def create(name,
pretrained=True,
channels=3,
classes=80,
autoshape=True,
verbose=True):
"""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
autoshape (bool): apply YOLOv5 .autoshape() wrapper to model
verbose (bool): print all information to screen
Returns:
YOLOv5 pytorch model
"""
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:
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s = 'Cache may be out of date, try `force_reload=True`. See %s for help.' % help_url
raise Exception(s) from e
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
# ------------------------- yolov5s 모델 import -------------------------
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)
device = None
name1 = 'yolov5_custom_pink.pt'
fname1 = Path(name1).with_suffix('.pt') # checkpoint filename
model1 = attempt_load(fname1, map_location=torch.device('cpu'))
model1 = model1.autoshape() # for file/URI/PIL/cv2/np inputs and NMS
model1.conf = 0.5
name2 = 'yolov5_custom.pt'
fname2 = Path(name2).with_suffix('.pt') # checkpoint filename
model2 = attempt_load(fname2, map_location=torch.device('cpu'))
model2 = model2.autoshape() # for file/URI/PIL/cv2/np inputs and NMS
model2.conf = 0.5
device = select_device('0' if torch.cuda.is_available() else 'cpu'
) if device is None else torch.device(device)
# ------------------------- 모델 import fin -------------------------
"""
on_connect는 subscriber가 브로커에 연결하면서 호출할 함수
rc가 0이면 정상접속이 됐다는 의미