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