def test_trainer_1(data): trainer = Trainer() assert trainer.device == torch.device('cpu') assert trainer.model is None assert trainer.optimizer is None assert trainer.lr_scheduler is None assert trainer.x_val is None assert trainer.y_val is None assert trainer.validate_dataset is None assert trainer._init_states is None assert trainer._optimizer_state is None assert trainer.total_epochs == 0 assert trainer.total_iterations == 0 assert trainer.training_info is None assert trainer.loss_type is None assert trainer.loss_func is None trainer = Trainer(optimizer=Adam(), loss_func=MSELoss(), lr_scheduler=ExponentialLR(gamma=0.99), clip_grad=ClipValue(clip_value=0.1)) assert isinstance(trainer._scheduler, ExponentialLR) assert isinstance(trainer._optim, Adam) assert isinstance(trainer.clip_grad, ClipValue) assert isinstance(trainer.loss_func, MSELoss)
def test_trainer_prediction_1(data): model = deepcopy(data[0]) trainer = Trainer(model=model, optimizer=Adam(lr=0.1), loss_func=MSELoss(), epochs=200) trainer.extend(TensorConverter()) trainer.fit(*data[1], *data[1]) trainer = Trainer(model=model).extend(TensorConverter()) y_p = trainer.predict(data[1][0]) assert np.any(np.not_equal(y_p, data[1][1].numpy())) assert np.allclose(y_p, data[1][1].numpy(), rtol=0, atol=0.2) y_p, y_t = trainer.predict(*data[1]) assert np.any(np.not_equal(y_p, y_t)) assert np.allclose(y_p, y_t, rtol=0, atol=0.2) val_set = DataLoader(TensorDataset(*data[1]), batch_size=50) y_p, y_t = trainer.predict(dataset=val_set) assert np.any(np.not_equal(y_p, y_t)) assert np.allclose(y_p, y_t, rtol=0, atol=0.2) with pytest.raises( RuntimeError, match='parameters <x_in> and <dataset> are mutually exclusive'): trainer.predict(*data[1], dataset='not none')
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_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_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_prediction_2(): model = _Net(n_feature=2, n_hidden=10, n_output=2) n_data = np.ones((100, 2)) x0 = np.random.normal(2 * n_data, 1) y0 = np.zeros(100) x1 = np.random.normal(-2 * n_data, 1) y1 = np.ones(100) x = np.vstack((x0, x1)) y = np.concatenate((y0, y1)) s = np.arange(x.shape[0]) np.random.shuffle(s) x, y = x[s], y[s] trainer = Trainer(model=model, optimizer=Adam(lr=0.1), loss_func=CrossEntropyLoss(), epochs=200) trainer.extend(TensorConverter(x_dtype=torch.float32, y_dtype=torch.long, argmax=True)) trainer.fit(x, y) y_p, y_t = trainer.predict(x, y) assert y_p.shape == (200,) assert np.all(y_p == y_t) # trainer.reset() val_set = DataLoader(ArrayDataset(x, y, dtypes=(torch.float, torch.long)), batch_size=20) trainer.extend(TensorConverter(x_dtype=torch.float32, y_dtype=torch.long, auto_reshape=False)) y_p, y_t = trainer.predict(dataset=val_set) assert y_p.shape == (200, 2) y_p = np.argmax(y_p, 1) assert np.all(y_p == y_t)
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_2(data): model = deepcopy(data[0]) trainer = Trainer(model=model, optimizer=Adam(), loss_func=MSELoss(), epochs=20) trainer.fit(*data[1], *data[1]) assert trainer.total_iterations == 20 assert trainer.total_epochs == 20 assert (trainer.x_val, trainer.y_val) == data[1] train_set = DataLoader(TensorDataset(*data[1])) val_set = DataLoader(TensorDataset(*data[1])) trainer.fit(training_dataset=train_set, validation_dataset=val_set) assert trainer.total_iterations == 2020 assert trainer.total_epochs == 40 assert isinstance(trainer.validate_dataset, DataLoader)
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)])
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)
def test_persist_1(data): model = deepcopy(data[0]) trainer = Trainer(model=model, optimizer=Adam(lr=0.1), loss_func=MSELoss(), epochs=200) trainer.extend(TensorConverter(), Persist('model_dir')) trainer.fit(*data[1], *data[1]) persist = trainer['persist'] checker = persist._checker assert isinstance(persist, Persist) assert isinstance(checker.model, torch.nn.Module) assert isinstance(checker.describe, dict) assert isinstance(checker.files, list) assert set(checker.files) == { 'model', 'init_state', 'model_structure', 'describe', 'training_info', 'final_state' } trainer = Trainer.load(checker) assert isinstance(trainer.training_info, pd.DataFrame) assert isinstance(trainer.model, torch.nn.Module) assert isinstance(trainer._training_info, list) assert trainer.optimizer is None assert trainer.lr_scheduler is None assert trainer.x_val is None assert trainer.y_val is None assert trainer.validate_dataset is None assert trainer._optimizer_state is None assert trainer.total_epochs == 0 assert trainer.total_iterations == 0 assert trainer.loss_type is None assert trainer.loss_func is None trainer = Trainer.load(from_=checker.path, optimizer=Adam(), loss_func=MSELoss(), lr_scheduler=ExponentialLR(gamma=0.99), clip_grad=ClipValue(clip_value=0.1)) assert isinstance(trainer._scheduler, ExponentialLR) assert isinstance(trainer._optim, Adam) assert isinstance(trainer.clip_grad, ClipValue) assert isinstance(trainer.loss_func, MSELoss)