本系列文章记录本人硕士阶段YOLO系列目标检测算法自学及其代码实现的过程。其中算法具体实现借鉴于ultralytics YOLO源码Github，删减了源码中部分内容，满足个人科研需求。
本系列文章主要以YOLOv5为例完成算法的实现，后续修改、增加相关模块即可实现其他版本的YOLO算法。
文章地址：
YOLOv5算法实现(一)：算法框架概述
YOLOv5算法实现(二)：模型加载
YOLOv5算法实现(三)：数据集加载
YOLOv5算法实现(四)：损失计算
YOLOv5算法实现(五)：预测结果后处理
YOLOv5算法实现(六)：评价指标及实现
YOLOv5算法实现(七)：模型训练
YOLOv5算法实现(八)：模型验证
YOLOv5算法实现(九)：模型预测(编辑中…)
本文目录
- 1 引言
- 2 模型验证(validation.py)
1 引言
本篇文章综合之前文章中的功能，实现模型的验证。模型验证的逻辑如图1所示。
2 模型验证(validation.py)
def validation(parser_data):device = torch.device(parser_data.device if torch.cuda.is_available() else "cpu")print("Using {} device validation.".format(device.type))# read class_indictlabel_json_path = './data/object.json'assert os.path.exists(label_json_path), "json file {} dose not exist.".format(label_json_path)with open(label_json_path, 'r') as f:class_dict = json.load(f)category_index = {v: k for k, v in class_dict.items()}data_dict = parse_data_cfg(parser_data.data)test_path = data_dict["valid"]# 注意这里的collate_fn是自定义的，因为读取的数据包括image和targets，不能直接使用默认的方法合成batchbatch_size = parser_data.batch_sizenw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) # number of workersprint('Using %g dataloader workers' % nw)# load validation data setval_dataset = LoadImagesAndLabels(test_path, parser_data.img_size, batch_size,hyp=parser_data.hyp,rect=False) # 将每个batch的图像调整到合适大小，可减少运算量(并不是512x512标准尺寸)val_dataset_loader = torch.utils.data.DataLoader(val_dataset,batch_size=batch_size,shuffle=True,num_workers=nw,pin_memory=True,collate_fn=val_dataset.collate_fn)# create modelmodel = Model(parser_data.cfg, ch=3, nc=parser_data.nc)weights_dict = torch.load(parser_data.weights, map_location='cpu')weights_dict = weights_dict["model"] if "model" in weights_dict else weights_dictmodel.load_state_dict(weights_dict, strict=False)model.to(device)# evaluate on the test dataset# 计算PR曲线和APstats = []iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95niou = iouv.numel()# 混淆矩阵confusion_matrix = ConfusionMatrix(nc=3, conf=0.6)model.eval()with torch.no_grad():for imgs, targets, paths, shapes, img_index in tqdm(val_dataset_loader, desc="validation..."):imgs = imgs.to(device).float() / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0nb, _, height, width = imgs.shape # batch size, channels, height, widthtargets = targets.to(device)preds = model(imgs)[0] # only get inference resultpreds = non_max_suppression(preds, conf_thres=0.3, iou_thres=0.6, multi_label=False)targets[:, 2:] *= torch.tensor((width, height, width, height), device=device)outputs = []for si, pred in enumerate(preds):'''labels: [clas, x, y, w, h] (训练图像上绝对坐标)pred: [x,y,x,y,obj,cls] (训练图像上绝对坐标)predn: [x,y,x,y,obj,cls] (输入图像上绝对坐标)labels: [x,y,x,y,class] (输入图像上绝对坐标)shapes[si][0]: 输入图像大小shapes[si][1]'''labels = targets[targets[:, 0] == si, 1:] # 当前图片的标签信息nl = labels.shape[0] # number of labels # 当前图片标签数量if pred is None:npr = 0else:npr = pred.shape[0] # 预测结果数量correct = torch.zeros(npr, niou, dtype=torch.bool, device=device) # 判断在不同IoU下预测是否预测正确path, shape = Path(paths[si]), shapes[si][0] # 当前图片shape(原图大小)if npr == 0: # 若没有预测结果if nl: # 没有预测结果但有实际目标# 不同IoU阈值下预测准确率,目标类别置信度,预测类别,实际类别stats.append((correct, *torch.zeros((2, 0), device=device), labels[:, 0]))# 混淆矩阵计算(类别信息)confusion_matrix.process_batch(detections=None, labels=labels[:, 0])continuepredn = pred.clone()scale_boxes(imgs[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space predif nl: # 有预测结果且有实际目标tbox = xywh2xyxy(labels[:, 1:5]) # target boxesscale_boxes(imgs[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labelslabelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labelscorrect = process_batch(predn, labelsn, iouv)confusion_matrix.process_batch(predn, labelsn)stats.append((correct, pred[:, 4], pred[:, 5], labels[:, 0])) # 预测结果在不同IoU是否预测正确, 预测置信度, 预测类别, 实际类别confusion_matrix.plot(save_dir=parser_data.save_path, names=["normal", 'defect', 'leakage'])# 图片:预测结果在不同IoU下预测结果,预测置信度,预测类别,实际类别stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpyif len(stats) and stats[0].any():tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, names=["normal", 'defect', 'leakage'])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()print(map50)if __name__ == "__main__":import argparseparser = argparse.ArgumentParser(description=__doc__)# 使用设备类型parser.add_argument('--device', default='cuda', help='device')# 检测目标类别数parser.add_argument('--nc', type=int, default=3, help='number of classes')file = 'yolov5s'cfg = f'cfg/models/{file}.yaml'parser.add_argument('--cfg', type=str, default=cfg, help="*.cfg path")parser.add_argument('--data', type=str, default='data/my_data.data', help='*.data path')parser.add_argument('--hyp', type=str, default='cfg/hyps/hyp.scratch-med.yaml', help='hyperparameters path')parser.add_argument('--img-size', type=int, default=640, help='test size')# 训练好的权重文件weight_1 = f'./weights/{file}/{file}' + '-best_map.pt'weight_2 = f'./weights/{file}/{file}' + '.pt'weight = weight_1 if os.path.exists(weight_1) else weight_2parser.add_argument('--weights', default=weight, type=str, help='training weights')parser.add_argument('--save_path', default=f'results/{file}', type=str, help='result save path')# batch sizeparser.add_argument('--batch_size', default=2, type=int, metavar='N',help='batch size when validation.')args = parser.parse_args()validation(args)
