diff --git a/.circleci/config.yml b/.circleci/config.yml index c139e561b3..f44928e7ed 100755 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -22,6 +22,7 @@ references: command: | python --version ; pip --version ; pip list py.test pytorch_lightning tests pl_examples -v --doctest-modules --junitxml=test-reports/pytest_junit.xml --flake8 + no_output_timeout: 15m jobs: diff --git a/pytorch_lightning/testing/__init__.py b/pytorch_lightning/testing/__init__.py index 1e33f0e677..326f67caf1 100644 --- a/pytorch_lightning/testing/__init__.py +++ b/pytorch_lightning/testing/__init__.py @@ -1,6 +1,6 @@ -from .test_module import LightningTestModel -from .test_module_base import LightningTestModelBase -from .test_module_mixins import ( +from .model import LightningTestModel +from .model_base import LightningTestModelBase +from .model_mixins import ( LightningValidationStepMixin, LightningValidationMixin, LightningValidationStepMultipleDataloadersMixin, diff --git a/pytorch_lightning/testing/test_module.py b/pytorch_lightning/testing/model.py similarity index 68% rename from pytorch_lightning/testing/test_module.py rename to pytorch_lightning/testing/model.py index 703787e4c1..51ed6b57b9 100644 --- a/pytorch_lightning/testing/test_module.py +++ b/pytorch_lightning/testing/model.py @@ -1,7 +1,7 @@ import torch -from .test_module_base import LightningTestModelBase -from .test_module_mixins import LightningValidationMixin, LightningTestMixin +from .model_base import LightningTestModelBase +from .model_mixins import LightningValidationMixin, LightningTestMixin class LightningTestModel(LightningValidationMixin, LightningTestMixin, LightningTestModelBase): diff --git a/pytorch_lightning/testing/test_module_base.py b/pytorch_lightning/testing/model_base.py similarity index 90% rename from pytorch_lightning/testing/test_module_base.py rename to pytorch_lightning/testing/model_base.py index c417e0e100..b83b8a2882 100644 --- a/pytorch_lightning/testing/test_module_base.py +++ b/pytorch_lightning/testing/model_base.py @@ -19,6 +19,22 @@ from pytorch_lightning import data_loader from pytorch_lightning.core.lightning import LightningModule +class TestingMNIST(MNIST): + + def __init__(self, root, train=True, transform=None, target_transform=None, + download=False, num_samples=8000): + super(TestingMNIST, self).__init__( + root, + train=train, + transform=transform, + target_transform=target_transform, + download=download + ) + # take just a subset of MNIST dataset + self.data = self.data[:num_samples] + self.targets = self.targets[:num_samples] + + class LightningTestModelBase(LightningModule): """ Base LightningModule for testing. Implements only the required @@ -137,8 +153,8 @@ class LightningTestModelBase(LightningModule): # init data generators transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))]) - dataset = MNIST(root=self.hparams.data_root, train=train, - transform=transform, download=True) + dataset = TestingMNIST(root=self.hparams.data_root, train=train, + transform=transform, download=True, num_samples=2000) # when using multi-node we need to add the datasampler train_sampler = None diff --git a/pytorch_lightning/testing/test_module_mixins.py b/pytorch_lightning/testing/model_mixins.py similarity index 100% rename from pytorch_lightning/testing/test_module_mixins.py rename to pytorch_lightning/testing/model_mixins.py diff --git a/tests/test_z_amp.py b/tests/test_amp.py similarity index 69% rename from tests/test_z_amp.py rename to tests/test_amp.py index faae07078c..0e19ce0e02 100644 --- a/tests/test_z_amp.py +++ b/tests/test_amp.py @@ -9,7 +9,7 @@ from pytorch_lightning.testing import ( LightningTestModel, ) from pytorch_lightning.utilities.debugging import MisconfigurationException -from . import testing_utils +import tests.utils as tutils def test_amp_single_gpu(): @@ -17,12 +17,12 @@ def test_amp_single_gpu(): Make sure DDP + AMP work :return: """ - testing_utils.reset_seed() + tutils.reset_seed() - if not testing_utils.can_run_gpu_test(): + if not tutils.can_run_gpu_test(): return - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) trainer_options = dict( @@ -33,7 +33,7 @@ def test_amp_single_gpu(): use_amp=True ) - testing_utils.run_gpu_model_test(trainer_options, model, hparams) + tutils.run_model_test(trainer_options, model, hparams) def test_no_amp_single_gpu(): @@ -41,12 +41,12 @@ def test_no_amp_single_gpu(): Make sure DDP + AMP work :return: """ - testing_utils.reset_seed() + tutils.reset_seed() - if not testing_utils.can_run_gpu_test(): + if not tutils.can_run_gpu_test(): return - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) trainer_options = dict( @@ -58,7 +58,7 @@ def test_no_amp_single_gpu(): ) with pytest.raises((MisconfigurationException, ModuleNotFoundError)): - testing_utils.run_gpu_model_test(trainer_options, model, hparams) + tutils.run_model_test(trainer_options, model, hparams) def test_amp_gpu_ddp(): @@ -66,13 +66,13 @@ def test_amp_gpu_ddp(): Make sure DDP + AMP work :return: """ - if not testing_utils.can_run_gpu_test(): + if not tutils.can_run_gpu_test(): return - testing_utils.reset_seed() - testing_utils.set_random_master_port() + tutils.reset_seed() + tutils.set_random_master_port() - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) trainer_options = dict( @@ -83,7 +83,7 @@ def test_amp_gpu_ddp(): use_amp=True ) - testing_utils.run_gpu_model_test(trainer_options, model, hparams) + tutils.run_model_test(trainer_options, model, hparams) def test_amp_gpu_ddp_slurm_managed(): @@ -91,16 +91,16 @@ def test_amp_gpu_ddp_slurm_managed(): Make sure DDP + AMP work :return: """ - if not testing_utils.can_run_gpu_test(): + if not tutils.can_run_gpu_test(): return - testing_utils.reset_seed() + tutils.reset_seed() # simulate setting slurm flags - testing_utils.set_random_master_port() + tutils.set_random_master_port() os.environ['SLURM_LOCALID'] = str(0) - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) trainer_options = dict( @@ -111,13 +111,13 @@ def test_amp_gpu_ddp_slurm_managed(): use_amp=True ) - save_dir = testing_utils.init_save_dir() + save_dir = tutils.init_save_dir() # exp file to get meta - logger = testing_utils.get_test_tube_logger(False) + logger = tutils.get_test_tube_logger(False) # exp file to get weights - checkpoint = testing_utils.init_checkpoint_callback(logger) + checkpoint = tutils.init_checkpoint_callback(logger) # add these to the trainer options trainer_options['checkpoint_callback'] = checkpoint @@ -138,12 +138,11 @@ def test_amp_gpu_ddp_slurm_managed(): assert trainer.resolve_root_node_address('abc[23-24, 45-40, 40]') == 'abc23' # test model loading with a map_location - pretrained_model = testing_utils.load_model(logger.experiment, - trainer.checkpoint_callback.filepath) + pretrained_model = tutils.load_model(logger.experiment, trainer.checkpoint_callback.filepath) # test model preds for dataloader in trainer.get_test_dataloaders(): - testing_utils.run_prediction(dataloader, pretrained_model) + tutils.run_prediction(dataloader, pretrained_model) if trainer.use_ddp: # on hpc this would work fine... but need to hack it for the purpose of the test @@ -158,7 +157,7 @@ def test_amp_gpu_ddp_slurm_managed(): model.freeze() model.unfreeze() - testing_utils.clear_save_dir() + tutils.clear_save_dir() def test_cpu_model_with_amp(): @@ -166,21 +165,21 @@ def test_cpu_model_with_amp(): Make sure model trains on CPU :return: """ - testing_utils.reset_seed() + tutils.reset_seed() trainer_options = dict( show_progress_bar=False, - logger=testing_utils.get_test_tube_logger(), + logger=tutils.get_test_tube_logger(), max_nb_epochs=1, train_percent_check=0.4, val_percent_check=0.4, use_amp=True ) - model, hparams = testing_utils.get_model() + model, hparams = tutils.get_model() with pytest.raises((MisconfigurationException, ModuleNotFoundError)): - testing_utils.run_gpu_model_test(trainer_options, model, hparams, on_gpu=False) + tutils.run_model_test(trainer_options, model, hparams, on_gpu=False) def test_amp_gpu_dp(): @@ -188,12 +187,12 @@ def test_amp_gpu_dp(): Make sure DP + AMP work :return: """ - testing_utils.reset_seed() + tutils.reset_seed() - if not testing_utils.can_run_gpu_test(): + if not tutils.can_run_gpu_test(): return - model, hparams = testing_utils.get_model() + model, hparams = tutils.get_model() trainer_options = dict( max_nb_epochs=1, gpus='0, 1', # test init with gpu string @@ -201,7 +200,7 @@ def test_amp_gpu_dp(): use_amp=True ) with pytest.raises(MisconfigurationException): - testing_utils.run_gpu_model_test(trainer_options, model, hparams) + tutils.run_model_test(trainer_options, model, hparams) if __name__ == '__main__': diff --git a/tests/test_cpu_models.py b/tests/test_cpu_models.py index 1b7a2573d7..87f0471ee5 100644 --- a/tests/test_cpu_models.py +++ b/tests/test_cpu_models.py @@ -12,7 +12,7 @@ from pytorch_lightning.testing import ( LightningTestModelBase, LightningTestMixin, ) -from . import testing_utils +import tests.utils as tutils def test_early_stopping_cpu_model(): @@ -20,9 +20,9 @@ def test_early_stopping_cpu_model(): Test each of the trainer options :return: """ - testing_utils.reset_seed() + tutils.reset_seed() - stopping = EarlyStopping(monitor='val_loss') + stopping = EarlyStopping(monitor='val_loss', min_delta=0.1) trainer_options = dict( early_stop_callback=stopping, gradient_clip_val=1.0, @@ -30,13 +30,13 @@ def test_early_stopping_cpu_model(): track_grad_norm=2, print_nan_grads=True, show_progress_bar=True, - logger=testing_utils.get_test_tube_logger(), + logger=tutils.get_test_tube_logger(), train_percent_check=0.1, val_percent_check=0.1 ) - model, hparams = testing_utils.get_model() - testing_utils.run_gpu_model_test(trainer_options, model, hparams, on_gpu=False) + model, hparams = tutils.get_model() + tutils.run_model_test(trainer_options, model, hparams, on_gpu=False) # test freeze on cpu model.freeze() @@ -48,7 +48,7 @@ def test_lbfgs_cpu_model(): Test each of the trainer options :return: """ - testing_utils.reset_seed() + tutils.reset_seed() trainer_options = dict( max_nb_epochs=1, @@ -59,11 +59,11 @@ def test_lbfgs_cpu_model(): val_percent_check=0.2 ) - model, hparams = testing_utils.get_model(use_test_model=True, lbfgs=True) - testing_utils.run_model_test_no_loggers(trainer_options, - model, hparams, on_gpu=False, min_acc=0.30) + model, hparams = tutils.get_model(use_test_model=True, lbfgs=True) + tutils.run_model_test_no_loggers(trainer_options, model, hparams, + on_gpu=False, min_acc=0.30) - testing_utils.clear_save_dir() + tutils.clear_save_dir() def test_default_logger_callbacks_cpu_model(): @@ -71,7 +71,7 @@ def test_default_logger_callbacks_cpu_model(): Test each of the trainer options :return: """ - testing_utils.reset_seed() + tutils.reset_seed() trainer_options = dict( max_nb_epochs=1, @@ -83,30 +83,30 @@ def test_default_logger_callbacks_cpu_model(): val_percent_check=0.01 ) - model, hparams = testing_utils.get_model() - testing_utils.run_model_test_no_loggers(trainer_options, model, hparams, on_gpu=False) + model, hparams = tutils.get_model() + tutils.run_model_test_no_loggers(trainer_options, model, hparams, on_gpu=False) # test freeze on cpu model.freeze() model.unfreeze() - testing_utils.clear_save_dir() + tutils.clear_save_dir() def test_running_test_after_fitting(): """Verify test() on fitted model""" - testing_utils.reset_seed() + tutils.reset_seed() - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) - save_dir = testing_utils.init_save_dir() + save_dir = tutils.init_save_dir() # logger file to get meta - logger = testing_utils.get_test_tube_logger(False) + logger = tutils.get_test_tube_logger(False) # logger file to get weights - checkpoint = testing_utils.init_checkpoint_callback(logger) + checkpoint = tutils.init_checkpoint_callback(logger) trainer_options = dict( show_progress_bar=False, @@ -127,29 +127,29 @@ def test_running_test_after_fitting(): trainer.test() # test we have good test accuracy - testing_utils.assert_ok_test_acc(trainer) + tutils.assert_ok_test_acc(trainer) - testing_utils.clear_save_dir() + tutils.clear_save_dir() def test_running_test_without_val(): - testing_utils.reset_seed() + tutils.reset_seed() """Verify test() works on a model with no val_loader""" class CurrentTestModel(LightningTestMixin, LightningTestModelBase): pass - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = CurrentTestModel(hparams) - save_dir = testing_utils.init_save_dir() + save_dir = tutils.init_save_dir() # logger file to get meta - logger = testing_utils.get_test_tube_logger(False) + logger = tutils.get_test_tube_logger(False) # logger file to get weights - checkpoint = testing_utils.init_checkpoint_callback(logger) + checkpoint = tutils.init_checkpoint_callback(logger) trainer_options = dict( show_progress_bar=False, @@ -170,15 +170,15 @@ def test_running_test_without_val(): trainer.test() # test we have good test accuracy - testing_utils.assert_ok_test_acc(trainer) + tutils.assert_ok_test_acc(trainer) - testing_utils.clear_save_dir() + tutils.clear_save_dir() def test_single_gpu_batch_parse(): - testing_utils.reset_seed() + tutils.reset_seed() - if not testing_utils.can_run_gpu_test(): + if not tutils.can_run_gpu_test(): return trainer = Trainer() @@ -224,12 +224,12 @@ def test_simple_cpu(): Verify continue training session on CPU :return: """ - testing_utils.reset_seed() + tutils.reset_seed() - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) - save_dir = testing_utils.init_save_dir() + save_dir = tutils.init_save_dir() # logger file to get meta trainer_options = dict( @@ -245,7 +245,7 @@ def test_simple_cpu(): # traning complete assert result == 1, 'amp + ddp model failed to complete' - testing_utils.clear_save_dir() + tutils.clear_save_dir() def test_cpu_model(): @@ -253,19 +253,19 @@ def test_cpu_model(): Make sure model trains on CPU :return: """ - testing_utils.reset_seed() + tutils.reset_seed() trainer_options = dict( show_progress_bar=False, - logger=testing_utils.get_test_tube_logger(), + logger=tutils.get_test_tube_logger(), max_nb_epochs=1, train_percent_check=0.4, val_percent_check=0.4 ) - model, hparams = testing_utils.get_model() + model, hparams = tutils.get_model() - testing_utils.run_gpu_model_test(trainer_options, model, hparams, on_gpu=False) + tutils.run_model_test(trainer_options, model, hparams, on_gpu=False) def test_all_features_cpu_model(): @@ -273,7 +273,7 @@ def test_all_features_cpu_model(): Test each of the trainer options :return: """ - testing_utils.reset_seed() + tutils.reset_seed() trainer_options = dict( gradient_clip_val=1.0, @@ -281,15 +281,15 @@ def test_all_features_cpu_model(): track_grad_norm=2, print_nan_grads=True, show_progress_bar=False, - logger=testing_utils.get_test_tube_logger(), + logger=tutils.get_test_tube_logger(), accumulate_grad_batches=2, max_nb_epochs=1, train_percent_check=0.4, val_percent_check=0.4 ) - model, hparams = testing_utils.get_model() - testing_utils.run_gpu_model_test(trainer_options, model, hparams, on_gpu=False) + model, hparams = tutils.get_model() + tutils.run_model_test(trainer_options, model, hparams, on_gpu=False) def test_tbptt_cpu_model(): @@ -297,9 +297,9 @@ def test_tbptt_cpu_model(): Test truncated back propagation through time works. :return: """ - testing_utils.reset_seed() + tutils.reset_seed() - save_dir = testing_utils.init_save_dir() + save_dir = tutils.init_save_dir() truncated_bptt_steps = 2 sequence_size = 30 @@ -354,7 +354,7 @@ def test_tbptt_cpu_model(): weights_summary=None, ) - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() hparams.batch_size = batch_size hparams.in_features = truncated_bptt_steps hparams.hidden_dim = truncated_bptt_steps @@ -368,7 +368,7 @@ def test_tbptt_cpu_model(): assert result == 1, 'training failed to complete' - testing_utils.clear_save_dir() + tutils.clear_save_dir() def test_single_gpu_model(): @@ -376,13 +376,13 @@ def test_single_gpu_model(): Make sure single GPU works (DP mode) :return: """ - testing_utils.reset_seed() + tutils.reset_seed() if not torch.cuda.is_available(): warnings.warn('test_single_gpu_model cannot run.' ' Rerun on a GPU node to run this test') return - model, hparams = testing_utils.get_model() + model, hparams = tutils.get_model() trainer_options = dict( show_progress_bar=False, @@ -392,7 +392,7 @@ def test_single_gpu_model(): gpus=1 ) - testing_utils.run_gpu_model_test(trainer_options, model, hparams) + tutils.run_model_test(trainer_options, model, hparams) if __name__ == '__main__': diff --git a/tests/test_gpu_models.py b/tests/test_gpu_models.py index 8d294f23da..987a914dac 100644 --- a/tests/test_gpu_models.py +++ b/tests/test_gpu_models.py @@ -15,7 +15,7 @@ from pytorch_lightning.trainer.dp_mixin import ( determine_root_gpu_device, ) from pytorch_lightning.utilities.debugging import MisconfigurationException -from . import testing_utils +import tests.utils as tutils PRETEND_N_OF_GPUS = 16 @@ -25,13 +25,13 @@ def test_multi_gpu_model_ddp2(): Make sure DDP2 works :return: """ - if not testing_utils.can_run_gpu_test(): + if not tutils.can_run_gpu_test(): return - testing_utils.reset_seed() - testing_utils.set_random_master_port() + tutils.reset_seed() + tutils.set_random_master_port() - model, hparams = testing_utils.get_model() + model, hparams = tutils.get_model() trainer_options = dict( show_progress_bar=True, max_nb_epochs=1, @@ -42,7 +42,7 @@ def test_multi_gpu_model_ddp2(): distributed_backend='ddp2' ) - testing_utils.run_gpu_model_test(trainer_options, model, hparams) + tutils.run_model_test(trainer_options, model, hparams) def test_multi_gpu_model_ddp(): @@ -50,13 +50,13 @@ def test_multi_gpu_model_ddp(): Make sure DDP works :return: """ - if not testing_utils.can_run_gpu_test(): + if not tutils.can_run_gpu_test(): return - testing_utils.reset_seed() - testing_utils.set_random_master_port() + tutils.reset_seed() + tutils.set_random_master_port() - model, hparams = testing_utils.get_model() + model, hparams = tutils.get_model() trainer_options = dict( show_progress_bar=False, max_nb_epochs=1, @@ -66,14 +66,14 @@ def test_multi_gpu_model_ddp(): distributed_backend='ddp' ) - testing_utils.run_gpu_model_test(trainer_options, model, hparams) + tutils.run_model_test(trainer_options, model, hparams) def test_optimizer_return_options(): - testing_utils.reset_seed() + tutils.reset_seed() trainer = Trainer() - model, hparams = testing_utils.get_model() + model, hparams = tutils.get_model() # single optimizer opt_a = torch.optim.Adam(model.parameters(), lr=0.002) @@ -105,15 +105,15 @@ def test_cpu_slurm_save_load(): Verify model save/load/checkpoint on CPU :return: """ - testing_utils.reset_seed() + tutils.reset_seed() - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) - save_dir = testing_utils.init_save_dir() + save_dir = tutils.init_save_dir() # logger file to get meta - logger = testing_utils.get_test_tube_logger(False) + logger = tutils.get_test_tube_logger(False) version = logger.version @@ -149,7 +149,7 @@ def test_cpu_slurm_save_load(): assert os.path.exists(saved_filepath) # new logger file to get meta - logger = testing_utils.get_test_tube_logger(False, version=version) + logger = tutils.get_test_tube_logger(False, version=version) trainer_options = dict( max_nb_epochs=1, @@ -174,7 +174,7 @@ def test_cpu_slurm_save_load(): # and our hook to predict using current model before any more weight updates trainer.fit(model) - testing_utils.clear_save_dir() + tutils.clear_save_dir() def test_multi_gpu_none_backend(): @@ -183,12 +183,12 @@ def test_multi_gpu_none_backend(): distributed_backend = None :return: """ - testing_utils.reset_seed() + tutils.reset_seed() - if not testing_utils.can_run_gpu_test(): + if not tutils.can_run_gpu_test(): return - model, hparams = testing_utils.get_model() + model, hparams = tutils.get_model() trainer_options = dict( show_progress_bar=False, max_nb_epochs=1, @@ -198,7 +198,7 @@ def test_multi_gpu_none_backend(): ) with pytest.raises(MisconfigurationException): - testing_utils.run_gpu_model_test(trainer_options, model, hparams) + tutils.run_model_test(trainer_options, model, hparams) def test_multi_gpu_model_dp(): @@ -206,12 +206,12 @@ def test_multi_gpu_model_dp(): Make sure DP works :return: """ - testing_utils.reset_seed() + tutils.reset_seed() - if not testing_utils.can_run_gpu_test(): + if not tutils.can_run_gpu_test(): return - model, hparams = testing_utils.get_model() + model, hparams = tutils.get_model() trainer_options = dict( show_progress_bar=False, distributed_backend='dp', @@ -221,7 +221,7 @@ def test_multi_gpu_model_dp(): gpus='-1' ) - testing_utils.run_gpu_model_test(trainer_options, model, hparams) + tutils.run_model_test(trainer_options, model, hparams) # test memory helper functions memory.get_memory_profile('min_max') @@ -232,16 +232,16 @@ def test_ddp_sampler_error(): Make sure DDP + AMP work :return: """ - if not testing_utils.can_run_gpu_test(): + if not tutils.can_run_gpu_test(): return - testing_utils.reset_seed() - testing_utils.set_random_master_port() + tutils.reset_seed() + tutils.set_random_master_port() - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams, force_remove_distributed_sampler=True) - logger = testing_utils.get_test_tube_logger(True) + logger = tutils.get_test_tube_logger(True) trainer = Trainer( logger=logger, @@ -255,7 +255,7 @@ def test_ddp_sampler_error(): with pytest.warns(UserWarning): trainer.get_dataloaders(model) - testing_utils.clear_save_dir() + tutils.clear_save_dir() @pytest.fixture diff --git a/tests/test_y_logging.py b/tests/test_logging.py similarity index 82% rename from tests/test_y_logging.py rename to tests/test_logging.py index cfa979956c..4a0f9c379e 100644 --- a/tests/test_y_logging.py +++ b/tests/test_logging.py @@ -7,26 +7,20 @@ import torch from pytorch_lightning import Trainer from pytorch_lightning.testing import LightningTestModel from pytorch_lightning.logging import LightningLoggerBase, rank_zero_only -from . import testing_utils - -RANDOM_FILE_PATHS = list(np.random.randint(12000, 19000, 1000)) -ROOT_SEED = 1234 -torch.manual_seed(ROOT_SEED) -np.random.seed(ROOT_SEED) -RANDOM_SEEDS = list(np.random.randint(0, 10000, 1000)) +import tests.utils as tutils def test_testtube_logger(): """ verify that basic functionality of test tube logger works """ - reset_seed() - hparams = testing_utils.get_hparams() + tutils.reset_seed() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) - save_dir = testing_utils.init_save_dir() + save_dir = tutils.init_save_dir() - logger = testing_utils.get_test_tube_logger(False) + logger = tutils.get_test_tube_logger(False) trainer_options = dict( max_nb_epochs=1, @@ -39,21 +33,21 @@ def test_testtube_logger(): assert result == 1, "Training failed" - testing_utils.clear_save_dir() + tutils.clear_save_dir() def test_testtube_pickle(): """ Verify that pickling a trainer containing a test tube logger works """ - reset_seed() + tutils.reset_seed() - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) - save_dir = testing_utils.init_save_dir() + save_dir = tutils.init_save_dir() - logger = testing_utils.get_test_tube_logger(False) + logger = tutils.get_test_tube_logger(False) logger.log_hyperparams(hparams) logger.save() @@ -68,21 +62,21 @@ def test_testtube_pickle(): trainer2 = pickle.loads(pkl_bytes) trainer2.logger.log_metrics({"acc": 1.0}) - testing_utils.clear_save_dir() + tutils.clear_save_dir() def test_mlflow_logger(): """ verify that basic functionality of mlflow logger works """ - reset_seed() + tutils.reset_seed() try: from pytorch_lightning.logging import MLFlowLogger except ModuleNotFoundError: return - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) root_dir = os.path.dirname(os.path.realpath(__file__)) @@ -102,21 +96,21 @@ def test_mlflow_logger(): print('result finished') assert result == 1, "Training failed" - testing_utils.clear_save_dir() + tutils.clear_save_dir() def test_mlflow_pickle(): """ verify that pickling trainer with mlflow logger works """ - reset_seed() + tutils.reset_seed() try: from pytorch_lightning.logging import MLFlowLogger except ModuleNotFoundError: return - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) root_dir = os.path.dirname(os.path.realpath(__file__)) @@ -134,21 +128,21 @@ def test_mlflow_pickle(): trainer2 = pickle.loads(pkl_bytes) trainer2.logger.log_metrics({"acc": 1.0}) - testing_utils.clear_save_dir() + tutils.clear_save_dir() def test_comet_logger(): """ verify that basic functionality of Comet.ml logger works """ - reset_seed() + tutils.reset_seed() try: from pytorch_lightning.logging import CometLogger except ModuleNotFoundError: return - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) root_dir = os.path.dirname(os.path.realpath(__file__)) @@ -173,21 +167,21 @@ def test_comet_logger(): print('result finished') assert result == 1, "Training failed" - testing_utils.clear_save_dir() + tutils.clear_save_dir() def test_comet_pickle(): """ verify that pickling trainer with comet logger works """ - reset_seed() + tutils.reset_seed() try: from pytorch_lightning.logging import CometLogger except ModuleNotFoundError: return - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) root_dir = os.path.dirname(os.path.realpath(__file__)) @@ -210,7 +204,7 @@ def test_comet_pickle(): trainer2 = pickle.loads(pkl_bytes) trainer2.logger.log_metrics({"acc": 1.0}) - testing_utils.clear_save_dir() + tutils.clear_save_dir() def test_custom_logger(tmpdir): @@ -241,7 +235,7 @@ def test_custom_logger(tmpdir): def version(self): return "1" - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) logger = CustomLogger() @@ -259,9 +253,3 @@ def test_custom_logger(tmpdir): assert logger.hparams_logged == hparams assert logger.metrics_logged != {} assert logger.finalized_status == "success" - - -def reset_seed(): - SEED = RANDOM_SEEDS.pop() - torch.manual_seed(SEED) - np.random.seed(SEED) diff --git a/tests/test_a_restore_models.py b/tests/test_restore_models.py similarity index 75% rename from tests/test_a_restore_models.py rename to tests/test_restore_models.py index 8e366c8806..318b70bdc1 100644 --- a/tests/test_a_restore_models.py +++ b/tests/test_restore_models.py @@ -7,27 +7,27 @@ import torch from pytorch_lightning import Trainer from pytorch_lightning.callbacks import ModelCheckpoint from pytorch_lightning.testing import LightningTestModel -from . import testing_utils +import tests.utils as tutils def test_running_test_pretrained_model_ddp(): """Verify test() on pretrained model""" - if not testing_utils.can_run_gpu_test(): + if not tutils.can_run_gpu_test(): return - testing_utils.reset_seed() - testing_utils.set_random_master_port() + tutils.reset_seed() + tutils.set_random_master_port() - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) - save_dir = testing_utils.init_save_dir() + save_dir = tutils.init_save_dir() # exp file to get meta - logger = testing_utils.get_test_tube_logger(False) + logger = tutils.get_test_tube_logger(False) # exp file to get weights - checkpoint = testing_utils.init_checkpoint_callback(logger) + checkpoint = tutils.init_checkpoint_callback(logger) trainer_options = dict( show_progress_bar=False, @@ -49,38 +49,38 @@ def test_running_test_pretrained_model_ddp(): # correct result and ok accuracy assert result == 1, 'training failed to complete' - pretrained_model = testing_utils.load_model(logger.experiment, - trainer.checkpoint_callback.filepath, - module_class=LightningTestModel) + pretrained_model = tutils.load_model(logger.experiment, + trainer.checkpoint_callback.filepath, + module_class=LightningTestModel) # run test set new_trainer = Trainer(**trainer_options) new_trainer.test(pretrained_model) for dataloader in model.test_dataloader(): - testing_utils.run_prediction(dataloader, pretrained_model) + tutils.run_prediction(dataloader, pretrained_model) - testing_utils.clear_save_dir() + tutils.clear_save_dir() def test_running_test_pretrained_model(): - testing_utils.reset_seed() + tutils.reset_seed() """Verify test() on pretrained model""" - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) - save_dir = testing_utils.init_save_dir() + save_dir = tutils.init_save_dir() # logger file to get meta - logger = testing_utils.get_test_tube_logger(False) + logger = tutils.get_test_tube_logger(False) # logger file to get weights - checkpoint = testing_utils.init_checkpoint_callback(logger) + checkpoint = tutils.init_checkpoint_callback(logger) trainer_options = dict( show_progress_bar=False, - max_nb_epochs=1, + max_nb_epochs=4, train_percent_check=0.4, val_percent_check=0.2, checkpoint_callback=checkpoint, @@ -93,7 +93,7 @@ def test_running_test_pretrained_model(): # correct result and ok accuracy assert result == 1, 'training failed to complete' - pretrained_model = testing_utils.load_model( + pretrained_model = tutils.load_model( logger.experiment, trainer.checkpoint_callback.filepath, module_class=LightningTestModel ) @@ -101,18 +101,18 @@ def test_running_test_pretrained_model(): new_trainer.test(pretrained_model) # test we have good test accuracy - testing_utils.assert_ok_test_acc(new_trainer) - testing_utils.clear_save_dir() + tutils.assert_ok_test_acc(new_trainer) + tutils.clear_save_dir() def test_load_model_from_checkpoint(): - testing_utils.reset_seed() + tutils.reset_seed() """Verify test() on pretrained model""" - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) - save_dir = testing_utils.init_save_dir() + save_dir = tutils.init_save_dir() trainer_options = dict( show_progress_bar=False, @@ -142,27 +142,27 @@ def test_load_model_from_checkpoint(): new_trainer.test(pretrained_model) # test we have good test accuracy - testing_utils.assert_ok_test_acc(new_trainer) - testing_utils.clear_save_dir() + tutils.assert_ok_test_acc(new_trainer) + tutils.clear_save_dir() def test_running_test_pretrained_model_dp(): - testing_utils.reset_seed() + tutils.reset_seed() """Verify test() on pretrained model""" - if not testing_utils.can_run_gpu_test(): + if not tutils.can_run_gpu_test(): return - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) - save_dir = testing_utils.init_save_dir() + save_dir = tutils.init_save_dir() # logger file to get meta - logger = testing_utils.get_test_tube_logger(False) + logger = tutils.get_test_tube_logger(False) # logger file to get weights - checkpoint = testing_utils.init_checkpoint_callback(logger) + checkpoint = tutils.init_checkpoint_callback(logger) trainer_options = dict( show_progress_bar=True, @@ -181,16 +181,16 @@ def test_running_test_pretrained_model_dp(): # correct result and ok accuracy assert result == 1, 'training failed to complete' - pretrained_model = testing_utils.load_model(logger.experiment, - trainer.checkpoint_callback.filepath, - module_class=LightningTestModel) + pretrained_model = tutils.load_model(logger.experiment, + trainer.checkpoint_callback.filepath, + module_class=LightningTestModel) new_trainer = Trainer(**trainer_options) new_trainer.test(pretrained_model) # test we have good test accuracy - testing_utils.assert_ok_test_acc(new_trainer) - testing_utils.clear_save_dir() + tutils.assert_ok_test_acc(new_trainer) + tutils.clear_save_dir() def test_dp_resume(): @@ -198,12 +198,12 @@ def test_dp_resume(): Make sure DP continues training correctly :return: """ - if not testing_utils.can_run_gpu_test(): + if not tutils.can_run_gpu_test(): return - testing_utils.reset_seed() + tutils.reset_seed() - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) trainer_options = dict( @@ -213,14 +213,14 @@ def test_dp_resume(): distributed_backend='dp', ) - save_dir = testing_utils.init_save_dir() + save_dir = tutils.init_save_dir() # get logger - logger = testing_utils.get_test_tube_logger(debug=False) + logger = tutils.get_test_tube_logger(debug=False) # exp file to get weights # logger file to get weights - checkpoint = testing_utils.init_checkpoint_callback(logger) + checkpoint = tutils.init_checkpoint_callback(logger) # add these to the trainer options trainer_options['logger'] = logger @@ -244,7 +244,7 @@ def test_dp_resume(): trainer.hpc_save(save_dir, logger) # init new trainer - new_logger = testing_utils.get_test_tube_logger(version=logger.version) + new_logger = tutils.get_test_tube_logger(version=logger.version) trainer_options['logger'] = new_logger trainer_options['checkpoint_callback'] = ModelCheckpoint(save_dir) trainer_options['train_percent_check'] = 0.2 @@ -262,7 +262,7 @@ def test_dp_resume(): dp_model.eval() dataloader = trainer.get_train_dataloader() - testing_utils.run_prediction(dataloader, dp_model, dp=True) + tutils.run_prediction(dataloader, dp_model, dp=True) # new model model = LightningTestModel(hparams) @@ -275,7 +275,7 @@ def test_dp_resume(): model.freeze() model.unfreeze() - testing_utils.clear_save_dir() + tutils.clear_save_dir() def test_cpu_restore_training(): @@ -283,16 +283,16 @@ def test_cpu_restore_training(): Verify continue training session on CPU :return: """ - testing_utils.reset_seed() + tutils.reset_seed() - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) - save_dir = testing_utils.init_save_dir() + save_dir = tutils.init_save_dir() # logger file to get meta test_logger_version = 10 - logger = testing_utils.get_test_tube_logger(False, version=test_logger_version) + logger = tutils.get_test_tube_logger(False, version=test_logger_version) trainer_options = dict( max_nb_epochs=2, @@ -314,7 +314,7 @@ def test_cpu_restore_training(): # wipe-out trainer and model # retrain with not much data... this simulates picking training back up after slurm # we want to see if the weights come back correctly - new_logger = testing_utils.get_test_tube_logger(False, version=test_logger_version) + new_logger = tutils.get_test_tube_logger(False, version=test_logger_version) trainer_options = dict( max_nb_epochs=2, val_check_interval=0.50, @@ -335,7 +335,7 @@ def test_cpu_restore_training(): # haven't trained with the new loaded model trainer.model.eval() for dataloader in trainer.get_val_dataloaders(): - testing_utils.run_prediction(dataloader, trainer.model) + tutils.run_prediction(dataloader, trainer.model) model.on_sanity_check_start = assert_good_acc @@ -343,7 +343,7 @@ def test_cpu_restore_training(): # and our hook to predict using current model before any more weight updates trainer.fit(model) - testing_utils.clear_save_dir() + tutils.clear_save_dir() def test_model_saving_loading(): @@ -351,15 +351,15 @@ def test_model_saving_loading(): Tests use case where trainer saves the model, and user loads it from tags independently :return: """ - testing_utils.reset_seed() + tutils.reset_seed() - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) - save_dir = testing_utils.init_save_dir() + save_dir = tutils.init_save_dir() # logger file to get meta - logger = testing_utils.get_test_tube_logger(False) + logger = tutils.get_test_tube_logger(False) trainer_options = dict( max_nb_epochs=1, @@ -402,7 +402,7 @@ def test_model_saving_loading(): new_pred = model_2(x) assert torch.all(torch.eq(pred_before_saving, new_pred)).item() == 1 - testing_utils.clear_save_dir() + tutils.clear_save_dir() if __name__ == '__main__': diff --git a/tests/test_trainer.py b/tests/test_trainer.py index 8f2d830c02..bd7c558d3e 100644 --- a/tests/test_trainer.py +++ b/tests/test_trainer.py @@ -16,7 +16,7 @@ from pytorch_lightning.testing import ( ) from pytorch_lightning.trainer import trainer_io from pytorch_lightning.trainer.logging_mixin import TrainerLoggingMixin -from . import testing_utils +import tests.utils as tutils def test_no_val_module(): @@ -24,19 +24,19 @@ def test_no_val_module(): Tests use case where trainer saves the model, and user loads it from tags independently :return: """ - testing_utils.reset_seed() + tutils.reset_seed() - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() class CurrentTestModel(LightningTestModelBase): pass model = CurrentTestModel(hparams) - save_dir = testing_utils.init_save_dir() + save_dir = tutils.init_save_dir() # logger file to get meta - logger = testing_utils.get_test_tube_logger(False) + logger = tutils.get_test_tube_logger(False) trainer_options = dict( max_nb_epochs=1, @@ -63,7 +63,7 @@ def test_no_val_module(): model_2.eval() # make prediction - testing_utils.clear_save_dir() + tutils.clear_save_dir() def test_no_val_end_module(): @@ -71,18 +71,18 @@ def test_no_val_end_module(): Tests use case where trainer saves the model, and user loads it from tags independently :return: """ - testing_utils.reset_seed() + tutils.reset_seed() class CurrentTestModel(LightningValidationStepMixin, LightningTestModelBase): pass - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = CurrentTestModel(hparams) - save_dir = testing_utils.init_save_dir() + save_dir = tutils.init_save_dir() # logger file to get meta - logger = testing_utils.get_test_tube_logger(False) + logger = tutils.get_test_tube_logger(False) trainer_options = dict( max_nb_epochs=1, @@ -109,11 +109,11 @@ def test_no_val_end_module(): model_2.eval() # make prediction - testing_utils.clear_save_dir() + tutils.clear_save_dir() def test_gradient_accumulation_scheduling(): - testing_utils.reset_seed() + tutils.reset_seed() """ Test grad accumulation by the freq of optimizer updates @@ -170,7 +170,7 @@ def test_gradient_accumulation_scheduling(): # clear gradients optimizer.zero_grad() - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) schedule = {1: 2, 3: 4} @@ -187,13 +187,13 @@ def test_gradient_accumulation_scheduling(): def test_loading_meta_tags(): - testing_utils.reset_seed() + tutils.reset_seed() from argparse import Namespace - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() # save tags - logger = testing_utils.get_test_tube_logger(False) + logger = tutils.get_test_tube_logger(False) logger.log_hyperparams(Namespace(some_str='a_str', an_int=1, a_float=2.0)) logger.log_hyperparams(hparams) logger.save() @@ -206,12 +206,12 @@ def test_loading_meta_tags(): assert tags.batch_size == 32 and tags.hidden_dim == 1000 - testing_utils.clear_save_dir() + tutils.clear_save_dir() def test_dp_output_reduce(): mixin = TrainerLoggingMixin() - testing_utils.reset_seed() + tutils.reset_seed() # test identity when we have a single gpu out = torch.rand(3, 1) @@ -240,11 +240,11 @@ def test_model_checkpoint_options(): def mock_save_function(filepath): open(filepath, 'a').close() - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) # simulated losses - save_dir = testing_utils.init_save_dir() + save_dir = tutils.init_save_dir() losses = [10, 9, 2.8, 5, 2.5] # ----------------- @@ -262,7 +262,7 @@ def test_model_checkpoint_options(): for i in range(0, len(losses)): assert f'_ckpt_epoch_{i}.ckpt' in file_lists - testing_utils.clear_save_dir() + tutils.clear_save_dir() # ----------------- # CASE K=0 (none) @@ -275,7 +275,7 @@ def test_model_checkpoint_options(): assert len(file_lists) == 0, "Should save 0 models when save_top_k=0" - testing_utils.clear_save_dir() + tutils.clear_save_dir() # ----------------- # CASE K=1 (2.5, epoch 4) @@ -289,7 +289,7 @@ def test_model_checkpoint_options(): assert len(file_lists) == 1, "Should save 1 model when save_top_k=1" assert 'test_prefix_ckpt_epoch_4.ckpt' in file_lists - testing_utils.clear_save_dir() + tutils.clear_save_dir() # ----------------- # CASE K=2 (2.5 epoch 4, 2.8 epoch 2) @@ -308,7 +308,7 @@ def test_model_checkpoint_options(): assert '_ckpt_epoch_2.ckpt' in file_lists assert 'other_file.ckpt' in file_lists - testing_utils.clear_save_dir() + tutils.clear_save_dir() # ----------------- # CASE K=4 (save all 4 models) @@ -323,7 +323,7 @@ def test_model_checkpoint_options(): assert len(file_lists) == 4, 'Should save all 4 models when save_top_k=4 within same epoch' - testing_utils.clear_save_dir() + tutils.clear_save_dir() # ----------------- # CASE K=3 (save the 2nd, 3rd, 4th model) @@ -341,13 +341,13 @@ def test_model_checkpoint_options(): assert '_ckpt_epoch_0_v1.ckpt' in file_lists assert '_ckpt_epoch_0.ckpt' in file_lists - testing_utils.clear_save_dir() + tutils.clear_save_dir() def test_model_freeze_unfreeze(): - testing_utils.reset_seed() + tutils.reset_seed() - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = LightningTestModel(hparams) model.freeze() @@ -359,7 +359,7 @@ def test_multiple_val_dataloader(): Verify multiple val_dataloader :return: """ - testing_utils.reset_seed() + tutils.reset_seed() class CurrentTestModel( LightningValidationMultipleDataloadersMixin, @@ -367,7 +367,7 @@ def test_multiple_val_dataloader(): ): pass - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = CurrentTestModel(hparams) # logger file to get meta @@ -390,7 +390,7 @@ def test_multiple_val_dataloader(): # make sure predictions are good for each val set for dataloader in trainer.get_val_dataloaders(): - testing_utils.run_prediction(dataloader, trainer.model) + tutils.run_prediction(dataloader, trainer.model) def test_multiple_test_dataloader(): @@ -398,7 +398,7 @@ def test_multiple_test_dataloader(): Verify multiple test_dataloader :return: """ - testing_utils.reset_seed() + tutils.reset_seed() class CurrentTestModel( LightningTestMultipleDataloadersMixin, @@ -406,7 +406,7 @@ def test_multiple_test_dataloader(): ): pass - hparams = testing_utils.get_hparams() + hparams = tutils.get_hparams() model = CurrentTestModel(hparams) # logger file to get meta @@ -426,7 +426,7 @@ def test_multiple_test_dataloader(): # make sure predictions are good for each test set for dataloader in trainer.get_test_dataloaders(): - testing_utils.run_prediction(dataloader, trainer.model) + tutils.run_prediction(dataloader, trainer.model) # run the test method trainer.test() diff --git a/tests/testing_utils.py b/tests/utils.py similarity index 99% rename from tests/testing_utils.py rename to tests/utils.py index a956ffad0f..1fcc68287c 100644 --- a/tests/testing_utils.py +++ b/tests/utils.py @@ -52,7 +52,7 @@ def run_model_test_no_loggers(trainer_options, model, hparams, on_gpu=True, min_ clear_save_dir() -def run_gpu_model_test(trainer_options, model, hparams, on_gpu=True): +def run_model_test(trainer_options, model, hparams, on_gpu=True): save_dir = init_save_dir() # logger file to get meta