class ModelTest(ModelFittingTestCase): # pylint: disable=too-many-public-methods def setUp(self): super().setUp() torch.manual_seed(42) self.pytorch_network = nn.Linear(1, 1) self.loss_function = nn.MSELoss() self.optimizer = torch.optim.SGD(self.pytorch_network.parameters(), lr=1e-3) self.batch_metrics = [ some_batch_metric_1, ('custom_name', some_batch_metric_2), repeat_batch_metric, repeat_batch_metric ] self.batch_metrics_names = [ 'some_batch_metric_1', 'custom_name', 'repeat_batch_metric1', 'repeat_batch_metric2' ] self.batch_metrics_values = [ some_metric_1_value, some_metric_2_value, repeat_batch_metric_value, repeat_batch_metric_value ] self.epoch_metrics = [SomeConstantEpochMetric()] self.epoch_metrics_names = ['some_constant_epoch_metric'] self.epoch_metrics_values = [some_constant_epoch_metric_value] self.model = Model(self.pytorch_network, self.optimizer, self.loss_function, batch_metrics=self.batch_metrics, epoch_metrics=self.epoch_metrics) def test_fitting_tensor_generator(self): train_generator = some_data_tensor_generator(ModelTest.batch_size) valid_generator = some_data_tensor_generator(ModelTest.batch_size) logs = self.model.fit_generator( train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=ModelTest.steps_per_epoch, validation_steps=ModelTest.steps_per_epoch, callbacks=[self.mock_callback]) params = { 'epochs': ModelTest.epochs, 'steps': ModelTest.steps_per_epoch } self._test_callbacks_train(params, logs) def test_fitting_without_valid_generator(self): train_generator = some_data_tensor_generator(ModelTest.batch_size) logs = self.model.fit_generator( train_generator, None, epochs=ModelTest.epochs, steps_per_epoch=ModelTest.steps_per_epoch, callbacks=[self.mock_callback]) params = { 'epochs': ModelTest.epochs, 'steps': ModelTest.steps_per_epoch } self._test_callbacks_train(params, logs, has_valid=False) def test_correct_optim_calls_1_batch_per_step(self): train_generator = some_data_tensor_generator(ModelTest.batch_size) mocked_optimizer = some_mocked_optimizer() mocked_optim_model = Model(self.pytorch_network, mocked_optimizer, self.loss_function, batch_metrics=self.batch_metrics, epoch_metrics=self.epoch_metrics) mocked_optim_model.fit_generator(train_generator, None, epochs=1, steps_per_epoch=1, batches_per_step=1) self.assertEqual(1, mocked_optimizer.step.call_count) self.assertEqual(1, mocked_optimizer.zero_grad.call_count) def test_correct_optim_calls__valid_n_batches_per_step(self): n_batches = 5 items_per_batch = int(ModelTest.batch_size / n_batches) x = torch.rand(n_batches, items_per_batch, 1) y = torch.rand(n_batches, items_per_batch, 1) mocked_optimizer = some_mocked_optimizer() mocked_optim_model = Model(self.pytorch_network, mocked_optimizer, self.loss_function, batch_metrics=self.batch_metrics, epoch_metrics=self.epoch_metrics) mocked_optim_model.fit_generator(list(zip(x, y)), None, epochs=1, batches_per_step=n_batches) self.assertEqual(1, mocked_optimizer.step.call_count) self.assertEqual(1, mocked_optimizer.zero_grad.call_count) def test_fitting_generator_n_batches_per_step(self): total_batch_size = 6 x = torch.rand(1, total_batch_size, 1) y = torch.rand(1, total_batch_size, 1) initial_params = self.model.get_weight_copies() self.model.fit_generator(list(zip(x, y)), None, epochs=1, batches_per_step=1) expected_params = list(self.model.get_weight_copies().values()) for mini_batch_size in [1, 2, 5]: self.model.set_weights(initial_params) n_batches_per_step = int(total_batch_size / mini_batch_size) x.resize_((n_batches_per_step, mini_batch_size, 1)) y.resize_((n_batches_per_step, mini_batch_size, 1)) self.model.fit_generator(list(zip(x, y)), None, epochs=1, batches_per_step=n_batches_per_step) returned_params = list(self.model.get_weight_copies().values()) np.testing.assert_almost_equal(returned_params, expected_params, decimal=4) def test_fitting_generator_n_batches_per_step_higher_than_num_batches( self): total_batch_size = 6 x = torch.rand(1, total_batch_size, 1) y = torch.rand(1, total_batch_size, 1) initial_params = self.model.get_weight_copies() self.model.fit_generator(list(zip(x, y)), None, epochs=1, batches_per_step=1) expected_params = list(self.model.get_weight_copies().values()) self.model.set_weights(initial_params) self.model.fit_generator(list(zip(x, y)), None, epochs=1, batches_per_step=2) returned_params = list(self.model.get_weight_copies().values()) np.testing.assert_almost_equal(returned_params, expected_params, decimal=4) def test_fitting_generator_n_batches_per_step_uneven_batches(self): total_batch_size = 6 x = torch.rand(1, total_batch_size, 1) y = torch.rand(1, total_batch_size, 1) initial_params = self.model.get_weight_copies() self.model.fit_generator(list(zip(x, y)), None, epochs=1, batches_per_step=1) expected_params = list(self.model.get_weight_copies().values()) x.squeeze_(dim=0) y.squeeze_(dim=0) uneven_chunk_sizes = [4, 5] for chunk_size in uneven_chunk_sizes: self.model.set_weights(initial_params) splitted_x = x.split(chunk_size) splitted_y = y.split(chunk_size) n_batches_per_step = ceil(total_batch_size / chunk_size) self.model.fit_generator(list(zip(splitted_x, splitted_y)), None, epochs=1, batches_per_step=n_batches_per_step) returned_params = list(self.model.get_weight_copies().values()) np.testing.assert_almost_equal(returned_params, expected_params, decimal=4) def test_fitting_ndarray_generator(self): train_generator = some_ndarray_generator(ModelTest.batch_size) valid_generator = some_ndarray_generator(ModelTest.batch_size) logs = self.model.fit_generator( train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=ModelTest.steps_per_epoch, validation_steps=ModelTest.steps_per_epoch, callbacks=[self.mock_callback]) params = { 'epochs': ModelTest.epochs, 'steps': ModelTest.steps_per_epoch } self._test_callbacks_train(params, logs) def test_fitting_with_data_loader(self): train_real_steps_per_epoch = 30 train_batch_size = ModelTest.batch_size train_final_batch_missing_samples = 7 train_size = train_real_steps_per_epoch * train_batch_size - \ train_final_batch_missing_samples train_x = torch.rand(train_size, 1) train_y = torch.rand(train_size, 1) train_dataset = TensorDataset(train_x, train_y) train_generator = DataLoader(train_dataset, train_batch_size) valid_real_steps_per_epoch = 10 valid_batch_size = 15 valid_final_batch_missing_samples = 3 valid_size = valid_real_steps_per_epoch * valid_batch_size - \ valid_final_batch_missing_samples valid_x = torch.rand(valid_size, 1) valid_y = torch.rand(valid_size, 1) valid_dataset = TensorDataset(valid_x, valid_y) valid_generator = DataLoader(valid_dataset, valid_batch_size) logs = self.model.fit_generator(train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=None, validation_steps=None, callbacks=[self.mock_callback]) params = { 'epochs': ModelTest.epochs, 'steps': train_real_steps_per_epoch } self._test_callbacks_train(params, logs) def test_fitting_generator_calls(self): train_real_steps_per_epoch = 30 train_batch_size = ModelTest.batch_size train_final_batch_missing_samples = 7 train_size = train_real_steps_per_epoch * train_batch_size - \ train_final_batch_missing_samples train_x = torch.rand(train_size, 1) train_y = torch.rand(train_size, 1) train_dataset = TensorDataset(train_x, train_y) train_generator = DataLoader(train_dataset, train_batch_size) valid_real_steps_per_epoch = 10 valid_batch_size = 15 valid_final_batch_missing_samples = 3 valid_size = valid_real_steps_per_epoch * valid_batch_size - \ valid_final_batch_missing_samples valid_x = torch.rand(valid_size, 1) valid_y = torch.rand(valid_size, 1) valid_dataset = TensorDataset(valid_x, valid_y) valid_generator = DataLoader(valid_dataset, valid_batch_size) class IterableMock: def __init__(self, iterable): self.iterable = iterable self.iter = None self.calls = [] def __iter__(self): self.calls.append('__iter__') self.iter = iter(self.iterable) return self def __next__(self): self.calls.append('__next__') return next(self.iter) def __len__(self): self.calls.append('__len__') return len(self.iterable) mock_train_generator = IterableMock(train_generator) mock_valid_generator = IterableMock(valid_generator) self.model.fit_generator(mock_train_generator, mock_valid_generator, epochs=ModelTest.epochs) expected_train_calls = ['__len__'] + \ (['__iter__'] + ['__next__'] * train_real_steps_per_epoch) * ModelTest.epochs expected_valid_calls = ['__len__'] + \ (['__iter__'] + ['__next__'] * valid_real_steps_per_epoch) * ModelTest.epochs self.assertEqual(mock_train_generator.calls, expected_train_calls) self.assertEqual(mock_valid_generator.calls, expected_valid_calls) def test_fitting_with_tensor(self): train_real_steps_per_epoch = 30 train_batch_size = ModelTest.batch_size train_final_batch_missing_samples = 7 train_size = train_real_steps_per_epoch * train_batch_size - \ train_final_batch_missing_samples train_x = torch.rand(train_size, 1) train_y = torch.rand(train_size, 1) valid_real_steps_per_epoch = 10 # valid_batch_size will be the same as train_batch_size in the fit method. valid_batch_size = train_batch_size valid_final_batch_missing_samples = 3 valid_size = valid_real_steps_per_epoch * valid_batch_size - \ valid_final_batch_missing_samples valid_x = torch.rand(valid_size, 1) valid_y = torch.rand(valid_size, 1) logs = self.model.fit(train_x, train_y, validation_data=(valid_x, valid_y), epochs=ModelTest.epochs, batch_size=train_batch_size, steps_per_epoch=None, validation_steps=None, callbacks=[self.mock_callback]) params = { 'epochs': ModelTest.epochs, 'steps': train_real_steps_per_epoch } self._test_callbacks_train(params, logs) def test_fitting_with_np_array(self): train_real_steps_per_epoch = 30 train_batch_size = ModelTest.batch_size train_final_batch_missing_samples = 7 train_size = train_real_steps_per_epoch * train_batch_size - \ train_final_batch_missing_samples train_x = np.random.rand(train_size, 1).astype(np.float32) train_y = np.random.rand(train_size, 1).astype(np.float32) valid_real_steps_per_epoch = 10 # valid_batch_size will be the same as train_batch_size in the fit method. valid_batch_size = train_batch_size valid_final_batch_missing_samples = 3 valid_size = valid_real_steps_per_epoch * valid_batch_size - \ valid_final_batch_missing_samples valid_x = np.random.rand(valid_size, 1).astype(np.float32) valid_y = np.random.rand(valid_size, 1).astype(np.float32) logs = self.model.fit(train_x, train_y, validation_data=(valid_x, valid_y), epochs=ModelTest.epochs, batch_size=train_batch_size, steps_per_epoch=None, validation_steps=None, callbacks=[self.mock_callback]) params = { 'epochs': ModelTest.epochs, 'steps': train_real_steps_per_epoch } self._test_callbacks_train(params, logs) def test_fitting_with_generator_with_len(self): train_real_steps_per_epoch = 30 train_generator = SomeDataGeneratorWithLen( batch_size=ModelTest.batch_size, length=train_real_steps_per_epoch, num_missing_samples=7) valid_generator = SomeDataGeneratorWithLen(batch_size=15, length=10, num_missing_samples=3) logs = self.model.fit_generator(train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=None, validation_steps=None, callbacks=[self.mock_callback]) params = { 'epochs': ModelTest.epochs, 'steps': train_real_steps_per_epoch } self._test_callbacks_train(params, logs) def test_fitting_with_generator_with_stop_iteration(self): train_real_steps_per_epoch = 30 train_generator = SomeDataGeneratorUsingStopIteration( batch_size=ModelTest.batch_size, length=train_real_steps_per_epoch) valid_generator = SomeDataGeneratorUsingStopIteration(batch_size=15, length=10) logs = self.model.fit_generator(train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=None, validation_steps=None, callbacks=[self.mock_callback]) params = {'epochs': ModelTest.epochs, 'steps': None} self._test_callbacks_train(params, logs, steps=train_real_steps_per_epoch) def test_tensor_train_on_batch(self): x = torch.rand(ModelTest.batch_size, 1) y = torch.rand(ModelTest.batch_size, 1) loss, metrics = self.model.train_on_batch(x, y) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), self.batch_metrics_values) def test_train_on_batch_with_pred(self): x = torch.rand(ModelTest.batch_size, 1) y = torch.rand(ModelTest.batch_size, 1) loss, metrics, pred_y = self.model.train_on_batch(x, y, return_pred=True) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), self.batch_metrics_values) self.assertEqual(pred_y.shape, (ModelTest.batch_size, 1)) def test_ndarray_train_on_batch(self): x = np.random.rand(ModelTest.batch_size, 1).astype(np.float32) y = np.random.rand(ModelTest.batch_size, 1).astype(np.float32) loss, metrics = self.model.train_on_batch(x, y) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), self.batch_metrics_values) def test_evaluate(self): x = torch.rand(ModelTest.evaluate_dataset_len, 1) y = torch.rand(ModelTest.evaluate_dataset_len, 1) loss, metrics = self.model.evaluate(x, y, batch_size=ModelTest.batch_size) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), self.batch_metrics_values + self.epoch_metrics_values) def test_evaluate_with_pred(self): x = torch.rand(ModelTest.evaluate_dataset_len, 1) y = torch.rand(ModelTest.evaluate_dataset_len, 1) # We also test the unpacking. _, _, pred_y = self.model.evaluate(x, y, batch_size=ModelTest.batch_size, return_pred=True) self.assertEqual(pred_y.shape, (ModelTest.evaluate_dataset_len, 1)) def test_evaluate_with_callback(self): x = torch.rand(ModelTest.evaluate_dataset_len, 1) y = torch.rand(ModelTest.evaluate_dataset_len, 1) # We also test the unpacking. _, _, pred_y = self.model.evaluate(x, y, batch_size=ModelTest.batch_size, return_pred=True, callbacks=[self.mock_callback]) self.assertEqual(pred_y.shape, (ModelTest.evaluate_dataset_len, 1)) def test_evaluate_with_np_array(self): x = np.random.rand(ModelTest.evaluate_dataset_len, 1).astype(np.float32) y = np.random.rand(ModelTest.evaluate_dataset_len, 1).astype(np.float32) loss, metrics, pred_y = self.model.evaluate( x, y, batch_size=ModelTest.batch_size, return_pred=True) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), self.batch_metrics_values + self.epoch_metrics_values) self.assertEqual(pred_y.shape, (ModelTest.evaluate_dataset_len, 1)) def test_evaluate_data_loader(self): x = torch.rand(ModelTest.evaluate_dataset_len, 1) y = torch.rand(ModelTest.evaluate_dataset_len, 1) dataset = TensorDataset(x, y) generator = DataLoader(dataset, ModelTest.batch_size) loss, metrics, pred_y = self.model.evaluate_generator(generator, return_pred=True) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), self.batch_metrics_values + self.epoch_metrics_values) self.assertEqual(pred_y.shape, (ModelTest.evaluate_dataset_len, 1)) def test_evaluate_generator(self): num_steps = 10 generator = some_data_tensor_generator(ModelTest.batch_size) loss, metrics, pred_y = self.model.evaluate_generator(generator, steps=num_steps, return_pred=True) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), self.batch_metrics_values + self.epoch_metrics_values) self.assertEqual(type(pred_y), np.ndarray) self.assertEqual(pred_y.shape, (num_steps * ModelTest.batch_size, 1)) def test_evaluate_generator_with_callback(self): num_steps = 10 generator = some_data_tensor_generator(ModelTest.batch_size) result_log = self.model.evaluate_generator( generator, steps=num_steps, return_pred=True, callbacks=[self.mock_callback]) params = {'batch': ModelTest.epochs} self._test_callbacks_test(params, result_log) def test_evaluate_generator_with_ground_truth(self): num_steps = 10 generator = some_data_tensor_generator(ModelTest.batch_size) loss, metrics, pred_y, true_y = self.model.evaluate_generator( generator, steps=num_steps, return_pred=True, return_ground_truth=True) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), self.batch_metrics_values + self.epoch_metrics_values) self.assertEqual(type(pred_y), np.ndarray) self.assertEqual(type(true_y), np.ndarray) self.assertEqual(pred_y.shape, (num_steps * ModelTest.batch_size, 1)) self.assertEqual(true_y.shape, (num_steps * ModelTest.batch_size, 1)) def test_evaluate_generator_with_no_concatenation(self): num_steps = 10 generator = some_data_tensor_generator(ModelTest.batch_size) loss, metrics, pred_y, true_y = self.model.evaluate_generator( generator, steps=num_steps, return_pred=True, return_ground_truth=True, concatenate_returns=False) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), self.batch_metrics_values + self.epoch_metrics_values) self.assertEqual(type(pred_y), list) for pred in pred_y: self.assertEqual(type(pred), np.ndarray) self.assertEqual(pred.shape, (ModelTest.batch_size, 1)) self.assertEqual(type(true_y), list) for true in true_y: self.assertEqual(type(true), np.ndarray) self.assertEqual(true.shape, (ModelTest.batch_size, 1)) def test_evaluate_with_only_one_metric(self): model = Model(self.pytorch_network, self.optimizer, self.loss_function, batch_metrics=self.batch_metrics[:1]) x = torch.rand(ModelTest.evaluate_dataset_len, 1) y = torch.rand(ModelTest.evaluate_dataset_len, 1) loss, first_metric = model.evaluate(x, y, batch_size=ModelTest.batch_size) self.assertEqual(type(loss), float) self.assertEqual(type(first_metric), float) self.assertEqual(first_metric, some_metric_1_value) def test_metrics_integration(self): num_steps = 10 model = Model(self.pytorch_network, self.optimizer, self.loss_function, batch_metrics=[F.mse_loss]) train_generator = some_data_tensor_generator(ModelTest.batch_size) valid_generator = some_data_tensor_generator(ModelTest.batch_size) model.fit_generator(train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=ModelTest.steps_per_epoch, validation_steps=ModelTest.steps_per_epoch, callbacks=[self.mock_callback]) generator = some_data_tensor_generator(ModelTest.batch_size) loss, mse = model.evaluate_generator(generator, steps=num_steps) self.assertEqual(type(loss), float) self.assertEqual(type(mse), float) def test_epoch_metrics_integration(self): model = Model(self.pytorch_network, self.optimizer, self.loss_function, epoch_metrics=[SomeEpochMetric()]) train_generator = some_data_tensor_generator(ModelTest.batch_size) valid_generator = some_data_tensor_generator(ModelTest.batch_size) logs = model.fit_generator(train_generator, valid_generator, epochs=1, steps_per_epoch=ModelTest.steps_per_epoch, validation_steps=ModelTest.steps_per_epoch) actual_value = logs[-1]['some_epoch_metric'] val_actual_value = logs[-1]['val_some_epoch_metric'] expected_value = 5 self.assertEqual(val_actual_value, expected_value) self.assertEqual(actual_value, expected_value) def test_evaluate_with_no_metric(self): model = Model(self.pytorch_network, self.optimizer, self.loss_function) x = torch.rand(ModelTest.evaluate_dataset_len, 1) y = torch.rand(ModelTest.evaluate_dataset_len, 1) loss = model.evaluate(x, y, batch_size=ModelTest.batch_size) self.assertEqual(type(loss), float) def test_tensor_evaluate_on_batch(self): x = torch.rand(ModelTest.batch_size, 1) y = torch.rand(ModelTest.batch_size, 1) loss, metrics = self.model.evaluate_on_batch(x, y) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), self.batch_metrics_values) def test_evaluate_on_batch_with_pred(self): x = torch.rand(ModelTest.batch_size, 1) y = torch.rand(ModelTest.batch_size, 1) loss, metrics, pred_y = self.model.evaluate_on_batch(x, y, return_pred=True) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), self.batch_metrics_values) self.assertEqual(pred_y.shape, (ModelTest.batch_size, 1)) def test_ndarray_evaluate_on_batch(self): x = np.random.rand(ModelTest.batch_size, 1).astype(np.float32) y = np.random.rand(ModelTest.batch_size, 1).astype(np.float32) loss, metrics = self.model.evaluate_on_batch(x, y) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), self.batch_metrics_values) def test_predict(self): x = torch.rand(ModelTest.evaluate_dataset_len, 1) pred_y = self.model.predict(x, batch_size=ModelTest.batch_size) self.assertEqual(pred_y.shape, (ModelTest.evaluate_dataset_len, 1)) def test_predict_with_np_array(self): x = np.random.rand(ModelTest.evaluate_dataset_len, 1).astype(np.float32) pred_y = self.model.predict(x, batch_size=ModelTest.batch_size) self.assertEqual(type(pred_y), np.ndarray) self.assertEqual(pred_y.shape, (ModelTest.evaluate_dataset_len, 1)) def test_predict_data_loader(self): x = torch.rand(ModelTest.evaluate_dataset_len, 1) generator = DataLoader(x, ModelTest.batch_size) pred_y = self.model.predict_generator(generator) self.assertEqual(type(pred_y), np.ndarray) self.assertEqual(pred_y.shape, (ModelTest.evaluate_dataset_len, 1)) def test_predict_generator(self): num_steps = 10 generator = some_data_tensor_generator(ModelTest.batch_size) generator = (x for x, _ in generator) pred_y = self.model.predict_generator(generator, steps=num_steps) self.assertEqual(type(pred_y), np.ndarray) self.assertEqual(pred_y.shape, (num_steps * ModelTest.batch_size, 1)) def test_predict_generator_with_no_concatenation(self): num_steps = 10 generator = some_data_tensor_generator(ModelTest.batch_size) generator = (x for x, _ in generator) pred_y = self.model.predict_generator(generator, steps=num_steps, concatenate_returns=False) self.assertEqual(type(pred_y), list) for pred in pred_y: self.assertEqual(type(pred), np.ndarray) self.assertEqual(pred.shape, (ModelTest.batch_size, 1)) def test_tensor_predict_on_batch(self): x = torch.rand(ModelTest.batch_size, 1) pred_y = self.model.predict_on_batch(x) self.assertEqual(pred_y.shape, (ModelTest.batch_size, 1)) def test_ndarray_predict_on_batch(self): x = np.random.rand(ModelTest.batch_size, 1).astype(np.float32) pred_y = self.model.predict_on_batch(x) self.assertEqual(pred_y.shape, (ModelTest.batch_size, 1)) @skipIf(not torch.cuda.is_available(), "no gpu available") def test_cpu_cuda(self): train_generator = some_data_tensor_generator(ModelTest.batch_size) valid_generator = some_data_tensor_generator(ModelTest.batch_size) with torch.cuda.device(ModelTest.cuda_device): self.model.cuda() self.model.fit_generator( train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=ModelTest.steps_per_epoch, validation_steps=ModelTest.steps_per_epoch, callbacks=[self.mock_callback]) # The context manager is also used here because of this bug: # https://github.com/pytorch/pytorch/issues/7320 with torch.cuda.device(ModelTest.cuda_device): self.model.cuda(ModelTest.cuda_device) self._test_device( torch.device('cuda:' + str(ModelTest.cuda_device))) self.model.fit_generator( train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=ModelTest.steps_per_epoch, validation_steps=ModelTest.steps_per_epoch, callbacks=[self.mock_callback]) self.model.cpu() self._test_device(torch.device('cpu')) self.model.fit_generator( train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=ModelTest.steps_per_epoch, validation_steps=ModelTest.steps_per_epoch, callbacks=[self.mock_callback]) self.model.to(torch.device('cuda:' + str(ModelTest.cuda_device))) self._test_device( torch.device('cuda:' + str(ModelTest.cuda_device))) self.model.fit_generator( train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=ModelTest.steps_per_epoch, validation_steps=ModelTest.steps_per_epoch, callbacks=[self.mock_callback]) self.model.to(torch.device('cpu')) self._test_device(torch.device('cpu')) self.model.fit_generator( train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=ModelTest.steps_per_epoch, validation_steps=ModelTest.steps_per_epoch, callbacks=[self.mock_callback]) def _test_device(self, device): for p in self.pytorch_network.parameters(): self.assertEqual(p.device, device) def test_get_batch_size(self): batch_size = ModelTest.batch_size x = np.random.rand(batch_size, 1).astype(np.float32) y = np.random.rand(batch_size, 1).astype(np.float32) batch_size2 = ModelTest.batch_size + 1 x2 = np.random.rand(batch_size2, 1).astype(np.float32) y2 = np.random.rand(batch_size2, 1).astype(np.float32) other_batch_size = batch_size2 + 1 inf_batch_size = self.model.get_batch_size(x, y) self.assertEqual(inf_batch_size, batch_size) inf_batch_size = self.model.get_batch_size(x2, y2) self.assertEqual(inf_batch_size, batch_size2) inf_batch_size = self.model.get_batch_size(x, y2) self.assertEqual(inf_batch_size, batch_size) inf_batch_size = self.model.get_batch_size(x2, y) self.assertEqual(inf_batch_size, batch_size2) inf_batch_size = self.model.get_batch_size((x, x2), y) self.assertEqual(inf_batch_size, batch_size) inf_batch_size = self.model.get_batch_size((x2, x), y) self.assertEqual(inf_batch_size, batch_size) inf_batch_size = self.model.get_batch_size((x, x2), (y, y2)) self.assertEqual(inf_batch_size, batch_size) inf_batch_size = self.model.get_batch_size((x2, x), (y, y2)) self.assertEqual(inf_batch_size, batch_size2) inf_batch_size = self.model.get_batch_size([x, x2], y) self.assertEqual(inf_batch_size, batch_size) inf_batch_size = self.model.get_batch_size([x2, x], y) self.assertEqual(inf_batch_size, batch_size) inf_batch_size = self.model.get_batch_size([x, x2], [y, y2]) self.assertEqual(inf_batch_size, batch_size) inf_batch_size = self.model.get_batch_size([x2, x], [y, y2]) self.assertEqual(inf_batch_size, batch_size2) inf_batch_size = self.model.get_batch_size( { 'batch_size': other_batch_size, 'x': x }, {'y': y}) self.assertEqual(inf_batch_size, other_batch_size) inf_batch_size = self.model.get_batch_size({'x': x}, { 'batch_size': other_batch_size, 'y': y }) self.assertEqual(inf_batch_size, other_batch_size) inf_batch_size = self.model.get_batch_size({'x': x}, {'y': y}) self.assertEqual(inf_batch_size, batch_size) inf_batch_size = self.model.get_batch_size( OrderedDict([('x1', x), ('x2', x2)]), {'y': y}) self.assertEqual(inf_batch_size, batch_size) inf_batch_size = self.model.get_batch_size( OrderedDict([('x1', x2), ('x2', x)]), {'y': y}) self.assertEqual(inf_batch_size, batch_size2) inf_batch_size = self.model.get_batch_size([1, 2, 3], {'y': y}) self.assertEqual(inf_batch_size, batch_size) def test_get_batch_size_warning(self): with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") inf_batch_size = self.model.get_batch_size([1, 2, 3], [4, 5, 6]) self.assertEqual(inf_batch_size, 1) self.assertEqual(len(w), 1) with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") warning_settings['batch_size'] = 'ignore' inf_batch_size = self.model.get_batch_size([1, 2, 3], [4, 5, 6]) self.assertEqual(inf_batch_size, 1) self.assertEqual(len(w), 0)
class ModelMultiInputTest(ModelFittingTestCase): def setUp(self): super().setUp() torch.manual_seed(42) self.pytorch_network = MultiIOModel(num_input=1, num_output=1) self.loss_function = nn.MSELoss() self.optimizer = torch.optim.SGD(self.pytorch_network.parameters(), lr=1e-3) self.model = Model(self.pytorch_network, self.optimizer, self.loss_function, batch_metrics=self.batch_metrics, epoch_metrics=self.epoch_metrics) def test_fitting_tensor_generator_multi_input(self): train_generator = some_data_tensor_generator_multi_input( ModelMultiInputTest.batch_size) valid_generator = some_data_tensor_generator_multi_input( ModelMultiInputTest.batch_size) logs = self.model.fit_generator( train_generator, valid_generator, epochs=ModelMultiInputTest.epochs, steps_per_epoch=ModelMultiInputTest.steps_per_epoch, validation_steps=ModelMultiInputTest.steps_per_epoch, callbacks=[self.mock_callback]) params = { 'epochs': ModelMultiInputTest.epochs, 'steps': ModelMultiInputTest.steps_per_epoch } self._test_callbacks_train(params, logs) def test_fitting_with_tensor_multi_input(self): train_real_steps_per_epoch = 30 train_batch_size = ModelMultiInputTest.batch_size train_final_batch_missing_samples = 7 train_size = train_real_steps_per_epoch * train_batch_size - \ train_final_batch_missing_samples train_x = (torch.rand(train_size, 1), torch.rand(train_size, 1)) train_y = torch.rand(train_size, 1) valid_real_steps_per_epoch = 10 # valid_batch_size will be the same as train_batch_size in the fit method. valid_batch_size = train_batch_size valid_final_batch_missing_samples = 3 valid_size = valid_real_steps_per_epoch * valid_batch_size - \ valid_final_batch_missing_samples valid_x = (torch.rand(valid_size, 1), torch.rand(valid_size, 1)) valid_y = torch.rand(valid_size, 1) logs = self.model.fit(train_x, train_y, validation_data=(valid_x, valid_y), epochs=ModelMultiInputTest.epochs, batch_size=train_batch_size, steps_per_epoch=None, validation_steps=None, callbacks=[self.mock_callback]) params = { 'epochs': ModelMultiInputTest.epochs, 'steps': train_real_steps_per_epoch } self._test_callbacks_train(params, logs) def test_tensor_train_on_batch_multi_input(self): x1 = torch.rand(ModelMultiInputTest.batch_size, 1) x2 = torch.rand(ModelMultiInputTest.batch_size, 1) y = torch.rand(ModelMultiInputTest.batch_size, 1) loss = self.model.train_on_batch((x1, x2), y) self.assertEqual(type(loss), float) def test_train_on_batch_with_pred_multi_input(self): x1 = torch.rand(ModelMultiInputTest.batch_size, 1) x2 = torch.rand(ModelMultiInputTest.batch_size, 1) y = torch.rand(ModelMultiInputTest.batch_size, 1) loss, pred_y = self.model.train_on_batch((x1, x2), y, return_pred=True) self.assertEqual(type(loss), float) self.assertEqual(pred_y.shape, (ModelMultiInputTest.batch_size, 1)) def test_ndarray_train_on_batch_multi_input(self): x1 = np.random.rand(ModelMultiInputTest.batch_size, 1).astype(np.float32) x2 = np.random.rand(ModelMultiInputTest.batch_size, 1).astype(np.float32) y = np.random.rand(ModelMultiInputTest.batch_size, 1).astype(np.float32) loss = self.model.train_on_batch((x1, x2), y) self.assertEqual(type(loss), float) def test_evaluate_multi_input(self): x = (torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1), torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1)) y = torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1) loss = self.model.evaluate(x, y, batch_size=ModelMultiInputTest.batch_size) self.assertEqual(type(loss), float) def test_evaluate_with_pred_multi_input(self): x = (torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1), torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1)) y = torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1) # We also test the unpacking. _, pred_y = self.model.evaluate( x, y, batch_size=ModelMultiInputTest.batch_size, return_pred=True) self.assertEqual(pred_y.shape, (ModelMultiInputTest.evaluate_dataset_len, 1)) def test_evaluate_with_np_array_multi_input(self): x1 = np.random.rand(ModelMultiInputTest.evaluate_dataset_len, 1).astype(np.float32) x2 = np.random.rand(ModelMultiInputTest.evaluate_dataset_len, 1).astype(np.float32) x = (x1, x2) y = np.random.rand(ModelMultiInputTest.evaluate_dataset_len, 1).astype(np.float32) loss, pred_y = self.model.evaluate( x, y, batch_size=ModelMultiInputTest.batch_size, return_pred=True) self.assertEqual(type(loss), float) self.assertEqual(pred_y.shape, (ModelMultiInputTest.evaluate_dataset_len, 1)) def test_evaluate_data_loader_multi_input(self): x1 = torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1) x2 = torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1) y = torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1) dataset = TensorDataset((x1, x2), y) generator = DataLoader(dataset, ModelMultiInputTest.batch_size) loss, pred_y = self.model.evaluate_generator(generator, return_pred=True) self.assertEqual(type(loss), float) self.assertEqual(pred_y.shape, (ModelMultiInputTest.evaluate_dataset_len, 1)) def test_evaluate_generator_multi_input(self): num_steps = 10 generator = some_data_tensor_generator_multi_input( ModelMultiInputTest.batch_size) loss, pred_y = self.model.evaluate_generator(generator, steps=num_steps, return_pred=True) self.assertEqual(type(loss), float) self.assertEqual(pred_y.shape, (num_steps * ModelMultiInputTest.batch_size, 1)) def test_tensor_evaluate_on_batch_multi_input(self): x1 = torch.rand(ModelMultiInputTest.batch_size, 1) x2 = torch.rand(ModelMultiInputTest.batch_size, 1) y = torch.rand(ModelMultiInputTest.batch_size, 1) loss = self.model.evaluate_on_batch((x1, x2), y) self.assertEqual(type(loss), float) def test_predict_multi_input(self): x = (torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1), torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1)) pred_y = self.model.predict(x, batch_size=ModelMultiInputTest.batch_size) self.assertEqual(pred_y.shape, (ModelMultiInputTest.evaluate_dataset_len, 1)) def test_predict_with_np_array_multi_input(self): x1 = np.random.rand(ModelMultiInputTest.evaluate_dataset_len, 1).astype(np.float32) x2 = np.random.rand(ModelMultiInputTest.evaluate_dataset_len, 1).astype(np.float32) x = (x1, x2) pred_y = self.model.predict(x, batch_size=ModelMultiInputTest.batch_size) self.assertEqual(pred_y.shape, (ModelMultiInputTest.evaluate_dataset_len, 1)) def test_predict_generator_multi_input(self): num_steps = 10 generator = some_data_tensor_generator_multi_input( ModelMultiInputTest.batch_size) generator = (x for x, _ in generator) pred_y = self.model.predict_generator(generator, steps=num_steps) self.assertEqual(type(pred_y), np.ndarray) self.assertEqual(pred_y.shape, (num_steps * ModelMultiInputTest.batch_size, 1)) def test_tensor_predict_on_batch_multi_input(self): x1 = torch.rand(ModelMultiInputTest.batch_size, 1) x2 = torch.rand(ModelMultiInputTest.batch_size, 1) pred_y = self.model.predict_on_batch((x1, x2)) self.assertEqual(pred_y.shape, (ModelMultiInputTest.batch_size, 1))
class ModelTest(TestCase): # pylint: disable=too-many-public-methods epochs = 10 steps_per_epoch = 5 batch_size = 20 evaluate_dataset_len = 107 cuda_device = int(os.environ.get('CUDA_DEVICE', 0)) def setUp(self): torch.manual_seed(42) self.pytorch_module = nn.Linear(1, 1) self.loss_function = nn.MSELoss() self.optimizer = torch.optim.SGD(self.pytorch_module.parameters(), lr=1e-3) self.metrics = [some_metric_1, some_metric_2] self.metrics_names = ['some_metric_1', 'some_metric_2'] self.metrics_values = [some_metric_1_value, some_metric_2_value] self.model = Model(self.pytorch_module, self.optimizer, self.loss_function, metrics=self.metrics) self.mock_callback = MagicMock() def test_fitting_tensor_generator(self): train_generator = some_data_tensor_generator(ModelTest.batch_size) valid_generator = some_data_tensor_generator(ModelTest.batch_size) logs = self.model.fit_generator( train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=ModelTest.steps_per_epoch, validation_steps=ModelTest.steps_per_epoch, callbacks=[self.mock_callback]) params = { 'epochs': ModelTest.epochs, 'steps': ModelTest.steps_per_epoch } self._test_fitting(params, logs) def test_fitting_without_valid_generator(self): train_generator = some_data_tensor_generator(ModelTest.batch_size) logs = self.model.fit_generator( train_generator, None, epochs=ModelTest.epochs, steps_per_epoch=ModelTest.steps_per_epoch, callbacks=[self.mock_callback]) params = { 'epochs': ModelTest.epochs, 'steps': ModelTest.steps_per_epoch } self._test_fitting(params, logs, has_valid=False) def test_fitting_ndarray_generator(self): train_generator = some_ndarray_generator(ModelTest.batch_size) valid_generator = some_ndarray_generator(ModelTest.batch_size) logs = self.model.fit_generator( train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=ModelTest.steps_per_epoch, validation_steps=ModelTest.steps_per_epoch, callbacks=[self.mock_callback]) params = { 'epochs': ModelTest.epochs, 'steps': ModelTest.steps_per_epoch } self._test_fitting(params, logs) def test_fitting_with_data_loader(self): # pylint: disable=too-many-locals train_real_steps_per_epoch = 30 train_batch_size = ModelTest.batch_size train_final_batch_missing_samples = 7 train_size = train_real_steps_per_epoch * train_batch_size - \ train_final_batch_missing_samples train_x = torch.rand(train_size, 1) train_y = torch.rand(train_size, 1) train_dataset = TensorDataset(train_x, train_y) train_generator = DataLoader(train_dataset, train_batch_size) valid_real_steps_per_epoch = 10 valid_batch_size = 15 valid_final_batch_missing_samples = 3 valid_size = valid_real_steps_per_epoch * valid_batch_size - \ valid_final_batch_missing_samples valid_x = torch.rand(valid_size, 1) valid_y = torch.rand(valid_size, 1) valid_dataset = TensorDataset(valid_x, valid_y) valid_generator = DataLoader(valid_dataset, valid_batch_size) logs = self.model.fit_generator(train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=None, validation_steps=None, callbacks=[self.mock_callback]) params = { 'epochs': ModelTest.epochs, 'steps': train_real_steps_per_epoch } self._test_fitting(params, logs) def test_fitting_with_tensor(self): # pylint: disable=too-many-locals train_real_steps_per_epoch = 30 train_batch_size = ModelTest.batch_size train_final_batch_missing_samples = 7 train_size = train_real_steps_per_epoch * train_batch_size - \ train_final_batch_missing_samples train_x = torch.rand(train_size, 1) train_y = torch.rand(train_size, 1) valid_real_steps_per_epoch = 10 # valid_batch_size will be the same as train_batch_size in the fit method. valid_batch_size = train_batch_size valid_final_batch_missing_samples = 3 valid_size = valid_real_steps_per_epoch * valid_batch_size - \ valid_final_batch_missing_samples valid_x = torch.rand(valid_size, 1) valid_y = torch.rand(valid_size, 1) logs = self.model.fit(train_x, train_y, validation_x=valid_x, validation_y=valid_y, epochs=ModelTest.epochs, batch_size=train_batch_size, steps_per_epoch=None, validation_steps=None, callbacks=[self.mock_callback]) params = { 'epochs': ModelTest.epochs, 'steps': train_real_steps_per_epoch } self._test_fitting(params, logs) def test_fitting_with_np_array(self): # pylint: disable=too-many-locals train_real_steps_per_epoch = 30 train_batch_size = ModelTest.batch_size train_final_batch_missing_samples = 7 train_size = train_real_steps_per_epoch * train_batch_size - \ train_final_batch_missing_samples train_x = np.random.rand(train_size, 1).astype(np.float32) train_y = np.random.rand(train_size, 1).astype(np.float32) valid_real_steps_per_epoch = 10 # valid_batch_size will be the same as train_batch_size in the fit method. valid_batch_size = train_batch_size valid_final_batch_missing_samples = 3 valid_size = valid_real_steps_per_epoch * valid_batch_size - \ valid_final_batch_missing_samples valid_x = np.random.rand(valid_size, 1).astype(np.float32) valid_y = np.random.rand(valid_size, 1).astype(np.float32) logs = self.model.fit(train_x, train_y, validation_x=valid_x, validation_y=valid_y, epochs=ModelTest.epochs, batch_size=train_batch_size, steps_per_epoch=None, validation_steps=None, callbacks=[self.mock_callback]) params = { 'epochs': ModelTest.epochs, 'steps': train_real_steps_per_epoch } self._test_fitting(params, logs) def test_fitting_with_generator_with_len(self): train_real_steps_per_epoch = 30 train_generator = SomeDataGeneratorWithLen( batch_size=ModelTest.batch_size, length=train_real_steps_per_epoch, num_missing_samples=7) valid_generator = SomeDataGeneratorWithLen(batch_size=15, length=10, num_missing_samples=3) logs = self.model.fit_generator(train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=None, validation_steps=None, callbacks=[self.mock_callback]) params = { 'epochs': ModelTest.epochs, 'steps': train_real_steps_per_epoch } self._test_fitting(params, logs) def test_fitting_with_generator_with_stop_iteration(self): train_real_steps_per_epoch = 30 train_generator = SomeDataGeneratorUsingStopIteration( batch_size=ModelTest.batch_size, length=train_real_steps_per_epoch) valid_generator = SomeDataGeneratorUsingStopIteration(batch_size=15, length=10) logs = self.model.fit_generator(train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=None, validation_steps=None, callbacks=[self.mock_callback]) params = {'epochs': ModelTest.epochs, 'steps': None} self._test_fitting(params, logs, steps=train_real_steps_per_epoch) def _test_fitting(self, params, logs, has_valid=True, steps=None): if steps is None: steps = params['steps'] self.assertEqual(len(logs), params['epochs']) train_dict = dict(zip(self.metrics_names, self.metrics_values), loss=ANY, time=ANY) if has_valid: val_metrics_names = [ 'val_' + metric_name for metric_name in self.metrics_names ] val_dict = dict(zip(val_metrics_names, self.metrics_values), val_loss=ANY) log_dict = {**train_dict, **val_dict} else: log_dict = train_dict for epoch, log in enumerate(logs, 1): self.assertEqual(log, dict(log_dict, epoch=epoch)) call_list = [] call_list.append(call.on_train_begin({})) for epoch in range(1, params['epochs'] + 1): call_list.append(call.on_epoch_begin(epoch, {})) for step in range(1, steps + 1): call_list.append(call.on_batch_begin(step, {})) call_list.append(call.on_backward_end(step)) call_list.append( call.on_batch_end(step, { 'batch': step, 'size': ANY, **train_dict })) call_list.append( call.on_epoch_end(epoch, { 'epoch': epoch, **log_dict })) call_list.append(call.on_train_end({})) method_calls = self.mock_callback.method_calls self.assertIn(call.set_model(self.model), method_calls[:2]) self.assertIn(call.set_params(params), method_calls[:2]) self.assertEqual(len(method_calls), len(call_list) + 2) self.assertEqual(method_calls[2:], call_list) def test_tensor_train_on_batch(self): x = torch.rand(ModelTest.batch_size, 1) y = torch.rand(ModelTest.batch_size, 1) loss, metrics = self.model.train_on_batch(x, y) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), [some_metric_1_value, some_metric_2_value]) def test_train_on_batch_with_pred(self): x = torch.rand(ModelTest.batch_size, 1) y = torch.rand(ModelTest.batch_size, 1) loss, metrics, pred_y = self.model.train_on_batch(x, y, return_pred=True) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), [some_metric_1_value, some_metric_2_value]) self.assertEqual(pred_y.shape, (ModelTest.batch_size, 1)) def test_ndarray_train_on_batch(self): x = np.random.rand(ModelTest.batch_size, 1).astype(np.float32) y = np.random.rand(ModelTest.batch_size, 1).astype(np.float32) loss, metrics = self.model.train_on_batch(x, y) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), [some_metric_1_value, some_metric_2_value]) def test_evaluate(self): x = torch.rand(ModelTest.evaluate_dataset_len, 1) y = torch.rand(ModelTest.evaluate_dataset_len, 1) loss, metrics = self.model.evaluate(x, y, batch_size=ModelTest.batch_size) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), [some_metric_1_value, some_metric_2_value]) def test_evaluate_with_pred(self): x = torch.rand(ModelTest.evaluate_dataset_len, 1) y = torch.rand(ModelTest.evaluate_dataset_len, 1) # We also test the unpacking. # pylint: disable=unused-variable loss, metrics, pred_y = self.model.evaluate( x, y, batch_size=ModelTest.batch_size, return_pred=True) self.assertEqual(pred_y.shape, (ModelTest.evaluate_dataset_len, 1)) def test_evaluate_with_np_array(self): x = np.random.rand(ModelTest.evaluate_dataset_len, 1).astype(np.float32) y = np.random.rand(ModelTest.evaluate_dataset_len, 1).astype(np.float32) loss, metrics, pred_y = self.model.evaluate( x, y, batch_size=ModelTest.batch_size, return_pred=True) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), [some_metric_1_value, some_metric_2_value]) self.assertEqual(pred_y.shape, (ModelTest.evaluate_dataset_len, 1)) def test_evaluate_data_loader(self): x = torch.rand(ModelTest.evaluate_dataset_len, 1) y = torch.rand(ModelTest.evaluate_dataset_len, 1) dataset = TensorDataset(x, y) generator = DataLoader(dataset, ModelTest.batch_size) loss, metrics, pred_y = self.model.evaluate_generator(generator, return_pred=True) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), [some_metric_1_value, some_metric_2_value]) self._test_predictions_for_evaluate_and_predict_generator(pred_y) def test_evaluate_generator(self): num_steps = 10 generator = some_data_tensor_generator(ModelTest.batch_size) loss, metrics, pred_y = self.model.evaluate_generator(generator, steps=num_steps, return_pred=True) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), [some_metric_1_value, some_metric_2_value]) for pred in pred_y: self.assertEqual(type(pred), np.ndarray) self.assertEqual(pred.shape, (ModelTest.batch_size, 1)) self.assertEqual( np.concatenate(pred_y).shape, (num_steps * ModelTest.batch_size, 1)) def test_evaluate_with_only_one_metric(self): self.model = Model(self.pytorch_module, self.optimizer, self.loss_function, metrics=self.metrics[:1]) x = torch.rand(ModelTest.evaluate_dataset_len, 1) y = torch.rand(ModelTest.evaluate_dataset_len, 1) loss, first_metric = self.model.evaluate( x, y, batch_size=ModelTest.batch_size) self.assertEqual(type(loss), float) self.assertEqual(type(first_metric), float) self.assertEqual(first_metric, some_metric_1_value) def test_metrics_integration(self): num_steps = 10 self.model = Model(self.pytorch_module, self.optimizer, self.loss_function, metrics=[F.mse_loss]) train_generator = some_data_tensor_generator(ModelTest.batch_size) valid_generator = some_data_tensor_generator(ModelTest.batch_size) self.model.fit_generator(train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=ModelTest.steps_per_epoch, validation_steps=ModelTest.steps_per_epoch, callbacks=[self.mock_callback]) generator = some_data_tensor_generator(ModelTest.batch_size) loss, mse = self.model.evaluate_generator(generator, steps=num_steps) self.assertEqual(type(loss), float) self.assertEqual(type(mse), float) def test_evaluate_with_no_metric(self): self.model = Model(self.pytorch_module, self.optimizer, self.loss_function) x = torch.rand(ModelTest.evaluate_dataset_len, 1) y = torch.rand(ModelTest.evaluate_dataset_len, 1) loss = self.model.evaluate(x, y, batch_size=ModelTest.batch_size) self.assertEqual(type(loss), float) def test_tensor_evaluate_on_batch(self): x = torch.rand(ModelTest.batch_size, 1) y = torch.rand(ModelTest.batch_size, 1) loss, metrics = self.model.evaluate_on_batch(x, y) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), [some_metric_1_value, some_metric_2_value]) def test_evaluate_on_batch_with_pred(self): x = torch.rand(ModelTest.batch_size, 1) y = torch.rand(ModelTest.batch_size, 1) loss, metrics, pred_y = self.model.evaluate_on_batch(x, y, return_pred=True) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), [some_metric_1_value, some_metric_2_value]) self.assertEqual(pred_y.shape, (ModelTest.batch_size, 1)) def test_ndarray_evaluate_on_batch(self): x = np.random.rand(ModelTest.batch_size, 1).astype(np.float32) y = np.random.rand(ModelTest.batch_size, 1).astype(np.float32) loss, metrics = self.model.evaluate_on_batch(x, y) self.assertEqual(type(loss), float) self.assertEqual(type(metrics), np.ndarray) self.assertEqual(metrics.tolist(), [some_metric_1_value, some_metric_2_value]) def test_predict(self): x = torch.rand(ModelTest.evaluate_dataset_len, 1) pred_y = self.model.predict(x, batch_size=ModelTest.batch_size) self.assertEqual(pred_y.shape, (ModelTest.evaluate_dataset_len, 1)) def test_predict_with_np_array(self): x = np.random.rand(ModelTest.evaluate_dataset_len, 1).astype(np.float32) pred_y = self.model.predict(x, batch_size=ModelTest.batch_size) self.assertEqual(pred_y.shape, (ModelTest.evaluate_dataset_len, 1)) def test_predict_data_loader(self): x = torch.rand(ModelTest.evaluate_dataset_len, 1) generator = DataLoader(x, ModelTest.batch_size) pred_y = self.model.predict_generator(generator) self._test_predictions_for_evaluate_and_predict_generator(pred_y) def test_predict_generator(self): num_steps = 10 generator = some_data_tensor_generator(ModelTest.batch_size) generator = (x for x, _ in generator) pred_y = self.model.predict_generator(generator, steps=num_steps) for pred in pred_y: self.assertEqual(type(pred), np.ndarray) self.assertEqual(pred.shape, (ModelTest.batch_size, 1)) self.assertEqual( np.concatenate(pred_y).shape, (num_steps * ModelTest.batch_size, 1)) def _test_predictions_for_evaluate_and_predict_generator(self, pred_y): self.assertEqual(type(pred_y), list) remaning_example = ModelTest.evaluate_dataset_len cur_batch_size = ModelTest.batch_size for pred in pred_y: self.assertEqual(type(pred), np.ndarray) if remaning_example < ModelTest.batch_size: cur_batch_size = remaning_example remaning_example = 0 else: remaning_example -= ModelTest.batch_size self.assertEqual(pred.shape, (cur_batch_size, 1)) self.assertEqual( np.concatenate(pred_y).shape, (ModelTest.evaluate_dataset_len, 1)) def test_tensor_predict_on_batch(self): x = torch.rand(ModelTest.batch_size, 1) pred_y = self.model.predict_on_batch(x) self.assertEqual(pred_y.shape, (ModelTest.batch_size, 1)) def test_ndarray_predict_on_batch(self): x = np.random.rand(ModelTest.batch_size, 1).astype(np.float32) pred_y = self.model.predict_on_batch(x) self.assertEqual(pred_y.shape, (ModelTest.batch_size, 1)) @skipIf(not torch.cuda.is_available(), "no gpu available") def test_cpu_cuda(self): train_generator = some_data_tensor_generator(ModelTest.batch_size) valid_generator = some_data_tensor_generator(ModelTest.batch_size) with torch.cuda.device(ModelTest.cuda_device): self.model.cuda() self.model.fit_generator( train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=ModelTest.steps_per_epoch, validation_steps=ModelTest.steps_per_epoch, callbacks=[self.mock_callback]) # The context manager is also used here because of this bug: # https://github.com/pytorch/pytorch/issues/7320 with torch.cuda.device(ModelTest.cuda_device): self.model.cuda(ModelTest.cuda_device) self._test_device( torch.device('cuda:' + str(ModelTest.cuda_device))) self.model.fit_generator( train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=ModelTest.steps_per_epoch, validation_steps=ModelTest.steps_per_epoch, callbacks=[self.mock_callback]) self.model.cpu() self._test_device(torch.device('cpu')) self.model.fit_generator( train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=ModelTest.steps_per_epoch, validation_steps=ModelTest.steps_per_epoch, callbacks=[self.mock_callback]) self.model.to(torch.device('cuda:' + str(ModelTest.cuda_device))) self._test_device( torch.device('cuda:' + str(ModelTest.cuda_device))) self.model.fit_generator( train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=ModelTest.steps_per_epoch, validation_steps=ModelTest.steps_per_epoch, callbacks=[self.mock_callback]) self.model.to(torch.device('cpu')) self._test_device(torch.device('cpu')) self.model.fit_generator( train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=ModelTest.steps_per_epoch, validation_steps=ModelTest.steps_per_epoch, callbacks=[self.mock_callback]) def _test_device(self, device): for p in self.pytorch_module.parameters(): self.assertEqual(p.device, device) def test_disable_batch_size_warning(self): import warnings def tuple_generator(batch_size): while True: x1 = torch.rand(batch_size, 1) x2 = torch.rand(batch_size, 1) y1 = torch.rand(batch_size, 1) y2 = torch.rand(batch_size, 1) yield (x1, x2), (y1, y2) class TupleModule(nn.Module): def __init__(self): super().__init__() self.l1 = nn.Linear(1, 1) self.l2 = nn.Linear(1, 1) def forward(self, x): # pylint: disable=arguments-differ x1, x2 = x return self.l1(x1), self.l2(x2) def loss_function(y_pred, y_true): return F.mse_loss(y_pred[0], y_true[0]) + F.mse_loss( y_pred[1], y_true[1]) pytorch_module = TupleModule() optimizer = torch.optim.SGD(pytorch_module.parameters(), lr=1e-3) model = Model(pytorch_module, optimizer, loss_function) train_generator = tuple_generator(ModelTest.batch_size) valid_generator = tuple_generator(ModelTest.batch_size) with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") model.fit_generator(train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=ModelTest.steps_per_epoch, validation_steps=ModelTest.steps_per_epoch) num_warnings = ModelTest.steps_per_epoch * 2 * ModelTest.epochs self.assertEqual(len(w), num_warnings) with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") warning_settings['batch_size'] = 'ignore' model.fit_generator(train_generator, valid_generator, epochs=ModelTest.epochs, steps_per_epoch=ModelTest.steps_per_epoch, validation_steps=ModelTest.steps_per_epoch) self.assertEqual(len(w), 0)
class ModelMultiDictIOTest(ModelFittingTestCase): def setUp(self): super().setUp() torch.manual_seed(42) self.pytorch_network = DictIOModel(['x1', 'x2'], ['y1', 'y2']) self.loss_function = dict_mse_loss self.optimizer = torch.optim.SGD(self.pytorch_network.parameters(), lr=1e-3) self.model = Model(self.pytorch_network, self.optimizer, self.loss_function, batch_metrics=self.batch_metrics, epoch_metrics=self.epoch_metrics) def test_fitting_tensor_generator_multi_dict_io(self): train_generator = some_data_tensor_generator_dict_io(ModelMultiDictIOTest.batch_size) valid_generator = some_data_tensor_generator_dict_io(ModelMultiDictIOTest.batch_size) logs = self.model.fit_generator(train_generator, valid_generator, epochs=ModelMultiDictIOTest.epochs, steps_per_epoch=ModelMultiDictIOTest.steps_per_epoch, validation_steps=ModelMultiDictIOTest.steps_per_epoch, callbacks=[self.mock_callback]) params = {'epochs': ModelMultiDictIOTest.epochs, 'steps': ModelMultiDictIOTest.steps_per_epoch} self._test_callbacks_train(params, logs) def test_tensor_train_on_batch_multi_dict_io(self): x, y = get_batch(ModelMultiDictIOTest.batch_size) loss = self.model.train_on_batch(x, y) self.assertEqual(type(loss), float) def test_train_on_batch_with_pred_multi_dict_io(self): x, y = get_batch(ModelMultiDictIOTest.batch_size) loss, pred_y = self.model.train_on_batch(x, y, return_pred=True) self.assertEqual(type(loss), float) for value in pred_y.values(): self.assertEqual(value.shape, (ModelMultiDictIOTest.batch_size, 1)) def test_ndarray_train_on_batch_multi_dict_io(self): x1 = np.random.rand(ModelMultiDictIOTest.batch_size, 1).astype(np.float32) x2 = np.random.rand(ModelMultiDictIOTest.batch_size, 1).astype(np.float32) y1 = np.random.rand(ModelMultiDictIOTest.batch_size, 1).astype(np.float32) y2 = np.random.rand(ModelMultiDictIOTest.batch_size, 1).astype(np.float32) x, y = dict(x1=x1, x2=x2), dict(y1=y1, y2=y2) loss = self.model.train_on_batch(x, y) self.assertEqual(type(loss), float) def test_evaluate_generator_multi_dict_io(self): num_steps = 10 generator = some_data_tensor_generator_dict_io(ModelMultiDictIOTest.batch_size) loss, pred_y = self.model.evaluate_generator(generator, steps=num_steps, return_pred=True) self.assertEqual(type(loss), float) self._test_size_and_type_for_generator(pred_y, (num_steps * ModelMultiDictIOTest.batch_size, 1)) def test_tensor_evaluate_on_batch_multi_dict_io(self): x, y = get_batch(ModelMultiDictIOTest.batch_size) loss = self.model.evaluate_on_batch(x, y) self.assertEqual(type(loss), float) def test_predict_generator_multi_dict_io(self): num_steps = 10 generator = some_data_tensor_generator_dict_io(ModelMultiDictIOTest.batch_size) generator = (x for x, _ in generator) pred_y = self.model.predict_generator(generator, steps=num_steps) self._test_size_and_type_for_generator(pred_y, (num_steps * ModelMultiDictIOTest.batch_size, 1)) def test_tensor_predict_on_batch_multi_dict_io(self): x1 = torch.rand(ModelMultiDictIOTest.batch_size, 1) x2 = torch.rand(ModelMultiDictIOTest.batch_size, 1) pred_y = self.model.predict_on_batch(dict(x1=x1, x2=x2)) self._test_size_and_type_for_generator(pred_y, (ModelMultiDictIOTest.batch_size, 1))