def load_DNet_for_train(cfg, dict_DB):
model = DNet(cfg=cfg)
model.cuda()
optimizer = torch.optim.Adam(params=model.parameters(),
lr=cfg.lr,
weight_decay=cfg.weight_decay)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer=optimizer,
milestones=cfg.milestones,
gamma=cfg.gamma)
if cfg.resume == False:
checkpoint = torch.load(cfg.weight_dir + 'checkpoint_DNet_final')
model.load_state_dict(checkpoint['model'], strict=False)
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer=optimizer,
milestones=cfg.milestones,
gamma=cfg.gamma,
last_epoch=checkpoint['epoch'])
dict_DB['epoch'] = checkpoint['epoch']
dict_DB['val_result'] = checkpoint['val_result']
loss_fn = Loss_Function()
dict_DB['DNet'] = model
dict_DB['optimizer'] = optimizer
dict_DB['scheduler'] = scheduler
dict_DB['loss_fn'] = loss_fn
return dict_DB
def load_DNet_for_test(cfg, dict_DB):
if cfg.run_mode == 'test_paper':
checkpoint = torch.load(cfg.paper_weight_dir + 'checkpoint_DNet_paper')
else:
# select ckpt from output_dir
checkpoint = torch.load(cfg.weight_dir + '')
model = DNet(cfg=cfg)
model.load_state_dict(checkpoint['model'], strict=False)
model.cuda()
dict_DB['DNet'] = model
return dict_DB