# 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, ROOT, check_requirements, intersect_dicts, logging from utils.torch_utils import select_device if not verbose: LOGGER.setLevel(logging.WARNING) check_requirements(ROOT / "requirements.txt", 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://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading" 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()