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""" |
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PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/ |
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Usage: |
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import torch |
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model = torch.hub.load('ultralytics/yolov5', 'yolov5s') |
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model = torch.hub.load('ultralytics/yolov5:master', 'custom', 'path/to/yolov5s.onnx') # file from branch |
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""" |
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import torch |
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def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): |
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"""Creates or loads a YOLOv5 model |
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Arguments: |
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name (str): model name 'yolov5s' or path 'path/to/best.pt' |
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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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autoshape (bool): apply YOLOv5 .autoshape() wrapper to model |
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verbose (bool): print all information to screen |
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device (str, torch.device, None): device to use for model parameters |
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Returns: |
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YOLOv5 model |
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""" |
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from pathlib import Path |
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from models.common import AutoShape, DetectMultiBackend |
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from models.yolo import Model |
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from utils.downloads import attempt_download |
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from utils.general import LOGGER, check_requirements, intersect_dicts, logging |
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from utils.torch_utils import select_device |
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if not verbose: |
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LOGGER.setLevel(logging.WARNING) |
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check_requirements(exclude=('tensorboard', 'thop', 'opencv-python')) |
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name = Path(name) |
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path = name.with_suffix('.pt') if name.suffix == '' else name |
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try: |
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device = select_device(('0' if torch.cuda.is_available() else 'cpu') if device is None else device) |
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if pretrained and channels == 3 and classes == 80: |
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model = DetectMultiBackend(path, device=device) |
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else: |
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cfg = list((Path(__file__).parent / 'models').rglob(f'{path.stem}.yaml'))[0] |
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model = Model(cfg, channels, classes) |
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if pretrained: |
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ckpt = torch.load(attempt_download(path), map_location=device) |
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csd = ckpt['model'].float().state_dict() |
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csd = intersect_dicts(csd, model.state_dict(), exclude=['anchors']) |
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model.load_state_dict(csd, 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 = AutoShape(model) |
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return model.to(device) |
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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 = f'{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help.' |
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raise Exception(s) from e |
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def custom(path='path/to/model.pt', autoshape=True, verbose=True, device=None): |
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return _create(path, autoshape=autoshape, verbose=verbose, device=device) |
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def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): |
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return _create('yolov5n', pretrained, channels, classes, autoshape, verbose, device) |
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def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): |
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return _create('yolov5s', pretrained, channels, classes, autoshape, verbose, device) |
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def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): |
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return _create('yolov5m', pretrained, channels, classes, autoshape, verbose, device) |
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def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): |
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return _create('yolov5l', pretrained, channels, classes, autoshape, verbose, device) |
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def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): |
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return _create('yolov5x', pretrained, channels, classes, autoshape, verbose, device) |
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def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): |
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return _create('yolov5n6', pretrained, channels, classes, autoshape, verbose, device) |
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def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): |
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return _create('yolov5s6', pretrained, channels, classes, autoshape, verbose, device) |
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def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): |
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return _create('yolov5m6', pretrained, channels, classes, autoshape, verbose, device) |
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def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): |
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return _create('yolov5l6', pretrained, channels, classes, autoshape, verbose, device) |
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def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): |
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return _create('yolov5x6', pretrained, channels, classes, autoshape, verbose, device) |
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if __name__ == '__main__': |
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model = _create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True, verbose=True) |
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from pathlib import Path |
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import numpy as np |
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from PIL import Image |
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from utils.general import cv2 |
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imgs = ['data/images/zidane.jpg', |
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Path('data/images/zidane.jpg'), |
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'https://ultralytics.com/images/zidane.jpg', |
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cv2.imread('data/images/bus.jpg')[:, :, ::-1], |
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Image.open('data/images/bus.jpg'), |
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np.zeros((320, 640, 3))] |
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results = model(imgs, size=320) |
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results.print() |
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results.save() |
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