def test_load_model_from_checkpoint(tmpdir): tutils.reset_seed() """Verify test() on pretrained model""" hparams = tutils.get_hparams() model = LightningTestModel(hparams) trainer_options = dict( show_progress_bar=False, max_num_epochs=1, train_percent_check=0.4, val_percent_check=0.2, checkpoint_callback=True, logger=False, default_save_path=tmpdir, ) # 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)
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_test_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): 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) # 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_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)
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()