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[UNET3D] Replace epochs with samples #684

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[UNET3D] Replace epochs with samples #684

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2 changes: 1 addition & 1 deletion image_segmentation/pytorch/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -64,7 +64,7 @@ def main():

if flags.exec_mode == 'train':
train(flags, model, train_dataloader, val_dataloader, loss_fn, score_fn,
device=device, callbacks=callbacks, is_distributed=is_distributed)
device=device, callbacks=callbacks, is_distributed=is_distributed, samples_per_epoch=samples_per_epoch)

elif flags.exec_mode == 'evaluate':
eval_metrics = evaluate(flags, model, val_dataloader, loss_fn, score_fn,
Expand Down
25 changes: 16 additions & 9 deletions image_segmentation/pytorch/runtime/training.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,8 @@ def lr_warmup(optimizer, init_lr, lr, current_epoch, warmup_epochs):
param_group['lr'] = init_lr + (lr - init_lr) * scale


def train(flags, model, train_loader, val_loader, loss_fn, score_fn, device, callbacks, is_distributed):
def train(flags, model, train_loader, val_loader, loss_fn, score_fn, device, callbacks,
is_distributed, samples_per_epoch):
rank = get_rank()
world_size = get_world_size()
torch.backends.cudnn.benchmark = flags.cudnn_benchmark
Expand All @@ -52,6 +53,7 @@ def train(flags, model, train_loader, val_loader, loss_fn, score_fn, device, cal

is_successful = False
diverged = False
epoch = 1
next_eval_at = flags.start_eval_at
model.train()
for callback in callbacks:
Expand All @@ -61,8 +63,9 @@ def train(flags, model, train_loader, val_loader, loss_fn, score_fn, device, cal
if epoch <= flags.lr_warmup_epochs and flags.lr_warmup_epochs > 0:
lr_warmup(optimizer, flags.init_learning_rate, flags.learning_rate, epoch, flags.lr_warmup_epochs)
mllog_start(key=CONSTANTS.BLOCK_START, sync=False,
metadata={CONSTANTS.FIRST_EPOCH_NUM: epoch, CONSTANTS.EPOCH_COUNT: 1})
mllog_start(key=CONSTANTS.EPOCH_START, metadata={CONSTANTS.EPOCH_NUM: epoch}, sync=False)
metadata={CONSTANTS.FIRST_EPOCH_NUM: epoch * samples_per_epoch,
CONSTANTS.EPOCH_COUNT: samples_per_epoch})
mllog_start(key=CONSTANTS.EPOCH_START, metadata={CONSTANTS.EPOCH_NUM: epoch * samples_per_epoch}, sync=False)

if is_distributed:
train_loader.sampler.set_epoch(epoch)
Expand Down Expand Up @@ -98,24 +101,26 @@ def train(flags, model, train_loader, val_loader, loss_fn, score_fn, device, cal
cumulative_loss.append(loss_value)

mllog_end(key=CONSTANTS.EPOCH_STOP, sync=False,
metadata={CONSTANTS.EPOCH_NUM: epoch, 'current_lr': optimizer.param_groups[0]['lr']})
metadata={CONSTANTS.EPOCH_NUM: epoch * samples_per_epoch,
'current_lr': optimizer.param_groups[0]['lr']})

if flags.lr_decay_epochs:
scheduler.step()

if epoch == next_eval_at:
next_eval_at += flags.evaluate_every
del output
mllog_start(key=CONSTANTS.EVAL_START, value=epoch, metadata={CONSTANTS.EPOCH_NUM: epoch}, sync=False)
mllog_start(key=CONSTANTS.EVAL_START, value=epoch * samples_per_epoch,
metadata={CONSTANTS.EPOCH_NUM: epoch * samples_per_epoch}, sync=False)

eval_metrics = evaluate(flags, model, val_loader, loss_fn, score_fn, device, epoch)
eval_metrics["train_loss"] = sum(cumulative_loss) / len(cumulative_loss)

mllog_event(key=CONSTANTS.EVAL_ACCURACY,
value=eval_metrics["mean_dice"],
metadata={CONSTANTS.EPOCH_NUM: epoch},
metadata={CONSTANTS.EPOCH_NUM: epoch * samples_per_epoch},
sync=False)
mllog_end(key=CONSTANTS.EVAL_STOP, metadata={CONSTANTS.EPOCH_NUM: epoch}, sync=False)
mllog_end(key=CONSTANTS.EVAL_STOP, metadata={CONSTANTS.EPOCH_NUM: epoch * samples_per_epoch}, sync=False)

for callback in callbacks:
callback.on_epoch_end(epoch=epoch, metrics=eval_metrics, model=model, optimizer=optimizer)
Expand All @@ -127,12 +132,14 @@ def train(flags, model, train_loader, val_loader, loss_fn, score_fn, device, cal
diverged = True

mllog_end(key=CONSTANTS.BLOCK_STOP, sync=False,
metadata={CONSTANTS.FIRST_EPOCH_NUM: epoch, CONSTANTS.EPOCH_COUNT: 1})
metadata={CONSTANTS.FIRST_EPOCH_NUM: epoch * samples_per_epoch,
CONSTANTS.EPOCH_COUNT: samples_per_epoch})

if is_successful or diverged:
break

mllog_end(key=CONSTANTS.RUN_STOP, sync=True,
metadata={CONSTANTS.STATUS: CONSTANTS.SUCCESS if is_successful else CONSTANTS.ABORTED})
metadata={CONSTANTS.STATUS: CONSTANTS.SUCCESS if is_successful else CONSTANTS.ABORTED,
CONSTANTS.EPOCH_COUNT: epoch * samples_per_epoch})
for callback in callbacks:
callback.on_fit_end()