ref: training flag tests (val_check_interval) (#3825)

* added test_val_check_interval tests

* added test_val_check_interval tests

* added test_val_check_interval tests

tests/trainer/flags/test_val_check_interval.py

@@ -0,0 +1,84 @@
+import pytest
+from tests.base import SimpleModule
+from pytorch_lightning.trainer import Trainer
+
+
+@pytest.mark.parametrize('max_epochs', [1, 2, 3])
+def test_val_check_interval_1(tmpdir, max_epochs):
+
+    class TestModel(SimpleModule):
+        def __init__(self):
+            super().__init__()
+            self.train_epoch_calls = 0
+            self.val_epoch_calls = 0
+
+        def on_train_epoch_start(self) -> None:
+            self.train_epoch_calls += 1
+
+        def on_validation_epoch_start(self) -> None:
+            if not self.trainer.running_sanity_check:
+                self.val_epoch_calls += 1
+
+    model = TestModel()
+    trainer = Trainer(
+        max_epochs=max_epochs,
+        val_check_interval=1.0,
+        logger=False,
+    )
+    trainer.fit(model)
+
+    assert model.val_epoch_calls == max_epochs
+
+
+@pytest.mark.parametrize('max_epochs', [1, 2, 3])
+def test_val_check_interval_quarter(tmpdir, max_epochs):
+
+    class TestModel(SimpleModule):
+        def __init__(self):
+            super().__init__()
+            self.train_epoch_calls = 0
+            self.val_epoch_calls = 0
+
+        def on_train_epoch_start(self) -> None:
+            self.train_epoch_calls += 1
+
+        def on_validation_epoch_start(self) -> None:
+            if not self.trainer.running_sanity_check:
+                self.val_epoch_calls += 1
+
+    model = TestModel()
+    trainer = Trainer(
+        max_epochs=max_epochs,
+        val_check_interval=0.25,
+        logger=False,
+    )
+    trainer.fit(model)
+
+    assert model.val_epoch_calls == max_epochs * 4
+
+
+@pytest.mark.parametrize('max_epochs', [1, 2, 3])
+def test_val_check_interval_third(tmpdir, max_epochs):
+
+    class TestModel(SimpleModule):
+        def __init__(self):
+            super().__init__()
+            self.train_epoch_calls = 0
+            self.val_epoch_calls = 0
+
+        def on_train_epoch_start(self) -> None:
+            self.train_epoch_calls += 1
+
+        def on_validation_epoch_start(self) -> None:
+            if not self.trainer.running_sanity_check:
+                self.val_epoch_calls += 1
+
+    model = TestModel()
+    trainer = Trainer(
+        max_epochs=max_epochs,
+        val_check_interval=0.33,
+        logger=False,
+    )
+    trainer.fit(model)
+
+    assert model.val_epoch_calls == max_epochs * 3