Update Actions with Lychee and GitHub Token (#12592)
* Add Ultralytics Actions * Auto-format by Ultralytics actions * Update format.yml Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> --------- Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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@@ -14,7 +14,8 @@ 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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"""
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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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@@ -39,9 +40,9 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
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if not verbose:
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LOGGER.setLevel(logging.WARNING)
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check_requirements(ROOT / 'requirements.txt', exclude=('opencv-python', 'tensorboard', 'thop'))
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check_requirements(ROOT / "requirements.txt", exclude=("opencv-python", "tensorboard", "thop"))
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name = Path(name)
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path = name.with_suffix('.pt') if name.suffix == '' and not name.is_dir() else name # checkpoint path
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path = name.with_suffix(".pt") if name.suffix == "" and not name.is_dir() else name # checkpoint path
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try:
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device = select_device(device)
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if pretrained and channels == 3 and classes == 80:
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@@ -49,91 +50,95 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
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model = DetectMultiBackend(path, device=device, fuse=autoshape) # detection model
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if autoshape:
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if model.pt and isinstance(model.model, ClassificationModel):
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LOGGER.warning('WARNING ⚠️ YOLOv5 ClassificationModel is not yet AutoShape compatible. '
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'You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224).')
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LOGGER.warning(
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"WARNING ⚠️ YOLOv5 ClassificationModel is not yet AutoShape compatible. "
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"You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224)."
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)
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elif model.pt and isinstance(model.model, SegmentationModel):
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LOGGER.warning('WARNING ⚠️ YOLOv5 SegmentationModel is not yet AutoShape compatible. '
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'You will not be able to run inference with this model.')
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LOGGER.warning(
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"WARNING ⚠️ YOLOv5 SegmentationModel is not yet AutoShape compatible. "
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"You will not be able to run inference with this model."
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)
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else:
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model = AutoShape(model) # for file/URI/PIL/cv2/np inputs and NMS
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except Exception:
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model = attempt_load(path, device=device, fuse=False) # arbitrary model
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else:
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cfg = list((Path(__file__).parent / 'models').rglob(f'{path.stem}.yaml'))[0] # model.yaml path
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cfg = list((Path(__file__).parent / "models").rglob(f"{path.stem}.yaml"))[0] # model.yaml path
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model = DetectionModel(cfg, channels, classes) # create model
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if pretrained:
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ckpt = torch.load(attempt_download(path), map_location=device) # load
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csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
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csd = intersect_dicts(csd, model.state_dict(), exclude=['anchors']) # intersect
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csd = ckpt["model"].float().state_dict() # checkpoint state_dict as FP32
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csd = intersect_dicts(csd, model.state_dict(), exclude=["anchors"]) # intersect
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model.load_state_dict(csd, strict=False) # load
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if len(ckpt['model'].names) == classes:
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model.names = ckpt['model'].names # set class names attribute
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if len(ckpt["model"].names) == classes:
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model.names = ckpt["model"].names # set class names attribute
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if not verbose:
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LOGGER.setLevel(logging.INFO) # reset to default
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return model.to(device)
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except Exception as e:
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help_url = 'https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading'
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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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help_url = "https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading"
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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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def custom(path="path/to/model.pt", autoshape=True, _verbose=True, device=None):
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# YOLOv5 custom or local model
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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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# YOLOv5-nano model https://github.com/ultralytics/yolov5
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return _create('yolov5n', pretrained, channels, classes, autoshape, _verbose, device)
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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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# YOLOv5-small model https://github.com/ultralytics/yolov5
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return _create('yolov5s', pretrained, channels, classes, autoshape, _verbose, device)
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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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# YOLOv5-medium model https://github.com/ultralytics/yolov5
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return _create('yolov5m', pretrained, channels, classes, autoshape, _verbose, device)
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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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# YOLOv5-large model https://github.com/ultralytics/yolov5
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return _create('yolov5l', pretrained, channels, classes, autoshape, _verbose, device)
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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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# YOLOv5-xlarge model https://github.com/ultralytics/yolov5
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return _create('yolov5x', pretrained, channels, classes, autoshape, _verbose, device)
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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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# YOLOv5-nano-P6 model https://github.com/ultralytics/yolov5
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return _create('yolov5n6', pretrained, channels, classes, autoshape, _verbose, device)
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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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# YOLOv5-small-P6 model https://github.com/ultralytics/yolov5
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return _create('yolov5s6', pretrained, channels, classes, autoshape, _verbose, device)
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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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# YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5
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return _create('yolov5m6', pretrained, channels, classes, autoshape, _verbose, device)
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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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# YOLOv5-large-P6 model https://github.com/ultralytics/yolov5
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return _create('yolov5l6', pretrained, channels, classes, autoshape, _verbose, device)
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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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# YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5
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return _create('yolov5x6', pretrained, channels, classes, autoshape, _verbose, device)
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return _create("yolov5x6", pretrained, channels, classes, autoshape, _verbose, device)
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if __name__ == '__main__':
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if __name__ == "__main__":
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import argparse
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from pathlib import Path
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@@ -144,7 +149,7 @@ if __name__ == '__main__':
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# Argparser
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parser = argparse.ArgumentParser()
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parser.add_argument('--model', type=str, default='yolov5s', help='model name')
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parser.add_argument("--model", type=str, default="yolov5s", help="model name")
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opt = parser.parse_args()
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print_args(vars(opt))
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@@ -154,12 +159,13 @@ if __name__ == '__main__':
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# Images
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imgs = [
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'data/images/zidane.jpg', # filename
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Path('data/images/zidane.jpg'), # Path
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'https://ultralytics.com/images/zidane.jpg', # URI
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cv2.imread('data/images/bus.jpg')[:, :, ::-1], # OpenCV
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Image.open('data/images/bus.jpg'), # PIL
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np.zeros((320, 640, 3))] # numpy
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"data/images/zidane.jpg", # filename
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Path("data/images/zidane.jpg"), # Path
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"https://ultralytics.com/images/zidane.jpg", # URI
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cv2.imread("data/images/bus.jpg")[:, :, ::-1], # OpenCV
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Image.open("data/images/bus.jpg"), # PIL
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np.zeros((320, 640, 3)),
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] # numpy
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# Inference
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results = model(imgs, size=320) # batched inference
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