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🐛 Describe the bug
Setting a list of float type values to padding argument of RandomCrop() gets the indirect error message as shown below:
from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import RandomCrop
my_data = OxfordIIITPet(
root="data", # ↓↓↓↓↓↓↓↓↓↓↓↓
transform=RandomCrop(size=[100, 200], padding=[10.8, 20.3])
)
my_data[0][0] # ErrorTypeError: randint() received an invalid combination of arguments - got (int, float, size=tuple), but expected one of:
* (int high, tuple of ints size, *, torch.Generator generator, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)
* (int high, tuple of ints size, *, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)
* (int low, int high, tuple of ints size, *, torch.Generator generator, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)
* (int low, int high, tuple of ints size, *, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)
So, the error message should be something direct like below:
TypeError:
paddingshould be an integer or tuple or list of integers, but got 10.8
Versions
import torchvision
torchvision.__version__ # '0.20.1'Metadata
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