Disable validation when val_percent_check=0 (#1251)

* fix disable validation

* add test

* update changelog

* update docs for val_percent_check

* make "fast training" docs consistent

CHANGELOG.md

@@ -29,11 +29,11 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
-
 - `Trainer.add_argparse_args` classmethod fixed. Now it adds a type for the arguments ([#1147](https://github.com/PyTorchLightning/pytorch-lightning/pull/1147)).
 - Fixed bug related to type cheking of `ReduceLROnPlateau` lr schedulers([#1114](https://github.com/PyTorchLightning/pytorch-lightning/issues/1114))
 - Fixed a bug to ensure lightning checkpoints to be backward compatible ([#1132](https://github.com/PyTorchLightning/pytorch-lightning/pull/1132))
 - Fixed all warnings and errors in the docs build process ([#1191](https://github.com/PyTorchLightning/pytorch-lightning/pull/1191))
+- Fixed an issue where `val_percent_check=0` would not disable validation ([#1251](https://github.com/PyTorchLightning/pytorch-lightning/pull/1251))
 
 ## [0.7.1] - 2020-03-07
 

docs/source/fast_training.rst

@@ -1,10 +1,10 @@
 Fast Training
-================
+=============
 There are multiple options to speed up different parts of the training by choosing to train
 on a subset of data. This could be done for speed or debugging purposes.
 
 Check validation every n epochs
--------------------------------------
+-------------------------------
 If you have a small dataset you might want to check validation every n epochs
 
 .. code-block:: python
@@ -13,7 +13,7 @@ If you have a small dataset you might want to check validation every n epochs
     trainer = Trainer(check_val_every_n_epoch=1)
 
 Force training for min or max epochs
--------------------------------------
+------------------------------------
 It can be useful to force training for a minimum number of epochs or limit to a max number.
 
 .. seealso::
@@ -26,7 +26,7 @@ It can be useful to force training for a minimum number of epochs or limit to a
 
 
 Set validation check frequency within 1 training epoch
--------------------------------------------------------
+------------------------------------------------------
 For large datasets it's often desirable to check validation multiple times within a training loop.
 Pass in a float to check that often within 1 training epoch. Pass in an int k to check every k training batches.
 Must use an int if using an IterableDataset.
@@ -43,7 +43,7 @@ Must use an int if using an IterableDataset.
     trainer = Trainer(val_check_interval=100)
 
 Use training data subset
-----------------------------------
+------------------------
 If you don't want to check 100% of the training set (for debugging or if it's huge), set this flag.
 
 .. code-block:: python
@@ -54,12 +54,11 @@ If you don't want to check 100% of the training set (for debugging or if it's hu
    # check 10% only
    trainer = Trainer(train_percent_check=0.1)
 
-.. note:: train_percent_check will be overwritten by overfit_pct if overfit_pct > 0
+.. note:: ``train_percent_check`` will be overwritten by ``overfit_pct`` if ``overfit_pct`` > 0.
 
 Use test data subset
--------------------------------------
-If you don't want to check 100% of the test set (for debugging or if it's huge), set this flag
-test_percent_check will be overwritten by overfit_pct if overfit_pct > 0.
+--------------------
+If you don't want to check 100% of the test set (for debugging or if it's huge), set this flag.
 
 .. code-block:: python
 
@@ -69,15 +68,19 @@ test_percent_check will be overwritten by overfit_pct if overfit_pct > 0.
     # check 10% only
     trainer = Trainer(test_percent_check=0.1)
 
+.. note:: ``test_percent_check`` will be overwritten by ``overfit_pct`` if ``overfit_pct`` > 0.
+
 Use validation data subset
---------------------------------------------
-If you don't want to check 100% of the validation set (for debugging or if it's huge), set this flag
-val_percent_check will be overwritten by overfit_pct if overfit_pct > 0
+--------------------------
+If you don't want to check 100% of the validation set (for debugging or if it's huge), set this flag.
 
 .. code-block:: python
 
     # DEFAULT
     trainer = Trainer(val_percent_check=1.0)
 
     # check 10% only
-    trainer = Trainer(val_percent_check=0.1)
+    trainer = Trainer(val_percent_check=0.1)
+
+.. note:: ``val_percent_check`` will be overwritten by ``overfit_pct`` if ``overfit_pct`` > 0 and ignored if
+    ``fast_dev_run=True``.

pytorch_lightning/trainer/trainer.py

@@ -876,7 +876,8 @@ def run_pretrain_routine(self, model: LightningModule):
             return
 
         # check if we should run validation during training
-        self.disable_validation = not self.is_overriden('validation_step') and not self.fast_dev_run
+        self.disable_validation = not (self.is_overriden('validation_step') and self.val_percent_check > 0) \
+            and not self.fast_dev_run
 
         # run tiny validation (if validation defined)
         # to make sure program won't crash during val

tests/models/test_cpu.py

@@ -14,6 +14,7 @@
     LightTrainDataloader,
     LightningTestModel,
     LightTestMixin,
+    LightValidationMixin
 )
 
 
@@ -156,6 +157,55 @@ class CurrentTestModel(LightTrainDataloader, LightTestMixin, TestModelBase):
     tutils.assert_ok_model_acc(trainer)
 
 
+def test_disabled_validation():
+    """Verify that `val_percent_check=0` disables the validation loop unless `fast_dev_run=True`."""
+    tutils.reset_seed()
+
+    class CurrentModel(LightTrainDataloader, LightValidationMixin, TestModelBase):
+
+        validation_step_invoked = False
+        validation_end_invoked = False
+
+        def validation_step(self, *args, **kwargs):
+            self.validation_step_invoked = True
+            return super().validation_step(*args, **kwargs)
+
+        def validation_end(self, *args, **kwargs):
+            self.validation_end_invoked = True
+            return super().validation_end(*args, **kwargs)
+
+    hparams = tutils.get_default_hparams()
+    model = CurrentModel(hparams)
+
+    trainer_options = dict(
+        show_progress_bar=False,
+        max_epochs=2,
+        train_percent_check=0.4,
+        val_percent_check=0.0,
+        fast_dev_run=False,
+    )
+
+    trainer = Trainer(**trainer_options)
+    result = trainer.fit(model)
+
+    # check that val_percent_check=0 turns off validation
+    assert result == 1, 'training failed to complete'
+    assert trainer.current_epoch == 1
+    assert not model.validation_step_invoked, '`validation_step` should not run when `val_percent_check=0`'
+    assert not model.validation_end_invoked, '`validation_end` should not run when `val_percent_check=0`'
+
+    # check that val_percent_check has no influence when fast_dev_run is turned on
+    model = CurrentModel(hparams)
+    trainer_options.update(fast_dev_run=True)
+    trainer = Trainer(**trainer_options)
+    result = trainer.fit(model)
+
+    assert result == 1, 'training failed to complete'
+    assert trainer.current_epoch == 0
+    assert model.validation_step_invoked, 'did not run `validation_step` with `fast_dev_run=True`'
+    assert model.validation_end_invoked, 'did not run `validation_end` with `fast_dev_run=True`'
+
+
 def test_single_gpu_batch_parse():
     tutils.reset_seed()