Delete hubconf.py

hubconf.py

@@ -1,169 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
-"""
-PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5
-
-Usage:
-    import torch
-    model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # official model
-    model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s')  # from branch
-    model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt')  # custom/local model
-    model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local')  # local repo
-"""
-
-import torch
-
-
-def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    """Creates or loads a YOLOv5 model
-
-    Arguments:
-        name (str): model name 'yolov5s' or path 'path/to/best.pt'
-        pretrained (bool): load pretrained weights into the model
-        channels (int): number of input channels
-        classes (int): number of model classes
-        autoshape (bool): apply YOLOv5 .autoshape() wrapper to model
-        verbose (bool): print all information to screen
-        device (str, torch.device, None): device to use for model parameters
-
-    Returns:
-        YOLOv5 model
-    """
-    from pathlib import Path
-
-    from models.common import AutoShape, DetectMultiBackend
-    from models.experimental import attempt_load
-    from models.yolo import ClassificationModel, DetectionModel, SegmentationModel
-    from utils.downloads import attempt_download
-    from utils.general import LOGGER, check_requirements, intersect_dicts, logging
-    from utils.torch_utils import select_device
-
-    if not verbose:
-        LOGGER.setLevel(logging.WARNING)
-    check_requirements(exclude=('opencv-python', 'tensorboard', 'thop'))
-    name = Path(name)
-    path = name.with_suffix('.pt') if name.suffix == '' and not name.is_dir() else name  # checkpoint path
-    try:
-        device = select_device(device)
-        if pretrained and channels == 3 and classes == 80:
-            try:
-                model = DetectMultiBackend(path, device=device, fuse=autoshape)  # detection model
-                if autoshape:
-                    if model.pt and isinstance(model.model, ClassificationModel):
-                        LOGGER.warning('WARNING ⚠️ YOLOv5 ClassificationModel is not yet AutoShape compatible. '
-                                       'You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224).')
-                    elif model.pt and isinstance(model.model, SegmentationModel):
-                        LOGGER.warning('WARNING ⚠️ YOLOv5 SegmentationModel is not yet AutoShape compatible. '
-                                       'You will not be able to run inference with this model.')
-                    else:
-                        model = AutoShape(model)  # for file/URI/PIL/cv2/np inputs and NMS
-            except Exception:
-                model = attempt_load(path, device=device, fuse=False)  # arbitrary model
-        else:
-            cfg = list((Path(__file__).parent / 'models').rglob(f'{path.stem}.yaml'))[0]  # model.yaml path
-            model = DetectionModel(cfg, channels, classes)  # create model
-            if pretrained:
-                ckpt = torch.load(attempt_download(path), map_location=device)  # load
-                csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32
-                csd = intersect_dicts(csd, model.state_dict(), exclude=['anchors'])  # intersect
-                model.load_state_dict(csd, strict=False)  # load
-                if len(ckpt['model'].names) == classes:
-                    model.names = ckpt['model'].names  # set class names attribute
-        if not verbose:
-            LOGGER.setLevel(logging.INFO)  # reset to default
-        return model.to(device)
-
-    except Exception as e:
-        help_url = 'https://github.com/ultralytics/yolov5/issues/36'
-        s = f'{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help.'
-        raise Exception(s) from e
-
-
-def custom(path='path/to/model.pt', autoshape=True, _verbose=True, device=None):
-    # YOLOv5 custom or local model
-    return _create(path, autoshape=autoshape, verbose=_verbose, device=device)
-
-
-def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
-    # YOLOv5-nano model https://github.com/ultralytics/yolov5
-    return _create('yolov5n', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
-    # YOLOv5-small model https://github.com/ultralytics/yolov5
-    return _create('yolov5s', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
-    # YOLOv5-medium model https://github.com/ultralytics/yolov5
-    return _create('yolov5m', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
-    # YOLOv5-large model https://github.com/ultralytics/yolov5
-    return _create('yolov5l', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
-    # YOLOv5-xlarge model https://github.com/ultralytics/yolov5
-    return _create('yolov5x', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
-    # YOLOv5-nano-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5n6', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
-    # YOLOv5-small-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5s6', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
-    # YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5m6', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
-    # YOLOv5-large-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5l6', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
-    # YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5x6', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-if __name__ == '__main__':
-    import argparse
-    from pathlib import Path
-
-    import numpy as np
-    from PIL import Image
-
-    from utils.general import cv2, print_args
-
-    # Argparser
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--model', type=str, default='yolov5s', help='model name')
-    opt = parser.parse_args()
-    print_args(vars(opt))
-
-    # Model
-    model = _create(name=opt.model, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True)
-    # model = custom(path='path/to/model.pt')  # custom
-
-    # Images
-    imgs = [
-        'data/images/zidane.jpg',  # filename
-        Path('data/images/zidane.jpg'),  # Path
-        'https://ultralytics.com/images/zidane.jpg',  # URI
-        cv2.imread('data/images/bus.jpg')[:, :, ::-1],  # OpenCV
-        Image.open('data/images/bus.jpg'),  # PIL
-        np.zeros((320, 640, 3))]  # numpy
-
-    # Inference
-    results = model(imgs, size=320)  # batched inference
-
-    # Results
-    results.print()
-    results.save()