1. 在导出onnx的时候可以把后处理的那部分注释掉，下图是我的处理方式，只在导出onnx时，myexport=1.。导出完毕后，onnx的结构如图。这样在后续转模型的时候就不会出现do not support dimension size > 4 了。
2. 得到onnx后，手写后处理，附赠onnx推理代码：

import onnxruntime
import numpy as np
import cv2
import os
import tqdm
# 指定 ONNX 模型文件路径
onnx_model_path = 'my.onnx'
sess = onnxruntime.InferenceSession(onnx_model_path)
strides = [8,16,32]  # 80*80 40*40 20*20
featuremaps = [80,40,20]coco_classes = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck','boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench','bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra','giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee','skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove','skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup','fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange','broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch','potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse','remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink','refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier','toothbrush'
]def sigmoid(x):return 1 / (1 + np.exp(-x))def get_color(idx):idx += 3return (37 * idx % 255, 17 * idx % 255, 29 * idx % 255)def onnx_infer(img_path):# 创建 ONNX 运行时的 Session# 构造输入数据img = cv2.imread(img_path)img = cv2.resize(img,(640,640))input_data = np.transpose(img,(2,0,1))[None,...]input_data = np.array(input_data,dtype=np.float32)# 进行推理output_data = sess.run(['output_0','344','367'], {'input_0': input_data})posted_output = []for i,output in enumerate(output_data):b,n,c = output.shapeoutput[..., 4:] = sigmoid(output[..., 4:])y = np.arange(featuremaps[i]).repeat(featuremaps[i]).reshape(-1,1)x = np.reshape([np.arange(featuremaps[i]) for _ in range(featuremaps[i])],(-1,1))grid = np.concatenate([x,y],axis=1)output[0,:, 0:2] = (output[0,:, 0:2] + grid) * strides[i]  # xyoutput[0,:, 2:4] = np.exp(output[0,:, 2:4]) * strides[i]  # whposted_output.append(output[0])posted_output = np.concatenate(posted_output)box_corner = np.zeros_like(posted_output)box_corner[:, 0] = posted_output[:, 0] - posted_output[:, 2] / 2 # 左上右下box_corner[:, 1] = posted_output[:, 1] - posted_output[:, 3] / 2box_corner[:, 2] = posted_output[:, 0] + posted_output[:, 2] / 2box_corner[:, 3] = posted_output[:, 1] + posted_output[:, 3] / 2posted_output[:, :4] = box_corner[:, :4]# Get score and class with highest confidence# class_conf, class_pred = np.max(posted_output[:, 5:85], axis=1)class_conf = np.max(posted_output[:, 5:85], axis=1)class_pred = np.argmax(posted_output[:, 5:85], axis=1)conf_mask = (posted_output[:, 4] * class_conf.squeeze() >= 0.25)# Detections ordered as (x1, y1, x2, y2, obj_conf, class_conf, class_pred)detections = posted_output[conf_mask]cls_scores = class_conf[conf_mask]cls_cat = class_pred[conf_mask]indices = cv2.dnn.NMSBoxes(detections[:,:4], detections[:,4]*cls_scores,score_threshold=0.1, nms_threshold=0.55)ans_bbox = detections[indices][:,:4]ans_conf = cls_scores[indices]*detections[indices][:,4] # 分类*定位ans_cat = cls_cat[indices]for bbox,conf,cat in zip(ans_bbox,ans_conf,ans_cat):x0,y0,x1,y1 = bboxcv2.rectangle(img,(int(x0),int(y0)),(int(x1),int(y1)),get_color(int(cat)),2,2)label = f'{coco_classes[int(cat)]}: {conf:.2f}'cv2.putText(img, label, (int(x0),int(y0) - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, get_color(int(cat)), 2)cv2.imwrite(os.path.join('onnx_infer',img_path.split('/')[-1]),img)if __name__ == '__main__':data_root = 'imgs/'imgs = os.listdir(data_root)for img in tqdm.tqdm(imgs):onnx_infer(os.path.join(data_root,img))