def test_main(): model = Net() model.load_state_dict(torch.load(model_file)) model.to(device) test_loader = get_test_loader(25) print('=========') print('Test set:') with torch.no_grad(): evaluate(model, test_loader)
def test_main(): print('Reading', model_file) model = Net() model.load_state_dict(torch.load(model_file)) model.to(device) test_loader = get_test_loader(25) print('=========') print('Simple:') with torch.no_grad(): evaluate(model, test_loader)
def main(): model = Net() batch_size = 25 train_loader = get_train_loader(batch_size) validation_loader = get_validation_loader(batch_size) trainer = pl.Trainer(gpus=-1, max_epochs=50, accelerator='ddp') # trainer = pl.Trainer(gpus=1, max_epochs=50, accelerator='horovod', checkpoint_callback=False) start_time = datetime.now() trainer.fit(model, train_loader, validation_loader) end_time = datetime.now() print('Total training time: {}.'.format(end_time - start_time)) # torch.save(model.state_dict(), model_file) # print('Wrote model to', model_file) test_loader = get_test_loader(batch_size) trainer.test(test_dataloaders=test_loader)
def test_main(): model = PretrainedNet() model.load_state_dict(torch.load(model_file)) model.to(device) test_loader = get_test_loader(25) print('=========') print('Pretrained:') with torch.no_grad(): evaluate(model, test_loader) model = PretrainedNet() model.load_state_dict(torch.load(model_file_ft)) model.to(device) print('=========') print('Finetuned:') with torch.no_grad(): evaluate(model, test_loader)