Add datamodule parameter to lr_find() (#3425)

* Add datamodule parameter to lr_find()

* Fixed missing import

* Move datamodule parameter to end

* Add datamodule parameter test with auto_lr_find

* Change test for datamodule parameter

* Apply suggestions from code review

Co-authored-by: Nicki Skafte <skaftenicki@gmail.com>

* Fix lr_find documentation

Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>

* formatting

* Add description to datamodule param in lr_find

* pep8: remove trailing whitespace on line 105

* added changelog

Co-authored-by: Nicki Skafte <skaftenicki@gmail.com>
Co-authored-by: Nicki Skafte <nugginea@gmail.com>
Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

CHANGELOG.md

@@ -27,6 +27,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Added support for datamodules to save and load checkpoints when training ([#3563]https://github.com/PyTorchLightning/pytorch-lightning/pull/3563)
 
+- Added support for datamodule in learning rate finder ([#3425](https://github.com/PyTorchLightning/pytorch-lightning/pull/3425))
+
 ### Changed
 
 - Changed `LearningRateLogger` to `LearningRateMonitor` ([#3251](https://github.com/PyTorchLightning/pytorch-lightning/pull/3251))

pytorch_lightning/tuner/lr_finder.py

@@ -11,21 +11,23 @@
 # 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 importlib
 import os
+from typing import List, Optional, Sequence, Union
+
+import numpy as np
 import torch
-from typing import Optional, Sequence, List, Union
+from torch.optim.lr_scheduler import _LRScheduler
 from torch.utils.data import DataLoader
+
+from pytorch_lightning import _logger as log
+from pytorch_lightning.callbacks import Callback
+from pytorch_lightning.core.datamodule import LightningDataModule
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.loggers.base import DummyLogger
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from torch.optim.lr_scheduler import _LRScheduler
-import importlib
-from pytorch_lightning import _logger as log
-import numpy as np
-from pytorch_lightning.callbacks import Callback
 from pytorch_lightning.utilities.parsing import lightning_hasattr, lightning_setattr
 
-
 # check if ipywidgets is installed before importing tqdm.auto
 # to ensure it won't fail and a progress bar is displayed
 if importlib.util.find_spec('ipywidgets') is not None:
@@ -71,6 +73,7 @@ def lr_find(
         num_training: int = 100,
         mode: str = 'exponential',
         early_stop_threshold: float = 4.0,
+        datamodule: Optional[LightningDataModule] = None,
 ):
     r"""
     lr_find enables the user to do a range test of good initial learning rates,
@@ -81,7 +84,7 @@ def lr_find(
 
         train_dataloader: A PyTorch
             DataLoader with training samples. If the model has
-            a predefined train_dataloader method this will be skipped.
+            a predefined train_dataloader method, this will be skipped.
 
         min_lr: minimum learning rate to investigate
 
@@ -98,6 +101,12 @@ def lr_find(
             loss at any point is larger than early_stop_threshold*best_loss
             then the search is stopped. To disable, set to None.
 
+        datamodule: An optional `LightningDataModule` which holds the training
+            and validation dataloader(s). Note that the `train_dataloader` and
+            `val_dataloaders` parameters cannot be used at the same time as
+            this parameter, or a `MisconfigurationException` will be raised.
+
+
     Example::
 
         # Setup model and trainer
@@ -167,7 +176,8 @@ def lr_find(
     # Fit, lr & loss logged in callback
     trainer.fit(model,
                 train_dataloader=train_dataloader,
-                val_dataloaders=val_dataloaders)
+                val_dataloaders=val_dataloaders,
+                datamodule=datamodule)
 
     # Prompt if we stopped early
     if trainer.global_step != num_training:

pytorch_lightning/tuner/tuning.py

@@ -15,6 +15,7 @@ from pytorch_lightning.tuner.batch_size_scaling import scale_batch_size
 from pytorch_lightning.tuner.auto_gpu_select import pick_multiple_gpus
 from pytorch_lightning.tuner.lr_finder import _run_lr_finder_internally, lr_find
 from pytorch_lightning.core.lightning import LightningModule
+from pytorch_lightning.core.datamodule import LightningDataModule
 from typing import Optional, List, Union
 from torch.utils.data import DataLoader
 
@@ -50,6 +51,7 @@ class Tuner:
             num_training: int = 100,
             mode: str = 'exponential',
             early_stop_threshold: float = 4.0,
+            datamodule: Optional[LightningDataModule] = None
     ):
         return lr_find(
             self.trainer,
@@ -60,7 +62,8 @@ class Tuner:
             max_lr,
             num_training,
             mode,
-            early_stop_threshold
+            early_stop_threshold,
+            datamodule,
         )
 
     def internal_find_lr(self, trainer, model: LightningModule):

tests/trainer/test_lr_finder.py

@@ -5,6 +5,7 @@ import torch
 from pytorch_lightning import Trainer
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.base import EvalModelTemplate
+from tests.base.datamodules import TrialMNISTDataModule
 
 
 def test_error_on_more_than_1_optimizer(tmpdir):
@@ -152,6 +153,30 @@ def test_call_to_trainer_method(tmpdir):
         'Learning rate was not altered after running learning rate finder'
 
 
+def test_datamodule_parameter(tmpdir):
+    """ Test that the datamodule parameter works """
+
+    # trial datamodule
+    dm = TrialMNISTDataModule(tmpdir)
+
+    hparams = EvalModelTemplate.get_default_hparams()
+    model = EvalModelTemplate(**hparams)
+
+    before_lr = hparams.get('learning_rate')
+    # logger file to get meta
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=2,
+    )
+
+    lrfinder = trainer.tuner.lr_find(model, datamodule=dm)
+    after_lr = lrfinder.suggestion()
+    model.learning_rate = after_lr
+
+    assert before_lr != after_lr, \
+        'Learning rate was not altered after running learning rate finder'
+
+
 def test_accumulation_and_early_stopping(tmpdir):
     """ Test that early stopping of learning rate finder works, and that
         accumulation also works for this feature """