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，但得到以下错误
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
以前的代码是
# 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
])
我刚刚为val_transform添加了T.RandomPerspective(distortion_scale = 0.8, p=0.5, fill=0.6)。在此之前，我还为train_transform添加了一些其他转换，但仍然得到了相同的错误。可能是什么问题？
fill参数必须是一个整数。
此转换不支持Tensor类型的fill参数；因此，如果要使用fill参数，则必须在ToTensor变换之前使用此变换。在这一点上，数据是积分的。