = { 3.0, }; diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_operators.py b/utensor_cgen/backend/utensor/code_generator/rearch/_operators.py index 9f34857a..e07c1f52 100644 --- a/utensor_cgen/backend/utensor/code_generator/rearch/_operators.py +++ b/utensor_cgen/backend/utensor/code_generator/rearch/_operators.py @@ -1,3 +1,5 @@ +from copy import deepcopy + from six import with_metaclass from utensor_cgen.backend.utensor.snippets._types import NP_TYPES_MAP @@ -61,6 +63,7 @@ def __new__(cls, op_info): self = object.__new__(cls) self.in_dtypes = in_dtypes self.out_dtypes = out_dtypes + self.attributes = deepcopy(op_info.op_attr) cls._cache[type_signature] = self return cls._cache[type_signature] From 1166e914b9ccc7998f84d3281624db2bf83768aa Mon Sep 17 00:00:00 2001 From: dboyliao Date: Wed, 1 Apr 2020 16:23:08 +0800 Subject: [PATCH 078/117] modify backend apply procedure utensor backend will be applied as following: 1. graph optimization 2. lowering to target specific graph 3. apply memory allocation planning 4. generate target files --- utensor_cgen/backend/utensor/_backend_impl.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/utensor_cgen/backend/utensor/_backend_impl.py b/utensor_cgen/backend/utensor/_backend_impl.py index 03b918a7..790a5815 100644 --- a/utensor_cgen/backend/utensor/_backend_impl.py +++ b/utensor_cgen/backend/utensor/_backend_impl.py @@ -57,10 +57,14 @@ def default_config(cls): return config def apply(self, ugraph): - self._graph_op_lower.apply(ugraph) - new_ugraph = self._graph_transformer.transform(ugraph) - self._graph_alloc_lower.apply(new_ugraph) - self._code_generator.apply(new_ugraph) + # 1. graph optimization + opt_ugraph = self._graph_transformer.transform(ugraph) + # 2. lowering to target specific graph + self._graph_op_lower.apply(opt_ugraph) + # 3. apply memory allocation planner + self._graph_alloc_lower.apply(opt_ugraph) + # 4. generate target files + self._code_generator.apply(opt_ugraph) def __call__(self, ugraph): return self.apply(ugraph) From c4bd90f502b2a4f8495ab95a693dae7c12ccd2d3 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Wed, 1 Apr 2020 18:43:44 +0800 Subject: [PATCH 079/117] nightly --- utensor_cgen/frontend/tflite.py | 268 +++++++++++++++++++++----------- 1 file changed, 175 insertions(+), 93 deletions(-) diff --git a/utensor_cgen/frontend/tflite.py b/utensor_cgen/frontend/tflite.py index be005694..d552d2c3 100644 --- a/utensor_cgen/frontend/tflite.py +++ b/utensor_cgen/frontend/tflite.py @@ -1,6 +1,7 @@ from __future__ import absolute_import import os import six +import re import numpy as np @@ -12,104 +13,185 @@ import flatbuffers from .tflite_flatbuffer.Model import Model +tensor_np_type = dict() +tensor_np_type[0] = np.float32 +tensor_np_type[1] = np.float16 +tensor_np_type[2] = np.int32 +tensor_np_type[3] = np.uint8 +tensor_np_type[4] = np.uint64 +tensor_np_type[5] = np.ubyte #FIXME: supposed to be string +tensor_np_type[6] = np.bool +tensor_np_type[7] = np.int16 +tensor_np_type[8] = np.cdouble +tensor_np_type[9] = np.int8 + +builtin_ops = {v: k for k, v in BuiltinOperator.__dict__.items()} + @FrontendSelector.register(target_exts=['.tflite']) class TFLiteParser(Parser): @classmethod - def parse(self, pb_file, output_nodes=None): - print("TF Lite Parser") - exit() -# buf = open(pb_file, 'rb').read() -# buf = bytearray(buf) -# model = Model.GetRootAsModel(buf, 0) -# #TODO: version check -# #print(model.Version()) - -# assert model.SubgraphsLength() == 1 -# subgraph = model.Subgraphs(0) - -# #table to convert opcode to op-class -# opcode_to_class= [] -# for i in range(0, model.OperatorCodesLength()): -# op_code = model.OperatorCodes(i) -# opcode_to_class.append(op_code) - -# #construct lookup objects for builtin ops -# from tflite.BuiltinOperator import BuiltinOperator -# from tflite.BuiltinOptions import BuiltinOptions - -# builtin_ops = {v: k for k, v in BuiltinOperator.__dict__.items()} -# op_options = {v: k for k, v in BuiltinOptions.__dict__.items()} -# builtin_option_class = self.get_builtin_option_class() - -# ugraph = uTensorGraph(output_nodes=output_nodes, -# backend="tensorflow") #FIXME: with the proper backend - -# #assuming this is the equivalent to ugraph's topological order -# for i in range(0, subgraph.OperatorsLength()): -# op = subgraph.Operators(i) -# opIndex = op.OpcodeIndex() -# op_class = opcode_to_class[opIndex] -# builtin_code = op_class.BuiltinCode() -# op_type = builtin_ops[builtin_code] #op's type in string - -# node_name = op_type + "_" + i - -# op_attr = dict() - -# if(op.CustomOptionsLength() < 1): -# option = builtin_option_class[op_type] -# builtin_data = op.BuiltinOptions() -# option.Init(builtin_data.Bytes, builtin_data.Pos) -# op_attr['option'] = option - -# if(op_type == 'FULLY_CONNECTED'): -# from tflite_flatbuffer.FullyConnectedOptionsWeightsFormat import FullyConnectedOptionsWeightsFormat -# w_formats = {v: k for k, v in FullyConnectedOptionsWeightsFormat.__dict__.items()} -# op_attr['weights_format'] = w_formats -# else: -# op_attr['custom_option'] = op.CustomOptionsAsNumpy() - -# fb_input_tensors = [subgraph.Tensors(input_idx) for input_idx in op.InputsAsNumpy()] -# in_tensors = [TensorInfo(name=tensor.Name(), -# ugraph=ugraph, -# op_name=tensor.op.name, #FIXME: who the fuck knows -# dtype=self.tensor_type_lookup(tensor.Type()), -# shape=tensor.ShapeAsNumpy()) -# for tensor in fb_input_tensors] - -# fb_output_tensors = [subgraph.Tensors(output_idx) for output_idx in op.OutputsAsNumpy()] -# out_tensors = [TensorInfo(name=tensor.Name(), -# ugraph=ugraph, -# op_name=tensor.op.name, #FIXME: who the fuck knows -# dtype=self.tensor_type_lookup(tensor.Type()), -# shape=tensor.ShapeAsNumpy()) -# for tensor in fb_output_tensors] - -# op_info = OperationInfo(name=node_name, -# input_tensors=in_tensors, -# output_tensors=out_tensors, -# op_type=op_type, -# backend='tensorflow', #FIXME: what should this be? -# op_attr=op_attr, -# ugraph=ugraph) - -# ugraph.ops_info[node_name] = op_info - -# topologic_order_graph(ugraph) -# return ugraph + def parse(self, tflite_file, output_nodes=None): + graph_name, _ = os.path.splitext(tflite_file) + buf = open(tflite_file, 'rb').read() + buf = bytearray(buf) + fb_model = Model.GetRootAsModel(buf, 0) + + ugraph = uTensorGraph( + name=graph_name, + output_nodes=[], + lib_name='tflite', + ops_info={}, + ) + + #print("TF Lite Parser") + + self._build_graph(fb_model, ugraph) + return ugraph + + def _build_graph(self, fb_model, ugraph): + self.tensor_names_map = {} #addresseed by indexi + self._build_tensor_map(fb_model, ugraph) + + self._build_param_ops(fb_model, ugraph) + #find and set input nodes + self._build_input_ops(fb_model, ugraph) + self._build_intermediate_ops(fb_model, ugraph) + #TODO: set output nodes + topologic_order_graph(ugraph) + + def _build_tensor_map(self, fb_model, ugraph): + subgraph = self._get_tflm_get_subgraph(fb_model) + for idx in range(0, subgraph.TensorsLength()): + tensor = subgraph.Tensors(idx) + + tensor_name = tensor.Name() + if tensor_name is '' or None: + tensor_name = 'tensor_' + str(idx) + + dtype=tensor_np_type[tensor.Type()] + + self.tensor_names_map[idx] = TensorInfo( + name=self._format_tensor_name('', tensor_name, 0), + op_name="", + dtype=dtype, + shape=tensor.ShapeAsNumpy(), + ugraph=ugraph + ) + + # 0 if intermediate + #buffer_index = tensor.Buffer() + #buffer_content = model.Buffers(buffer_index).DataAsNumpy().astype(dtype) + + #tensor.Type() + + # quantization missing + + def _build_param_ops(self, fb_model, ugraph): + """Find all tensors in initialization list in onnx_graph, normally constants + Note that this method will update op_types_cnt and tensor_names_map **inplace** + """ + subgraph = self._get_tflm_get_subgraph(fb_model) + + for idx in range(0, subgraph.TensorsLength()): + tensor = subgraph.Tensors(idx) + buffer_index = tensor.Buffer() + + if buffer_index == 0: + continue + + # TODO: quantization conversion + + node_name = self.tensor_names_map[idx].name + "_Const" + dtype = self.tensor_names_map[idx].dtype + + buffer_content = fb_model.Buffers(buffer_index).DataAsNumpy().astype(dtype) + + OperationInfo( + name=node_name, + input_tensors=[], + output_tensors=[self.tensor_names_map[idx]], + op_type='Const', + lib_name='tflm', + ugraph=ugraph, + op_attr={ + 'value': buffer_content + } + ) + + self._set_tensor_node(idx, node_name) + + + def _build_input_ops(self, fb_model, ugraph): + """Find placeholders + Attach placeholders to input tensors + Note this method will update inputs **inplace** + """ + subgraph_inputs_indexi = fb_model.InputsAsNumpy() + for index in subgraph_inputs_indexi: + node_name = self.tensor_names_map[index].name + "_Placeholder" + self._set_tensor_node(index, node_name) + OperationInfo( + name=node_name, + input_tensors=[], + output_tensors=[self.tensor_names_map[index]], + op_type='Placeholder', + ugraph=ugraph, + lib_name='tflm', + op_attr={} + ) + + def _build_intermediate_ops(self, fb_model, ugraph): + """Build all intermediate nodes, the nodes that is not in neither initialization list nor input list + """ + subgraphs_len = fb_model.SubgraphsLength() + assert subgraphs_len == 1, "only 1 subgraph is supported" + subgraph = fb_model.Subgraphs(0) + for i in range(0, subgraph.OperatorsLength()): + #topological order, op-index defined by schema + #BuiltinOperator: https://github.com/tensorflow/tensorflow/blob/031804922d8f4d18b61e3ad077f9f1b69273ff21/tensorflow/lite/schema/schema_v3.fbs#L71 + op = subgraph.Operators(i) + local_op_code = op.OpcodeIndex() + global_op_code = fb_model.OperatorCodes(local_op_code) + builtinOperator_code = global_op_code.BuiltinCode() + op_type = builtin_ops[builtinOperator_code] + + node_name = str(i) + "_" + op_type + + input_tensor_names = [self.tensor_names_map[input_index].name for input_index in op.InputsAsNumpy()] + output_tensor_names = [self.tensor_names_map[output_index].name for output_index in op.OutputsAsNumpy()] + + OperationInfo( + name=node_name, + input_tensors=input_tensor_names, + output_tensors=output_tensor_names, + op_type=op_type, + ugraph=ugraph, + lib_name='tflm', + op_attr={} + ) -# def get_builtin_option_class(self): -# from tflite_flatbuffer.FullyConnectedOptions import FullyConnectedOptions -# from tflite_flatbuffer.ArgMaxOptions import ArgMaxOptions + for tensor_index in op.OutputsAsNumpy(): + self._set_tensor_node(tensor_index, node_name) -# table = dict() -# table['FULLY_CONNECTED'] = FullyConnectedOptions() -# table['ARG_MAX'] = ArgMaxOptions() + def _get_tflm_get_subgraph(self, fb_model): + subgraphs_len = fb_model.SubgraphsLength() + assert subgraphs_len == 1, "only 1 subgraph is supported" + subgraph = fb_model.Subgraphs(0) -# return table - -# def tensor_type_lookup(self, int_type): -# #see TensorType.py -# return -1 + return subgraph + + def _set_tensor_node(self, idx, name): + assert self.tensor_names_map[idx].op_name != "" + self.tensor_names_map[idx].op_name = name + + def _format_node_name(self, node_name, op_type, op_cnt): + if node_name == '': + node_name = '{}_{}'.format(op_type, op_cnt) + return re.sub(r'[\.:/]', '_', node_name) + + def _format_tensor_name(self, name, node_name, offset): + if re.match(r'[a-zA-Z][a-zA-Z0-9]*:[0-9]+', name): + return name + return '{}:{}'.format(node_name, offset) From 95a1c50d7a0a4ec15c516698b337c2996f0aedaf Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 7 Apr 2020 14:47:41 +0800 Subject: [PATCH 080/117] memory planner: make output tensors optionally included in the planning --- .../backend/graph_lower/alloc_plan.py | 10 +- .../graph_lower/generic_graph_lower.py | 28 +++--- .../code_generator/legacy/_operators.py | 97 ++++--------------- utensor_cgen/cli.py | 3 +- 4 files changed, 43 insertions(+), 95 deletions(-) diff --git a/utensor_cgen/backend/graph_lower/alloc_plan.py b/utensor_cgen/backend/graph_lower/alloc_plan.py index e6416ee3..6e76c792 100644 --- a/utensor_cgen/backend/graph_lower/alloc_plan.py +++ b/utensor_cgen/backend/graph_lower/alloc_plan.py @@ -109,11 +109,15 @@ def __setitem__(self, entity_name, alloc): self._plan[entity_name] = alloc def __getitem__(self, entity_name): - if entity_name not in self._plan: + if entity_name not in self.plan: raise KeyError('%s not found' % entity_name) + return self.plan[entity_name] + + def __contains__(self, entity_name): + return entity_name in self.plan def __delitem__(self, entity_name): - del self._plan[entity_name] + del self.plan[entity_name] def __getattr__(self, attr_name): - return getattr(self._plan, attr_name) + return getattr(self.plan, attr_name) diff --git a/utensor_cgen/backend/graph_lower/generic_graph_lower.py b/utensor_cgen/backend/graph_lower/generic_graph_lower.py index 5d87f9f6..01f94dc3 100644 --- a/utensor_cgen/backend/graph_lower/generic_graph_lower.py +++ b/utensor_cgen/backend/graph_lower/generic_graph_lower.py @@ -11,6 +11,7 @@ from math import ceil, log10 import numpy as np + import tensorflow as tf from ortools.sat.python import cp_model from utensor_cgen.backend.base import BackendPart @@ -85,6 +86,7 @@ class TensorAllocationPlanner(BackendPart): def __init__(self, config): self.max_pool_size = self.config['max_pool_size'] self.include_inputs = self.config['include_inputs'] + self.include_outputs = self.config['include_outputs'] self.out_fname = self.config['out_fname'] if self.out_fname == 'None': self.out_fname = None @@ -105,28 +107,22 @@ def apply(self, ugraph): if time_alloc_plan is None: TopoOrderTensorTimeslotPlanner(config={}).apply(ugraph) time_alloc_plan = ugraph.attributes[TopoOrderTensorTimeslotPlanner.KWARGS_NAMESCOPE] - tensors_to_ignore = set( - chain(*[ - op.output_tensors - for op in ugraph.get_ops_by_type('Inline') - ]) + ops_to_ignore = set( + ugraph.get_ops_by_type('Inline') ) if not self.include_inputs: - tensors_to_ignore.update( - chain(*[ - op.output_tensors - for op in ugraph.get_ops_by_type('Placeholder') - ]) + ops_to_ignore.update( + ugraph.get_ops_by_type('Placeholder') + ) + if not self.include_outputs: + ops_to_ignore.update( + ugraph.output_ops ) tensors_to_schedule = set() nonoverlap_map = defaultdict(set) for time_slot, op_name in enumerate(ugraph.topo_order): op_info = ugraph.ops_info[op_name] - # ignore inline ops - if op_info.op_type == 'Inline': - continue - # ignore placeholders if not included - if not self.include_inputs and op_info.op_type == 'Placeholder': + if op_info in ops_to_ignore: continue # all output tensor should not overlap with tensors that's still alive for out_tensor, known_tensor in product(op_info.output_tensors, tensors_to_schedule): @@ -206,6 +202,7 @@ def _solve_space_alloc(self, tensors_to_schedule, nonoverlap_map): data_alignment=self.data_alignment, ) logger.info('optimal tensor allocation plan solved, total memory required: %i bytes', opt_mempool_size) + logger.info('number of tensors allocated: %i' % len(alloc_plan)) else: logger.info('tensor allocation plan not found, status: %s', solver.StatusName(status)) if status == cp_model.INFEASIBLE: @@ -247,6 +244,7 @@ def default_config(cls): return { 'max_pool_size': 1024*1024, # 1G bytes 'include_inputs': False, + 'include_outputs': True, # for RNN, this may be False 'out_fname': 'None', 'aesthetic_kwargs': { 'split_on_large_graph': True, diff --git a/utensor_cgen/backend/utensor/code_generator/legacy/_operators.py b/utensor_cgen/backend/utensor/code_generator/legacy/_operators.py index fd1d9ac3..2ec7a58d 100644 --- a/utensor_cgen/backend/utensor/code_generator/legacy/_operators.py +++ b/utensor_cgen/backend/utensor/code_generator/legacy/_operators.py @@ -4,8 +4,9 @@ ''' import os -import idx2numpy as idx2np import numpy as np + +import idx2numpy as idx2np from utensor_cgen.backend.graph_lower import TensorAllocationPlanner from utensor_cgen.backend.utensor.snippets.legacy import * # pylint: disable=W0401,W0614 from utensor_cgen.ir import OperationInfo, TensorInfo @@ -86,6 +87,14 @@ def snippet(self): def build_op_info(cls, ugraph, name, *args, **kwargs): raise NotImplementedError('%s does not have build_op_info method' % cls) + def get_mem_address(self, op_info): + alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) + address = [] + if alloc_plan: + for tensor in op_info.output_tensors: + if tensor.name in alloc_plan: + address.append(alloc_plan[tensor.name].offset_start) + return address @OperatorFactory.register @uTensorOpEqualityDelegate.is_compatible_with("Inline", _morphism.Const2InlineMorphism) @@ -225,13 +234,7 @@ def __init__(self, op_info, **kwargs): ) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) - if alloc_plan is not None: - address = [ - alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors - ] - else: - address = [] + address = self.get_mem_address(op_info) self._snippet = ArgMaxOpSnippet(inputs, output, in_dtype, out_dtype, ref_count, to_eval, address) @classmethod @@ -292,13 +295,7 @@ def __init__(self, op_info, **kwargs): ) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) - if alloc_plan is not None: - address = [ - alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors - ] - else: - address = [] + address = self.get_mem_address(op_info) self._snippet = DequantizeOpSnippet(inputs, output, out_dtype, ref_count, to_eval, address) @@ -323,13 +320,7 @@ def __init__(self, op_info, **kwargs): ) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) - if alloc_plan is not None: - address = [ - alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors - ] - else: - address = [] + address = self.get_mem_address(op_info) self._snippet = MaxOpSnippet(inputs, output, out_dtype, out_shape, ref_count, to_eval, address) @classmethod @@ -545,13 +536,7 @@ def __init__(self, op_info, **kwargs): ) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) - if alloc_plan is not None: - address = [ - alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors - ] - else: - address = [] + address = self.get_mem_address(op_info) self._snippet = MinOpSnippet(inputs, output, out_dtype, out_shape, ref_count, to_eval, address) @classmethod @@ -611,13 +596,7 @@ def __init__(self, op_info, **kwargs): ) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) - if alloc_plan is not None: - address = [ - alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors - ] - else: - address = [] + address = self.get_mem_address(op_info) self._snippet = QuantizeV2OpSnippet(inputs, outputs, out_dtype, ref_counts, to_eval, address) @@ -704,13 +683,7 @@ def __init__(self, op_info, **kwargs): ) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) - if alloc_plan is not None: - address = [ - alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors - ] - else: - address = [] + address = self.get_mem_address(op_info) self._snippet = QuantizedMatMulOpSnippet(inputs, outputs, x_dtype, w_dtype, out_dtype, ref_counts, to_eval, address) @@ -781,13 +754,7 @@ def __init__(self, op_info, **kwargs): ) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) - if alloc_plan is not None: - address = [ - alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors - ] - else: - address = [] + address = self.get_mem_address(op_info) self._snippet = QuantizedReluOpSnippet(inputs, outputs, in_dtype, out_dtypes, qout_dtype, ref_counts, to_eval, address) @@ -811,13 +778,7 @@ def __init__(self, op_info, **kwargs): ) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) - if alloc_plan is not None: - address = [ - alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors - ] - else: - address = [] + address = self.get_mem_address(op_info) self._snippet = QuantizedAddOpSnippet(inputs, outputs, x_dtype, w_dtype, out_dtype, ref_counts, to_eval, address) @@ -858,11 +819,7 @@ def __init__(self, op_info, **kwargs): op_info.op_attr) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) - if alloc_plan is not None: - address = [alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors] - else: - address = [] + address = self.get_mem_address(op_info) self._snippet = RequantizationRangeOpSnippet(inputs, outputs, out_dtype, ref_counts, to_eval, address) @@ -883,13 +840,7 @@ def __init__(self, op_info, **kwargs): ) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) - if alloc_plan is not None: - address = [ - alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors - ] - else: - address = [] + address = self.get_mem_address(op_info) self._snippet = RequantizeOpSnippet(inputs, outputs, qout_dtype, range_dtype, ref_counts, to_eval, address) @@ -911,13 +862,7 @@ def __init__(self, op_info, **kwargs): ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) dtype = op_info.input_tensors[0].dtype - alloc_plan = op_info.ugraph.attributes.get(TensorAllocationPlanner.KWARGS_NAMESCOPE) - if alloc_plan is not None: - address = [ - alloc_plan.plan[tensor.name].offset_start for tensor in op_info.output_tensors - ] - else: - address = [] + address = self.get_mem_address(op_info) self._snippet = ReshapeOpSnippet(inputs, output, dtype, ref_count, to_eval, address) diff --git a/utensor_cgen/cli.py b/utensor_cgen/cli.py index 47dc149b..e0a18ca2 100644 --- a/utensor_cgen/cli.py +++ b/utensor_cgen/cli.py @@ -65,7 +65,8 @@ def generate_config(target, output): ) with open(output, 'w') as fid: fid.write( - '# https://github.com/toml-lang/toml\n' + '# utensor-cli version {}\n'.format(__version__) + \ + '# https://github.com/toml-lang/toml\n' + \ '# ..\n' ) fid.write( From e23a17bedddd1b2e2ff418ef724d6fa1dc0ab23e Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Tue, 7 Apr 2020 23:29:50 +0800 Subject: [PATCH 081/117] before testing --- utensor_cgen/frontend/tflite.py | 29 +++++++++++++++++++++-------- 1 file changed, 21 insertions(+), 8 deletions(-) diff --git a/utensor_cgen/frontend/tflite.py b/utensor_cgen/frontend/tflite.py index d552d2c3..82bb4e2d 100644 --- a/utensor_cgen/frontend/tflite.py +++ b/utensor_cgen/frontend/tflite.py @@ -25,12 +25,12 @@ tensor_np_type[8] = np.cdouble tensor_np_type[9] = np.int8 +from .tflite_flatbuffer.BuiltinOperator import BuiltinOperator builtin_ops = {v: k for k, v in BuiltinOperator.__dict__.items()} @FrontendSelector.register(target_exts=['.tflite']) class TFLiteParser(Parser): - @classmethod def parse(self, tflite_file, output_nodes=None): graph_name, _ = os.path.splitext(tflite_file) buf = open(tflite_file, 'rb').read() @@ -47,6 +47,7 @@ def parse(self, tflite_file, output_nodes=None): #print("TF Lite Parser") self._build_graph(fb_model, ugraph) + return ugraph def _build_graph(self, fb_model, ugraph): @@ -57,9 +58,22 @@ def _build_graph(self, fb_model, ugraph): #find and set input nodes self._build_input_ops(fb_model, ugraph) self._build_intermediate_ops(fb_model, ugraph) - #TODO: set output nodes + self._set_output_ops(fb_model, ugraph) + topologic_order_graph(ugraph) - + + def _set_output_ops(self, fb_model, ugraph): + """identfy output nodes in fb_mdel + sets output_nodes in ugraph + Note this method will update ugraph **inplace** + """ + subgraph_outputs_indexi = fb_model.OutputsAsNumpy() #tensor indexi + output_node_names = set() + for index in subgraph_outputs_indexi: + output_node_names.add(self.tensor_names_map[index].op_name) + + ugraph.output_nodes = list(output_node_names) + def _build_tensor_map(self, fb_model, ugraph): subgraph = self._get_tflm_get_subgraph(fb_model) @@ -72,11 +86,15 @@ def _build_tensor_map(self, fb_model, ugraph): dtype=tensor_np_type[tensor.Type()] + attributes = dict() + attributes['quantizationParam'] = tensor.Quantization() + self.tensor_names_map[idx] = TensorInfo( name=self._format_tensor_name('', tensor_name, 0), op_name="", dtype=dtype, shape=tensor.ShapeAsNumpy(), + attributes=attributes, ugraph=ugraph ) @@ -86,8 +104,6 @@ def _build_tensor_map(self, fb_model, ugraph): #tensor.Type() - # quantization missing - def _build_param_ops(self, fb_model, ugraph): """Find all tensors in initialization list in onnx_graph, normally constants Note that this method will update op_types_cnt and tensor_names_map **inplace** @@ -101,8 +117,6 @@ def _build_param_ops(self, fb_model, ugraph): if buffer_index == 0: continue - # TODO: quantization conversion - node_name = self.tensor_names_map[idx].name + "_Const" dtype = self.tensor_names_map[idx].dtype @@ -122,7 +136,6 @@ def _build_param_ops(self, fb_model, ugraph): self._set_tensor_node(idx, node_name) - def _build_input_ops(self, fb_model, ugraph): """Find placeholders Attach placeholders to input tensors From 373bbc574e31ec10635a69e1862e0e4cd1122670 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Thu, 9 Apr 2020 14:17:39 +0800 Subject: [PATCH 082/117] seems to work on the first glance, expecting devils in the detail --- utensor_cgen/frontend/tflite.py | 46 +++++++++++++++++++++------------ 1 file changed, 29 insertions(+), 17 deletions(-) diff --git a/utensor_cgen/frontend/tflite.py b/utensor_cgen/frontend/tflite.py index 82bb4e2d..9c4a9d87 100644 --- a/utensor_cgen/frontend/tflite.py +++ b/utensor_cgen/frontend/tflite.py @@ -9,21 +9,22 @@ from utensor_cgen.frontend import FrontendSelector from utensor_cgen.ir.base import TensorInfo, OperationInfo, uTensorGraph from utensor_cgen.utils import topologic_order_graph +from utensor_cgen.ir.converter import GenericTensorConverterMixin import flatbuffers from .tflite_flatbuffer.Model import Model tensor_np_type = dict() -tensor_np_type[0] = np.float32 -tensor_np_type[1] = np.float16 -tensor_np_type[2] = np.int32 -tensor_np_type[3] = np.uint8 -tensor_np_type[4] = np.uint64 -tensor_np_type[5] = np.ubyte #FIXME: supposed to be string -tensor_np_type[6] = np.bool -tensor_np_type[7] = np.int16 -tensor_np_type[8] = np.cdouble -tensor_np_type[9] = np.int8 +tensor_np_type[0] = np.dtype('float32') +tensor_np_type[1] = np.dtype('float16') +tensor_np_type[2] = np.dtype('int32') +tensor_np_type[3] = np.dtype('uint8') +tensor_np_type[4] = np.dtype('uint64') +tensor_np_type[5] = np.dtype('ubyte') #FIXME: supposed to be string +tensor_np_type[6] = np.dtype('bool') +tensor_np_type[7] = np.dtype('int16') +tensor_np_type[8] = np.dtype('cdouble') +tensor_np_type[9] = np.dtype('int8') from .tflite_flatbuffer.BuiltinOperator import BuiltinOperator builtin_ops = {v: k for k, v in BuiltinOperator.__dict__.items()} @@ -67,7 +68,8 @@ def _set_output_ops(self, fb_model, ugraph): sets output_nodes in ugraph Note this method will update ugraph **inplace** """ - subgraph_outputs_indexi = fb_model.OutputsAsNumpy() #tensor indexi + subgraph = self._get_tflm_get_subgraph(fb_model) + subgraph_outputs_indexi = subgraph.OutputsAsNumpy() #tensor indexi output_node_names = set() for index in subgraph_outputs_indexi: output_node_names.add(self.tensor_names_map[index].op_name) @@ -89,11 +91,17 @@ def _build_tensor_map(self, fb_model, ugraph): attributes = dict() attributes['quantizationParam'] = tensor.Quantization() + + if type(tensor.ShapeAsNumpy()) == np.ndarray: + shape = tensor.ShapeAsNumpy().tolist() + else: + shape = [d for d in fb_model.Buffers(12).DataAsNumpy().shape] + self.tensor_names_map[idx] = TensorInfo( name=self._format_tensor_name('', tensor_name, 0), op_name="", dtype=dtype, - shape=tensor.ShapeAsNumpy(), + shape=shape, attributes=attributes, ugraph=ugraph ) @@ -120,6 +128,9 @@ def _build_param_ops(self, fb_model, ugraph): node_name = self.tensor_names_map[idx].name + "_Const" dtype = self.tensor_names_map[idx].dtype + buffer_array = fb_model.Buffers(buffer_index).DataAsNumpy() + if type(buffer_array) == int: + continue #somehow, sometimes, the buffer contains no data buffer_content = fb_model.Buffers(buffer_index).DataAsNumpy().astype(dtype) OperationInfo( @@ -130,7 +141,7 @@ def _build_param_ops(self, fb_model, ugraph): lib_name='tflm', ugraph=ugraph, op_attr={ - 'value': buffer_content + 'value': GenericTensorConverterMixin.GenericType(np_array=buffer_content) } ) @@ -141,7 +152,8 @@ def _build_input_ops(self, fb_model, ugraph): Attach placeholders to input tensors Note this method will update inputs **inplace** """ - subgraph_inputs_indexi = fb_model.InputsAsNumpy() + subgraph = self._get_tflm_get_subgraph(fb_model) + subgraph_inputs_indexi = subgraph.InputsAsNumpy() for index in subgraph_inputs_indexi: node_name = self.tensor_names_map[index].name + "_Placeholder" self._set_tensor_node(index, node_name) @@ -172,8 +184,8 @@ def _build_intermediate_ops(self, fb_model, ugraph): node_name = str(i) + "_" + op_type - input_tensor_names = [self.tensor_names_map[input_index].name for input_index in op.InputsAsNumpy()] - output_tensor_names = [self.tensor_names_map[output_index].name for output_index in op.OutputsAsNumpy()] + input_tensor_names = [self.tensor_names_map[input_index] for input_index in op.InputsAsNumpy()] + output_tensor_names = [self.tensor_names_map[output_index] for output_index in op.OutputsAsNumpy()] OperationInfo( name=node_name, @@ -196,7 +208,7 @@ def _get_tflm_get_subgraph(self, fb_model): return subgraph def _set_tensor_node(self, idx, name): - assert self.tensor_names_map[idx].op_name != "" + assert self.tensor_names_map[idx].op_name == "" self.tensor_names_map[idx].op_name = name def _format_node_name(self, node_name, op_type, op_cnt): From 142d7520cb987b390f628bfb443214c22bf71730 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Sun, 12 Apr 2020 18:25:54 +0800 Subject: [PATCH 083/117] checkpoint before reworking the quantization info --- .gitignore | 5 +- .../backend/utensor/snippets/_types.py | 2 + utensor_cgen/frontend/tflite.py | 146 ++++++++++++++++-- 3 files changed, 142 insertions(+), 11 deletions(-) diff --git a/.gitignore b/.gitignore index f65ce068..1817ee1a 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,6 @@ +#system files +__pycache__ +.DS_Store # virtual env .venv venv @@ -20,4 +23,4 @@ tests/deep_mlp/data .vscode # docs doc/build -doc/source/_build +doc/source/_build \ No newline at end of file diff --git a/utensor_cgen/backend/utensor/snippets/_types.py b/utensor_cgen/backend/utensor/snippets/_types.py index 24f875ef..c3e72d2c 100644 --- a/utensor_cgen/backend/utensor/snippets/_types.py +++ b/utensor_cgen/backend/utensor/snippets/_types.py @@ -55,6 +55,8 @@ def _init(cls): tensor_type_str="uint16_t"), np.dtype('int8'): _TYPE_MAP_VALUE(importer_type_str="int8", tensor_type_str="q7_t"), + np.dtype('uint8'): _TYPE_MAP_VALUE(importer_type_str="uint8", + tensor_type_str="uint8_t"), } cls._inited = True diff --git a/utensor_cgen/frontend/tflite.py b/utensor_cgen/frontend/tflite.py index 9c4a9d87..1295894c 100644 --- a/utensor_cgen/frontend/tflite.py +++ b/utensor_cgen/frontend/tflite.py @@ -9,7 +9,7 @@ from utensor_cgen.frontend import FrontendSelector from utensor_cgen.ir.base import TensorInfo, OperationInfo, uTensorGraph from utensor_cgen.utils import topologic_order_graph -from utensor_cgen.ir.converter import GenericTensorConverterMixin +from utensor_cgen.ir.converter import GenericTensorConverterMixin, AttrValueConverter import flatbuffers from .tflite_flatbuffer.Model import Model @@ -29,6 +29,131 @@ from .tflite_flatbuffer.BuiltinOperator import BuiltinOperator builtin_ops = {v: k for k, v in BuiltinOperator.__dict__.items()} +from .tflite_flatbuffer.ActivationFunctionType import ActivationFunctionType +from .tflite_flatbuffer.FullyConnectedOptionsWeightsFormat import FullyConnectedOptionsWeightsFormat +from .tflite_flatbuffer.CustomOptionsFormat import CustomOptionsFormat + +def class_option2str(obj, idx): + names_lookup = {v: k for k, v in obj.__dict__.items()} + name = names_lookup[idx] + return str(idx) + " (" + name + ")" + +customOptionFormat_lookup = {v: k for k, v in CustomOptionsFormat.__dict__.items()} + +def fully_connected_op_data(op): + option_dict = {} + if(op.CustomOptionsLength() < 1): + from .tflite_flatbuffer.FullyConnectedOptions import FullyConnectedOptions + option = FullyConnectedOptions() + builtin_data = op.BuiltinOptions() + option.Init(builtin_data.Bytes, builtin_data.Pos) + option_dict["FusedActivationFunction"] = class_option2str(ActivationFunctionType, option.FusedActivationFunction()) + option_dict["w_formats"] = class_option2str(FullyConnectedOptionsWeightsFormat, option.WeightsFormat()) + else: + option_dict[customOptionFormat_lookup[op.CustomOptionsFormat()]] = op.CustomOptionsAsNumpy() + return option_dict + +def depthwise_conv2d_op_data(op): + option_dict = {} + if(op.CustomOptionsLength() < 1): + from .tflite_flatbuffer.DepthwiseConv2DOptions import DepthwiseConv2DOptions + option = DepthwiseConv2DOptions() + builtin_data = op.BuiltinOptions() + option.Init(builtin_data.Bytes, builtin_data.Pos) + option_dict["Padding"] = option.Padding() + option_dict["StrideW"] = option.StrideW() + option_dict["StrideH"] = option.StrideH() + option_dict["DepthMultiplier"] = option.DepthMultiplier() + option_dict["FusedActivationFunction"] = class_option2str(ActivationFunctionType, option.FusedActivationFunction()) + option_dict["DilationWFactor"] = option.DilationWFactor() + option_dict["DilationHFactor"] = option.DilationHFactor() + + else: + option_dict[customOptionFormat_lookup[op.CustomOptionsFormat()]] = op.CustomOptionsAsNumpy() + + return option_dict + +def reshape_op_data(op): + option_dict = {} + if(op.CustomOptionsLength() < 1): + from .tflite_flatbuffer.ReshapeOptions import ReshapeOptions + option = ReshapeOptions() + builtin_data = op.BuiltinOptions() + option.Init(builtin_data.Bytes, builtin_data.Pos) + option_dict["NewShape"] = option.NewShapeAsNumpy() + else: + option_dict[customOptionFormat_lookup[op.CustomOptionsFormat()]] = op.CustomOptionsAsNumpy() + + return option_dict + +def dequantize_op_data(op): + if(op.CustomOptionsLength() < 1): + from .tflite_flatbuffer.DequantizeOptions import DequantizeOptions + option = DequantizeOptions() + builtin_data = op.BuiltinOptions() + if builtin_data is None: + return builtin_data + option.Init(builtin_data.Bytes, builtin_data.Pos) + return [option] + else: + custom_option = op.CustomOptionsAsNumpy() + return [custom_option] + #print("custom option format: ", op.CustomOptionsFormat()) + +def quantize_op_data(op): + if(op.CustomOptionsLength() < 1): + from .tflite_flatbuffer.QuantizeOptions import QuantizeOptions + option = QuantizeOptions() + builtin_data = op.BuiltinOptions() + if builtin_data is None: + return builtin_data + option.Init(builtin_data.Bytes, builtin_data.Pos) + return [option] + else: + custom_option = op.CustomOptionsAsNumpy() + return [custom_option] + #print("custom option format: ", op.CustomOptionsFormat()) + +def pool2d_op_data(op): + option_dict = {} + if(op.CustomOptionsLength() < 1): + from .tflite_flatbuffer.Pool2DOptions import Pool2DOptions + option = Pool2DOptions() + builtin_data = op.BuiltinOptions() + option.Init(builtin_data.Bytes, builtin_data.Pos) + option_dict["Padding"] = option.Padding() + option_dict["StrideW"] = option.StrideW() + option_dict["StrideH"] = option.StrideH() + option_dict["FilterWidth"] = option.FilterWidth() + option_dict["FilterHeight"] = option.FilterHeight() + option_dict["FusedActivationFunction"] = class_option2str(ActivationFunctionType, option.FusedActivationFunction()) + else: + option_dict[customOptionFormat_lookup[op.CustomOptionsFormat()]] = op.CustomOptionsAsNumpy() + + return option_dict + +def argmax_op_data(op): + option_dict = {} + if(op.CustomOptionsLength() < 1): + from .tflite_flatbuffer.ArgMaxOptions import ArgMaxOptions + option = ArgMaxOptions() + builtin_data = op.BuiltinOptions() + option.Init(builtin_data.Bytes, builtin_data.Pos) + option_dict["OutputType"] = option.OutputType() + else: + option_dict[customOptionFormat_lookup[op.CustomOptionsFormat()]] = op.CustomOptionsAsNumpy() + + return option_dict + +op_data_func = dict() +op_data_func["QUANTIZE"] = quantize_op_data +op_data_func["DEPTHWISE_CONV_2D"] = depthwise_conv2d_op_data +op_data_func["MAX_POOL_2D"] = pool2d_op_data +op_data_func["RESHAPE"] = reshape_op_data +op_data_func["FULLY_CONNECTED"] = fully_connected_op_data +op_data_func["DEQUANTIZE"] = dequantize_op_data +op_data_func["ARG_MAX"] = argmax_op_data + @FrontendSelector.register(target_exts=['.tflite']) class TFLiteParser(Parser): @@ -106,12 +231,6 @@ def _build_tensor_map(self, fb_model, ugraph): ugraph=ugraph ) - # 0 if intermediate - #buffer_index = tensor.Buffer() - #buffer_content = model.Buffers(buffer_index).DataAsNumpy().astype(dtype) - - #tensor.Type() - def _build_param_ops(self, fb_model, ugraph): """Find all tensors in initialization list in onnx_graph, normally constants Note that this method will update op_types_cnt and tensor_names_map **inplace** @@ -122,6 +241,7 @@ def _build_param_ops(self, fb_model, ugraph): tensor = subgraph.Tensors(idx) buffer_index = tensor.Buffer() + # buffer_index == 0 if intermediate if buffer_index == 0: continue @@ -130,7 +250,7 @@ def _build_param_ops(self, fb_model, ugraph): buffer_array = fb_model.Buffers(buffer_index).DataAsNumpy() if type(buffer_array) == int: - continue #somehow, sometimes, the buffer contains no data + continue #somehow, sometimes, the buffer contains no data, likely to be an intermediate tensor buffer_content = fb_model.Buffers(buffer_index).DataAsNumpy().astype(dtype) OperationInfo( @@ -141,7 +261,10 @@ def _build_param_ops(self, fb_model, ugraph): lib_name='tflm', ugraph=ugraph, op_attr={ - 'value': GenericTensorConverterMixin.GenericType(np_array=buffer_content) + 'value': AttrValueConverter.GenericType( + value_name="tensor", + value=GenericTensorConverterMixin.GenericType(np_array=buffer_content) + ) } ) @@ -187,6 +310,9 @@ def _build_intermediate_ops(self, fb_model, ugraph): input_tensor_names = [self.tensor_names_map[input_index] for input_index in op.InputsAsNumpy()] output_tensor_names = [self.tensor_names_map[output_index] for output_index in op.OutputsAsNumpy()] + op_attr = dict() + op_attr = op_data_func[op_type](op) + OperationInfo( name=node_name, input_tensors=input_tensor_names, @@ -194,7 +320,7 @@ def _build_intermediate_ops(self, fb_model, ugraph): op_type=op_type, ugraph=ugraph, lib_name='tflm', - op_attr={} + op_attr=op_attr ) for tensor_index in op.OutputsAsNumpy(): From 598f8243124dc0afd1edf42323e1b47b80718235 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Sun, 12 Apr 2020 18:42:51 +0800 Subject: [PATCH 084/117] quantization param in generic type --- utensor_cgen/frontend/tflite.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/utensor_cgen/frontend/tflite.py b/utensor_cgen/frontend/tflite.py index 1295894c..32a9f384 100644 --- a/utensor_cgen/frontend/tflite.py +++ b/utensor_cgen/frontend/tflite.py @@ -214,7 +214,11 @@ def _build_tensor_map(self, fb_model, ugraph): dtype=tensor_np_type[tensor.Type()] attributes = dict() - attributes['quantizationParam'] = tensor.Quantization() + + quantParam = tensor.Quantization() + if quantParam != None: + attributes['quantizationZeros'] = list([quantParam.ZeroPointAsNumpy()]) + attributes['quantizationScales'] = list([quantParam.ScaleAsNumpy()]) if type(tensor.ShapeAsNumpy()) == np.ndarray: From a4f21ec6e96910d2603ee7ea01b54ea86653bc5c Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Sun, 12 Apr 2020 18:54:27 +0800 Subject: [PATCH 085/117] minor cleanup --- utensor_cgen/frontend/tflite.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/utensor_cgen/frontend/tflite.py b/utensor_cgen/frontend/tflite.py index 32a9f384..f413f932 100644 --- a/utensor_cgen/frontend/tflite.py +++ b/utensor_cgen/frontend/tflite.py @@ -98,7 +98,6 @@ def dequantize_op_data(op): else: custom_option = op.CustomOptionsAsNumpy() return [custom_option] - #print("custom option format: ", op.CustomOptionsFormat()) def quantize_op_data(op): if(op.CustomOptionsLength() < 1): @@ -112,7 +111,6 @@ def quantize_op_data(op): else: custom_option = op.CustomOptionsAsNumpy() return [custom_option] - #print("custom option format: ", op.CustomOptionsFormat()) def pool2d_op_data(op): option_dict = {} @@ -170,8 +168,6 @@ def parse(self, tflite_file, output_nodes=None): ops_info={}, ) - #print("TF Lite Parser") - self._build_graph(fb_model, ugraph) return ugraph @@ -236,8 +232,7 @@ def _build_tensor_map(self, fb_model, ugraph): ) def _build_param_ops(self, fb_model, ugraph): - """Find all tensors in initialization list in onnx_graph, normally constants - Note that this method will update op_types_cnt and tensor_names_map **inplace** + """Const tensors are identified by buffer_index == 0. These tensors are converted to Const Op and added to ugraph """ subgraph = self._get_tflm_get_subgraph(fb_model) @@ -295,7 +290,7 @@ def _build_input_ops(self, fb_model, ugraph): ) def _build_intermediate_ops(self, fb_model, ugraph): - """Build all intermediate nodes, the nodes that is not in neither initialization list nor input list + """Build all intermediate nodes """ subgraphs_len = fb_model.SubgraphsLength() assert subgraphs_len == 1, "only 1 subgraph is supported" From d7fe924b550397aacb008501e5e1abfffb620068 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Thu, 16 Apr 2020 02:05:23 +0800 Subject: [PATCH 086/117] Add constructor signature for op factory --- .../code_generator/rearch/_operators.py | 36 +++++++++++++++--- utensor_cgen/utils.py | 37 +++++++++++++++++++ 2 files changed, 67 insertions(+), 6 deletions(-) diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_operators.py b/utensor_cgen/backend/utensor/code_generator/rearch/_operators.py index e07c1f52..3d3d8456 100644 --- a/utensor_cgen/backend/utensor/code_generator/rearch/_operators.py +++ b/utensor_cgen/backend/utensor/code_generator/rearch/_operators.py @@ -1,4 +1,5 @@ from copy import deepcopy +from typing import Hashable from six import with_metaclass @@ -6,6 +7,7 @@ from utensor_cgen.backend.utensor.snippets.rearch import (AddOpEvalSnippet, DeclareOpSnippet, RomTensorSnippet) +from utensor_cgen.utils import must_return_type __all__ = ['OperatorFactory', 'OpNotSupportedError'] @@ -49,6 +51,14 @@ class _OperatorMeta(type): def __new__(mcls, name, bases, attrib): attrib['_cache'] = {} + for key in ['get_type_signature', 'get_constructor_signature']: + func = attrib.get(key) + if func is None: + continue + if not must_return_type.return_type_is_ensured(func): + attrib[key] = must_return_type(Hashable)(func) + elif not issubclass(must_return_type.get_expect_type(func), Hashable): + raise RuntimeError('{}.{} must be ensured to return {}'.format(name, key, Hashable)) cls = type.__new__(mcls, name, bases, attrib) return cls @@ -56,16 +66,30 @@ def __new__(mcls, name, bases, attrib): class _Operator(with_metaclass(_OperatorMeta), object): def __new__(cls, op_info): - in_dtypes = tuple(t.dtype for t in op_info.input_tensors) - out_dtypes = tuple(t.dtype for t in op_info.output_tensors) - type_signature = (in_dtypes, out_dtypes) - if type_signature not in cls._cache: + type_signature = cls.get_type_signature(op_info) + construct_signature = cls.get_constructor_signature(op_info) + full_signature = (type_signature, construct_signature) + in_dtypes, out_dtypes = type_signature + if full_signature not in cls._cache: self = object.__new__(cls) self.in_dtypes = in_dtypes self.out_dtypes = out_dtypes + self.construct_params = construct_signature self.attributes = deepcopy(op_info.op_attr) - cls._cache[type_signature] = self - return cls._cache[type_signature] + cls._cache[full_signature] = self + return cls._cache[full_signature] + + @classmethod + @must_return_type(Hashable) + def get_type_signature(cls, op_info): + in_dtypes = tuple(t.dtype for t in op_info.input_tensors) + out_dtypes = tuple(t.dtype for t in op_info.output_tensors) + return (in_dtypes, out_dtypes) + + @classmethod + @must_return_type(Hashable) + def get_constructor_signature(cls, op_info): + return tuple() def get_declare_snippet(self, op_var_name, **kwargs): raise NotImplementedError( diff --git a/utensor_cgen/utils.py b/utensor_cgen/utils.py index 3f47219f..13c0bb58 100644 --- a/utensor_cgen/utils.py +++ b/utensor_cgen/utils.py @@ -496,6 +496,7 @@ def __getitem__(self, slice_obj): cls = type(self) return cls(funcs=self._funcs[slice_obj]) + class Configuration(object): def __init__(self, defaults=None, user_config=None): """ @@ -563,3 +564,39 @@ def __repr__(self): ' user_config={} \n' ')' ).format(self._defaults, self._user_config) + + +class must_return_type(object): + + def __init__(self, type_): + self._expected_type = type_ + + def __call__(self, func): + @wraps(func) + def wrapped(*args, **kwargs): + ret = func(*args, **kwargs) + ret_cls = type(ret) + if not issubclass(ret_cls, self._expected_type): + raise TypeError( + "expecting {} to return value of type {}, get {}".format( + func, + self._expected_type, + ret_cls + ) + ) + return ret + wrapped._has_return_type_check = True + wrapped._expecting = self._expected_type + return wrapped + + @staticmethod + def get_expect_type(wrapped): + if isinstance(wrapped, classmethod): + wrapped = wrapped.__func__ + return wrapped._expecting + + @staticmethod + def return_type_is_ensured(wrapped): + if isinstance(wrapped, classmethod): + wrapped = wrapped.__func__ + return getattr(wrapped, '_has_return_type_check', False) From 154d8ab6d60368149c4afd578c095fa62baf392b Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sat, 18 Apr 2020 10:43:19 +0800 Subject: [PATCH 087/117] Resolve review issues --- utensor_cgen/legalizer/onnx.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/utensor_cgen/legalizer/onnx.py b/utensor_cgen/legalizer/onnx.py index d9459252..170ad828 100644 --- a/utensor_cgen/legalizer/onnx.py +++ b/utensor_cgen/legalizer/onnx.py @@ -21,7 +21,11 @@ def _visit_all(self, ugraph): visitor(op_info) topologic_order_graph(ugraph) + # _visit_ methods will be invoked when an op_info of + # type is encountered during graph traversal + # ex: _visit_gemm -> will be invoked with an op_info with type Gemm def _visit_gemm(self, op_info): + # op_info.op_type == "Gemm" ugraph = op_info.ugraph op_info.op_type = 'MatMul' tensor_a, tensor_w, tensor_bias = op_info.input_tensors @@ -53,5 +57,5 @@ def _visit_gemm(self, op_info): op_info.output_tensors[0].op_name = add_op.name def _visit_id(self, op_info): - # identity op should have op_type == 'Identity' + # identity op should be op_info.op_type == 'Identity' return op_info From df943832f1d28a87cbe112939cff0405862514fa Mon Sep 17 00:00:00 2001 From: dboyliao Date: Mon, 20 Apr 2020 18:25:02 +0800 Subject: [PATCH 088/117] Formatting files --- utensor_cgen/frontend/tflite.py | 488 +++++++++++++++++--------------- 1 file changed, 265 insertions(+), 223 deletions(-) diff --git a/utensor_cgen/frontend/tflite.py b/utensor_cgen/frontend/tflite.py index f413f932..89be991f 100644 --- a/utensor_cgen/frontend/tflite.py +++ b/utensor_cgen/frontend/tflite.py @@ -1,62 +1,77 @@ from __future__ import absolute_import + import os -import six import re -import numpy as np +import six -from utensor_cgen.frontend.base import Parser +import flatbuffers +import numpy as np from utensor_cgen.frontend import FrontendSelector -from utensor_cgen.ir.base import TensorInfo, OperationInfo, uTensorGraph +from utensor_cgen.frontend.base import Parser +from utensor_cgen.ir.base import OperationInfo, TensorInfo, uTensorGraph +from utensor_cgen.ir.converter import (AttrValueConverter, + GenericTensorConverterMixin) from utensor_cgen.utils import topologic_order_graph -from utensor_cgen.ir.converter import GenericTensorConverterMixin, AttrValueConverter -import flatbuffers +from .tflite_flatbuffer.ActivationFunctionType import ActivationFunctionType +from .tflite_flatbuffer.BuiltinOperator import BuiltinOperator +from .tflite_flatbuffer.CustomOptionsFormat import CustomOptionsFormat +from .tflite_flatbuffer.FullyConnectedOptionsWeightsFormat import \ + FullyConnectedOptionsWeightsFormat from .tflite_flatbuffer.Model import Model tensor_np_type = dict() -tensor_np_type[0] = np.dtype('float32') -tensor_np_type[1] = np.dtype('float16') -tensor_np_type[2] = np.dtype('int32') -tensor_np_type[3] = np.dtype('uint8') -tensor_np_type[4] = np.dtype('uint64') -tensor_np_type[5] = np.dtype('ubyte') #FIXME: supposed to be string -tensor_np_type[6] = np.dtype('bool') -tensor_np_type[7] = np.dtype('int16') -tensor_np_type[8] = np.dtype('cdouble') -tensor_np_type[9] = np.dtype('int8') +tensor_np_type[0] = np.dtype("float32") +tensor_np_type[1] = np.dtype("float16") +tensor_np_type[2] = np.dtype("int32") +tensor_np_type[3] = np.dtype("uint8") +tensor_np_type[4] = np.dtype("uint64") +tensor_np_type[5] = np.dtype("ubyte") # FIXME: supposed to be string +tensor_np_type[6] = np.dtype("bool") +tensor_np_type[7] = np.dtype("int16") +tensor_np_type[8] = np.dtype("cdouble") +tensor_np_type[9] = np.dtype("int8") + -from .tflite_flatbuffer.BuiltinOperator import BuiltinOperator builtin_ops = {v: k for k, v in BuiltinOperator.__dict__.items()} -from .tflite_flatbuffer.ActivationFunctionType import ActivationFunctionType -from .tflite_flatbuffer.FullyConnectedOptionsWeightsFormat import FullyConnectedOptionsWeightsFormat -from .tflite_flatbuffer.CustomOptionsFormat import CustomOptionsFormat def class_option2str(obj, idx): names_lookup = {v: k for k, v in obj.__dict__.items()} name = names_lookup[idx] return str(idx) + " (" + name + ")" + customOptionFormat_lookup = {v: k for k, v in CustomOptionsFormat.__dict__.items()} + def fully_connected_op_data(op): option_dict = {} - if(op.CustomOptionsLength() < 1): + if op.CustomOptionsLength() < 1: from .tflite_flatbuffer.FullyConnectedOptions import FullyConnectedOptions + option = FullyConnectedOptions() builtin_data = op.BuiltinOptions() option.Init(builtin_data.Bytes, builtin_data.Pos) - option_dict["FusedActivationFunction"] = class_option2str(ActivationFunctionType, option.FusedActivationFunction()) - option_dict["w_formats"] = class_option2str(FullyConnectedOptionsWeightsFormat, option.WeightsFormat()) + option_dict["FusedActivationFunction"] = class_option2str( + ActivationFunctionType, option.FusedActivationFunction() + ) + option_dict["w_formats"] = class_option2str( + FullyConnectedOptionsWeightsFormat, option.WeightsFormat() + ) else: - option_dict[customOptionFormat_lookup[op.CustomOptionsFormat()]] = op.CustomOptionsAsNumpy() + option_dict[ + customOptionFormat_lookup[op.CustomOptionsFormat()] + ] = op.CustomOptionsAsNumpy() return option_dict - + + def depthwise_conv2d_op_data(op): option_dict = {} - if(op.CustomOptionsLength() < 1): + if op.CustomOptionsLength() < 1: from .tflite_flatbuffer.DepthwiseConv2DOptions import DepthwiseConv2DOptions + option = DepthwiseConv2DOptions() builtin_data = op.BuiltinOptions() option.Init(builtin_data.Bytes, builtin_data.Pos) @@ -64,31 +79,41 @@ def depthwise_conv2d_op_data(op): option_dict["StrideW"] = option.StrideW() option_dict["StrideH"] = option.StrideH() option_dict["DepthMultiplier"] = option.DepthMultiplier() - option_dict["FusedActivationFunction"] = class_option2str(ActivationFunctionType, option.FusedActivationFunction()) + option_dict["FusedActivationFunction"] = class_option2str( + ActivationFunctionType, option.FusedActivationFunction() + ) option_dict["DilationWFactor"] = option.DilationWFactor() option_dict["DilationHFactor"] = option.DilationHFactor() - + else: - option_dict[customOptionFormat_lookup[op.CustomOptionsFormat()]] = op.CustomOptionsAsNumpy() - + option_dict[ + customOptionFormat_lookup[op.CustomOptionsFormat()] + ] = op.CustomOptionsAsNumpy() + return option_dict - + + def reshape_op_data(op): option_dict = {} - if(op.CustomOptionsLength() < 1): + if op.CustomOptionsLength() < 1: from .tflite_flatbuffer.ReshapeOptions import ReshapeOptions + option = ReshapeOptions() builtin_data = op.BuiltinOptions() option.Init(builtin_data.Bytes, builtin_data.Pos) option_dict["NewShape"] = option.NewShapeAsNumpy() else: - option_dict[customOptionFormat_lookup[op.CustomOptionsFormat()]] = op.CustomOptionsAsNumpy() - + option_dict[ + customOptionFormat_lookup[op.CustomOptionsFormat()] + ] = op.CustomOptionsAsNumpy() + return option_dict - + + def dequantize_op_data(op): - if(op.CustomOptionsLength() < 1): + if op.CustomOptionsLength() < 1: from .tflite_flatbuffer.DequantizeOptions import DequantizeOptions + option = DequantizeOptions() builtin_data = op.BuiltinOptions() if builtin_data is None: @@ -98,10 +123,12 @@ def dequantize_op_data(op): else: custom_option = op.CustomOptionsAsNumpy() return [custom_option] - + + def quantize_op_data(op): - if(op.CustomOptionsLength() < 1): + if op.CustomOptionsLength() < 1: from .tflite_flatbuffer.QuantizeOptions import QuantizeOptions + option = QuantizeOptions() builtin_data = op.BuiltinOptions() if builtin_data is None: @@ -111,11 +138,13 @@ def quantize_op_data(op): else: custom_option = op.CustomOptionsAsNumpy() return [custom_option] - + + def pool2d_op_data(op): option_dict = {} - if(op.CustomOptionsLength() < 1): + if op.CustomOptionsLength() < 1: from .tflite_flatbuffer.Pool2DOptions import Pool2DOptions + option = Pool2DOptions() builtin_data = op.BuiltinOptions() option.Init(builtin_data.Bytes, builtin_data.Pos) @@ -124,25 +153,34 @@ def pool2d_op_data(op): option_dict["StrideH"] = option.StrideH() option_dict["FilterWidth"] = option.FilterWidth() option_dict["FilterHeight"] = option.FilterHeight() - option_dict["FusedActivationFunction"] = class_option2str(ActivationFunctionType, option.FusedActivationFunction()) + option_dict["FusedActivationFunction"] = class_option2str( + ActivationFunctionType, option.FusedActivationFunction() + ) else: - option_dict[customOptionFormat_lookup[op.CustomOptionsFormat()]] = op.CustomOptionsAsNumpy() - + option_dict[ + customOptionFormat_lookup[op.CustomOptionsFormat()] + ] = op.CustomOptionsAsNumpy() + return option_dict + def argmax_op_data(op): option_dict = {} - if(op.CustomOptionsLength() < 1): + if op.CustomOptionsLength() < 1: from .tflite_flatbuffer.ArgMaxOptions import ArgMaxOptions + option = ArgMaxOptions() builtin_data = op.BuiltinOptions() option.Init(builtin_data.Bytes, builtin_data.Pos) option_dict["OutputType"] = option.OutputType() else: - option_dict[customOptionFormat_lookup[op.CustomOptionsFormat()]] = op.CustomOptionsAsNumpy() - + option_dict[ + customOptionFormat_lookup[op.CustomOptionsFormat()] + ] = op.CustomOptionsAsNumpy() + return option_dict + op_data_func = dict() op_data_func["QUANTIZE"] = quantize_op_data op_data_func["DEPTHWISE_CONV_2D"] = depthwise_conv2d_op_data @@ -152,196 +190,200 @@ def argmax_op_data(op): op_data_func["DEQUANTIZE"] = dequantize_op_data op_data_func["ARG_MAX"] = argmax_op_data -@FrontendSelector.register(target_exts=['.tflite']) -class TFLiteParser(Parser): - def parse(self, tflite_file, output_nodes=None): - graph_name, _ = os.path.splitext(tflite_file) - buf = open(tflite_file, 'rb').read() - buf = bytearray(buf) - fb_model = Model.GetRootAsModel(buf, 0) +@FrontendSelector.register(target_exts=[".tflite"]) +class TFLiteParser(Parser): + def parse(self, tflite_file, output_nodes=None): + graph_name, _ = os.path.splitext(tflite_file) + buf = open(tflite_file, "rb").read() + buf = bytearray(buf) + fb_model = Model.GetRootAsModel(buf, 0) - ugraph = uTensorGraph( - name=graph_name, - output_nodes=[], - lib_name='tflite', - ops_info={}, - ) + ugraph = uTensorGraph( + name=graph_name, output_nodes=[], lib_name="tflite", ops_info={}, + ) - self._build_graph(fb_model, ugraph) + self._build_graph(fb_model, ugraph) - return ugraph + return ugraph - def _build_graph(self, fb_model, ugraph): - self.tensor_names_map = {} #addresseed by indexi - self._build_tensor_map(fb_model, ugraph) + def _build_graph(self, fb_model, ugraph): + self.tensor_names_map = {} # addresseed by indexi + self._build_tensor_map(fb_model, ugraph) - self._build_param_ops(fb_model, ugraph) - #find and set input nodes - self._build_input_ops(fb_model, ugraph) - self._build_intermediate_ops(fb_model, ugraph) - self._set_output_ops(fb_model, ugraph) + self._build_param_ops(fb_model, ugraph) + # find and set input nodes + self._build_input_ops(fb_model, ugraph) + self._build_intermediate_ops(fb_model, ugraph) + self._set_output_ops(fb_model, ugraph) - topologic_order_graph(ugraph) + topologic_order_graph(ugraph) - def _set_output_ops(self, fb_model, ugraph): - """identfy output nodes in fb_mdel + def _set_output_ops(self, fb_model, ugraph): + """identfy output nodes in fb_mdel sets output_nodes in ugraph Note this method will update ugraph **inplace** """ - subgraph = self._get_tflm_get_subgraph(fb_model) - subgraph_outputs_indexi = subgraph.OutputsAsNumpy() #tensor indexi - output_node_names = set() - for index in subgraph_outputs_indexi: - output_node_names.add(self.tensor_names_map[index].op_name) - - ugraph.output_nodes = list(output_node_names) - - def _build_tensor_map(self, fb_model, ugraph): - subgraph = self._get_tflm_get_subgraph(fb_model) - - for idx in range(0, subgraph.TensorsLength()): - tensor = subgraph.Tensors(idx) - - tensor_name = tensor.Name() - if tensor_name is '' or None: - tensor_name = 'tensor_' + str(idx) - - dtype=tensor_np_type[tensor.Type()] - - attributes = dict() - - quantParam = tensor.Quantization() - if quantParam != None: - attributes['quantizationZeros'] = list([quantParam.ZeroPointAsNumpy()]) - attributes['quantizationScales'] = list([quantParam.ScaleAsNumpy()]) - - - if type(tensor.ShapeAsNumpy()) == np.ndarray: - shape = tensor.ShapeAsNumpy().tolist() - else: - shape = [d for d in fb_model.Buffers(12).DataAsNumpy().shape] - - self.tensor_names_map[idx] = TensorInfo( - name=self._format_tensor_name('', tensor_name, 0), - op_name="", - dtype=dtype, - shape=shape, - attributes=attributes, - ugraph=ugraph - ) - - def _build_param_ops(self, fb_model, ugraph): - """Const tensors are identified by buffer_index == 0. These tensors are converted to Const Op and added to ugraph + subgraph = self._get_tflm_get_subgraph(fb_model) + subgraph_outputs_indexi = subgraph.OutputsAsNumpy() # tensor indexi + output_node_names = set() + for index in subgraph_outputs_indexi: + output_node_names.add(self.tensor_names_map[index].op_name) + + ugraph.output_nodes = list(output_node_names) + + def _build_tensor_map(self, fb_model, ugraph): + subgraph = self._get_tflm_get_subgraph(fb_model) + + for idx in range(0, subgraph.TensorsLength()): + tensor = subgraph.Tensors(idx) + + tensor_name = tensor.Name() + if tensor_name is "" or None: + tensor_name = "tensor_" + str(idx) + + dtype = tensor_np_type[tensor.Type()] + + attributes = dict() + + quantParam = tensor.Quantization() + if quantParam != None: + attributes["quantizationZeros"] = list([quantParam.ZeroPointAsNumpy()]) + attributes["quantizationScales"] = list([quantParam.ScaleAsNumpy()]) + + if type(tensor.ShapeAsNumpy()) == np.ndarray: + shape = tensor.ShapeAsNumpy().tolist() + else: + shape = [d for d in fb_model.Buffers(12).DataAsNumpy().shape] + + self.tensor_names_map[idx] = TensorInfo( + name=self._format_tensor_name("", tensor_name, 0), + op_name="", + dtype=dtype, + shape=shape, + attributes=attributes, + ugraph=ugraph, + ) + + def _build_param_ops(self, fb_model, ugraph): + """Const tensors are identified by buffer_index == 0. These tensors are converted to Const Op and added to ugraph """ - subgraph = self._get_tflm_get_subgraph(fb_model) - - for idx in range(0, subgraph.TensorsLength()): - tensor = subgraph.Tensors(idx) - buffer_index = tensor.Buffer() - - # buffer_index == 0 if intermediate - if buffer_index == 0: - continue - - node_name = self.tensor_names_map[idx].name + "_Const" - dtype = self.tensor_names_map[idx].dtype - - buffer_array = fb_model.Buffers(buffer_index).DataAsNumpy() - if type(buffer_array) == int: - continue #somehow, sometimes, the buffer contains no data, likely to be an intermediate tensor - buffer_content = fb_model.Buffers(buffer_index).DataAsNumpy().astype(dtype) - - OperationInfo( - name=node_name, - input_tensors=[], - output_tensors=[self.tensor_names_map[idx]], - op_type='Const', - lib_name='tflm', - ugraph=ugraph, - op_attr={ - 'value': AttrValueConverter.GenericType( - value_name="tensor", - value=GenericTensorConverterMixin.GenericType(np_array=buffer_content) - ) - } - ) - - self._set_tensor_node(idx, node_name) - - def _build_input_ops(self, fb_model, ugraph): - """Find placeholders + subgraph = self._get_tflm_get_subgraph(fb_model) + + for idx in range(0, subgraph.TensorsLength()): + tensor = subgraph.Tensors(idx) + buffer_index = tensor.Buffer() + + # buffer_index == 0 if intermediate + if buffer_index == 0: + continue + + node_name = self.tensor_names_map[idx].name + "_Const" + dtype = self.tensor_names_map[idx].dtype + + buffer_array = fb_model.Buffers(buffer_index).DataAsNumpy() + if type(buffer_array) == int: + + continue # somehow, sometimes, the buffer contains no data, likely to be an intermediate tensor + buffer_content = fb_model.Buffers(buffer_index).DataAsNumpy().astype(dtype) + + OperationInfo( + name=node_name, + input_tensors=[], + output_tensors=[self.tensor_names_map[idx]], + op_type="Const", + lib_name="tflm", + ugraph=ugraph, + op_attr={ + "value": AttrValueConverter.GenericType( + value_name="tensor", + value=GenericTensorConverterMixin.GenericType( + np_array=buffer_content + ), + ) + }, + ) + + self._set_tensor_node(idx, node_name) + + def _build_input_ops(self, fb_model, ugraph): + """Find placeholders Attach placeholders to input tensors Note this method will update inputs **inplace** """ - subgraph = self._get_tflm_get_subgraph(fb_model) - subgraph_inputs_indexi = subgraph.InputsAsNumpy() - for index in subgraph_inputs_indexi: - node_name = self.tensor_names_map[index].name + "_Placeholder" - self._set_tensor_node(index, node_name) - OperationInfo( - name=node_name, - input_tensors=[], - output_tensors=[self.tensor_names_map[index]], - op_type='Placeholder', - ugraph=ugraph, - lib_name='tflm', - op_attr={} - ) - - def _build_intermediate_ops(self, fb_model, ugraph): - """Build all intermediate nodes + subgraph = self._get_tflm_get_subgraph(fb_model) + subgraph_inputs_indexi = subgraph.InputsAsNumpy() + for index in subgraph_inputs_indexi: + node_name = self.tensor_names_map[index].name + "_Placeholder" + self._set_tensor_node(index, node_name) + OperationInfo( + name=node_name, + input_tensors=[], + output_tensors=[self.tensor_names_map[index]], + op_type="Placeholder", + ugraph=ugraph, + lib_name="tflm", + op_attr={}, + ) + + def _build_intermediate_ops(self, fb_model, ugraph): + """Build all intermediate nodes """ - subgraphs_len = fb_model.SubgraphsLength() - assert subgraphs_len == 1, "only 1 subgraph is supported" - subgraph = fb_model.Subgraphs(0) - for i in range(0, subgraph.OperatorsLength()): - #topological order, op-index defined by schema - #BuiltinOperator: https://github.com/tensorflow/tensorflow/blob/031804922d8f4d18b61e3ad077f9f1b69273ff21/tensorflow/lite/schema/schema_v3.fbs#L71 - op = subgraph.Operators(i) - local_op_code = op.OpcodeIndex() - global_op_code = fb_model.OperatorCodes(local_op_code) - builtinOperator_code = global_op_code.BuiltinCode() - op_type = builtin_ops[builtinOperator_code] - - node_name = str(i) + "_" + op_type - - input_tensor_names = [self.tensor_names_map[input_index] for input_index in op.InputsAsNumpy()] - output_tensor_names = [self.tensor_names_map[output_index] for output_index in op.OutputsAsNumpy()] - - op_attr = dict() - op_attr = op_data_func[op_type](op) - - OperationInfo( - name=node_name, - input_tensors=input_tensor_names, - output_tensors=output_tensor_names, - op_type=op_type, - ugraph=ugraph, - lib_name='tflm', - op_attr=op_attr - ) - - for tensor_index in op.OutputsAsNumpy(): - self._set_tensor_node(tensor_index, node_name) - - def _get_tflm_get_subgraph(self, fb_model): - subgraphs_len = fb_model.SubgraphsLength() - assert subgraphs_len == 1, "only 1 subgraph is supported" - subgraph = fb_model.Subgraphs(0) - - return subgraph - - def _set_tensor_node(self, idx, name): - assert self.tensor_names_map[idx].op_name == "" - self.tensor_names_map[idx].op_name = name - - def _format_node_name(self, node_name, op_type, op_cnt): - if node_name == '': - node_name = '{}_{}'.format(op_type, op_cnt) - return re.sub(r'[\.:/]', '_', node_name) - - def _format_tensor_name(self, name, node_name, offset): - if re.match(r'[a-zA-Z][a-zA-Z0-9]*:[0-9]+', name): - return name - return '{}:{}'.format(node_name, offset) + subgraphs_len = fb_model.SubgraphsLength() + assert subgraphs_len == 1, "only 1 subgraph is supported" + subgraph = fb_model.Subgraphs(0) + for i in range(0, subgraph.OperatorsLength()): + # topological order, op-index defined by schema + # BuiltinOperator: https://github.com/tensorflow/tensorflow/blob/031804922d8f4d18b61e3ad077f9f1b69273ff21/tensorflow/lite/schema/schema_v3.fbs#L71 + op = subgraph.Operators(i) + local_op_code = op.OpcodeIndex() + global_op_code = fb_model.OperatorCodes(local_op_code) + builtinOperator_code = global_op_code.BuiltinCode() + op_type = builtin_ops[builtinOperator_code] + + node_name = str(i) + "_" + op_type + + input_tensor_names = [ + self.tensor_names_map[input_index] for input_index in op.InputsAsNumpy() + ] + output_tensor_names = [ + self.tensor_names_map[output_index] + for output_index in op.OutputsAsNumpy() + ] + + op_attr = dict() + op_attr = op_data_func[op_type](op) + + OperationInfo( + name=node_name, + input_tensors=input_tensor_names, + output_tensors=output_tensor_names, + op_type=op_type, + ugraph=ugraph, + lib_name="tflm", + op_attr=op_attr, + ) + + for tensor_index in op.OutputsAsNumpy(): + self._set_tensor_node(tensor_index, node_name) + + def _get_tflm_get_subgraph(self, fb_model): + subgraphs_len = fb_model.SubgraphsLength() + assert subgraphs_len == 1, "only 1 subgraph is supported" + subgraph = fb_model.Subgraphs(0) + + return subgraph + + def _set_tensor_node(self, idx, name): + assert self.tensor_names_map[idx].op_name == "" + self.tensor_names_map[idx].op_name = name + + def _format_node_name(self, node_name, op_type, op_cnt): + if node_name == "": + node_name = "{}_{}".format(op_type, op_cnt) + return re.sub(r"[\.:/]", "_", node_name) + + def _format_tensor_name(self, name, node_name, offset): + if re.match(r"[a-zA-Z][a-zA-Z0-9]*:[0-9]+", name): + return name + return "{}:{}".format(node_name, offset) From 4577bf8afe917ed9d0df9c8f62f6436370cd0670 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Mon, 20 Apr 2020 23:34:09 +0800 Subject: [PATCH 089/117] update lockfile --- Pipfile.lock | 688 ++++++++++++++++++++++++++------------------------- 1 file changed, 356 insertions(+), 332 deletions(-) diff --git a/Pipfile.lock b/Pipfile.lock index 0c7b4cd7..86a93d24 100644 --- a/Pipfile.lock +++ b/Pipfile.lock @@ -36,15 +36,29 @@ }, "click": { "hashes": [ - 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names_lookup = {v: k for k, v in obj.__dict__.items()} - name = names_lookup[idx] - return str(idx) + " (" + name + ")" + names_lookup = {v: k for k, v in obj.__dict__.items()} + name = names_lookup[idx] + return str(idx) + " (" + name + ")" customOptionFormat_lookup = {v: k for k, v in CustomOptionsFormat.__dict__.items()} def fully_connected_op_data(op): - option_dict = {} - if op.CustomOptionsLength() < 1: - from .tflite_flatbuffer.FullyConnectedOptions import FullyConnectedOptions - - option = FullyConnectedOptions() - builtin_data = op.BuiltinOptions() - option.Init(builtin_data.Bytes, builtin_data.Pos) - option_dict["FusedActivationFunction"] = class_option2str( - ActivationFunctionType, option.FusedActivationFunction() - ) - option_dict["w_formats"] = class_option2str( - FullyConnectedOptionsWeightsFormat, option.WeightsFormat() - ) - else: - option_dict[ - customOptionFormat_lookup[op.CustomOptionsFormat()] - ] = op.CustomOptionsAsNumpy() - return option_dict + option_dict = {} + if op.CustomOptionsLength() < 1: + from .tflite_flatbuffer.FullyConnectedOptions import FullyConnectedOptions + + option = FullyConnectedOptions() + builtin_data = op.BuiltinOptions() + option.Init(builtin_data.Bytes, builtin_data.Pos) + option_dict["FusedActivationFunction"] = class_option2str( + ActivationFunctionType, option.FusedActivationFunction() + ) + option_dict["w_formats"] = class_option2str( + FullyConnectedOptionsWeightsFormat, option.WeightsFormat() + ) + else: + option_dict[ + customOptionFormat_lookup[op.CustomOptionsFormat()] + ] = op.CustomOptionsAsNumpy() + return option_dict def depthwise_conv2d_op_data(op): - option_dict = {} - if op.CustomOptionsLength() < 1: - from .tflite_flatbuffer.DepthwiseConv2DOptions import DepthwiseConv2DOptions - - option = DepthwiseConv2DOptions() - builtin_data = op.BuiltinOptions() - option.Init(builtin_data.Bytes, builtin_data.Pos) - option_dict["Padding"] = option.Padding() - option_dict["StrideW"] = option.StrideW() - option_dict["StrideH"] = option.StrideH() - option_dict["DepthMultiplier"] = option.DepthMultiplier() - option_dict["FusedActivationFunction"] = class_option2str( - ActivationFunctionType, option.FusedActivationFunction() - ) - option_dict["DilationWFactor"] = option.DilationWFactor() - option_dict["DilationHFactor"] = option.DilationHFactor() - - else: - option_dict[ - customOptionFormat_lookup[op.CustomOptionsFormat()] - ] = op.CustomOptionsAsNumpy() - - return option_dict + option_dict = {} + if op.CustomOptionsLength() < 1: + from .tflite_flatbuffer.DepthwiseConv2DOptions import DepthwiseConv2DOptions + + option = DepthwiseConv2DOptions() + builtin_data = op.BuiltinOptions() + option.Init(builtin_data.Bytes, builtin_data.Pos) + option_dict["Padding"] = option.Padding() + option_dict["StrideW"] = option.StrideW() + option_dict["StrideH"] = option.StrideH() + option_dict["DepthMultiplier"] = option.DepthMultiplier() + option_dict["FusedActivationFunction"] = class_option2str( + ActivationFunctionType, option.FusedActivationFunction() + ) + option_dict["DilationWFactor"] = option.DilationWFactor() + option_dict["DilationHFactor"] = option.DilationHFactor() + + else: + option_dict[ + customOptionFormat_lookup[op.CustomOptionsFormat()] + ] = op.CustomOptionsAsNumpy() + + return option_dict def reshape_op_data(op): - option_dict = {} - if op.CustomOptionsLength() < 1: - from .tflite_flatbuffer.ReshapeOptions import ReshapeOptions + option_dict = {} + if op.CustomOptionsLength() < 1: + from .tflite_flatbuffer.ReshapeOptions import ReshapeOptions - option = ReshapeOptions() - builtin_data = op.BuiltinOptions() - option.Init(builtin_data.Bytes, builtin_data.Pos) - option_dict["NewShape"] = option.NewShapeAsNumpy() - else: - option_dict[ - customOptionFormat_lookup[op.CustomOptionsFormat()] - ] = op.CustomOptionsAsNumpy() + option = ReshapeOptions() + builtin_data = op.BuiltinOptions() + option.Init(builtin_data.Bytes, builtin_data.Pos) + option_dict["NewShape"] = option.NewShapeAsNumpy() + else: + option_dict[ + customOptionFormat_lookup[op.CustomOptionsFormat()] + ] = op.CustomOptionsAsNumpy() - return option_dict + return option_dict def dequantize_op_data(op): - if op.CustomOptionsLength() < 1: - from .tflite_flatbuffer.DequantizeOptions import DequantizeOptions + if op.CustomOptionsLength() < 1: + from .tflite_flatbuffer.DequantizeOptions import DequantizeOptions - option = DequantizeOptions() - builtin_data = op.BuiltinOptions() - if builtin_data is None: - return builtin_data - option.Init(builtin_data.Bytes, builtin_data.Pos) - return [option] - else: - custom_option = op.CustomOptionsAsNumpy() - return [custom_option] + option = DequantizeOptions() + builtin_data = op.BuiltinOptions() + if builtin_data is None: + return builtin_data + option.Init(builtin_data.Bytes, builtin_data.Pos) + return [option] + else: + custom_option = op.CustomOptionsAsNumpy() + return [custom_option] def quantize_op_data(op): - if op.CustomOptionsLength() < 1: - from .tflite_flatbuffer.QuantizeOptions import QuantizeOptions + if op.CustomOptionsLength() < 1: + from .tflite_flatbuffer.QuantizeOptions import QuantizeOptions - option = QuantizeOptions() - builtin_data = op.BuiltinOptions() - if builtin_data is None: - return builtin_data - option.Init(builtin_data.Bytes, builtin_data.Pos) - return [option] - else: - custom_option = op.CustomOptionsAsNumpy() - return [custom_option] + option = QuantizeOptions() + builtin_data = op.BuiltinOptions() + if builtin_data is None: + return builtin_data + option.Init(builtin_data.Bytes, builtin_data.Pos) + return [option] + else: + custom_option = op.CustomOptionsAsNumpy() + return [custom_option] def pool2d_op_data(op): - option_dict = {} - if op.CustomOptionsLength() < 1: - from .tflite_flatbuffer.Pool2DOptions import Pool2DOptions - - option = Pool2DOptions() - builtin_data = op.BuiltinOptions() - option.Init(builtin_data.Bytes, builtin_data.Pos) - option_dict["Padding"] = option.Padding() - option_dict["StrideW"] = option.StrideW() - option_dict["StrideH"] = option.StrideH() - option_dict["FilterWidth"] = option.FilterWidth() - option_dict["FilterHeight"] = option.FilterHeight() - option_dict["FusedActivationFunction"] = class_option2str( - ActivationFunctionType, option.FusedActivationFunction() - ) - else: - option_dict[ - customOptionFormat_lookup[op.CustomOptionsFormat()] - ] = op.CustomOptionsAsNumpy() - - return option_dict + option_dict = {} + if op.CustomOptionsLength() < 1: + from .tflite_flatbuffer.Pool2DOptions import Pool2DOptions + + option = Pool2DOptions() + builtin_data = op.BuiltinOptions() + option.Init(builtin_data.Bytes, builtin_data.Pos) + option_dict["Padding"] = option.Padding() + option_dict["StrideW"] = option.StrideW() + option_dict["StrideH"] = option.StrideH() + option_dict["FilterWidth"] = option.FilterWidth() + option_dict["FilterHeight"] = option.FilterHeight() + option_dict["FusedActivationFunction"] = class_option2str( + ActivationFunctionType, option.FusedActivationFunction() + ) + else: + option_dict[ + customOptionFormat_lookup[op.CustomOptionsFormat()] + ] = op.CustomOptionsAsNumpy() + + return option_dict def argmax_op_data(op): - option_dict = {} - if op.CustomOptionsLength() < 1: - from .tflite_flatbuffer.ArgMaxOptions import ArgMaxOptions + option_dict = {} + if op.CustomOptionsLength() < 1: + from .tflite_flatbuffer.ArgMaxOptions import ArgMaxOptions - option = ArgMaxOptions() - builtin_data = op.BuiltinOptions() - option.Init(builtin_data.Bytes, builtin_data.Pos) - option_dict["OutputType"] = option.OutputType() - else: - option_dict[ - customOptionFormat_lookup[op.CustomOptionsFormat()] - ] = op.CustomOptionsAsNumpy() + option = ArgMaxOptions() + builtin_data = op.BuiltinOptions() + option.Init(builtin_data.Bytes, builtin_data.Pos) + option_dict["OutputType"] = option.OutputType() + else: + option_dict[ + customOptionFormat_lookup[op.CustomOptionsFormat()] + ] = op.CustomOptionsAsNumpy() - return option_dict + return option_dict op_data_func = dict() @@ -193,197 +193,197 @@ def argmax_op_data(op): @FrontendSelector.register(target_exts=[".tflite"]) class TFLiteParser(Parser): - def parse(self, tflite_file, output_nodes=None): - graph_name, _ = os.path.splitext(tflite_file) - buf = open(tflite_file, "rb").read() - buf = bytearray(buf) - fb_model = Model.GetRootAsModel(buf, 0) + def parse(self, tflite_file, output_nodes=None): + graph_name, _ = os.path.splitext(tflite_file) + buf = open(tflite_file, "rb").read() + buf = bytearray(buf) + fb_model = Model.GetRootAsModel(buf, 0) - ugraph = uTensorGraph( - name=graph_name, output_nodes=[], lib_name="tflite", ops_info={}, - ) + ugraph = uTensorGraph( + name=graph_name, output_nodes=[], lib_name="tflite", ops_info={}, + ) - self._build_graph(fb_model, ugraph) + self._build_graph(fb_model, ugraph) - return ugraph + return ugraph - def _build_graph(self, fb_model, ugraph): - self.tensor_names_map = {} # addresseed by indexi - self._build_tensor_map(fb_model, ugraph) + def _build_graph(self, fb_model, ugraph): + self.tensor_names_map = {} # addresseed by indexi + self._build_tensor_map(fb_model, ugraph) - self._build_param_ops(fb_model, ugraph) - # find and set input nodes - self._build_input_ops(fb_model, ugraph) - self._build_intermediate_ops(fb_model, ugraph) - self._set_output_ops(fb_model, ugraph) + self._build_param_ops(fb_model, ugraph) + # find and set input nodes + self._build_input_ops(fb_model, ugraph) + self._build_intermediate_ops(fb_model, ugraph) + self._set_output_ops(fb_model, ugraph) - topologic_order_graph(ugraph) + topologic_order_graph(ugraph) - def _set_output_ops(self, fb_model, ugraph): - """identfy output nodes in fb_mdel + def _set_output_ops(self, fb_model, ugraph): + """identfy output nodes in fb_mdel sets output_nodes in ugraph Note this method will update ugraph **inplace** """ - subgraph = self._get_tflm_get_subgraph(fb_model) - subgraph_outputs_indexi = subgraph.OutputsAsNumpy() # tensor indexi - output_node_names = set() - for index in subgraph_outputs_indexi: - output_node_names.add(self.tensor_names_map[index].op_name) + subgraph = self._get_tflm_get_subgraph(fb_model) + subgraph_outputs_indexi = subgraph.OutputsAsNumpy() # tensor indexi + output_node_names = set() + for index in subgraph_outputs_indexi: + output_node_names.add(self.tensor_names_map[index].op_name) - ugraph.output_nodes = list(output_node_names) + ugraph.output_nodes = list(output_node_names) - def _build_tensor_map(self, fb_model, ugraph): - subgraph = self._get_tflm_get_subgraph(fb_model) + def _build_tensor_map(self, fb_model, ugraph): + subgraph = self._get_tflm_get_subgraph(fb_model) - for idx in range(0, subgraph.TensorsLength()): - tensor = subgraph.Tensors(idx) + for idx in range(0, subgraph.TensorsLength()): + tensor = subgraph.Tensors(idx) - tensor_name = tensor.Name() - if tensor_name is "" or None: - tensor_name = "tensor_" + str(idx) + tensor_name = tensor.Name() + if tensor_name is "" or None: + tensor_name = "tensor_" + str(idx) - dtype = tensor_np_type[tensor.Type()] + dtype = tensor_np_type[tensor.Type()] - attributes = dict() + attributes = dict() - quantParam = tensor.Quantization() - if quantParam != None: - attributes["quantizationZeros"] = list([quantParam.ZeroPointAsNumpy()]) - attributes["quantizationScales"] = list([quantParam.ScaleAsNumpy()]) + quantParam = tensor.Quantization() + if quantParam != None: + attributes["quantizationZeros"] = list([quantParam.ZeroPointAsNumpy()]) + attributes["quantizationScales"] = list([quantParam.ScaleAsNumpy()]) - if type(tensor.ShapeAsNumpy()) == np.ndarray: - shape = tensor.ShapeAsNumpy().tolist() - else: - shape = [d for d in fb_model.Buffers(12).DataAsNumpy().shape] + if type(tensor.ShapeAsNumpy()) == np.ndarray: + shape = tensor.ShapeAsNumpy().tolist() + else: + shape = [d for d in fb_model.Buffers(12).DataAsNumpy().shape] - self.tensor_names_map[idx] = TensorInfo( - name=self._format_tensor_name("", tensor_name, 0), - op_name="", - dtype=dtype, - shape=shape, - attributes=attributes, - ugraph=ugraph, - ) + self.tensor_names_map[idx] = TensorInfo( + name=self._format_tensor_name("", tensor_name, 0), + op_name="", + dtype=dtype, + shape=shape, + attributes=attributes, + ugraph=ugraph, + ) - def _build_param_ops(self, fb_model, ugraph): - """Const tensors are identified by buffer_index == 0. These tensors are converted to Const Op and added to ugraph + def _build_param_ops(self, fb_model, ugraph): + """Const tensors are identified by buffer_index == 0. These tensors are converted to Const Op and added to ugraph """ - subgraph = self._get_tflm_get_subgraph(fb_model) - - for idx in range(0, subgraph.TensorsLength()): - tensor = subgraph.Tensors(idx) - buffer_index = tensor.Buffer() - - # buffer_index == 0 if intermediate - if buffer_index == 0: - continue - - node_name = self.tensor_names_map[idx].name + "_Const" - dtype = self.tensor_names_map[idx].dtype - - buffer_array = fb_model.Buffers(buffer_index).DataAsNumpy() - if type(buffer_array) == int: - - continue # somehow, sometimes, the buffer contains no data, likely to be an intermediate tensor - buffer_content = fb_model.Buffers(buffer_index).DataAsNumpy().astype(dtype) - - OperationInfo( - name=node_name, - input_tensors=[], - output_tensors=[self.tensor_names_map[idx]], - op_type="Const", - lib_name="tflm", - ugraph=ugraph, - op_attr={ - "value": AttrValueConverter.GenericType( - value_name="tensor", - value=GenericTensorConverterMixin.GenericType( - np_array=buffer_content - ), - ) - }, - ) - - self._set_tensor_node(idx, node_name) - - def _build_input_ops(self, fb_model, ugraph): - """Find placeholders + subgraph = self._get_tflm_get_subgraph(fb_model) + + for idx in range(0, subgraph.TensorsLength()): + tensor = subgraph.Tensors(idx) + buffer_index = tensor.Buffer() + + # buffer_index == 0 if intermediate + if buffer_index == 0: + continue + + node_name = self.tensor_names_map[idx].name + "_Const" + dtype = self.tensor_names_map[idx].dtype + + buffer_array = fb_model.Buffers(buffer_index).DataAsNumpy() + if type(buffer_array) == int: + + continue # somehow, sometimes, the buffer contains no data, likely to be an intermediate tensor + buffer_content = fb_model.Buffers(buffer_index).DataAsNumpy().astype(dtype) + + OperationInfo( + name=node_name, + input_tensors=[], + output_tensors=[self.tensor_names_map[idx]], + op_type="Const", + lib_name="tflm", + ugraph=ugraph, + op_attr={ + "value": AttrValueConverter.GenericType( + value_name="tensor", + value=GenericTensorConverterMixin.GenericType( + np_array=buffer_content + ), + ) + }, + ) + + self._set_tensor_node(idx, node_name) + + def _build_input_ops(self, fb_model, ugraph): + """Find placeholders Attach placeholders to input tensors Note this method will update inputs **inplace** """ - subgraph = self._get_tflm_get_subgraph(fb_model) - subgraph_inputs_indexi = subgraph.InputsAsNumpy() - for index in subgraph_inputs_indexi: - node_name = self.tensor_names_map[index].name + "_Placeholder" - self._set_tensor_node(index, node_name) - OperationInfo( - name=node_name, - input_tensors=[], - output_tensors=[self.tensor_names_map[index]], - op_type="Placeholder", - ugraph=ugraph, - lib_name="tflm", - op_attr={}, - ) - - def _build_intermediate_ops(self, fb_model, ugraph): - """Build all intermediate nodes + subgraph = self._get_tflm_get_subgraph(fb_model) + subgraph_inputs_indexi = subgraph.InputsAsNumpy() + for index in subgraph_inputs_indexi: + node_name = self.tensor_names_map[index].name + "_Placeholder" + self._set_tensor_node(index, node_name) + OperationInfo( + name=node_name, + input_tensors=[], + output_tensors=[self.tensor_names_map[index]], + op_type="Placeholder", + ugraph=ugraph, + lib_name="tflm", + op_attr={}, + ) + + def _build_intermediate_ops(self, fb_model, ugraph): + """Build all intermediate nodes """ - subgraphs_len = fb_model.SubgraphsLength() - assert subgraphs_len == 1, "only 1 subgraph is supported" - subgraph = fb_model.Subgraphs(0) - for i in range(0, subgraph.OperatorsLength()): - # topological order, op-index defined by schema - # BuiltinOperator: https://github.com/tensorflow/tensorflow/blob/031804922d8f4d18b61e3ad077f9f1b69273ff21/tensorflow/lite/schema/schema_v3.fbs#L71 - op = subgraph.Operators(i) - local_op_code = op.OpcodeIndex() - global_op_code = fb_model.OperatorCodes(local_op_code) - builtinOperator_code = global_op_code.BuiltinCode() - op_type = builtin_ops[builtinOperator_code] - - node_name = str(i) + "_" + op_type - - input_tensor_names = [ - self.tensor_names_map[input_index] for input_index in op.InputsAsNumpy() - ] - output_tensor_names = [ - self.tensor_names_map[output_index] - for output_index in op.OutputsAsNumpy() - ] - - op_attr = dict() - op_attr = op_data_func[op_type](op) - - OperationInfo( - name=node_name, - input_tensors=input_tensor_names, - output_tensors=output_tensor_names, - op_type=op_type, - ugraph=ugraph, - lib_name="tflm", - op_attr=op_attr, - ) - - for tensor_index in op.OutputsAsNumpy(): - self._set_tensor_node(tensor_index, node_name) - - def _get_tflm_get_subgraph(self, fb_model): - subgraphs_len = fb_model.SubgraphsLength() - assert subgraphs_len == 1, "only 1 subgraph is supported" - subgraph = fb_model.Subgraphs(0) - - return subgraph - - def _set_tensor_node(self, idx, name): - assert self.tensor_names_map[idx].op_name == "" - self.tensor_names_map[idx].op_name = name - - def _format_node_name(self, node_name, op_type, op_cnt): - if node_name == "": - node_name = "{}_{}".format(op_type, op_cnt) - return re.sub(r"[\.:/]", "_", node_name) - - def _format_tensor_name(self, name, node_name, offset): - if re.match(r"[a-zA-Z][a-zA-Z0-9]*:[0-9]+", name): - return name - return "{}:{}".format(node_name, offset) + subgraphs_len = fb_model.SubgraphsLength() + assert subgraphs_len == 1, "only 1 subgraph is supported" + subgraph = fb_model.Subgraphs(0) + for i in range(0, subgraph.OperatorsLength()): + # topological order, op-index defined by schema + # BuiltinOperator: https://github.com/tensorflow/tensorflow/blob/031804922d8f4d18b61e3ad077f9f1b69273ff21/tensorflow/lite/schema/schema_v3.fbs#L71 + op = subgraph.Operators(i) + local_op_code = op.OpcodeIndex() + global_op_code = fb_model.OperatorCodes(local_op_code) + builtinOperator_code = global_op_code.BuiltinCode() + op_type = builtin_ops[builtinOperator_code] + + node_name = str(i) + "_" + op_type + + input_tensor_names = [ + self.tensor_names_map[input_index] for input_index in op.InputsAsNumpy() + ] + output_tensor_names = [ + self.tensor_names_map[output_index] + for output_index in op.OutputsAsNumpy() + ] + + op_attr = dict() + op_attr = op_data_func[op_type](op) + + OperationInfo( + name=node_name, + input_tensors=input_tensor_names, + output_tensors=output_tensor_names, + op_type=op_type, + ugraph=ugraph, + lib_name="tflm", + op_attr=op_attr, + ) + + for tensor_index in op.OutputsAsNumpy(): + self._set_tensor_node(tensor_index, node_name) + + def _get_tflm_get_subgraph(self, fb_model): + subgraphs_len = fb_model.SubgraphsLength() + assert subgraphs_len == 1, "only 1 subgraph is supported" + subgraph = fb_model.Subgraphs(0) + + return subgraph + + def _set_tensor_node(self, idx, name): + assert self.tensor_names_map[idx].op_name == "" + self.tensor_names_map[idx].op_name = name + + def _format_node_name(self, node_name, op_type, op_cnt): + if node_name == "": + node_name = "{}_{}".format(op_type, op_cnt) + return re.sub(r"[\.:/]", "_", node_name) + + def _format_tensor_name(self, name, node_name, offset): + if re.match(r"[a-zA-Z][a-zA-Z0-9]*:[0-9]+", name): + return name + return "{}:{}".format(node_name, offset) From 56066939fa12408a60ffee1defa33b2008891e60 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Thu, 23 Apr 2020 15:34:02 +0800 Subject: [PATCH 091/117] Fix "b"-prefix in the tensor names (with decoding to utf8) --- utensor_cgen/frontend/tflite.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utensor_cgen/frontend/tflite.py b/utensor_cgen/frontend/tflite.py index 4f7f1dc3..13b48e24 100644 --- a/utensor_cgen/frontend/tflite.py +++ b/utensor_cgen/frontend/tflite.py @@ -238,7 +238,7 @@ def _build_tensor_map(self, fb_model, ugraph): for idx in range(0, subgraph.TensorsLength()): tensor = subgraph.Tensors(idx) - tensor_name = tensor.Name() + tensor_name = tensor.Name().decode('utf8') if tensor_name is "" or None: tensor_name = "tensor_" + str(idx) From 21da98c77701b9fcbe5103f627167a3baba7f3cb Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Fri, 24 Apr 2020 12:35:31 +0800 Subject: [PATCH 092/117] addressing issues mentioned in the PR107 comments --- utensor_cgen/frontend/tflite.py | 28 +++++++++++++++------------- 1 file changed, 15 insertions(+), 13 deletions(-) diff --git a/utensor_cgen/frontend/tflite.py b/utensor_cgen/frontend/tflite.py index 13b48e24..4544fc22 100644 --- a/utensor_cgen/frontend/tflite.py +++ b/utensor_cgen/frontend/tflite.py @@ -3,9 +3,6 @@ import os import re -import six - -import flatbuffers import numpy as np from utensor_cgen.frontend import FrontendSelector from utensor_cgen.frontend.base import Parser @@ -27,7 +24,7 @@ tensor_np_type[2] = np.dtype("int32") tensor_np_type[3] = np.dtype("uint8") tensor_np_type[4] = np.dtype("uint64") -tensor_np_type[5] = np.dtype("ubyte") # FIXME: supposed to be string +tensor_np_type[5] = np.dtype("str") tensor_np_type[6] = np.dtype("bool") tensor_np_type[7] = np.dtype("int16") tensor_np_type[8] = np.dtype("cdouble") @@ -111,34 +108,40 @@ def reshape_op_data(op): def dequantize_op_data(op): + option_dict = {} if op.CustomOptionsLength() < 1: from .tflite_flatbuffer.DequantizeOptions import DequantizeOptions option = DequantizeOptions() builtin_data = op.BuiltinOptions() if builtin_data is None: - return builtin_data + return option_dict option.Init(builtin_data.Bytes, builtin_data.Pos) - return [option] + option_dict['builtin'] = option else: - custom_option = op.CustomOptionsAsNumpy() - return [custom_option] + option_dict[ + customOptionFormat_lookup[op.CustomOptionsFormat()] + ] = op.CustomOptionsAsNumpy() + return option_dict def quantize_op_data(op): + option_dict = {} if op.CustomOptionsLength() < 1: from .tflite_flatbuffer.QuantizeOptions import QuantizeOptions option = QuantizeOptions() builtin_data = op.BuiltinOptions() if builtin_data is None: - return builtin_data + return option_dict option.Init(builtin_data.Bytes, builtin_data.Pos) - return [option] + option_dict['builtin'] = option else: - custom_option = op.CustomOptionsAsNumpy() - return [custom_option] + option_dict[ + customOptionFormat_lookup[op.CustomOptionsFormat()] + ] = op.CustomOptionsAsNumpy() + return option_dict def pool2d_op_data(op): option_dict = {} @@ -351,7 +354,6 @@ def _build_intermediate_ops(self, fb_model, ugraph): for output_index in op.OutputsAsNumpy() ] - op_attr = dict() op_attr = op_data_func[op_type](op) OperationInfo( From bda37bb17058305d9c85aeff86a34dae59c9c931 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Fri, 24 Apr 2020 14:56:31 +0800 Subject: [PATCH 093/117] fix node_name --- utensor_cgen/frontend/tflite.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utensor_cgen/frontend/tflite.py b/utensor_cgen/frontend/tflite.py index 4544fc22..0c880a56 100644 --- a/utensor_cgen/frontend/tflite.py +++ b/utensor_cgen/frontend/tflite.py @@ -281,7 +281,7 @@ def _build_param_ops(self, fb_model, ugraph): if buffer_index == 0: continue - node_name = self.tensor_names_map[idx].name + "_Const" + node_name = re.sub(r':\d+', '', self.tensor_names_map[idx].name) + "_Const" dtype = self.tensor_names_map[idx].dtype buffer_array = fb_model.Buffers(buffer_index).DataAsNumpy() From 3903dfe3c239de06ff0b311852f5cef4d84d6175 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Fri, 24 Apr 2020 15:17:09 +0800 Subject: [PATCH 094/117] Fix linting issues --- utensor_cgen/frontend/tflite.py | 96 +++++++++++++++------------------ 1 file changed, 43 insertions(+), 53 deletions(-) diff --git a/utensor_cgen/frontend/tflite.py b/utensor_cgen/frontend/tflite.py index 0c880a56..07fc4330 100644 --- a/utensor_cgen/frontend/tflite.py +++ b/utensor_cgen/frontend/tflite.py @@ -1,5 +1,3 @@ -from __future__ import absolute_import - import os import re @@ -18,31 +16,13 @@ FullyConnectedOptionsWeightsFormat from .tflite_flatbuffer.Model import Model -tensor_np_type = dict() -tensor_np_type[0] = np.dtype("float32") -tensor_np_type[1] = np.dtype("float16") -tensor_np_type[2] = np.dtype("int32") -tensor_np_type[3] = np.dtype("uint8") -tensor_np_type[4] = np.dtype("uint64") -tensor_np_type[5] = np.dtype("str") -tensor_np_type[6] = np.dtype("bool") -tensor_np_type[7] = np.dtype("int16") -tensor_np_type[8] = np.dtype("cdouble") -tensor_np_type[9] = np.dtype("int8") - - -builtin_ops = {v: k for k, v in BuiltinOperator.__dict__.items()} - +_CUSTOM_OPTION_FORMAT_MAP = {v: k for k, v in CustomOptionsFormat.__dict__.items()} def class_option2str(obj, idx): names_lookup = {v: k for k, v in obj.__dict__.items()} name = names_lookup[idx] return str(idx) + " (" + name + ")" - -customOptionFormat_lookup = {v: k for k, v in CustomOptionsFormat.__dict__.items()} - - def fully_connected_op_data(op): option_dict = {} if op.CustomOptionsLength() < 1: @@ -59,11 +39,10 @@ def fully_connected_op_data(op): ) else: option_dict[ - customOptionFormat_lookup[op.CustomOptionsFormat()] + _CUSTOM_OPTION_FORMAT_MAP[op.CustomOptionsFormat()] ] = op.CustomOptionsAsNumpy() return option_dict - def depthwise_conv2d_op_data(op): option_dict = {} if op.CustomOptionsLength() < 1: @@ -84,12 +63,11 @@ def depthwise_conv2d_op_data(op): else: option_dict[ - customOptionFormat_lookup[op.CustomOptionsFormat()] + _CUSTOM_OPTION_FORMAT_MAP[op.CustomOptionsFormat()] ] = op.CustomOptionsAsNumpy() return option_dict - def reshape_op_data(op): option_dict = {} if op.CustomOptionsLength() < 1: @@ -101,12 +79,11 @@ def reshape_op_data(op): option_dict["NewShape"] = option.NewShapeAsNumpy() else: option_dict[ - customOptionFormat_lookup[op.CustomOptionsFormat()] + _CUSTOM_OPTION_FORMAT_MAP[op.CustomOptionsFormat()] ] = op.CustomOptionsAsNumpy() return option_dict - def dequantize_op_data(op): option_dict = {} if op.CustomOptionsLength() < 1: @@ -120,7 +97,7 @@ def dequantize_op_data(op): option_dict['builtin'] = option else: option_dict[ - customOptionFormat_lookup[op.CustomOptionsFormat()] + _CUSTOM_OPTION_FORMAT_MAP[op.CustomOptionsFormat()] ] = op.CustomOptionsAsNumpy() return option_dict @@ -138,7 +115,7 @@ def quantize_op_data(op): option_dict['builtin'] = option else: option_dict[ - customOptionFormat_lookup[op.CustomOptionsFormat()] + _CUSTOM_OPTION_FORMAT_MAP[op.CustomOptionsFormat()] ] = op.CustomOptionsAsNumpy() return option_dict @@ -161,12 +138,11 @@ def pool2d_op_data(op): ) else: option_dict[ - customOptionFormat_lookup[op.CustomOptionsFormat()] + _CUSTOM_OPTION_FORMAT_MAP[op.CustomOptionsFormat()] ] = op.CustomOptionsAsNumpy() return option_dict - def argmax_op_data(op): option_dict = {} if op.CustomOptionsLength() < 1: @@ -178,34 +154,52 @@ def argmax_op_data(op): option_dict["OutputType"] = option.OutputType() else: option_dict[ - customOptionFormat_lookup[op.CustomOptionsFormat()] + _CUSTOM_OPTION_FORMAT_MAP[op.CustomOptionsFormat()] ] = op.CustomOptionsAsNumpy() return option_dict -op_data_func = dict() -op_data_func["QUANTIZE"] = quantize_op_data -op_data_func["DEPTHWISE_CONV_2D"] = depthwise_conv2d_op_data -op_data_func["MAX_POOL_2D"] = pool2d_op_data -op_data_func["RESHAPE"] = reshape_op_data -op_data_func["FULLY_CONNECTED"] = fully_connected_op_data -op_data_func["DEQUANTIZE"] = dequantize_op_data -op_data_func["ARG_MAX"] = argmax_op_data +_OP_DATA_FUNC_MAP = dict() +_OP_DATA_FUNC_MAP["QUANTIZE"] = quantize_op_data +_OP_DATA_FUNC_MAP["DEPTHWISE_CONV_2D"] = depthwise_conv2d_op_data +_OP_DATA_FUNC_MAP["MAX_POOL_2D"] = pool2d_op_data +_OP_DATA_FUNC_MAP["RESHAPE"] = reshape_op_data +_OP_DATA_FUNC_MAP["FULLY_CONNECTED"] = fully_connected_op_data +_OP_DATA_FUNC_MAP["DEQUANTIZE"] = dequantize_op_data +_OP_DATA_FUNC_MAP["ARG_MAX"] = argmax_op_data @FrontendSelector.register(target_exts=[".tflite"]) class TFLiteParser(Parser): + _TENSOR_NP_TYPE = { + 0:np.dtype("float32"), + 1: np.dtype("float16"), + 2: np.dtype("int32"), + 3: np.dtype("uint8"), + 4: np.dtype("uint64"), + 5: np.dtype("str"), + 6: np.dtype("bool"), + 7: np.dtype("int16"), + 8: np.dtype("cdouble"), + 9: np.dtype("int8"), + } + _BUILTIN_OPS = {v: k for k, v in BuiltinOperator.__dict__.items()} + def parse(self, tflite_file, output_nodes=None): + if output_nodes is None: + output_nodes = [] graph_name, _ = os.path.splitext(tflite_file) buf = open(tflite_file, "rb").read() buf = bytearray(buf) fb_model = Model.GetRootAsModel(buf, 0) ugraph = uTensorGraph( - name=graph_name, output_nodes=[], lib_name="tflite", ops_info={}, + name=graph_name, + output_nodes=output_nodes, + lib_name="tflite", + ops_info={}, ) - self._build_graph(fb_model, ugraph) return ugraph @@ -240,24 +234,21 @@ def _build_tensor_map(self, fb_model, ugraph): for idx in range(0, subgraph.TensorsLength()): tensor = subgraph.Tensors(idx) - tensor_name = tensor.Name().decode('utf8') if tensor_name is "" or None: tensor_name = "tensor_" + str(idx) - dtype = tensor_np_type[tensor.Type()] - + dtype = self._TENSOR_NP_TYPE[tensor.Type()] attributes = dict() - quantParam = tensor.Quantization() - if quantParam != None: + if quantParam is not None: attributes["quantizationZeros"] = list([quantParam.ZeroPointAsNumpy()]) attributes["quantizationScales"] = list([quantParam.ScaleAsNumpy()]) - if type(tensor.ShapeAsNumpy()) == np.ndarray: + if isinstance(tensor.ShapeAsNumpy(), np.ndarray): shape = tensor.ShapeAsNumpy().tolist() else: - shape = [d for d in fb_model.Buffers(12).DataAsNumpy().shape] + shape = list(fb_model.Buffers(12).DataAsNumpy().shape) self.tensor_names_map[idx] = TensorInfo( name=self._format_tensor_name("", tensor_name, 0), @@ -285,8 +276,7 @@ def _build_param_ops(self, fb_model, ugraph): dtype = self.tensor_names_map[idx].dtype buffer_array = fb_model.Buffers(buffer_index).DataAsNumpy() - if type(buffer_array) == int: - + if isinstance(buffer_array, int): continue # somehow, sometimes, the buffer contains no data, likely to be an intermediate tensor buffer_content = fb_model.Buffers(buffer_index).DataAsNumpy().astype(dtype) @@ -342,7 +332,7 @@ def _build_intermediate_ops(self, fb_model, ugraph): local_op_code = op.OpcodeIndex() global_op_code = fb_model.OperatorCodes(local_op_code) builtinOperator_code = global_op_code.BuiltinCode() - op_type = builtin_ops[builtinOperator_code] + op_type = self._BUILTIN_OPS[builtinOperator_code] node_name = str(i) + "_" + op_type @@ -354,7 +344,7 @@ def _build_intermediate_ops(self, fb_model, ugraph): for output_index in op.OutputsAsNumpy() ] - op_attr = op_data_func[op_type](op) + op_attr = _OP_DATA_FUNC_MAP[op_type](op) OperationInfo( name=node_name, From a8c904effe59f3f85eaa07f4233b4baef76a7a95 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Fri, 24 Apr 2020 15:36:36 +0800 Subject: [PATCH 095/117] fix minor issues and add test for tflite parser --- tests/test_frontend/conftest.py | 13 +++++++++++++ .../model_files/quant_mnist_cnn.tflite | Bin 0 -> 698160 bytes tests/test_frontend/test_tflite.py | 11 +++++++++++ utensor_cgen/frontend/tflite.py | 4 ++-- 4 files changed, 26 insertions(+), 2 deletions(-) create mode 100644 tests/test_frontend/conftest.py create mode 100644 tests/test_frontend/model_files/quant_mnist_cnn.tflite create mode 100644 tests/test_frontend/test_tflite.py diff --git a/tests/test_frontend/conftest.py b/tests/test_frontend/conftest.py new file mode 100644 index 00000000..909f17c1 --- /dev/null +++ b/tests/test_frontend/conftest.py @@ -0,0 +1,13 @@ +import os + +import pytest + + +@pytest.fixture(name='tflm_mnist_path', scope='session') +def tflm_mnist_path(): + model_path = os.path.join( + os.path.dirname(__file__), + 'model_files', + 'quant_mnist_cnn.tflite' + ) + return model_path diff --git a/tests/test_frontend/model_files/quant_mnist_cnn.tflite b/tests/test_frontend/model_files/quant_mnist_cnn.tflite new file mode 100644 index 0000000000000000000000000000000000000000..4c50ff0de4c26fddf9dade2fa3b4de9139693686 GIT binary patch literal 698160 zcmXWDd9W=>dLNWom6^5I+3$Ye)~~x?Pxs8UW|hVxOO}KKumr*(g2mb*kT4Qq*&&1p z{$Y3!A;88C+x%f6aEQQ`B5+_JgfSA9>=Cj;8fo_KnciN%fA 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