Non-functional clean ups related to lr schedulers

While working on the fixes in this PR, I also cleaned up some lr
scheduler code. These clean ups are non-functional.

1. We imported CyclicLR as TorchCyclicLR. I'm not sure why but it is
   somehow related to very old PyTorch versions we no longer support, so I
   removed this.
2. Fixed some indentations for conditional checks to improve
   readability.

skorch/callbacks/lr_scheduler.py

@@ -9,17 +9,12 @@
 import torch
 from torch.optim.lr_scheduler import _LRScheduler
 from torch.optim.lr_scheduler import CosineAnnealingLR
+from torch.optim.lr_scheduler import CyclicLR
 from torch.optim.lr_scheduler import ExponentialLR
 from torch.optim.lr_scheduler import LambdaLR
 from torch.optim.lr_scheduler import MultiStepLR
 from torch.optim.lr_scheduler import ReduceLROnPlateau
 from torch.optim.lr_scheduler import StepLR
-
-try:
-    from torch.optim.lr_scheduler import CyclicLR as TorchCyclicLR
-except ImportError:
-    # Backward compatibility with torch >= 1.0 && < 1.1
-    TorchCyclicLR = None
 from torch.optim.optimizer import Optimizer
 from skorch.callbacks import Callback
 
@@ -152,7 +147,7 @@ def _step(self, net, lr_scheduler, score=None):
            certain conditions.
 
         For more info on the latter, see:
-        https://huggingface.co/docs/accelerate/v0.21.0/en/quicktour#mixed-precision-training
+        https://huggingface.co/docs/accelerate/quicktour#mixed-precision-training
 
         """
         accelerator_maybe = getattr(net, 'accelerator', None)
@@ -187,28 +182,33 @@ def on_epoch_end(self, net, **kwargs):
             self._step(net, self.lr_scheduler_, score=score)
             # ReduceLROnPlateau does not expose the current lr so it can't be recorded
         else:
-            if self.event_name is not None and hasattr(
-                    self.lr_scheduler_, "get_last_lr"):
-                net.history.record(self.event_name,
-                                   self.lr_scheduler_.get_last_lr()[0])
+            if (
+                    (self.event_name is not None)
+                    and hasattr(self.lr_scheduler_, "get_last_lr")
+            ):
+                net.history.record(self.event_name, self.lr_scheduler_.get_last_lr()[0])
             self._step(net, self.lr_scheduler_)
 
     def on_batch_end(self, net, training, **kwargs):
         if not training or self.step_every != 'batch':
             return
-        if self.event_name is not None and hasattr(
-                self.lr_scheduler_, "get_last_lr"):
-            net.history.record_batch(self.event_name,
-                                     self.lr_scheduler_.get_last_lr()[0])
+        if (
+                (self.event_name is not None)
+                and hasattr(self.lr_scheduler_, "get_last_lr")
+        ):
+            net.history.record_batch(
+                self.event_name, self.lr_scheduler_.get_last_lr()[0])
         self._step(net, self.lr_scheduler_)
         self.batch_idx_ += 1
 
     def _get_scheduler(self, net, policy, **scheduler_kwargs):
         """Return scheduler, based on indicated policy, with appropriate
         parameters.
         """
-        if policy not in [ReduceLROnPlateau] and \
-                'last_epoch' not in scheduler_kwargs:
+        if (
+                (policy not in [ReduceLROnPlateau])
+                and ('last_epoch' not in scheduler_kwargs)
+        ):
             last_epoch = len(net.history) - 1
             scheduler_kwargs['last_epoch'] = last_epoch
 

skorch/tests/callbacks/test_lr_scheduler.py

@@ -3,7 +3,6 @@
 
 import numpy as np
 import pytest
-import torch
 from sklearn.base import clone
 from torch.optim import SGD
 from torch.optim.lr_scheduler import CosineAnnealingLR
@@ -12,7 +11,7 @@
 from torch.optim.lr_scheduler import MultiStepLR
 from torch.optim.lr_scheduler import ReduceLROnPlateau
 from torch.optim.lr_scheduler import StepLR
-from torch.optim.lr_scheduler import CyclicLR as TorchCyclicLR
+from torch.optim.lr_scheduler import CyclicLR
 
 from skorch import NeuralNetClassifier
 from skorch.callbacks.lr_scheduler import WarmRestartLR, LRScheduler
@@ -28,7 +27,7 @@ def test_simulate_lrs_epoch_step(self, policy):
         expected = np.array([1.0, 1.0, 0.1, 0.1, 0.01, 0.01])
         assert np.allclose(expected, lrs)
 
-    @pytest.mark.parametrize('policy', [TorchCyclicLR])
+    @pytest.mark.parametrize('policy', [CyclicLR])
     def test_simulate_lrs_batch_step(self, policy):
         lr_sch = LRScheduler(
             policy, base_lr=1, max_lr=5, step_size_up=4, step_every='batch')
@@ -96,7 +95,7 @@ def test_lr_callback_steps_correctly(
         assert lr_policy.lr_scheduler_.last_epoch == max_epochs
 
     @pytest.mark.parametrize('policy, kwargs', [
-        (TorchCyclicLR, {'base_lr': 1e-3, 'max_lr': 6e-3, 'step_every': 'batch'}),
+        (CyclicLR, {'base_lr': 1e-3, 'max_lr': 6e-3, 'step_every': 'batch'}),
     ])
     def test_lr_callback_batch_steps_correctly(
             self,
@@ -125,7 +124,7 @@ def test_lr_callback_batch_steps_correctly(
         assert lr_policy.batch_idx_ == expected
 
     @pytest.mark.parametrize('policy, kwargs', [
-        (TorchCyclicLR, {'base_lr': 1e-3, 'max_lr': 6e-3, 'step_every': 'batch'}),
+        (CyclicLR, {'base_lr': 1e-3, 'max_lr': 6e-3, 'step_every': 'batch'}),
     ])
     def test_lr_callback_batch_steps_correctly_fallback(
             self,
@@ -177,7 +176,7 @@ def test_lr_scheduler_cloneable(self):
 
     def test_lr_scheduler_set_params(self, classifier_module, classifier_data):
         scheduler = LRScheduler(
-            TorchCyclicLR, base_lr=123, max_lr=999, step_every='batch')
+            CyclicLR, base_lr=123, max_lr=999, step_every='batch')
         net = NeuralNetClassifier(
             classifier_module,
             max_epochs=0,
@@ -212,7 +211,7 @@ def test_lr_scheduler_record_batch_step(self, classifier_module, classifier_data
         batch_size = 128
 
         scheduler = LRScheduler(
-            TorchCyclicLR,
+            CyclicLR,
             base_lr=1,
             max_lr=5,
             step_size_up=4,