98 lines
4.2 KiB
Python
98 lines
4.2 KiB
Python
"""YOLOv3 PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov3/
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Usage:
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import torch
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model = torch.hub.load('ultralytics/yolov3', 'yolov3_tiny')
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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 a specified YOLOv3 model
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Arguments:
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name (str): name of model, i.e. 'yolov3'
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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 YOLOv3 .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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YOLOv3 pytorch model
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"""
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from pathlib import Path
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from models.yolo import Model, attempt_load
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from utils.general import check_requirements, set_logging
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from utils.google_utils import attempt_download
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from utils.torch_utils import select_device
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check_requirements(Path(__file__).parent / 'requirements.txt', exclude=('tensorboard', 'pycocotools', 'thop'))
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set_logging(verbose=verbose)
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fname = Path(name).with_suffix('.pt') # checkpoint filename
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try:
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if pretrained and channels == 3 and classes == 80:
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model = attempt_load(fname, map_location=torch.device('cpu')) # download/load FP32 model
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else:
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cfg = list((Path(__file__).parent / 'models').rglob(f'{name}.yaml'))[0] # model.yaml path
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model = Model(cfg, channels, classes) # create model
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if pretrained:
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ckpt = torch.load(attempt_download(fname), map_location=torch.device('cpu')) # load
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msd = model.state_dict() # model state_dict
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csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
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csd = {k: v for k, v in csd.items() if msd[k].shape == v.shape} # filter
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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 autoshape:
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model = model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS
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device = select_device('0' if torch.cuda.is_available() else 'cpu') if device is None else torch.device(device)
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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 = 'Cache may be out of date, try `force_reload=True`. See %s for help.' % help_url
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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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# YOLOv3 custom or local model
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return _create(path, autoshape=autoshape, verbose=verbose, device=device)
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def yolov3(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
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# YOLOv3 model https://github.com/ultralytics/yolov3
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return _create('yolov3', pretrained, channels, classes, autoshape, verbose, device)
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def yolov3_spp(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
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# YOLOv3-SPP model https://github.com/ultralytics/yolov3
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return _create('yolov3-spp', pretrained, channels, classes, autoshape, verbose, device)
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def yolov3_tiny(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
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# YOLOv3-tiny model https://github.com/ultralytics/yolov3
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return _create('yolov3-tiny', pretrained, channels, classes, autoshape, verbose, device)
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if __name__ == '__main__':
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model = _create(name='yolov3', pretrained=True, channels=3, classes=80, autoshape=True, verbose=True) # pretrained
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# model = custom(path='path/to/model.pt') # custom
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# Verify inference
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import cv2
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import numpy as np
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from PIL import Image
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imgs = ['data/images/zidane.jpg', # filename
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'https://github.com/ultralytics/yolov5/releases/download/v1.0/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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results = model(imgs) # batched inference
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results.print()
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results.save()
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