def eval_epoch(
epoch: int,
model: BaseModel,
dataset,
device,
tracker: BaseTracker,
checkpoint: ModelCheckpoint,
visualizer: Visualizer,
early_break: bool,
):
model.eval()
tracker.reset("val")
visualizer.reset(epoch, "val")
loader = dataset.val_dataloader()
with Ctq(loader) as tq_val_loader:
for data in tq_val_loader:
data = data.to(device)
with torch.no_grad():
model.set_input(data)
model.forward()
tracker.track(model)
tq_val_loader.set_postfix(**tracker.get_metrics(),
color=COLORS.VAL_COLOR)
if visualizer.is_active:
visualizer.save_visuals(model.get_current_visuals())
if early_break:
break
metrics = tracker.publish(epoch)
tracker.print_summary()
checkpoint.save_best_models_under_current_metrics(model, metrics)
def run_epoch(model: BaseModel, loader, device: str, num_batches: int):
model.eval()
with Ctq(loader) as tq_loader:
for batch_idx, data in enumerate(tq_loader):
if batch_idx < num_batches:
process(model, data, device)
else:
break
def test_epoch(model: BaseModel, dataset, device, tracker: BaseTracker,
checkpoint: ModelCheckpoint, log):
model.eval()
tracker.reset("test")
loader = dataset.test_dataloader()
with Ctq(loader) as tq_test_loader:
for data in tq_test_loader:
data = data.to(device)
with torch.no_grad():
model.set_input(data)
model.forward()
tracker.track(model)
tq_test_loader.set_postfix(**tracker.get_metrics(),
color=COLORS.TEST_COLOR)
metrics = tracker.publish()
tracker.print_summary()
checkpoint.save_best_models_under_current_metrics(model, metrics)
def test_epoch(
epoch: int,
model: BaseModel,
dataset,
device,
tracker: BaseTracker,
checkpoint: ModelCheckpoint,
visualizer: Visualizer,
early_break: bool,
):
model.eval()
loaders = dataset.test_dataloaders()
for idx, loader in enumerate(loaders):
stage_name = dataset.get_test_dataset_name(idx)
tracker.reset(stage_name)
visualizer.reset(epoch, stage_name)
with Ctq(loader) as tq_test_loader:
for data in tq_test_loader:
data = data.to(device)
with torch.no_grad():
model.set_input(data)
model.forward()
tracker.track(model)
tq_test_loader.set_postfix(**tracker.get_metrics(),
color=COLORS.TEST_COLOR)
if visualizer.is_active:
visualizer.save_visuals(model.get_current_visuals())
if early_break:
break
metrics = tracker.publish(epoch)
tracker.print_summary()
checkpoint.save_best_models_under_current_metrics(model, metrics)
