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Merge pull request #3765 from wanghaoshuang/pad_op

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Add pad op
enforce_failed
whs 8 years ago committed by GitHub
parent aed51dde32 23381dd16a
commit 08f9b72dbf
6 changed files with 322 additions and 1 deletions
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112
paddle/operators/pad_op.cc
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/* 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/pad_op.h"
namespace paddle {
namespace operators {
using framework::Tensor;
class PadOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto x_dim = ctx.Input<Tensor>("X")->dims();
auto paddings = Attr<std::vector<int>>("paddings");
PADDLE_ENFORCE_EQ(x_dim.size() * 2, int64_t(paddings.size()),
"Size of paddings should be equal to 2 * dimension size "
"of input tensor.");
std::vector<int64_t> out_dims(x_dim.size());
for (int i = 0; i < x_dim.size(); ++i) {
out_dims[i] = x_dim[i] + paddings[i * 2] + paddings[i * 2 + 1];
}
ctx.Output<Tensor>("Out")->Resize(framework::make_ddim(out_dims));
}
};
class PadOpMaker : public framework::OpProtoAndCheckerMaker {
public:
PadOpMaker(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)");
AddOutput("Out",
"The output of pad op."
"A tensor with the same shape as X.")
.NotInGradient();
AddComment(R"DOC(
Pad input into output, as specified by paddings and pad_value. The input should be a k-D tensor(k > 0 and k < 7). As an example:
Given:
X = [[1, 2],
[3, 4]]
and
paddings = [0, 1, 1, 2]
and
pad_value = 0
then we get
Out = [[0, 1, 2, 0, 0]
[0, 3, 4, 0, 0]
[0, 0, 0, 0, 0]]
)DOC");
AddAttr<std::vector<int>>(
"paddings",
"A list<int> to describes padding rules for each dimension."
" For 2-D image tensor, paddings=[0, 1, 2, 3] means"
" padding 0 row to top, 1 row to bottom, 2 columns to left"
" and 3 columns to right.Size of paddings should be equal to"
" 2 * dimension size of input tensor.");
AddAttr<float>("pad_value",
"(float) default to 0; "
"The value to fill padded areas.")
.SetDefault(0.0f);
}
};
class PadOpGrad : 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<Tensor>("X")->dims();
auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
if (x_grad != nullptr) {
x_grad->Resize(x_dims);
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(pad, ops::PadOp, ops::PadOpMaker, pad_grad, ops::PadOpGrad);
REGISTER_OP_CPU_KERNEL(pad, ops::PadKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(pad_grad,
ops::PadGradKernel<paddle::platform::CPUPlace, float>);

21
paddle/operators/pad_op.cu
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/* 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/pad_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(pad, ops::PadKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(pad_grad,
ops::PadGradKernel<paddle::platform::GPUPlace, float>);

132
paddle/operators/pad_op.h
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/* 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. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
template <typename Place, typename T, size_t D>
void PadFunction(const framework::ExecutionContext& context) {
auto pads = context.Attr<std::vector<int>>("paddings");
Eigen::array<std::pair<int, int>, D> paddings;
for (size_t i = 0; i < paddings.size(); ++i) {
paddings[i].first = pads[i * 2];
paddings[i].second = pads[i * 2 + 1];
}
T pad_value = context.Attr<T>("pad_value");
auto* x = context.Input<Tensor>("X");
auto* out = context.Output<Tensor>("Out");
out->mutable_data<T>(context.GetPlace());
auto x_tensor = EigenTensor<T, D>::From(*x);
auto out_tensor = EigenTensor<T, D>::From(*out);
auto place = context.GetEigenDevice<Place>();
out_tensor.device(place) = x_tensor.pad(paddings, pad_value);
}
template <typename Place, typename T>
class PadKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
int rank = context.Input<Tensor>("X")->dims().size();
switch (rank) {
case 1:
PadFunction<Place, T, 1>(context);
break;
case 2:
PadFunction<Place, T, 2>(context);
break;
case 3:
PadFunction<Place, T, 3>(context);
break;
case 4:
PadFunction<Place, T, 4>(context);
break;
case 5:
PadFunction<Place, T, 5>(context);
break;
case 6:
PadFunction<Place, T, 6>(context);
break;
default:
PADDLE_THROW(
"PadOp only support tensors with no more than 6 dimensions.");
}
}
};
template <typename Place, typename T, size_t D>
void PadGradFunction(const framework::ExecutionContext& context) {
auto pads = context.Attr<std::vector<int>>("paddings");
Eigen::array<std::pair<int, int>, D> paddings;
for (size_t i = 0; i < paddings.size(); ++i) {
paddings[i].first = -pads[i * 2];
paddings[i].second = -pads[i * 2 + 1];
}
auto* d_out = context.Input<Tensor>(framework::GradVarName("Out"));
auto* d_x = context.Output<Tensor>(framework::GradVarName("X"));
if (d_x != nullptr) {
d_x->mutable_data<T>(context.GetPlace());
auto d_x_tensor = EigenTensor<T, D>::From(*d_x);
auto d_out_tensor = EigenTensor<T, D>::From(*d_out);
auto place = context.GetEigenDevice<Place>();
d_x_tensor.device(place) = d_out_tensor.pad(paddings, 0);
}
}
template <typename Place, typename T>
class PadGradKernel : 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:
PadGradFunction<Place, T, 1>(context);
break;
case 2:
PadGradFunction<Place, T, 2>(context);
break;
case 3:
PadGradFunction<Place, T, 3>(context);
break;
case 4:
PadGradFunction<Place, T, 4>(context);
break;
case 5:
PadGradFunction<Place, T, 5>(context);
break;
case 6:
PadGradFunction<Place, T, 6>(context);
break;
default:
PADDLE_THROW(
"PadOp only support tensors with no more than 6 dimensions.");
}
}
};
} // namespace operators
} // namespace paddle

1
paddle/pybind/pybind.cc
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@ -50,6 +50,7 @@ USE_NO_KERNEL_OP(identity);
USE_OP(minus);
USE_OP(cos_sim);
USE_CPU_ONLY_OP(gather);
USE_OP(pad);
USE_CPU_ONLY_OP(scatter);
USE_CPU_ONLY_OP(concat);
USE_OP(top_k);

2
python/paddle/v2/framework/op.py
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@ -97,7 +97,7 @@ class OpDescCreationMethod(object):
new_attr.strings.extend(user_defined_attr)
elif attr.type == framework_pb2.INT_PAIRS:
for p in user_defined_attr:
pair = new_attr.pairs.add()
pair = new_attr.int_pairs.add()
pair.first = p[0]
pair.second = p[1]
else:

55
python/paddle/v2/framework/tests/test_pad_op.py
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import unittest
import numpy as np
from op_test import OpTest
class TestPadOp(OpTest):
def setUp(self):
self.initTestCase()
self.op_type = "pad"
self.inputs = {'X': np.random.random(self.shape).astype("float32"), }
self.attrs = {}
self.attrs['paddings'] = np.array(self.paddings).flatten()
self.attrs['pad_value'] = self.pad_value
self.outputs = {
'Out': np.pad(self.inputs['X'],
self.paddings,
mode='constant',
constant_values=self.pad_value)
}
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['X'], 'Out')
def initTestCase(self):
self.shape = (16, 16)
self.paddings = [(0, 1), (2, 3)]
self.pad_value = 0
class TestCase1(TestPadOp):
def initTestCase(self):
self.shape = (2, 3, 4, 4)
self.paddings = [(0, 1), (2, 3), (2, 1), (1, 1)]
self.pad_value = 0.5
class TestCase2(TestPadOp):
def initTestCase(self):
self.shape = (2, 2, 2)
self.paddings = [(0, 0), (0, 0), (1, 2)]
self.pad_value = 1
class TestCase3(TestPadOp):
def initTestCase(self):
self.shape = (8)
self.paddings = [(0, 1)]
self.pad_value = 0.9
if __name__ == '__main__':
unittest.main()
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