def test_film(self, resnet_size, enabled_blocks): image = tf.zeros((2, 224, 224, 3), dtype=tf.float32) embedding = tf.zeros((2, 100), dtype=tf.float32) film_generator_fn = functools.partial( resnet.linear_film_generator, enabled_block_layers=enabled_blocks) _ = resnet.resnet_model(image, is_training=True, num_classes=1001, resnet_size=resnet_size, return_intermediate_values=True, film_generator_fn=film_generator_fn, film_generator_input=embedding)
def test_intermediate_values(self, scope): with tf.variable_scope(scope): image = tf.zeros((2, 224, 224, 3), dtype=tf.float32) end_points = resnet.resnet_model(image, is_training=True, num_classes=1001, return_intermediate_values=True) tensors = ['initial_conv', 'initial_max_pool', 'pre_final_pool', 'final_reduce_mean', 'final_dense'] tensors += [ 'block_layer{}'.format(i + 1) for i in range(4)] self.assertEqual(set(tensors), set(end_points.keys()))
def test_malformed_film_raises(self): image = tf.zeros((2, 224, 224, 3), dtype=tf.float32) embedding = tf.zeros((2, 100), dtype=tf.float32) film_generator_fn = functools.partial(resnet.linear_film_generator, enabled_block_layers=[True] * 5) with self.assertRaises(ValueError): _ = resnet.resnet_model(image, is_training=True, num_classes=1001, resnet_size=18, return_intermediate_values=True, film_generator_fn=film_generator_fn, film_generator_input=embedding)