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| """File for accessing YOLOv5 via PyTorch Hub https://pytorch.org/hub/ | |
| Usage: | |
| import torch | |
| model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True, channels=3, classes=80) | |
| """ | |
| dependencies = ['torch', 'yaml'] | |
| import os | |
| import torch | |
| from models.yolo import Model | |
| from utils.google_utils import attempt_download | |
| def create(name, pretrained, channels, classes): | |
| """Creates a specified YOLOv5 model | |
| Arguments: | |
| name (str): name of model, i.e. 'yolov5s' | |
| pretrained (bool): load pretrained weights into the model | |
| channels (int): number of input channels | |
| classes (int): number of model classes | |
| Returns: | |
| pytorch model | |
| """ | |
| config = os.path.join(os.path.dirname(__file__), 'models', '%s.yaml' % name) # model.yaml path | |
| try: | |
| model = Model(config, channels, classes) | |
| if pretrained: | |
| ckpt = '%s.pt' % name # checkpoint filename | |
| attempt_download(ckpt) # download if not found locally | |
| state_dict = torch.load(ckpt, map_location=torch.device('cpu'))['model'].float().state_dict() # to FP32 | |
| state_dict = {k: v for k, v in state_dict.items() if model.state_dict()[k].shape == v.shape} # filter | |
| model.load_state_dict(state_dict, strict=False) # load | |
| return model | |
| except Exception as e: | |
| help_url = 'https://github.com/ultralytics/yolov5/issues/36' | |
| s = 'Cache maybe be out of date, deleting cache and retrying may solve this. See %s for help.' % help_url | |
| raise Exception(s) from e | |
| def yolov5s(pretrained=False, channels=3, classes=80): | |
| """YOLOv5-small model from https://github.com/ultralytics/yolov5 | |
| Arguments: | |
| pretrained (bool): load pretrained weights into the model, default=False | |
| channels (int): number of input channels, default=3 | |
| classes (int): number of model classes, default=80 | |
| Returns: | |
| pytorch model | |
| """ | |
| return create('yolov5s', pretrained, channels, classes) | |
| def yolov5m(pretrained=False, channels=3, classes=80): | |
| """YOLOv5-medium model from https://github.com/ultralytics/yolov5 | |
| Arguments: | |
| pretrained (bool): load pretrained weights into the model, default=False | |
| channels (int): number of input channels, default=3 | |
| classes (int): number of model classes, default=80 | |
| Returns: | |
| pytorch model | |
| """ | |
| return create('yolov5m', pretrained, channels, classes) | |
| def yolov5l(pretrained=False, channels=3, classes=80): | |
| """YOLOv5-large model from https://github.com/ultralytics/yolov5 | |
| Arguments: | |
| pretrained (bool): load pretrained weights into the model, default=False | |
| channels (int): number of input channels, default=3 | |
| classes (int): number of model classes, default=80 | |
| Returns: | |
| pytorch model | |
| """ | |
| return create('yolov5l', pretrained, channels, classes) | |
| def yolov5x(pretrained=False, channels=3, classes=80): | |
| """YOLOv5-xlarge model from https://github.com/ultralytics/yolov5 | |
| Arguments: | |
| pretrained (bool): load pretrained weights into the model, default=False | |
| channels (int): number of input channels, default=3 | |
| classes (int): number of model classes, default=80 | |
| Returns: | |
| pytorch model | |
| """ | |
| return create('yolov5x', pretrained, channels, classes) | |