[bugfix] Resolve memory leak for evaluation

pytorch_lightning/trainer/evaluation_loop.py

@@ -203,6 +203,10 @@ def __run_eval_epoch_end(self, num_dataloaders):
 
         # with a single dataloader don't pass an array
         outputs = self.outputs
+
+        # free memory
+        self.outputs = []
+
         eval_results = outputs
         if num_dataloaders == 1:
             eval_results = outputs[0]

tests/callbacks/test_pruning.py

@@ -11,7 +11,6 @@
 # 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.
-import os
 from collections import OrderedDict
 from logging import INFO
 
@@ -22,7 +21,7 @@
 from torch.nn import Sequential
 
 from pytorch_lightning import seed_everything, Trainer
-from pytorch_lightning.callbacks import ModelPruning, ModelCheckpoint
+from pytorch_lightning.callbacks import ModelCheckpoint, ModelPruning
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.helpers import BoringModel
 from tests.helpers.runif import RunIf
@@ -274,6 +273,7 @@ def test_permanent_when_model_is_saved_multiple_times(tmpdir, caplog):
     seed_everything(0)
 
     class TestPruning(ModelPruning):
+
         def on_save_checkpoint(self, trainer, pl_module, checkpoint):
             super().on_save_checkpoint(trainer, pl_module, checkpoint)
             assert "layer.mlp_3.weight_orig" not in checkpoint["state_dict"]

tests/trainer/logging_/test_eval_loop_logging_1_0.py → tests/trainer/logging/test_eval_loop_logging_1_0.py

@@ -110,6 +110,7 @@ def training_step(self, batch, batch_idx):
             return acc
 
         def validation_step(self, batch, batch_idx):
+            self.batch_idx = batch_idx
             acc = self.step(batch, batch_idx)
             acc = acc + batch_idx
             self.log('c', acc)
@@ -126,6 +127,7 @@ def validation_step_end(self, acc):
         def validation_epoch_end(self, outputs):
             self.log('g', torch.tensor(2, device=self.device), on_epoch=True)
             self.validation_epoch_end_called = True
+            assert len(self.trainer.evaluation_loop.outputs) == 0
 
         def backward(self, loss, optimizer, optimizer_idx):
             return LightningModule.backward(self, loss, optimizer, optimizer_idx)