def custom(path_or_model='path/to/model.pt', autoshape=True):
"""YOLOv5-custom model from 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
"""
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 create(name, pretrained, channels, classes, autoshape, verbose):
"""Creates a specified YOLOv5 model
Arguments:
name (str): name of model, i.e. 'yolov5s'
pretrained (bool): load pretrained weights into the model
channels (int): number of input channels
classes (int): number of model classes
Returns:
pytorch model
"""
try:
set_logging(verbose=verbose)
cfg = list((Path(__file__).parent /
'models').rglob(f'{name}.yaml'))[0] # model.yaml path
model = Model(cfg, channels, classes)
if pretrained:
fname = f'{name}.pt' # checkpoint filename
attempt_download(fname) # download if not found locally
ckpt = torch.load(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') # default to GPU if available
return model.to(device)
except Exception as e:
help_url = 'https://github.com/ultralytics/yolov5/issues/36'
s = 'Cache maybe be out of date, try force_reload=True. See %s for help.' % help_url
raise Exception(s) from e
def custom(path_or_model="path/to/model.pt", autoshape=True):
"""YOLOv5-custom model from 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
"""
model = (torch.load(path_or_model) if isinstance(path_or_model, str) else
path_or_model) # load checkpoint
if isinstance(model, dict):
model = model["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
return hub_model.autoshape() if autoshape else hub_model
def create(name, pretrained, channels, classes, autoshape):
"""Creates a specified YOLOv5 model
Arguments:
name (str): name of model, i.e. 'yolov5s'
pretrained (bool): load pretrained weights into the model
channels (int): number of input channels
classes (int): number of model classes
Returns:
pytorch model
"""
config = Path(
__file__).parent / "models" / f"{name}.yaml" # model.yaml path
try:
model = Model(config, channels, classes)
if pretrained:
fname = f"{name}.pt" # checkpoint filename
attempt_download(fname) # download if not found locally
ckpt = torch.load(fname, map_location=torch.device("cpu")) # load
state_dict = ckpt["model"].float().state_dict() # to FP32
state_dict = {
k: v
for k, v in state_dict.items()
if model.state_dict()[k].shape == v.shape
} # filter
model.load_state_dict(state_dict, 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
return model
except Exception as e:
help_url = "https://github.com/ultralytics/yolov5/issues/36"
s = (
"Cache maybe be out of date, try force_reload=True. See %s for help."
% help_url)
raise Exception(s) from e