#!/usr/bin/env python # -*- coding: utf-8 -*- import os import unittest os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' import tensorlayer as tl from tests.utils import CustomTestCase class Layer_Convolution_1D_Test(CustomTestCase): @classmethod def setUpClass(self): self.batch_size = 8 self.inputs_shape = [self.batch_size, 100, 1] self.input_layer = tl.layers.Input(self.inputs_shape, name='input_layer') self.conv1dlayer1 = tl.layers.Conv1d(in_channels=1, n_filter=32, filter_size=5, stride=2) self.n1 = self.conv1dlayer1(self.input_layer) self.conv1dlayer2 = tl.layers.Conv1d(in_channels=32, n_filter=32, filter_size=5, stride=2) self.n2 = self.conv1dlayer2(self.n1) self.dconv1dlayer1 = tl.layers.DeConv1d(n_filter=64, in_channels=32, filter_size=5, name='deconv1dlayer') self.n3 = self.dconv1dlayer1(self.n2) self.separableconv1d1 = tl.layers.SeparableConv1d(in_channels=1, n_filter=16, filter_size=3, stride=2) self.n4 = self.separableconv1d1(self.input_layer) self.separableconv1d2 = tl.layers.SeparableConv1d( in_channels=1, n_filter=16, filter_size=3, stride=2, depth_multiplier=4 ) self.n5 = self.separableconv1d2(self.input_layer) self.separableconv1d3 = tl.layers.SeparableConv1d( in_channels=1, n_filter=16, filter_size=3, stride=2, depth_multiplier=4, b_init=None ) self.n6 = self.separableconv1d3(self.input_layer) @classmethod def tearDownClass(self): pass def test_layer_n1(self): self.assertEqual(len(self.conv1dlayer1.all_weights), 2) self.assertEqual(tl.get_tensor_shape(self.n1), [self.batch_size, 50, 32]) def test_layer_n2(self): self.assertEqual(len(self.conv1dlayer2.all_weights), 2) self.assertEqual(tl.get_tensor_shape(self.n2), [self.batch_size, 25, 32]) def test_layer_n3(self): self.assertEqual(len(self.dconv1dlayer1.all_weights), 2) self.assertEqual(tl.get_tensor_shape(self.n3), [self.batch_size, 25, 64]) def test_layer_n4(self): self.assertEqual(len(self.separableconv1d1.all_weights), 3) self.assertEqual(tl.get_tensor_shape(self.n4), [self.batch_size, 50, 16]) def test_layer_n5(self): self.assertEqual(len(self.separableconv1d2.all_weights), 3) self.assertEqual(tl.get_tensor_shape(self.n5), [self.batch_size, 50, 16]) def test_layer_n6(self): self.assertEqual(len(self.separableconv1d3.all_weights), 2) self.assertEqual(tl.get_tensor_shape(self.n6), [self.batch_size, 50, 16]) class Layer_Convolution_2D_Test(CustomTestCase): @classmethod def setUpClass(self): self.batch_size = 5 self.inputs_shape = [self.batch_size, 400, 400, 3] self.input_layer = tl.layers.Input(self.inputs_shape, name='input_layer') self.conv2dlayer1 = tl.layers.Conv2d( n_filter=32, in_channels=3, strides=(2, 2), filter_size=(5, 5), padding='SAME', b_init=tl.initializers.truncated_normal(0.01), name='conv2dlayer' ) self.n1 = self.conv2dlayer1(self.input_layer) self.conv2dlayer2 = tl.layers.Conv2d( n_filter=32, in_channels=32, filter_size=(3, 3), strides=(2, 2), act=None, name='conv2d' ) self.n2 = self.conv2dlayer2(self.n1) self.conv2dlayer3 = tl.layers.Conv2d( in_channels=32, n_filter=32, filter_size=(3, 3), strides=(2, 2), act=tl.ReLU, b_init=None, name='conv2d_no_bias' ) self.n3 = self.conv2dlayer3(self.n2) self.dconv2dlayer = tl.layers.DeConv2d( n_filter=32, in_channels=32, filter_size=(5, 5), strides=(2, 2), name='deconv2dlayer' ) self.n4 = self.dconv2dlayer(self.n3) self.dwconv2dlayer = tl.layers.DepthwiseConv2d( in_channels=32, filter_size=(3, 3), strides=(1, 1), dilation_rate=(2, 2), act=tl.ReLU, depth_multiplier=2, name='depthwise' ) self.n5 = self.dwconv2dlayer(self.n4) self.separableconv2d = tl.layers.SeparableConv2d( in_channels=3, filter_size=(3, 3), strides=(2, 2), dilation_rate=(2, 2), act=tl.ReLU, depth_multiplier=3, name='separableconv2d' ) self.n6 = self.separableconv2d(self.input_layer) self.groupconv2d = tl.layers.GroupConv2d( in_channels=3, n_filter=18, filter_size=(3, 3), strides=(2, 2), dilation_rate=(3, 3), n_group=3, act=tl.ReLU, name='groupconv2d' ) self.n7 = self.groupconv2d(self.input_layer) self.binaryconv2d = tl.layers.BinaryConv2d( in_channels=3, n_filter=32, filter_size=(3, 3), strides=(2, 2), dilation_rate=(2, 2), act=tl.ReLU, name='binaryconv2d' ) self.n8 = self.binaryconv2d(self.input_layer) self.dorefaconv2d = tl.layers.DorefaConv2d( bitA=2, bitW=8, in_channels=3, n_filter=16, filter_size=(3, 3), strides=(2, 2), dilation_rate=(2, 2), act=tl.ReLU, name='dorefaconv2d' ) self.n9 = self.dorefaconv2d(self.input_layer) @classmethod def tearDownClass(cls): pass # tf.reset_default_graph() def test_layer_n1(self): self.assertEqual(len(self.conv2dlayer1.all_weights), 2) self.assertEqual(tl.get_tensor_shape(self.n1), [self.batch_size, 200, 200, 32]) def test_layer_n2(self): self.assertEqual(len(self.conv2dlayer2.all_weights), 2) self.assertEqual(tl.get_tensor_shape(self.n2), [self.batch_size, 100, 100, 32]) def test_layer_n3(self): self.assertEqual(len(self.conv2dlayer3.all_weights), 1) # b_init is None self.assertEqual(tl.get_tensor_shape(self.n3), [self.batch_size, 50, 50, 32]) def test_layer_n4(self): self.assertEqual(len(self.dconv2dlayer.all_weights), 2) self.assertEqual(tl.get_tensor_shape(self.n4), [self.batch_size, 100, 100, 32]) def test_layer_n5(self): self.assertEqual(len(self.dwconv2dlayer.all_weights), 2) self.assertEqual(tl.get_tensor_shape(self.n5), [self.batch_size, 100, 100, 64]) def test_layer_n6(self): self.assertEqual(len(self.separableconv2d.all_weights), 3) self.assertEqual(tl.get_tensor_shape(self.n6), [self.batch_size, 198, 198, 32]) def test_layer_n7(self): self.assertEqual(len(self.groupconv2d.all_weights), 2) self.assertEqual(tl.get_tensor_shape(self.n7), [self.batch_size, 200, 200, 18]) def test_layer_n8(self): self.assertEqual(len(self.binaryconv2d.all_weights), 2) self.assertEqual(tl.get_tensor_shape(self.n8), [self.batch_size, 198, 198, 32]) def test_layer_n9(self): self.assertEqual(len(self.dorefaconv2d.all_weights), 2) self.assertEqual(tl.get_tensor_shape(self.n9), [self.batch_size, 200, 200, 16]) class Layer_Convolution_3D_Test(CustomTestCase): @classmethod def setUpClass(self): print("\n#################################") self.batch_size = 5 self.inputs_shape = [self.batch_size, 20, 20, 20, 3] self.input_layer = tl.layers.Input(self.inputs_shape, name='input_layer') self.conv3dlayer1 = tl.layers.Conv3d(n_filter=32, in_channels=3, filter_size=(2, 2, 2), strides=(2, 2, 2)) self.n1 = self.conv3dlayer1(self.input_layer) self.deconv3dlayer = tl.layers.DeConv3d(n_filter=128, in_channels=32, filter_size=(2, 2, 2), strides=(2, 2, 2)) self.n2 = self.deconv3dlayer(self.n1) self.conv3dlayer2 = tl.layers.Conv3d( n_filter=64, in_channels=128, filter_size=(3, 3, 3), strides=(3, 3, 3), act=tl.ReLU, b_init=None, name='conv3d_no_bias' ) self.n3 = self.conv3dlayer2(self.n2) @classmethod def tearDownClass(self): pass def test_layer_n1(self): self.assertEqual(len(self.conv3dlayer1.all_weights), 2) self.assertEqual(tl.get_tensor_shape(self.n1), [self.batch_size, 10, 10, 10, 32]) def test_layer_n2(self): self.assertEqual(len(self.deconv3dlayer.all_weights), 2) self.assertEqual(tl.get_tensor_shape(self.n2), [self.batch_size, 20, 20, 20, 128]) def test_layer_n3(self): self.assertEqual(len(self.conv3dlayer2.all_weights), 1) # b_init is None self.assertEqual(tl.get_tensor_shape(self.n3), [self.batch_size, 7, 7, 7, 64]) if __name__ == '__main__': tl.logging.set_verbosity(tl.logging.DEBUG) unittest.main()