def test_trainer_2(data):
trainer = Trainer()
with pytest.raises(RuntimeError, match='no model for training'):
trainer.fit(*data[1])
with pytest.raises(
TypeError,
match='parameter `m` must be a instance of <torch.nn.modules>'):
trainer.model = {}
trainer.model = data[0]
assert isinstance(trainer.model, torch.nn.Module)
with pytest.raises(RuntimeError, match='no loss function for training'):
trainer.fit(*data[1])
trainer.loss_func = MSELoss()
assert trainer.loss_type == 'train_mse_loss'
assert trainer.loss_func.__class__ == MSELoss
with pytest.raises(RuntimeError, match='no optimizer for training'):
trainer.fit(*data[1])
trainer.optimizer = Adam()
assert isinstance(trainer.optimizer, torch.optim.Adam)
assert isinstance(trainer._optimizer_state, dict)
assert isinstance(trainer._init_states, dict)
trainer.lr_scheduler = ExponentialLR(gamma=0.99)
assert isinstance(trainer.lr_scheduler,
torch.optim.lr_scheduler.ExponentialLR)
def test_trainer_3(data):
model = data[0]
trainer = Trainer(model=model, optimizer=Adam(), loss_func=MSELoss())
assert isinstance(trainer.model, torch.nn.Module)
assert isinstance(trainer.optimizer, torch.optim.Adam)
assert isinstance(trainer._optimizer_state, dict)
assert isinstance(trainer._init_states, dict)
assert trainer.clip_grad is None
assert trainer.lr_scheduler is None
trainer.lr_scheduler = ExponentialLR(gamma=0.1)
assert isinstance(trainer.lr_scheduler, torch.optim.lr_scheduler.ExponentialLR)
trainer.optimizer = SGD()
assert isinstance(trainer.optimizer, torch.optim.SGD)
trainer.clip_grad = ClipNorm(max_norm=0.4)
assert isinstance(trainer.clip_grad, ClipNorm)