request for adding 4 bands-image training

[Floyd2yh]

Aerial and satellite images taken with a drone are,typically in 4-band geotiff format. A 4-band image is an RBGN image, which is a 3-band RGB plus near-infrared (NIR) to the 3-band RGB. This modification is needed to train the 4band-image without processing.

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:test_tube: See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/vision/8405

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[abhi-glitchhg]

If image has 4 channels, doesn't mean we should load all 4 channels. It would be better if pil_loader takes optional parameter which indicates whether to return RGB only or PIL image without any conversion

Because sometimes images have alpha channel (RGBA image) which are mostly constant value. In those cases, it's required to get rid of alpha channel.

[Floyd2yh]

abhi-glitchhg, thank you for your advice. I agree with your point about PNG (RBGA). However, adding an optional argument would also cause changes to the caller, which would be a major modification. Also, the conversion to RGBX is necessary even for a 4-band image. I have uploaded a corrected version. What do you think?

[Floyd2yh]

uploaded modified code

[abhi-glitchhg]

I think we need to take a review/comments from maintainers in this approach. Maybe @pmeier or @NicolasHug ?? What do you guys think??

[Floyd2yh]

abhi-glitchhg, ok I understood. but how can I set loader?

train_data = datasets.ImageFolder(root=train_dataset_dir, transform=train_transforms, loader=????????) will you teach me?

[NicolasHug]

Thanks for the PR @Floyd2yh .

The ImageFolder class exposes a loader attribute that you can override to a custom loader. I'd suggest simply doing that locally. Unfortunately we won't be able to include the change within torchvision, because our transforms expect RGB images with 3 channels instead of 4.