# 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') model = torch.hub.load('ultralytics/yolov5:master', 'custom', 'path/to/yolov5s.onnx') # file from branch """ 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 Model 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=('tensorboard', 'thop', 'opencv-python')) 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: 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 = Model(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()