revert changes to the tests, enforce str contract in code

pytorch_lightning/trainer/trainer.py

@@ -371,10 +371,12 @@ def __init__(
                            ' val and test loop using a single batch')
 
         # set default save path if user didn't provide one
-        self.default_root_dir = default_root_dir
-
-        if self.default_root_dir is None:
+        if default_root_dir is None:
             self.default_root_dir = os.getcwd()
+        else:
+            # we have to do str() because the unit tests violate type annotation and pass path objecto
+            self.default_root_dir = str(default_root_dir)
+
 
         # training bookeeping
         self.total_batch_idx = 0

pytorch_lightning/trainer/training_io.py

@@ -375,7 +375,7 @@ def restore_hpc_weights_if_needed(self, model: LightningModule):
         did_restore = False
 
         # look for hpc weights
-        folderpath = self.weights_save_path
+        folderpath = str(self.weights_save_path)
         if gfile.exists(folderpath):
             files = gfile.listdir(folderpath)
             hpc_weight_paths = [x for x in files if 'hpc_ckpt' in x]
@@ -452,6 +452,7 @@ def restore_training_state(self, checkpoint):
     # ----------------------------------
     def hpc_save(self, folderpath: str, logger):
         # make sure the checkpoint folder exists
+        folderpath = str(folderpath)  # because the tests pass a path object
         if not gfile.exists(folderpath):
             gfile.makedirs(folderpath)
 
@@ -511,7 +512,7 @@ def hpc_load(self, folderpath, on_gpu):
         log.info(f'restored hpc model from: {filepath}')
 
     def max_ckpt_in_folder(self, path, name_key='ckpt_'):
-        files = gfile.listdir(path)
+        files = gfile.listdir(str(path))
         files = [x for x in files if name_key in x]
         if len(files) == 0:
             return 0

tests/loggers/test_all.py

@@ -46,7 +46,7 @@ def log_metrics(self, metrics, step):
             super().log_metrics(metrics, step)
             self.history.append((step, metrics))
 
-    logger_args = _get_logger_args(logger_class, str(tmpdir))
+    logger_args = _get_logger_args(logger_class, tmpdir)
     logger = StoreHistoryLogger(**logger_args)
 
     trainer = Trainer(
@@ -82,7 +82,7 @@ def test_loggers_pickle(tmpdir, monkeypatch, logger_class):
     import atexit
     monkeypatch.setattr(atexit, 'register', lambda _: None)
 
-    logger_args = _get_logger_args(logger_class, str(tmpdir))
+    logger_args = _get_logger_args(logger_class, tmpdir)
     logger = logger_class(**logger_args)
 
     # test pickling loggers
@@ -109,7 +109,7 @@ def test_logger_reset_correctly(tmpdir, extra_params):
     model = EvalModelTemplate()
 
     trainer = Trainer(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         **extra_params
     )
     logger1 = trainer.logger

tests/models/test_cpu.py

@@ -19,7 +19,7 @@ def test_cpu_slurm_save_load(tmpdir):
     model = EvalModelTemplate(**hparams)
 
     # logger file to get meta
-    logger = tutils.get_default_logger(str(tmpdir))
+    logger = tutils.get_default_logger(tmpdir)
     version = logger.version
 
     # fit model
@@ -28,7 +28,7 @@ def test_cpu_slurm_save_load(tmpdir):
         logger=logger,
         train_percent_check=0.2,
         val_percent_check=0.2,
-        checkpoint_callback=ModelCheckpoint(str(tmpdir)),
+        checkpoint_callback=ModelCheckpoint(tmpdir)
     )
     result = trainer.fit(model)
     real_global_step = trainer.global_step
@@ -54,13 +54,17 @@ def test_cpu_slurm_save_load(tmpdir):
 
     # test HPC saving
     # simulate snapshot on slurm
-    saved_filepath = trainer.hpc_save(str(tmpdir), logger)
+    saved_filepath = trainer.hpc_save(tmpdir, logger)
     assert os.path.exists(saved_filepath)
 
     # new logger file to get meta
-    logger = tutils.get_default_logger(str(tmpdir), version=version)
+    logger = tutils.get_default_logger(tmpdir, version=version)
 
-    trainer = Trainer(max_epochs=1, logger=logger, checkpoint_callback=ModelCheckpoint(str(tmpdir)),)
+    trainer = Trainer(
+        max_epochs=1,
+        logger=logger,
+        checkpoint_callback=ModelCheckpoint(tmpdir),
+    )
     model = EvalModelTemplate(**hparams)
 
     # set the epoch start hook so we can predict before the model does the full training
@@ -83,7 +87,7 @@ def test_early_stopping_cpu_model(tmpdir):
     """Test each of the trainer options."""
     stopping = EarlyStopping(monitor='val_loss', min_delta=0.1)
     trainer_options = dict(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         early_stop_callback=stopping,
         max_epochs=2,
         gradient_clip_val=1.0,
@@ -112,7 +116,7 @@ def test_multi_cpu_model_ddp(tmpdir):
     tutils.set_random_master_port()
 
     trainer_options = dict(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         progress_bar_refresh_rate=0,
         max_epochs=1,
         train_percent_check=0.4,
@@ -129,7 +133,7 @@ def test_multi_cpu_model_ddp(tmpdir):
 def test_lbfgs_cpu_model(tmpdir):
     """Test each of the trainer options."""
     trainer_options = dict(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         max_epochs=1,
         progress_bar_refresh_rate=0,
         weights_summary='top',
@@ -148,7 +152,7 @@ def test_lbfgs_cpu_model(tmpdir):
 def test_default_logger_callbacks_cpu_model(tmpdir):
     """Test each of the trainer options."""
     trainer_options = dict(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         max_epochs=1,
         gradient_clip_val=1.0,
         overfit_pct=0.20,
@@ -170,14 +174,14 @@ def test_running_test_after_fitting(tmpdir):
     model = EvalModelTemplate()
 
     # logger file to get meta
-    logger = tutils.get_default_logger(str(tmpdir))
+    logger = tutils.get_default_logger(tmpdir)
 
     # logger file to get weights
     checkpoint = tutils.init_checkpoint_callback(logger)
 
     # fit model
     trainer = Trainer(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         progress_bar_refresh_rate=0,
         max_epochs=2,
         train_percent_check=0.4,
@@ -201,7 +205,7 @@ def test_running_test_no_val(tmpdir):
     model = EvalModelTemplate()
 
     # logger file to get meta
-    logger = tutils.get_default_logger(str(tmpdir))
+    logger = tutils.get_default_logger(tmpdir)
 
     # logger file to get weights
     checkpoint = tutils.init_checkpoint_callback(logger)
@@ -280,7 +284,7 @@ def test_simple_cpu(tmpdir):
 
     # fit model
     trainer = Trainer(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         max_epochs=1,
         val_percent_check=0.1,
         train_percent_check=0.1,
@@ -294,7 +298,7 @@ def test_simple_cpu(tmpdir):
 def test_cpu_model(tmpdir):
     """Make sure model trains on CPU."""
     trainer_options = dict(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         progress_bar_refresh_rate=0,
         max_epochs=1,
         train_percent_check=0.4,
@@ -309,7 +313,7 @@ def test_cpu_model(tmpdir):
 def test_all_features_cpu_model(tmpdir):
     """Test each of the trainer options."""
     trainer_options = dict(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         gradient_clip_val=1.0,
         overfit_pct=0.20,
         track_grad_norm=2,
@@ -383,7 +387,7 @@ def train_dataloader(self):
 
     # fit model
     trainer = Trainer(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         max_epochs=1,
         truncated_bptt_steps=truncated_bptt_steps,
         val_percent_check=0,

tests/models/test_gpu.py

@@ -18,7 +18,7 @@
 def test_single_gpu_model(tmpdir, gpus):
     """Make sure single GPU works (DP mode)."""
     trainer_options = dict(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         progress_bar_refresh_rate=0,
         max_epochs=1,
         train_percent_check=0.1,
@@ -38,7 +38,7 @@ def test_multi_gpu_model(tmpdir, backend):
     tutils.set_random_master_port()
 
     trainer_options = dict(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         max_epochs=1,
         train_percent_check=0.4,
         val_percent_check=0.2,
@@ -84,7 +84,7 @@ def test_ddp_all_dataloaders_passed_to_fit(tmpdir):
 def test_multi_gpu_none_backend(tmpdir):
     """Make sure when using multiple GPUs the user can't use `distributed_backend = None`."""
     trainer_options = dict(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         progress_bar_refresh_rate=0,
         max_epochs=1,
         train_percent_check=0.1,

tests/trainer/test_lr_finder.py

@@ -14,7 +14,7 @@ def test_error_on_more_than_1_optimizer(tmpdir):
 
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         max_epochs=1
     )
 
@@ -29,7 +29,7 @@ def test_model_reset_correctly(tmpdir):
 
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         max_epochs=1
     )
 
@@ -51,7 +51,7 @@ def test_trainer_reset_correctly(tmpdir):
 
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         max_epochs=1
     )
 
@@ -81,7 +81,7 @@ def test_trainer_arg_bool(tmpdir):
 
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         max_epochs=2,
         auto_lr_find=True
     )
@@ -100,7 +100,7 @@ def test_trainer_arg_str(tmpdir):
     before_lr = model.my_fancy_lr
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         max_epochs=2,
         auto_lr_find='my_fancy_lr'
     )
@@ -120,7 +120,7 @@ def test_call_to_trainer_method(tmpdir):
     before_lr = hparams.get('learning_rate')
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         max_epochs=2,
     )
 
@@ -144,7 +144,7 @@ def test_accumulation_and_early_stopping(tmpdir):
     before_lr = hparams.get('learning_rate')
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         accumulate_grad_batches=2,
     )
 
@@ -167,7 +167,7 @@ def test_suggestion_parameters_work(tmpdir):
 
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         max_epochs=3,
     )
 
@@ -187,7 +187,7 @@ def test_suggestion_with_non_finite_values(tmpdir):
 
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=str(tmpdir),
+        default_root_dir=tmpdir,
         max_epochs=3
     )