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我克隆了迁移学习库repo 并致力于最大分类器差异。我正在尝试更改增强但收到以下错误

Traceback (most recent call last):
  File "mcd.py", line 378, in <module>
    main(args)
  File "mcd.py", line 145, in main
    results = validate(val_loader, G, F1, F2, args)
  File "mcd.py", line 290, in validate
    for i, (images, target) in enumerate(val_loader):
  File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py", line 521, in __next__
    data = self._next_data()
  File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py", line 1203, in _next_data
    return self._process_data(data)
  File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py", line 1229, in _process_data
    data.reraise()
  File "/usr/local/lib/python3.7/dist-packages/torch/_utils.py", line 425, in reraise
    raise self.exc_type(msg)
TypeError: Caught TypeError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
    data = fetcher.fetch(index)
  File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "../../../common/vision/datasets/imagelist.py", line 48, in __getitem__
    img = self.transform(img)
  File "/usr/local/lib/python3.7/dist-packages/torchvision/transforms/transforms.py", line 60, in __call__
    img = t(img)
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/torchvision/transforms/transforms.py", line 750, in forward
    return F.perspective(img, startpoints, endpoints, self.interpolation, fill)
  File "/usr/local/lib/python3.7/dist-packages/torchvision/transforms/functional.py", line 647, in perspective
    return F_pil.perspective(img, coeffs, interpolation=pil_interpolation, fill=fill)
  File "/usr/local/lib/python3.7/dist-packages/torchvision/transforms/functional_pil.py", line 289, in perspective
    return img.transform(img.size, Image.PERSPECTIVE, perspective_coeffs, interpolation, **opts)
  File "/usr/local/lib/python3.7/dist-packages/PIL/Image.py", line 2371, in transform
    im = new(self.mode, size, fillcolor)
  File "/usr/local/lib/python3.7/dist-packages/PIL/Image.py", line 2578, in new
    return im._new(core.fill(mode, size, color))
TypeError: integer argument expected, got float

之前的代码是

 # Data loading code
    normalize = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    if args.center_crop:
        train_transform = T.Compose([
            ResizeImage(256),
            T.CenterCrop(224),
            T.RandomHorizontalFlip(),
            T.ToTensor(),
            normalize
        ])
    else:
        train_transform = T.Compose([
            ResizeImage(256),
            T.RandomResizedCrop(224),
            T.RandomHorizontalFlip(),
            T.ToTensor(),
            normalize
        ])
    val_transform = T.Compose([
        ResizeImage(256),
        T.CenterCrop(224),
        T.ToTensor(),
        normalize
    ])

我刚刚添加 T.RandomPerspective(distortion_scale = 0.8, p=0.5, fill=0.6)了 val_transform。在此之前，我还为 train_transform 添加了一些其他转换，但仍然遇到相同的错误。可能是什么问题呢？

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1 回答 1

0

fill参数必须是整数。

此转换不支持类型的fill参数Tensor；因此，如果您希望使用该fill参数，则必须在转换之前使用此ToTensor转换。此时，数据是完整的。

于 2021-07-08T18:45:59.763 回答