Add Accel implementation.

deeplab/_init_paths.py

@@ -0,0 +1,19 @@
+# --------------------------------------------------------
+# Deformable Convolutional Networks
+# Copyright (c) 2016 by Contributors
+# Copyright (c) 2017 Microsoft
+# Licensed under The Apache-2.0 License [see LICENSE for details]
+# Modified by Zheng Zhang
+# --------------------------------------------------------
+
+import os.path as osp
+import sys
+
+def add_path(path):
+    if path not in sys.path:
+        sys.path.insert(0, path)
+
+this_dir = osp.dirname(__file__)
+
+lib_path = osp.join(this_dir, '..', 'lib')
+add_path(lib_path)

deeplab/config/config.py

@@ -0,0 +1,96 @@
+# --------------------------------------------------------
+# Deformable Convolutional Networks
+# Copyright (c) 2016 by Contributors
+# Copyright (c) 2017 Microsoft
+# Licensed under The Apache-2.0 License [see LICENSE for details]
+# Modified by Zheng Zhang
+# --------------------------------------------------------
+
+import yaml
+import numpy as np
+from easydict import EasyDict as edict
+
+config = edict()
+
+config.MXNET_VERSION = ''
+config.output_path = ''
+config.symbol = ''
+config.gpus = ''
+config.CLASS_AGNOSTIC = True
+config.SCALES = [(360, 600)]  # first is scale (the shorter side); second is max size
+
+# default training
+config.default = edict()
+config.default.frequent = 1000
+config.default.kvstore = 'device'
+
+# network related params
+config.network = edict()
+config.network.pretrained = '../model/pretrained_model/resnet_v1-101'
+config.network.pretrained_epoch = 0
+config.network.PIXEL_MEANS = np.array([103.06, 115.90, 123.15])
+config.network.IMAGE_STRIDE = 0
+config.network.FIXED_PARAMS = ['conv1', 'bn_conv1', 'res2', 'bn2', 'gamma', 'beta']
+
+# dataset related params
+config.dataset = edict()
+config.dataset.dataset = 'cityscapes'
+config.dataset.image_set = 'leftImg8bit_train'
+config.dataset.test_image_set = 'leftImg8bit_val'
+config.dataset.root_path = '../data'
+config.dataset.dataset_path = '../data/cityscapes'
+config.dataset.NUM_CLASSES = 19
+config.dataset.annotation_prefix = 'gtFine'
+
+config.TRAIN = edict()
+config.TRAIN.lr = 0
+config.TRAIN.lr_step = ''
+config.TRAIN.warmup = False
+config.TRAIN.warmup_lr = 0
+config.TRAIN.warmup_step = 0
+config.TRAIN.momentum = 0.9
+config.TRAIN.wd = 0.0005
+config.TRAIN.begin_epoch = 0
+config.TRAIN.end_epoch = 0
+config.TRAIN.model_prefix = 'deeplab'
+
+# whether resume training
+config.TRAIN.RESUME = False
+# whether flip image
+config.TRAIN.FLIP = True
+# whether shuffle image
+config.TRAIN.SHUFFLE = True
+# whether use OHEM
+config.TRAIN.ENABLE_OHEM = False
+# size of images for each device, 2 for rcnn, 1 for rpn and e2e
+config.TRAIN.BATCH_IMAGES = 1
+
+config.TEST = edict()
+# size of images for each device
+config.TEST.BATCH_IMAGES = 1
+
+# Test Model Epoch
+config.TEST.test_epoch = 0
+
+def update_config(config_file):
+    exp_config = None
+    with open(config_file) as f:
+        exp_config = edict(yaml.load(f))
+        for k, v in exp_config.items():
+            if k in config:
+                if isinstance(v, dict):
+                    if k == 'TRAIN':
+                        if 'BBOX_WEIGHTS' in v:
+                            v['BBOX_WEIGHTS'] = np.array(v['BBOX_WEIGHTS'])
+                    elif k == 'network':
+                        if 'PIXEL_MEANS' in v:
+                            v['PIXEL_MEANS'] = np.array(v['PIXEL_MEANS'])
+                    for vk, vv in v.items():
+                        config[k][vk] = vv
+                else:
+                    if k == 'SCALES':
+                        config[k][0] = (tuple(v))
+                    else:
+                        config[k] = v
+            else:
+                raise ValueError("key must exist in config.py")