train的时候调用test发生ValueError: not enough values to unpack (expected 3, got 2)报错

[z0v0z]

您好！
我用自己的数据集，按照您的格式：
├── images # xx.jpg example
│ ├── train2017
│ │ ├── 000001.jpg
│ │ ├── 000002.jpg
│ │ └── 000003.jpg
│ └── val2017
│ ├── 100001.jpg
│ ├── 100002.jpg
│ └── 100003.jpg
└── labels # xx.txt example
├── train2017
│ ├── 000001.txt
│ ├── 000002.txt
│ └── 000003.txt
└── val2017
├── 100001.txt
├── 100002.txt
└── 100003.txt
运行： python train.py --data data/fruit.yaml --cfg models/v5Lite-e.yaml --weights v5lite-e.pt --batch-size 64 --device 0 --name z_frui
错误是：
Image sizes 640 train, 640 test
Using 8 dataloader workers
Logging results to runs/train/z_fruit6
Starting training for 300 epochs...

 Epoch   gpu_mem       box       obj       cls     total    labels  img_size
 0/299    0.902G   0.09024   0.02284   0.03911    0.1522        75       640: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [00:14<00:00,  4.42it/s]
           Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95:   0%|                                                                                                                            | 0/4 [00:00<?, ?it/s]

Traceback (most recent call last):
File "train.py", line 544, in
train(hyp, opt, device, tb_writer)
File "train.py", line 355, in train
results, maps, times = test.test(data_dict,
File "/home/zhongzw/fruit_dataset/YOLOv5-Lite/test.py", line 110, in test
out, _, train_out = model(img, augment=augment) # inference and training outputs
ValueError: not enough values to unpack (expected 3, got 2)
您知道是什么原因导致这个输入输出不一样吗？
parser = argparse.ArgumentParser()
parser.add_argument('--weights', type=str, default='weights/v5lite-s.pt', help='initial weights path')
parser.add_argument('--cfg', type=str, default='models/v5ite-s.yaml', help='model.yaml path')
parser.add_argument('--data', type=str, default='data/coco.yaml', help='data.yaml path')
parser.add_argument('--hyp', type=str, default='data/hyp.scratch.yaml', help='hyperparameters path')
parser.add_argument('--epochs', type=int, default=300)
parser.add_argument('--batch-size', type=int, default=128, help='total batch size for all GPUs')
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')
parser.add_argument('--rect', action='store_true', help='rectangular training')
parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
parser.add_argument('--notest', action='store_true', help='only test final epoch')
parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')
parser.add_argument('--project', default='runs/train', help='save to project/name')
parser.add_argument('--entity', default=None, help='W&B entity')
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')
parser.add_argument('--quad', action='store_true', help='quad dataloader')
parser.add_argument('--linear-lr', action='store_true', help='linear LR')
parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')
parser.add_argument('--upload_dataset', action='store_true', help='Upload dataset as W&B artifact table')
parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval for W&B')
parser.add_argument('--save_period', type=int, default=-1, help='Log model after every "save_period" epoch')
parser.add_argument('--artifact_alias', type=str, default="latest", help='version of dataset artifact to be used')
opt = parser.parse_args()
这部分我是没有改的；

[z0v0z]

对了，然后我把报错的这行代码
out, _, train_out = model(img, augment=augment)
改成：
out, train_out = model(img, augment=augment)就可以跑了，不知道我改的对不对。

[ppogg]

应该是没问题的，之前做heatmap实验遗留下来的小问题，忘记改回去了

[zhjai]

用Tags v1.4版本，这个版本没问题的