mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 09:07:10 +08:00
100 lines
4.1 KiB
C++
Executable File
100 lines
4.1 KiB
C++
Executable File
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
|
//
|
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
|
// you may not use this file except in compliance with the License.
|
|
// You may obtain a copy of the License at
|
|
//
|
|
// http://www.apache.org/licenses/LICENSE-2.0
|
|
//
|
|
// Unless required by applicable law or agreed to in writing, software
|
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
// See the License for the specific language governing permissions and
|
|
// limitations under the License.
|
|
|
|
#ifndef NON_64_PLATFORM
|
|
|
|
#include "adaptive_pool2d.h"
|
|
|
|
namespace fastdeploy {
|
|
struct OrtTensorDimensions : std::vector<int64_t> {
|
|
OrtTensorDimensions(Ort::CustomOpApi ort, const OrtValue* value) {
|
|
OrtTensorTypeAndShapeInfo* info = ort.GetTensorTypeAndShape(value);
|
|
std::vector<int64_t>::operator=(ort.GetTensorShape(info));
|
|
ort.ReleaseTensorTypeAndShapeInfo(info);
|
|
}
|
|
};
|
|
|
|
void AdaptivePool2dKernel::CpuAdaptivePool(const std::vector<int64_t>& input_size, const std::vector<int64_t>& output_size, const float* input_data, float* output_data){
|
|
int64_t in_bc_offset = input_size[2] * input_size[3];
|
|
int64_t out_bc_offset = output_size[2] * output_size[3];
|
|
for (int64_t b = 0; b < output_size[0] ; b++) {
|
|
for (int64_t c = 0; c < output_size[1] ; c++) {
|
|
for(int64_t h = 0; h < output_size[2]; h++){
|
|
int64_t hstart = std::floor( static_cast<float>(h * input_size[2]) / output_size[2]);
|
|
int64_t hend = std::ceil(static_cast<float>((h + 1) * input_size[2]) / output_size[2]);
|
|
for(int64_t w = 0; w < output_size[3]; w++){
|
|
int64_t wstart = std::floor(static_cast<float>(w * input_size[3]) / output_size[3]);
|
|
int64_t wend = std::ceil(static_cast<float>((w + 1) * input_size[3]) / output_size[3]);
|
|
int64_t out_offset = h * output_size[3] + w;
|
|
output_data[out_offset] = 0;
|
|
for(auto i = hstart; i < hend; i++){
|
|
for(auto j = wstart; j< wend; j++){
|
|
if(pooling_type_ == "avg"){
|
|
output_data[out_offset] += input_data[i * input_size[3] + j];
|
|
}
|
|
if(pooling_type_ == "max"){
|
|
output_data[out_offset] = std::max(output_data[out_offset], input_data[i * input_size[3] + j]);
|
|
}
|
|
}
|
|
}
|
|
if(pooling_type_ == "avg"){
|
|
output_data[out_offset] /= ((hend - hstart) * (wend - wstart));
|
|
}
|
|
}
|
|
}
|
|
output_data += out_bc_offset;
|
|
input_data += in_bc_offset;
|
|
}
|
|
}
|
|
}
|
|
|
|
void AdaptivePool2dKernel::Compute(OrtKernelContext* context) {
|
|
const OrtValue* input = ort_.KernelContext_GetInput(context, 0);
|
|
|
|
const float* input_data =
|
|
reinterpret_cast<const float*>(ort_.GetTensorData<float>(input));
|
|
|
|
OrtTensorDimensions input_dim(ort_, input);
|
|
output_size_[0] = input_dim[0];
|
|
std::vector<int64_t> input_size;
|
|
for(auto i: input_dim){
|
|
input_size.push_back(i);
|
|
}
|
|
|
|
OrtValue* output = ort_.KernelContext_GetOutput(
|
|
context, 0, output_size_.data(), output_size_.size());
|
|
|
|
float* output_data = ort_.GetTensorMutableData<float>(output);
|
|
if(!strcmp(this->provider_, "CUDAExecutionProvider")){
|
|
#ifdef WITH_GPU
|
|
auto compute_stream = ort_.KernelContext_GetGPUComputeStream(context);
|
|
CudaAdaptivePool(input_size, output_size_, output_data, input_data, compute_stream, pooling_type_);
|
|
#else
|
|
FDWARNING << "FastDeploy didn't compile with WITH_GPU. "
|
|
<< "Will force to use CPU to run." << std::endl;
|
|
CpuAdaptivePool(input_size, output_size_, input_data, output_data);
|
|
#endif
|
|
} else {
|
|
CpuAdaptivePool(input_size, output_size_, input_data, output_data);
|
|
}
|
|
}
|
|
|
|
void AdaptivePool2dKernel::GetAttribute(const OrtKernelInfo* info) {
|
|
pooling_type_ = ort_.KernelInfoGetAttribute<std::string>(info, "pooling_type");
|
|
output_size_ = ort_.KernelInfoGetAttribute<std::vector<int64_t>>(info, "output_size");
|
|
FDASSERT(output_size_.size() == 4 && output_size_[2] > 0 && output_size_[3] > 0, "The output size of adaptive pool must be positive.");
|
|
}
|
|
} // namespace fastdeploy
|
|
|
|
#endif |