import torch from copy import deepcopy 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