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"""File for accessing YOLOv5 via PyTorch Hub https://pytorch.org/hub/ |
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Usage: |
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import torch |
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model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True, channels=3, classes=80) |
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""" |
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from pathlib import Path |
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import torch |
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from models.yolo import Model |
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from utils.general import set_logging |
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from utils.google_utils import attempt_download |
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dependencies = ['torch', 'yaml'] |
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set_logging() |
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def create(name, pretrained, channels, classes, autoshape): |
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"""Creates a specified YOLOv5 model |
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Arguments: |
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name (str): name of model, i.e. 'yolov5s' |
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pretrained (bool): load pretrained weights into the model |
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channels (int): number of input channels |
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classes (int): number of model classes |
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Returns: |
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pytorch model |
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""" |
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config = Path(__file__).parent / 'models' / f'{name}.yaml' |
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try: |
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model = Model(config, channels, classes) |
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if pretrained: |
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fname = f'{name}.pt' |
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attempt_download(fname) |
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ckpt = torch.load(fname, map_location=torch.device('cpu')) |
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state_dict = ckpt['model'].float().state_dict() |
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state_dict = {k: v for k, v in state_dict.items() if model.state_dict()[k].shape == v.shape} |
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model.load_state_dict(state_dict, strict=False) |
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if len(ckpt['model'].names) == classes: |
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model.names = ckpt['model'].names |
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if autoshape: |
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model = model.autoshape() |
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return model |
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except Exception as e: |
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help_url = 'https://github.com/ultralytics/yolov5/issues/36' |
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s = 'Cache maybe be out of date, try force_reload=True. See %s for help.' % help_url |
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raise Exception(s) from e |
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def yolov5s(pretrained=False, channels=3, classes=80, autoshape=True): |
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"""YOLOv5-small model from https://github.com/ultralytics/yolov5 |
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Arguments: |
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pretrained (bool): load pretrained weights into the model, default=False |
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channels (int): number of input channels, default=3 |
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classes (int): number of model classes, default=80 |
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Returns: |
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pytorch model |
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""" |
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return create('yolov5s', pretrained, channels, classes, autoshape) |
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def yolov5m(pretrained=False, channels=3, classes=80, autoshape=True): |
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"""YOLOv5-medium model from https://github.com/ultralytics/yolov5 |
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Arguments: |
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pretrained (bool): load pretrained weights into the model, default=False |
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channels (int): number of input channels, default=3 |
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classes (int): number of model classes, default=80 |
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Returns: |
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pytorch model |
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""" |
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return create('yolov5m', pretrained, channels, classes, autoshape) |
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def yolov5l(pretrained=False, channels=3, classes=80, autoshape=True): |
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"""YOLOv5-large model from https://github.com/ultralytics/yolov5 |
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Arguments: |
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pretrained (bool): load pretrained weights into the model, default=False |
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channels (int): number of input channels, default=3 |
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classes (int): number of model classes, default=80 |
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Returns: |
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pytorch model |
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""" |
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return create('yolov5l', pretrained, channels, classes, autoshape) |
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def yolov5x(pretrained=False, channels=3, classes=80, autoshape=True): |
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"""YOLOv5-xlarge model from https://github.com/ultralytics/yolov5 |
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Arguments: |
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pretrained (bool): load pretrained weights into the model, default=False |
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channels (int): number of input channels, default=3 |
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classes (int): number of model classes, default=80 |
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Returns: |
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pytorch model |
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""" |
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return create('yolov5x', pretrained, channels, classes, autoshape) |
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def custom(path_or_model='path/to/model.pt', autoshape=True): |
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"""YOLOv5-custom model from https://github.com/ultralytics/yolov5 |
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Arguments (3 options): |
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path_or_model (str): 'path/to/model.pt' |
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path_or_model (dict): torch.load('path/to/model.pt') |
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path_or_model (nn.Module): torch.load('path/to/model.pt')['model'] |
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Returns: |
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pytorch model |
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""" |
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model = torch.load(path_or_model) if isinstance(path_or_model, str) else path_or_model |
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if isinstance(model, dict): |
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model = model['ema' if model.get('ema') else 'model'] |
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hub_model = Model(model.yaml).to(next(model.parameters()).device) |
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hub_model.load_state_dict(model.float().state_dict()) |
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hub_model.names = model.names |
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return hub_model.autoshape() if autoshape else hub_model |
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if __name__ == '__main__': |
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model = create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True) |
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import numpy as np |
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from PIL import Image |
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imgs = [Image.open('data/images/bus.jpg'), |
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'data/images/zidane.jpg', |
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'https://github.com/ultralytics/yolov5/raw/master/data/images/bus.jpg', |
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np.zeros((640, 480, 3))] |
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results = model(imgs) |
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results.print() |
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results.save() |
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