class ModelTestMultiGPU(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) self.default_main_device = ModelTestMultiGPU.cuda_device def _test_multiple_gpu_mode(self, devices): if devices == "all": expected = torch.cuda.device_count() else: expected = len(devices) self.assertEqual(len([self.model.device] + self.model.other_device), expected) def _test_single_gpu_mode(self): self.assertIsNone(self.model.other_device) self.assertEqual(len([self.model.device]), 1) def test_back_and_forth_gpu_cpu_multi_gpus(self): devices = "all" train_generator = some_data_tensor_generator( ModelTestMultiGPU.batch_size) valid_generator = some_data_tensor_generator( ModelTestMultiGPU.batch_size) with torch.cuda.device(self.default_main_device): self.model.to(devices) self.model.fit_generator( train_generator, valid_generator, epochs=ModelTestMultiGPU.epochs, steps_per_epoch=ModelTestMultiGPU.steps_per_epoch, validation_steps=ModelTestMultiGPU.steps_per_epoch, callbacks=[self.mock_callback]) self._test_device( torch.device('cuda:' + str(self.default_main_device))) self._test_multiple_gpu_mode(devices=devices) self.model.cpu() self.model.fit_generator( train_generator, valid_generator, epochs=ModelTestMultiGPU.epochs, steps_per_epoch=ModelTestMultiGPU.steps_per_epoch, validation_steps=ModelTestMultiGPU.steps_per_epoch, callbacks=[self.mock_callback]) self._test_device(torch.device('cpu')) self._test_single_gpu_mode() self.model.to(devices) self.model.fit_generator( train_generator, valid_generator, epochs=ModelTestMultiGPU.epochs, steps_per_epoch=ModelTestMultiGPU.steps_per_epoch, validation_steps=ModelTestMultiGPU.steps_per_epoch, callbacks=[self.mock_callback]) self._test_device( torch.device('cuda:' + str(ModelTestMultiGPU.cuda_device))) self._test_multiple_gpu_mode(devices=devices) self.model.to(torch.device('cpu')) self.model.fit_generator( train_generator, valid_generator, epochs=ModelTestMultiGPU.epochs, steps_per_epoch=ModelTestMultiGPU.steps_per_epoch, validation_steps=ModelTestMultiGPU.steps_per_epoch, callbacks=[self.mock_callback]) self._test_device(torch.device('cpu')) self._test_single_gpu_mode() def test_back_and_forth_cuda_cpu_to_multi_gpus(self): devices = "all" train_generator = some_data_tensor_generator( ModelTestMultiGPU.batch_size) valid_generator = some_data_tensor_generator( ModelTestMultiGPU.batch_size) self.model.to(devices) self.model.fit_generator( train_generator, valid_generator, epochs=ModelTestMultiGPU.epochs, steps_per_epoch=ModelTestMultiGPU.steps_per_epoch, validation_steps=ModelTestMultiGPU.steps_per_epoch, callbacks=[self.mock_callback]) with torch.cuda.device(self.default_main_device): self.model.cuda() self.model.fit_generator( train_generator, valid_generator, epochs=ModelTestMultiGPU.epochs, steps_per_epoch=ModelTestMultiGPU.steps_per_epoch, validation_steps=ModelTestMultiGPU.steps_per_epoch, callbacks=[self.mock_callback]) self._test_device( torch.device('cuda:' + str(self.default_main_device))) self._test_single_gpu_mode() self.model.cpu() self.model.fit_generator( train_generator, valid_generator, epochs=ModelTestMultiGPU.epochs, steps_per_epoch=ModelTestMultiGPU.steps_per_epoch, validation_steps=ModelTestMultiGPU.steps_per_epoch, callbacks=[self.mock_callback]) self._test_device(torch.device('cpu')) self._test_single_gpu_mode() self.model.to(devices) self.model.fit_generator( train_generator, valid_generator, epochs=ModelTestMultiGPU.epochs, steps_per_epoch=ModelTestMultiGPU.steps_per_epoch, validation_steps=ModelTestMultiGPU.steps_per_epoch, callbacks=[self.mock_callback]) self._test_device( torch.device('cuda:' + str(ModelTestMultiGPU.cuda_device))) self._test_multiple_gpu_mode(devices=devices) self.model.cuda() self.model.fit_generator( train_generator, valid_generator, epochs=ModelTestMultiGPU.epochs, steps_per_epoch=ModelTestMultiGPU.steps_per_epoch, validation_steps=ModelTestMultiGPU.steps_per_epoch, callbacks=[self.mock_callback]) self._test_device( torch.device('cuda:' + str(self.default_main_device))) self._test_single_gpu_mode() self.model.to(torch.device('cpu')) self.model.fit_generator( train_generator, valid_generator, epochs=ModelTestMultiGPU.epochs, steps_per_epoch=ModelTestMultiGPU.steps_per_epoch, validation_steps=ModelTestMultiGPU.steps_per_epoch, callbacks=[self.mock_callback]) self._test_device(torch.device('cpu')) self._test_single_gpu_mode() def test_devices_settings(self): train_generator = some_data_tensor_generator( ModelTestMultiGPU.batch_size) valid_generator = some_data_tensor_generator( ModelTestMultiGPU.batch_size) devices = "all" self.model.to(devices) self.model.fit_generator( train_generator, valid_generator, epochs=ModelTestMultiGPU.epochs, steps_per_epoch=ModelTestMultiGPU.steps_per_epoch, validation_steps=ModelTestMultiGPU.steps_per_epoch, callbacks=[self.mock_callback]) self._test_multiple_gpu_mode(devices=devices) devices = ["cuda:0", "cuda:1"] self.model.to(devices) self.model.fit_generator( train_generator, valid_generator, epochs=ModelTestMultiGPU.epochs, steps_per_epoch=ModelTestMultiGPU.steps_per_epoch, validation_steps=ModelTestMultiGPU.steps_per_epoch, callbacks=[self.mock_callback]) self._test_multiple_gpu_mode(devices=devices) devices = [torch.device("cuda:0"), torch.device("cuda:1")] self.model.to(devices) self.model.fit_generator( train_generator, valid_generator, epochs=ModelTestMultiGPU.epochs, steps_per_epoch=ModelTestMultiGPU.steps_per_epoch, validation_steps=ModelTestMultiGPU.steps_per_epoch, callbacks=[self.mock_callback]) self._test_multiple_gpu_mode(devices=devices) devices = ["cuda:0"] self.model.to(devices) self.assertIsNone(self.model.other_device) self.model.fit_generator( train_generator, valid_generator, epochs=ModelTestMultiGPU.epochs, steps_per_epoch=ModelTestMultiGPU.steps_per_epoch, validation_steps=ModelTestMultiGPU.steps_per_epoch, callbacks=[self.mock_callback]) self._test_device(torch.device('cuda:0')) self._test_single_gpu_mode()
num_valid_samples = 200 valid_x = np.random.randn(num_valid_samples, num_features).astype('float32') valid_y = np.random.randint(num_classes, size=num_valid_samples).astype('int64') num_test_samples = 200 test_x = np.random.randn(num_test_samples, num_features).astype('float32') test_y = np.random.randint(num_classes, size=num_test_samples).astype('int64') cuda_device = 0 device = torch.device("cuda:%d" % cuda_device if torch.cuda.is_available() else "cpu") # Define the network network = nn.Sequential(nn.Linear(num_features, hidden_state_size), nn.ReLU(), nn.Linear(hidden_state_size, num_classes)) # Train model = Model(network, 'sgd', 'cross_entropy', batch_metrics=['accuracy'], epoch_metrics=['f1']) model.to(device) model.fit(train_x, train_y, validation_data=(valid_x, valid_y), epochs=5, batch_size=32)
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.Adam(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, 'valid_steps': ModelTest.steps_per_epoch } self._test_callbacks_train(params, logs, valid_steps=ModelTest.steps_per_epoch) 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 = self.model.get_weight_copies() 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 = self.model.get_weight_copies() self.assertEqual(returned_params.keys(), expected_params.keys()) for k in expected_params.keys(): np.testing.assert_almost_equal(returned_params[k].numpy(), expected_params[k].numpy(), 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 = self.model.get_weight_copies() self.model.set_weights(initial_params) self.model.fit_generator(list(zip(x, y)), None, epochs=1, batches_per_step=2) returned_params = self.model.get_weight_copies() self.assertEqual(returned_params.keys(), expected_params.keys()) for k in expected_params.keys(): np.testing.assert_almost_equal(returned_params[k].numpy(), expected_params[k].numpy(), 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 = self.model.get_weight_copies() 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 = self.model.get_weight_copies() self.assertEqual(returned_params.keys(), expected_params.keys()) for k in expected_params.keys(): np.testing.assert_almost_equal(returned_params[k].numpy(), expected_params[k].numpy(), 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, 'valid_steps': ModelTest.steps_per_epoch } self._test_callbacks_train(params, logs, valid_steps=ModelTest.steps_per_epoch) 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, 'valid_steps': valid_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) 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_generator_calls_with_longer_validation_set(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 = 40 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) 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, 'valid_steps': valid_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, 'valid_steps': valid_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_real_steps_per_epoch = 10 valid_generator = SomeDataGeneratorWithLen( batch_size=15, length=valid_real_steps_per_epoch, 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, 'valid_steps': valid_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_return_dict(self): x = torch.rand(ModelTest.evaluate_dataset_len, 1) y = torch.rand(ModelTest.evaluate_dataset_len, 1) logs = self.model.evaluate(x, y, batch_size=ModelTest.batch_size, return_dict_format=True) self._test_return_dict_logs(logs) 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_stop_iteration(self): test_generator = SomeDataGeneratorUsingStopIteration( ModelTest.batch_size, 10) loss, _ = self.model.evaluate_generator(test_generator) self.assertEqual(type(loss), float) def test_evaluate_generator_with_callback(self): num_steps = 10 generator = some_data_tensor_generator(ModelTest.batch_size) self.model.evaluate_generator(generator, steps=num_steps, callbacks=[self.mock_callback]) params = {'steps': ModelTest.epochs} self._test_callbacks_test(params) def test_evaluate_generator_with_return_dict(self): num_steps = 10 generator = some_data_tensor_generator(ModelTest.batch_size) logs = self.model.evaluate_generator(generator, steps=num_steps, return_dict_format=True) self._test_return_dict_logs(logs) 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) self._capture_output() 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_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)