Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Update hubconf.py for unified loading #3005

Merged
merged 1 commit into from
May 1, 2021
Merged

Update hubconf.py for unified loading #3005

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
34 changes: 7 additions & 27 deletions hubconf.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@
check_requirements(Path(__file__).parent / 'requirements.txt', exclude=('tensorboard', 'pycocotools', 'thop'))


def create(name, pretrained, channels, classes, autoshape, verbose):
def create(name, pretrained, channels=3, classes=80, autoshape=True, verbose=True):
"""Creates a specified YOLOv5 model
Arguments:
Expand All @@ -33,7 +33,7 @@ def create(name, pretrained, channels, classes, autoshape, verbose):
YOLOv5 pytorch model
"""
set_logging(verbose=verbose)
fname = f'{name}.pt' # checkpoint filename
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
Expand All @@ -60,30 +60,9 @@ def create(name, pretrained, channels, classes, autoshape, verbose):
raise Exception(s) from e


def custom(path_or_model='path/to/model.pt', autoshape=True, verbose=True):
"""YOLOv5-custom model https://github.com/ultralytics/yolov5
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
"""
set_logging(verbose=verbose)

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 custom(path='path/to/model.pt', autoshape=True, verbose=True):
# YOLOv5 custom or local model
return create(path, autoshape, verbose)


def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True):
Expand Down Expand Up @@ -127,7 +106,8 @@ def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=Tr


if __name__ == '__main__':
model = create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True, verbose=True) # pretrained
model = create(name='weights/yolov5s.pt', pretrained=True, channels=3, classes=80, autoshape=True,
verbose=True) # pretrained
# model = custom(path_or_model='path/to/model.pt') # custom

# Verify inference
Expand Down