建设银行积分商城网站,全文wordpress主题,报修网站模板,搜索关键词的网站Fast SAM C推理部署—TensorRT 核心源代码在结尾处有获取方式 晓理紫 0 XX开局一张图#xff0c;剩下… 1 为什么需要trt部署 主要是在GPU上推理可以获得更高的推理速度。可与onnxruntim推理向比较一下 对比视频
2 TensorRt部署
2.1 环境与条件
需要配置TensorRt相关环境 这… Fast SAM C推理部署—TensorRT 核心源代码在结尾处有获取方式 晓理紫 0 XX开局一张图剩下… 1 为什么需要trt部署 主要是在GPU上推理可以获得更高的推理速度。可与onnxruntim推理向比较一下 对比视频
2 TensorRt部署
2.1 环境与条件
需要配置TensorRt相关环境 这个就需要有显卡安装驱动CUDA以及TensorRT 需要把原始权重模型转为trt模型
2.2 trt模型转换
trt模型转换有多种方式本文采用的是先把pt模型转成onnx模型再把onnx通过trtexec工具进行转换。这里假设已经有onxx模型转换命令如下
trtexec --onnxfastsam.onnx --saveEnginefasrsam.engine 注意 trtexec -h查看帮助转fp16或者int8等参数
2.3 部署核心代码 模型转换完成以后剩下的就是部署推理。部署推理里面最为重要也是最难搞的是数据解析部分。其中模型加载是很标准的流程当然我这里不一定是标准的。 加载模型并初始化核心代码 std::ifstream file(engine_file_path, std::ios::binary);assert(file.good());file.seekg(0, std::ios::end);auto size file.tellg();std::ostringstream fmt;file.seekg(0, std::ios::beg);char *trtModelStream new char[size];assert(trtModelStream);file.read(trtModelStream, size);file.close();initLibNvInferPlugins(this-gLogger, );this-runtime nvinfer1::createInferRuntime(this-gLogger);assert(this-runtime ! nullptr);this-engine this-runtime-deserializeCudaEngine(trtModelStream, size);assert(this-engine ! nullptr);this-context this-engine-createExecutionContext();assert(this-context ! nullptr);cudaStreamCreate(this-stream);const nvinfer1::Dims input_dims this-engine-getBindingDimensions(this-engine-getBindingIndex(INPUT));this-in_size get_size_by_dims(input_dims);CHECK(cudaMalloc(this-buffs[0], this-in_size * sizeof(float)));this-context-setBindingDimensions(0, input_dims);const int32_t output0_idx this-engine-getBindingIndex(OUTPUT0);const nvinfer1::Dims output0_dims this-context-getBindingDimensions(output0_idx);this-out_sizes[output0_idx - NUM_INPUT].first get_size_by_dims(output0_dims);this-out_sizes[output0_idx - NUM_INPUT].second DataTypeToSize(this-engine-getBindingDataType(output0_idx));const int32_t output1_idx this-engine-getBindingIndex(OUTPUT1);const nvinfer1::Dims output1_dims this-context-getBindingDimensions(output1_idx);this-out_sizes[output1_idx - NUM_INPUT].first get_size_by_dims(output1_dims);this-out_sizes[output1_idx - NUM_INPUT].second DataTypeToSize(this-engine-getBindingDataType(output1_idx));const int32_t Reshape_1252_idx this-engine-getBindingIndex(Reshape_1252);const nvinfer1::Dims Reshape_1252_dims this-context-getBindingDimensions(Reshape_1252_idx);this-out_sizes[Reshape_1252_idx - NUM_INPUT].first get_size_by_dims(Reshape_1252_dims);this-out_sizes[Reshape_1252_idx - NUM_INPUT].second DataTypeToSize(this-engine-getBindingDataType(Reshape_1252_idx));const int32_t Reshape_1271_idx this-engine-getBindingIndex(Reshape_1271);const nvinfer1::Dims Reshape_1271_dims this-context-getBindingDimensions(Reshape_1271_idx);this-out_sizes[Reshape_1271_idx - NUM_INPUT].first get_size_by_dims(Reshape_1271_dims);this-out_sizes[Reshape_1271_idx - NUM_INPUT].second DataTypeToSize(this-engine-getBindingDataType(Reshape_1271_idx));const int32_t Concat_1213_idx this-engine-getBindingIndex(Concat_1213);const nvinfer1::Dims Concat_1213_dims this-context-getBindingDimensions(Concat_1213_idx);this-out_sizes[Concat_1213_idx - NUM_INPUT].first get_size_by_dims(Concat_1213_dims);this-out_sizes[Concat_1213_idx - NUM_INPUT].second DataTypeToSize(this-engine-getBindingDataType(Concat_1213_idx));const int32_t OUTPUT1167_idx this-engine-getBindingIndex(OUTPUT1167);const nvinfer1::Dims OUTPUT1167_dims this-context-getBindingDimensions(OUTPUT1167_idx);this-out_sizes[OUTPUT1167_idx - NUM_INPUT].first get_size_by_dims(OUTPUT1167_dims);this-out_sizes[OUTPUT1167_idx - NUM_INPUT].second DataTypeToSize(this-engine-getBindingDataType(OUTPUT1167_idx));for (int i 0; i NUM_OUTPUT; i) {const int osize this-out_sizes[i].first * out_sizes[i].second;CHECK(cudaHostAlloc(this-outputs[i], osize, 0));CHECK(cudaMalloc(this-buffs[NUM_INPUT i], osize));}if (warmup) {for (int i 0; i 10; i) {size_t isize this-in_size * sizeof(float);auto *tmp new float[isize];CHECK(cudaMemcpyAsync(this-buffs[0], tmp, isize, cudaMemcpyHostToDevice,this-stream));this-xiaoliziinfer();}}模型加载以后就可以送入数据进行推理 送入数据并推理 float height (float)image.rows;float width (float)image.cols;float r std::min(INPUT_H / height, INPUT_W / width);int padw (int)std::round(width * r);int padh (int)std::round(height * r);if ((int)width ! padw || (int)height ! padh) {cv::resize(image, tmp, cv::Size(padw, padh));} else {tmp image.clone();}float _dw INPUT_W - padw;float _dh INPUT_H - padh;_dw / 2.0f;_dh / 2.0f;int top int(std::round(_dh - 0.1f));int bottom int(std::round(_dh 0.1f));int left int(std::round(_dw - 0.1f));int right int(std::round(_dw 0.1f));cv::copyMakeBorder(tmp, tmp, top, bottom, left, right, cv::BORDER_CONSTANT,PAD_COLOR);cv::dnn::blobFromImage(tmp, tmp, 1 / 255.f, cv::Size(), cv::Scalar(0, 0, 0),true, false, CV_32F);CHECK(cudaMemcpyAsync(this-buffs[0], tmp.ptrfloat(),this-in_size * sizeof(float), cudaMemcpyHostToDevice,this-stream));this-context-enqueueV2(buffs.data(), this-stream, nullptr);for (int i 0; i NUM_OUTPUT; i) {const int osize this-out_sizes[i].first * out_sizes[i].second;CHECK(cudaMemcpyAsync(this-outputs[i], this-buffs[NUM_INPUT i], osize,cudaMemcpyDeviceToHost, this-stream));}cudaStreamSynchronize(this-stream); 推理以后就可以获取数据并进行解析 数据获取
cv::Mat matData(37, OUTPUT0w, CV_32F, pdata);matVec.push_back(matData);float *pdata1 nullptr;pdata1 static_castfloat *(this-outputs[2]);if (pdata1 nullptr) {return;}cv::Mat matData1(105, OUTPUT1w * OUTPUT1w, CV_32F, pdata1);matVec.push_back(matData1);float *pdata2 nullptr;pdata2 static_castfloat *(this-outputs[3]);if (pdata2 nullptr) {return;}cv::Mat matData2(105, Reshape_1252w * Reshape_1252w, CV_32F, pdata2);matVec.push_back(matData2);float *pdata3 nullptr;pdata3 static_castfloat *(this-outputs[4]);if (pdata3 nullptr) {return;}cv::Mat matData3(105, Reshape_1271w * Reshape_1271w, CV_32F, pdata3);matVec.push_back(matData3);float *pdata4 nullptr;pdata4 static_castfloat *(this-outputs[1]);if (pdata4 nullptr) {return;}cv::Mat matData4(Concat_1213w, 32, CV_32F, pdata4);matVec.push_back(matData4);float *pdata5 nullptr;pdata5 static_castfloat *(this-outputs[0]);if (pdata5 nullptr) {return;}cv::Mat matData5(32, OUTPUT1167w * OUTPUT1167w, CV_32F, pdata5);matVec.push_back(matData5);数据解析 首先是对数据进行分割处理并进行NMS获取box、lab以及mask相关信息 cv::Mat box;
cv::Mat cls;
cv::Mat mask;
box temData.colRange(0, 4).clone();
cls temData.colRange(4, 5).clone();
mask temData.colRange(5, temData.cols).clone();
cv::Mat j cv::Mat::zeros(cls.size(), CV_32F);
cv::Mat dst;
cv::hconcat(box, cls, dst); // dst[A B]
cv::hconcat(dst, j, dst);
cv::hconcat(dst, mask, dst);
std::vectorfloat scores;
std::vectorcv::Rect boxes;
pxvec dst.ptrfloat(0);
for (int i 0; i dst.rows; i) {pxvec dst.ptrfloat(i);boxes.push_back(cv::Rect(pxvec[0], pxvec[1], pxvec[2], pxvec[3]));scores.push_back(pxvec[4]);
}
std::vectorint indices;
xiaoliziNMSBoxes(boxes, scores, conf_thres, iou_thres, indices);
cv::Mat reMat;
for (int i 0; i indices.size() i max_det; i) {int index indices[i];reMat.push_back(dst.rowRange(index, index 1).clone());
}
box reMat.colRange(0, 6).clone();
xiaolizixywh2xyxy(box);
mask reMat.colRange(6, reMat.cols).clone(); 其次是获取mask相关数据 for (int i 0; i bboxes.rows; i) {pxvec bboxes.ptrfloat(i);cv::Mat dest, mask;cv::exp(-maskChannels[i], dest);dest 1.0 / (1.0 dest);dest dest(roi);cv::resize(dest, mask, frmae.size(), cv::INTER_LINEAR);cv::Rect roi(pxvec[0], pxvec[1], pxvec[2] - pxvec[0], pxvec[3] - pxvec[1]);cv::Mat temmask mask(roi);cv::Mat boxMask cv::Mat(frmae.size(), mask.type(), cv::Scalar(0.0));float rx std::max(pxvec[0], 0.0f);float ry std::max(pxvec[1], 0.0f);for (int y ry, my 0; my temmask.rows; y, my) {float *ptemmask temmask.ptrfloat(my);float *pboxmask boxMask.ptrfloat(y);for (int x rx, mx 0; mx temmask.cols; x, mx) {pboxmask[x] ptemmask[mx] 0.5 ? 1.0 : 0.0;}}vremat.push_back(boxMask);}最后是画出相关信息 cv::Mat bbox vremat[0];float *pxvec bbox.ptrfloat(0);for (int i 0; i bbox.rows; i) {pxvec bbox.ptrfloat(i);cv::rectangle(image, cv::Point(pxvec[0], pxvec[1]),cv::Point(int(pxvec[2]), int(pxvec[3])),cv::Scalar(0, 0, 255), 2);}for (int i 1; i vremat.size(); i) {cv::Mat mask vremat[i];int indx (rand() % (80 - 0)) 0;for (int y 0; y mask.rows; y) {const float *mp mask.ptrfloat(y);uchar *p image.ptruchar(y);for (int x 0; x mask.cols; x) {if (mp[x] 1.0) {p[0] cv::saturate_castuchar(p[0] * 0.5 COLORS[indx][0] * 0.5);p[1] cv::saturate_castuchar(p[1] * 0.5 COLORS[indx][1] * 0.5);p[2] cv::saturate_castuchar(p[2] * 0.5 COLORS[indx][2] * 0.5);}p 3;}}}3 核心代码 扫一扫关注并回复fastsamtrt获取核心代码 晓理紫爱学习爱记录爱分享
