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| # 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, 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() | |