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hubconf.py
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/
Usage:
import torch
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
model = torch.hub.load('ultralytics/yolov5:master', 'custom', 'path/to/yolov5s.onnx') # file from branch
"""
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.yolo import Model
from utils.downloads import attempt_download
from utils.general import LOGGER, check_requirements, intersect_dicts, logging
from utils.torch_utils import select_device
if not verbose:
LOGGER.setLevel(logging.WARNING)
check_requirements(exclude=('tensorboard', 'thop', 'opencv-python'))
name = Path(name)
path = name.with_suffix('.pt') if name.suffix == '' and not name.is_dir() else name # checkpoint path
try:
device = select_device(('0' if torch.cuda.is_available() else 'cpu') if device is None else device)
if pretrained and channels == 3 and classes == 80:
model = DetectMultiBackend(path, device=device) # download/load FP32 model
# model = models.experimental.attempt_load(path, map_location=device) # download/load FP32 model
else:
cfg = list((Path(__file__).parent / 'models').rglob(f'{path.stem}.yaml'))[0] # model.yaml path
model = Model(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 autoshape:
model = AutoShape(model) # for file/URI/PIL/cv2/np inputs and NMS
return model.to(device)
except Exception as e:
help_url = 'https://github.com/ultralytics/yolov5/issues/36'
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__':
model = _create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True, verbose=True)
# model = custom(path='path/to/model.pt') # custom
# Verify inference
from pathlib import Path
import numpy as np
from PIL import Image
from utils.general import cv2
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
results = model(imgs, size=320) # batched inference
results.print()
results.save()