642 train 3d model with lucchi data #650
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* Update faq.md * Add described answer to the issue
Updates to SAM 3d training --------- Co-authored-by: Constantin Pape <constantin.pape@informatik.uni-goettingen.de>
…er (#645) Add simple 3d wrapper and enable freezing the encoder in sam 3d wrapper, simplify lora support
Minor fix to trainable sam model functionality
Clean up interfaces related to 3d models and PEFT
| assert os.path.exists(image_path), image_path | ||
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| # Perform segmentation only on the semantic class | ||
| for i, (semantic_class_name, _) in enumerate(semantic_class_map.items()): |
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I would suggest to remove this part already, it doesn't make sense in the context here.
| def predict(args): | ||
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| device = "cuda" if torch.cuda.is_available() else "cpu" | ||
| if args.checkpoint_path is not None: |
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I am not sure why you would ever run prediction without a checkpoint. I would not make this optional.
| lora_rank=4, | ||
| model_type=args.model_type, | ||
| checkpoint_path=cp_path | ||
| ) |
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This will not work to actually load the checkpoint. Please read the code I send you carefully and see how I use torch_em.util.load_model to get the checkpoint. Not that this quite complex way is now necessary because of the way how we mix the 3d adapter and LoRA. I will think about a better way at some point, but for now we need to use this work-around.
| return raw | ||
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| class LucchiSegmentationDataset(SegmentationDataset): |
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This can now be removed.
… default_sam_dataset
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