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 discriminator(image_batch, unused_conditioning=None):
"""A thin wrapper around the Pix2Pix discriminator to conform to TFGAN API."""
with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
logits_4d, _ = pix2pix.pix2pix_discriminator(
image_batch, num_filters=[64, 128, 256, 512])
logits_4d.shape.assert_has_rank(4)
# Output of logits is 4D. Reshape to 2D, for TFGAN.
logits_2d = tf.contrib.layers.flatten(logits_4d)
return logits_2d
def discriminator(image_batch, unused_conditioning=None):
"""A thin wrapper around the Pix2Pix discriminator to conform to TFGAN API."""
with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
logits_4d, _ = pix2pix.pix2pix_discriminator(
image_batch, num_filters=[64, 128, 256, 512])
logits_4d.shape.assert_has_rank(4)
# Output of logits is 4D. Reshape to 2D, for TFGAN.
logits_2d = tf.contrib.layers.flatten(logits_4d)
return logits_2d
def pix2pix_D(image_batch, unused_conditioning=None):
with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
logits_4d, _ = pix2pix.pix2pix_discriminator(
image_batch, num_filters=[64, 128, 256, 512])
logits_4d.shape.assert_has_rank(4)
# Output of logits is 4D. Reshape to 2D, for TFGAN.
net = layers.flatten(logits_4d)
# net = layers.fully_connected(net, 1024, normalizer_fn=layers.layer_norm)
net = layers.fully_connected(
net, 1024, activation_fn=None) # default: normalizer_fn=None
return net
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 discriminator(image_batch, unused_conditioning=None, depth=64):
"""A thin wrapper around the pix2pix discriminator to conform to TFGAN API."""
logits, _ = pix2pix.pix2pix_discriminator(
image_batch, num_filters=[depth, 2 * depth, 4 * depth, 8 * depth])
return tf.layers.flatten(logits)
def discriminator(image_batch, unused_conditioning=None, depth=64):
"""A thin wrapper around the pix2pix discriminator to conform to TFGAN API."""
logits, _ = pix2pix.pix2pix_discriminator(
image_batch, num_filters=[depth, 2 * depth, 4 * depth, 8 * depth])
return tf.layers.flatten(logits)