def testBasicGrayscaleToRGB(self):
# 4-D input with batch dimension.
x_np = np.array([[1, 2]], dtype=np.uint8).reshape([1, 1, 2, 1])
y_np = np.array([[1, 1, 1], [2, 2, 2]], dtype=np.uint8).reshape([1, 1, 2, 3])
with self.test_session():
x_tf = constant_op.constant(x_np, shape=x_np.shape)
y = image_ops.grayscale_to_rgb(x_tf)
y_tf = y.eval()
self.assertAllEqual(y_tf, y_np)
# 3-D input with no batch dimension.
x_np = np.array([[1, 2]], dtype=np.uint8).reshape([1, 2, 1])
y_np = np.array([[1, 1, 1], [2, 2, 2]], dtype=np.uint8).reshape([1, 2, 3])
with self.test_session():
x_tf = constant_op.constant(x_np, shape=x_np.shape)
y = image_ops.grayscale_to_rgb(x_tf)
y_tf = y.eval()
self.assertAllEqual(y_tf, y_np)
def testBasicGrayscaleToRGB(self):
# 4-D input with batch dimension.
x_np = np.array([[1, 2]], dtype=np.uint8).reshape([1, 1, 2, 1])
y_np = np.array([[1, 1, 1], [2, 2, 2]],
dtype=np.uint8).reshape([1, 1, 2, 3])
with self.test_session():
x_tf = constant_op.constant(x_np, shape=x_np.shape)
y = image_ops.grayscale_to_rgb(x_tf)
y_tf = y.eval()
self.assertAllEqual(y_tf, y_np)
# 3-D input with no batch dimension.
x_np = np.array([[1, 2]], dtype=np.uint8).reshape([1, 2, 1])
y_np = np.array([[1, 1, 1], [2, 2, 2]], dtype=np.uint8).reshape([1, 2, 3])
with self.test_session():
x_tf = constant_op.constant(x_np, shape=x_np.shape)
y = image_ops.grayscale_to_rgb(x_tf)
y_tf = y.eval()
self.assertAllEqual(y_tf, y_np)
def testShapeInference(self):
# Shape inference works and produces expected output where possible
rgb_shape = [7, None, 19, 3]
gray_shape = rgb_shape[:-1] + [1]
with self.test_session():
rgb_tf = array_ops.placeholder(dtypes.uint8, shape=rgb_shape)
gray = image_ops.rgb_to_grayscale(rgb_tf)
self.assertEqual(gray_shape, gray.get_shape().as_list())
with self.test_session():
gray_tf = array_ops.placeholder(dtypes.uint8, shape=gray_shape)
rgb = image_ops.grayscale_to_rgb(gray_tf)
self.assertEqual(rgb_shape, rgb.get_shape().as_list())
# Shape inference does not break for unknown shapes
with self.test_session():
rgb_tf_unknown = array_ops.placeholder(dtypes.uint8)
gray_unknown = image_ops.rgb_to_grayscale(rgb_tf_unknown)
self.assertFalse(gray_unknown.get_shape())
with self.test_session():
gray_tf_unknown = array_ops.placeholder(dtypes.uint8)
rgb_unknown = image_ops.grayscale_to_rgb(gray_tf_unknown)
self.assertFalse(rgb_unknown.get_shape())
def testShapeInference(self):
# Shape inference works and produces expected output where possible
rgb_shape = [7, None, 19, 3]
gray_shape = rgb_shape[:-1] + [1]
with self.test_session():
rgb_tf = array_ops.placeholder(dtypes.uint8, shape=rgb_shape)
gray = image_ops.rgb_to_grayscale(rgb_tf)
self.assertEqual(gray_shape, gray.get_shape().as_list())
with self.test_session():
gray_tf = array_ops.placeholder(dtypes.uint8, shape=gray_shape)
rgb = image_ops.grayscale_to_rgb(gray_tf)
self.assertEqual(rgb_shape, rgb.get_shape().as_list())
# Shape inference does not break for unknown shapes
with self.test_session():
rgb_tf_unknown = array_ops.placeholder(dtypes.uint8)
gray_unknown = image_ops.rgb_to_grayscale(rgb_tf_unknown)
self.assertFalse(gray_unknown.get_shape())
with self.test_session():
gray_tf_unknown = array_ops.placeholder(dtypes.uint8)
rgb_unknown = image_ops.grayscale_to_rgb(gray_tf_unknown)
self.assertFalse(rgb_unknown.get_shape())
