def __init__(self, model):
super().__init__()
LOGGER.info('Adding AutoShape... ')
copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=()) # copy attributes
self.dmb = isinstance(model, DetectMultiBackend) # DetectMultiBackend() instance
self.pt = not self.dmb or model.pt # PyTorch model
self.model = model.eval()
def autoshape(self): # add autoShape module
print('Adding autoShape... ')
m = autoShape(self) # wrap model
copy_attr(m,
self,
include=('yaml', 'nc', 'hyp', 'names', 'stride'),
exclude=()) # copy attributes
return m
def __init__(self, model):
super().__init__()
LOGGER.info('Adding AutoShape... ')
copy_attr(self,
model,
include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'),
exclude=()) # copy attributes
self.model = model.eval()
def autoshape(self): # add AutoShape module
logger.info('Adding AutoShape... ')
m = AutoShape(self) # wrap model
copy_attr(m,
self,
include=('yaml', 'nc', 'hyp', 'names', 'stride'),
exclude=()) # copy attributes
return m
def autoshape(self): # add autoShape module
print("Adding autoShape... ")
m = autoShape(self) # wrap model
copy_attr(m,
self,
include=("yaml", "nc", "hyp", "names", "stride"),
exclude=()) # copy attributes
return m
def load_model(self):
m = Model(cfg=self.cfg, nc=self.nc)
ckpt = torch.load(self.weights)['model'] # load checkpoint
m.load_state_dict(ckpt.state_dict())
m.names = ckpt.names
#load detections params
self._detections_params()
autoShape.iou = self.iou_thr
autoShape.conf = self.conf
#sacado de ultralytics/yolo
m = m.fuse()
model = autoShape(m)
copy_attr(model,
m,
include=('yaml', 'nc', 'hyp', 'names', 'stride'),
exclude=()) # copy attributes
self.model = model
self.model.cuda()
#fp32 to fp16
if self.half:
self.model.half()
def ema_update_attr(self, include=(),
exclude=("process_group", "reducer")):
copy_attr(self.ema, self.model, include, exclude)
