def train_process(self):
"""Whole train and validate process for the fully train cyclesr."""
# self._init_all_settings()
init_log(level=General.logger.level,
log_file="log_worker_{}.txt".format(self.trainer.worker_id),
log_path=self.trainer.local_log_path)
self._init_report()
if self.cfg.cuda:
self.trainer._init_cuda_setting()
self.model = self._init_model()
if self.cfg.distributed:
self._horovod_init_optimizer()
self._init_horovod_setting()
self.train_data = self._init_dataloader('train')
self.valid_data = self._init_dataloader('test')
train_dataloader = Adapter(self.train_data).loader
valid_dataloader = Adapter(self.valid_data).loader
writer = SummaryWriter(self.worker_path)
start_time = time.time()
train_time = 0
best_psnr = -np.inf
best_epoch = 0
logging.info("==> Start training")
val_gan_imgs = self.getValImg(self.train_data, val_num=5)
for epoch in range(self.cfg.epoch_count, self.cfg.n_epoch + self.cfg.n_epoch_decay + 1):
self.model.update_learning_rate(
epoch,
self.cfg.model_desc.custom.cyc_lr,
self.cfg.model_desc.custom.SR_lr,
self.cfg.n_epoch,
self.cfg.n_epoch_decay)
start_train_time = time.time()
self._train(train_dataloader, writer, epoch, self.model, print_freq=self.cfg.print_freq)
train_time += round(time.time() - start_train_time)
# validation
###############################################################################
if epoch % self.cfg.eval_epoch == 0:
logging.info("==> Validng")
self._evalGAN(self.model, val_gan_imgs, epoch, writer)
val_ave_psnr = self._valid(self.model, valid_dataloader, epoch, self.cfg.eval_epoch, writer,
self.cfg.val_ps_offset)
if val_ave_psnr is not None:
logging.info("==> Current ave psnr is {:.3f}".format(val_ave_psnr))
if val_ave_psnr > best_psnr:
best_psnr = val_ave_psnr
best_epoch = epoch
logging.info(
"==> Best PSNR on val dataset {:.3f}, achieved at epoch {}".format(best_psnr, best_epoch))
self._save_checkpoint(epoch, best=True)
self._broadcast(epoch, {"psnr": val_ave_psnr})
model_name = 'epoch' + str(epoch)
logging.info("Saving checkpoints to {}".format(model_name))
self._save_checkpoint(epoch)
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
train_time = str(datetime.timedelta(seconds=train_time))
logging.info("Finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}.".format(elapsed, train_time))
def init_trainer(self):
"""Init Train Op."""
init_log(level=General.logger.level,
log_file="log_worker_{}.txt".format(self.worker_id),
log_path=self.local_log_path)
self._set_default_funcs()
self._set_condition()
self._init_callbacks()
self.callbacks.init_trainer()
self.init_train_op()
def train_process(self):
"""Validate process for the model validate worker."""
init_log(level=General.logger.level,
log_file="host_evaluator_{}.log".format(self.worker_id),
log_path=self.local_log_path)
logging.info("start evaluate process")
self.load_model()
self.valid_loader = self._init_dataloader(mode='test')
performance = self.valid(self.valid_loader)
self._broadcast(performance)
logging.info("the model (id {}) is evaluated on the host".format(self.worker_id))
def train_process(self):
"""Whole train process of the TrainWorker specified in config.
After training, the model and validation results are saved to local_worker_path and s3_path.
"""
init_log(level=General.logger.level,
log_file="log_worker_{}.txt".format(self.worker_id),
log_path=self.local_log_path)
self._set_default_funcs()
self._set_condition()
self._init_callbacks()
self.callbacks.init_trainer()
if not self.lazy_built:
self.build()
self._train_loop()
def _init_env():
if sys.version_info < (3, 6):
sys.exit('Sorry, Python < 3.6 is not supported.')
init_log(level=General.logger.level, log_path=TaskOps().local_log_path)
General.env = init_cluster_args()
_print_task_id()