Remove evaluation loop legacy dict returns for *_epoch_end hooks

pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py

@@ -25,7 +25,7 @@
 from pytorch_lightning.trainer.connectors.logger_connector.epoch_result_store import EpochResultStore
 from pytorch_lightning.trainer.connectors.logger_connector.metrics_holder import MetricsHolder
 from pytorch_lightning.trainer.states import RunningStage, TrainerState
-from pytorch_lightning.utilities import DeviceType, flatten_dict
+from pytorch_lightning.utilities import DeviceType
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.model_helpers import is_overridden
 
@@ -297,33 +297,6 @@ def get_evaluate_epoch_results(self):
         self.eval_loop_results = []
         return results
 
-    def _track_callback_metrics(self, eval_results):
-        if len(eval_results) > 0 and (eval_results[0] is None or not isinstance(eval_results[0], Result)):
-            return
-
-        flat = {}
-        if isinstance(eval_results, list):
-            for eval_result in eval_results:
-                # with a scalar return, auto set it to "val_loss" for callbacks
-                if isinstance(eval_result, torch.Tensor):
-                    flat = {'val_loss': eval_result}
-                elif isinstance(eval_result, dict):
-                    flat = flatten_dict(eval_result)
-
-                self.trainer.logger_connector.callback_metrics.update(flat)
-                if self.trainer.state in (TrainerState.TESTING, TrainerState.VALIDATING):
-                    self.trainer.logger_connector.evaluation_callback_metrics.update(flat)
-        else:
-            # with a scalar return, auto set it to "val_loss" for callbacks
-            if isinstance(eval_results, torch.Tensor):
-                flat = {'val_loss': eval_results}
-            else:
-                flat = flatten_dict(eval_results)
-
-            self.trainer.logger_connector.callback_metrics.update(flat)
-            if self.trainer.state in (TrainerState.TESTING, TrainerState.VALIDATING):
-                self.trainer.logger_connector.evaluation_callback_metrics.update(flat)
-
     def on_train_epoch_end(self):
         # inform cached logger connector epoch finished
         self.cached_results.has_batch_loop_finished = True

pytorch_lightning/trainer/evaluation_loop.py

@@ -190,61 +190,25 @@ def evaluation_epoch_end(self):
         # unset dataloder_idx in model
         self.trainer.logger_connector.evaluation_epoch_end()
 
-        # call the model epoch end
-        deprecated_results = self.__run_eval_epoch_end(self.num_dataloaders)
-
-        # enable returning anything
-        for i, r in enumerate(deprecated_results):
-            if not isinstance(r, (dict, Result, torch.Tensor)):
-                deprecated_results[i] = []
-
-        return deprecated_results
-
-    def log_epoch_metrics_on_evaluation_end(self):
-        # get the final loop results
-        eval_loop_results = self.trainer.logger_connector.get_evaluate_epoch_results()
-        return eval_loop_results
-
-    def __run_eval_epoch_end(self, num_dataloaders):
-        model = self.trainer.lightning_module
-
-        # with a single dataloader don't pass an array
         outputs = self.outputs
+        # with a single dataloader don't pass an array
+        eval_results = outputs[0] if self.num_dataloaders == 1 else outputs
 
-        eval_results = outputs
-        if num_dataloaders == 1:
-            eval_results = outputs[0]
-
-        user_reduced = False
+        # call the model epoch end
+        model = self.trainer.lightning_module
 
         if self.trainer.testing:
             if is_overridden('test_epoch_end', model=model):
                 model._current_fx_name = 'test_epoch_end'
-                eval_results = model.test_epoch_end(eval_results)
-                user_reduced = True
+                model.test_epoch_end(eval_results)
 
         else:
             if is_overridden('validation_epoch_end', model=model):
                 model._current_fx_name = 'validation_epoch_end'
-                eval_results = model.validation_epoch_end(eval_results)
-                user_reduced = True
+                model.validation_epoch_end(eval_results)
 
         # capture logging
         self.trainer.logger_connector.cache_logged_metrics()
-        # depre warning
-        if eval_results is not None and user_reduced:
-            step = 'testing_epoch_end' if self.trainer.testing else 'validation_epoch_end'
-            self.warning_cache.warn(
-                f'The {step} should not return anything as of 9.1.'
-                ' To log, use self.log(...) or self.write(...) directly in the LightningModule'
-            )
-
-        if not isinstance(eval_results, list):
-            eval_results = [eval_results]
-
-        self.trainer.logger_connector._track_callback_metrics(eval_results)
-
-        return eval_results
 
     def __gather_epoch_end_eval_results(self, outputs):
         eval_results = []

pytorch_lightning/trainer/trainer.py

@@ -712,7 +712,7 @@ def run_evaluation(self, on_epoch=False):
             self.evaluation_loop.outputs.append(dl_outputs)
 
         # lightning module method
-        deprecated_eval_results = self.evaluation_loop.evaluation_epoch_end()
+        self.evaluation_loop.evaluation_epoch_end()
 
         # hook
         self.evaluation_loop.on_evaluation_epoch_end()
@@ -725,7 +725,7 @@ def run_evaluation(self, on_epoch=False):
         self.evaluation_loop.on_evaluation_end()
 
         # log epoch metrics
-        eval_loop_results = self.evaluation_loop.log_epoch_metrics_on_evaluation_end()
+        eval_loop_results = self.logger_connector.get_evaluate_epoch_results()
 
         # save predictions to disk
         self.evaluation_loop.predictions.to_disk()
@@ -735,7 +735,7 @@ def run_evaluation(self, on_epoch=False):
 
         torch.set_grad_enabled(True)
 
-        return eval_loop_results, deprecated_eval_results
+        return eval_loop_results
 
     def track_output_for_epoch_end(self, outputs, output):
         if output is not None:
@@ -757,7 +757,7 @@ def run_evaluate(self):
         assert self.evaluating
 
         with self.profiler.profile(f"run_{self._running_stage}_evaluation"):
-            eval_loop_results, _ = self.run_evaluation()
+            eval_loop_results = self.run_evaluation()
 
         if len(eval_loop_results) == 0:
             return 1
@@ -831,7 +831,7 @@ def run_sanity_check(self, ref_model):
             self.on_sanity_check_start()
 
             # run eval step
-            _, eval_results = self.run_evaluation()
+            self.run_evaluation()
 
             self.on_sanity_check_end()
 

tests/trainer/data_flow/test_eval_loop_flow.py

@@ -12,10 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
-Tests to ensure that the training loop works with a dict (1.0)
+Tests the evaluation loop
 """
 
-import pytest
 import torch
 
 from pytorch_lightning import Trainer
@@ -189,8 +188,6 @@ def validation_epoch_end(self, outputs):
             assert out_a == self.out_a
             assert out_b == self.out_b
 
-            return {'no returns needed'}
-
         def backward(self, loss, optimizer, optimizer_idx):
             return LightningModule.backward(self, loss, optimizer, optimizer_idx)
 
@@ -206,8 +203,7 @@ def backward(self, loss, optimizer, optimizer_idx):
         weights_summary=None,
     )
 
-    with pytest.warns(UserWarning, match=r".*should not return anything as of 9.1.*"):
-        trainer.fit(model)
+    trainer.fit(model)
 
     # make sure correct steps were called
     assert model.validation_step_called
@@ -254,8 +250,6 @@ def validation_epoch_end(self, outputs):
             assert out_a == self.out_a
             assert out_b == self.out_b
 
-            return {'no returns needed'}
-
         def backward(self, loss, optimizer, optimizer_idx):
             return LightningModule.backward(self, loss, optimizer, optimizer_idx)
 
@@ -270,8 +264,7 @@ def backward(self, loss, optimizer, optimizer_idx):
         weights_summary=None,
     )
 
-    with pytest.warns(UserWarning, match=r".*should not return anything as of 9.1.*"):
-        trainer.fit(model)
+    trainer.fit(model)
 
     # make sure correct steps were called
     assert model.validation_step_called