def make_inference_graph(model_name, patch_dim):
"""Build the inference graph for either the X2Y or Y2X GAN.
Args:
model_name: The var scope name 'ModelX2Y' or 'ModelY2X'.
patch_dim: An integer size of patches to feed to the generator.
Returns:
Tuple of (input_placeholder, generated_tensor).
"""
input_hwc_pl = tf.placeholder(tf.float32, [None, None, 3])
# Expand HWC to NHWC
images_x = tf.expand_dims(
data_provider.full_image_to_patch(input_hwc_pl, patch_dim), 0)
with tf.variable_scope(model_name):
with tf.variable_scope('Generator'):
generated = networks.generator(images_x)
return input_hwc_pl, generated
def test_generator_run_multi_channel(self):
img_batch = tf.zeros([3, 128, 128, 5])
model_output = networks.generator(img_batch)
with self.cached_session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(model_output)
def test_generator_invalid_input(self):
with self.assertRaisesRegexp(ValueError, 'must have rank 4'):
networks.generator(tf.zeros([28, 28, 3]))