* Copyright(C) 2021. Huawei Technologies Co.,Ltd. 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.
*/
#include "SamplePostProcess.h"
#include "MxBase/Log/Log.h"
#include "MxBase/Maths/FastMath.h"
namespace MxBase
{
SamplePostProcess &SamplePostProcess::operator=(const SamplePostProcess &other)
{
if (this == &other)
{
return *this;
}
ClassPostProcessBase::operator=(other);
softmax_ = other.softmax_;
classNum_ = other.classNum_;
topK_ = other.topK_;
return *this;
}
APP_ERROR SamplePostProcess::Init(const std::map<std::string, std::string> &postConfig)
{
LogDebug << "Start to Init SamplePostProcess.";
APP_ERROR ret = ClassPostProcessBase::Init(postConfig);
if (ret != APP_ERR_OK)
{
LogError << GetErrorInfo(ret) << "Fail to superInit in ClassPostProcessBase.";
return ret;
}
configData_.GetFileValue("SOFTMAX", softmax_);
configData_.GetFileValue("CLASS_NUM", classNum_);
configData_.GetFileValue("TOP_K", topK_);
LogDebug << "End to Init SamplePostProcess.";
return APP_ERR_OK;
}
APP_ERROR SamplePostProcess::DeInit() { return APP_ERR_OK; }
bool SamplePostProcess::IsValidTensors(const std::vector<TensorBase> &tensors) const
{
if (tensors.empty())
{
return false;
}
const uint32_t softmaxTensorIndex = 0;
auto softmaxTensor = tensors[softmaxTensorIndex];
auto softmaxShape = softmaxTensor.GetShape();
if (softmaxShape[1] != classNum_)
{
LogError << "input size(" << softmaxShape[1] << ") " << "classNumber(" << classNum_ << ")";
return false;
}
return true;
}
APP_ERROR SamplePostProcess::Process(const std::vector<TensorBase> &tensors,
std::vector<std::vector<ClassInfo>> &classInfos,
const std::map<std::string, std::shared_ptr<void>> &configParamMap)
{
LogDebug << "Start to Process SamplePostProcess.";
APP_ERROR ret = APP_ERR_OK;
auto inputs = tensors;
ret = CheckAndMoveTensors(inputs);
if (ret != APP_ERR_OK)
{
LogError << "CheckAndMoveTensors failed. ret=" << ret;
return ret;
}
const uint32_t softmaxTensorIndex = 0;
auto softmaxTensor = inputs[softmaxTensorIndex];
auto shape = softmaxTensor.GetShape();
uint32_t batchSize = shape[0];
void *softmaxTensorPtr = softmaxTensor.GetBuffer();
uint32_t topk = std::min(topK_, classNum_);
for (uint32_t i = 0; i < batchSize; i++)
{
std::vector<uint32_t> idx = {};
for (uint32_t j = 0; j < classNum_; j++)
{
idx.push_back(j);
}
std::vector<float> softmax = {};
for (uint32_t j = 0; j < classNum_; j++)
{
float value = *((float *)softmaxTensorPtr + i * classNum_ + j);
softmax.push_back(value);
}
if (softmax_)
{
fastmath::softmax(softmax);
}
auto cmp = [&softmax](uint32_t index_1, uint32_t index_2) { return softmax[index_1] > softmax[index_2]; };
std::sort(idx.begin(), idx.end(), cmp);
std::vector<ClassInfo> topkClassInfos = {};
for (uint32_t j = 0; j < topk; j++)
{
ClassInfo clsInfo = {};
clsInfo.classId = idx[j];
clsInfo.confidence = softmax[idx[j]];
clsInfo.className = configData_.GetClassName(idx[j]);
topkClassInfos.push_back(clsInfo);
}
classInfos.push_back(topkClassInfos);
}
LogDebug << "End to Process SamplePostProcess.";
return APP_ERR_OK;
}
extern "C"
{
std::shared_ptr<MxBase::SamplePostProcess> GetClassInstance()
{
LogInfo << "Begin to get SamplePostProcess instance.";
auto instance = std::make_shared<MxBase::SamplePostProcess>();
LogInfo << "End to get SamplePostProcess instance.";
return instance;
}
}
}