def _ResizeBilinearGrad(op, grad):
"""The derivatives for bilinear resizing.
Args:
op: The ResizeBilinear op.
grad: The tensor representing the gradient w.r.t. the output.
Returns:
The gradients w.r.t. the input.
"""
# pylint: disable=protected-access
grad0 = gen_image_ops._resize_bilinear_grad(
grad, op.inputs[0], align_corners=op.get_attr("align_corners"))
# pylint: enable=protected-access
return [grad0, None]
def _ResizeBilinearGrad(op, grad):
"""The derivatives for bilinear resizing.
Args:
op: The ResizeBilinear op.
grad: The tensor representing the gradient w.r.t. the output.
Returns:
The gradients w.r.t. the input.
"""
# pylint: disable=protected-access
grad0 = gen_image_ops._resize_bilinear_grad(
grad, op.inputs[0], align_corners=op.get_attr("align_corners"))
# pylint: enable=protected-access
return [grad0, None]
def _ResizeBilinearGrad(op, grad):
"""The derivatives for bilinear resizing.
Args:
op: The ResizeBilinear op.
grad: The tensor representing the gradient w.r.t. the output.
Returns:
The gradients w.r.t. the input.
"""
allowed_types = [dtypes.float32, dtypes.float64]
grad0 = None
if op.inputs[0].dtype in allowed_types:
# pylint: disable=protected-access
grad0 = gen_image_ops._resize_bilinear_grad(grad, op.inputs[0])
# pylint: enable=protected-access
return [grad0, None]
def _ResizeBilinearGrad(op, grad):
"""The derivatives for bilinear resizing.
Args:
op: The ResizeBilinear op.
grad: The tensor representing the gradient w.r.t. the output.
Returns:
The gradients w.r.t. the input.
"""
allowed_types = [dtypes.float32, dtypes.float64]
grad0 = None
if op.inputs[0].dtype in allowed_types:
# pylint: disable=protected-access
grad0 = gen_image_ops._resize_bilinear_grad(grad, op.inputs[0])
# pylint: enable=protected-access
return [grad0, None]
def _assertBackwardOpMatchesExpected(self,
grads_np,
input_shape=None,
dtype=None,
expected=None):
if input_shape is None:
self.fail("input_shape must be specified")
if expected is None:
self.fail("expected must be specified")
with self.test_session() as sess, self.test_scope():
dtype = dtype or np.float32
grads = array_ops.placeholder(np.float32)
resized = gen_image_ops._resize_bilinear_grad(
grads,
np.zeros([1, input_shape[0], input_shape[1], 1], dtype=dtype),
align_corners=True)
out = sess.run(resized, {grads: grads_np[np.newaxis, :, :, np.newaxis]})
self.assertAllClose(expected[np.newaxis, :, :, np.newaxis], out)
def _assertBackwardOpMatchesExpected(self,
grads_np,
input_shape=None,
dtype=None,
expected=None):
if input_shape is None:
self.fail("input_shape must be specified")
if expected is None:
self.fail("expected must be specified")
with self.test_session() as sess, self.test_scope():
dtype = dtype or np.float32
grads = array_ops.placeholder(np.float32)
resized = gen_image_ops._resize_bilinear_grad(
grads,
np.zeros([1, input_shape[0], input_shape[1], 1], dtype=dtype),
align_corners=True)
out = sess.run(resized,
{grads: grads_np[np.newaxis, :, :, np.newaxis]})
self.assertAllClose(expected[np.newaxis, :, :, np.newaxis], out)
