Add SemanticSam3dLogger #643
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| model_type="vit_b", | ||
| checkpoint_path=None, | ||
| ): | ||
| _, sam = get_sam_model( |
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Can't we just pass the checkpoint here? Then we don't need to duplicate the code below.
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Ah, that won't work as we need to pass the weights to the wrapper model (to initialize the adapter blocks)
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Hmm, I don't fully understand. Let's discuss tomorrow :) .
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Okay now I see already where your suspicion comes from.
TLDR: I think we might have to merge the get_3d_sam_model into get_sam_model for the best possible design (which leads to flexibly loading SAM checkpoints for finetuning, and the 3d-SAM checkpoints for downstream semantic inference).
I have a plan on this. I'll take care of this first thing in the morning. Thanks for spotting.
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I think the current design should work at least for intialization. We can revisit this later to discuss how we do this for actually loading the trained 3d models.
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Okie, I'll leave it to here for now then. Thanks!
| model_type="vit_b", | ||
| checkpoint_path=None, | ||
| ): | ||
| _, sam = get_sam_model( |
There was a problem hiding this comment.
Hmm, I don't fully understand. Let's discuss tomorrow :) .
Updates to SAM 3d training --------- Co-authored-by: Constantin Pape <constantin.pape@informatik.uni-goettingen.de>
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