/
random_crop_ops.py
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random_crop_ops.py
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# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Operations for random tensor cropping."""
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_assert
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import stateless_random_ops
from tensorflow.python.util import deprecation
from tensorflow.python.util import dispatch
from tensorflow.python.util.tf_export import tf_export
@tf_export("image.random_crop", v1=["image.random_crop", "random_crop"])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints("random_crop")
def random_crop(value, size, seed=None, name=None):
"""Randomly crops a tensor to a given size.
Slices a shape `size` portion out of `value` at a uniformly chosen offset.
Requires `value.shape >= size`.
If a dimension should not be cropped, pass the full size of that dimension.
For example, RGB images can be cropped with
`size = [crop_height, crop_width, 3]`.
Example usage:
>>> image = [[1, 2, 3], [4, 5, 6]]
>>> result = tf.image.random_crop(value=image, size=(1, 3))
>>> result.shape.as_list()
[1, 3]
For producing deterministic results given a `seed` value, use
`tf.image.stateless_random_crop`. Unlike using the `seed` param with
`tf.image.random_*` ops, `tf.image.stateless_random_*` ops guarantee the same
results given the same seed independent of how many times the function is
called, and independent of global seed settings (e.g. tf.random.set_seed).
Args:
value: Input tensor to crop.
size: 1-D tensor with size the rank of `value`.
seed: Python integer. Used to create a random seed. See
`tf.random.set_seed`
for behavior.
name: A name for this operation (optional).
Returns:
A cropped tensor of the same rank as `value` and shape `size`.
"""
with ops.name_scope(name, "random_crop", [value, size]) as name:
value = ops.convert_to_tensor(value, name="value")
size = ops.convert_to_tensor(size, dtype=dtypes.int32, name="size")
shape = array_ops.shape(value)
check = control_flow_assert.Assert(
math_ops.reduce_all(shape >= size),
["Need value.shape >= size, got ", shape, size],
summarize=1000)
shape = control_flow_ops.with_dependencies([check], shape)
limit = shape - size + 1
offset = random_ops.random_uniform(
array_ops.shape(shape),
dtype=size.dtype,
maxval=size.dtype.max,
seed=seed) % limit
return array_ops.slice(value, offset, size, name=name)
@tf_export("image.stateless_random_crop", v1=[])
@dispatch.add_dispatch_support
def stateless_random_crop(value, size, seed, name=None):
"""Randomly crops a tensor to a given size in a deterministic manner.
Slices a shape `size` portion out of `value` at a uniformly chosen offset.
Requires `value.shape >= size`.
If a dimension should not be cropped, pass the full size of that dimension.
For example, RGB images can be cropped with
`size = [crop_height, crop_width, 3]`.
Guarantees the same results given the same `seed` independent of how many
times the function is called, and independent of global seed settings (e.g.
`tf.random.set_seed`).
Usage Example:
>>> image = [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]
>>> seed = (1, 2)
>>> tf.image.stateless_random_crop(value=image, size=(1, 2, 3), seed=seed)
<tf.Tensor: shape=(1, 2, 3), dtype=int32, numpy=
array([[[1, 2, 3],
[4, 5, 6]]], dtype=int32)>
Args:
value: Input tensor to crop.
size: 1-D tensor with size the rank of `value`.
seed: A shape [2] Tensor, the seed to the random number generator. Must have
dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.)
name: A name for this operation (optional).
Returns:
A cropped tensor of the same rank as `value` and shape `size`.
"""
with ops.name_scope(name, "random_crop", [value, size]) as name:
value = ops.convert_to_tensor(value, name="value")
size = ops.convert_to_tensor(size, dtype=dtypes.int32, name="size")
shape = array_ops.shape(value)
check = control_flow_assert.Assert(
math_ops.reduce_all(shape >= size),
["Need value.shape >= size, got ", shape, size],
summarize=1000)
shape = control_flow_ops.with_dependencies([check], shape)
limit = shape - size + 1
offset = stateless_random_ops.stateless_random_uniform(
array_ops.shape(shape),
dtype=size.dtype,
maxval=size.dtype.max,
seed=seed) % limit
return array_ops.slice(value, offset, size, name=name)