wehub-resource-sync
217 lines
8.8 KiB
C++
217 lines
8.8 KiB
C++
// Copyright (c) 2024 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/funcs/math/unpooling.h"
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namespace phi {
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namespace math {
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template <typename T>
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class Unpool2dMaxFunctor<CPUContext, T> {
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public:
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void operator()(const CPUContext& context,
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const DenseTensor& input,
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const DenseTensor& indices,
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DenseTensor* output) {
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const int batch_size = static_cast<int>(input.dims()[0]);
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const int input_height = static_cast<int>(input.dims()[2]);
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const int input_width = static_cast<int>(input.dims()[3]);
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const int output_channels = static_cast<int>(output->dims()[1]);
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const int output_height = static_cast<int>(output->dims()[2]);
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const int output_width = static_cast<int>(output->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 T* input_data = input.data<T>();
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const int* indices_data = indices.data<int>();
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T* output_data = context.template Alloc<T>(output);
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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 (int64_t i = 0; i < input_feasize; ++i) {
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int 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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output_data[index] = input_data[i];
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}
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input_data += input_feasize;
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indices_data += input_feasize;
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output_data += output_feasize;
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}
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}
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}
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};
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template <class T>
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class Unpool2dMaxGradFunctor<CPUContext, T> {
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public:
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void operator()(const CPUContext& context,
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const DenseTensor& input,
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const DenseTensor& indices,
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const DenseTensor& output,
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const DenseTensor& output_grad,
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DenseTensor* input_grad) {
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const int batch_size = static_cast<int>(input.dims()[0]);
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const int input_height = static_cast<int>(input.dims()[2]);
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const int input_width = static_cast<int>(input.dims()[3]);
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const int output_channels = static_cast<int>(output.dims()[1]);
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const int output_height = static_cast<int>(output.dims()[2]);
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const int output_width = static_cast<int>(output.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 int* indices_data = indices.data<int>();
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const T* output_grad_data = output_grad.data<T>();
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T* input_grad_data = context.template Alloc<T>(input_grad);
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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 (int64_t i = 0; i < input_feasize; ++i) {
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int 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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};
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template <typename T>
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class Unpool3dMaxFunctor<CPUContext, T> {
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public:
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void operator()(const CPUContext& context,
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const DenseTensor& input,
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const DenseTensor& indices,
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DenseTensor* output) {
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const int batch_size = static_cast<int>(input.dims()[0]);
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const int input_depth = static_cast<int>(input.dims()[2]);
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const int input_height = static_cast<int>(input.dims()[3]);
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const int input_width = static_cast<int>(input.dims()[4]);
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const int output_channels = static_cast<int>(output->dims()[1]);
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const int output_depth = static_cast<int>(output->dims()[2]);
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const int output_height = static_cast<int>(output->dims()[3]);
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const int output_width = static_cast<int>(output->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 T* input_data = input.data<T>();
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const int* indices_data = indices.data<int>();
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T* output_data = context.template Alloc<T>(output);
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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 (int64_t i = 0; i < input_feasize; ++i) {
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int 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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output_data[index] = input_data[i];
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}
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input_data += input_feasize;
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indices_data += input_feasize;
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output_data += output_feasize;
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}
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}
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}
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};
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template <class T>
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class Unpool3dMaxGradFunctor<CPUContext, T> {
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public:
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void operator()(const CPUContext& context,
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const DenseTensor& input,
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const DenseTensor& indices,
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const DenseTensor& output,
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const DenseTensor& output_grad,
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DenseTensor* input_grad) {
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const int batch_size = static_cast<int>(input.dims()[0]);
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const int input_depth = static_cast<int>(input.dims()[2]);
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const int input_height = static_cast<int>(input.dims()[3]);
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const int input_width = static_cast<int>(input.dims()[4]);
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const int output_channels = static_cast<int>(output.dims()[1]);
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const int output_depth = static_cast<int>(output.dims()[2]);
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const int output_height = static_cast<int>(output.dims()[3]);
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const int output_width = static_cast<int>(output.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 int* indices_data = indices.data<int>();
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const T* output_grad_data = output_grad.data<T>();
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T* input_grad_data = context.template Alloc<T>(input_grad);
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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 (int64_t i = 0; i < input_feasize; ++i) {
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int 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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};
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template class Unpool2dMaxGradFunctor<CPUContext, float>;
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template class Unpool2dMaxGradFunctor<CPUContext, double>;
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template class Unpool2dMaxFunctor<CPUContext, float>;
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template class Unpool2dMaxFunctor<CPUContext, double>;
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template class Unpool3dMaxGradFunctor<CPUContext, float>;
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template class Unpool3dMaxGradFunctor<CPUContext, double>;
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template class Unpool3dMaxFunctor<CPUContext, float>;
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template class Unpool3dMaxFunctor<CPUContext, double>;
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} // namespace math
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} // namespace phi
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