def test_transform_data_types(self):
for dtype in _DTYPES:
image = tf.constant([[1, 2], [3, 4]], dtype=dtype)
with self.test_session(use_gpu=True):
self.assertAllEqual(
np.array([[4, 4], [4, 4]]).astype(dtype.as_numpy_dtype()),
transform_ops.transform(image, [1] * 8))
def test_extreme_projective_transform(self):
for dtype in _DTYPES:
image = tf.constant(
[[1, 0, 1, 0], [0, 1, 0, 1], [1, 0, 1, 0], [0, 1, 0, 1]],
dtype=dtype)
transformation = tf.constant([1, 0, 0, 0, 1, 0, -1, 0],
tf.dtypes.float32)
image_transformed = transform_ops.transform(image, transformation)
self.assertAllEqual(
[[1, 0, 0, 0], [0, 0, 0, 0], [1, 0, 0, 0], [0, 0, 0, 0]],
image_transformed)
def _test_grad(self, shape_to_test):
with self.cached_session():
test_image_shape = shape_to_test
test_image = np.random.randn(*test_image_shape)
test_image_tensor = tf.constant(test_image, shape=test_image_shape)
test_transform = transform_ops.angles_to_projective_transforms(
np.pi / 2, 4, 4)
output_shape = test_image_shape
output = transform_ops.transform(test_image_tensor, test_transform)
left_err = gradient_checker.compute_gradient_error(
test_image_tensor,
test_image_shape,
output,
output_shape,
x_init_value=test_image)
self.assertLess(left_err, 1e-10)
def test_compose(self):
for dtype in _DTYPES:
image = tf.constant(
[[1, 1, 1, 0], [1, 0, 0, 0], [1, 1, 1, 0], [0, 0, 0, 0]],
dtype=dtype)
# Rotate counter-clockwise by pi / 2.
rotation = transform_ops.angles_to_projective_transforms(
np.pi / 2, 4, 4)
# Translate right by 1 (the transformation matrix is always inverted,
# hence the -1).
translation = tf.constant([1, 0, -1, 0, 1, 0, 0, 0],
dtype=tf.dtypes.float32)
composed = transform_ops.compose_transforms(rotation, translation)
image_transformed = transform_ops.transform(image, composed)
self.assertAllEqual(
[[0, 0, 0, 0], [0, 1, 0, 1], [0, 1, 0, 1], [0, 1, 1, 1]],
image_transformed)
def _test_grad_different_shape(self, input_shape, output_shape):
with self.cached_session():
test_image_shape = input_shape
test_image = np.random.randn(*test_image_shape)
test_image_tensor = tf.constant(test_image, shape=test_image_shape)
test_transform = transform_ops.angles_to_projective_transforms(
np.pi / 2, 4, 4)
if len(output_shape) == 2:
resize_shape = output_shape
elif len(output_shape) == 3:
resize_shape = output_shape[0:2]
elif len(output_shape) == 4:
resize_shape = output_shape[1:3]
output = transform_ops.transform(images=test_image_tensor,
transforms=test_transform,
output_shape=resize_shape)
left_err = gradient_checker.compute_gradient_error(
test_image_tensor,
test_image_shape,
output,
output_shape,
x_init_value=test_image)
self.assertLess(left_err, 1e-10)
def test_transform_static_output_shape(self):
image = tf.constant([[1., 2.], [3., 4.]])
result = transform_ops.transform(image,
tf.random.uniform([8], -1, 1),
output_shape=tf.constant([3, 5]))
self.assertAllEqual([3, 5], result.shape)
def test_transform_eager(self):
image = tf.constant([[1., 2.], [3., 4.]])
self.assertAllEqual(np.array([[4, 4], [4, 4]]),
transform_ops.transform(image, [1] * 8))
