chargedmonk / social-distancing-using-yolov5 Goto Github PK

    detect()
  File "detect.py", line 62, in detect
    pred = model(img, augment=opt.augment)[0]
  File "/Users/s-yasui/anaconda3/envs/yolov5/lib/python3.8/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/Users/s-yasui/Documents/ai/pytorch/Social-Distancing-using-YOLOv5/models/yolo.py", line 91, in forward
    return self.forward_once(x, profile)  # single-scale inference, train
  File "/Users/s-yasui/Documents/ai/pytorch/Social-Distancing-using-YOLOv5/models/yolo.py", line 108, in forward_once
    x = m(x)  # run
  File "/Users/s-yasui/anaconda3/envs/yolov5/lib/python3.8/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/Users/s-yasui/Documents/ai/pytorch/Social-Distancing-using-YOLOv5/models/yolo.py", line 28, in forward
    x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
RuntimeError: shape '[1, 3, 85, 56, 80]' is invalid for input of size 573440

Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', fourcc='mp4v', img_size=640, iou_thres=0.5, output='inference/output', save_txt=False, source='./inference/videos/video.mp4', view_img=False, weights='weights/yolov5s.pt')
Using CUDA device0 _CudaDeviceProperties(name='GeForce RTX 2070 SUPER', total_memory=8192MB)

Traceback (most recent call last):
  File "C:\Users\XPC\anaconda3\envs\py36\lib\tarfile.py", line 189, in nti
    n = int(s.strip() or "0", 8)
ValueError: invalid literal for int() with base 8: 'py\nndarr'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Users\XPC\anaconda3\envs\py36\lib\tarfile.py", line 2297, in next
    tarinfo = self.tarinfo.fromtarfile(self)
  File "C:\Users\XPC\anaconda3\envs\py36\lib\tarfile.py", line 1093, in fromtarfile
    obj = cls.frombuf(buf, tarfile.encoding, tarfile.errors)
  File "C:\Users\XPC\anaconda3\envs\py36\lib\tarfile.py", line 1035, in frombuf
    chksum = nti(buf[148:156])
  File "C:\Users\XPC\anaconda3\envs\py36\lib\tarfile.py", line 191, in nti
    raise InvalidHeaderError("invalid header")
tarfile.InvalidHeaderError: invalid header

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Users\XPC\anaconda3\envs\py36\lib\site-packages\torch\serialization.py", line 555, in _load
    return legacy_load(f)
  File "C:\Users\XPC\anaconda3\envs\py36\lib\site-packages\torch\serialization.py", line 466, in legacy_load
    with closing(tarfile.open(fileobj=f, mode='r:', format=tarfile.PAX_FORMAT)) as tar, \
  File "C:\Users\XPC\anaconda3\envs\py36\lib\tarfile.py", line 1589, in open
    return func(name, filemode, fileobj, **kwargs)
  File "C:\Users\XPC\anaconda3\envs\py36\lib\tarfile.py", line 1619, in taropen
    return cls(name, mode, fileobj, **kwargs)
  File "C:\Users\XPC\anaconda3\envs\py36\lib\tarfile.py", line 1482, in __init__
    self.firstmember = self.next()
  File "C:\Users\XPC\anaconda3\envs\py36\lib\tarfile.py", line 2309, in next
    raise ReadError(str(e))
tarfile.ReadError: invalid header

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "detect.py", line 166, in <module>
    detect()
  File "detect.py", line 21, in detect
    model = torch.load(weights, map_location=device)['model'].float()  # load to FP32
  File "C:\Users\XPC\anaconda3\envs\py36\lib\site-packages\torch\serialization.py", line 386, in load
    return _load(f, map_location, pickle_module, **pickle_load_args)
  File "C:\Users\XPC\anaconda3\envs\py36\lib\site-packages\torch\serialization.py", line 559, in _load
    raise RuntimeError("{} is a zip archive (did you mean to use torch.jit.load()?)".format(f.name))
RuntimeError: weights/yolov5s.pt is a zip archive (did you mean to use torch.jit.load()?)

assert self.nF > 0, 'No images or videos found in %s. Supported formats are:\nimages: %s\nvideos: %s' % \