rebase

CHANGELOG.md

@@ -15,7 +15,6 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added `checkpoint` parameter to callback's `on_save_checkpoint` hook ([#6072](https://github.com/PyTorchLightning/pytorch-lightning/pull/6072))
 
 
-<<<<<<< HEAD
 - Added `RunningStage.SANITY_CHECKING` ([#4945](https://github.com/PyTorchLightning/pytorch-lightning/pull/4945))
 
 

pytorch_lightning/overrides/base.py

@@ -46,7 +46,7 @@ def forward(self, *inputs, **kwargs):
             # it is done manually in ``LightningModule.manual_backward``
             # `require_backward_grad_sync` will be reset in the
             # ddp_plugin ``post_training_step`` hook
-            if self.module.automatic_optimization:
+            if not self.module.automatic_optimization:
                 trainer.model.require_backward_grad_sync = False
         elif trainer and trainer.testing:
             output = self.module.test_step(*inputs, **kwargs)

pytorch_lightning/plugins/training_type/ddp_spawn.py

@@ -214,8 +214,7 @@ def transfer_distrib_spawn_state_on_fit_end(self, results):
             # save the last weights
             last_path = None
             if (
-                self.lightning_module.trainer.state == TrainerState.FITTING
-                and best_model_path is not None
+                self.lightning_module.trainer.state == TrainerState.FITTING and best_model_path is not None
                 and len(best_model_path) > 0
             ):
                 last_path = re.sub(".ckpt", ".tmp_end.ckpt", best_model_path)

pytorch_lightning/plugins/training_type/tpu_spawn.py

@@ -139,8 +139,7 @@ def transfer_distrib_spawn_state_on_fit_end(self, results):
             # save the last weights
             last_path = None
             if (
-                self.lightning_module.trainer.state == TrainerState.FITTING
-                and best_model_path is not None
+                self.lightning_module.trainer.state == TrainerState.FITTING and best_model_path is not None
                 and len(best_model_path) > 0
             ):
                 last_path = re.sub(".ckpt", ".tmp_end.ckpt", best_model_path)

pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py

@@ -297,9 +297,7 @@ def get_evaluate_epoch_results(self):
 
         # log results of evaluation
         if (
-            self.trainer.state != TrainerState.FITTING
-            and self.trainer.evaluating
-            and self.trainer.is_global_zero
+            self.trainer.state != TrainerState.FITTING and self.trainer.evaluating and self.trainer.is_global_zero
             and self.trainer.verbose_evaluate
         ):
             print('-' * 80)

pytorch_lightning/trainer/evaluation_loop.py

@@ -60,8 +60,7 @@ def get_evaluation_dataloaders(self):
                 self.trainer.reset_val_dataloader(model)
             if self.trainer.sanity_checking:
                 self.trainer.num_sanity_val_batches = [
-                    min(self.trainer.num_sanity_val_steps, val_batches)
-                    for val_batches in self.trainer.num_val_batches
+                    min(self.trainer.num_sanity_val_steps, val_batches) for val_batches in self.trainer.num_val_batches
                 ]
                 max_batches = self.trainer.num_sanity_val_batches
             else:

pytorch_lightning/trainer/trainer.py

@@ -875,8 +875,7 @@ def test(
         # Attach datamodule to get setup/prepare_data added to model before the call to it below
         self.data_connector.attach_datamodule(model, datamodule)
         results = (
-            self.__evaluate_given_model(model, dataloaders=test_dataloaders)
-            if model_provided else
+            self.__evaluate_given_model(model, dataloaders=test_dataloaders) if model_provided else
             self.__evaluate_using_weights(model, ckpt_path=ckpt_path, dataloaders=test_dataloaders)
         )
 

tests/overrides/test_data_parallel.py

@@ -19,12 +19,14 @@
     LightningParallelModule,
     LightningDistributedModule,
 ])
-@pytest.mark.parametrize("stage", [
-    ("training", "training_step"),
-    ("testing", "test_step"),
-    ("validating", "validation_step"),
-    ("predicting", "predict"),
-])
+@pytest.mark.parametrize(
+    "stage", [
+        ("training", "training_step"),
+        ("testing", "test_step"),
+        ("validating", "validation_step"),
+        ("predicting", "predict"),
+    ]
+)
 def test_lightning_wrapper_module_methods(wrapper_class, stage):
     """ Test that the LightningWrapper redirects .forward() to the LightningModule methods. """
     pl_module = MagicMock()

tests/trainer/test_states.py

@@ -34,6 +34,7 @@ def test_trainer_state_while_running(tmpdir, extra_params):
     trainer = Trainer(default_root_dir=tmpdir, **extra_params, auto_lr_find=True)
 
     class TestModel(BoringModel):
+
         def __init__(self, expected_state):
             super().__init__()
             self.expected_state = expected_state
@@ -78,6 +79,7 @@ def test_interrupt_state_on_keyboard_interrupt(tmpdir, extra_params):
     model = BoringModel()
 
     class InterruptCallback(Callback):
+
         def on_batch_start(self, trainer, pl_module):
             raise KeyboardInterrupt
 

tests/utilities/test_parsing.py

@@ -233,15 +233,19 @@ class UnpicklableClass:
 
 
 def test_parse_class_init_keys(tmpdir):
+
     class Class:
+
         def __init__(self, hparams, *my_args, anykw=42, **my_kwargs):
             pass
 
     assert parse_class_init_keys(Class) == ("self", "my_args", "my_kwargs")
 
 
 def test_get_init_args(tmpdir):
+
     class AutomaticArgsModel:
+
         def __init__(self, anyarg, anykw=42, **kwargs):
             super().__init__()
 
@@ -259,7 +263,9 @@ def get_init_args_wrapper(self):
 
 
 def test_collect_init_args():
+
     class AutomaticArgsParent:
+
         def __init__(self, anyarg, anykw=42, **kwargs):
             super().__init__()
             self.get_init_args_wrapper()
@@ -269,6 +275,7 @@ def get_init_args_wrapper(self):
             self.result = collect_init_args(frame, [])
 
     class AutomaticArgsChild(AutomaticArgsParent):
+
         def __init__(self, anyarg, childarg, anykw=42, childkw=42, **kwargs):
             super().__init__(anyarg, anykw=anykw, **kwargs)
 
@@ -299,10 +306,7 @@ def test_attribute_dict(tmpdir):
 
 
 def test_flatten_dict(tmpdir):
-    d = {
-        '1': 1,
-        '_': {'2': 2, '_': {'3': 3, '4': 4}}
-    }
+    d = {'1': 1, '_': {'2': 2, '_': {'3': 3, '4': 4}}}
 
     expected = {
         '1': 1,