def test_generator_graph(self):
for shape in ([4, 32, 32], [3, 128, 128], [2, 80, 400]):
tf.reset_default_graph()
img = tf.ones(shape + [3])
output_imgs = networks.generator(img)
self.assertAllEqual(shape + [3], output_imgs.shape.as_list())
def test_generator_graph(self):
for shape in ([4, 32, 32], [3, 128, 128], [2, 80, 400]):
tf.reset_default_graph()
img = tf.ones(shape + [3])
output_imgs = networks.generator(img)
self.assertAllEqual(shape + [3], output_imgs.shape.as_list())
def test_generator_invalid_channels(self):
with self.assertRaisesRegexp(
ValueError, 'Last dimension shape must be known but is None'):
img = tf.placeholder(tf.float32, shape=[4, 32, 32, None])
networks.generator(img)
def test_generator_run_multi_channel(self):
img_batch = tf.zeros([3, 128, 128, 5])
model_output = networks.generator(img_batch)
with self.test_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]))
def test_generator_graph_unknown_batch_dim(self):
img = tf.placeholder(tf.float32, shape=[None, 32, 32, 3])
output_imgs = networks.generator(img)
self.assertAllEqual([None, 32, 32, 3], output_imgs.shape.as_list())
def test_generator_invalid_input(self):
with self.assertRaisesRegexp(ValueError, 'must have rank 4'):
networks.generator(tf.zeros([28, 28, 3]))
def test_generator_graph_unknown_batch_dim(self):
img = tf.placeholder(tf.float32, shape=[None, 32, 32, 3])
output_imgs = networks.generator(img)
self.assertAllEqual([None, 32, 32, 3], output_imgs.shape.as_list())
def test_generator_run(self):
img_batch = tf.zeros([3, 128, 128, 3])
model_output = networks.generator(img_batch)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(model_output)
