def create_ema_model(model):
ema_model = Res_Deeplab(num_classes=num_classes)
for param in ema_model.parameters():
param.detach_()
mp = list(model.parameters())
mcp = list(ema_model.parameters())
n = len(mp)
for i in range(0, n):
mcp[i].data[:] = mp[i].data[:].clone()
if len(gpus) > 1:
if use_sync_batchnorm:
ema_model = convert_model(ema_model)
ema_model = DataParallelWithCallback(ema_model, device_ids=gpus)
else:
ema_model = torch.nn.DataParallel(ema_model, device_ids=gpus)
return ema_model
def create_ema_model(model):
#ema_model = getattr(models, config['arch']['type'])(self.train_loader.dataset.num_classes, **config['arch']['args']).to(self.device)
ema_model = Res_Deeplab(num_classes=num_classes)
for param in ema_model.parameters():
param.detach_()
mp = list(model.parameters())
mcp = list(ema_model.parameters())
n = len(mp)
for i in range(0, n):
mcp[i].data[:] = mp[i].data[:].clone()
#_, availble_gpus = self._get_available_devices(self.config['n_gpu'])
#ema_model = torch.nn.DataParallel(ema_model, device_ids=availble_gpus)
if len(gpus) > 1:
#return torch.nn.DataParallel(ema_model, device_ids=gpus)
if use_sync_batchnorm:
ema_model = convert_model(ema_model)
ema_model = DataParallelWithCallback(ema_model, device_ids=gpus)
else:
ema_model = torch.nn.DataParallel(ema_model, device_ids=gpus)
return ema_model