def _train_g(self, G: ModelTrainer, real, backward=True):
G.zero_grad()
gen_pred = torch.nn.functional.sigmoid(G.forward(real))
loss_G = G.compute_and_update_train_loss("MSELoss", gen_pred, real)
metric = G.compute_metric("MeanSquaredError", gen_pred, real)
G.update_train_metric("MeanSquaredError", metric / 32768)
if backward:
loss_G.backward()
G.step()
return gen_pred
class ModelTrainerTest(unittest.TestCase):
MODEL_NAME_1 = "Harry Potter"
MODEL_NAME_2 = "Hagrid"
LOSS_NAME = "CrossEntropy"
def setUp(self) -> None:
self._model = SimpleNet()
self._criterion_mock = spy(nn.CrossEntropyLoss())
self._optimizer_mock = spy(SGD(self._model.parameters(), lr=0.001))
self._scheduler_mock = mock(lr_scheduler)
self._accuracy_computer_mock = spy(Accuracy())
self._recall_computer_mock = spy(Recall())
self._gradient_clipping_strategy = None
self._model_trainer = ModelTrainer(
self.MODEL_NAME_1, self._model,
{self.LOSS_NAME: self._criterion_mock}, self._optimizer_mock,
self._scheduler_mock, {
"Accuracy": self._accuracy_computer_mock,
"Recall": self._recall_computer_mock
}, self._gradient_clipping_strategy)
self._gradient_clipping_strategy = mock(GradientClippingStrategy)
def tearDown(self) -> None:
super().tearDown()
def test_should_compute_train_metric_and_update_state(self):
metric = self._model_trainer.compute_metrics(MODEL_PREDICTION_CLASS_0,
TARGET_CLASS_0)
self._model_trainer.update_train_metrics(metric)
assert_that(self._model_trainer.train_metrics["Accuracy"],
equal_to(ONE))
assert_that(self._model_trainer.step_train_metrics["Accuracy"],
equal_to(ONE))
verify(self._accuracy_computer_mock).update(
(MODEL_PREDICTION_CLASS_0, TARGET_CLASS_0))
metric = self._model_trainer.compute_metrics(MODEL_PREDICTION_CLASS_1,
TARGET_CLASS_0)
self._model_trainer.update_train_metrics(metric)
assert_that(self._model_trainer.train_metrics["Accuracy"],
equal_to(ONE / 2))
assert_that(self._model_trainer.step_train_metrics["Accuracy"],
equal_to(ZERO))
verify(self._accuracy_computer_mock, times=2).compute()
assert_that(self._model_trainer.valid_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.test_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.step_valid_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.step_test_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.step_train_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.step_valid_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.step_test_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.train_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.valid_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.test_loss,
equal_to({self.LOSS_NAME: ZERO}))
def test_should_compute_valid_metric_and_update_state(self):
metric = self._model_trainer.compute_metrics(MODEL_PREDICTION_CLASS_0,
TARGET_CLASS_0)
self._model_trainer.update_valid_metrics(metric)
assert_that(self._model_trainer.valid_metrics["Accuracy"],
equal_to(ONE))
assert_that(self._model_trainer.step_valid_metrics["Accuracy"],
equal_to(ONE))
verify(self._accuracy_computer_mock).update(
(MODEL_PREDICTION_CLASS_0, TARGET_CLASS_0))
metric = self._model_trainer.compute_metrics(MODEL_PREDICTION_CLASS_1,
TARGET_CLASS_0)
self._model_trainer.update_valid_metrics(metric)
assert_that(self._model_trainer.valid_metrics["Accuracy"],
equal_to(ONE / 2))
assert_that(self._model_trainer.step_valid_metrics["Accuracy"],
equal_to(ZERO))
verify(self._accuracy_computer_mock, times=2).compute()
assert_that(self._model_trainer.train_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.test_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.step_train_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.step_test_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.step_train_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.step_valid_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.step_test_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.train_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.valid_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.test_loss,
equal_to({self.LOSS_NAME: ZERO}))
def test_should_compute_test_metric_and_update_state(self):
metric = self._model_trainer.compute_metrics(MODEL_PREDICTION_CLASS_0,
TARGET_CLASS_0)
self._model_trainer.update_test_metrics(metric)
assert_that(self._model_trainer.test_metrics["Accuracy"],
equal_to(ONE))
assert_that(self._model_trainer.step_test_metrics["Accuracy"],
equal_to(ONE))
verify(self._accuracy_computer_mock).update(
(MODEL_PREDICTION_CLASS_0, TARGET_CLASS_0))
metric = self._model_trainer.compute_metrics(MODEL_PREDICTION_CLASS_1,
TARGET_CLASS_0)
self._model_trainer.update_test_metrics(metric)
assert_that(self._model_trainer.test_metrics["Accuracy"],
equal_to(ONE / 2))
assert_that(self._model_trainer.step_test_metrics["Accuracy"],
equal_to(ZERO))
verify(self._accuracy_computer_mock, times=2).compute()
assert_that(self._model_trainer.train_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.valid_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.step_train_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.step_valid_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.step_train_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.step_valid_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.step_test_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.train_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.valid_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.test_loss,
equal_to({self.LOSS_NAME: ZERO}))
def test_should_compute_train_loss_and_update_state(self):
loss = self._model_trainer.compute_and_update_train_loss(
self.LOSS_NAME, MODEL_PREDICTION_CLASS_0, TARGET_CLASS_0)
assert_that(loss._loss,
close_to(MINIMUM_BINARY_CROSS_ENTROPY_LOSS, DELTA))
assert_that(self._model_trainer.step_train_loss[self.LOSS_NAME],
close_to(MINIMUM_BINARY_CROSS_ENTROPY_LOSS, DELTA))
assert_that(self._model_trainer.train_loss[self.LOSS_NAME],
close_to(MINIMUM_BINARY_CROSS_ENTROPY_LOSS, DELTA))
loss = self._model_trainer.compute_and_update_train_loss(
self.LOSS_NAME, MODEL_PREDICTION_CLASS_0, TARGET_CLASS_1)
assert_that(loss._loss,
close_to(MAXIMUM_BINARY_CROSS_ENTROPY_LOSS, DELTA))
assert_that(self._model_trainer.step_train_loss[self.LOSS_NAME],
close_to(MAXIMUM_BINARY_CROSS_ENTROPY_LOSS, DELTA))
assert_that(self._model_trainer.train_loss[self.LOSS_NAME],
close_to(AVERAGED_BINARY_CROSS_ENTROPY_LOSS, DELTA))
assert_that(self._model_trainer.train_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.valid_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.test_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.step_train_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.step_valid_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.step_test_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.step_valid_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.step_test_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.valid_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.test_loss,
equal_to({self.LOSS_NAME: ZERO}))
def test_should_compute_valid_loss_and_update_state(self):
loss = self._model_trainer.compute_and_update_valid_loss(
self.LOSS_NAME, MODEL_PREDICTION_CLASS_0, TARGET_CLASS_0)
assert_that(loss._loss,
close_to(MINIMUM_BINARY_CROSS_ENTROPY_LOSS, DELTA))
assert_that(self._model_trainer.step_valid_loss[self.LOSS_NAME],
close_to(MINIMUM_BINARY_CROSS_ENTROPY_LOSS, DELTA))
assert_that(self._model_trainer.valid_loss[self.LOSS_NAME],
close_to(MINIMUM_BINARY_CROSS_ENTROPY_LOSS, DELTA))
loss = self._model_trainer.compute_and_update_valid_loss(
self.LOSS_NAME, MODEL_PREDICTION_CLASS_0, TARGET_CLASS_1)
assert_that(loss._loss,
close_to(MAXIMUM_BINARY_CROSS_ENTROPY_LOSS, DELTA))
assert_that(self._model_trainer.step_valid_loss[self.LOSS_NAME],
close_to(MAXIMUM_BINARY_CROSS_ENTROPY_LOSS, DELTA))
assert_that(self._model_trainer.valid_loss[self.LOSS_NAME],
close_to(AVERAGED_BINARY_CROSS_ENTROPY_LOSS, DELTA))
assert_that(self._model_trainer.train_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.valid_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.test_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.step_train_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.step_valid_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.step_test_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.step_train_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.step_test_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.train_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.test_loss,
equal_to({self.LOSS_NAME: ZERO}))
def test_should_compute_test_loss_and_update_state(self):
loss = self._model_trainer.compute_and_update_test_loss(
self.LOSS_NAME, MODEL_PREDICTION_CLASS_0, TARGET_CLASS_0)
assert_that(loss._loss,
close_to(MINIMUM_BINARY_CROSS_ENTROPY_LOSS, DELTA))
assert_that(self._model_trainer.step_test_loss[self.LOSS_NAME],
close_to(MINIMUM_BINARY_CROSS_ENTROPY_LOSS, DELTA))
assert_that(self._model_trainer.test_loss[self.LOSS_NAME],
close_to(MINIMUM_BINARY_CROSS_ENTROPY_LOSS, DELTA))
loss = self._model_trainer.compute_and_update_test_losses(
MODEL_PREDICTION_CLASS_0, TARGET_CLASS_1)
assert_that(loss[self.LOSS_NAME]._loss,
close_to(MAXIMUM_BINARY_CROSS_ENTROPY_LOSS, DELTA))
assert_that(self._model_trainer.step_test_loss[self.LOSS_NAME],
close_to(MAXIMUM_BINARY_CROSS_ENTROPY_LOSS, DELTA))
assert_that(self._model_trainer.test_loss[self.LOSS_NAME],
close_to(AVERAGED_BINARY_CROSS_ENTROPY_LOSS, DELTA))
assert_that(self._model_trainer.train_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.valid_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.test_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.step_train_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.step_valid_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.step_test_metrics["Accuracy"],
equal_to(ZERO))
assert_that(self._model_trainer.step_train_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.step_valid_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.train_loss,
equal_to({self.LOSS_NAME: ZERO}))
assert_that(self._model_trainer.valid_loss,
equal_to({self.LOSS_NAME: ZERO}))