Comments (4)
this is without DDP? do you think that torchmetrics would take care of that?
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Not dependent on DDP really, its about how we aggregate metrics. Not sure if torch metrics would take care of it, the issue lies within how we do metrics over multiple data points. I've fixed it for now, I'll push the update in a bit.
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the issue lies within how we do metrics over multiple data points
you mean how we deal with data points in the batch, no? if that's the case, we can actually get rid of how we do it and just use torchmetrics since it handles batches anyways
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Nope, I mean doing it over, for example, multiple patients - then we average over each patient, if we already average over batch, then the final average will be incorrect.
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Related Issues (20)
- Windowed images not displayed correctly in tensorboard
- Switch to monai's `decollate_batch`
- Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)? HOT 1
- Add more functionality tests: HOT 1
- make default imagedataset have different behavior for train vs val/test modes HOT 4
- metrics calculation not working for batch_size > 1
- Better logging names for losses, metric, G and D, and visuals
- metrics all over the place in GAN implementations HOT 1
- Add warning for when dataset size is smaller than number of processes in DDP * batch size HOT 1
- PatchGAN output channels set equal to in_channels? Change it to 1 HOT 10
- Figure out logging for multimodal images
- Make CycleGAN's separate channel config cleaner and more readable
- Document + support framework for fine-tuning on a different dataset HOT 2
- Out channels missing in PatchGAN 3D HOT 1
- Volumetric probability map based PatchSampler HOT 5
- Add structure-constrained GANs to the list of available GANs
- separate out medical utils
- CLI tab autocompletion not working
- Restructure docs to separate package overview from tutorials
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