From 3e33d444c6c0861d448755627982284736ed01a0 Mon Sep 17 00:00:00 2001
From: Geoffrey Irving <geoffreyi@google.com>
Date: Mon, 8 Feb 2016 12:02:44 -0800
Subject: [PATCH] Generalize tf.image.random_crop to dimension-independent
tf.random_crop
The C++ 3-D-only RandomCrop op is now deprecated at GraphDef version 8, replaced
with a python tf.random_crop that works for any dimension. This will allow
random_crop to be used for other purposes.
Unfortunately, tf.image.random_crop took 2 sizes rather than 3 for 3-D tensors.
The new tf.random_crop always takes n sizes for rank n tensors; pass 3 as the
last element if you want to not crop a last dimension of size 3.
Change: 114135451
---
RELEASE.md | 4 +
tensorflow/core/kernels/random_crop_op.cc | 1 +
.../core/kernels/random_crop_op_test.cc | 75 ------------------
tensorflow/core/public/version.h | 3 +-
.../models/image/cifar10/cifar10_input.py | 2 +-
tensorflow/python/BUILD | 1 +
.../python/kernel_tests/random_crop_test.py | 77 +++++++++++++++++++
tensorflow/python/ops/constant_op.py | 1 +
tensorflow/python/ops/image_ops.py | 41 ----------
tensorflow/python/ops/image_ops_test.py | 51 ------------
tensorflow/python/ops/random_ops.py | 40 ++++++++++
11 files changed, 127 insertions(+), 169 deletions(-)
delete mode 100644 tensorflow/core/kernels/random_crop_op_test.cc
create mode 100644 tensorflow/python/kernel_tests/random_crop_test.py
diff --git a/RELEASE.md b/RELEASE.md
index fd46bcdff36..b8146610c80 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -33,6 +33,10 @@
maintained for short-term compatibility but will be removed.
* The non-public `nn.rnn` and the various `nn.seq2seq` methods now return
just the final state instead of the list of all states.
+* `tf.image.random_crop(image, [height, width])` is now
+ `tf.random_crop(image, [height, width, depth])`, and `tf.random_crop` works
+ for any rank (not just 3-D images). The C++ `RandomCrop` op has been replaced
+ with pure Python.
## Bug fixes
diff --git a/tensorflow/core/kernels/random_crop_op.cc b/tensorflow/core/kernels/random_crop_op.cc
index e27de3b4e04..80b4041e05f 100644
--- a/tensorflow/core/kernels/random_crop_op.cc
+++ b/tensorflow/core/kernels/random_crop_op.cc
@@ -28,6 +28,7 @@ template <typename T>
class RandomCropOp : public OpKernel {
public:
explicit RandomCropOp(OpKernelConstruction* context) : OpKernel(context) {
+ OP_DEPRECATED(context, 8, "Random crop is now pure Python");
OP_REQUIRES_OK(context, generator_.Init(context));
}
diff --git a/tensorflow/core/kernels/random_crop_op_test.cc b/tensorflow/core/kernels/random_crop_op_test.cc
deleted file mode 100644
index 18bdbd4b6b5..00000000000
--- a/tensorflow/core/kernels/random_crop_op_test.cc
+++ /dev/null
@@ -1,75 +0,0 @@
-/* Copyright 2015 Google Inc. 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.
-==============================================================================*/
-
-#include "tensorflow/core/framework/allocator.h"
-#include "tensorflow/core/framework/fake_input.h"
-#include "tensorflow/core/framework/graph.pb.h"
-#include "tensorflow/core/framework/node_def_builder.h"
-#include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/tensor.h"
-#include "tensorflow/core/framework/tensor_testutil.h"
-#include "tensorflow/core/framework/types.h"
-#include "tensorflow/core/framework/types.pb.h"
-#include "tensorflow/core/kernels/ops_testutil.h"
-#include "tensorflow/core/kernels/ops_util.h"
-#include "tensorflow/core/lib/core/status_test_util.h"
-#include "tensorflow/core/platform/test.h"
-
-namespace tensorflow {
-
-class RandomCropOpTest : public OpsTestBase {
- protected:
- RandomCropOpTest() {
- RequireDefaultOps();
- TF_EXPECT_OK(NodeDefBuilder("random_crop_op", "RandomCrop")
- .Input(FakeInput(DT_UINT8))
- .Input(FakeInput(DT_INT64))
- .Attr("T", DT_UINT8)
- .Finalize(node_def()));
- TF_EXPECT_OK(InitOp());
- }
-};
-
-TEST_F(RandomCropOpTest, Basic) {
- AddInputFromArray<uint8>(TensorShape({1, 2, 1}), {2, 2});
- AddInputFromArray<int64>(TensorShape({2}), {1, 1});
- TF_ASSERT_OK(RunOpKernel());
-
- Tensor expected(allocator(), DT_UINT8, TensorShape({1, 1, 1}));
- test::FillValues<uint8>(&expected, {2});
- test::ExpectTensorEqual<uint8>(expected, *GetOutput(0));
-}
-
-TEST_F(RandomCropOpTest, SameSizeOneChannel) {
- AddInputFromArray<uint8>(TensorShape({2, 1, 1}), {1, 2});
- AddInputFromArray<int64>(TensorShape({2}), {2, 1});
- TF_ASSERT_OK(RunOpKernel());
-
- Tensor expected(allocator(), DT_UINT8, TensorShape({2, 1, 1}));
- test::FillValues<uint8>(&expected, {1, 2});
- test::ExpectTensorEqual<uint8>(expected, *GetOutput(0));
-}
-
-TEST_F(RandomCropOpTest, SameSizeMultiChannel) {
- AddInputFromArray<uint8>(TensorShape({2, 1, 3}), {1, 2, 3, 4, 5, 6});
- AddInputFromArray<int64>(TensorShape({2}), {2, 1});
- TF_ASSERT_OK(RunOpKernel());
-
- Tensor expected(allocator(), DT_UINT8, TensorShape({2, 1, 3}));
- test::FillValues<uint8>(&expected, {1, 2, 3, 4, 5, 6});
- test::ExpectTensorEqual<uint8>(expected, *GetOutput(0));
-}
-
-} // namespace tensorflow
diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h
index 9ec1aa9b7f2..39cf5b06e9b 100644
--- a/tensorflow/core/public/version.h
+++ b/tensorflow/core/public/version.h
@@ -61,8 +61,9 @@ limitations under the License.
// 5. Graphs are wholly-validated during Session::Create() (7jan2016).
// 6. TensorFlow is scalar strict within Google (27jan2016).
// 7. Remove TopK in favor of TopKV2 (5feb2016).
+// 8. Replace RandomCrop from C++ with pure Python (5feb2016).
#define TF_GRAPH_DEF_VERSION_MIN_PRODUCER 0
#define TF_GRAPH_DEF_VERSION_MIN_CONSUMER 0
-#define TF_GRAPH_DEF_VERSION 7
+#define TF_GRAPH_DEF_VERSION 8
#endif // TENSORFLOW_CORE_PUBLIC_VERSION_H_
diff --git a/tensorflow/models/image/cifar10/cifar10_input.py b/tensorflow/models/image/cifar10/cifar10_input.py
index ffe8facd27d..f7d7083d735 100644
--- a/tensorflow/models/image/cifar10/cifar10_input.py
+++ b/tensorflow/models/image/cifar10/cifar10_input.py
@@ -161,7 +161,7 @@ def distorted_inputs(data_dir, batch_size):
# distortions applied to the image.
# Randomly crop a [height, width] section of the image.
- distorted_image = tf.image.random_crop(reshaped_image, [height, width])
+ distorted_image = tf.random_crop(reshaped_image, [height, width, 3])
# Randomly flip the image horizontally.
distorted_image = tf.image.random_flip_left_right(distorted_image)
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 979897c985a..ee4dae35453 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -509,6 +509,7 @@ tf_gen_op_wrapper_py(
tf_gen_op_wrapper_py(
name = "image_ops",
hidden = [
+ "RandomCrop",
"ResizeBilinearGrad",
"ResizeNearestNeighborGrad",
"AdjustContrastv2",
diff --git a/tensorflow/python/kernel_tests/random_crop_test.py b/tensorflow/python/kernel_tests/random_crop_test.py
new file mode 100644
index 00000000000..b682b22b88b
--- /dev/null
+++ b/tensorflow/python/kernel_tests/random_crop_test.py
@@ -0,0 +1,77 @@
+# Copyright 2015 Google Inc. 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.
+# ==============================================================================
+
+"""Tests for random_crop."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow.python.platform
+
+import numpy as np
+import tensorflow as tf
+
+
+class RandomCropTest(tf.test.TestCase):
+
+ def testNoOp(self):
+ # No random cropping is performed since the size is value.shape.
+ for shape in (2, 1, 1), (2, 1, 3), (4, 5, 3):
+ value = np.arange(0, np.prod(shape), dtype=np.int32).reshape(shape)
+ with self.test_session():
+ crop = tf.random_crop(value, shape).eval()
+ self.assertAllEqual(crop, value)
+
+ def testContains(self):
+ with self.test_session():
+ shape = (3, 5, 7)
+ target = (2, 3, 4)
+ value = np.random.randint(1000000, size=shape)
+ value_set = set(tuple(value[i:i + 2, j:j + 3, k:k + 4].ravel())
+ for i in range(2) for j in range(3) for k in range(4))
+ crop = tf.random_crop(value, size=target)
+ for _ in range(20):
+ y = crop.eval()
+ self.assertAllEqual(y.shape, target)
+ self.assertTrue(tuple(y.ravel()) in value_set)
+
+ def testRandomization(self):
+ # Run 1x1 crop num_samples times in an image and ensure that one finds each
+ # pixel 1/size of the time.
+ num_samples = 1000
+ shape = [5, 4, 1]
+ size = np.prod(shape)
+ single = [1, 1, 1]
+ value = np.arange(size).reshape(shape)
+
+ with self.test_session():
+ crop = tf.random_crop(value, single, seed=7)
+ counts = np.zeros(size, dtype=np.int32)
+ for _ in range(num_samples):
+ y = crop.eval()
+ self.assertAllEqual(y.shape, single)
+ counts[y] += 1
+
+ # Calculate the mean and 4 * standard deviation.
+ mean = np.repeat(num_samples / size, size)
+ four_stddev = 4.0 * np.sqrt(mean)
+
+ # Ensure that each entry is observed in 1/size of the samples
+ # within 4 standard deviations.
+ self.assertAllClose(counts, mean, atol=four_stddev)
+
+
+if __name__ == '__main__':
+ tf.test.main()
diff --git a/tensorflow/python/ops/constant_op.py b/tensorflow/python/ops/constant_op.py
index a5f1a9c8ee1..5d04389c731 100644
--- a/tensorflow/python/ops/constant_op.py
+++ b/tensorflow/python/ops/constant_op.py
@@ -91,6 +91,7 @@ print(sess.run(var))
@@truncated_normal
@@random_uniform
@@random_shuffle
+@@random_crop
@@set_random_seed
"""
diff --git a/tensorflow/python/ops/image_ops.py b/tensorflow/python/ops/image_ops.py
index 2eeef95d99d..d278b53d709 100644
--- a/tensorflow/python/ops/image_ops.py
+++ b/tensorflow/python/ops/image_ops.py
@@ -70,7 +70,6 @@ resized_image = tf.image.resize_images(image, 299, 299)
@@pad_to_bounding_box
@@crop_to_bounding_box
-@@random_crop
@@extract_glimpse
## Flipping and Transposing
@@ -156,7 +155,6 @@ import tensorflow.python.platform
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
-from tensorflow.python.framework import random_seed
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
@@ -832,45 +830,6 @@ def _ImageEncodeShape(op):
return [tensor_shape.scalar()]
-@ops.RegisterShape('RandomCrop')
-def _random_cropShape(op):
- """Shape function for the random_crop op."""
- input_shape = op.inputs[0].get_shape().with_rank(3)
- unused_size_shape = op.inputs[1].get_shape().merge_with(
- tensor_shape.vector(2))
- size = tensor_util.constant_value(op.inputs[1])
- if size is not None:
- height = size[0]
- width = size[1]
- else:
- height = None
- width = None
- channels = input_shape[2]
- return [tensor_shape.TensorShape([height, width, channels])]
-
-
-def random_crop(image, size, seed=None, name=None):
- """Randomly crops `image` to size `[target_height, target_width]`.
-
- The offset of the output within `image` is uniformly random. `image` always
- fully contains the result.
-
- Args:
- image: 3-D tensor of shape `[height, width, channels]`
- size: 1-D tensor with two elements, specifying target `[height, width]`
- seed: A Python integer. Used to create a random seed. See
- [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
- for behavior.
- name: A name for this operation (optional).
-
- Returns:
- A cropped 3-D tensor of shape `[target_height, target_width, channels]`.
- """
- seed1, seed2 = random_seed.get_seed(seed)
- return gen_image_ops.random_crop(image, size, seed=seed1, seed2=seed2,
- name=name)
-
-
def saturate_cast(image, dtype):
"""Performs a safe cast of image data to `dtype`.
diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py
index d09004556ca..bb19902960c 100644
--- a/tensorflow/python/ops/image_ops_test.py
+++ b/tensorflow/python/ops/image_ops_test.py
@@ -443,57 +443,6 @@ class AdjustBrightnessTest(test_util.TensorFlowTestCase):
self._testBrightness(x_np, y_np, delta=-10. / 255.)
-class RandomCropTest(test_util.TensorFlowTestCase):
-
- def testNoOp(self):
- # No random cropping is performed since the target width and height
- # are match the image dimensions.
- height = 4
- width = 5
- x_shape = [height, width, 3]
- x_np = np.arange(0, np.prod(x_shape), dtype=np.int32).reshape(x_shape)
- target_shape_np = np.array([height, width], dtype=np.int64)
-
- with self.test_session():
- x = constant_op.constant(x_np, shape=x_shape)
- target_shape = constant_op.constant(target_shape_np, shape=[2])
- y = image_ops.random_crop(x, target_shape)
- y_tf = y.eval()
- self.assertAllEqual(y_tf, x_np)
-
- def testRandomization(self):
- # Run 1x1 crop num_samples times in an image and ensure that one finds each
- # pixel 1/num_pixels of the time.
- num_samples = 1000
- height = 5
- width = 4
-
- num_pixels = height * width
- data = np.arange(num_pixels).reshape([height, width, 1])
- x_np = np.array(data).astype(np.int32)
-
- target_shape_np = np.array([1, 1], dtype=np.int64)
-
- y = []
- with self.test_session():
- x = constant_op.constant(x_np, shape=x_np.shape)
- target_shape = constant_op.constant(target_shape_np, shape=[2])
- y_tf = image_ops.random_crop(x, target_shape)
- for _ in xrange(num_samples):
- y_np = y_tf.eval()
- self.assertAllEqual(y_np.shape, [1, 1, 1])
- y.extend(y_np.flatten())
-
- # Calculate the mean and 4 * standard deviation.
- mean = [num_samples / num_pixels] * num_pixels
- four_stddev = 4.0 * np.sqrt(mean)
-
- # Ensure that each entry is observed in 1/num_pixels of the samples
- # within 4 standard deviations.
- counts = np.bincount(y)
- self.assertAllClose(counts, mean, atol=four_stddev)
-
-
class PerImageWhiteningTest(test_util.TensorFlowTestCase):
def _NumpyPerImageWhitening(self, x):
diff --git a/tensorflow/python/ops/random_ops.py b/tensorflow/python/ops/random_ops.py
index 1416e4a8c14..15cfcebc291 100644
--- a/tensorflow/python/ops/random_ops.py
+++ b/tensorflow/python/ops/random_ops.py
@@ -24,8 +24,11 @@ from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.framework import random_seed
+from tensorflow.python.ops import array_ops
from tensorflow.python.ops import common_shapes
+from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gen_random_ops
+from tensorflow.python.ops import logging_ops
from tensorflow.python.ops import math_ops
# pylint: disable=wildcard-import
from tensorflow.python.ops.gen_random_ops import *
@@ -209,6 +212,43 @@ def random_shuffle(value, seed=None, name=None):
name=name)
+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]`.
+
+ 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
+ [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
+ for behavior.
+ name: A name for this operation (optional).
+
+ Returns:
+ A cropped tensor of the same rank as `value` and shape `size`.
+ """
+ # TODO(shlens): Implement edge case to guarantee output size dimensions.
+ # If size > value.shape, zero pad the result so that it always has shape
+ # exactly size.
+ with ops.op_scope([value, size], name, "random_crop") 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 = logging_ops.Assert(math_ops.reduce_all(shape >= size),
+ ["Need value.shape >= size, got ", shape, size])
+ shape = control_flow_ops.with_dependencies([check], shape)
+ limit = shape - size + 1
+ offset = 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)
+
+
ops.NoGradient("RandomUniform")