mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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128 lines
4.1 KiB
C++
128 lines
4.1 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 "fastdeploy/core/fd_tensor.h"
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#include "fastdeploy/utils/utils.h"
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#ifdef WITH_GPU
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#include <cuda_runtime_api.h>
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#endif
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namespace fastdeploy {
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void* FDTensor::MutableData() {
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if (external_data_ptr != nullptr) {
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return external_data_ptr;
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}
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return data.data();
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}
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void* FDTensor::Data() {
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if (external_data_ptr != nullptr) {
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if (device == Device::GPU) {
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#ifdef WITH_GPU
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// need to copy cuda mem to cpu first
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temporary_cpu_buffer.resize(Nbytes());
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FDASSERT(cudaMemcpy(temporary_cpu_buffer.data(), external_data_ptr,
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Nbytes(), cudaMemcpyDeviceToHost) == 0,
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"[ERROR] Error occurs while copy memory from GPU to CPU");
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return temporary_cpu_buffer.data();
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#else
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FDASSERT(false,
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"The FastDeploy didn't compile under -DWITH_GPU=ON, so this is "
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"an unexpected problem happend.");
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#endif
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} else {
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return external_data_ptr;
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}
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}
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return data.data();
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}
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void FDTensor::SetExternalData(const std::vector<int>& new_shape,
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const FDDataType& data_type, void* data_buffer) {
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dtype = data_type;
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shape.assign(new_shape.begin(), new_shape.end());
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external_data_ptr = data_buffer;
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}
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void FDTensor::Allocate(const std::vector<int>& new_shape,
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const FDDataType& data_type,
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const std::string& tensor_name) {
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dtype = data_type;
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name = tensor_name;
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shape.assign(new_shape.begin(), new_shape.end());
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int unit = FDDataTypeSize(data_type);
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int total_size =
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std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>());
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data.resize(total_size * unit);
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}
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int FDTensor::Nbytes() const { return Numel() * FDDataTypeSize(dtype); }
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int FDTensor::Numel() const {
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return std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>());
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}
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template <typename T>
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void CalculateStatisInfo(void* src_ptr, int size, double* mean, double* max,
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double* min) {
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T* ptr = static_cast<T*>(src_ptr);
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*mean = 0;
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*max = -99999999;
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*min = 99999999;
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for (int i = 0; i < size; ++i) {
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if (*(ptr + i) > *max) {
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*max = *(ptr + i);
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}
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if (*(ptr + i) < *min) {
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*min = *(ptr + i);
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}
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*mean += *(ptr + i);
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}
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*mean = *mean / size;
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}
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void FDTensor::PrintInfo(const std::string& prefix) {
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double mean = 0;
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double max = -99999999;
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double min = 99999999;
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if (dtype == FDDataType::FP32) {
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CalculateStatisInfo<float>(Data(), Numel(), &mean, &max, &min);
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} else if (dtype == FDDataType::FP64) {
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CalculateStatisInfo<double>(Data(), Numel(), &mean, &max, &min);
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} else if (dtype == FDDataType::INT8) {
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CalculateStatisInfo<int8_t>(Data(), Numel(), &mean, &max, &min);
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} else if (dtype == FDDataType::UINT8) {
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CalculateStatisInfo<uint8_t>(Data(), Numel(), &mean, &max, &min);
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} else if (dtype == FDDataType::INT32) {
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CalculateStatisInfo<int32_t>(Data(), Numel(), &mean, &max, &min);
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} else if (dtype == FDDataType::INT64) {
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CalculateStatisInfo<int64_t>(Data(), Numel(), &mean, &max, &min);
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} else {
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FDASSERT(false,
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"PrintInfo function doesn't support current situation, maybe you "
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"need enhance this function now.")
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}
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std::cout << prefix << ": shape=";
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for (int i = 0; i < shape.size(); ++i) {
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std::cout << shape[i] << " ";
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}
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std::cout << ", dtype=" << FDDataTypeStr(dtype) << ", mean=" << mean
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<< ", max=" << max << ", min=" << min << std::endl;
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}
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FDTensor::FDTensor(const std::string& tensor_name) { name = tensor_name; }
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} // namespace fastdeploy
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