add test

.pre-commit-config.yaml

@@ -17,7 +17,7 @@ default_language_version:
 
 repos:
  - repo: https://github.com/pre-commit/pre-commit-hooks
- rev: v2.3.0
+ rev: v3.4.0
  hooks:
  - id: trailing-whitespace
  - id: end-of-file-fixer
@@ -35,6 +35,6 @@ repos:
  args: [--parallel, --in-place]
 
  - repo: https://github.com/pre-commit/mirrors-mypy
- rev: v0.790
+ rev: v0.812
  hooks:
  - id: mypy

pytorch_lightning/callbacks/progress.py

@@ -149,9 +149,10 @@ def total_val_batches(self) -> int:
  validation dataloader is of infinite size.
  """
  total_val_batches = 0
- if not self.trainer.disable_validation:
+ if self.trainer.enable_validation:
  is_val_epoch = (self.trainer.current_epoch + 1) % self.trainer.check_val_every_n_epoch == 0
  total_val_batches = sum(self.trainer.num_val_batches) if is_val_epoch else 0
+
  return total_val_batches
 
  @property

tests/trainer/flags/test_check_val_every_n_epoch.py

@@ -0,0 +1,56 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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 pytest
+
+from pytorch_lightning.trainer import Trainer
+from pytorch_lightning.trainer.states import TrainerState
+from tests.helpers import BoringModel
+
+
+@pytest.mark.parametrize(
+ 'max_epochs,expected_val_loop_calls,expected_val_batches', [
+ (1, 0, [0]),
+ (4, 2, [0, 2, 0, 2]),
+ (5, 2, [0, 2, 0, 2, 0]),
+ ]
+)
+def test_check_val_every_n_epoch(tmpdir, max_epochs, expected_val_loop_calls, expected_val_batches):
+
+ class TestModel(BoringModel):
+
+ def __init__(self):
+ super().__init__()
+ self.val_epoch_calls = 0
+ self.val_batches = []
+
+ def on_train_epoch_end(self, *args, **kwargs):
+ self.val_batches.append(self.trainer.progress_bar_callback.total_val_batches)
+
+ def on_validation_epoch_start(self) -> None:
+ self.val_epoch_calls += 1
+
+ model = TestModel()
+ trainer = Trainer(
+ default_root_dir=tmpdir,
+ max_epochs=max_epochs,
+ num_sanity_val_steps=0,
+ limit_val_batches=2,
+ check_val_every_n_epoch=2,
+ logger=False,
+ )
+ trainer.fit(model)
+ assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
+
+ assert model.val_epoch_calls == expected_val_loop_calls
+ assert model.val_batches == expected_val_batches