From 87c22e0499698bf00f769c06b8b7f9ecb0c8641d Mon Sep 17 00:00:00 2001 From: Edward Yang Date: Sat, 8 Feb 2025 13:30:58 +1100 Subject: [PATCH] remove hubconf.py --- hubconf.py | 168 ----------------------------------------------------- 1 file changed, 168 deletions(-) delete mode 100644 hubconf.py diff --git a/hubconf.py b/hubconf.py deleted file mode 100644 index 5a9af66e2..000000000 --- a/hubconf.py +++ /dev/null @@ -1,168 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-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, intersect_dicts, logging - from utils.torch_utils import select_device - - if not verbose: - LOGGER.setLevel(logging.WARNING) - 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()