add more test

tests/trainer/test_trainer.py

@@ -789,9 +789,42 @@ def test_tpu_choice(tmpdir, tpu_cores, expected_tpu_id, error_expected):
     pytest.param(0.0),  # this should run no sanity checks
     pytest.param(1),
     pytest.param(1.0),
-    pytest.param(0.3),
+    pytest.param(0.5),
+    pytest.param(5),
 ])
 def test_num_sanity_val_steps(tmpdir, limit_val_batches):
+    """
+    Test that num_sanity_val_steps!=-1 runs through all validation data once.
+    Makes sure the number of sanity check batches is clipped to limit_val_batches.
+    """
+    model = EvalModelTemplate()
+    model.validation_step = model.validation_step__multiple_dataloaders
+    model.validation_epoch_end = model.validation_epoch_end__multiple_dataloaders
+    num_sanity_val_steps = 4
+
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        num_sanity_val_steps=num_sanity_val_steps,
+        limit_val_batches=limit_val_batches,  # should have no influence
+        max_steps=1,
+    )
+    assert trainer.num_sanity_val_steps == num_sanity_val_steps
+    val_dataloaders = model.val_dataloader__multiple_mixed_length()
+
+    with patch.object(trainer, 'evaluation_forward', wraps=trainer.evaluation_forward) as mocked:
+        trainer.fit(model, val_dataloaders=val_dataloaders)
+        assert mocked.call_count == sum(
+            min(num_sanity_val_steps, num_batches) for num_batches in trainer.num_val_batches
+        )
+
+
+@pytest.mark.parametrize(['limit_val_batches'], [
+    pytest.param(0.0),  # this should run no sanity checks
+    pytest.param(1),
+    pytest.param(1.0),
+    pytest.param(0.3),
+])
+def test_num_sanity_val_steps_neg_one(tmpdir, limit_val_batches):
     """
     Test that num_sanity_val_steps=-1 runs through all validation data once.
     Makes sure the number of sanity check batches is clipped to limit_val_batches.
@@ -810,10 +843,7 @@ def test_num_sanity_val_steps(tmpdir, limit_val_batches):
 
     with patch.object(trainer, 'evaluation_forward', wraps=trainer.evaluation_forward) as mocked:
         trainer.fit(model, val_dataloaders=val_dataloaders)
-        if isinstance(limit_val_batches, float):
-            assert mocked.call_count == sum(len(dl) * limit_val_batches for dl in val_dataloaders)
-        if isinstance(limit_val_batches, int):
-            assert mocked.call_count == sum(limit_val_batches for dl in val_dataloaders)
+        assert mocked.call_count == sum(trainer.num_val_batches)
 
 
 @pytest.mark.parametrize("trainer_kwargs,expected", [