`torchvision.transforms.v2.functional.pad` should have maintained the `channels_last` format of the input

🐛 Describe the bug

import torch
import torchvision.transforms.v2.functional
from torchcodec.decoders import VideoDecoder

a=VideoDecoder(r"F:\....gif",device='cuda')

# When input is CHW channels_last 
c=torchvision.transforms.v2.functional.crop(a[0], 10,10,200,282)
c.shape, c.stride()
# (torch.Size([3, 200, 200]), (1, 876, 3))

d=torchvision.transforms.v2.functional.resize(a[0], (300,300))
d.shape, d.stride()
# (torch.Size([3, 300, 300]), (1, 900, 3))

e=torchvision.transforms.v2.functional.pad(a[0], (500,500), 255, 'constant')
e.shape, e.stride()
# (torch.Size([3, 1241, 1292]), (1603372, 1292, 1))  # channels_last broken!

f=torch.nn.functional.pad(a[0], (500,500,500,500), 'constant', 255)
f.shape, f.stride()
# (torch.Size([3, 1241, 1292]), (1603372, 1292, 1))  # channels_last broken!

g=torchvision.transforms.v2.functional.crop(a[0], 10,10,500,500)
g.shape, g.stride()
# (torch.Size([3, 500, 500]), (250000, 500, 1))


# When input is NCHW channels_last 
aa=a[0].unsqueeze(0)
aa.shape,aa.stride()

cc=torchvision.transforms.v2.functional.crop(aa, 10,10,200,200)
print(cc.shape, cc.stride())

dd=torchvision.transforms.v2.functional.resize(aa, (300,300))
print(dd.shape, dd.stride())

ee=torchvision.transforms.v2.functional.pad(aa, (500,500), 255, 'constant')
print(ee.shape, ee.stride())

ff=torch.nn.functional.pad(aa, (500,500,500,500), 'constant', 255)
print(ff.shape, ff.stride())

gg=torchvision.transforms.v2.functional.crop(aa, 10,10,500,500)
print(gg.shape, gg.stride())

# torch.Size([1, 3, 200, 200]) (3, 1, 876, 3)
# torch.Size([1, 3, 300, 300]) (3, 1, 900, 3)
# torch.Size([1, 3, 1241, 1292]) (4810116, 1603372, 1292, 1)  # channels_last broken!
# torch.Size([1, 3, 1241, 1292]) (4810116, 1603372, 1292, 1)  # channels_last broken!
# torch.Size([1, 3, 500, 500]) (750000, 250000, 500, 1)


# When input is NCHW channels_last for sure! perfect and stictly channels_last stride.
aa=a[0].unsqueeze(0).to(memory_format=torch.channels_last)
aa.shape,aa.stride()

cc=torchvision.transforms.v2.functional.crop(aa, 10,10,200,200)
print(cc.shape, cc.stride())

dd=torchvision.transforms.v2.functional.resize(aa, (300,300))
print(dd.shape, dd.stride())

ee=torchvision.transforms.v2.functional.pad(aa, (500,500), 255, 'constant')
print(ee.shape, ee.stride())

ff=torch.nn.functional.pad(aa, (500,500,500,500), 'constant', 255)
print(ff.shape, ff.stride())

gg=torchvision.transforms.v2.functional.crop(aa, 10,10,500,500)
print(gg.shape, gg.stride())

# torch.Size([1, 3, 200, 200]) (211116, 1, 876, 3)
# torch.Size([1, 3, 300, 300]) (3, 1, 900, 3)
# torch.Size([1, 3, 1241, 1292]) (4810116, 1603372, 1292, 1)  # channels_last broken!
# torch.Size([1, 3, 1241, 1292]) (4810116, 1, 3876, 3)
# torch.Size([1, 3, 500, 500]) (750000, 250000, 500, 1)

As we can see,

• resize and crop they both support "CHW" and "NCHW", "channels_last", "strict stride channels_last" memory_format.
• torch.nn.functional.pad supports only "NCHW", "strict stride channels_last" memory_format, not "CHW" nor "(unstrict) channels_last".
• torchvision.transforms.v2.functional.pad does not even support what torch.nn.functional.pad supported.

The "channels_last" is needed and has higher performance in image process where R G B are together so filters can process them at onece, and gpu need it to accelarate in its tensor cores.

So torchvision.transforms.v2.functional.pad should have the ablity to keep the channels_last format of the input like resize and crop, instead of breaking it within the pipeline process in torchvision.

Thank your time for reading.
I would like to make a PR.

Versions

torchvision.version
'0.27.1+cu126'

Start at the torchvision.transforms.v2.functional.pad entry point and compare its behavior with torch.nn.functional.pad, resize, and crop using the CHW and NCHW examples in the issue. Add coverage for the shown stride and memory-format cases, with done meaning pad preserves the input's channels_last format without breaking supported layouts.