Modify UNet to take an arbitrary input dimension?
One must normalize sizes of images with preprocessing, see torchvision.transforms.functional.resize.
I have a network that's pretty much UNet. However, the model crashed when I feed in input size of 3x1x1 (channel =3, height =1, width=1) since the first max pooling (with kernel size =2 and stride =2) will reduce the dimension into 3x0x0.
How do I modify Unet model such that it can take my 3x1x1 input and handle arbitrary number of poolings? Any help is appreciated!