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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 (
EfficientViTCls,
efficientvit_cls_b0,
efficientvit_cls_b1,
efficientvit_cls_b2,
efficientvit_cls_b3,
efficientvit_cls_l1,
efficientvit_cls_l2,
efficientvit_cls_l3,
)
from efficientvit.models.nn.norm import set_norm_eps
from efficientvit.models.utils import load_state_dict_from_file
__all__ = ["create_cls_model"]
REGISTERED_CLS_MODEL: dict[str, str] = {
"b0-r224": "assets/checkpoints/cls/b0-r224.pt",
###############################################
"b1-r224": "assets/checkpoints/cls/b1-r224.pt",
"b1-r256": "assets/checkpoints/cls/b1-r256.pt",
"b1-r288": "assets/checkpoints/cls/b1-r288.pt",
###############################################
"b2-r224": "assets/checkpoints/cls/b2-r224.pt",
"b2-r256": "assets/checkpoints/cls/b2-r256.pt",
"b2-r288": "assets/checkpoints/cls/b2-r288.pt",
###############################################
"b3-r224": "assets/checkpoints/cls/b3-r224.pt",
"b3-r256": "assets/checkpoints/cls/b3-r256.pt",
"b3-r288": "assets/checkpoints/cls/b3-r288.pt",
###############################################
"l1-r224": "assets/checkpoints/cls/l1-r224.pt",
###############################################
"l2-r224": "assets/checkpoints/cls/l2-r224.pt",
"l2-r256": "assets/checkpoints/cls/l2-r256.pt",
"l2-r288": "assets/checkpoints/cls/l2-r288.pt",
"l2-r320": "assets/checkpoints/cls/l2-r320.pt",
"l2-r384": "assets/checkpoints/cls/l2-r384.pt",
###############################################
"l3-r224": "assets/checkpoints/cls/l3-r224.pt",
"l3-r256": "assets/checkpoints/cls/l3-r256.pt",
"l3-r288": "assets/checkpoints/cls/l3-r288.pt",
"l3-r320": "assets/checkpoints/cls/l3-r320.pt",
"l3-r384": "assets/checkpoints/cls/l3-r384.pt",
}
def create_cls_model(name: str, pretrained=True, weight_url: str or None = None, **kwargs) -> EfficientViTCls:
model_dict = {
"b0": efficientvit_cls_b0,
"b1": efficientvit_cls_b1,
"b2": efficientvit_cls_b2,
"b3": efficientvit_cls_b3,
#########################
"l1": efficientvit_cls_l1,
"l2": efficientvit_cls_l2,
"l3": efficientvit_cls_l3,
}
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](**kwargs)
if model_id in ["l1", "l2", "l3"]:
set_norm_eps(model, 1e-7)
if pretrained:
weight_url = weight_url or REGISTERED_CLS_MODEL.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
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