SuperYOLO模型单RGB分支评估及不同尺度mAP50、FPS打印

github：https://github.com/icey-zhang/SuperYOLO
article：https://www.sfu.ca/~zhenman/files/J12-TGRS2023-SuperYOLO.pdf

环境：

PyTorch 2.5.1
Python 3.12(ubuntu22.04)
CUDA 12.4
GPU RTX 4090D(24GB) * 1升降配置
CPU 18 vCPU AMD EPYC 9754 128-Core Processor
内存 60GB

由于不习惯SuperYOLO的数据集读取方式，此处使用原YOLOv5方式。
此处使用的数据集为VisDrone2019。
大中小三种尺度划分：

Small < 32×32 < Medium < 96×96 < Large

data.yaml内容示例：

点击查看代码

train: /root/autodl-tmp/VisDrone/VisDrone2019-DET-train/images # train images (relative to 'path')  6471 images
val: /root/autodl-tmp/VisDrone/VisDrone2019-DET-val/images # val images (relative to 'path')  548 images
test: /root/autodl-tmp/VisDrone/VisDrone2019-DET-test-dev/images # test images (optional)  1610 images
# Classes
nc: 10
names: ['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor']

相应的改动：
utils/general.py：

点击查看代码

# 替换原check_dataset()函数
def check_dataset(data, autodownload=True):# Download (optional)extract_dir = ''if isinstance(data, (str, Path)) and str(data).endswith('.zip'):  # i.e. gs://bucket/dir/coco128.zipdownload(data, dir='../datasets', unzip=True, delete=False, curl=False, threads=1)data = next((Path('../datasets') / Path(data).stem).rglob('*.yaml'))extract_dir, autodownload = data.parent, False# Read yaml (optional)if isinstance(data, (str, Path)):with open(data, errors='ignore') as f:data = yaml.safe_load(f)  # dictionary# Parse yamlpath = extract_dir or Path(data.get('path') or '')  # optional 'path' default to '.'for k in 'train', 'val', 'test':if data.get(k):  # prepend pathdata[k] = str(path / data[k]) if isinstance(data[k], str) else [str(path / x) for x in data[k]]assert 'nc' in data, "Dataset 'nc' key missing."if 'names' not in data:data['names'] = [f'class{i}' for i in range(data['nc'])]  # assign class names if missingtrain, val, test, s = (data.get(x) for x in ('train', 'val', 'test', 'download'))if val:val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])]  # val pathif not all(x.exists() for x in val):print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()])if s and autodownload:  # download scriptroot = path.parent if 'path' in data else '..'  # unzip directory i.e. '../'if s.startswith('http') and s.endswith('.zip'):  # URLf = Path(s).name  # filenameprint(f'Downloading {s} to {f}...')torch.hub.download_url_to_file(s, f)Path(root).mkdir(parents=True, exist_ok=True)  # create rootZipFile(f).extractall(path=root)  # unzipPath(f).unlink()  # remove zipr = None  # successelif s.startswith('bash '):  # bash scriptprint(f'Running {s} ...')r = os.system(s)else:  # python scriptr = exec(s, {'yaml': data})  # return Noneprint(f"Dataset autodownload {f'success, saved to {root}' if r in (0, None) else 'failure'}\n")else:raise Exception('Dataset not found.')return data  # dictionary

单RGB分支评估完整代码(test.py)：

点击查看代码

import argparse
import json
import os
from pathlib import Path
from threading import Threadimport numpy as np
import torch
import yaml
from tqdm import tqdmfrom models.experimental import attempt_load
from utils.datasets import create_dataloader, create_dataloader_sr
from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, check_requirements, \box_iou, non_max_suppression,weighted_boxes, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, increment_path, colorstr
from utils.metrics import ap_per_class, ConfusionMatrix
from utils.plots import plot_images, output_to_target, plot_study_txt
from utils.torch_utils import select_device, time_synchronizedfrom torchvision import transforms
from PIL import Image
unloader = transforms.ToPILImage()
def tensor_to_PIL(tensor):image = tensor.cpu().clone()image = image.squeeze(0)image = unloader(image)image.save('a.png')return imagedef process_batch(detections, labels, iouv):correct = torch.zeros(detections.shape[0], iouv.shape[0], dtype=torch.bool, device=iouv.device)iou = box_iou(labels[:, 1:], detections[:, :4])x = torch.where((iou >= iouv[0]) & (labels[:, 0:1] == detections[:, 5]))  # IoU above threshold and classes matchif x[0].shape[0]:matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()  # [label, detection, iou]if x[0].shape[0] > 1:matches = matches[matches[:, 2].argsort()[::-1]]matches = matches[np.unique(matches[:, 1], return_index=True)[1]]matches = matches[np.unique(matches[:, 0], return_index=True)[1]]matches = torch.Tensor(matches).to(iouv.device)correct[matches[:, 1].long()] = matches[:, 2:3] >= iouvreturn correctdef test(data,weights=None,batch_size=32,imgsz=640,input_mode = None,conf_thres=0.001,iou_thres=0.6,  # for NMSsave_json=False,single_cls=False,augment=False,verbose=False,model=None,dataloader=None,save_dir=Path(''),  # for saving imagessave_txt=False,  # for auto-labellingsave_hybrid=False,  # for hybrid auto-labellingsave_conf=False,  # save auto-label confidencesplots=True,wandb_logger=None,compute_loss=None,is_coco=False):print(f"*******************当前权重：{weights}*******************")# Initialize/load model and set devicetraining = model is not Noneif training:  # called by train.pydevice = next(model.parameters()).device  # get model deviceelse:  # called directlyset_logging()device = select_device(opt.device, batch_size=batch_size)# Directoriessave_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir# Load modelmodel = attempt_load(weights, map_location=device)  # load FP32 modelprint(model.yaml_file)gs = max(int(model.stride.max()), 32)  # grid size (max stride)imgsz = check_img_size(imgsz, s=gs)  # check img_sizedata = check_dataset(data)  # checkhalf = device.type != 'cpu'model.half() if half else model.float()# Configuremodel.eval()# print(model)if isinstance(data, str):is_coco = data.endswith('coco.yaml')with open(data) as f:data = yaml.load(f, Loader=yaml.SafeLoader)check_dataset(data)  # checknc = 1 if single_cls else int(data['nc'])  # number of classesiouv = torch.linspace(0.5, 0.95, 10).to(device)  # iou vector for mAP@0.5:0.95 zjqniou = iouv.numel()stats_small, stats_medium, stats_large = [], [], []# Logginglog_imgs = 0if wandb_logger and wandb_logger.wandb:log_imgs = min(wandb_logger.log_imgs, 100)if not training:task = opt.task if opt.task in ('train', 'val', 'test') else 'val'  # path to train/val/test imagesif opt.data.endswith('vedai.yaml') or opt.data.endswith('SRvedai.yaml'):from utils.datasets import create_dataloader_sr as create_dataloaderelse:from utils.datasets import create_dataloaderdataloader, dataset = create_dataloader(data[task], imgsz, batch_size, gs, opt, augment=augment, cache=True, rect=True,  quad=False, prefix=colorstr(f'{task}: '))seen = 0confusion_matrix = ConfusionMatrix(nc=nc)names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}coco91class = coco80_to_coco91_class()s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.loss = torch.zeros(3, device=device)jdict, stats, ap, ap_class, wandb_images = [], [], [], [], []for batch_i, (img, ir, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)): #zjq# t1 = time_sync()img = img.to(device, non_blocking=True).float()img = img.half() if half else img.float()  # uint8 to fp16/32img /= 255.0  # 0 - 255 to 0.0 - 1.0ir = ir.to(device, non_blocking=True).float()# ir = ir.half() if half else ir.float()  # uint8 to fp16/32ir /= 255.0  # 0 - 255 to 0.0 - 1.0targets = targets.to(device)nb, _, height, width = img.shape  # batch size, channels, height, widthwith torch.no_grad():# Run modelt = time_synchronized()try:out, train_out = model(img,ir,input_mode=input_mode) #zjq inference and training outputsexcept:out, train_out,_ = model(img,ir,input_mode=input_mode) #zjq inference and training outputst0 += time_synchronized() - t# Compute lossif compute_loss:loss += compute_loss([x.float() for x in train_out], targets)[1][:3]  # box, obj, cls# Run NMStargets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device)  # to pixelslb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else []  # for autolabellingt = time_synchronized()out = non_max_suppression(out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb, multi_label=True)# out = weighted_boxes(out,image_size=imgsz, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb, multi_label=True)t1 += time_synchronized() - t# Statistics per imagefor si, pred in enumerate(out):labels = targets[targets[:, 0] == si, 1:]nl = len(labels)tcls = labels[:, 0].tolist() if nl else []  # target class# path = Path(paths[si])path, shape = Path(paths[si]), shapes[si][0]seen += 1if len(pred) == 0:if nl:stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))# Calculate the xyxy format of the ground truthtbox = xywh2xyxy(labels[:, 1:5])scale_coords(img[si].shape[1:], tbox, shape, shapes[si][1])gt_boxes = torch.cat((labels[:, 0:1], tbox), 1)else:stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), []))gt_boxes = torch.empty((0, 5), device=img.device)# Divide ground truth by scalesmall_thresh = 32 * 32medium_thresh = 96 * 96if gt_boxes.shape[0] > 0:gt_areas = (gt_boxes[:, 3] - gt_boxes[:, 1]) * (gt_boxes[:, 4] - gt_boxes[:, 2])small_gt_idx = gt_areas < small_threshmedium_gt_idx = (gt_areas >= small_thresh) & (gt_areas < medium_thresh)large_gt_idx = gt_areas >= medium_threshsmall_gts = gt_boxes[small_gt_idx]medium_gts = gt_boxes[medium_gt_idx]large_gts = gt_boxes[large_gt_idx]else:small_gts = medium_gts = large_gts = torch.empty((0, 5), device=img.device)stats_small.append((torch.zeros(0, iouv.shape[0], dtype=torch.bool),torch.Tensor(), torch.Tensor(),small_gts[:, 0].cpu() if small_gts.shape[0] > 0 else torch.Tensor()))stats_medium.append((torch.zeros(0, iouv.shape[0], dtype=torch.bool),torch.Tensor(), torch.Tensor(),medium_gts[:, 0].cpu() if medium_gts.shape[0] > 0 else torch.Tensor()))stats_large.append((torch.zeros(0, iouv.shape[0], dtype=torch.bool),torch.Tensor(), torch.Tensor(),large_gts[:, 0].cpu() if large_gts.shape[0] > 0 else torch.Tensor()))continue# If there are prediction results, convert them to the original image scaleif single_cls:pred[:, 5] = 0# Predictionspredn = pred.clone()scale_coords(img[si].shape[1:], predn[:, :4], shape, shapes[si][1])  # native-space predif nl:tbox = xywh2xyxy(labels[:, 1:5])scale_coords(img[si].shape[1:], tbox, shape, shapes[si][1])labelsn = torch.cat((labels[:, 0:1], tbox), 1)correct = process_batch(predn, labelsn, iouv)if plots:confusion_matrix.process_batch(predn, labelsn)else:correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool)stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))# Calculate the statistical information of scale groupingif nl:gt_boxes = torch.cat((labels[:, 0:1], tbox), 1)else:gt_boxes = torch.empty((0, 5), device=img.device)def compute_area(boxes):return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])if predn.shape[0] > 0:pred_areas = compute_area(predn[:, :4])else:pred_areas = torch.tensor([], device=img.device)if gt_boxes.shape[0] > 0:gt_areas = compute_area(gt_boxes[:, 1:5])else:gt_areas = torch.tensor([], device=img.device)small_thresh = 32 * 32medium_thresh = 96 * 96if pred_areas.numel() > 0:small_pred_idx = pred_areas < small_threshmedium_pred_idx = (pred_areas >= small_thresh) & (pred_areas < medium_thresh)large_pred_idx = pred_areas >= medium_threshsmall_preds = predn[small_pred_idx]medium_preds = predn[medium_pred_idx]large_preds = predn[large_pred_idx]else:small_preds = medium_preds = large_preds = torch.empty((0, 6), device=img.device)if gt_boxes.shape[0] > 0:small_gt_idx = gt_areas < small_threshmedium_gt_idx = (gt_areas >= small_thresh) & (gt_areas < medium_thresh)large_gt_idx = gt_areas >= medium_threshsmall_gts = gt_boxes[small_gt_idx]medium_gts = gt_boxes[medium_gt_idx]large_gts = gt_boxes[large_gt_idx]else:small_gts = medium_gts = large_gts = torch.empty((0, 5), device=img.device)# Smallif small_preds.shape[0] == 0 and small_gts.shape[0] == 0:stats_small.append((torch.zeros(0, iouv.shape[0], dtype=torch.bool),torch.Tensor(), torch.Tensor(), torch.Tensor()))else:if small_preds.shape[0] > 0 and small_gts.shape[0] > 0:correct_small = process_batch(small_preds, small_gts, iouv)else:correct_small = torch.zeros(small_preds.shape[0], iouv.shape[0], dtype=torch.bool, device=iouv.device) if small_preds.shape[0] > 0 else torch.zeros(0, iouv.shape[0], dtype=torch.bool, device=iouv.device)stats_small.append((correct_small.cpu(),small_preds[:, 4].cpu() if small_preds.shape[0] > 0 else torch.Tensor(),small_preds[:, 5].cpu() if small_preds.shape[0] > 0 else torch.Tensor(),small_gts[:, 0].cpu() if small_gts.shape[0] > 0 else torch.Tensor()))# Mediumif medium_preds.shape[0] == 0 and medium_gts.shape[0] == 0:stats_medium.append((torch.zeros(0, iouv.shape[0], dtype=torch.bool),torch.Tensor(), torch.Tensor(), torch.Tensor()))else:if medium_preds.shape[0] > 0 and medium_gts.shape[0] > 0:correct_medium = process_batch(medium_preds, medium_gts, iouv)else:correct_medium = torch.zeros(medium_preds.shape[0], iouv.shape[0], dtype=torch.bool, device=iouv.device) if medium_preds.shape[0] > 0 else torch.zeros(0, iouv.shape[0], dtype=torch.bool, device=iouv.device)stats_medium.append((correct_medium.cpu(),medium_preds[:, 4].cpu() if medium_preds.shape[0] > 0 else torch.Tensor(),medium_preds[:, 5].cpu() if medium_preds.shape[0] > 0 else torch.Tensor(),medium_gts[:, 0].cpu() if medium_gts.shape[0] > 0 else torch.Tensor()))# Largeif large_preds.shape[0] == 0 and large_gts.shape[0] == 0:stats_large.append((torch.zeros(0, iouv.shape[0], dtype=torch.bool),torch.Tensor(), torch.Tensor(), torch.Tensor()))else:if large_preds.shape[0] > 0 and large_gts.shape[0] > 0:correct_large = process_batch(large_preds, large_gts, iouv)else:correct_large = torch.zeros(large_preds.shape[0], iouv.shape[0], dtype=torch.bool, device=iouv.device) if large_preds.shape[0] > 0 else torch.zeros(0, iouv.shape[0], dtype=torch.bool, device=iouv.device)stats_large.append((correct_large.cpu(),large_preds[:, 4].cpu() if large_preds.shape[0] > 0 else torch.Tensor(),large_preds[:, 5].cpu() if large_preds.shape[0] > 0 else torch.Tensor(),large_gts[:, 0].cpu() if large_gts.shape[0] > 0 else torch.Tensor()))# end for each image in batch# Append to text fileif save_txt:gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]]  # normalization gain whwhfor *xyxy, conf, cls in predn.tolist():xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywhline = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label formatwith open(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f:f.write(('%g ' * len(line)).rstrip() % line + '\n')# W&B logging - Media Panel Plotsif len(wandb_images) < log_imgs and wandb_logger.current_epoch > 0:  # Check for test operationif wandb_logger.current_epoch % wandb_logger.bbox_interval == 0:box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},"class_id": int(cls),"box_caption": "%s %.3f" % (names[cls], conf),"scores": {"class_score": conf},"domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-spacewandb_images.append(wandb_logger.wandb.Image(img[si], boxes=boxes, caption=path.name))wandb_logger.log_training_progress(predn, path, names) if wandb_logger and wandb_logger.wandb_run else None# Append to pycocotools JSON dictionaryif save_json:# [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...image_id = int(path.stem) if path.stem.isnumeric() else path.stembox = xyxy2xywh(predn[:, :4])  # xywhbox[:, :2] -= box[:, 2:] / 2  # xy center to top-left cornerfor p, b in zip(pred.tolist(), box.tolist()):jdict.append({'image_id': image_id,'category_id': coco91class[int(p[5])] if is_coco else int(p[5]),'bbox': [round(x, 3) for x in b],'score': round(p[4], 5)})# Plot imagesif plots: #and batch_i < 3: #zjqf = save_dir / f'test_batch{batch_i}_labels.png'  # labels# f = '/home/data/zhangjiaqing/dataset/VEDAI/train_label/'+paths[0].split('/')[-1].replace('_co','_label') #zjqif input_mode == 'IR':Thread(target=plot_images, args=(ir, targets, paths, f, names), daemon=True).start()else:Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start()f = save_dir / f'test_batch{batch_i}_pred.png'  # predictionsif input_mode == 'IR':Thread(target=plot_images, args=(ir, output_to_target(out), paths, f, names), daemon=True).start()else:Thread(target=plot_images, args=(img, output_to_target(out), paths, f, names), daemon=True).start()# Compute statisticsstats = [np.concatenate(x, 0) for x in zip(*stats)]  # to numpyif len(stats) and stats[0].any():p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)ap50, ap = ap[:, 0], ap.mean(1)  # AP@0.5, AP@0.5:0.95mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()nt = np.bincount(stats[3].astype(np.int64), minlength=nc)  # number of targets per classelse:nt = torch.zeros(1)# Calculate s-m-l mAPif len(stats_small) and np.concatenate(list(zip(*stats_small))[0]).size > 0:stats_small_agg = [np.concatenate(x, 0) for x in zip(*stats_small)]p_small, r_small, ap_small, f1_small, ap_class_small = ap_per_class(*stats_small_agg, plot=False, names=names)mAP_small = ap_small[:, 0].mean()else:mAP_small = 0.0if len(stats_medium) and np.concatenate(list(zip(*stats_medium))[0]).size > 0:stats_medium_agg = [np.concatenate(x, 0) for x in zip(*stats_medium)]p_medium, r_medium, ap_medium, f1_medium, ap_class_medium = ap_per_class(*stats_medium_agg, plot=False, names=names)mAP_medium = ap_medium[:, 0].mean()else:mAP_medium = 0.0if len(stats_large) and np.concatenate(list(zip(*stats_large))[0]).size > 0:stats_large_agg = [np.concatenate(x, 0) for x in zip(*stats_large)]p_large, r_large, ap_large, f1_large, ap_class_large = ap_per_class(*stats_large_agg, plot=False, names=names)mAP_large = ap_large[:, 0].mean()else:mAP_large = 0.0# Print resultspf = '%20s' + '%12i' * 2 + '%12.4g' * 4  # print formatprint(pf % ('all', seen, nt.sum(), mp, mr, map50, map))# with open("trying.txt", 'a+') as f:#     f.write((pf % ('all', seen, nt.sum(), mp, mr, map50, map)) + '\n')  # append metrics, val_loss# Print results per classif (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):for i, c in enumerate(ap_class):print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))# Print s-m-l Scale mAPprint("\nScale-based mAP results:")print(f"  Small objects mAP:  {mAP_small:.4f}")print(f"  Medium objects mAP: {mAP_medium:.4f}")print(f"  Large objects mAP:  {mAP_large:.4f}")# Print speedst = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size)  # tupleif not training:print('Speed: %.3f/%.3f/%.3f ms inference/NMS/total per %gx%g image at batch-size %g' % t)fps = 1000 / (t[0] + t[1] + t[2])print(f'fps = {fps:.2f}')# Plotsif plots:confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))if wandb_logger and wandb_logger.wandb:val_batches = [wandb_logger.wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]wandb_logger.log({"Validation": val_batches})if wandb_images:wandb_logger.log({"Bounding Box Debugger/Images": wandb_images})# Save JSONif save_json and len(jdict):w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else ''  # weightsanno_json = '../coco/annotations/instances_val2017.json'  # annotations jsonpred_json = str(save_dir / f"{w}_predictions.json")  # predictions jsonprint('\nEvaluating pycocotools mAP... saving %s...' % pred_json)with open(pred_json, 'w') as f:json.dump(jdict, f)try:  # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynbfrom pycocotools.coco import COCOfrom pycocotools.cocoeval import COCOevalanno = COCO(anno_json)  # init annotations apipred = anno.loadRes(pred_json)  # init predictions apieval = COCOeval(anno, pred, 'bbox')if is_coco:eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files]  # image IDs to evaluateeval.evaluate()eval.accumulate()eval.summarize()map, map50 = eval.stats[:2]  # update results (mAP@0.5:0.95, mAP@0.5)except Exception as e:print(f'pycocotools unable to run: {e}')# Return resultsmodel.float()  # for trainingif not training:s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''print(f"Results saved to {save_dir}{s}")maps = np.zeros(nc) + mapfor i, c in enumerate(ap_class):maps[c] = ap[i]return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, tif __name__ == '__main__':parser = argparse.ArgumentParser(prog='test.py')parser.add_argument('--weights', nargs='+', type=str, default='/root/SuperYOLO-main/runs/train/exp20/weights/best.pt', help='model.pt path(s)')parser.add_argument('--data', type=str, default='/root/SuperYOLO-main/visDrone-Copy1.yaml', help='*.data path')parser.add_argument('--batch-size', type=int, default=8, help='size of each image batch')parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')parser.add_argument('--input_mode', type=str, default='RGB') #RGB IR RGB+IR RGB+IR+fusionparser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS')parser.add_argument('--task', default='val', help='train, val, test, speed or study')parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')parser.add_argument('--augment', action='store_true', help='augmented inference')parser.add_argument('--verbose', action='store_true', help='report mAP by class')parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')parser.add_argument('--project', default='runs/test', help='save to project/name')parser.add_argument('--name', default='exp', help='save to project/name')parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')opt = parser.parse_args()opt.save_json |= opt.data.endswith('coco.yaml')opt.data = check_file(opt.data)  # check fileprint(opt)check_requirements()if opt.task in ('train', 'val', 'test'):  # run normallytest(opt.data,opt.weights,opt.batch_size,opt.img_size,opt.input_mode,opt.conf_thres,opt.iou_thres,opt.save_json,opt.single_cls,opt.augment,opt.verbose,save_txt=opt.save_txt | opt.save_hybrid,save_hybrid=opt.save_hybrid,save_conf=opt.save_conf,)elif opt.task == 'speed':  # speed benchmarksfor w in opt.weights:test(opt.data, w, opt.batch_size, opt.img_size, 0.25, 0.45, save_json=False, plots=False)elif opt.task == 'study':  # run over a range of settings and save/plot# python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s.pt yolov5m.pt yolov5l.pt yolov5x.ptx = list(range(256, 1536 + 128, 128))  # x axis (image sizes)for w in opt.weights:f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt'  # filename to save toy = []  # y axisfor i in x:  # img-sizeprint(f'\nRunning {f} point {i}...')r, _, t = test(opt.data, w, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json,plots=False)y.append(r + t)  # results and timesnp.savetxt(f, y, fmt='%10.4g')  # saveos.system('zip -r study.zip study_*.txt')plot_study_txt(x=x)  # plot

碎碎念：
作为以YOLO为baseline改进而来的模型，如果是初次使用SuperYOLO，想必会遇到各种奇怪的问题(
相当一部分是因为长期未得到更新导致的，其中不少报错可以在YOLOv5官方github的issue中找到解决方法。
至于剩下的，在我眼中可称为“开门劝退”的，就是那奇怪的数据集加载方式。个人的建议是，如果像我一样只需要使用RGB分支，还是想办法datasets.py中的代码调整回YOLOv5的经典形式最为省事，可以回避掉许多奇奇怪怪的问题。
当然，就效果而言，SuperYOLO还是相当不错的，即使不使用“RGB+IR”的双分支结构（VisDrone2019数据集）