Fix lr key name in case of param groups (#1719)

* Fix lr key name in case of param groups

* Add tests

* Update test and added configure_optimizers__param_groups

* Update CHANGELOG

CHANGELOG.md

@@ -44,6 +44,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed a bug in Trainer that prepended the checkpoint path with `version_` when it shouldn't ([#1748](https://github.com/PyTorchLightning/pytorch-lightning/pull/1748))
 
+- Fixed lr key name in case of param groups in LearningRateLogger ([#1719](https://github.com/PyTorchLightning/pytorch-lightning/pull/1719))
+
 ## [0.7.5] - 2020-04-27
 
 ### Changed

pytorch_lightning/callbacks/lr_logger.py

@@ -80,7 +80,7 @@ def _extract_lr(self, trainer, interval):
                 param_groups = scheduler['scheduler'].optimizer.param_groups
                 if len(param_groups) != 1:
                     for i, pg in enumerate(param_groups):
-                        lr, key = pg['lr'], f'{name}/{i + 1}'
+                        lr, key = pg['lr'], f'{name}/pg{i + 1}'
                         self.lrs[key].append(lr)
                         latest_stat[key] = lr
                 else:
@@ -109,7 +109,7 @@ def _find_names(self, lr_schedulers):
             param_groups = sch.optimizer.param_groups
             if len(param_groups) != 1:
                 for i, pg in enumerate(param_groups):
-                    temp = name + '/pg' + str(i + 1)
+                    temp = f'{name}/pg{i + 1}'
                     names.append(temp)
             else:
                 names.append(name)

tests/base/model_optimizers.py

@@ -59,3 +59,13 @@ def configure_optimizers__reduce_lr_on_plateau(self):
         optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
         lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer)
         return [optimizer], [lr_scheduler]
+
+    def configure_optimizers__param_groups(self):
+        param_groups = [
+            {'params': list(self.parameters())[:2], 'lr': self.hparams.learning_rate * 0.1},
+            {'params': list(self.parameters())[2:], 'lr': self.hparams.learning_rate}
+        ]
+
+        optimizer = optim.Adam(param_groups)
+        lr_scheduler = optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.1)
+        return [optimizer], [lr_scheduler]

tests/callbacks/test_callbacks.py

@@ -331,3 +331,27 @@ def test_lr_logger_multi_lrs(tmpdir):
         '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_logger.lrs.keys()]), \
         'Names of learning rates not set correctly'
+
+
+def test_lr_logger_param_groups(tmpdir):
+    """ Test that learning rates are extracted and logged for single lr scheduler"""
+    tutils.reset_seed()
+
+    model = EvalModelTemplate()
+    model.configure_optimizers = model.configure_optimizers__param_groups
+
+    lr_logger = LearningRateLogger()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=5,
+        val_percent_check=0.1,
+        train_percent_check=0.5,
+        callbacks=[lr_logger]
+    )
+    results = trainer.fit(model)
+
+    assert lr_logger.lrs, 'No learning rates logged'
+    assert len(lr_logger.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_logger.lrs.keys()]), \
+        'Names of learning rates not set correctly'