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 = constant_op.constant(
test_image, shape=test_image_shape)
test_transform = image_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 = image_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_grad_different_shape(self, input_shape, output_shape):
with self.test_session():
test_image_shape = input_shape
test_image = np.random.randn(*test_image_shape)
test_image_tensor = constant_op.constant(test_image,
shape=test_image_shape)
test_transform = image_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 = image_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_data_types(self):
for dtype in _DTYPES:
image = constant_op.constant([[1, 2], [3, 4]], dtype=dtype)
value = image_ops.transform(image, [1] * 8)
with self.test_session(use_gpu=True):
self.assertAllEqual(
value.eval(),
np.array([[4, 4], [4, 4]]).astype(dtype.as_numpy_dtype()))
def test_transform_data_types(self):
for dtype in _DTYPES:
image = constant_op.constant([[1, 2], [3, 4]], dtype=dtype)
value = image_ops.transform(image, [1] * 8)
with self.test_session(use_gpu=True):
self.assertAllEqual(
value.eval(),
np.array([[4, 4], [4, 4]]).astype(dtype.as_numpy_dtype()))
def test_extreme_projective_transform(self):
for dtype in _DTYPES:
with self.test_session():
image = constant_op.constant(
[[1, 0, 1, 0], [0, 1, 0, 1], [1, 0, 1, 0], [0, 1, 0, 1]],
dtype=dtype)
transformation = constant_op.constant(
[1, 0, 0, 0, 1, 0, -1, 0], dtypes.float32)
image_transformed = image_ops.transform(image, transformation)
self.assertAllEqual(
image_transformed.eval(),
[[1, 0, 0, 0], [0, 0, 0, 0], [1, 0, 0, 0], [0, 0, 0, 0]])
def test_extreme_projective_transform(self):
for dtype in _DTYPES:
with self.cached_session():
image = constant_op.constant(
[[1, 0, 1, 0],
[0, 1, 0, 1],
[1, 0, 1, 0],
[0, 1, 0, 1]], dtype=dtype)
transformation = constant_op.constant([1, 0, 0, 0, 1, 0, -1, 0],
dtypes.float32)
image_transformed = image_ops.transform(image, transformation)
self.assertAllEqual(image_transformed.eval(),
[[1, 0, 0, 0],
[0, 0, 0, 0],
[1, 0, 0, 0],
[0, 0, 0, 0]])
def _test_grad(self, shape_to_test):
with self.test_session():
test_image_shape = shape_to_test
test_image = np.random.randn(*test_image_shape)
test_image_tensor = constant_op.constant(test_image,
shape=test_image_shape)
test_transform = image_ops.angles_to_projective_transforms(np.pi / 2,
4,
4)
test_transform_shape = test_transform.shape
output_shape = test_image_shape
output = image_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_grad(self, shape_to_test):
with self.test_session():
test_image_shape = shape_to_test
test_image = np.random.randn(*test_image_shape)
test_image_tensor = constant_op.constant(test_image,
shape=test_image_shape)
test_transform = image_ops.angles_to_projective_transforms(
np.pi / 2, 4, 4)
output_shape = test_image_shape
output = image_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:
with self.test_session():
image = constant_op.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 = image_ops.angles_to_projective_transforms(
np.pi / 2, 4, 4)
# Translate right by 1 (the transformation matrix is always inverted,
# hence the -1).
translation = constant_op.constant([1, 0, -1, 0, 1, 0, 0, 0],
dtype=dtypes.float32)
composed = image_ops.compose_transforms(rotation, translation)
image_transformed = image_ops.transform(image, composed)
self.assertAllEqual(
image_transformed.eval(),
[[0, 0, 0, 0], [0, 1, 0, 1], [0, 1, 0, 1], [0, 1, 1, 1]])
def test_compose(self):
for dtype in _DTYPES:
with self.cached_session():
image = constant_op.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 = image_ops.angles_to_projective_transforms(np.pi / 2, 4, 4)
# Translate right by 1 (the transformation matrix is always inverted,
# hence the -1).
translation = constant_op.constant([1, 0, -1,
0, 1, 0,
0, 0],
dtype=dtypes.float32)
composed = image_ops.compose_transforms(rotation, translation)
image_transformed = image_ops.transform(image, composed)
self.assertAllEqual(image_transformed.eval(),
[[0, 0, 0, 0],
[0, 1, 0, 1],
[0, 1, 0, 1],
[0, 1, 1, 1]])
def test_transform_static_output_shape(self):
image = constant_op.constant([[1., 2.], [3., 4.]])
result = image_ops.transform(
image, random_ops.random_uniform([8], -1, 1),
output_shape=constant_op.constant([3, 5]))
self.assertAllEqual([3, 5], result.get_shape())
def test_transform_static_output_shape(self):
image = constant_op.constant([[1., 2.], [3., 4.]])
result = image_ops.transform(image,
random_ops.random_uniform([8], -1, 1),
output_shape=constant_op.constant([3, 5]))
self.assertAllEqual([3, 5], result.get_shape())
def test_transform_eager(self):
image = constant_op.constant([[1., 2.], [3., 4.]])
value = image_ops.transform(image, [1] * 8)
with self.test_session(use_gpu=True):
self.assertAllEqual(self.evaluate(value), np.array([[4, 4], [4,
4]]))
def test_transform_eager(self):
image = constant_op.constant([[1., 2.], [3., 4.]])
value = image_ops.transform(image, [1] * 8)
with self.test_session(use_gpu=True):
self.assertAllEqual(self.evaluate(value), np.array([[4, 4], [4, 4]]))
