mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-24 15:18:46 +00:00
82 lines
3 KiB
Python
82 lines
3 KiB
Python
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import argparse
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from torch import Tensor
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from refiners.fluxion.utils import load_tensors, save_to_safetensors
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def main() -> None:
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parser = argparse.ArgumentParser(description="Convert HQ SAM model to Refiners state_dict format")
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parser.add_argument(
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"--from",
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type=str,
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dest="source_path",
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required=True,
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default="sam_hq_vit_h.pth",
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help="Path to the source model checkpoint.",
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)
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parser.add_argument(
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"--to",
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type=str,
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dest="output_path",
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required=True,
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default="refiners_sam_hq_vit_h.safetensors",
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help="Path to save the converted model in Refiners format.",
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)
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args = parser.parse_args()
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source_state_dict = load_tensors(args.source_path)
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state_dict: dict[str, Tensor] = {}
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for suffix in ["weight", "bias"]:
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state_dict[f"HQFeatures.CompressViTFeat.ConvTranspose2d_1.{suffix}"] = source_state_dict[
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f"mask_decoder.compress_vit_feat.0.{suffix}"
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]
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state_dict[f"HQFeatures.EmbeddingEncoder.ConvTranspose2d_1.{suffix}"] = source_state_dict[
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f"mask_decoder.embedding_encoder.0.{suffix}"
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]
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state_dict[f"EmbeddingMaskfeature.Conv2d_1.{suffix}"] = source_state_dict[
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f"mask_decoder.embedding_maskfeature.0.{suffix}"
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]
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state_dict[f"HQFeatures.CompressViTFeat.LayerNorm2d.{suffix}"] = source_state_dict[
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f"mask_decoder.compress_vit_feat.1.{suffix}"
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]
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state_dict[f"HQFeatures.EmbeddingEncoder.LayerNorm2d.{suffix}"] = source_state_dict[
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f"mask_decoder.embedding_encoder.1.{suffix}"
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]
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state_dict[f"EmbeddingMaskfeature.LayerNorm2d.{suffix}"] = source_state_dict[
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f"mask_decoder.embedding_maskfeature.1.{suffix}"
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]
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state_dict[f"HQFeatures.CompressViTFeat.ConvTranspose2d_2.{suffix}"] = source_state_dict[
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f"mask_decoder.compress_vit_feat.3.{suffix}"
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]
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state_dict[f"HQFeatures.EmbeddingEncoder.ConvTranspose2d_2.{suffix}"] = source_state_dict[
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f"mask_decoder.embedding_encoder.3.{suffix}"
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]
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state_dict[f"EmbeddingMaskfeature.Conv2d_2.{suffix}"] = source_state_dict[
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f"mask_decoder.embedding_maskfeature.3.{suffix}"
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]
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state_dict = {f"Chain.HQSAMMaskPrediction.Chain.DenseEmbeddingUpscalingHQ.{k}": v for k, v in state_dict.items()}
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# HQ Token
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state_dict["MaskDecoderTokensExtender.hq_token.weight"] = source_state_dict["mask_decoder.hf_token.weight"]
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# HQ MLP
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for i in range(3):
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state_dict[f"Chain.HQSAMMaskPrediction.HQTokenMLP.MultiLinear.Linear_{i+1}.weight"] = source_state_dict[
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f"mask_decoder.hf_mlp.layers.{i}.weight"
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]
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state_dict[f"Chain.HQSAMMaskPrediction.HQTokenMLP.MultiLinear.Linear_{i+1}.bias"] = source_state_dict[
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f"mask_decoder.hf_mlp.layers.{i}.bias"
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]
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save_to_safetensors(path=args.output_path, tensors=state_dict)
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if __name__ == "__main__":
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main()
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