wehub-resource-sync
111 lines
3.6 KiB
C++
111 lines
3.6 KiB
C++
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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/phi/kernels/crop_grad_kernel.h"
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#include <vector>
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#include "paddle/phi/core/tensor_utils.h"
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#include "paddle/phi/kernels/full_kernel.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
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namespace phi {
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template <typename Context, typename T, size_t D>
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void CropTensorGradFunction(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out_grad,
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const IntArray& offsets,
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DenseTensor* x_grad) {
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if (x_grad != nullptr) {
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x_grad->Resize(x.dims());
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dev_ctx.template Alloc<T>(x_grad);
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auto offsets_vec = offsets.GetData();
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std::array<std::pair<int64_t, int64_t>, D> paddings;
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for (size_t i = 0; i < D; ++i) {
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paddings[i].first = offsets_vec[i];
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paddings[i].second =
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x_grad->dims()[i] - out_grad.dims()[i] - offsets_vec[i];
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}
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auto x_grad_tensor = EigenTensor<T, D>::From(*x_grad);
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auto out_grad_tensor = EigenTensor<T, D>::From(out_grad);
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auto& place = *dev_ctx.eigen_device();
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funcs::EigenPad<std::decay_t<decltype(place)>, T, D>::Eval(
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place, x_grad_tensor, out_grad_tensor, paddings, static_cast<T>(0));
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}
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}
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template <typename T, typename Context>
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void CropGradKernel(const Context& dev_ctx,
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const DenseTensor& out_grad,
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const DenseTensor& x,
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const IntArray& offsets,
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DenseTensor* x_grad) {
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// x[3, 5], shape[2, 0], out[2, 0]
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if (out_grad.numel() == 0 && x_grad != nullptr) {
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Full<T, Context>(dev_ctx, x_grad->dims(), 0, x_grad);
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return;
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}
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size_t rank = out_grad.dims().size();
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PADDLE_ENFORCE_GE(
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rank,
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1,
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errors::InvalidArgument(
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"The number of dimensions of the input 'Out@GRAD' for "
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"Op(crop_tensor_grad) must be greater than or equal to 1, but the "
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"value received is %d.",
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rank));
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PADDLE_ENFORCE_LE(
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rank,
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6,
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errors::InvalidArgument(
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"The number of dimensions of the input 'Out@GRAD' for "
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"Op(crop_tensor_grad) must be less than or equal to 6, but the "
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"value received is %d.",
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rank));
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switch (rank) {
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case 1:
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CropTensorGradFunction<Context, T, 1>(
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dev_ctx, out_grad, x, offsets, x_grad);
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break;
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case 2:
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CropTensorGradFunction<Context, T, 2>(
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dev_ctx, out_grad, x, offsets, x_grad);
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break;
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case 3:
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CropTensorGradFunction<Context, T, 3>(
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dev_ctx, out_grad, x, offsets, x_grad);
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break;
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case 4:
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CropTensorGradFunction<Context, T, 4>(
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dev_ctx, out_grad, x, offsets, x_grad);
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break;
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case 5:
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CropTensorGradFunction<Context, T, 5>(
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dev_ctx, out_grad, x, offsets, x_grad);
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break;
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case 6:
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CropTensorGradFunction<Context, T, 6>(
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dev_ctx, out_grad, x, offsets, x_grad);
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break;
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}
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}
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} // namespace phi
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