Issue with pt models

[pranav-deo]

🐛 Bug

A clear and concise description of what the bug is.

Traceback (most recent call last):
  File "detect.py", line 162, in <module>
    detect()
  File "detect.py", line 48, in detect
    names = model.names if hasattr(model, 'names') else model.modules.names
AttributeError: 'function' object has no attribute 'names'

To Reproduce

!python detect.py --source ./inference/images/ --weights yolov5s.pt --conf 0.4

Expected behavior

Expected the download of the model with weights and inference to happen

Environment

Google Colab with GPU (name='Tesla K80', total_memory=11441MB)

Additional context

This issue seems to arise on all yolov5 weights downloaded since 1-2 days back, but not on yolov3-spp.pt

[github-actions]

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[glenn-jocher]

@pranav2812 thank you for the bug report! Yes, you are correct, it seems we forgot to attach the names lists to the updated models. We have fixed this and re-uploaded all models. Please try again.

[pranav-deo]

Thanks for the quick resolvement! 😃

[mon28]

I see the models were updated again a couple of days back and this issue is reoccuring

[glenn-jocher]

@mon28 we designed a set of unit tests which include this exact use case, and they are all passing in our colab notebook with no errors on the latest repository. If you can't run these successfully on your system, then you are using old/modified code, or your environment is a problem.

# Unit tests
%%shell
git pull
python3 -c "from utils.google_utils import *; gdrive_download('1n_oKgR81BJtqk75b00eAjdv03qVCQn2f', 'coco128.zip')" && mv ./coco128 ../
for d in 0 # device
do
  for x in yolov5s #yolov5m yolov5l yolov5x # models
  do
    python train.py --weights $x.pt --cfg $x.yaml --epochs 3 --img 320 --device $d --name test
    python detect.py --weights weights/last_test.pt --device $d
    python detect.py --weights weights/best_test.pt --device $d
  done
done

Output:

Already up to date.
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   408    0   408    0     0    137      0 --:--:--  0:00:02 --:--:--   137
  0     0    0     0    0     0      0      0 --:--:--  0:00:03 --:--:--     0
  0     0    0     0    0     0      0      0 --:--:--  0:00:04 --:--:--     0
100 21.0M    0 21.0M    0     0  4699k      0 --:--:--  0:00:04 --:--:--  237M
Downloading https://drive.google.com/uc?export=download&id=1n_oKgR81BJtqk75b00eAjdv03qVCQn2f as coco128.zip... unzipping... Done (8.7s)
Apex recommended for faster mixed precision training: https://github.com/NVIDIA/apex
{'lr0': 0.01, 'momentum': 0.937, 'weight_decay': 0.0005, 'giou': 0.05, 'cls': 0.58, 'cls_pw': 1.0, 'obj': 1.0, 'obj_pw': 1.0, 'iou_t': 0.2, 'anchor_t': 4.0, 'fl_gamma': 0.0, 'hsv_h': 0.014, 'hsv_s': 0.68, 'hsv_v': 0.36, 'degrees': 0.0, 'translate': 0.0, 'scale': 0.5, 'shear': 0.0}
Namespace(adam=False, batch_size=16, bucket='', cache_images=False, cfg='./models/yolov5s.yaml', data='data/coco128.yaml', device='0', epochs=3, evolve=False, img_size=[320], multi_scale=False, name='test', noautoanchor=False, nosave=False, notest=False, rect=False, resume=False, single_cls=False, weights='yolov5s.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)

2020-06-19 16:38:28.749236: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/

              from  n    params  module                                  arguments                     
  0             -1  1      3520  models.common.Focus                     [3, 32, 3]                    
  1             -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                
  2             -1  1     20672  models.common.Bottleneck                [64, 64]                      
  3             -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  4             -1  1    161152  models.common.BottleneckCSP             [128, 128, 3]                 
  5             -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              
  6             -1  1    641792  models.common.BottleneckCSP             [256, 256, 3]                 
  7             -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              
  8             -1  1    656896  models.common.SPP                       [512, 512, [5, 9, 13]]        
  9             -1  1   1905152  models.common.BottleneckCSP             [512, 512, 2]                 
 10             -1  1   1248768  models.common.BottleneckCSP             [512, 512, 1, False]          
 11             -1  1    130815  torch.nn.modules.conv.Conv2d            [512, 255, 1, 1]              
 12             -2  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 13        [-1, 6]  1         0  models.common.Concat                    [1]                           
 14             -1  1    197120  models.common.Conv                      [768, 256, 1, 1]              
 15             -1  1    313088  models.common.BottleneckCSP             [256, 256, 1, False]          
 16             -1  1     65535  torch.nn.modules.conv.Conv2d            [256, 255, 1, 1]              
 17             -2  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 18        [-1, 4]  1         0  models.common.Concat                    [1]                           
 19             -1  1     49408  models.common.Conv                      [384, 128, 1, 1]              
 20             -1  1     78720  models.common.BottleneckCSP             [128, 128, 1, False]          
 21             -1  1     32895  torch.nn.modules.conv.Conv2d            [128, 255, 1, 1]              
 22   [-1, 16, 11]  1         0  models.yolo.Detect                      [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]]]
Model Summary: 165 layers, 7.07417e+06 parameters, 7.07417e+06 gradients

Optimizer groups: 54 .bias, 60 conv.weight, 51 other
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   408    0   408    0     0    447      0 --:--:-- --:--:-- --:--:--   446
  0     0    0     0    0     0      0      0 --:--:--  0:00:01 --:--:--     0
  0     0    0     0    0     0      0      0 --:--:--  0:00:01 --:--:--     0
100 13.6M    0 13.6M    0     0  7962k      0 --:--:--  0:00:01 --:--:-- 7962k
Downloading https://drive.google.com/uc?export=download&id=1R5T6rIyy3lLwgFXNms8whc-387H0tMQO as yolov5s.pt... Done (3.7s)
Reading image shapes: 100% 128/128 [00:00<00:00, 4405.17it/s]
Caching labels ../coco128/labels/train2017 (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 100% 128/128 [00:00<00:00, 7694.21it/s]
Caching labels ../coco128/labels/train2017 (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 100% 128/128 [00:00<00:00, 10106.76it/s]

Analyzing anchors... Best Possible Recall (BPR) = 0.9623. Attempting to generate improved anchors, please wait...
WARNING: Extremely small objects found. 84 of 929 labels are < 4 pixels in width or height.
Running kmeans for 9 anchors on 916 points...
thr=0.25: 0.9645 best possible recall, 3.74 anchors past thr
n=9, img_size=320, metric_all=0.261/0.653-mean/best, past_thr=0.472-mean: 9,12,  32,19,  27,48,  74,43,  54,92,  77,161,  161,107,  174,237,  299,195
Evolving anchors with Genetic Algorithm: fitness = 0.6670: 100% 1000/1000 [00:00<00:00, 1604.71it/s]
thr=0.25: 0.9849 best possible recall, 3.79 anchors past thr
n=9, img_size=320, metric_all=0.263/0.663-mean/best, past_thr=0.471-mean: 8,10,  23,12,  23,33,  58,37,  48,89,  68,145,  145,110,  181,199,  310,221
New anchors saved to model. Update model *.yaml to use these anchors in the future.

Image sizes 320 train, 320 test
Using 2 dataloader workers
Starting training for 3 epochs...

     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size
       0/2     2.31G   0.07842    0.1905   0.03977    0.3087       177       320: 100% 8/8 [00:04<00:00,  1.84it/s]
               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 8/8 [00:03<00:00,  2.28it/s]
                 all         128         929       0.172       0.609       0.408        0.24

     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size
       1/2     2.31G   0.06492    0.1971   0.04188    0.3039       140       320: 100% 8/8 [00:03<00:00,  2.29it/s]
               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 8/8 [00:02<00:00,  3.25it/s]
                 all         128         929       0.174       0.646       0.464       0.275

     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size
       2/2     2.32G   0.06308    0.1993   0.03756       0.3       191       320: 100% 8/8 [00:03<00:00,  2.34it/s]
               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 8/8 [00:02<00:00,  3.24it/s]
                 all         128         929       0.149       0.658       0.494       0.305
Optimizer stripped from weights/last_test.pt
Optimizer stripped from weights/best_test.pt
3 epochs completed in 0.006 hours.

Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='0', fourcc='mp4v', img_size=640, iou_thres=0.5, output='inference/output', save_txt=False, source='inference/images', view_img=False, weights='weights/last_test.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)

image 1/2 inference/images/bus.jpg: 640x512 5 persons, 1 buss, 2 skateboards, Done. (0.010s)
image 2/2 inference/images/zidane.jpg: 384x640 3 persons, 2 ties, Done. (0.011s)
Results saved to /content/yolov5/inference/output
Done. (0.123s)
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='0', fourcc='mp4v', img_size=640, iou_thres=0.5, output='inference/output', save_txt=False, source='inference/images', view_img=False, weights='weights/best_test.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)

image 1/2 inference/images/bus.jpg: 640x512 4 persons, 1 buss, 2 skateboards, Done. (0.010s)
image 2/2 inference/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.010s)
Results saved to /content/yolov5/inference/output
Done. (0.104s)