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Merge pull request #3906 from wanghaoshuang/crop_op

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Add crop op
update-doc-pybind
whs 8 years ago committed by GitHub
parent e53dc8a2e4 8b6fda6fd8
commit da2aabb628
6 changed files with 364 additions and 0 deletions
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8
paddle/framework/ddim.cc
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@ -292,5 +292,13 @@ DDim flatten_to_2d(const DDim& src, int num_col_dims) {
DDim flatten_to_1d(const DDim& src) { return make_ddim({product(src)}); }
DDim stride(const DDim& ddim) {
std::vector<int64_t> strides(ddim.size());
strides[ddim.size() - 1] = 1;
for (int i = ddim.size() - 2; i >= 0; --i) {
strides[i] = strides[i + 1] * ddim[i + 1];
}
return framework::make_ddim(strides);
}
} // namespace framework
} // namespace paddle

1
paddle/framework/ddim.h
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@ -121,6 +121,7 @@ DDim flatten_to_2d(const DDim& src, int num_col_dims);
DDim flatten_to_1d(const DDim& src);
DDim stride(const DDim& ddim);
} // namespace framework
} // namespace paddle

139
paddle/operators/crop_op.cc
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@ -0,0 +1,139 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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 "paddle/operators/crop_op.h"
#include <boost/lexical_cast.hpp>
namespace paddle {
namespace operators {
using framework::Tensor;
using framework::LoDTensor;
class CropOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of CropOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of CropOp should not be null.");
auto x_dim = ctx.Input<LoDTensor>("X")->dims();
auto *y = ctx.Input<LoDTensor>("Y");
auto *out = ctx.Output<LoDTensor>("Out");
if (y == nullptr) {
auto shape = Attr<std::vector<int>>("shape");
PADDLE_ENFORCE_EQ(
int64_t(shape.size()), x_dim.size(),
"Shape size should be equal to dimention size of input tensor.");
std::vector<int64_t> tensor_shape(shape.size());
for (size_t i = 0; i < shape.size(); ++i) {
tensor_shape[i] = static_cast<int64_t>(shape[i]);
}
out->Resize(framework::make_ddim(tensor_shape));
} else {
PADDLE_ENFORCE_EQ(framework::arity(x_dim), framework::arity(y->dims()),
"Tensor rank of both CropOp's "
"inputs must be same.");
out->Resize(y->dims());
}
}
};
class CropOpMaker : public framework::OpProtoAndCheckerMaker {
public:
CropOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"The input of pad op. "
"The input should be a k-D tensor(k > 0 and k < 7)");
AddInput("Y",
"The input used as reference for cropping"
" with the same dimension as X. ");
AddOutput("Out",
"The output of crop op "
"with the same dimension as X.");
AddAttr<std::vector<int>>("offsets",
"A list<int> describing offsets to be cropped."
"The size of offsets list should be as same as "
"dimension size of input X.");
AddAttr<std::vector<int>>("shape",
"A list<int> describing the shape of output."
"The size of shape list should be as same as "
"dimension size of input X.")
.SetDefault(std::vector<int>());
AddComment(R"DOC(
Crop Operator.
Crop input into output, as specified by offsets and shape.
There are two ways to set shape:
1. referenc input: crop input X as shape as reference input.
The dimension of reference input should
be as same as input X.
2. shape list: crop input X by shape described by a list<int>.
The size of shape list should be as same as
dimension size of input X.
The input should be a k-D tensor(k > 0 and k < 7). As an example:
Given:
X = [[0, 1, 2, 0, 0]
[0, 3, 4, 0, 0]
[0, 0, 0, 0, 0]]
and
offsets = [0, 1]
and
shape = [2, 2]
then we get
Out = [[1, 2],
[3, 4]]
)DOC");
}
};
class CropOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto x_dims = ctx.Input<LoDTensor>("X")->dims();
auto *x_grad = ctx.Output<LoDTensor>(framework::GradVarName("X"));
if (x_grad != nullptr) {
x_grad->Resize(x_dims);
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(crop, ops::CropOp, ops::CropOpMaker, crop_grad, ops::CropOpGrad);
REGISTER_OP_CPU_KERNEL(crop, ops::CropKernel<float>);
REGISTER_OP_CPU_KERNEL(crop_grad,
ops::CropGradKernel<paddle::platform::CPUPlace, float>);

21
paddle/operators/crop_op.cu
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@ -0,0 +1,21 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#define EIGEN_USE_GPU
#include "paddle/operators/crop_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(crop, ops::CropKernel<float>);
REGISTER_OP_GPU_KERNEL(crop_grad,
ops::CropGradKernel<paddle::platform::GPUPlace, float>);

104
paddle/operators/crop_op.h
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@ -0,0 +1,104 @@
/* Copyright (c) 2016 CropdleCropdle Authors. All Rights Reserve.
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. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/strided_memcpy.h"
namespace paddle {
namespace operators { // Internal
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
using framework::Tensor;
template <typename T>
class CropKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* x = context.Input<Tensor>("X");
auto* out = context.Output<Tensor>("Out");
const T* x_data = x->data<T>();
T* out_data = out->mutable_data<T>(context.GetPlace());
auto x_stride = framework::stride(x->dims());
auto out_stride = framework::stride(out->dims());
auto offsets = context.Attr<std::vector<int>>("offsets");
PADDLE_ENFORCE_EQ(
x->dims().size(), offsets.size(),
"Offsets size should be equal to dimension size of input tensor.");
int64_t offset = 0;
for (int i = 0; i < offsets.size(); ++i) {
offset += (x_stride[i] * offsets[i]);
}
StridedMemcpy<T>(context.device_context(), x_data + offset, x_stride,
out->dims(), out_stride, out_data);
}
};
template <typename Place, typename T, size_t D>
void CropGradFunction(const framework::ExecutionContext& context) {
auto* d_x = context.Output<Tensor>(framework::GradVarName("X"));
if (d_x != nullptr) {
auto* d_out = context.Input<Tensor>(framework::GradVarName("Out"));
d_x->mutable_data<T>(context.GetPlace());
auto offsets = context.Attr<std::vector<int>>("offsets");
Eigen::array<std::pair<int, int>, D> paddings;
for (int i = 0; i < D; ++i) {
paddings[i].first = offsets[i];
paddings[i].second = d_x->dims()[i] - d_out->dims()[i] - offsets[i];
}
auto d_x_tensor = EigenTensor<T, D>::From(*d_x);
auto d_out_tensor = EigenTensor<T, D>::From(*d_out);
d_x_tensor.device(context.GetEigenDevice<Place>()) =
d_out_tensor.pad(paddings, 0);
}
}
template <typename Place, typename T>
class CropGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
size_t rank =
context.Input<Tensor>(framework::GradVarName("Out"))->dims().size();
switch (rank) {
case 1:
CropGradFunction<Place, T, 1>(context);
break;
case 2:
CropGradFunction<Place, T, 2>(context);
break;
case 3:
CropGradFunction<Place, T, 3>(context);
break;
case 4:
CropGradFunction<Place, T, 4>(context);
break;
case 5:
CropGradFunction<Place, T, 5>(context);
break;
case 6:
CropGradFunction<Place, T, 6>(context);
break;
default:
PADDLE_THROW(
"CropOp only support tensors with no more than 6 dimensions.");
}
}
};
} // namespace operators
} // namespace paddle

91
python/paddle/v2/framework/tests/test_crop_op.py
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@ -0,0 +1,91 @@
import unittest
import numpy as np
from op_test import OpTest
def crop(data, offsets, crop_shape):
def indexOf(shape, index):
result = []
for dim in reversed(shape):
result.append(index % dim)
index = index / dim
return result[::-1]
result = []
for i, value in enumerate(data.flatten()):
index = indexOf(data.shape, i)
selected = True
if len(index) == len(offsets):
for j, offset in enumerate(offsets):
selected = selected and index[j] >= offset and index[
j] < crop_shape[j] + offset
if selected:
result.append(value)
return np.array(result).reshape(crop_shape)
class TestCropOp(OpTest):
def setUp(self):
self.op_type = "crop"
self.crop_by_input = False
self.attrs = {}
self.initTestCase()
self.attrs['offsets'] = self.offsets
if self.crop_by_input:
self.inputs = {
'X': np.random.random(self.x_shape).astype("float32"),
'Y': np.random.random(self.crop_shape).astype("float32")
}
else:
self.attrs['shape'] = self.crop_shape
self.inputs = {
'X': np.random.random(self.x_shape).astype("float32"),
}
self.outputs = {
'Out': crop(self.inputs['X'], self.offsets, self.crop_shape)
}
def initTestCase(self):
self.x_shape = (8, 8)
self.crop_shape = (2, 2)
self.offsets = [1, 2]
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['X'], 'Out', max_relative_error=0.006)
class TestCase1(TestCropOp):
def initTestCase(self):
self.x_shape = (16, 8, 32)
self.crop_shape = [2, 2, 3]
self.offsets = [1, 5, 3]
class TestCase2(TestCropOp):
def initTestCase(self):
self.x_shape = (4, 8)
self.crop_shape = [4, 8]
self.offsets = [0, 0]
class TestCase3(TestCropOp):
def initTestCase(self):
self.x_shape = (4, 8, 16)
self.crop_shape = [2, 2, 3]
self.offsets = [1, 5, 3]
self.crop_by_input = True
class TestCase4(TestCropOp):
def initTestCase(self):
self.x_shape = (4, 4)
self.crop_shape = [4, 4]
self.offsets = [0, 0]
self.crop_by_input = True
if __name__ == '__main__':
unittest.main()
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