Do not warn when the name key is used in the lr_scheduler dict (#5057)

* Do not warn when the name key is used

* Missing line

* Consistency

* Update pytorch_lightning/callbacks/lr_monitor.py

* Update docs

* Update pytorch_lightning/core/lightning.py

Co-authored-by: Rohit Gupta <[email protected]>

* Update CHANGELOG

Co-authored-by: Rohit Gupta <[email protected]>

Author: carmocca

CHANGELOG.md

@@ -45,6 +45,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed `LightningOptimizer` exposes optimizer attributes ([#5095](https://github.com/PyTorchLightning/pytorch-lightning/pull/5095))
 
 
+- Do not warn when the `name` key is used in the `lr_scheduler` dict ([#5057](https://github.com/PyTorchLightning/pytorch-lightning/pull/5057))
+
 
 ## [1.1.0] - 2020-12-09
 

pytorch_lightning/callbacks/lr_monitor.py

@@ -157,7 +157,7 @@ def _find_names(self, lr_schedulers) -> List[str]:
         names = []
         for scheduler in lr_schedulers:
             sch = scheduler['scheduler']
-            if 'name' in scheduler:
+            if scheduler['name'] is not None:
                 name = scheduler['name']
             else:
                 opt_name = 'lr-' + sch.optimizer.__class__.__name__

pytorch_lightning/core/lightning.py

@@ -990,7 +990,7 @@ def configure_optimizers(
             - List or Tuple - List of optimizers.
             - Two lists - The first list has multiple optimizers, the second a list of LR schedulers (or lr_dict).
             - Dictionary, with an 'optimizer' key, and (optionally) a 'lr_scheduler'
-              key which value is a single LR scheduler or lr_dict.
+              key whose value is a single LR scheduler or lr_dict.
             - Tuple of dictionaries as described, with an optional 'frequency' key.
             - None - Fit will run without any optimizer.
 
@@ -1002,21 +1002,22 @@ def configure_optimizers(
             In the former case, all optimizers will operate on the given batch in each optimization step.
             In the latter, only one optimizer will operate on the given batch at every step.
 
-            The lr_dict is a dictionary which contains scheduler and its associated configuration.
-            It has five keys. The default configuration is shown below.
+            The lr_dict is a dictionary which contains the scheduler and its associated configuration.
+            The default configuration is shown below.
 
             .. code-block:: python
 
                 {
-                    'scheduler': lr_scheduler, # The LR schduler
+                    'scheduler': lr_scheduler, # The LR scheduler instance (required)
                     'interval': 'epoch', # The unit of the scheduler's step size
                     'frequency': 1, # The frequency of the scheduler
                     'reduce_on_plateau': False, # For ReduceLROnPlateau scheduler
                     'monitor': 'val_loss', # Metric for ReduceLROnPlateau to monitor
-                    'strict': True # Whether to crash the training if `monitor` is not found
+                    'strict': True, # Whether to crash the training if `monitor` is not found
+                    'name': None, # Custom name for LearningRateMonitor to use
                 }
 
-            If user only provides LR schedulers, then their configuration will set to default as shown above.
+            Only the ``scheduler`` key is required, the rest will be set to the defaults above.
 
         Examples:
             .. code-block:: python

pytorch_lightning/trainer/optimizers.py

@@ -94,6 +94,7 @@ def configure_schedulers(self, schedulers: list, monitor: Optional[str] = None):
         lr_schedulers = []
         default_config = {
             'scheduler': None,
+            'name': None,  # no custom name
             'interval': 'epoch',  # after epoch is over
             'frequency': 1,  # every epoch/batch
             'reduce_on_plateau': False,  # most often not ReduceLROnPlateau scheduler

tests/callbacks/test_lr_monitor.py

@@ -13,11 +13,11 @@
 # limitations under the License.
 import pytest
 
+import tests.base.develop_utils as tutils
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import LearningRateMonitor
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from tests.base import EvalModelTemplate
-import tests.base.develop_utils as tutils
+from tests.base import BoringModel, EvalModelTemplate
 
 
 def test_lr_monitor_single_lr(tmpdir):
@@ -43,7 +43,7 @@ def test_lr_monitor_single_lr(tmpdir):
         'Momentum should not be logged by default'
     assert len(lr_monitor.lrs) == len(trainer.lr_schedulers), \
         'Number of learning rates logged does not match number of lr schedulers'
-    assert all([k in ['lr-Adam'] for k in lr_monitor.lrs.keys()]), \
+    assert lr_monitor.lr_sch_names == list(lr_monitor.lrs.keys()) == ['lr-Adam'], \
         'Names of learning rates not set correctly'
 
 
@@ -134,7 +134,7 @@ def test_lr_monitor_multi_lrs(tmpdir, logging_interval):
     assert lr_monitor.lrs, 'No learning rates logged'
     assert len(lr_monitor.lrs) == len(trainer.lr_schedulers), \
         'Number of learning rates logged does not match number of lr schedulers'
-    assert all([k in ['lr-Adam', 'lr-Adam-1'] for k in lr_monitor.lrs.keys()]), \
+    assert lr_monitor.lr_sch_names == ['lr-Adam', 'lr-Adam-1'], \
         'Names of learning rates not set correctly'
 
     if logging_interval == 'step':
@@ -167,5 +167,27 @@ def test_lr_monitor_param_groups(tmpdir):
     assert lr_monitor.lrs, 'No learning rates logged'
     assert len(lr_monitor.lrs) == 2 * len(trainer.lr_schedulers), \
         'Number of learning rates logged does not match number of param groups'
-    assert all([k in ['lr-Adam/pg1', 'lr-Adam/pg2'] for k in lr_monitor.lrs.keys()]), \
+    assert lr_monitor.lr_sch_names == ['lr-Adam']
+    assert list(lr_monitor.lrs.keys()) == ['lr-Adam/pg1', 'lr-Adam/pg2'], \
         'Names of learning rates not set correctly'
+
+
+def test_lr_monitor_custom_name(tmpdir):
+    class TestModel(BoringModel):
+        def configure_optimizers(self):
+            optimizer, [scheduler] = super().configure_optimizers()
+            lr_scheduler = {'scheduler': scheduler, 'name': 'my_logging_name'}
+            return optimizer, [lr_scheduler]
+
+    lr_monitor = LearningRateMonitor()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=2,
+        limit_val_batches=0.1,
+        limit_train_batches=0.5,
+        callbacks=[lr_monitor],
+        progress_bar_refresh_rate=0,
+        weights_summary=None,
+    )
+    trainer.fit(TestModel())
+    assert lr_monitor.lr_sch_names == list(lr_monitor.lrs.keys()) == ['my_logging_name']

tests/trainer/test_optimizers.py

@@ -15,7 +15,6 @@
 import torch
 
 from pytorch_lightning import Callback, Trainer
-from pytorch_lightning.core.optimizer import LightningOptimizer
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.base import EvalModelTemplate
 from tests.base.boring_model import BoringModel
@@ -177,6 +176,7 @@ def test_reducelronplateau_scheduling(tmpdir):
         frequency=1,
         reduce_on_plateau=True,
         strict=True,
+        name=None,
     ), 'lr scheduler was not correctly converted to dict'
 
 
@@ -215,7 +215,13 @@ def test_optimizer_return_options(enable_pl_optimizer):
     assert len(freq) == 0
     assert optim[0] == opt_a
     assert lr_sched[0] == dict(
-        scheduler=scheduler_a, interval='epoch', frequency=1, reduce_on_plateau=False, monitor=None, strict=True
+        scheduler=scheduler_a,
+        interval='epoch',
+        frequency=1,
+        reduce_on_plateau=False,
+        monitor=None,
+        strict=True,
+        name=None,
     )
 
     # opt tuple of 1 list
@@ -225,7 +231,13 @@ def test_optimizer_return_options(enable_pl_optimizer):
     assert len(freq) == 0
     assert optim[0] == opt_a
     assert lr_sched[0] == dict(
-        scheduler=scheduler_a, interval='epoch', frequency=1, reduce_on_plateau=False, monitor=None, strict=True
+        scheduler=scheduler_a,
+        interval='epoch',
+        frequency=1,
+        reduce_on_plateau=False,
+        monitor=None,
+        strict=True,
+        name=None,
     )
 
     # opt single dictionary
@@ -235,7 +247,13 @@ def test_optimizer_return_options(enable_pl_optimizer):
     assert len(freq) == 0
     assert optim[0] == opt_a
     assert lr_sched[0] == dict(
-        scheduler=scheduler_a, interval='epoch', frequency=1, reduce_on_plateau=False, monitor=None, strict=True
+        scheduler=scheduler_a,
+        interval='epoch',
+        frequency=1,
+        reduce_on_plateau=False,
+        monitor=None,
+        strict=True,
+        name=None,
     )
 
     # opt multiple dictionaries with frequencies
@@ -247,7 +265,13 @@ def test_optimizer_return_options(enable_pl_optimizer):
     assert len(optim) == len(lr_sched) == len(freq) == 2
     assert optim[0] == opt_a
     assert lr_sched[0] == dict(
-        scheduler=scheduler_a, interval='epoch', frequency=1, reduce_on_plateau=False, monitor=None, strict=True
+        scheduler=scheduler_a,
+        interval='epoch',
+        frequency=1,
+        reduce_on_plateau=False,
+        monitor=None,
+        strict=True,
+        name=None,
     )
     assert freq == [1, 5]