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- from copy import deepcopy
- import torch
- from ..utils import load_file_from_url
- from .retinaface import RetinaFace
- def init_detection_model(model_name, half=False, device="cuda", model_rootpath=None):
- if model_name == "retinaface_resnet50":
- model = RetinaFace(network_name="resnet50", half=half, device=device)
- model_url = "https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth"
- elif model_name == "retinaface_mobile0.25":
- model = RetinaFace(network_name="mobile0.25", half=half, device=device)
- model_url = "https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_mobilenet0.25_Final.pth"
- else:
- raise NotImplementedError(f"{model_name} is not implemented.")
- model_path = load_file_from_url(
- url=model_url,
- model_dir="facexlib/weights",
- progress=True,
- file_name=None,
- save_dir=model_rootpath,
- )
- # TODO: clean pretrained model
- load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
- # remove unnecessary 'module.'
- for k, v in deepcopy(load_net).items():
- if k.startswith("module."):
- load_net[k[7:]] = v
- load_net.pop(k)
- model.load_state_dict(load_net, strict=True)
- model.eval()
- model = model.to(device)
- return model
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