[skip ci] Add doctest for LinearCyclicalScheduler

docs/source/conf.py

@@ -332,6 +332,8 @@ def run(self):
 
 # doctest config
 doctest_global_setup = """
+from collections import OrderedDict
+
 import torch
 from torch import nn, optim
 
@@ -340,18 +342,40 @@ def run(self):
 from ignite.metrics import *
 from ignite.utils import *
 from ignite.contrib.metrics.regression import *
-
 from ignite.contrib.metrics import *
 
-manual_seed(666)
-
 # create default evaluator for doctests
 
-def process_function(engine, batch):
+def eval_step(engine, batch):
     y_pred, y = batch
     return y_pred, y
 
-default_evaluator = Engine(process_function)
+default_evaluator = Engine(eval_step)
+
+# create default optimizer for doctests
+
+param_tensor = torch.zeros([1], requires_grad=True)
+default_optimizer = torch.optim.SGD([param_tensor], lr=0)
+
+# create default trainer for doctests
+# as handlers could be attached to the trainer,
+# each test must defined his own trainer using `.. testsetup:`
+
+def get_default_trainer():
+
+    def train_step(engine, batch):
+        return 0.0
+
+    return Engine(train_step)
+
+# create default model for doctests
+
+default_model = nn.Sequential(OrderedDict([
+    ('base', nn.Linear(4, 2)),
+    ('fc', nn.Linear(2, 1))
+]))
+
+manual_seed(666)
 """
 
 

ignite/handlers/param_scheduler.py

@@ -360,15 +360,71 @@ class LinearCyclicalScheduler(CyclicalScheduler):
         usually be the number of batches in an epoch.
 
     Examples:
-        .. code-block:: python
+
+        .. testsetup:: *
+
+            default_trainer = get_default_trainer()
+
+        .. testcode:: 1
 
             from ignite.handlers.param_scheduler import LinearCyclicalScheduler
 
-            scheduler = LinearCyclicalScheduler(optimizer, 'lr', 1e-3, 1e-1, len(train_loader))
-            trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)
-            #
-            # Linearly increases the learning rate from 1e-3 to 1e-1 and back to 1e-3
-            # over the course of 1 epoch
+            # Linearly increases the learning rate from 0.0 to 1.0 and back to 0.0
+            # over a cycle of 4 iterations
+            scheduler = LinearCyclicalScheduler(default_optimizer, "lr", 0.0, 1.0, 4)
+
+            default_trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)
+
+            @default_trainer.on(Events.ITERATION_COMPLETED)
+            def print_lr():
+                print(default_optimizer.param_groups[0]["lr"])
+
+            default_trainer.run([0] * 9, max_epochs=1)
+
+        .. testoutput:: 1
+
+            0.0
+            0.5
+            1.0
+            0.5
+            ...
+
+        .. testcode:: 2
+
+            from ignite.handlers.param_scheduler import LinearCyclicalScheduler
+
+            optimizer = torch.optim.SGD(
+                [
+                    {"params": default_model.base.parameters(), "lr": 0.001},
+                    {"params": default_model.fc.parameters(), "lr": 0.01},
+                ]
+            )
+
+            # Linearly increases the learning rate from 0.0 to 1.0 and back to 0.0
+            # over a cycle of 4 iterations
+            scheduler1 = LinearCyclicalScheduler(optimizer, "lr", 0.0, 1.0, 4, param_group_index=0)
+
+            # Linearly increases the learning rate from 1.0 to 0.0 and back to 0.1
+            # over a cycle of 4 iterations
+            scheduler2 = LinearCyclicalScheduler(optimizer, "lr", 0.0, 0.1, 4, param_group_index=1)
+
+            default_trainer.add_event_handler(Events.ITERATION_STARTED, scheduler1)
+            default_trainer.add_event_handler(Events.ITERATION_STARTED, scheduler2)
+
+            @default_trainer.on(Events.ITERATION_COMPLETED)
+            def print_lr():
+                print(optimizer.param_groups[0]["lr"],
+                      optimizer.param_groups[1]["lr"])
+
+            default_trainer.run([0] * 9, max_epochs=1)
+
+        .. testoutput:: 2
+
+            0.0 0.0
+            0.5 0.05
+            1.0 0.1
+            0.5 0.05
+            ...
 
     .. versionadded:: 0.4.5
     """