Invertd is not able to invert Resized for data points that aren't changed in size. Consider the following very basic example: I have a pipeline that resizes all axial slices to a size of 128x128:

transform = Compose([Resized('X', (-1,128,128))])

Suppose I have the following data I want to resize. Some of the data already has slices of size 128x128, but not all:

test_input = [{'X': torch.ones((1,10,128,128))}, {'X': torch.ones((1,10,144,144))}]

I can apply the transform to resize all data points such that they have axial slices of size 128x128:

test_output = transform(test_input)

The issue then comes if I want to invert resize some output data. For example, consider the very basic neural network that simply multiplies all values by 2:

for d in test_output:
    d['Y'] = 2*d['X']

I want to resize the Ys such that they correspond with the original Xs. I use an inverse transform:

transform_inverse = Invertd(keys='Y', transform=transform, orig_keys='X')

Now the following command will run successfully (recall that index 1 refers to the data point that was initially 144x144):

transform_inverse(test_output[1])

but the following won't run (recall that index 0 refers to the data point that was initially 128x128, and so Resized wouldn't modify it):

transform_inverse(test_output[0])

I believe this is because Resized isn't actually applied to this data point (because its already the right size) and thus it isn't tracked in the transforms applied to X (and thus the inverse can't be applied to Y). Obviously there are hack-ish ways to get around this, but it becomes annoying when you're dealing with a transform that has many different operations, and dealing with a large dataset.

Solution: I just commented out 845-846 of monai.transforms.spatial.array

if tuple(img.shape[1:]) == spatial_size_:  # spatial shape is already the desired
    return convert_to_tensor(img, track_meta=get_track_meta())  # type: ignore

but you might want to modify them so that transform metadata is stored.