def main():
epochs = 5
num_class = 10
output_path = './output/catalyst'
# Use if you want to fix seed
# catalyst.utils.set_global_seed(42)
# catalyst.utils.prepare_cudnn(deterministic=True)
model = get_model()
train_loader, val_loader = get_loaders()
loaders = {"train": train_loader, "valid": val_loader}
optimizer, lr_scheduler = get_optimizer(model=model)
criterion = get_criterion()
runner = SupervisedRunner(device=catalyst.utils.get_device())
runner.train(
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=lr_scheduler,
loaders=loaders,
logdir=output_path,
callbacks=[AccuracyCallback(num_classes=num_class, accuracy_args=[1])],
num_epochs=epochs,
main_metric="accuracy01",
minimize_metric=False,
fp16=None,
verbose=True
)
def main():
epochs = 10
train_loader, val_loader = get_loaders()
model = get_model()
device = 'cuda' if torch.cuda.is_available() else 'cpu'
optimizer, scheduler = get_optimizer(model)
criterion = get_criterion()
run(
epochs=epochs,
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
train_loader=train_loader,
val_loader=val_loader,
device=device,
)
def main():
epochs = 10
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = get_model()
train_loader, val_loader = get_loaders()
optimizer, lr_scheduler = get_optimizer(model=model)
criterion = get_criterion()
# Model を multi-gpu したり、FP16 対応したりする
model = model.to(device)
print('Train start !')
for epoch in range(epochs):
print(f'epoch {epoch} start !')
metrics_train = train(model, train_loader, criterion, optimizer,
device)
metrics_eval = eval(model, val_loader, criterion, device)
lr_scheduler.step()
# Logger 周りの処理
# print するためのごちゃごちゃした処理
print(f'epoch: {epoch} ', metrics_train, metrics_eval)
def val_dataloader(self):
# OPTIONAL
return get_loaders()[1]
def train_dataloader(self):
# REQUIRED
return get_loaders()[0]