TRT4-trt-integrate - 3 使用onnxruntime进行onnx的模型推理过程

前言：

• onnx是microsoft开发的一个中间格式，而onnxruntime简称ort是microsoft为onnx开发的推理引擎。
• 允许使用onnx作为输入进行直接推理得到结果。

py接口的推理过程：

 main函数：

if __name__ == "__main__":session = onnxruntime.InferenceSession("workspace/yolov5s.onnx", providers=["CPUExecutionProvider"])#建立一个InferenceSession，塞进去的是onnx的路径实际运算image = cv2.imread("workspace/car.jpg")image_input, M, IM = preprocess(image)pred = session.run(["output"], {"images": image_input})[0]boxes = post_process(pred, IM)for obj in boxes:left, top, right, bottom = map(int, obj[:4])confidence = obj[4]label = int(obj[6])cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2)cv2.putText(image, f"{label}: {confidence:.2f}", (left, top+20), 0, 1, (0, 0, 255), 2, 16)cv2.imwrite("workspace/python-ort.jpg", image)

 session = onnxruntime.InferenceSession("workspace/yolov5s.onnx", providers=["CPUExecutionProvider"])

建立一个InferenceSession，塞进去的是onnx的路径，实际运算的后端选用的是CPU

也可以选用cuda等等

    image = cv2.imread("workspace/car.jpg")image_input, M, IM = preprocess(image)

之后就是预处理

    pred = session.run(["output"], {"images": image_input})[0]boxes = post_process(pred, IM)

session.run就是运行的inference过程

 输入第一个是output的name，决定了哪几个节点作为输出，就将这个名字传递给他

第二个是input的dict，这个意思就是如果有好多个输入，那应该是将名字与输入进行一一对应，比如"input1 ":input1  ,   "input2":input2....

那么在这里output就是一个输出的list，然后我们取第0项

就是这个样子。

预处理：

def preprocess(image, input_w=640, input_h=640):scale = min(input_h / image.shape[0], input_w / image.shape[1])ox = (-scale * image.shape[1] + input_w + scale  - 1) * 0.5oy = (-scale * image.shape[0] + input_h + scale  - 1) * 0.5M = np.array([[scale, 0, ox],[0, scale, oy]], dtype=np.float32)IM = cv2.invertAffineTransform(M)image_prep = cv2.warpAffine(image, M, (input_w, input_h), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=(114, 114, 114))image_prep = (image_prep[..., ::-1] / 255.0).astype(np.float32)image_prep = image_prep.transpose(2, 0, 1)[None]return image_prep, M, IM

 

后处理：

def nms(boxes, threshold=0.5):keep = []remove_flags = [False] * len(boxes)for i in range(len(boxes)):if remove_flags[i]:continueib = boxes[i]keep.append(ib)for j in range(len(boxes)):if remove_flags[j]:continuejb = boxes[j]# class mismatch or image_id mismatchif ib[6] != jb[6] or ib[5] != jb[5]:continuecleft,  ctop    = max(ib[:2], jb[:2])#例子：#将 ib 的前两个元素 [2, 3] 与 jb 的前两个元素 [4, 1] 进行比较，并取其中较大的值。所以结果是 [4, 3]。cright, cbottom = min(ib[2:4], jb[2:4])cross = max(0, cright - cleft) * max(0, cbottom - ctop)union = max(0, ib[2] - ib[0]) * max(0, ib[3] - ib[1]) + max(0, jb[2] - jb[0]) * max(0, jb[3] - jb[1]) - crossiou = cross / unionif iou >= threshold:remove_flags[j] = Truereturn keepdef post_process(pred, IM, threshold=0.25):# b, n, 85boxes = []for image_id, box_id in zip(*np.where(pred[..., 4] >= threshold)):item = pred[image_id, box_id]cx, cy, w, h, objness = item[:5]label = item[5:].argmax()confidence = item[5 + label] * objnessif confidence < threshold:continueboxes.append([cx - w * 0.5, cy - h * 0.5, cx + w * 0.5, cy + h * 0.5, confidence, image_id, label])boxes = np.array(boxes)lr = boxes[:, [0, 2]]tb = boxes[:, [1, 3]]boxes[:, [0, 2]] = lr * IM[0, 0] + IM[0, 2]boxes[:, [1, 3]] = tb * IM[1, 1] + IM[1, 2]# left, top, right, bottom, confidence, image_id, labelboxes = sorted(boxes.tolist(), key=lambda x:x[4], reverse=True)return nms(boxes)

 我们可以发现，真正的onnxruntime只有两行，一个onnxruntime.InferenceSession,一个run就结束了。其余的都是和之前一样的，这是非常好用便捷的，所以如果有模型需要作测试，是非常推荐用onnxruntime的

CPP接口推理过程：

Inference：

在main函数中只有一个inference

int main(){inference();return 0;
}

所以我们直接来到inference的解读中

 auto engine_data = load_file("yolov5s.onnx");
//读onnx文件Ort::Env env(ORT_LOGGING_LEVEL_INFO, "onnx");
//设置打印的日志级别    Ort::SessionOptions session_options;
//定义sessionoptions 类似于python中的    session =     onnxruntime.InferenceSession("workspace/yolov5s.onnx", providers=["CPUExecutionProvider"])auto mem = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
//设置MemoryInfosession_options.SetIntraOpNumThreads(1);session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_EXTENDED);
//启动一些扩展

    Ort::Session session(env, "yolov5s.onnx", session_options);//创建session，将选项传进去auto output_dims = session.GetOutputTypeInfo(0).GetTensorTypeAndShapeInfo().GetShape();//获取output的shapeconst char *input_names[] = {"images"}, *output_names[] = {"output"};

    int input_batch = 1;int input_channel = 3;int input_height = 640;int input_width = 640;int64_t input_shape[] = {input_batch, input_channel, input_height, input_width};int input_numel = input_batch * input_channel * input_height * input_width;float* input_data_host = new float[input_numel];auto input_tensor = Ort::Value::CreateTensor(mem, input_data_host, input_numel, input_shape, 4);//创建一个Tensor，引用input_data_host中的数据

预处理：

///// letter boxauto image = cv::imread("car.jpg");float scale_x = input_width / (float)image.cols;float scale_y = input_height / (float)image.rows;float scale = std::min(scale_x, scale_y);float i2d[6], d2i[6];i2d[0] = scale;  i2d[1] = 0;  i2d[2] = (-scale * image.cols + input_width + scale  - 1) * 0.5;i2d[3] = 0;  i2d[4] = scale;  i2d[5] = (-scale * image.rows + input_height + scale - 1) * 0.5;cv::Mat m2x3_i2d(2, 3, CV_32F, i2d);cv::Mat m2x3_d2i(2, 3, CV_32F, d2i);cv::invertAffineTransform(m2x3_i2d, m2x3_d2i);cv::Mat input_image(input_height, input_width, CV_8UC3);cv::warpAffine(image, input_image, m2x3_i2d, input_image.size(), cv::INTER_LINEAR, cv::BORDER_CONSTANT, cv::Scalar::all(114));cv::imwrite("input-image.jpg", input_image);int image_area = input_image.cols * input_image.rows;unsigned char* pimage = input_image.data;float* phost_b = input_data_host + image_area * 0;float* phost_g = input_data_host + image_area * 1;float* phost_r = input_data_host + image_area * 2;for(int i = 0; i < image_area; ++i, pimage += 3){// 注意这里的顺序rgb调换了*phost_r++ = pimage[0] / 255.0f;*phost_g++ = pimage[1] / 255.0f;*phost_b++ = pimage[2] / 255.0f;}///

制作输出矩阵并运行：

    // 3x3输入，对应3x3输出int output_numbox = output_dims[1];int output_numprob = output_dims[2];int num_classes = output_numprob - 5;int output_numel = input_batch * output_numbox * output_numprob;float* output_data_host = new float[output_numel];int64_t output_shape[] = {input_batch, output_numbox, output_numprob};auto output_tensor = Ort::Value::CreateTensor(mem, output_data_host, output_numel, output_shape, 3);Ort::RunOptions options;session.Run(options, (const char* const*)input_names, &input_tensor, 1, (const char* const*)output_names, &output_tensor, 1);
//指定输入输出的name，tensor和个数，传入tensor进行推理

后处理：

 // decode boxvector<vector<float>> bboxes;float confidence_threshold = 0.25;float nms_threshold = 0.5;for(int i = 0; i < output_numbox; ++i){float* ptr = output_data_host + i * output_numprob;float objness = ptr[4];if(objness < confidence_threshold)continue;float* pclass = ptr + 5;int label     = std::max_element(pclass, pclass + num_classes) - pclass;float prob    = pclass[label];float confidence = prob * objness;if(confidence < confidence_threshold)continue;float cx     = ptr[0];float cy     = ptr[1];float width  = ptr[2];float height = ptr[3];float left   = cx - width * 0.5;float top    = cy - height * 0.5;float right  = cx + width * 0.5;float bottom = cy + height * 0.5;float image_base_left   = d2i[0] * left   + d2i[2];float image_base_right  = d2i[0] * right  + d2i[2];float image_base_top    = d2i[0] * top    + d2i[5];float image_base_bottom = d2i[0] * bottom + d2i[5];bboxes.push_back({image_base_left, image_base_top, image_base_right, image_base_bottom, (float)label, confidence});}printf("decoded bboxes.size = %d\n", bboxes.size());// nmsstd::sort(bboxes.begin(), bboxes.end(), [](vector<float>& a, vector<float>& b){return a[5] > b[5];});std::vector<bool> remove_flags(bboxes.size());std::vector<vector<float>> box_result;box_result.reserve(bboxes.size());auto iou = [](const vector<float>& a, const vector<float>& b){float cross_left   = std::max(a[0], b[0]);float cross_top    = std::max(a[1], b[1]);float cross_right  = std::min(a[2], b[2]);float cross_bottom = std::min(a[3], b[3]);float cross_area = std::max(0.0f, cross_right - cross_left) * std::max(0.0f, cross_bottom - cross_top);float union_area = std::max(0.0f, a[2] - a[0]) * std::max(0.0f, a[3] - a[1]) + std::max(0.0f, b[2] - b[0]) * std::max(0.0f, b[3] - b[1]) - cross_area;if(cross_area == 0 || union_area == 0) return 0.0f;return cross_area / union_area;};for(int i = 0; i < bboxes.size(); ++i){if(remove_flags[i]) continue;auto& ibox = bboxes[i];box_result.emplace_back(ibox);for(int j = i + 1; j < bboxes.size(); ++j){if(remove_flags[j]) continue;auto& jbox = bboxes[j];if(ibox[4] == jbox[4]){// class matchedif(iou(ibox, jbox) >= nms_threshold)remove_flags[j] = true;}}}printf("box_result.size = %d\n", box_result.size());for(int i = 0; i < box_result.size(); ++i){auto& ibox = box_result[i];float left = ibox[0];float top = ibox[1];float right = ibox[2];float bottom = ibox[3];int class_label = ibox[4];float confidence = ibox[5];cv::Scalar color;tie(color[0], color[1], color[2]) = random_color(class_label);cv::rectangle(image, cv::Point(left, top), cv::Point(right, bottom), color, 3);auto name      = cocolabels[class_label];auto caption   = cv::format("%s %.2f", name, confidence);int text_width = cv::getTextSize(caption, 0, 1, 2, nullptr).width + 10;cv::rectangle(image, cv::Point(left-3, top-33), cv::Point(left + text_width, top), color, -1);cv::putText(image, caption, cv::Point(left, top-5), 0, 1, cv::Scalar::all(0), 2, 16);}cv::imwrite("image-draw.jpg", image);delete[] input_data_host;delete[] output_data_host;
}

小结：

可以看到，这个与我们之前yolov5后处理没什么太大的区别，关键只在于对于output_tensor和output作关联，input_tensor和input作关联。