Add ChangeDetectionTask #2422
Add ChangeDetectionTask #2422
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| from .base import BaseTask | ||
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| class FocalJaccardLoss(nn.Module): |
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Should go in torchgeo/losses/ ?
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That makes sense, I'll put this and XEntJaccardLoss there.
| loss: 'ce' | ||
| model: 'unet' | ||
| backbone: 'resnet18' | ||
| in_channels: 13 |
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If stacking the channels should this be 2 * 13?
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We are no longer stacking channels, we are making all time series datasets (including change detection) into B x T x C x H x W
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OK and it is not necessary to config T?
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in_channels is multiplied by 2 in configure_models when initializing Unet.
| labels: Optional labels to use for classes in metrics | ||
| e.g. ["background", "change"] | ||
| loss: Name of the loss function, currently supports | ||
| 'ce', 'jaccard', 'focal' or 'focal-jaccard' loss. |
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| class XEntJaccardLoss(nn.Module): | ||
| """XEntJaccardLoss.""" |
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Is there a paper ref for this?
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Not that I'm aware of, and I didn't find one from a quick search. This loss function was already in there with what I started from.
| """ | ||
| loss: str = self.hparams["loss"] | ||
| ignore_index = self.hparams["ignore_index"] | ||
| if loss == "ce": |
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Noting that for change detection BCE may be more appropriate
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I wonder if we should limit the scope to change between two timesteps and binary change - then we can use binary metrics and provide a template for the plot methods. I say this because this is the most common change detection task by a mile. Might also simplify the augmentations approach? Treating as a video sequence seems overkill. |
I'm personally okay with this, although @hfangcat has a recent work using multiple pre-event images that would be nice to support someday (could be a subclass if necessary).
Again, this would probably be fine as a starting point, although I would someday like to make all trainers support binary/multiclass/multilabel, e.g., #2219.
Could also do this in the datasets (at least for benchmark NonGeoDatasets). We're also trying to remove explicit plotting in the trainers: #2184
Agreed.
I actually like the video augmentations, but let me loop in the Kornia folks to get their opinion: @edgarriba @johnnv1
Correct, see #2382 for the big picture (I think I also sent you a recording of my presented plan). |
Can you try
@ashnair1 would this work directly with |
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I will go ahead and make changes for this to be for binary change and two timesteps, sounds like a good starting point.
I tried this and it didn't get rid of the other dimension. I also looked into
I was going to add plotting in the trainer, but would you rather not then? What would this look like in the dataset? |
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Perhaps there should even be a base class ChangeDetection and subclasses for BinaryChangeDetection etc? |
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That's exactly what I'm trying to undo in #2219. |
We can copy-n-paste the
See |
This PR is to add a change detection trainer as mentioned in #2382.
Key points/items to discuss:
AugmentationSequentialdoesn’t work, but can be combined withVideoSequentialto support the temporal dimension (see Kornia docs). I overrodeself.augin theOSCDDataModuleto do this but not sure if this should be incorporated into theBaseDataModuleinstead.VideoSequentialadds a temporal dimension to the mask. Not sure if there is a way to avoid this, or if this is desirable, but I added an if statement to theAugmentationSequentialwrapper to check for and remove this added dimension._RandomNCropaugmentation, but this does not work for time series data. I'm not sure how to modify_RandomNCropto fix this and would appreciate some help/guidance.cc @robmarkcole