[2/2] Remove outputs from evaluation epoch end hooks

CHANGELOG.md

@@ -88,9 +88,6 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added support for the PyTorch 1.8.1 autograd profiler ([#6618](https://github.com/PyTorchLightning/pytorch-lightning/pull/6618))
 
 
-- Added `outputs` parameter to callback's `on_validation_epoch_end` & `on_test_epoch_end` hooks ([#6120](https://github.com/PyTorchLightning/pytorch-lightning/pull/6120))
-
-
 - Added `configure_sharded_model` hook ([#6679](https://github.com/PyTorchLightning/pytorch-lightning/pull/6679))
 
 
@@ -213,6 +210,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated `Trainer.truncated_bptt_steps` in favor of `LightningModule.truncated_bptt_steps` ([#7323](https://github.com/PyTorchLightning/pytorch-lightning/pull/7323))
 
 
+- Deprecated `outputs` in both `LightningModule.on_train_epoch_end` and `Callback.on_train_epoch_end` hooks ([#7339](https://github.com/PyTorchLightning/pytorch-lightning/pull/7339))
+
+
 - Deprecated `LightningModule.grad_norm` in favor of `pytorch_lightning.utilities.grads.grad_norm` ([#7292](https://github.com/PyTorchLightning/pytorch-lightning/pull/7292))
 
 

pytorch_lightning/callbacks/base.py

@@ -22,7 +22,7 @@
 from torch.optim import Optimizer
 
 import pytorch_lightning as pl
-from pytorch_lightning.utilities.types import EPOCH_OUTPUT, STEP_OUTPUT
+from pytorch_lightning.utilities.types import STEP_OUTPUT
 
 
 class Callback(abc.ABC):
@@ -108,17 +108,15 @@ def on_validation_epoch_start(self, trainer: 'pl.Trainer', pl_module: 'pl.Lightn
         """Called when the val epoch begins."""
         pass
 
-    def on_validation_epoch_end(
-        self, trainer: 'pl.Trainer', pl_module: 'pl.LightningModule', outputs: EPOCH_OUTPUT
-    ) -> None:
+    def on_validation_epoch_end(self, trainer: 'pl.Trainer', pl_module: 'pl.LightningModule') -> None:
         """Called when the val epoch ends."""
         pass
 
     def on_test_epoch_start(self, trainer: 'pl.Trainer', pl_module: 'pl.LightningModule') -> None:
         """Called when the test epoch begins."""
         pass
 
-    def on_test_epoch_end(self, trainer: 'pl.Trainer', pl_module: 'pl.LightningModule', outputs: EPOCH_OUTPUT) -> None:
+    def on_test_epoch_end(self, trainer: 'pl.Trainer', pl_module: 'pl.LightningModule') -> None:
         """Called when the test epoch ends."""
         pass
 

pytorch_lightning/core/hooks.py

@@ -20,7 +20,7 @@
 from torch.utils.data import DataLoader
 
 from pytorch_lightning.utilities import move_data_to_device, rank_zero_warn
-from pytorch_lightning.utilities.types import EPOCH_OUTPUT, STEP_OUTPUT
+from pytorch_lightning.utilities.types import STEP_OUTPUT
 
 
 class ModelHooks:
@@ -245,7 +245,7 @@ def on_validation_epoch_start(self) -> None:
         Called in the validation loop at the very beginning of the epoch.
         """
 
-    def on_validation_epoch_end(self, outputs: EPOCH_OUTPUT) -> None:
+    def on_validation_epoch_end(self) -> None:
         """
         Called in the validation loop at the very end of the epoch.
         """
@@ -255,7 +255,7 @@ def on_test_epoch_start(self) -> None:
         Called in the test loop at the very beginning of the epoch.
         """
 
-    def on_test_epoch_end(self, outputs: EPOCH_OUTPUT) -> None:
+    def on_test_epoch_end(self) -> None:
         """
         Called in the test loop at the very end of the epoch.
         """

pytorch_lightning/trainer/callback_hook.py

@@ -111,44 +111,20 @@ def on_validation_epoch_start(self):
         for callback in self.callbacks:
             callback.on_validation_epoch_start(self, self.lightning_module)
 
-    def on_validation_epoch_end(self, outputs: EPOCH_OUTPUT):
-        """Called when the epoch ends.
-
-        Args:
-            outputs: List of outputs on each ``validation`` epoch
-        """
+    def on_validation_epoch_end(self):
+        """Called when the validation epoch ends."""
         for callback in self.callbacks:
-            if is_param_in_hook_signature(callback.on_validation_epoch_end, "outputs"):
-                callback.on_validation_epoch_end(self, self.lightning_module, outputs)
-            else:
-                warning_cache.warn(
-                    "`Callback.on_validation_epoch_end` signature has changed in v1.3."
-                    " `outputs` parameter has been added."
-                    " Support for the old signature will be removed in v1.5", DeprecationWarning
-                )
-                callback.on_validation_epoch_end(self, self.lightning_module)
+            callback.on_validation_epoch_end(self, self.lightning_module)
 
     def on_test_epoch_start(self):
         """Called when the epoch begins."""
         for callback in self.callbacks:
             callback.on_test_epoch_start(self, self.lightning_module)
 
-    def on_test_epoch_end(self, outputs: EPOCH_OUTPUT):
-        """Called when the epoch ends.
-
-        Args:
-            outputs: List of outputs on each ``test`` epoch
-        """
+    def on_test_epoch_end(self):
+        """Called when the test epoch ends."""
         for callback in self.callbacks:
-            if is_param_in_hook_signature(callback.on_test_epoch_end, "outputs"):
-                callback.on_test_epoch_end(self, self.lightning_module, outputs)
-            else:
-                warning_cache.warn(
-                    "`Callback.on_test_epoch_end` signature has changed in v1.3."
-                    " `outputs` parameter has been added."
-                    " Support for the old signature will be removed in v1.5", DeprecationWarning
-                )
-                callback.on_test_epoch_end(self, self.lightning_module)
+            callback.on_test_epoch_end(self, self.lightning_module)
 
     def on_predict_epoch_start(self) -> None:
         """Called when the epoch begins."""

pytorch_lightning/trainer/evaluation_loop.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, List, Optional, Tuple, Union
 
 from torch.utils.data import DataLoader
 
@@ -20,7 +20,6 @@
 from pytorch_lightning.trainer.states import TrainerFn
 from pytorch_lightning.trainer.supporters import PredictionCollection
 from pytorch_lightning.utilities.model_helpers import is_overridden
-from pytorch_lightning.utilities.signature_utils import is_param_in_hook_signature
 from pytorch_lightning.utilities.types import EPOCH_OUTPUT, STEP_OUTPUT
 from pytorch_lightning.utilities.warnings import WarningCache
 
@@ -76,6 +75,7 @@ def should_skip_evaluation(self, max_batches: List[Union[int, float]]) -> bool:
         return sum(max_batches) == 0
 
     def on_evaluation_start(self, *args: Any, **kwargs: Any) -> None:
+        self.should_track_batch_outputs_for_epoch_end: bool = self._should_track_batch_outputs_for_epoch_end()
         if self.trainer.testing:
             self.trainer.call_hook('on_test_start', *args, **kwargs)
         else:
@@ -188,6 +188,13 @@ def evaluation_step_end(self, *args: Any, **kwargs: Any) -> Optional[STEP_OUTPUT
             output = self.trainer.call_hook('validation_step_end', *args, **kwargs)
         return output
 
+    def _should_track_batch_outputs_for_epoch_end(self) -> bool:
+        model = self.trainer.lightning_module
+        if self.trainer.testing:
+            return is_overridden('test_epoch_end', model=model)
+        else:
+            return is_overridden('validation_epoch_end', model=model)
+
     def evaluation_epoch_end(self, outputs: EPOCH_OUTPUT) -> None:
         # unset dataloder_idx in model
         self.trainer.logger_connector.evaluation_epoch_end()
@@ -241,7 +248,7 @@ def store_predictions(self, output: Optional[STEP_OUTPUT], batch_idx: int, datal
         # track debug metrics
         self.trainer.dev_debugger.track_eval_loss_history(batch_idx, dataloader_idx, output)
 
-    def on_evaluation_epoch_end(self, outputs: Union[List[List[Dict]], List[Dict]]) -> None:
+    def on_evaluation_epoch_end(self) -> None:
         model_ref = self.trainer.lightning_module
         hook_name = "on_test_epoch_end" if self.trainer.testing else "on_validation_epoch_end"
 
@@ -251,18 +258,11 @@ def on_evaluation_epoch_end(self, outputs: Union[List[List[Dict]], List[Dict]])
 
             if hasattr(self.trainer, hook_name):
                 on_evaluation_epoch_end_hook = getattr(self.trainer, hook_name)
-                on_evaluation_epoch_end_hook(outputs)
+                on_evaluation_epoch_end_hook()
 
             if is_overridden(hook_name, model_ref):
                 model_hook_fx = getattr(model_ref, hook_name)
-                if is_param_in_hook_signature(model_hook_fx, "outputs"):
-                    model_hook_fx(outputs)
-                else:
-                    self.warning_cache.warn(
-                        f"`ModelHooks.{hook_name}` signature has changed in v1.3. `outputs` parameter has been added."
-                        " Support for the old signature will be removed in v1.5", DeprecationWarning
-                    )
-                    model_hook_fx()
+                model_hook_fx()
 
         self.trainer._cache_logged_metrics()
 

pytorch_lightning/trainer/trainer.py

@@ -972,22 +972,23 @@ def run_evaluation(self, on_epoch: bool = False) -> _EVALUATE_OUTPUT:
                 dl_outputs = self.track_output_for_epoch_end(dl_outputs, output)
 
             # store batch level output per dataloader
-            self.evaluation_loop.outputs.append(dl_outputs)
+            if self.evaluation_loop.should_track_batch_outputs_for_epoch_end:
+                self.evaluation_loop.outputs.append(dl_outputs)
 
         outputs = self.evaluation_loop.outputs
 
         # reset outputs
         self.evaluation_loop.outputs = []
 
         # with a single dataloader don't pass a 2D list
-        if self.evaluation_loop.num_dataloaders == 1:
+        if len(outputs) > 0 and self.evaluation_loop.num_dataloaders == 1:
             outputs = outputs[0]
 
         # lightning module method
         self.evaluation_loop.evaluation_epoch_end(outputs)
 
         # hook
-        self.evaluation_loop.on_evaluation_epoch_end(outputs)
+        self.evaluation_loop.on_evaluation_epoch_end()
 
         # update epoch-level lr_schedulers
         if on_epoch:
@@ -1212,8 +1213,8 @@ def _cache_logged_metrics(self):
 
     def call_hook(self, hook_name: str, *args, **kwargs) -> Any:
         # Note this implementation is copy/pasted into the TrainLoop class in TrainLoop._on_train_epoch_end_hook
-        # This was done to manage the deprecation of an argument to on_train_epoch_end
-        # If making chnages to this function, ensure that those changes are also made to
+        # This was done to manage the deprecation of the `outputs` argument to on_train_epoch_end
+        # If making changes to this function, ensure that those changes are also made to
         # TrainLoop._on_train_epoch_end_hook
 
         # set hook_name to model + reset Result obj

tests/callbacks/test_callback_hook_outputs.py

@@ -65,48 +65,6 @@ def training_epoch_end(self, outputs) -> None:
     trainer.fit(model)
 
 
-def test_on_val_epoch_end_outputs(tmpdir):
-
-    class CB(Callback):
-
-        def on_validation_epoch_end(self, trainer, pl_module, outputs):
-            if trainer.running_sanity_check:
-                assert len(outputs) == trainer.num_sanity_val_batches[0]
-            else:
-                assert len(outputs) == trainer.num_val_batches[0]
-
-    model = BoringModel()
-
-    trainer = Trainer(
-        callbacks=CB(),
-        default_root_dir=tmpdir,
-        limit_train_batches=2,
-        limit_val_batches=2,
-        max_epochs=1,
-        weights_summary=None,
-    )
-
-    trainer.fit(model)
-
-
-def test_on_test_epoch_end_outputs(tmpdir):
-
-    class CB(Callback):
-
-        def on_test_epoch_end(self, trainer, pl_module, outputs):
-            assert len(outputs) == trainer.num_test_batches[0]
-
-    model = BoringModel()
-
-    trainer = Trainer(
-        callbacks=CB(),
-        default_root_dir=tmpdir,
-        weights_summary=None,
-    )
-
-    trainer.test(model)
-
-
 def test_free_memory_on_eval_outputs(tmpdir):
 
     class CB(Callback):

tests/callbacks/test_callbacks.py

@@ -58,7 +58,7 @@ def test_trainer_callback_hook_system_fit(_, tmpdir):
         call.on_validation_epoch_start(trainer, model),
         call.on_validation_batch_start(trainer, model, ANY, 0, 0),
         call.on_validation_batch_end(trainer, model, ANY, ANY, 0, 0),
-        call.on_validation_epoch_end(trainer, model, ANY),
+        call.on_validation_epoch_end(trainer, model),
         call.on_epoch_end(trainer, model),
         call.on_validation_end(trainer, model),
         call.on_sanity_check_end(trainer, model),
@@ -90,7 +90,7 @@ def test_trainer_callback_hook_system_fit(_, tmpdir):
         call.on_validation_epoch_start(trainer, model),
         call.on_validation_batch_start(trainer, model, ANY, 0, 0),
         call.on_validation_batch_end(trainer, model, ANY, ANY, 0, 0),
-        call.on_validation_epoch_end(trainer, model, ANY),
+        call.on_validation_epoch_end(trainer, model),
         call.on_epoch_end(trainer, model),
         call.on_validation_end(trainer, model),
         call.on_save_checkpoint(trainer, model),  # should take ANY but we are inspecting signature for BC
@@ -128,7 +128,7 @@ def test_trainer_callback_hook_system_test(tmpdir):
         call.on_test_batch_end(trainer, model, ANY, ANY, 0, 0),
         call.on_test_batch_start(trainer, model, ANY, 1, 0),
         call.on_test_batch_end(trainer, model, ANY, ANY, 1, 0),
-        call.on_test_epoch_end(trainer, model, ANY),
+        call.on_test_epoch_end(trainer, model),
         call.on_epoch_end(trainer, model),
         call.on_test_end(trainer, model),
         call.teardown(trainer, model, 'test'),
@@ -163,7 +163,7 @@ def test_trainer_callback_hook_system_validate(tmpdir):
         call.on_validation_batch_end(trainer, model, ANY, ANY, 0, 0),
         call.on_validation_batch_start(trainer, model, ANY, 1, 0),
         call.on_validation_batch_end(trainer, model, ANY, ANY, 1, 0),
-        call.on_validation_epoch_end(trainer, model, ANY),
+        call.on_validation_epoch_end(trainer, model),
         call.on_epoch_end(trainer, model),
         call.on_validation_end(trainer, model),
         call.teardown(trainer, model, 'validate'),