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- #!/usr/bin/env python
- # -*- coding: utf-8 -*-
-
- import os
- import unittest
-
- os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
- import tensorlayer as tl
- from tensorlayer.layers import *
- from tests.utils import CustomTestCase
-
-
- class Laye_BatchNorm_Test(CustomTestCase):
-
- @classmethod
- def setUpClass(cls):
-
- x_0_input_shape = [None, 10]
- x_1_input_shape = [None, 100, 1]
- x_2_input_shape = [None, 100, 100, 3]
- x_3_input_shape = [None, 100, 100, 100, 3]
- batchsize = 2
-
- cls.x0 = tl.ops.truncated_normal(shape=[batchsize] + x_0_input_shape[1:])
- cls.x1 = tl.ops.truncated_normal([batchsize] + x_1_input_shape[1:])
- cls.x2 = tl.ops.truncated_normal([batchsize] + x_2_input_shape[1:])
- cls.x3 = tl.ops.truncated_normal([batchsize] + x_3_input_shape[1:])
-
- ## Base
- ni_1 = Input(x_1_input_shape, name='test_ni1')
- nn_1 = Conv1d(n_filter=32, filter_size=5, stride=2, name='test_conv1d')(ni_1)
- n1_b = BatchNorm(name='test_conv')(nn_1)
- cls.n1_b = n1_b
-
- ni_2 = Input(x_2_input_shape, name='test_ni2')
- nn_2 = Conv2d(n_filter=32, filter_size=(3, 3), strides=(2, 2), name='test_conv2d')(ni_2)
- n2_b = BatchNorm(name='test_bn2d')(nn_2)
- cls.n2_b = n2_b
-
- ni_3 = Input(x_3_input_shape, name='test_ni2')
- nn_3 = Conv3d(n_filter=32, filter_size=(3, 3, 3), strides=(2, 2, 2), name='test_conv3d')(ni_3)
- n3_b = BatchNorm(name='test_bn3d')(nn_3)
- cls.n3_b = n3_b
-
- class bn_0d_model(tl.layers.Module):
-
- def __init__(self):
- super(bn_0d_model, self).__init__()
- self.fc = Dense(32, in_channels=10)
- self.bn = BatchNorm(num_features=32, name='test_bn1d')
-
- def forward(self, x):
- x = self.bn(self.fc(x))
- return x
-
- dynamic_base = bn_0d_model()
- cls.n0_b = dynamic_base(cls.x0)
-
- ## 0D ========================================================================
-
- nin_0 = Input(x_0_input_shape, name='test_in1')
-
- n0 = Dense(32)(nin_0)
- n0 = BatchNorm1d(name='test_bn0d')(n0)
-
- cls.n0 = n0
-
- class bn_0d_model(tl.layers.Module):
-
- def __init__(self):
- super(bn_0d_model, self).__init__(name='test_bn_0d_model')
- self.fc = Dense(32, in_channels=10)
- self.bn = BatchNorm1d(num_features=32, name='test_bn1d')
-
- def forward(self, x):
- x = self.bn(self.fc(x))
- return x
-
- cls.dynamic_0d = bn_0d_model()
-
- ## 1D ========================================================================
-
- nin_1 = Input(x_1_input_shape, name='test_in1')
-
- n1 = Conv1d(n_filter=32, filter_size=5, stride=2, name='test_conv1d')(nin_1)
- n1 = BatchNorm1d(name='test_bn1d')(n1)
-
- cls.n1 = n1
-
- class bn_1d_model(tl.layers.Module):
-
- def __init__(self):
- super(bn_1d_model, self).__init__(name='test_bn_1d_model')
- self.conv = Conv1d(n_filter=32, filter_size=5, stride=2, name='test_conv1d', in_channels=1)
- self.bn = BatchNorm1d(num_features=32, name='test_bn1d')
-
- def forward(self, x):
- x = self.bn(self.conv(x))
- return x
-
- cls.dynamic_1d = bn_1d_model()
-
- ## 2D ========================================================================
-
- nin_2 = Input(x_2_input_shape, name='test_in2')
-
- n2 = Conv2d(n_filter=32, filter_size=(3, 3), strides=(2, 2), name='test_conv2d')(nin_2)
- n2 = BatchNorm2d(name='test_bn2d')(n2)
-
- cls.n2 = n2
-
- class bn_2d_model(tl.layers.Module):
-
- def __init__(self):
- super(bn_2d_model, self).__init__(name='test_bn_2d_model')
- self.conv = Conv2d(n_filter=32, filter_size=(3, 3), strides=(2, 2), name='test_conv2d', in_channels=3)
- self.bn = BatchNorm2d(num_features=32, name='test_bn2d')
-
- def forward(self, x):
- x = self.bn(self.conv(x))
- return x
-
- cls.dynamic_2d = bn_2d_model()
-
- ## 3D ========================================================================
-
- nin_3 = Input(x_3_input_shape, name='test_in3')
-
- n3 = Conv3d(n_filter=32, filter_size=(3, 3, 3), strides=(2, 2, 2), name='test_conv3d')(nin_3)
- n3 = BatchNorm3d(name='test_bn3d', act=tl.ReLU)(n3)
-
- cls.n3 = n3
-
- class bn_3d_model(tl.layers.Module):
-
- def __init__(self):
- super(bn_3d_model, self).__init__(name='test_bn_3d_model')
- self.conv = Conv3d(
- n_filter=32, filter_size=(3, 3, 3), strides=(2, 2, 2), name='test_conv3d', in_channels=3
- )
- self.bn = BatchNorm3d(num_features=32, name='test_bn3d')
-
- def forward(self, x):
- x = self.bn(self.conv(x))
- return x
-
- cls.dynamic_3d = bn_3d_model()
-
-
- @classmethod
- def tearDownClass(cls):
- pass
- # tf.reset_default_graph()
-
- def test_BatchNorm(self):
- self.assertEqual(self.n1_b.shape[1:], (50, 32))
-
- self.assertEqual(self.n2_b.shape[1:], (50, 50, 32))
-
- self.assertEqual(self.n3_b.shape[1:], (50, 50, 50, 32))
-
- self.assertEqual(self.n0_b.shape[1:], (32))
- print("test_BatchNorm OK")
-
- def test_BatchNorm0d(self):
- self.assertEqual(self.n0.shape[1:], (32))
-
- def test_BatchNorm1d(self):
- self.assertEqual(self.n1.shape[1:], (50, 32))
-
- def test_BatchNorm2d(self):
- self.assertEqual(self.n2.shape[1:], (50, 50, 32))
-
- def test_BatchNorm3d(self):
- self.assertEqual(self.n3.shape[1:], (50, 50, 50, 32))
-
- def test_dataformat(self):
- bn1d = BatchNorm1d(data_format='channels_first', num_features=32)
- bn2d = BatchNorm2d(data_format='channels_first', num_features=32)
- bn3d = BatchNorm3d(data_format='channels_first', num_features=32)
- bn = BatchNorm(data_format='channels_first')
-
- try:
- bn_fail = BatchNorm1d(data_format='xyz', num_features=32)
- except Exception as e:
- self.assertIsInstance(e, ValueError)
- print(e)
-
- def test_exception(self):
- try:
- bn = BatchNorm(num_features=32)
- except Exception as e:
- self.assertIsInstance(e, ValueError)
- print(e)
-
- try:
- ni = Input([None, 100, 1], name='test_ni1')
- bn = BatchNorm(decay=1.5)(ni)
- except Exception as e:
- self.assertIsInstance(e, ValueError)
- print(e)
-
-
- if __name__ == '__main__':
-
- tl.logging.set_verbosity(tl.logging.DEBUG)
-
- unittest.main()
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