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test_layers_dense.py
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test_layers_dense.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import unittest
import numpy as np
import tensorflow as tf
import tensorlayer as tl
from tensorlayer.layers import *
from tensorlayer.models import *
from tests.utils import CustomTestCase
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
class Layer_BinaryDense_Test(CustomTestCase):
@classmethod
def setUpClass(cls):
print("-" * 20, "Layer_BinaryDense_Test", "-" * 20)
cls.batch_size = 4
cls.inputs_shape = [cls.batch_size, 10]
cls.ni = Input(cls.inputs_shape, name='input_layer')
cls.layer1 = BinaryDense(n_units=5)
nn = cls.layer1(cls.ni)
cls.layer1._nodes_fixed = True
cls.M = Model(inputs=cls.ni, outputs=nn)
cls.layer2 = BinaryDense(n_units=5, in_channels=10)
cls.layer2._nodes_fixed = True
cls.inputs = tf.ones((cls.inputs_shape))
cls.n1 = cls.layer1(cls.inputs)
cls.n2 = cls.layer2(cls.inputs)
cls.n3 = cls.M(cls.inputs, is_train=True)
print(cls.layer1)
print(cls.layer2)
@classmethod
def tearDownClass(cls):
pass
def test_layer_n1(self):
print(self.n1[0])
self.assertEqual(tf.reduce_sum(self.n1).numpy() % 1, 0.0) # should be integer
def test_layer_n2(self):
print(self.n2[0])
self.assertEqual(tf.reduce_sum(self.n2).numpy() % 1, 0.0) # should be integer
def test_model_n3(self):
print(self.n3[0])
self.assertEqual(tf.reduce_sum(self.n3).numpy() % 1, 0.0) # should be integer
def test_exception(self):
try:
layer = BinaryDense(n_units=5)
inputs = Input([4, 10, 5], name='ill_inputs')
out = layer(inputs)
self.fail('ill inputs')
except Exception as e:
print(e)
try:
layer = BinaryDense(n_units=5, use_gemm=True)
out = layer(self.ni)
self.fail('use gemm')
except Exception as e:
print(e)
class Layer_DorefaDense_Test(CustomTestCase):
@classmethod
def setUpClass(cls):
print("-" * 20, "Layer_DorefaDense_Test", "-" * 20)
cls.batch_size = 4
cls.inputs_shape = [cls.batch_size, 10]
cls.ni = Input(cls.inputs_shape, name='input_layer')
cls.layer1 = DorefaDense(n_units=5)
nn = cls.layer1(cls.ni)
cls.layer1._nodes_fixed = True
cls.M = Model(inputs=cls.ni, outputs=nn)
cls.layer2 = DorefaDense(n_units=5, in_channels=10)
cls.layer2._nodes_fixed = True
cls.inputs = tf.ones((cls.inputs_shape))
cls.n1 = cls.layer1(cls.inputs)
cls.n2 = cls.layer2(cls.inputs)
cls.n3 = cls.M(cls.inputs, is_train=True)
print(cls.layer1)
print(cls.layer2)
@classmethod
def tearDownClass(cls):
pass
def test_layer_n1(self):
print(self.n1[0])
# self.assertEqual(tf.reduce_sum(self.n1).numpy() % 1, 0.0) # should be integer
def test_layer_n2(self):
print(self.n2[0])
# self.assertEqual(tf.reduce_sum(self.n2).numpy() % 1, 0.0) # should be integer
def test_model_n3(self):
print(self.n3[0])
# self.assertEqual(tf.reduce_sum(self.n3).numpy() % 1, 0.0) # should be integer
def test_exception(self):
try:
layer = DorefaDense(n_units=5)
inputs = Input([4, 10, 5], name='ill_inputs')
out = layer(inputs)
self.fail('ill inputs')
except Exception as e:
print(e)
try:
layer = DorefaDense(n_units=5, use_gemm=True)
out = layer(self.ni)
self.fail('use gemm')
except Exception as e:
print(e)
class Layer_DropconnectDense_Test(CustomTestCase):
@classmethod
def setUpClass(cls):
print("-" * 20, "Layer_DropconnectDense_Test", "-" * 20)
cls.batch_size = 4
cls.inputs_shape = [cls.batch_size, 10]
cls.ni = Input(cls.inputs_shape, name='input_layer')
cls.layer1 = DropconnectDense(n_units=5, keep=1.0)
nn = cls.layer1(cls.ni)
cls.layer1._nodes_fixed = True
cls.M = Model(inputs=cls.ni, outputs=nn)
cls.layer2 = DropconnectDense(n_units=5, in_channels=10, keep=0.01)
cls.layer2._nodes_fixed = True
cls.inputs = tf.ones((cls.inputs_shape))
cls.n1 = cls.layer1(cls.inputs)
cls.n2 = cls.layer2(cls.inputs)
cls.n3 = cls.M(cls.inputs, is_train=True)
print(cls.layer1)
print(cls.layer2)
@classmethod
def tearDownClass(cls):
pass
def test_layer_n1(self):
print(self.n1[0])
def test_layer_n2(self):
zero_rate = tf.reduce_mean(tf.cast(tf.equal(self.n2, 0.0), tf.float32))
print(zero_rate)
self.assertGreater(zero_rate, 0.0)
print(self.n2[0])
def test_model_n3(self):
print(self.n3[0])
def test_exception(self):
try:
layer = DropconnectDense(n_units=5)
inputs = Input([4, 10, 5], name='ill_inputs')
out = layer(inputs)
self.fail('ill inputs')
except Exception as e:
print(e)
try:
layer = DropconnectDense(n_units=5, keep=0.0)
self.fail('keep no elements')
except Exception as e:
self.assertIsInstance(e, ValueError)
print(e)
class Layer_QuanDense_Test(CustomTestCase):
@classmethod
def setUpClass(cls):
print("-" * 20, "Layer_QuanDense_Test", "-" * 20)
cls.batch_size = 4
cls.inputs_shape = [cls.batch_size, 10]
cls.ni = Input(cls.inputs_shape, name='input_layer')
cls.layer1 = QuanDense(n_units=5)
nn = cls.layer1(cls.ni)
cls.layer1._nodes_fixed = True
cls.M = Model(inputs=cls.ni, outputs=nn)
cls.layer2 = QuanDense(n_units=5, in_channels=10)
cls.layer2._nodes_fixed = True
cls.inputs = tf.random.uniform((cls.inputs_shape))
cls.n1 = cls.layer1(cls.inputs)
cls.n2 = cls.layer2(cls.inputs)
cls.n3 = cls.M(cls.inputs, is_train=True)
print(cls.layer1)
print(cls.layer2)
@classmethod
def tearDownClass(cls):
pass
def test_layer_n1(self):
print(self.n1[0])
def test_layer_n2(self):
print(self.n2[0])
def test_model_n3(self):
print(self.n3[0])
def test_exception(self):
try:
layer = QuanDense(n_units=5)
inputs = Input([4, 10, 5], name='ill_inputs')
out = layer(inputs)
self.fail('ill inputs')
except Exception as e:
print(e)
try:
layer = QuanDense(n_units=5, use_gemm=True)
out = layer(self.ni)
self.fail('use gemm')
except Exception as e:
print(e)
class Layer_QuanDenseWithBN_Test(CustomTestCase):
@classmethod
def setUpClass(cls):
print("-" * 20, "Layer_QuanDenseWithBN_Test", "-" * 20)
cls.batch_size = 4
cls.inputs_shape = [cls.batch_size, 10]
cls.ni = Input(cls.inputs_shape, name='input_layer')
cls.layer1 = QuanDenseWithBN(n_units=5)
nn = cls.layer1(cls.ni)
cls.layer1._nodes_fixed = True
cls.M = Model(inputs=cls.ni, outputs=nn)
cls.layer2 = QuanDenseWithBN(n_units=5, in_channels=10)
cls.layer2._nodes_fixed = True
cls.inputs = tf.random.uniform((cls.inputs_shape))
cls.n1 = cls.layer1(cls.inputs)
cls.n2 = cls.layer2(cls.inputs)
cls.n3 = cls.M(cls.inputs, is_train=True)
print(cls.layer1)
print(cls.layer2)
@classmethod
def tearDownClass(cls):
pass
def test_layer_n1(self):
print(self.n1[0])
def test_layer_n2(self):
print(self.n2[0])
def test_model_n3(self):
print(self.n3[0])
def test_exception(self):
try:
layer = QuanDenseWithBN(n_units=5)
inputs = Input([4, 10, 5], name='ill_inputs')
out = layer(inputs)
self.fail('ill inputs')
except Exception as e:
print(e)
try:
layer = QuanDenseWithBN(n_units=5, use_gemm=True)
out = layer(self.ni)
self.fail('use gemm')
except Exception as e:
print(e)
class Layer_TernaryDense_Test(CustomTestCase):
@classmethod
def setUpClass(cls):
print("-" * 20, "Layer_BinaryDense_Test", "-" * 20)
cls.batch_size = 4
cls.inputs_shape = [cls.batch_size, 10]
cls.ni = Input(cls.inputs_shape, name='input_layer')
cls.layer1 = TernaryDense(n_units=5)
nn = cls.layer1(cls.ni)
cls.layer1._nodes_fixed = True
cls.M = Model(inputs=cls.ni, outputs=nn)
cls.layer2 = TernaryDense(n_units=5, in_channels=10)
cls.layer2._nodes_fixed = True
cls.inputs = tf.ones((cls.inputs_shape))
cls.n1 = cls.layer1(cls.inputs)
cls.n2 = cls.layer2(cls.inputs)
cls.n3 = cls.M(cls.inputs, is_train=True)
print(cls.layer1)
print(cls.layer2)
@classmethod
def tearDownClass(cls):
pass
def test_layer_n1(self):
print(np.unique(self.n1.numpy().reshape(-1)))
print(self.n1[0])
def test_layer_n2(self):
print(np.unique(self.n2.numpy().reshape(-1)))
print(self.n2[0])
def test_model_n3(self):
print(np.unique(self.n3.numpy().reshape(-1)))
print(self.n3[0])
def test_exception(self):
try:
layer = TernaryDense(n_units=5)
inputs = Input([4, 10, 5], name='ill_inputs')
out = layer(inputs)
self.fail('ill inputs')
except Exception as e:
print(e)
try:
layer = TernaryDense(n_units=5, use_gemm=True)
out = layer(self.ni)
self.fail('use gemm')
except Exception as e:
print(e)
if __name__ == '__main__':
tl.logging.set_verbosity(tl.logging.DEBUG)
unittest.main()