def test_four_layers_negative_padding(self):
batch_size = 2
input_size = 256
images = tf.ones((batch_size, input_size, input_size, 3))
with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
with self.assertRaises(ValueError):
pix2pix.pix2pix_discriminator(
images, num_filters=[64, 128, 256, 512], padding=-1)
def test_four_layers_negative_padding(self):
batch_size = 2
input_size = 256
images = tf.ones((batch_size, input_size, input_size, 3))
with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
with self.assertRaises(ValueError):
pix2pix.pix2pix_discriminator(images,
num_filters=[64, 128, 256, 512],
padding=-1)
def test_nonsquare_inputs_raise_exception(self):
batch_size = 2
height, width = 240, 320
num_outputs = 4
images = tf.ones((batch_size, height, width, 3))
with self.assertRaises(ValueError):
with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
pix2pix.pix2pix_generator(
images, num_outputs, upsample_method='nn_upsample_conv')
def test_nonsquare_inputs_raise_exception(self):
batch_size = 2
height, width = 240, 320
num_outputs = 4
images = tf.ones((batch_size, height, width, 3))
with self.assertRaises(ValueError):
with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
pix2pix.pix2pix_generator(images,
num_outputs,
upsample_method='nn_upsample_conv')
def test_output_size_conv2d_transpose(self):
batch_size = 2
height, width = 256, 256
num_outputs = 4
images = tf.ones((batch_size, height, width, 3))
with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
logits, _ = pix2pix.pix2pix_generator(
images, num_outputs, blocks=self._reduced_default_blocks(),
upsample_method='conv2d_transpose')
with self.test_session() as session:
session.run(tf.global_variables_initializer())
np_outputs = session.run(logits)
self.assertListEqual([batch_size, height, width, num_outputs],
list(np_outputs.shape))
def test_output_size_conv2d_transpose(self):
batch_size = 2
height, width = 256, 256
num_outputs = 4
images = tf.ones((batch_size, height, width, 3))
with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
logits, _ = pix2pix.pix2pix_generator(
images,
num_outputs,
blocks=self._reduced_default_blocks(),
upsample_method='conv2d_transpose')
with self.test_session() as session:
session.run(tf.global_variables_initializer())
np_outputs = session.run(logits)
self.assertListEqual([batch_size, height, width, num_outputs],
list(np_outputs.shape))
def test_four_layers_no_padding(self):
batch_size = 2
input_size = 256
output_size = self._layer_output_size(input_size, pad=0)
output_size = self._layer_output_size(output_size, pad=0)
output_size = self._layer_output_size(output_size, pad=0)
output_size = self._layer_output_size(output_size, stride=1, pad=0)
output_size = self._layer_output_size(output_size, stride=1, pad=0)
images = tf.ones((batch_size, input_size, input_size, 3))
with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
logits, end_points = pix2pix.pix2pix_discriminator(
images, num_filters=[64, 128, 256, 512], padding=0)
self.assertListEqual([batch_size, output_size, output_size, 1],
logits.shape.as_list())
self.assertListEqual([batch_size, output_size, output_size, 1],
end_points['predictions'].shape.as_list())
def test_four_layers_no_padding(self):
batch_size = 2
input_size = 256
output_size = self._layer_output_size(input_size, pad=0)
output_size = self._layer_output_size(output_size, pad=0)
output_size = self._layer_output_size(output_size, pad=0)
output_size = self._layer_output_size(output_size, stride=1, pad=0)
output_size = self._layer_output_size(output_size, stride=1, pad=0)
images = tf.ones((batch_size, input_size, input_size, 3))
with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
logits, end_points = pix2pix.pix2pix_discriminator(
images, num_filters=[64, 128, 256, 512], padding=0)
self.assertListEqual([batch_size, output_size, output_size, 1],
logits.shape.as_list())
self.assertListEqual([batch_size, output_size, output_size, 1],
end_points['predictions'].shape.as_list())
def test_block_number_dictates_number_of_layers(self):
batch_size = 2
height, width = 256, 256
num_outputs = 4
images = tf.ones((batch_size, height, width, 3))
blocks = [
pix2pix.Block(64, 0.5),
pix2pix.Block(128, 0),
]
with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
_, end_points = pix2pix.pix2pix_generator(images, num_outputs,
blocks)
num_encoder_layers = 0
num_decoder_layers = 0
for end_point in end_points:
if end_point.startswith('encoder'):
num_encoder_layers += 1
elif end_point.startswith('decoder'):
num_decoder_layers += 1
self.assertEqual(num_encoder_layers, len(blocks))
self.assertEqual(num_decoder_layers, len(blocks))
def test_block_number_dictates_number_of_layers(self):
batch_size = 2
height, width = 256, 256
num_outputs = 4
images = tf.ones((batch_size, height, width, 3))
blocks = [
pix2pix.Block(64, 0.5),
pix2pix.Block(128, 0),
]
with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
_, end_points = pix2pix.pix2pix_generator(
images, num_outputs, blocks)
num_encoder_layers = 0
num_decoder_layers = 0
for end_point in end_points:
if end_point.startswith('encoder'):
num_encoder_layers += 1
elif end_point.startswith('decoder'):
num_decoder_layers += 1
self.assertEqual(num_encoder_layers, len(blocks))
self.assertEqual(num_decoder_layers, len(blocks))