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Batch support for deterministic image ops #15726

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merged 6 commits into from
Feb 15, 2018
Merged

Batch support for deterministic image ops #15726

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3a7b31d
Initial 4-D support in flip/rotate/transpose
JoshVarty Dec 29, 2017
02f31ec
Implement tests
JoshVarty Dec 29, 2017
6496017
Make sure random ops are tested properly
JoshVarty Dec 30, 2017
b54e237
Correct comment
JoshVarty Dec 30, 2017
1753869
Merging master
JoshVarty Feb 13, 2018
0f689f8
Indentation fix.
martinwicke Feb 14, 2018
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152 changes: 116 additions & 36 deletions tensorflow/python/ops/image_ops_impl.py
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Expand Up @@ -262,104 +262,184 @@ def random_flip_left_right(image, seed=None):
def flip_left_right(image):
"""Flip an image horizontally (left to right).

Outputs the contents of `image` flipped along the second dimension, which is
`width`.
Outputs the contents of `image` flipped along the width dimension.

See also `reverse()`.

Args:
image: A 3-D tensor of shape `[height, width, channels].`
image: 4-D Tensor of shape `[batch, height, width, channels]` or
3-D Tensor of shape `[height, width, channels]`.

Returns:
A 3-D tensor of the same type and shape as `image`.
A tensor of the same type and shape as `image`.

Raises:
ValueError: if the shape of `image` not supported.
"""
image = ops.convert_to_tensor(image, name='image')
image = control_flow_ops.with_dependencies(
_Check3DImage(image, require_static=False), image)
return fix_image_flip_shape(image, array_ops.reverse(image, [1]))
_CheckAtLeast3DImage(image, require_static=False), image)

shape = image.get_shape()
if shape.ndims == 3 or shape.ndims is None:
return fix_image_flip_shape(image, array_ops.reverse(image, [1]))
elif shape.ndims == 4:
return array_ops.reverse(image, [2])
else:
raise ValueError('\'image\' must have either 3 or 4 dimensions.')


def flip_up_down(image):
"""Flip an image vertically (upside down).

Outputs the contents of `image` flipped along the first dimension, which is
`height`.
Outputs the contents of `image` flipped along the height dimension.

See also `reverse()`.

Args:
image: A 3-D tensor of shape `[height, width, channels].`
image: 4-D Tensor of shape `[batch, height, width, channels]` or
3-D Tensor of shape `[height, width, channels]`.

Returns:
A 3-D tensor of the same type and shape as `image`.
A tensor of the same type and shape as `image`.

Raises:
ValueError: if the shape of `image` not supported.
"""
image = ops.convert_to_tensor(image, name='image')
image = control_flow_ops.with_dependencies(
_Check3DImage(image, require_static=False), image)
return fix_image_flip_shape(image, array_ops.reverse(image, [0]))
_CheckAtLeast3DImage(image, require_static=False), image)
shape = image.get_shape()
if shape.ndims == 3 or shape.ndims is None:
return fix_image_flip_shape(image, array_ops.reverse(image, [0]))
elif shape.ndims == 4:
return array_ops.reverse(image, [1])
else:
raise ValueError('\'image\' must have either 3 or 4 dimensions.')



def rot90(image, k=1, name=None):
"""Rotate an image counter-clockwise by 90 degrees.
"""Rotate image(s) counter-clockwise by 90 degrees.

Args:
image: A 3-D tensor of shape `[height, width, channels]`.
image: 4-D Tensor of shape `[batch, height, width, channels]` or
3-D Tensor of shape `[height, width, channels]`.
k: A scalar integer. The number of times the image is rotated by 90 degrees.
name: A name for this operation (optional).

Returns:
A rotated 3-D tensor of the same type and shape as `image`.
A rotated tensor of the same type and shape as `image`.

Raises:
ValueError: if the shape of `image` not supported.
"""
with ops.name_scope(name, 'rot90', [image, k]) as scope:
image = ops.convert_to_tensor(image, name='image')
image = control_flow_ops.with_dependencies(
_Check3DImage(image, require_static=False), image)
_CheckAtLeast3DImage(image, require_static=False), image)
k = ops.convert_to_tensor(k, dtype=dtypes.int32, name='k')
k.get_shape().assert_has_rank(0)
k = math_ops.mod(k, 4)

def _rot90():
return array_ops.transpose(array_ops.reverse_v2(image, [1]),
[1, 0, 2])
def _rot180():
return array_ops.reverse_v2(image, [0, 1])
def _rot270():
return array_ops.reverse_v2(array_ops.transpose(image, [1, 0, 2]),
[1])
cases = [(math_ops.equal(k, 1), _rot90),
(math_ops.equal(k, 2), _rot180),
(math_ops.equal(k, 3), _rot270)]
shape = image.get_shape()
if shape.ndims == 3 or shape.ndims is None:
return _rot90_3D(image, k, scope)
elif shape.ndims == 4:
return _rot90_4D(image, k, scope)
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Introducing two methods for _rot90_3D and _rot90_4D is unfortunate. I'd definitely welcome a better suggestion. The other alternative was to stagger if shape.ndims == 3 checks throughout the method which didn't really help readability either.

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I think this is fine.

else:
raise ValueError('\'image\' must have either 3 or 4 dimensions.')

ret = control_flow_ops.case(cases, default=lambda: image, exclusive=True,
name=scope)
ret.set_shape([None, None, image.get_shape()[2]])
return ret

def _rot90_3D(image, k, name_scope):
"""Rotate image counter-clockwise by 90 degrees `k` times.

Args:
image: 3-D Tensor of shape `[height, width, channels]`.
k: A scalar integer. The number of times the image is rotated by 90 degrees.
name_scope: A valid TensorFlow name scope.

Returns:
A 3-D tensor of the same type and shape as `image`.

"""
def _rot90():
return array_ops.transpose(array_ops.reverse_v2(image, [1]),
[1, 0, 2])
def _rot180():
return array_ops.reverse_v2(image, [0, 1])
def _rot270():
return array_ops.reverse_v2(array_ops.transpose(image, [1, 0, 2]),
[1])
cases = [(math_ops.equal(k, 1), _rot90),
(math_ops.equal(k, 2), _rot180),
(math_ops.equal(k, 3), _rot270)]

result = control_flow_ops.case(cases, default=lambda: image, exclusive=True,
name=name_scope)
result.set_shape([None, None, image.get_shape()[2]])
return result

def _rot90_4D(images, k, name_scope):
"""Rotate batch of images counter-clockwise by 90 degrees `k` times.

Args:
images: 4-D Tensor of shape `[height, width, channels]`.
k: A scalar integer. The number of times the images are rotated by 90 degrees.
name_scope: A valid TensorFlow name scope.

Returns:
A 4-D tensor of the same type and shape as `images`.

"""
def _rot90():
return array_ops.transpose(array_ops.reverse_v2(images, [2]),
[0, 2, 1, 3])
def _rot180():
return array_ops.reverse_v2(images, [1, 2])
def _rot270():
return array_ops.reverse_v2(array_ops.transpose(images, [0, 2, 1, 3]),
[2])

cases = [(math_ops.equal(k, 1), _rot90),
(math_ops.equal(k, 2), _rot180),
(math_ops.equal(k, 3), _rot270)]

result = control_flow_ops.case(cases, default=lambda: images, exclusive=True,
name=name_scope)
shape = result.get_shape()
result.set_shape([shape[0], None, None, shape[3]])
return result

def transpose_image(image):
"""Transpose an image by swapping the first and second dimension.
"""Transpose image(s) by swapping the height and width dimension.

See also `transpose()`.

Args:
image: 3-D tensor of shape `[height, width, channels]`
image: 4-D Tensor of shape `[batch, height, width, channels]` or
3-D Tensor of shape `[height, width, channels]`.

Returns:
A 3-D tensor of shape `[width, height, channels]`
If `image` was 4-D, a 4-D float Tensor of shape
`[batch, width, height, channels]`
If `image` was 3-D, a 3-D float Tensor of shape
`[width, height, channels]`

Raises:
ValueError: if the shape of `image` not supported.
"""
image = ops.convert_to_tensor(image, name='image')
image = control_flow_ops.with_dependencies(
_Check3DImage(image, require_static=False), image)
return array_ops.transpose(image, [1, 0, 2], name='transpose_image')
_CheckAtLeast3DImage(image, require_static=False), image)

shape = image.get_shape()
if shape.ndims == 3 or shape.ndims is None:
return array_ops.transpose(image, [1, 0, 2], name='transpose_image')
elif shape.ndims == 4:
return array_ops.transpose(image, [0, 2, 1, 3], name='transpose_image')
else:
raise ValueError('\'image\' must have either 3 or 4 dimensions.')


def central_crop(image, central_fraction):
Expand Down