Added substation segementation dataset #2352
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Hi @rijuld, thanks for the contribution! If you're new to creating PyTorch datasets, I highly recommend reading the following tutorials:
The only difference between datasets in torchvision and NonGeoDatasets in TorchGeo is that our Most of your issues seem to be due to the use of |
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Hi @adamjstewart , thanks a ton for the feedback! I will go through this tutorial. |
torchgeo/datasets/substation_seg.py
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| image = image[:4, :, :, :] if self.use_timepoints else image[0] | ||
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| def _apply_transforms(self, image: torch.Tensor, mask: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: |
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Hi @rijuld, thank you for contributing this dataset. As another pointer, torchgeo datasets usually have an accompanying datamodule that defines things like the train/val/test split, but also common data augmentations, like flips, color augmentations etc through the kornia package. So in essence, torchgeo datasets simply load a particular sample and the augmentations are applied on GPU over the batch.
For example in this dataset, the getitem method loads the image and mask, and then we have a corresponding datamodule where we define augmentations like resizing and others, which will automatically be applied with a lightning training setup. This helps streamlining the datasets and keep them "minimal" and also make use of existing augmentation implementations like Kornia.
Let me know if I can help with any further questions.
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Hi @nilsleh , thank you for the detailed explanation!
That makes perfect sense. I will try to make this minimal, implement this today and reach out if I have any further questions.
Thanks again!
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Hi @nilsleh,
Hope you're doing well! I wanted to clarify if it's essential to shift all data augmentations to the datamodule. If so, could you guide me on which specific parts of the dataset should be moved there?
I've already removed the geotransform and color transform and plan to add them to the datamodule in my next pull request. If there are other elements you’d suggest removing, I can address those too. Once these adjustments are made, would it be possible to merge this PR (pending review) without the datamodule updates?
Thank you very much for your help!
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Apologies for the late response. Adam prefers having all data normalization in the datamodule for consistency, But I also don't think it is terrible to do in the dataset. If you move it to the datamodule, you can use the kornina Normalize module, that you can add to the augmentation series. Then it will be applied to on_after_batch_transfer in the LightningDataModule.
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@microsoft-github-policy-service agree |
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Not a bad data loader, it just doesn't match a single other data loader in TorchGeo. I highly recommend looking at some of the 80+ existing data loaders and unit tests for those data loaders already builtin before adding a new one from scratch. Especially for unit testing, you can probably just copy-n-paste most of the existing test code for a similar dataset.
alphabetical order
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Update non_geo_datasets.csv
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Can you resolve the merge conflict? The order of |
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