wehub-resource-sync
189 lines
7.1 KiB
C++
189 lines
7.1 KiB
C++
// 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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#include "paddle/phi/kernels/unpool_grad_kernel.h"
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#include <algorithm>
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#include <string>
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#include <vector>
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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namespace phi {
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template <typename T, typename IndT, typename Context>
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void UnpoolGrad(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& indices,
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const DenseTensor& out,
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const DenseTensor& out_grad,
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DenseTensor* x_grad) {
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T* input_grad_data = dev_ctx.template Alloc<T>(x_grad);
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const T* output_grad_data = out_grad.data<T>();
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funcs::SetConstant<Context, T> zero;
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zero(dev_ctx, x_grad, static_cast<T>(0));
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const int batch_size = static_cast<int>(x.dims()[0]);
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const int input_height = static_cast<int>(x.dims()[2]);
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const int input_width = static_cast<int>(x.dims()[3]);
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const int output_channels = static_cast<int>(out.dims()[1]);
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const int output_height = static_cast<int>(out.dims()[2]);
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const int output_width = static_cast<int>(out.dims()[3]);
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int64_t input_feasize = static_cast<int64_t>(input_height) * input_width;
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int64_t output_feasize = static_cast<int64_t>(output_height) * output_width;
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const IndT* indices_data = indices.data<IndT>();
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for (int b = 0; b < batch_size; ++b) {
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for (int c = 0; c < output_channels; ++c) {
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for (int i = 0; i < input_feasize; ++i) {
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IndT index = indices_data[i];
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PADDLE_ENFORCE_LT(
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index,
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output_feasize,
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common::errors::InvalidArgument(
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"index should less than output tensor height * output tensor "
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"width. Expected %ld < %ld, but got "
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"%ld >= %ld. Please check input value.",
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index,
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output_feasize,
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index,
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output_feasize));
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input_grad_data[i] = output_grad_data[index];
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}
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input_grad_data += input_feasize;
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indices_data += input_feasize;
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output_grad_data += output_feasize;
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}
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}
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}
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template <typename T, typename Context>
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void UnpoolGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& indices,
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const DenseTensor& out,
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const DenseTensor& out_grad,
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const std::vector<int>& ksize UNUSED,
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const std::vector<int>& strides UNUSED,
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const std::vector<int>& paddings UNUSED,
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const IntArray& output_size UNUSED,
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const std::string& data_format UNUSED,
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DenseTensor* x_grad) {
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if (x_grad && x_grad->numel() == 0) {
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dev_ctx.template Alloc<T>(x_grad);
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return;
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}
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const auto& indices_type = indices.dtype();
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if (indices_type == DataType::INT32) {
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UnpoolGrad<T, int, Context>(dev_ctx, x, indices, out, out_grad, x_grad);
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} else {
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UnpoolGrad<T, int64_t, Context>(dev_ctx, x, indices, out, out_grad, x_grad);
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}
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}
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template <typename T, typename IndT, typename Context>
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void Unpool3dGrad(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& indices,
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const DenseTensor& out,
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const DenseTensor& out_grad,
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DenseTensor* x_grad) {
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T* input_grad_data = dev_ctx.template Alloc<T>(x_grad);
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const T* output_grad_data = out_grad.data<T>();
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funcs::SetConstant<Context, T> zero;
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zero(dev_ctx, x_grad, static_cast<T>(0));
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const int batch_size = static_cast<int>(x.dims()[0]);
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const int input_depth = static_cast<int>(x.dims()[2]);
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const int input_height = static_cast<int>(x.dims()[3]);
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const int input_width = static_cast<int>(x.dims()[4]);
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const int output_channels = static_cast<int>(out.dims()[1]);
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const int output_depth = static_cast<int>(out.dims()[2]);
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const int output_height = static_cast<int>(out.dims()[3]);
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const int output_width = static_cast<int>(out.dims()[4]);
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int64_t input_feasize =
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static_cast<int64_t>(input_depth) * input_height * input_width;
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int64_t output_feasize =
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static_cast<int64_t>(output_depth) * output_height * output_width;
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const IndT* indices_data = indices.data<IndT>();
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for (int b = 0; b < batch_size; ++b) {
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for (int c = 0; c < output_channels; ++c) {
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for (int i = 0; i < input_feasize; ++i) {
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IndT index = indices_data[i];
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PADDLE_ENFORCE_LT(
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index,
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output_feasize,
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common::errors::InvalidArgument(
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"index should less than output tensor depth * output tensor "
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"height "
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"* output tensor width. Expected %ld < %ld, but got "
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"%ld >= %ld. Please check input value.",
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index,
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output_feasize,
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index,
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output_feasize));
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input_grad_data[i] = output_grad_data[index];
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}
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input_grad_data += input_feasize;
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indices_data += input_feasize;
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output_grad_data += output_feasize;
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}
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}
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}
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template <typename T, typename Context>
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void Unpool3dGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& indices,
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const DenseTensor& out,
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const DenseTensor& out_grad,
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const std::vector<int>& ksize UNUSED,
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const std::vector<int>& strides,
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const std::vector<int>& paddings UNUSED,
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const std::vector<int>& output_size UNUSED,
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const std::string& data_format UNUSED,
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DenseTensor* x_grad) {
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if (x_grad && x_grad->numel() == 0) {
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dev_ctx.template Alloc<T>(x_grad);
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return;
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}
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const auto& indices_type = indices.dtype();
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if (indices_type == DataType::INT32) {
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Unpool3dGrad<T, int, Context>(dev_ctx, x, indices, out, out_grad, x_grad);
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} else {
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Unpool3dGrad<T, int64_t, Context>(
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dev_ctx, x, indices, out, out_grad, x_grad);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(unpool_grad,
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CPU,
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ALL_LAYOUT,
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phi::UnpoolGradKernel,
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float,
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double,
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int64_t) {}
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PD_REGISTER_KERNEL(unpool3d_grad,
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CPU,
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ALL_LAYOUT,
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phi::Unpool3dGradKernel,
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float,
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double,
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int64_t) {}
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