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)