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122 lines
4.0 KiB
122 lines
4.0 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/operators/pad_op.h"
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namespace paddle {
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namespace operators {
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using framework::Tensor;
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class PadOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContextBase* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of PadOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of PadOp should not be null.");
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auto x_dim = ctx->GetInputDim("X");
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auto paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
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PADDLE_ENFORCE_EQ(x_dim.size() * 2, int64_t(paddings.size()),
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"Size of paddings should be equal to 2 * dimension size "
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"of input tensor.");
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std::vector<int64_t> out_dims(x_dim.size());
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for (int i = 0; i < x_dim.size(); ++i) {
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out_dims[i] = x_dim[i] + paddings[i * 2] + paddings[i * 2 + 1];
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}
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ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
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if (out_dims[0] == x_dim[0]) {
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// Only pass LoD when the first dimension is equal between
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// output and input.
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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}
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};
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class PadOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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PadOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X",
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"The input of pad op. "
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"The input should be a k-D tensor(k > 0 and k < 7)");
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AddOutput("Out",
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"The output of pad op."
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"A tensor with the same shape as X.")
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.NotInGradient();
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AddComment(R"DOC(
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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:
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Given:
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X = [[1, 2],
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[3, 4]]
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and
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paddings = [0, 1, 1, 2]
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and
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pad_value = 0
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then we get
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Out = [[0, 1, 2, 0, 0]
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[0, 3, 4, 0, 0]
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[0, 0, 0, 0, 0]]
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)DOC");
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AddAttr<std::vector<int>>(
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"paddings",
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"A list<int> to describes padding rules for each dimension."
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" For 2-D image tensor, paddings=[0, 1, 2, 3] means"
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" padding 0 row to top, 1 row to bottom, 2 columns to left"
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" and 3 columns to right.Size of paddings should be equal to"
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" 2 * dimension size of input tensor.");
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AddAttr<float>("pad_value",
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"(float) default to 0; "
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"The value to fill padded areas.")
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.SetDefault(0.0f);
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}
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};
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class PadOpGrad : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContextBase* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
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"Input(Out@GRAD) should not be null");
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auto x_dims = ctx->GetInputDim("X");
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auto x_grad_name = framework::GradVarName("X");
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if (ctx->HasOutput(x_grad_name)) {
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ctx->SetOutputDim(x_grad_name, x_dims);
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP(pad, ops::PadOp, ops::PadOpMaker, pad_grad, ops::PadOpGrad);
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REGISTER_OP_CPU_KERNEL(pad, ops::PadKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(pad_grad,
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ops::PadGradKernel<paddle::platform::CPUPlace, float>);
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