def test_trainer_fit_4(data):
model = deepcopy(data[0])
trainer = Trainer(model=model,
optimizer=Adam(),
loss_func=MSELoss(),
clip_grad=ClipValue(0.1),
lr_scheduler=ReduceLROnPlateau(),
epochs=10)
count = 1
for i in trainer(*data[1]):
assert isinstance(i, dict)
assert i['i_epoch'] == count
if count == 3:
trainer.early_stop('stop')
count += 1
assert trainer.total_epochs == 3
assert trainer._early_stopping == (True, 'stop')
trainer.reset()
train_set = DataLoader(TensorDataset(*data[1]))
count = 1
for i in trainer(training_dataset=train_set):
assert isinstance(i, dict)
assert i['i_batch'] == count
if count == 3:
trainer.early_stop('stop!!!')
count += 1
assert trainer.total_iterations == 3
assert trainer._early_stopping == (True, 'stop!!!')
def test_trainer_fit_1(data):
model = deepcopy(data[0])
trainer = Trainer(model=model, optimizer=Adam(), loss_func=MSELoss())
trainer.fit(*data[1])
assert trainer.total_iterations == 200
assert trainer.total_epochs == 200
trainer.fit(*data[1], epochs=20)
assert trainer.total_iterations == 220
assert trainer.total_epochs == 220
trainer.reset()
assert trainer.total_iterations == 0
assert trainer.total_epochs == 0
trainer.fit(*data[1], epochs=20)
assert trainer.total_iterations == 20
assert trainer.total_epochs == 20
assert isinstance(trainer.training_info, pd.DataFrame)
assert 'i_epoch' in trainer.training_info.columns
ret = trainer.to_namedtuple()
assert isinstance(ret, trainer.results_tuple)
train_set = DataLoader(TensorDataset(*data[1]))
with pytest.raises(RuntimeError, match='parameter <training_dataset> is exclusive of <x_train> and <y_train>'):
trainer.fit(*data[1], training_dataset=train_set)
with pytest.raises(RuntimeError, match='missing parameter <x_train> or <y_train>'):
trainer.fit(data[1][0])
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)])
