import os import pytest import torch from pytorch_lightning import Trainer from pytorch_lightning.callbacks import ( ModelCheckpoint, ) from pytorch_lightning.testing import ( LightningTestModel, LightningTestModelBase, LightningValidationStepMixin, LightningValidationMultipleDataloadersMixin, LightningTestMixin, LightningTestMultipleDataloadersMixin, ) from pytorch_lightning.trainer import trainer_io from pytorch_lightning.trainer.logging_mixin import TrainerLoggingMixin import tests.utils as tutils def test_no_val_module(): """ Tests use case where trainer saves the model, and user loads it from tags independently :return: """ tutils.reset_seed() hparams = tutils.get_hparams() class CurrentTestModel(LightningTestModelBase): pass 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) # training 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_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_gradient_accumulation_scheduling(): tutils.reset_seed() """ Test grad accumulation by the freq of optimizer updates """ # test incorrect configs with pytest.raises(IndexError): assert Trainer(accumulate_grad_batches={0: 3, 1: 4, 4: 6}) assert Trainer(accumulate_grad_batches={-2: 3}) with pytest.raises(TypeError): assert Trainer(accumulate_grad_batches={}) assert Trainer(accumulate_grad_batches=[[2, 3], [4, 6]]) assert Trainer(accumulate_grad_batches={1: 2, 3.: 4}) assert Trainer(accumulate_grad_batches={1: 2.5, 3: 5}) # test optimizer call freq matches scheduler def optimizer_step(self, epoch_nb, batch_nb, optimizer, optimizer_i, second_order_closure=None): # only test the first 12 batches in epoch if batch_nb < 12: if epoch_nb == 0: # reset counter when starting epoch if batch_nb == 0: self.prev_called_batch_nb = 0 # use this opportunity to test once assert self.trainer.accumulate_grad_batches == 1 assert batch_nb == self.prev_called_batch_nb self.prev_called_batch_nb += 1 elif 1 <= epoch_nb <= 2: # reset counter when starting epoch if batch_nb == 1: self.prev_called_batch_nb = 1 # use this opportunity to test once assert self.trainer.accumulate_grad_batches == 2 assert batch_nb == self.prev_called_batch_nb self.prev_called_batch_nb += 2 else: if batch_nb == 3: self.prev_called_batch_nb = 3 # use this opportunity to test once assert self.trainer.accumulate_grad_batches == 4 assert batch_nb == self.prev_called_batch_nb self.prev_called_batch_nb += 3 optimizer.step() # clear gradients optimizer.zero_grad() hparams = tutils.get_hparams() model = LightningTestModel(hparams) schedule = {1: 2, 3: 4} trainer = Trainer(accumulate_grad_batches=schedule, train_percent_check=0.1, val_percent_check=0.1, max_nb_epochs=4) # for the test trainer.optimizer_step = optimizer_step model.prev_called_batch_nb = 0 trainer.fit(model) 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_dp_output_reduce(): mixin = TrainerLoggingMixin() tutils.reset_seed() # test identity when we have a single gpu out = torch.rand(3, 1) assert mixin.reduce_distributed_output(out, nb_gpus=1) is out # average when we have multiples assert mixin.reduce_distributed_output(out, nb_gpus=2) == out.mean() # when we have a dict of vals out = { 'a': out, 'b': { 'c': out } } reduced = mixin.reduce_distributed_output(out, nb_gpus=3) assert reduced['a'] == out['a'] assert reduced['b']['c'] == out['b']['c'] 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() def test_model_freeze_unfreeze(): tutils.reset_seed() hparams = tutils.get_hparams() model = LightningTestModel(hparams) model.freeze() model.unfreeze() def test_multiple_val_dataloader(): """ Verify multiple val_dataloader :return: """ tutils.reset_seed() class CurrentTestModel( LightningValidationMultipleDataloadersMixin, LightningTestModelBase ): pass hparams = tutils.get_hparams() model = CurrentTestModel(hparams) # logger file to get meta trainer_options = dict( max_nb_epochs=1, val_percent_check=0.1, train_percent_check=1.0, ) # fit model trainer = Trainer(**trainer_options) result = trainer.fit(model) # verify training completed assert result == 1 # verify there are 2 val loaders assert len(trainer.get_val_dataloaders()) == 2, \ 'Multiple val_dataloaders not initiated properly' # make sure predictions are good for each val set for dataloader in trainer.get_val_dataloaders(): tutils.run_prediction(dataloader, trainer.model) def test_multiple_test_dataloader(): """ Verify multiple test_dataloader :return: """ tutils.reset_seed() class CurrentTestModel( LightningTestMultipleDataloadersMixin, LightningTestModelBase ): pass hparams = tutils.get_hparams() model = CurrentTestModel(hparams) # 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) # verify there are 2 val loaders assert len(trainer.get_test_dataloaders()) == 2, \ 'Multiple test_dataloaders not initiated properly' # make sure predictions are good for each test set for dataloader in trainer.get_test_dataloaders(): tutils.run_prediction(dataloader, trainer.model) # run the test method trainer.test() if __name__ == '__main__': pytest.main([__file__])