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92 lines
2.9 KiB
92 lines
2.9 KiB
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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#pragma once
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#include <utility>
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#include <vector>
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/tensor.h"
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#include "paddle/fluid/framework/tensor_util.h"
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#include "paddle/fluid/operators/math/padding.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class PadConstantLikeKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto in_x = context.Input<framework::Tensor>("X");
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auto in_y = context.Input<framework::Tensor>("Y");
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auto* out = context.Output<framework::Tensor>("Out");
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if (in_x->dims() == in_y->dims()) {
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framework::TensorCopy(*in_y, context.GetPlace(), out);
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return;
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}
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T pad_value = static_cast<T>(context.Attr<float>("pad_value"));
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out->mutable_data<T>(context.GetPlace());
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int rank = context.Input<framework::Tensor>("X")->dims().size();
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std::vector<int> pads(rank * 2, 0);
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for (int j = 0; j < rank; ++j) {
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pads[j * 2] = 0;
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pads[j * 2 + 1] = static_cast<int>(in_x->dims()[j] - in_y->dims()[j]);
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}
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math::PaddingFunctor<DeviceContext, T>(rank, context, pads, pad_value,
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*in_y, out);
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}
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};
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template <typename DeviceContext, typename T>
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class PadConstantLikeGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto in_y = context.Input<framework::Tensor>("Y");
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auto in_dout =
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context.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto* d_y = context.Output<framework::Tensor>(framework::GradVarName("Y"));
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if (d_y == nullptr) {
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return;
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}
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if (in_dout->dims() == in_y->dims()) {
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framework::TensorCopy(*in_dout, context.GetPlace(), d_y);
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return;
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}
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d_y->mutable_data<T>(context.GetPlace());
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int rank = in_dout->dims().size();
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std::vector<int> pads(static_cast<size_t>(rank) * 2, 0);
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for (int j = 0; j < rank; ++j) {
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pads[j * 2] = 0;
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pads[j * 2 + 1] = static_cast<int>(in_dout->dims()[j] - in_y->dims()[j]);
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}
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math::PaddingGradFunctor<DeviceContext, T>(rank, context, pads, *in_dout,
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d_y);
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}
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};
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} // namespace operators
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} // namespace paddle
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