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hubconf.py
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hubconf.py
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"""YOLOv3 PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov3/
Usage:
import torch
model = torch.hub.load('ultralytics/yolov3', 'yolov3_tiny')
"""
import torch
def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
"""Creates a specified YOLOv3 model
Arguments:
name (str): name of model, i.e. 'yolov3'
pretrained (bool): load pretrained weights into the model
channels (int): number of input channels
classes (int): number of model classes
autoshape (bool): apply YOLOv3 .autoshape() wrapper to model
verbose (bool): print all information to screen
device (str, torch.device, None): device to use for model parameters
Returns:
YOLOv3 pytorch model
"""
from pathlib import Path
from models.yolo import Model, attempt_load
from utils.general import check_requirements, set_logging
from utils.google_utils import attempt_download
from utils.torch_utils import select_device
check_requirements(Path(__file__).parent / 'requirements.txt', exclude=('tensorboard', 'pycocotools', 'thop'))
set_logging(verbose=verbose)
fname = Path(name).with_suffix('.pt') # checkpoint filename
try:
if pretrained and channels == 3 and classes == 80:
model = attempt_load(fname, map_location=torch.device('cpu')) # download/load FP32 model
else:
cfg = list((Path(__file__).parent / 'models').rglob(f'{name}.yaml'))[0] # model.yaml path
model = Model(cfg, channels, classes) # create model
if pretrained:
ckpt = torch.load(attempt_download(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') if device is None else torch.device(device)
return model.to(device)
except Exception as e:
help_url = 'https://github.com/ultralytics/yolov5/issues/36'
s = 'Cache may be out of date, try `force_reload=True`. See %s for help.' % help_url
raise Exception(s) from e
def custom(path='path/to/model.pt', autoshape=True, verbose=True, device=None):
# YOLOv3 custom or local model
return _create(path, autoshape=autoshape, verbose=verbose, device=device)
def yolov3(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
# YOLOv3 model https://github.com/ultralytics/yolov3
return _create('yolov3', pretrained, channels, classes, autoshape, verbose, device)
def yolov3_spp(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
# YOLOv3-SPP model https://github.com/ultralytics/yolov3
return _create('yolov3-spp', pretrained, channels, classes, autoshape, verbose, device)
def yolov3_tiny(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
# YOLOv3-tiny model https://github.com/ultralytics/yolov3
return _create('yolov3-tiny', pretrained, channels, classes, autoshape, verbose, device)
if __name__ == '__main__':
model = _create(name='yolov3', pretrained=True, channels=3, classes=80, autoshape=True, verbose=True) # pretrained
# model = custom(path='path/to/model.pt') # custom
# Verify inference
import cv2
import numpy as np
from PIL import Image
imgs = ['data/images/zidane.jpg', # filename
'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg', # URI
cv2.imread('data/images/bus.jpg')[:, :, ::-1], # OpenCV
Image.open('data/images/bus.jpg'), # PIL
np.zeros((320, 640, 3))] # numpy
results = model(imgs) # batched inference
results.print()
results.save()