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64 lines
2.1 KiB
64 lines
2.1 KiB
/* Copyright (c) 2016 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/operators/math/padding.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename DeviceContext, typename T>
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class PadKernel : 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 pads = context.Attr<std::vector<int>>("paddings");
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float pad_value = context.Attr<float>("pad_value");
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auto* x = context.Input<Tensor>("X");
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auto* out = context.Output<Tensor>("Out");
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out->mutable_data<T>(context.GetPlace());
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int rank = x->dims().size();
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math::PaddingFunctor<DeviceContext, T>(rank, context, pads,
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static_cast<T>(pad_value), *x, out);
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}
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};
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template <typename DeviceContext, typename T>
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class PadGradKernel : 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 pads = context.Attr<std::vector<int>>("paddings");
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auto* d_out = context.Input<Tensor>(framework::GradVarName("Out"));
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auto* d_x = context.Output<Tensor>(framework::GradVarName("X"));
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if (d_x == nullptr) {
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return;
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}
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d_x->mutable_data<T>(context.GetPlace());
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int rank = d_out->dims().size();
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math::PaddingGradFunctor<DeviceContext, T>(rank, context, pads, *d_out,
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d_x);
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}
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};
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} // namespace operators
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} // namespace paddle
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