* Copyright(C) 2020. 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 "Yolov3PostProcess.h"
#include "MxBase/Log/Log.h"
#include "MxBase/Maths/FastMath.h"
#include "MxBase/CV/ObjectDetection/Nms/Nms.h"
namespace {
const int SCALE = 32;
const int BIASESDIM = 2;
const int OFFSETWIDTH = 2;
const int OFFSETHEIGHT = 3;
const int OFFSETBIASES = 1;
const int OFFSETOBJECTNESS = 1;
const int NHWC_HEIGHTINDEX = 1;
const int NHWC_WIDTHINDEX = 2;
const int NCHW_HEIGHTINDEX = 2;
const int NCHW_WIDTHINDEX = 3;
const int YOLO_INFO_DIM = 5;
auto uint8Deleter = [] (uint8_t* p) { };
}
namespace MxBase {
Yolov3PostProcess &Yolov3PostProcess::operator=(const Yolov3PostProcess &other) {
if (this == &other) {
return *this;
}
ObjectPostProcessBase::operator=(other);
objectnessThresh_ = other.objectnessThresh_;
iouThresh_ = other.iouThresh_;
anchorDim_ = other.anchorDim_;
biasesNum_ = other.biasesNum_;
yoloType_ = other.yoloType_;
modelType_ = other.modelType_;
yoloType_ = other.yoloType_;
inputType_ = other.inputType_;
biases_ = other.biases_;
return *this;
}
APP_ERROR Yolov3PostProcess::Init(const std::map<std::string, std::string> &postConfig) {
LogDebug << "Start to Init Yolov3PostProcess.";
APP_ERROR ret = ObjectPostProcessBase::Init(postConfig);
if (ret != APP_ERR_OK) {
LogError << GetError(ret) << "Fail to superInit in ObjectPostProcessBase.";
return ret;
}
configData_.GetFileValue<int>("BIASES_NUM", biasesNum_);
std::string str;
configData_.GetFileValue<std::string>("BIASES", str);
configData_.GetFileValue<float>("OBJECTNESS_THRESH", objectnessThresh_);
configData_.GetFileValue<float>("IOU_THRESH", iouThresh_);
configData_.GetFileValue<int>("YOLO_TYPE", yoloType_);
configData_.GetFileValue<int>("MODEL_TYPE", modelType_);
configData_.GetFileValue<int>("YOLO_VERSION", yoloVersion_);
configData_.GetFileValue<int>("INPUT_TYPE", inputType_);
configData_.GetFileValue<int>("ANCHOR_DIM", anchorDim_);
ret = GetBiases(str);
if (ret != APP_ERR_OK) {
LogError << GetError(ret) << "Failed to get biases.";
return ret;
}
LogDebug << "End to Init Yolov3PostProcess.";
return APP_ERR_OK;
}
APP_ERROR Yolov3PostProcess::DeInit() {
return APP_ERR_OK;
}
bool Yolov3PostProcess::IsValidTensors(const std::vector <TensorBase> &tensors) const {
if (tensors.size() != (size_t) yoloType_) {
LogError << "number of tensors (" << tensors.size() << ") " << "is unequal to yoloType_("
<< yoloType_ << ")";
return false;
}
if (yoloVersion_ == YOLOV3_VERSION) {
for (size_t i = 0; i < tensors.size(); i++) {
auto shape = tensors[i].GetShape();
if (shape.size() < VECTOR_FIFTH_INDEX) {
LogError << "dimensions of tensor [" << i << "] is less than " << VECTOR_FIFTH_INDEX << ".";
return false;
}
uint32_t channelNumber = 1;
int startIndex = modelType_ ? VECTOR_SECOND_INDEX : VECTOR_FOURTH_INDEX;
int endIndex = modelType_ ? (shape.size() - VECTOR_THIRD_INDEX) : shape.size();
for (int i = startIndex; i < endIndex; i++) {
channelNumber *= shape[i];
}
if (channelNumber != anchorDim_ * (classNum_ + YOLO_INFO_DIM)) {
LogError << "channelNumber(" << channelNumber << ") != anchorDim_ * (classNum_ + 5).";
return false;
}
}
return true;
} else {
return true;
}
}
void Yolov3PostProcess::ObjectDetectionOutput(const std::vector <TensorBase> &tensors,
std::vector <std::vector<ObjectInfo>> &objectInfos,
const std::vector <ResizedImageInfo> &resizedImageInfos) {
LogDebug << "Yolov3PostProcess start to write results.";
if (tensors.size() == 0) {
return;
}
auto shape = tensors[0].GetShape();
if (shape.size() == 0) {
return;
}
uint32_t batchSize = shape[0];
for (uint32_t i = 0; i < batchSize; i++) {
std::vector <std::shared_ptr<void>> featLayerData = {};
std::vector <std::vector<size_t>> featLayerShapes = {};
for (uint32_t j = 0; j < tensors.size(); j++) {
auto dataPtr = (uint8_t *) GetBuffer(tensors[j], i);
std::shared_ptr<void> tmpPointer;
tmpPointer.reset(dataPtr, uint8Deleter);
featLayerData.push_back(tmpPointer);
shape = tensors[j].GetShape();
std::vector <size_t> featLayerShape = {};
for (auto s : shape) {
featLayerShape.push_back((size_t) s);
}
featLayerShapes.push_back(featLayerShape);
}
std::vector <ObjectInfo> objectInfo;
GenerateBbox(featLayerData, objectInfo, featLayerShapes, resizedImageInfos[i].widthResize,
resizedImageInfos[i].heightResize);
MxBase::NmsSort(objectInfo, iouThresh_);
objectInfos.push_back(objectInfo);
}
LogDebug << "Yolov3PostProcess write results successed.";
}
APP_ERROR Yolov3PostProcess::Process(const std::vector <TensorBase> &tensors,
std::vector <std::vector<ObjectInfo>> &objectInfos,
const std::vector <ResizedImageInfo> &resizedImageInfos,
const std::map <std::string, std::shared_ptr<void>> ¶mMap) {
LogDebug << "Start to Process Yolov3PostProcess.";
APP_ERROR ret = APP_ERR_OK;
if (resizedImageInfos.size() == 0) {
ret = APP_ERR_INPUT_NOT_MATCH;
LogError << GetError(ret) << "resizedImageInfos is not provided which is necessary for Yolov3PostProcess.";
return ret;
}
auto inputs = tensors;
ret = CheckAndMoveTensors(inputs);
if (ret != APP_ERR_OK) {
LogError << GetError(ret) << "CheckAndMoveTensors failed.";
return ret;
}
ObjectDetectionOutput(inputs, objectInfos, resizedImageInfos);
for (uint32_t i = 0; i < resizedImageInfos.size(); i++) {
CoordinatesReduction(i, resizedImageInfos[i], objectInfos[i]);
}
LogObjectInfos(objectInfos);
LogDebug << "End to Process Yolov3PostProcess.";
return APP_ERR_OK;
}
* @description: Compare the confidences between 2 classes and get the larger one
*/
void Yolov3PostProcess::CompareProb(int &classID, float &maxProb, float classProb, int classNum) {
if (classProb > maxProb) {
maxProb = classProb;
classID = classNum;
}
}
* @description: Select the highest confidence class name for each predicted box
* @param netout The feature data which contains box coordinates, objectness value and confidence of each class
* @param info Yolo layer info which contains class number, box dim and so on
* @param detBoxes ObjectInfo vector where all ObjectInfoes's confidences are greater than threshold
* @param stride Stride of output feature data
* @param layer Yolo output layer
*/
void Yolov3PostProcess::SelectClassNCHW(std::shared_ptr<void> netout, NetInfo info,
std::vector <MxBase::ObjectInfo> &detBoxes, int stride, OutputLayer layer) {
for (int j = 0; j < stride; ++j) {
for (int k = 0; k < info.anchorDim; ++k) {
int bIdx = (info.bboxDim + 1 + info.classNum) * stride * k + j;
int oIdx = bIdx + info.bboxDim * stride;
float objectness = fastmath::sigmoid(static_cast<float *>(netout.get())[oIdx]);
if (objectness <= objectnessThresh_) {
continue;
}
int classID = -1;
float maxProb = scoreThresh_;
float classProb;
for (int c = 0; c < info.classNum; ++c) {
classProb = fastmath::sigmoid(static_cast<float *>(netout.get())[bIdx +
(info.bboxDim + OFFSETOBJECTNESS +
c) * stride]) * objectness;
CompareProb(classID, maxProb, classProb, c);
}
if (classID < 0) continue;
MxBase::ObjectInfo det;
int row = j / layer.width;
int col = j % layer.width;
float x = (col + fastmath::sigmoid(static_cast<float *>(netout.get())[bIdx])) / layer.width;
float y = (row + fastmath::sigmoid(static_cast<float *>(netout.get())[bIdx + stride])) / layer.height;
float width = fastmath::exp(static_cast<float *>(netout.get())[bIdx + OFFSETWIDTH * stride]) *
layer.anchors[BIASESDIM * k] / info.netWidth;
float height = fastmath::exp(static_cast<float *>(netout.get())[bIdx + OFFSETHEIGHT * stride]) *
layer.anchors[BIASESDIM * k + OFFSETBIASES] / info.netHeight;
det.x0 = std::max(0.0f, x - width / COORDINATE_PARAM);
det.x1 = std::min(1.0f, x + width / COORDINATE_PARAM);
det.y0 = std::max(0.0f, y - height / COORDINATE_PARAM);
det.y1 = std::min(1.0f, y + height / COORDINATE_PARAM);
det.classId = classID;
det.className = configData_.GetClassName(classID);
det.confidence = maxProb;
if (det.confidence < separateScoreThresh_[classID]) {
continue;
}
detBoxes.emplace_back(det);
}
}
}
void Yolov3PostProcess::SelectClassNCHWC(std::shared_ptr<void> netout, NetInfo info,
std::vector <MxBase::ObjectInfo> &detBoxes, int stride,
OutputLayer layer) {
LogDebug << " out size " << sizeof(netout.get());
const int offsetY = 1;
for (int j = 0; j < stride; ++j) {
for (int k = 0; k < info.anchorDim; ++k) {
int bIdx = (info.bboxDim + 1 + info.classNum) * stride * k +
j * (info.bboxDim + 1 + info.classNum);
int oIdx = bIdx + info.bboxDim;
float objectness = fastmath::sigmoid(static_cast<float *>(netout.get())[oIdx]);
if (objectness <= objectnessThresh_) {
continue;
}
int classID = -1;
float maxProb = scoreThresh_;
float classProb;
for (int c = 0; c < info.classNum; ++c) {
classProb = fastmath::sigmoid(static_cast<float *>(netout.get())[bIdx +
(info.bboxDim + OFFSETOBJECTNESS +
c)]) * objectness;
CompareProb(classID, maxProb, classProb, c);
}
if (classID < 0) continue;
MxBase::ObjectInfo det;
int row = j / layer.width;
int col = j % layer.width;
float x = (col + fastmath::sigmoid(static_cast<float *>(netout.get())[bIdx]) * COORDINATE_PARAM -
MEAN_PARAM) / layer.width;
float y = (row + fastmath::sigmoid(static_cast<float *>(netout.get())[bIdx + offsetY]) *
COORDINATE_PARAM - MEAN_PARAM) / layer.height;
float width = (fastmath::sigmoid(static_cast<float *>(netout.get())[bIdx + OFFSETWIDTH]) *
COORDINATE_PARAM) * (fastmath::sigmoid(static_cast<float *>(netout.get())[bIdx +
OFFSETWIDTH]) *
COORDINATE_PARAM) * layer.anchors[BIASESDIM * k] / info.netWidth;
float height = (fastmath::sigmoid(static_cast<float *>(netout.get())[bIdx + OFFSETHEIGHT]) *
COORDINATE_PARAM) * (fastmath::sigmoid(static_cast<float *>(netout.get())[bIdx +
OFFSETHEIGHT]) *
COORDINATE_PARAM) * layer.anchors[BIASESDIM * k + OFFSETBIASES] /
info.netHeight;
det.x0 = std::max(0.0f, x - width / COORDINATE_PARAM);
det.x1 = std::min(1.0f, x + width / COORDINATE_PARAM);
det.y0 = std::max(0.0f, y - height / COORDINATE_PARAM);
det.y1 = std::min(1.0f, y + height / COORDINATE_PARAM);
det.classId = classID;
det.className = configData_.GetClassName(classID);
det.confidence = maxProb;
if (det.confidence < separateScoreThresh_[classID]) continue;
detBoxes.emplace_back(det);
}
}
}
* @description: Select the highest confidence class label for each predicted box and save into detBoxes
* @param netout The feature data which contains box coordinates, objectness value and confidence of each class
* @param info Yolo layer info which contains class number, box dim and so on
* @param detBoxes ObjectInfo vector where all ObjectInfoes's confidences are greater than threshold
* @param stride Stride of output feature data
* @param layer Yolo output layer
*/
void Yolov3PostProcess::SelectClassNHWC(std::shared_ptr<void> netout, NetInfo info,
std::vector <MxBase::ObjectInfo> &detBoxes, int stride, OutputLayer layer) {
const int offsetY = 1;
for (int j = 0; j < stride; ++j) {
for (int k = 0; k < info.anchorDim; ++k) {
int bIdx = (info.bboxDim + 1 + info.classNum) * info.anchorDim * j +
k * (info.bboxDim + 1 + info.classNum);
int oIdx = bIdx + info.bboxDim;
float objectness = fastmath::sigmoid(static_cast<float *>(netout.get())[oIdx]);
if (objectness <= objectnessThresh_) {
continue;
}
int classID = -1;
float maxProb = scoreThresh_;
float classProb;
for (int c = 0; c < info.classNum; ++c) {
classProb = fastmath::sigmoid(static_cast<float *>(netout.get())[bIdx +
(info.bboxDim + OFFSETOBJECTNESS +
c)]) * objectness;
CompareProb(classID, maxProb, classProb, c);
}
if (classID < 0) continue;
MxBase::ObjectInfo det;
int row = j / layer.width;
int col = j % layer.width;
float x = (col + fastmath::sigmoid(static_cast<float *>(netout.get())[bIdx])) / layer.width;
float y = (row + fastmath::sigmoid(static_cast<float *>(netout.get())[bIdx + offsetY])) / layer.height;
float width = fastmath::exp(static_cast<float *>(netout.get())[bIdx + OFFSETWIDTH]) *
layer.anchors[BIASESDIM * k] / info.netWidth;
float height = fastmath::exp(static_cast<float *>(netout.get())[bIdx + OFFSETHEIGHT]) *
layer.anchors[BIASESDIM * k + OFFSETBIASES] / info.netHeight;
det.x0 = std::max(0.0f, x - width / COORDINATE_PARAM);
det.x1 = std::min(1.0f, x + width / COORDINATE_PARAM);
det.y0 = std::max(0.0f, y - height / COORDINATE_PARAM);
det.y1 = std::min(1.0f, y + height / COORDINATE_PARAM);
det.classId = classID;
det.className = configData_.GetClassName(classID);
det.confidence = maxProb;
if (det.confidence < separateScoreThresh_[classID]) {
continue;
}
detBoxes.emplace_back(det);
}
}
}
* @description: According to the yolo layer structure, encapsulate the anchor box data of each feature into detBoxes
* @param featLayerData Vector of 3 output feature data
* @param info Yolo layer info which contains anchors dim, bbox dim, class number, net width, net height and
3 outputlayer(eg. 13*13, 26*26, 52*52)
* @param detBoxes ObjectInfo vector where all ObjectInfoes's confidences are greater than threshold
*/
void Yolov3PostProcess::GenerateBbox(std::vector <std::shared_ptr<void>> featLayerData,
std::vector <MxBase::ObjectInfo> &detBoxes,
const std::vector <std::vector<size_t>> &featLayerShapes, const int netWidth,
const int netHeight) {
NetInfo netInfo;
netInfo.anchorDim = anchorDim_;
netInfo.bboxDim = BOX_DIM;
netInfo.classNum = classNum_;
netInfo.netWidth = netWidth;
netInfo.netHeight = netHeight;
for (int i = 0; i < yoloType_; ++i) {
int widthIndex_ = modelType_ ? NCHW_WIDTHINDEX : NHWC_WIDTHINDEX;
int heightIndex_ = modelType_ ? NCHW_HEIGHTINDEX : NHWC_HEIGHTINDEX;
OutputLayer layer = {featLayerShapes[i][widthIndex_], featLayerShapes[i][heightIndex_]};
int logOrder = log(featLayerShapes[i][widthIndex_] * SCALE / netWidth) / log(BIASESDIM);
int startIdx = (yoloType_ - 1 - logOrder) * netInfo.anchorDim * BIASESDIM;
int endIdx = startIdx + netInfo.anchorDim * BIASESDIM;
int idx = 0;
for (int j = startIdx; j < endIdx; ++j) {
layer.anchors[idx++] = biases_[j];
}
int stride = layer.width * layer.height;
std::shared_ptr<void> netout = featLayerData[i];
if (modelType_ == 0) {
SelectClassNHWC(netout, netInfo, detBoxes, stride, layer);
} else {
if (yoloVersion_ == YOLOV3_VERSION) {
SelectClassNCHW(netout, netInfo, detBoxes, stride, layer);
} else {
SelectClassNCHWC(netout, netInfo, detBoxes, stride, layer);
}
}
}
}
APP_ERROR Yolov3PostProcess::GetBiases(std::string &strBiases) {
if (biasesNum_ <= 0) {
LogError << GetError(APP_ERR_COMM_INVALID_PARAM) << "Failed to get biasesNum (" << biasesNum_ << ").";
return APP_ERR_COMM_INVALID_PARAM;
}
biases_.clear();
int i = 0;
int num = strBiases.find(",");
while (num >= 0 && i < biasesNum_) {
std::string tmp = strBiases.substr(0, num);
num++;
strBiases = strBiases.substr(num, strBiases.size());
biases_.push_back(stof(tmp));
i++;
num = strBiases.find(",");
}
if (i != biasesNum_ - 1 || strBiases.size() <= 0) {
LogError << GetError(APP_ERR_COMM_INVALID_PARAM) << "biasesNum (" << biasesNum_
<< ") is not equal to total number of biases (" << strBiases << ").";
return APP_ERR_COMM_INVALID_PARAM;
}
biases_.push_back(stof(strBiases));
return APP_ERR_OK;
}
extern "C" {
std::shared_ptr <MxBase::Yolov3PostProcess> GetObjectInstance() {
LogInfo << "Begin to get Yolov3PostProcess instance.";
auto instance = std::make_shared<MxBase::Yolov3PostProcess>();
LogInfo << "End to get Yolov3PostProcess instance.";
return instance;
}
}
}
