def test_running_test_after_fitting(tmpdir): """Verify test() on fitted model.""" tutils.reset_seed() hparams = tutils.get_hparams() model = LightningTestModel(hparams) # logger file to get meta logger = tutils.get_test_tube_logger(tmpdir, False) # logger file to get weights checkpoint = tutils.init_checkpoint_callback(logger) trainer_options = dict(default_save_path=tmpdir, show_progress_bar=False, max_epochs=4, 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_model_acc(trainer)
def test_running_test_pretrained_model(tmpdir): tutils.reset_seed() """Verify test() on pretrained model""" hparams = tutils.get_hparams() model = LightningTestModel(hparams) # logger file to get meta logger = tutils.get_test_tube_logger(tmpdir, False) # logger file to get weights checkpoint = tutils.init_checkpoint_callback(logger) trainer_options = dict(show_progress_bar=False, max_num_epochs=4, train_percent_check=0.4, val_percent_check=0.2, checkpoint_callback=checkpoint, logger=logger) # 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)
def test_running_test_without_val(tmpdir): """Verify `test()` works on a model with no `val_loader`.""" tutils.reset_seed() class CurrentTestModel(LightningTestMixin, LightningTestModelBase): pass hparams = tutils.get_hparams() model = CurrentTestModel(hparams) # logger file to get meta logger = tutils.get_test_tube_logger(tmpdir, False) # logger file to get weights checkpoint = tutils.init_checkpoint_callback(logger) trainer_options = dict(show_progress_bar=False, max_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_model_acc(trainer)
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_amp_gpu_ddp_slurm_managed(tmpdir): """Make sure DDP + AMP work.""" 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_epochs=1, gpus=[0], distributed_backend='ddp', use_amp=True) # exp file to get meta logger = tutils.get_test_tube_logger(tmpdir, 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'
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_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()