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| """PyTorch Hub models | |
| Usage: | |
| import torch | |
| model = torch.hub.load('repo', 'model') | |
| """ | |
| from pathlib import Path | |
| import torch | |
| from models.yolo import Model | |
| from utils.general import check_requirements, set_logging | |
| from utils.google_utils import attempt_download | |
| from utils.torch_utils import select_device | |
| dependencies = ['torch', 'yaml'] | |
| check_requirements(Path(__file__).parent / 'requirements.txt', exclude=('pycocotools', 'thop')) | |
| set_logging() | |
| def create(name, pretrained, channels, classes, autoshape): | |
| """Creates a specified model | |
| Arguments: | |
| name (str): name of model, i.e. 'yolov7' | |
| pretrained (bool): load pretrained weights into the model | |
| channels (int): number of input channels | |
| classes (int): number of model classes | |
| Returns: | |
| pytorch model | |
| """ | |
| try: | |
| cfg = list((Path(__file__).parent / 'cfg').rglob(f'{name}.yaml'))[0] # model.yaml path | |
| model = Model(cfg, channels, classes) | |
| if pretrained: | |
| fname = f'{name}.pt' # checkpoint filename | |
| attempt_download(fname) # download if not found locally | |
| ckpt = torch.load(fname, map_location=torch.device('cpu')) # load | |
| msd = model.state_dict() # model state_dict | |
| csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 | |
| csd = {k: v for k, v in csd.items() if msd[k].shape == v.shape} # filter | |
| model.load_state_dict(csd, strict=False) # load | |
| if len(ckpt['model'].names) == classes: | |
| model.names = ckpt['model'].names # set class names attribute | |
| if autoshape: | |
| model = model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS | |
| device = select_device('0' if torch.cuda.is_available() else 'cpu') # default to GPU if available | |
| return model.to(device) | |
| except Exception as e: | |
| s = 'Cache maybe be out of date, try force_reload=True.' | |
| raise Exception(s) from e | |
| def custom(path_or_model='path/to/model.pt', autoshape=True): | |
| """custom mode | |
| Arguments (3 options): | |
| path_or_model (str): 'path/to/model.pt' | |
| path_or_model (dict): torch.load('path/to/model.pt') | |
| path_or_model (nn.Module): torch.load('path/to/model.pt')['model'] | |
| Returns: | |
| pytorch model | |
| """ | |
| model = torch.load(path_or_model) if isinstance(path_or_model, str) else path_or_model # load checkpoint | |
| if isinstance(model, dict): | |
| model = model['ema' if model.get('ema') else 'model'] # load model | |
| hub_model = Model(model.yaml).to(next(model.parameters()).device) # create | |
| hub_model.load_state_dict(model.float().state_dict()) # load state_dict | |
| hub_model.names = model.names # class names | |
| if autoshape: | |
| hub_model = hub_model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS | |
| device = select_device('0' if torch.cuda.is_available() else 'cpu') # default to GPU if available | |
| return hub_model.to(device) | |
| def yolov7(pretrained=True, channels=3, classes=80, autoshape=True): | |
| return create('yolov7', pretrained, channels, classes, autoshape) | |
| if __name__ == '__main__': | |
| model = custom(path_or_model='yolov7.pt') # custom example | |
| # model = create(name='yolov7', pretrained=True, channels=3, classes=80, autoshape=True) # pretrained example | |
| # Verify inference | |
| import numpy as np | |
| from PIL import Image | |
| imgs = [np.zeros((640, 480, 3))] | |
| results = model(imgs) # batched inference | |
| results.print() | |
| results.save() | |