def test_running_test_pretrained_model_cpu(tmpdir): """Verify test() on pretrained model.""" model = EvalModelTemplate() # logger file to get meta logger = tutils.get_default_logger(tmpdir) # logger file to get weights checkpoint = tutils.init_checkpoint_callback(logger) trainer_options = dict(progress_bar_refresh_rate=0, max_epochs=3, 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, trainer.checkpoint_callback.dirpath, module_class=EvalModelTemplate) new_trainer = Trainer(**trainer_options) new_trainer.test(pretrained_model) # test we have good test accuracy tutils.assert_ok_model_acc(new_trainer)
def test_running_test_pretrained_model_distrib(tmpdir, backend): """Verify `test()` on pretrained model.""" tutils.reset_seed() tutils.set_random_master_port() hparams = tutils.get_default_hparams() model = LightningTestModel(hparams) # exp file to get meta logger = tutils.get_default_logger(tmpdir) # exp file to get weights checkpoint = tutils.init_checkpoint_callback(logger) trainer_options = dict( progress_bar_refresh_rate=0, max_epochs=2, train_percent_check=0.4, val_percent_check=0.2, checkpoint_callback=checkpoint, logger=logger, gpus=[0, 1], distributed_backend=backend, ) # fit model trainer = Trainer(**trainer_options) result = trainer.fit(model) log.info(os.listdir(tutils.get_data_path(logger, path_dir=tmpdir))) # correct result and ok accuracy assert result == 1, 'training failed to complete' pretrained_model = tutils.load_model(logger, trainer.checkpoint_callback.dirpath, module_class=LightningTestModel) # run test set new_trainer = Trainer(**trainer_options) new_trainer.test(pretrained_model) # test we have good test accuracy tutils.assert_ok_model_acc(new_trainer) dataloaders = model.test_dataloader() if not isinstance(dataloaders, list): dataloaders = [dataloaders] for dataloader in dataloaders: tutils.run_prediction(dataloader, pretrained_model)
def test_running_test_pretrained_model_dp(tmpdir): """Verify test() on pretrained model.""" tutils.reset_seed() if not tutils.can_run_gpu_test(): return hparams = tutils.get_default_hparams() model = LightningTestModel(hparams) # logger file to get meta logger = tutils.get_default_testtube_logger(tmpdir, False) # logger file to get weights checkpoint = tutils.init_checkpoint_callback(logger) trainer_options = dict(show_progress_bar=True, max_epochs=4, 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, trainer.checkpoint_callback.dirpath, module_class=LightningTestModel) new_trainer = Trainer(**trainer_options) new_trainer.test(pretrained_model) # test we have good test accuracy tutils.assert_ok_model_acc(new_trainer)