def do_continuous_train(cfg, train_loader, valid_dict,
source_tr_comp: TrainComponent,
current_tr_comp: TrainComponent, saver):
# tb_log = TensorBoardXLog(cfg, saver.save_dir)
trainer = create_supervised_trainer(source_tr_comp.model,
current_tr_comp.model,
current_tr_comp.optimizer,
current_tr_comp.loss,
device=cfg.MODEL.DEVICE,
apex=cfg.APEX.IF_ON)
run(cfg, train_loader, valid_dict, current_tr_comp, saver, trainer)
def fine_tune_current_model(cfg, train_loader, valid_dict, tr_comp, saver):
for param in tr_comp.model.base.parameters():
param.requires_grad = False
trainer = create_supervised_trainer(tr_comp.model,
tr_comp.optimizer,
tr_comp.loss,
device=cfg.MODEL.DEVICE,
apex=cfg.APEX.IF_ON)
evaler = Eval(valid_dict, cfg.MODEL.DEVICE)
evaler.get_valid_eval_map(cfg, tr_comp.model)
copy_cfg = copy.deepcopy(cfg)
copy_cfg["TRAIN"]["MAX_EPOCHS"] = 60
run(copy_cfg, train_loader, tr_comp, saver, trainer, evaler)
def train_autoencoder(cfg, train_loader, valid_dict,
source_tr_comp: TrainComponent,
current_tr_comp: TrainComponent, saver):
trainer = create_autoencoder_trainer(source_tr_comp.model,
current_tr_comp.model,
current_tr_comp.optimizer,
current_tr_comp.loss,
device=cfg.MODEL.DEVICE,
apex=cfg.APEX.IF_ON)
evaler = Eval(valid_dict, cfg.MODEL.DEVICE)
evaler.get_valid_eval_map_autoencoder(cfg, source_tr_comp.model,
current_tr_comp.model)
copy_cfg = copy.deepcopy(cfg)
copy_cfg["TRAIN"]["MAX_EPOCHS"] = 90
run(copy_cfg, train_loader, current_tr_comp, saver, trainer, evaler)
def ebll_train(cfg, train_loader, valid_dict, source_tr_comp, current_tr_comp,
autoencoder_tr, saver):
for param in current_tr_comp.model.base.parameters():
param.requires_grad = True
trainer = create_ebll_trainer(source_tr_comp.model,
autoencoder_tr.model,
current_tr_comp.model,
current_tr_comp.optimizer,
current_tr_comp.loss,
apex=cfg.APEX.IF_ON,
device=cfg.MODEL.DEVICE)
evaler = Eval(valid_dict, cfg.MODEL.DEVICE)
evaler.get_valid_eval_map_ebll(cfg, source_tr_comp.model,
current_tr_comp.model)
run(cfg, train_loader, current_tr_comp, saver, trainer, evaler)
base_model.eval()
if diff_attention_model is not None:
diff_attention_model = diff_attention_model.to(device)
diff_attention_model.eval()
logger.info('Get model.')
# Get data loaders.
train_loader, query_loader, gallery_loader = loader.get_data_loaders(config, base_model=base_model, device=device)
logger.info('Get data loaders.')
# Get loss.
loss, center_loss = loss.get_loss(config, device)
logger.info('Get loss.')
# Get optimizer.
base_optimizer, base_scheduler, diff_optimizer, diff_scheduler, center_optimizer, center_scheduler = optimizer.get_optimizer(
config, base_model, diff_attention_model, center_loss=center_loss)
logger.info('Get optimizer.')
# Get trainer.
trainer = trainer.get_trainer(config, base_model, diff_attention_model, loss, device, logger, query_loader,
gallery_loader, base_optimizer=base_optimizer, base_scheduler=base_scheduler,
diff_optimizer=diff_optimizer, diff_scheduler=diff_scheduler,
center_optimizer=center_optimizer, center_scheduler=center_scheduler)
logger.info('Get trainer.')
# Do train.
logger.info('Start training.')
trainer.run(train_loader, max_epochs=config['trainer'].getint('epochs'))
logger.info('Finish training.')
