'cuda',
batch_size=batch_size,
save_path=base_path)
data_path_root_test = pathlib.Path('test/')
test_anno = pd.DataFrame({
'id': [f'pic{num:06}' for num in range(10699)],
'category': [0 for num in range(10699)]
})
test_dataset = MyDataset(data_dir=data_path_root_test,
data_anno=test_anno,
phase='train')
test_dataloader = DataLoader(test_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=0)
all_preds_list = []
for i in range(7):
all_preds_list.append(
trainer.predict(test_dataloader, labels=True).flatten())
print((all_preds_list[0] != all_preds_list[1]).mean())
val, count = mode(all_preds_list, axis=0)
preds = val.ravel().tolist()
submit = pd.DataFrame({
'id': [f'pic{num:06}' for num in range(10699)],
'category': [TRAFFIC_LABELS[pred] for pred in preds]
})
submit.to_csv(base_path / 'submit_tta.csv', index=False)
print('Done')
criterion = nn.CrossEntropyLoss()
# Scheduler
scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=1.5 * 1e-3, steps_per_epoch=len(train_dataloader),
epochs=num_epochs)
# Training
# optim_gen = lambda parameters, lr: SGD(parameters, lr=lr)
# find_lr(model, optim_gen, min_lr, max_lr, num_epochs, train_dataloader, val_dataloader, criterion, device, batch_size,
# batches_per_epoch, comet_experiment)
save_path = pathlib.Path('models') / name
save_path.mkdir(parents=True, exist_ok=True)
trainer = Trainer(model, train_dataloader, val_dataloader, criterion, optimizer, None, device, TRAFFIC_LABELS,
num_epochs, batch_size, batches_per_epoch, comet_experiment, save_path)
try:
trainer.fit()
except KeyboardInterrupt:
pass
# Prediction
data_path_root_test = pathlib.Path('test/')
test_anno = pd.DataFrame({'id': [f'pic{num:06}' for num in range(10699)]})
test_dataset = MyDataset(data_dir=data_path_root_test, data_anno=test_anno, phase='test')
test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=0)
preds = trainer.predict(test_dataloader)
submit = pd.DataFrame({'id': [f'pic{num:06}' for num in range(10699)],
'category': [TRAFFIC_LABELS[pred] for pred in preds]})
submit.to_csv(save_path / 'submit.csv')
print('Done')
