def test_validator_1(data):
model = deepcopy(data[0])
trainer = Trainer(model=model, optimizer=Adam(lr=0.1), loss_func=MSELoss(), epochs=20)
trainer.extend(TensorConverter(), Validator('regress', early_stop=30, trace_order=1, warming_up=0, mae=0))
trainer.fit(*data[1], *data[1])
assert trainer.get_checkpoint() == ['mae']
model = deepcopy(data[0])
trainer = Trainer(model=model, optimizer=Adam(lr=0.1), loss_func=MSELoss(), epochs=20)
trainer.extend(TensorConverter(), Validator('regress', early_stop=30, trace_order=5, warming_up=50, mae=0))
trainer.fit(*data[1], *data[1])
assert trainer.get_checkpoint() == []
def test_trainer_fit_3(data):
model = deepcopy(data[0])
trainer = Trainer(model=model,
optimizer=Adam(),
loss_func=MSELoss(),
epochs=5)
trainer.fit(*data[1])
assert len(trainer.checkpoints.keys()) == 0
trainer.reset()
assert trainer.total_iterations == 0
assert trainer.total_epochs == 0
assert len(trainer.get_checkpoint()) == 0
trainer.fit(*data[1], checkpoint=True)
assert len(trainer.get_checkpoint()) == 5
assert isinstance(trainer.get_checkpoint(2), trainer.checkpoint_tuple)
assert isinstance(trainer.get_checkpoint('cp_2'), trainer.checkpoint_tuple)
with pytest.raises(TypeError, match='parameter <cp> must be str or int'):
trainer.get_checkpoint([])
trainer.reset(to=3, remove_checkpoints=False)
assert len(trainer.get_checkpoint()) == 5
assert isinstance(trainer.get_checkpoint(2), trainer.checkpoint_tuple)
assert isinstance(trainer.get_checkpoint('cp_2'), trainer.checkpoint_tuple)
trainer.reset(to='cp_3')
assert trainer.total_iterations == 0
assert trainer.total_epochs == 0
assert len(trainer.get_checkpoint()) == 0
with pytest.raises(
TypeError,
match='parameter <to> must be torch.nnModule, int, or str'):
trainer.reset(to=[])
# todo: need a real testing
trainer.fit(*data[1], checkpoint=True)
trainer.predict(*data[1], checkpoint=3)
trainer.reset()
trainer.fit(*data[1], checkpoint=lambda i: (True, f'new:{i}'))
assert len(trainer.get_checkpoint()) == 5
assert trainer.get_checkpoint() == list([f'new:{i + 1}' for i in range(5)])