Fix validation progress counter with check_val_every_n_epoch > 1 (Lig…

…htning-AI#5952)

Co-authored-by: rohitgr7 <[email protected]>
Co-authored-by: Carlos Mocholí <[email protected]>

pytorch_lightning/callbacks/progress.py

@@ -146,9 +146,10 @@ def total_val_batches(self) -> int:
         validation dataloader is of infinite size.
         """
         total_val_batches = 0
-        if not self.trainer.disable_validation:
-            is_val_epoch = (self.trainer.current_epoch) % self.trainer.check_val_every_n_epoch == 0
+        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 +1,53 @@
+# 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):
+        val_epoch_calls = 0
+        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