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152 lines
6.0 KiB
Python
152 lines
6.0 KiB
Python
from copy import deepcopy
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from unittest import mock
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import tensorflow as tf
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def test_safe(func):
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"""
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Isolate tests
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"""
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def func_wrapper(*args):
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with tf.Graph().as_default():
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result = func(*args)
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print('Tests Passed')
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return result
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return func_wrapper
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def _assert_tensor_shape(tensor, shape, display_name):
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assert tf.assert_rank(tensor, len(shape), message='{} has wrong rank'.format(display_name))
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tensor_shape = tensor.get_shape().as_list() if len(shape) else []
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wrong_dimension = [ten_dim for ten_dim, cor_dim in zip(tensor_shape, shape)
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if cor_dim is not None and ten_dim != cor_dim]
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assert not wrong_dimension, \
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'{} has wrong shape. Found {}'.format(display_name, tensor_shape)
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def _check_input(tensor, shape, display_name, tf_name=None):
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assert tensor.op.type == 'Placeholder', \
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'{} is not a Placeholder.'.format(display_name)
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_assert_tensor_shape(tensor, shape, 'Real Input')
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if tf_name:
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assert tensor.name == tf_name, \
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'{} has bad name. Found name {}'.format(display_name, tensor.name)
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class TmpMock():
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"""
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Mock a attribute. Restore attribute when exiting scope.
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"""
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def __init__(self, module, attrib_name):
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self.original_attrib = deepcopy(getattr(module, attrib_name))
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setattr(module, attrib_name, mock.MagicMock())
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self.module = module
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self.attrib_name = attrib_name
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def __enter__(self):
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return getattr(self.module, self.attrib_name)
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def __exit__(self, type, value, traceback):
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setattr(self.module, self.attrib_name, self.original_attrib)
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@test_safe
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def test_model_inputs(model_inputs):
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image_width = 28
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image_height = 28
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image_channels = 3
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z_dim = 100
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input_real, input_z, learn_rate = model_inputs(image_width, image_height, image_channels, z_dim)
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_check_input(input_real, [None, image_width, image_height, image_channels], 'Real Input')
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_check_input(input_z, [None, z_dim], 'Z Input')
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_check_input(learn_rate, [], 'Learning Rate')
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@test_safe
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def test_discriminator(discriminator, tf_module):
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with TmpMock(tf_module, 'variable_scope') as mock_variable_scope:
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image = tf.placeholder(tf.float32, [None, 28, 28, 3])
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output, logits = discriminator(image)
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_assert_tensor_shape(output, [None, 1], 'Discriminator Training(reuse=false) output')
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_assert_tensor_shape(logits, [None, 1], 'Discriminator Training(reuse=false) Logits')
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assert mock_variable_scope.called,\
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'tf.variable_scope not called in Discriminator Training(reuse=false)'
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assert mock_variable_scope.call_args == mock.call('discriminator', reuse=False), \
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'tf.variable_scope called with wrong arguments in Discriminator Training(reuse=false)'
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mock_variable_scope.reset_mock()
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output_reuse, logits_reuse = discriminator(image, True)
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_assert_tensor_shape(output_reuse, [None, 1], 'Discriminator Inference(reuse=True) output')
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_assert_tensor_shape(logits_reuse, [None, 1], 'Discriminator Inference(reuse=True) Logits')
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assert mock_variable_scope.called, \
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'tf.variable_scope not called in Discriminator Inference(reuse=True)'
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assert mock_variable_scope.call_args == mock.call('discriminator', reuse=True), \
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'tf.variable_scope called with wrong arguments in Discriminator Inference(reuse=True)'
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@test_safe
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def test_generator(generator, tf_module):
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with TmpMock(tf_module, 'variable_scope') as mock_variable_scope:
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z = tf.placeholder(tf.float32, [None, 100])
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out_channel_dim = 5
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output = generator(z, out_channel_dim)
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_assert_tensor_shape(output, [None, 28, 28, out_channel_dim], 'Generator output (is_train=True)')
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assert mock_variable_scope.called, \
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'tf.variable_scope not called in Generator Training(reuse=false)'
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assert mock_variable_scope.call_args == mock.call('generator', reuse=False), \
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'tf.variable_scope called with wrong arguments in Generator Training(reuse=false)'
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mock_variable_scope.reset_mock()
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output = generator(z, out_channel_dim, False)
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_assert_tensor_shape(output, [None, 28, 28, out_channel_dim], 'Generator output (is_train=False)')
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assert mock_variable_scope.called, \
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'tf.variable_scope not called in Generator Inference(reuse=True)'
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assert mock_variable_scope.call_args == mock.call('generator', reuse=True), \
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'tf.variable_scope called with wrong arguments in Generator Inference(reuse=True)'
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@test_safe
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def test_model_loss(model_loss):
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out_channel_dim = 4
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input_real = tf.placeholder(tf.float32, [None, 28, 28, out_channel_dim])
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input_z = tf.placeholder(tf.float32, [None, 100])
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d_loss, g_loss = model_loss(input_real, input_z, out_channel_dim)
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_assert_tensor_shape(d_loss, [], 'Discriminator Loss')
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_assert_tensor_shape(d_loss, [], 'Generator Loss')
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@test_safe
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def test_model_opt(model_opt, tf_module):
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with TmpMock(tf_module, 'trainable_variables') as mock_trainable_variables:
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with tf.variable_scope('discriminator'):
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discriminator_logits = tf.Variable(tf.zeros([3, 3]))
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with tf.variable_scope('generator'):
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generator_logits = tf.Variable(tf.zeros([3, 3]))
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mock_trainable_variables.return_value = [discriminator_logits, generator_logits]
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d_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
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logits=discriminator_logits,
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labels=[[0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0]]))
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g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
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logits=generator_logits,
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labels=[[0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0]]))
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learning_rate = 0.001
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beta1 = 0.9
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d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
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assert mock_trainable_variables.called,\
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'tf.mock_trainable_variables not called'
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