def test_running_test_pretrained_model_cpu(tmpdir): """Verify test() on pretrained model.""" tutils.reset_seed() hparams = tutils.get_default_hparams() model = LightningTestModel(hparams) # 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=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, 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)
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, limit_train_batches=0.4, limit_val_batches=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_amp_gpu_ddp_slurm_managed(tmpdir): """Make sure DDP + AMP work.""" # simulate setting slurm flags tutils.set_random_master_port() os.environ['SLURM_LOCALID'] = str(0) model = EvalModelTemplate(tutils.get_default_hparams()) # exp file to get meta logger = tutils.get_default_logger(tmpdir) # exp file to get weights checkpoint = tutils.init_checkpoint_callback(logger) # fit model trainer = Trainer( max_epochs=1, gpus=[0], distributed_backend='ddp', precision=16, checkpoint_callback=checkpoint, logger=logger, ) 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_running_test_after_fitting(tmpdir): """Verify test() on fitted 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) # fit model trainer = Trainer(default_root_dir=tmpdir, progress_bar_refresh_rate=0, max_epochs=8, train_percent_check=0.4, val_percent_check=0.2, test_percent_check=0.2, checkpoint_callback=checkpoint, logger=logger) 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, thr=0.5)
def test_running_test_no_val(tmpdir): """Verify `test()` works on a model with no `val_loader`.""" model = EvalModelTemplate() # logger file to get meta logger = tutils.get_default_logger(tmpdir) # logger file to get weights checkpoint = tutils.init_checkpoint_callback(logger) # fit model trainer = Trainer(progress_bar_refresh_rate=0, max_epochs=1, train_percent_check=0.4, val_percent_check=0.2, test_percent_check=0.2, checkpoint_callback=checkpoint, logger=logger, early_stop_callback=False) 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_after_fitting(tmpdir): """Verify test() on fitted model.""" tutils.reset_seed() 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( default_root_dir=tmpdir, progress_bar_refresh_rate=0, max_epochs=8, 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, thr=0.5)
def test_running_test_no_val(tmpdir): """Verify `test()` works on a model with no `val_loader`.""" tutils.reset_seed() class CurrentTestModel(LightTrainDataloader, LightTestMixin, TestModelBase): pass hparams = tutils.get_default_hparams() model = CurrentTestModel(hparams) # logger file to get meta logger = tutils.get_default_logger(tmpdir) # logger file to get weights checkpoint = tutils.init_checkpoint_callback(logger) # fit model trainer = Trainer(progress_bar_refresh_rate=0, max_epochs=1, train_percent_check=0.4, val_percent_check=0.2, test_percent_check=0.2, checkpoint_callback=checkpoint, logger=logger, early_stop_callback=False) 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_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_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_default_hparams() model = LightningTestModel(hparams) trainer_options = dict(show_progress_bar=True, max_epochs=1, gpus=[0], distributed_backend='ddp', precision=16) # exp file to get meta logger = tutils.get_default_testtube_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_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)
def test_dp_resume(tmpdir): """Make sure DP continues training correctly.""" hparams = EvalModelTemplate.get_default_hparams() model = EvalModelTemplate(**hparams) trainer_options = dict( max_epochs=1, gpus=2, distributed_backend='dp', ) # get logger logger = tutils.get_default_logger(tmpdir) # 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. Increment since we finished the current epoch, don't want to rerun real_global_epoch = trainer.current_epoch + 1 # correct result and ok accuracy assert result == 1, 'amp + dp model failed to complete' # --------------------------- # HPC LOAD/SAVE # --------------------------- # save trainer.hpc_save(tmpdir, logger) # init new trainer new_logger = tutils.get_default_logger(tmpdir, version=logger.version) trainer_options['logger'] = new_logger trainer_options['checkpoint_callback'] = ModelCheckpoint(tmpdir) trainer_options['limit_train_batches'] = 0.5 trainer_options['limit_val_batches'] = 0.2 trainer_options['max_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.train_dataloader tutils.run_prediction(dataloader, dp_model, dp=True) # new model model = EvalModelTemplate(**hparams) model.on_train_start = assert_good_acc # fit new model which should load hpc weights new_trainer.fit(model) # test freeze on gpu model.freeze() model.unfreeze()