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| import os | |
| import torch | |
| from torch import nn | |
| from copy import deepcopy | |
| from facelib.utils import load_file_from_url | |
| from facelib.utils import download_pretrained_models | |
| from facelib.detection.yolov5face.models.common import Conv | |
| from .retinaface.retinaface import RetinaFace | |
| from .yolov5face.face_detector import YoloDetector | |
| def init_detection_model(model_name, half=False, device='cuda'): | |
| if 'retinaface' in model_name: | |
| model = init_retinaface_model(model_name, half, device) | |
| elif 'YOLOv5' in model_name: | |
| model = init_yolov5face_model(model_name, device) | |
| else: | |
| raise NotImplementedError(f'{model_name} is not implemented.') | |
| return model | |
| def init_retinaface_model(model_name, half=False, device='cuda'): | |
| if model_name == 'retinaface_resnet50': | |
| model = RetinaFace(network_name='resnet50', half=half) | |
| 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) | |
| 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='weights/facelib', progress=True, file_name=None) | |
| 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 | |
| def init_yolov5face_model(model_name, device='cuda'): | |
| if model_name == 'YOLOv5l': | |
| model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5l.yaml', device=device) | |
| model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5l-face.pth' | |
| elif model_name == 'YOLOv5n': | |
| model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5n.yaml', device=device) | |
| model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5n-face.pth' | |
| else: | |
| raise NotImplementedError(f'{model_name} is not implemented.') | |
| model_path = load_file_from_url(url=model_url, model_dir='weights/facelib', progress=True, file_name=None) | |
| load_net = torch.load(model_path, map_location=lambda storage, loc: storage) | |
| model.detector.load_state_dict(load_net, strict=True) | |
| model.detector.eval() | |
| model.detector = model.detector.to(device).float() | |
| for m in model.detector.modules(): | |
| if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]: | |
| m.inplace = True # pytorch 1.7.0 compatibility | |
| elif isinstance(m, Conv): | |
| m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility | |
| return model | |
| # Download from Google Drive | |
| # def init_yolov5face_model(model_name, device='cuda'): | |
| # if model_name == 'YOLOv5l': | |
| # model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5l.yaml', device=device) | |
| # f_id = {'yolov5l-face.pth': '131578zMA6B2x8VQHyHfa6GEPtulMCNzV'} | |
| # elif model_name == 'YOLOv5n': | |
| # model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5n.yaml', device=device) | |
| # f_id = {'yolov5n-face.pth': '1fhcpFvWZqghpGXjYPIne2sw1Fy4yhw6o'} | |
| # else: | |
| # raise NotImplementedError(f'{model_name} is not implemented.') | |
| # model_path = os.path.join('weights/facelib', list(f_id.keys())[0]) | |
| # if not os.path.exists(model_path): | |
| # download_pretrained_models(file_ids=f_id, save_path_root='weights/facelib') | |
| # load_net = torch.load(model_path, map_location=lambda storage, loc: storage) | |
| # model.detector.load_state_dict(load_net, strict=True) | |
| # model.detector.eval() | |
| # model.detector = model.detector.to(device).float() | |
| # for m in model.detector.modules(): | |
| # if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]: | |
| # m.inplace = True # pytorch 1.7.0 compatibility | |
| # elif isinstance(m, Conv): | |
| # m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility | |
| # return model |