def generator(inputs, targets):
"""Generator module.
Piece everything together for the Generator.
PyTorch Version:
https://github.com/yunjey/StarGAN/blob/fbdb6a6ce2a4a92e1dc034faec765e0dbe4b8164/model.py#L22
Args:
inputs: Tensor of shape (batch_size, h, w, c) representing the
images/information that we want to transform.
targets: Tensor of shape (batch_size, num_domains) representing the target
domain the generator should transform the image/information to.
Returns:
Tensor of shape (batch_size, h, w, c) as the inputs.
"""
with tf.variable_scope('generator'):
input_with_condition = ops.condition_input_with_pixel_padding(
inputs, targets)
down_sample = layers.generator_down_sample(input_with_condition)
bottleneck = layers.generator_bottleneck(down_sample)
up_sample = layers.generator_up_sample(bottleneck, inputs.shape[-1])
return up_sample
def test_condition_input_with_pixel_padding(self):
n = 2
h = 128
w = h
c = 3
num_label = 5
input_tensor = tf.random.uniform((n, h, w, c))
label_tensor = tf.random.uniform((n, num_label))
output_tensor = ops.condition_input_with_pixel_padding(
input_tensor, label_tensor)
with self.cached_session() as sess:
labels, outputs = sess.run([label_tensor, output_tensor])
self.assertTupleEqual((n, h, w, c + num_label), outputs.shape)
for label, output in zip(labels, outputs):
for i in range(output.shape[0]):
for j in range(output.shape[1]):
self.assertListEqual(label.tolist(), output[i, j, c:].tolist())
