remove hubconf.py
Edward Yang
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2cb269df45
commit
87c22e0499
168
hubconf.py
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hubconf.py
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5
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Usage:
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import torch
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model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # official model
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model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s') # from branch
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model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt') # custom/local model
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model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local') # local repo
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"""
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import torch
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def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
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"""Creates or loads a YOLOv5 model
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Arguments:
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name (str): model name 'yolov5s' or path 'path/to/best.pt'
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pretrained (bool): load pretrained weights into the model
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channels (int): number of input channels
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classes (int): number of model classes
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autoshape (bool): apply YOLOv5 .autoshape() wrapper to model
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verbose (bool): print all information to screen
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device (str, torch.device, None): device to use for model parameters
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Returns:
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YOLOv5 model
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"""
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from pathlib import Path
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from models.common import AutoShape, DetectMultiBackend
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from models.experimental import attempt_load
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from models.yolo import ClassificationModel, DetectionModel, SegmentationModel
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from utils.downloads import attempt_download
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from utils.general import LOGGER, intersect_dicts, logging
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from utils.torch_utils import select_device
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if not verbose:
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LOGGER.setLevel(logging.WARNING)
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name = Path(name)
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path = name.with_suffix('.pt') if name.suffix == '' and not name.is_dir() else name # checkpoint path
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try:
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device = select_device(device)
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if pretrained and channels == 3 and classes == 80:
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try:
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model = DetectMultiBackend(path, device=device, fuse=autoshape) # detection model
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if autoshape:
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if model.pt and isinstance(model.model, ClassificationModel):
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LOGGER.warning('WARNING ⚠️ YOLOv5 ClassificationModel is not yet AutoShape compatible. '
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'You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224).')
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elif model.pt and isinstance(model.model, SegmentationModel):
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LOGGER.warning('WARNING ⚠️ YOLOv5 SegmentationModel is not yet AutoShape compatible. '
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'You will not be able to run inference with this model.')
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else:
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model = AutoShape(model) # for file/URI/PIL/cv2/np inputs and NMS
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except Exception:
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model = attempt_load(path, device=device, fuse=False) # arbitrary model
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else:
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cfg = list((Path(__file__).parent / 'models').rglob(f'{path.stem}.yaml'))[0] # model.yaml path
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model = DetectionModel(cfg, channels, classes) # create model
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if pretrained:
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ckpt = torch.load(attempt_download(path), map_location=device) # load
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csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
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csd = intersect_dicts(csd, model.state_dict(), exclude=['anchors']) # intersect
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model.load_state_dict(csd, strict=False) # load
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if len(ckpt['model'].names) == classes:
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model.names = ckpt['model'].names # set class names attribute
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if not verbose:
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LOGGER.setLevel(logging.INFO) # reset to default
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return model.to(device)
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except Exception as e:
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help_url = 'https://github.com/ultralytics/yolov5/issues/36'
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s = f'{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help.'
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raise Exception(s) from e
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def custom(path='path/to/model.pt', autoshape=True, _verbose=True, device=None):
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# YOLOv5 custom or local model
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return _create(path, autoshape=autoshape, verbose=_verbose, device=device)
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def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
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# YOLOv5-nano model https://github.com/ultralytics/yolov5
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return _create('yolov5n', pretrained, channels, classes, autoshape, _verbose, device)
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def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
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# YOLOv5-small model https://github.com/ultralytics/yolov5
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return _create('yolov5s', pretrained, channels, classes, autoshape, _verbose, device)
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def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
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# YOLOv5-medium model https://github.com/ultralytics/yolov5
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return _create('yolov5m', pretrained, channels, classes, autoshape, _verbose, device)
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def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
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# YOLOv5-large model https://github.com/ultralytics/yolov5
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return _create('yolov5l', pretrained, channels, classes, autoshape, _verbose, device)
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def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
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# YOLOv5-xlarge model https://github.com/ultralytics/yolov5
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return _create('yolov5x', pretrained, channels, classes, autoshape, _verbose, device)
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def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
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# YOLOv5-nano-P6 model https://github.com/ultralytics/yolov5
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return _create('yolov5n6', pretrained, channels, classes, autoshape, _verbose, device)
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def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
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# YOLOv5-small-P6 model https://github.com/ultralytics/yolov5
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return _create('yolov5s6', pretrained, channels, classes, autoshape, _verbose, device)
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def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
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# YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5
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return _create('yolov5m6', pretrained, channels, classes, autoshape, _verbose, device)
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def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
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# YOLOv5-large-P6 model https://github.com/ultralytics/yolov5
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return _create('yolov5l6', pretrained, channels, classes, autoshape, _verbose, device)
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def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
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# YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5
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return _create('yolov5x6', pretrained, channels, classes, autoshape, _verbose, device)
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if __name__ == '__main__':
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import argparse
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from pathlib import Path
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import numpy as np
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from PIL import Image
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from utils.general import cv2, print_args
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# Argparser
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parser = argparse.ArgumentParser()
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parser.add_argument('--model', type=str, default='yolov5s', help='model name')
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opt = parser.parse_args()
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print_args(vars(opt))
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# Model
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model = _create(name=opt.model, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True)
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# model = custom(path='path/to/model.pt') # custom
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# Images
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imgs = [
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'data/images/zidane.jpg', # filename
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Path('data/images/zidane.jpg'), # Path
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'https://ultralytics.com/images/zidane.jpg', # URI
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cv2.imread('data/images/bus.jpg')[:, :, ::-1], # OpenCV
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Image.open('data/images/bus.jpg'), # PIL
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np.zeros((320, 640, 3))] # numpy
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# Inference
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results = model(imgs, size=320) # batched inference
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# Results
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results.print()
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results.save()
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