yapf

Author: Borda

tests/helpers/pipelines.py

@@ -33,18 +33,18 @@ def run_model_test_without_loggers(
     # correct result and ok accuracy
     assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
 
-    pretrained_model = load_model_from_checkpoint(
+    model2 = load_model_from_checkpoint(
         trainer.logger, trainer.checkpoint_callback.best_model_path, type(model)
     )
 
     # test new model accuracy
-    test_loaders = model.test_dataloader() if not data else data.test_dataloader()
+    test_loaders = model2.test_dataloader() if not data else data.test_dataloader()
     if not isinstance(test_loaders, list):
         test_loaders = [test_loaders]
 
-    if not isinstance(model, BoringModel):
+    if not isinstance(model2, BoringModel):
         for dataloader in test_loaders:
-            run_prediction_eval_model_template(model, dataloader, min_acc=min_acc)
+            run_prediction_eval_model_template(model2, dataloader, min_acc=min_acc)
 
 
 def run_model_test(

tests/models/test_horovod.py

@@ -32,8 +32,6 @@
 from pytorch_lightning.utilities import _APEX_AVAILABLE, _HOROVOD_AVAILABLE, _NATIVE_AMP_AVAILABLE
 from tests.helpers import BoringModel
 from tests.helpers.advanced_models import BasicGAN
-from tests.helpers.datamodules import ClassifDataModule
-from tests.helpers.simple_models import ClassificationModel
 
 if _HOROVOD_AVAILABLE:
     import horovod

tests/models/test_tpu.py

@@ -226,8 +226,16 @@ def test_dataloaders_passed_to_fit(tmpdir):
     tutils.reset_seed()
     model = BoringModel()
 
-    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, tpu_cores=8,)
-    trainer.fit(model, train_dataloader=model.train_dataloader(), val_dataloaders=model.val_dataloader(),)
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=1,
+        tpu_cores=8,
+    )
+    trainer.fit(
+        model,
+        train_dataloader=model.train_dataloader(),
+        val_dataloaders=model.val_dataloader(),
+    )
     assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"