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| # EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction | |
| # Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han | |
| # International Conference on Computer Vision (ICCV), 2023 | |
| from efficientvit.models.efficientvit import ( | |
| EfficientViTSeg, | |
| efficientvit_seg_b0, | |
| efficientvit_seg_b1, | |
| efficientvit_seg_b2, | |
| efficientvit_seg_b3, | |
| efficientvit_seg_l1, | |
| efficientvit_seg_l2, | |
| ) | |
| from efficientvit.models.nn.norm import set_norm_eps | |
| from efficientvit.models.utils import load_state_dict_from_file | |
| __all__ = ["create_seg_model"] | |
| REGISTERED_SEG_MODEL: dict[str, dict[str, str]] = { | |
| "cityscapes": { | |
| "b0": "assets/checkpoints/seg/cityscapes/b0.pt", | |
| "b1": "assets/checkpoints/seg/cityscapes/b1.pt", | |
| "b2": "assets/checkpoints/seg/cityscapes/b2.pt", | |
| "b3": "assets/checkpoints/seg/cityscapes/b3.pt", | |
| ################################################ | |
| "l1": "assets/checkpoints/seg/cityscapes/l1.pt", | |
| "l2": "assets/checkpoints/seg/cityscapes/l2.pt", | |
| }, | |
| "ade20k": { | |
| "b1": "assets/checkpoints/seg/ade20k/b1.pt", | |
| "b2": "assets/checkpoints/seg/ade20k/b2.pt", | |
| "b3": "assets/checkpoints/seg/ade20k/b3.pt", | |
| ################################################ | |
| "l1": "assets/checkpoints/seg/ade20k/l1.pt", | |
| "l2": "assets/checkpoints/seg/ade20k/l2.pt", | |
| }, | |
| } | |
| def create_seg_model( | |
| name: str, dataset: str, pretrained=True, weight_url: str or None = None, **kwargs | |
| ) -> EfficientViTSeg: | |
| model_dict = { | |
| "b0": efficientvit_seg_b0, | |
| "b1": efficientvit_seg_b1, | |
| "b2": efficientvit_seg_b2, | |
| "b3": efficientvit_seg_b3, | |
| ######################### | |
| "l1": efficientvit_seg_l1, | |
| "l2": efficientvit_seg_l2, | |
| } | |
| model_id = name.split("-")[0] | |
| if model_id not in model_dict: | |
| raise ValueError(f"Do not find {name} in the model zoo. List of models: {list(model_dict.keys())}") | |
| else: | |
| model = model_dict[model_id](dataset=dataset, **kwargs) | |
| if model_id in ["l1", "l2"]: | |
| set_norm_eps(model, 1e-7) | |
| if pretrained: | |
| weight_url = weight_url or REGISTERED_SEG_MODEL[dataset].get(name, None) | |
| if weight_url is None: | |
| raise ValueError(f"Do not find the pretrained weight of {name}.") | |
| else: | |
| weight = load_state_dict_from_file(weight_url) | |
| model.load_state_dict(weight) | |
| return model | |