def test_testtube_pickle(): """ Verify that pickling a trainer containing a test tube logger works """ tutils.reset_seed() hparams = tutils.get_hparams() model = LightningTestModel(hparams) save_dir = tutils.init_save_dir() logger = tutils.get_test_tube_logger(False) logger.log_hyperparams(hparams) logger.save() trainer_options = dict(max_nb_epochs=1, train_percent_check=0.01, logger=logger) trainer = Trainer(**trainer_options) pkl_bytes = pickle.dumps(trainer) trainer2 = pickle.loads(pkl_bytes) trainer2.logger.log_metrics({"acc": 1.0}) tutils.clear_save_dir()
def test_comet_pickle(): """ verify that pickling trainer with comet logger works """ tutils.reset_seed() try: from pytorch_lightning.logging import CometLogger except ModuleNotFoundError: return hparams = tutils.get_hparams() model = LightningTestModel(hparams) root_dir = os.path.dirname(os.path.realpath(__file__)) comet_dir = os.path.join(root_dir, "cometruns") # We test CometLogger in offline mode with local saves logger = CometLogger( save_dir=comet_dir, project_name="general", workspace="dummy-test", ) trainer_options = dict(max_nb_epochs=1, logger=logger) trainer = Trainer(**trainer_options) pkl_bytes = pickle.dumps(trainer) trainer2 = pickle.loads(pkl_bytes) trainer2.logger.log_metrics({"acc": 1.0}) tutils.clear_save_dir()
def test_default_logger_callbacks_cpu_model(): """ Test each of the trainer options :return: """ tutils.reset_seed() trainer_options = dict(max_nb_epochs=1, gradient_clip_val=1.0, overfit_pct=0.20, print_nan_grads=True, show_progress_bar=False, train_percent_check=0.01, val_percent_check=0.01) 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() tutils.clear_save_dir()
def test_running_test_after_fitting(): """Verify test() on fitted model""" tutils.reset_seed() hparams = tutils.get_hparams() model = LightningTestModel(hparams) save_dir = tutils.init_save_dir() # logger file to get meta logger = tutils.get_test_tube_logger(False) # logger file to get weights checkpoint = tutils.init_checkpoint_callback(logger) trainer_options = dict(show_progress_bar=False, max_nb_epochs=1, train_percent_check=0.4, val_percent_check=0.2, test_percent_check=0.2, checkpoint_callback=checkpoint, logger=logger) # fit model trainer = Trainer(**trainer_options) result = trainer.fit(model) assert result == 1, 'training failed to complete' trainer.test() # test we have good test accuracy tutils.assert_ok_test_acc(trainer) tutils.clear_save_dir()
def test_mlflow_pickle(): """ verify that pickling trainer with mlflow logger works """ tutils.reset_seed() try: from pytorch_lightning.logging import MLFlowLogger except ModuleNotFoundError: return hparams = tutils.get_hparams() model = LightningTestModel(hparams) root_dir = os.path.dirname(os.path.realpath(__file__)) mlflow_dir = os.path.join(root_dir, "mlruns") logger = MLFlowLogger("test", f"file://{mlflow_dir}") trainer_options = dict(max_nb_epochs=1, logger=logger) trainer = Trainer(**trainer_options) pkl_bytes = pickle.dumps(trainer) trainer2 = pickle.loads(pkl_bytes) trainer2.logger.log_metrics({"acc": 1.0}) tutils.clear_save_dir()
def test_mlflow_logger(): """ verify that basic functionality of mlflow logger works """ tutils.reset_seed() try: from pytorch_lightning.logging import MLFlowLogger except ModuleNotFoundError: return hparams = tutils.get_hparams() model = LightningTestModel(hparams) root_dir = os.path.dirname(os.path.realpath(__file__)) mlflow_dir = os.path.join(root_dir, "mlruns") logger = MLFlowLogger("test", f"file://{mlflow_dir}") trainer_options = dict(max_nb_epochs=1, train_percent_check=0.01, logger=logger) trainer = Trainer(**trainer_options) result = trainer.fit(model) print('result finished') assert result == 1, "Training failed" tutils.clear_save_dir()
def test_ddp_sampler_error(): """ Make sure DDP + AMP work :return: """ if not tutils.can_run_gpu_test(): return tutils.reset_seed() tutils.set_random_master_port() hparams = tutils.get_hparams() model = LightningTestModel(hparams, force_remove_distributed_sampler=True) logger = tutils.get_test_tube_logger(True) trainer = Trainer(logger=logger, show_progress_bar=False, max_nb_epochs=1, gpus=[0, 1], distributed_backend='ddp', use_amp=True) with pytest.warns(UserWarning): trainer.get_dataloaders(model) tutils.clear_save_dir()
def test_simple_cpu(): """ Verify continue training session on CPU :return: """ tutils.reset_seed() hparams = tutils.get_hparams() model = LightningTestModel(hparams) save_dir = tutils.init_save_dir() # logger file to get meta trainer_options = dict( max_nb_epochs=1, val_percent_check=0.1, train_percent_check=0.1, ) # fit model trainer = Trainer(**trainer_options) result = trainer.fit(model) # traning complete assert result == 1, 'amp + ddp model failed to complete' tutils.clear_save_dir()
def test_comet_logger(): """ verify that basic functionality of Comet.ml logger works """ tutils.reset_seed() try: from pytorch_lightning.logging import CometLogger except ModuleNotFoundError: return hparams = tutils.get_hparams() model = LightningTestModel(hparams) root_dir = os.path.dirname(os.path.realpath(__file__)) comet_dir = os.path.join(root_dir, "cometruns") # We test CometLogger in offline mode with local saves logger = CometLogger( save_dir=comet_dir, project_name="general", workspace="dummy-test", ) trainer_options = dict(max_nb_epochs=1, train_percent_check=0.01, logger=logger) trainer = Trainer(**trainer_options) result = trainer.fit(model) print('result finished') assert result == 1, "Training failed" tutils.clear_save_dir()
def test_model_saving_loading(): """ Tests use case where trainer saves the model, and user loads it from tags independently :return: """ tutils.reset_seed() hparams = tutils.get_hparams() model = LightningTestModel(hparams) save_dir = tutils.init_save_dir() # logger file to get meta logger = tutils.get_test_tube_logger(False) trainer_options = dict( max_nb_epochs=1, logger=logger, checkpoint_callback=ModelCheckpoint(save_dir) ) # fit model trainer = Trainer(**trainer_options) result = trainer.fit(model) # traning complete assert result == 1, 'amp + ddp model failed to complete' # make a prediction for dataloader in model.test_dataloader(): for batch in dataloader: break x, y = batch x = x.view(x.size(0), -1) # generate preds before saving model model.eval() pred_before_saving = model(x) # save model new_weights_path = os.path.join(save_dir, 'save_test.ckpt') trainer.save_checkpoint(new_weights_path) # load new model tags_path = logger.experiment.get_data_path(logger.experiment.name, logger.experiment.version) tags_path = os.path.join(tags_path, 'meta_tags.csv') model_2 = LightningTestModel.load_from_metrics(weights_path=new_weights_path, tags_csv=tags_path) model_2.eval() # make prediction # assert that both predictions are the same new_pred = model_2(x) assert torch.all(torch.eq(pred_before_saving, new_pred)).item() == 1 tutils.clear_save_dir()
def test_running_test_pretrained_model_ddp(): """Verify test() on pretrained model""" if not tutils.can_run_gpu_test(): return tutils.reset_seed() tutils.set_random_master_port() hparams = tutils.get_hparams() model = LightningTestModel(hparams) save_dir = tutils.init_save_dir() # exp file to get meta logger = tutils.get_test_tube_logger(False) # exp file to get weights checkpoint = tutils.init_checkpoint_callback(logger) trainer_options = dict( show_progress_bar=False, max_nb_epochs=1, train_percent_check=0.4, val_percent_check=0.2, checkpoint_callback=checkpoint, logger=logger, gpus=[0, 1], distributed_backend='ddp' ) # fit model trainer = Trainer(**trainer_options) result = trainer.fit(model) exp = logger.experiment logging.info(os.listdir(exp.get_data_path(exp.name, exp.version))) # correct result and ok accuracy assert result == 1, 'training failed to complete' 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(): tutils.run_prediction(dataloader, pretrained_model) tutils.clear_save_dir()
def test_running_test_pretrained_model_dp(): tutils.reset_seed() """Verify test() on pretrained model""" if not tutils.can_run_gpu_test(): return hparams = tutils.get_hparams() model = LightningTestModel(hparams) save_dir = tutils.init_save_dir() # logger file to get meta logger = tutils.get_test_tube_logger(False) # logger file to get weights checkpoint = tutils.init_checkpoint_callback(logger) trainer_options = dict( show_progress_bar=True, max_nb_epochs=1, train_percent_check=0.4, val_percent_check=0.2, checkpoint_callback=checkpoint, logger=logger, gpus=[0, 1], distributed_backend='dp' ) # fit model trainer = Trainer(**trainer_options) result = trainer.fit(model) # correct result and ok accuracy assert result == 1, 'training failed to complete' 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 tutils.assert_ok_test_acc(new_trainer) tutils.clear_save_dir()
def test_no_val_end_module(): """ Tests use case where trainer saves the model, and user loads it from tags independently :return: """ tutils.reset_seed() class CurrentTestModel(LightningValidationStepMixin, LightningTestModelBase): pass hparams = tutils.get_hparams() model = CurrentTestModel(hparams) save_dir = tutils.init_save_dir() # logger file to get meta logger = tutils.get_test_tube_logger(False) trainer_options = dict(max_nb_epochs=1, logger=logger, checkpoint_callback=ModelCheckpoint(save_dir)) # fit model trainer = Trainer(**trainer_options) result = trainer.fit(model) # traning complete assert result == 1, 'amp + ddp model failed to complete' # save model new_weights_path = os.path.join(save_dir, 'save_test.ckpt') trainer.save_checkpoint(new_weights_path) # load new model tags_path = logger.experiment.get_data_path(logger.experiment.name, logger.experiment.version) tags_path = os.path.join(tags_path, 'meta_tags.csv') model_2 = LightningTestModel.load_from_metrics( weights_path=new_weights_path, tags_csv=tags_path) model_2.eval() # make prediction tutils.clear_save_dir()
def test_loading_meta_tags(): tutils.reset_seed() from argparse import Namespace hparams = tutils.get_hparams() # save tags 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() # load tags tags_path = logger.experiment.get_data_path( logger.experiment.name, logger.experiment.version) + '/meta_tags.csv' tags = trainer_io.load_hparams_from_tags_csv(tags_path) assert tags.batch_size == 32 and tags.hidden_dim == 1000 tutils.clear_save_dir()
def test_testtube_logger(): """ verify that basic functionality of test tube logger works """ tutils.reset_seed() hparams = tutils.get_hparams() model = LightningTestModel(hparams) save_dir = tutils.init_save_dir() logger = tutils.get_test_tube_logger(False) trainer_options = dict(max_nb_epochs=1, train_percent_check=0.01, logger=logger) trainer = Trainer(**trainer_options) result = trainer.fit(model) assert result == 1, "Training failed" tutils.clear_save_dir()
def test_load_model_from_checkpoint(): tutils.reset_seed() """Verify test() on pretrained model""" hparams = tutils.get_hparams() model = LightningTestModel(hparams) save_dir = tutils.init_save_dir() trainer_options = dict( show_progress_bar=False, max_nb_epochs=1, train_percent_check=0.4, val_percent_check=0.2, checkpoint_callback=True, logger=False, default_save_path=save_dir ) # fit model trainer = Trainer(**trainer_options) result = trainer.fit(model) # correct result and ok accuracy assert result == 1, 'training failed to complete' pretrained_model = LightningTestModel.load_from_checkpoint( os.path.join(trainer.checkpoint_callback.filepath, "_ckpt_epoch_0.ckpt") ) # test that hparams loaded correctly for k, v in vars(hparams).items(): assert getattr(pretrained_model.hparams, k) == v new_trainer = Trainer(**trainer_options) new_trainer.test(pretrained_model) # test we have good test accuracy tutils.assert_ok_test_acc(new_trainer) tutils.clear_save_dir()
def test_lbfgs_cpu_model(): """ Test each of the trainer options :return: """ tutils.reset_seed() trainer_options = dict(max_nb_epochs=1, print_nan_grads=True, show_progress_bar=False, weights_summary='top', train_percent_check=1.0, val_percent_check=0.2) 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) tutils.clear_save_dir()
def test_running_test_without_val(): tutils.reset_seed() """Verify test() works on a model with no val_loader""" class CurrentTestModel(LightningTestMixin, LightningTestModelBase): pass hparams = tutils.get_hparams() model = CurrentTestModel(hparams) save_dir = tutils.init_save_dir() # logger file to get meta logger = tutils.get_test_tube_logger(False) # logger file to get weights checkpoint = tutils.init_checkpoint_callback(logger) trainer_options = dict(show_progress_bar=False, max_nb_epochs=1, train_percent_check=0.4, val_percent_check=0.2, test_percent_check=0.2, checkpoint_callback=checkpoint, logger=logger) # fit model trainer = Trainer(**trainer_options) result = trainer.fit(model) assert result == 1, 'training failed to complete' trainer.test() # test we have good test accuracy tutils.assert_ok_test_acc(trainer) tutils.clear_save_dir()
def test_cpu_restore_training(): """ Verify continue training session on CPU :return: """ tutils.reset_seed() hparams = tutils.get_hparams() model = LightningTestModel(hparams) save_dir = tutils.init_save_dir() # logger file to get meta test_logger_version = 10 logger = tutils.get_test_tube_logger(False, version=test_logger_version) trainer_options = dict( max_nb_epochs=2, val_check_interval=0.50, val_percent_check=0.2, train_percent_check=0.2, logger=logger, checkpoint_callback=ModelCheckpoint(save_dir) ) # fit model trainer = Trainer(**trainer_options) result = trainer.fit(model) real_global_epoch = trainer.current_epoch # traning complete assert result == 1, 'amp + ddp model failed to complete' # 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 = tutils.get_test_tube_logger(False, version=test_logger_version) trainer_options = dict( max_nb_epochs=2, val_check_interval=0.50, val_percent_check=0.2, train_percent_check=0.2, logger=new_logger, checkpoint_callback=ModelCheckpoint(save_dir), ) trainer = Trainer(**trainer_options) model = LightningTestModel(hparams) # set the epoch start hook so we can predict before the model does the full training def assert_good_acc(): assert trainer.current_epoch == real_global_epoch assert trainer.current_epoch >= 0 # if model and state loaded correctly, predictions will be good even though we # haven't trained with the new loaded model trainer.model.eval() for dataloader in trainer.get_val_dataloaders(): tutils.run_prediction(dataloader, trainer.model) model.on_sanity_check_start = assert_good_acc # by calling fit again, we trigger training, loading weights from the cluster # and our hook to predict using current model before any more weight updates trainer.fit(model) tutils.clear_save_dir()
def test_dp_resume(): """ Make sure DP continues training correctly :return: """ if not tutils.can_run_gpu_test(): return tutils.reset_seed() hparams = tutils.get_hparams() model = LightningTestModel(hparams) trainer_options = dict( show_progress_bar=True, max_nb_epochs=2, gpus=2, distributed_backend='dp', ) save_dir = tutils.init_save_dir() # get logger logger = tutils.get_test_tube_logger(debug=False) # exp file to get weights # logger file to get weights checkpoint = tutils.init_checkpoint_callback(logger) # add these to the trainer options trainer_options['logger'] = logger trainer_options['checkpoint_callback'] = checkpoint # fit model trainer = Trainer(**trainer_options) trainer.is_slurm_managing_tasks = True result = trainer.fit(model) # track epoch before saving real_global_epoch = trainer.current_epoch # correct result and ok accuracy assert result == 1, 'amp + dp model failed to complete' # --------------------------- # HPC LOAD/SAVE # --------------------------- # save trainer.hpc_save(save_dir, logger) # init new trainer 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 trainer_options['val_percent_check'] = 0.2 trainer_options['max_nb_epochs'] = 1 new_trainer = Trainer(**trainer_options) # set the epoch start hook so we can predict before the model does the full training def assert_good_acc(): assert new_trainer.current_epoch == real_global_epoch and new_trainer.current_epoch > 0 # if model and state loaded correctly, predictions will be good even though we # haven't trained with the new loaded model dp_model = new_trainer.model dp_model.eval() dataloader = trainer.get_train_dataloader() tutils.run_prediction(dataloader, dp_model, dp=True) # new model model = LightningTestModel(hparams) model.on_sanity_check_start = assert_good_acc # fit new model which should load hpc weights new_trainer.fit(model) # test freeze on gpu model.freeze() model.unfreeze() tutils.clear_save_dir()
def test_cpu_slurm_save_load(): """ Verify model save/load/checkpoint on CPU :return: """ tutils.reset_seed() hparams = tutils.get_hparams() model = LightningTestModel(hparams) save_dir = tutils.init_save_dir() # logger file to get meta logger = tutils.get_test_tube_logger(False) version = logger.version trainer_options = dict(max_nb_epochs=1, logger=logger, checkpoint_callback=ModelCheckpoint(save_dir)) # fit model trainer = Trainer(**trainer_options) result = trainer.fit(model) real_global_step = trainer.global_step # traning complete assert result == 1, 'amp + ddp model failed to complete' # predict with trained model before saving # make a prediction for dataloader in model.test_dataloader(): for batch in dataloader: break x, y = batch x = x.view(x.size(0), -1) model.eval() pred_before_saving = model(x) # test HPC saving # simulate snapshot on slurm saved_filepath = trainer.hpc_save(save_dir, logger) assert os.path.exists(saved_filepath) # new logger file to get meta logger = tutils.get_test_tube_logger(False, version=version) trainer_options = dict( max_nb_epochs=1, logger=logger, checkpoint_callback=ModelCheckpoint(save_dir), ) trainer = Trainer(**trainer_options) model = LightningTestModel(hparams) # set the epoch start hook so we can predict before the model does the full training def assert_pred_same(): assert trainer.global_step == real_global_step and trainer.global_step > 0 # predict with loaded model to make sure answers are the same trainer.model.eval() new_pred = trainer.model(x) assert torch.all(torch.eq(pred_before_saving, new_pred)).item() == 1 model.on_epoch_start = assert_pred_same # by calling fit again, we trigger training, loading weights from the cluster # and our hook to predict using current model before any more weight updates trainer.fit(model) tutils.clear_save_dir()
def test_tbptt_cpu_model(): """ Test truncated back propagation through time works. :return: """ tutils.reset_seed() save_dir = tutils.init_save_dir() truncated_bptt_steps = 2 sequence_size = 30 batch_size = 30 x_seq = torch.rand(batch_size, sequence_size, 1) y_seq_list = torch.rand(batch_size, sequence_size, 1).tolist() class MockSeq2SeqDataset(torch.utils.data.Dataset): def __getitem__(self, i): return x_seq, y_seq_list def __len__(self): return 1 class BpttTestModel(LightningTestModelBase): def __init__(self, hparams): super().__init__(hparams) self.test_hidden = None def training_step(self, batch, batch_idx, hiddens): assert hiddens == self.test_hidden, "Hidden state not persistent between tbptt steps" self.test_hidden = torch.rand(1) x_tensor, y_list = batch assert x_tensor.shape[ 1] == truncated_bptt_steps, "tbptt split Tensor failed" y_tensor = torch.tensor(y_list, dtype=x_tensor.dtype) assert y_tensor.shape[ 1] == truncated_bptt_steps, "tbptt split list failed" pred = self.forward(x_tensor.view(batch_size, truncated_bptt_steps)) loss_val = torch.nn.functional.mse_loss( pred, y_tensor.view(batch_size, truncated_bptt_steps)) return { 'loss': loss_val, 'hiddens': self.test_hidden, } @data_loader def train_dataloader(self): return torch.utils.data.DataLoader( dataset=MockSeq2SeqDataset(), batch_size=batch_size, shuffle=False, sampler=None, ) trainer_options = dict( max_nb_epochs=1, truncated_bptt_steps=truncated_bptt_steps, val_percent_check=0, weights_summary=None, ) hparams = tutils.get_hparams() hparams.batch_size = batch_size hparams.in_features = truncated_bptt_steps hparams.hidden_dim = truncated_bptt_steps hparams.out_features = truncated_bptt_steps model = BpttTestModel(hparams) # fit model trainer = Trainer(**trainer_options) result = trainer.fit(model) assert result == 1, 'training failed to complete' tutils.clear_save_dir()
def test_amp_gpu_ddp_slurm_managed(): """ Make sure DDP + AMP work :return: """ if not tutils.can_run_gpu_test(): return tutils.reset_seed() # simulate setting slurm flags tutils.set_random_master_port() os.environ['SLURM_LOCALID'] = str(0) hparams = tutils.get_hparams() model = LightningTestModel(hparams) trainer_options = dict(show_progress_bar=True, max_nb_epochs=1, gpus=[0], distributed_backend='ddp', use_amp=True) save_dir = tutils.init_save_dir() # exp file to get meta logger = tutils.get_test_tube_logger(False) # exp file to get weights checkpoint = tutils.init_checkpoint_callback(logger) # add these to the trainer options trainer_options['checkpoint_callback'] = checkpoint trainer_options['logger'] = logger # fit model trainer = Trainer(**trainer_options) trainer.is_slurm_managing_tasks = True result = trainer.fit(model) # correct result and ok accuracy assert result == 1, 'amp + ddp model failed to complete' # test root model address assert trainer.resolve_root_node_address('abc') == 'abc' assert trainer.resolve_root_node_address('abc[23]') == 'abc23' assert trainer.resolve_root_node_address('abc[23-24]') == 'abc23' assert trainer.resolve_root_node_address( 'abc[23-24, 45-40, 40]') == 'abc23' # test model loading with a map_location pretrained_model = tutils.load_model(logger.experiment, trainer.checkpoint_callback.filepath) # test model preds for dataloader in trainer.get_test_dataloaders(): 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 trainer.model = pretrained_model trainer.optimizers, trainer.lr_schedulers = pretrained_model.configure_optimizers( ) # test HPC loading / saving trainer.hpc_save(save_dir, logger) trainer.hpc_load(save_dir, on_gpu=True) # test freeze on gpu model.freeze() model.unfreeze() tutils.clear_save_dir()
def test_model_checkpoint_options(): """ Test ModelCheckpoint options :return: """ def mock_save_function(filepath): open(filepath, 'a').close() hparams = tutils.get_hparams() model = LightningTestModel(hparams) # simulated losses save_dir = tutils.init_save_dir() losses = [10, 9, 2.8, 5, 2.5] # ----------------- # CASE K=-1 (all) w = ModelCheckpoint(save_dir, save_top_k=-1, verbose=1) w.save_function = mock_save_function for i, loss in enumerate(losses): w.on_epoch_end(i, logs={'val_loss': loss}) file_lists = set(os.listdir(save_dir)) assert len(file_lists) == len( losses), "Should save all models when save_top_k=-1" # verify correct naming for i in range(0, len(losses)): assert f'_ckpt_epoch_{i}.ckpt' in file_lists tutils.clear_save_dir() # ----------------- # CASE K=0 (none) w = ModelCheckpoint(save_dir, save_top_k=0, verbose=1) w.save_function = mock_save_function for i, loss in enumerate(losses): w.on_epoch_end(i, logs={'val_loss': loss}) file_lists = os.listdir(save_dir) assert len(file_lists) == 0, "Should save 0 models when save_top_k=0" tutils.clear_save_dir() # ----------------- # CASE K=1 (2.5, epoch 4) w = ModelCheckpoint(save_dir, save_top_k=1, verbose=1, prefix='test_prefix') w.save_function = mock_save_function for i, loss in enumerate(losses): w.on_epoch_end(i, logs={'val_loss': loss}) file_lists = set(os.listdir(save_dir)) assert len(file_lists) == 1, "Should save 1 model when save_top_k=1" assert 'test_prefix_ckpt_epoch_4.ckpt' in file_lists tutils.clear_save_dir() # ----------------- # CASE K=2 (2.5 epoch 4, 2.8 epoch 2) # make sure other files don't get deleted w = ModelCheckpoint(save_dir, save_top_k=2, verbose=1) open(f'{save_dir}/other_file.ckpt', 'a').close() w.save_function = mock_save_function for i, loss in enumerate(losses): w.on_epoch_end(i, logs={'val_loss': loss}) file_lists = set(os.listdir(save_dir)) assert len(file_lists) == 3, 'Should save 2 model when save_top_k=2' assert '_ckpt_epoch_4.ckpt' in file_lists assert '_ckpt_epoch_2.ckpt' in file_lists assert 'other_file.ckpt' in file_lists tutils.clear_save_dir() # ----------------- # CASE K=4 (save all 4 models) # multiple checkpoints within same epoch w = ModelCheckpoint(save_dir, save_top_k=4, verbose=1) w.save_function = mock_save_function for loss in losses: w.on_epoch_end(0, logs={'val_loss': loss}) file_lists = set(os.listdir(save_dir)) assert len( file_lists ) == 4, 'Should save all 4 models when save_top_k=4 within same epoch' tutils.clear_save_dir() # ----------------- # CASE K=3 (save the 2nd, 3rd, 4th model) # multiple checkpoints within same epoch w = ModelCheckpoint(save_dir, save_top_k=3, verbose=1) w.save_function = mock_save_function for loss in losses: w.on_epoch_end(0, logs={'val_loss': loss}) file_lists = set(os.listdir(save_dir)) assert len(file_lists) == 3, 'Should save 3 models when save_top_k=3' assert '_ckpt_epoch_0_v2.ckpt' in file_lists assert '_ckpt_epoch_0_v1.ckpt' in file_lists assert '_ckpt_epoch_0.ckpt' in file_lists tutils.clear_save_dir()