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