test: add LinearCyclicalScheduler using use_sawtooth option test

tests/ignite/handlers/test_param_scheduler.py

@@ -68,6 +68,11 @@ def test_linear_scheduler_asserts():
     with pytest.raises(ValueError, match=r"Argument cycle_size should be positive and larger than 1"):
         LinearCyclicalScheduler(optimizer, "lr", 1, 0, cycle_size=1)
 
+    with pytest.raises(
+        ValueError, match=r"can not use use_sawtooth option and warmup duration please remove one of them"
+    ):
+        LinearCyclicalScheduler(optimizer, "lr", 1, 0, cycle_size=2, warmup_duration=1, use_sawtooth=True)
+
 
 def test_linear_scheduler():
     tensor = torch.zeros([1], requires_grad=True)
@@ -144,6 +149,79 @@ def save_lr(engine):
         scheduler.load_state_dict(state_dict)
 
 
+def test_linear_scheduler_use_sawtooth():
+    tensor = torch.zeros([1], requires_grad=True)
+    optimizer = torch.optim.SGD([tensor], lr=0)
+    scheduler = LinearCyclicalScheduler(optimizer, "lr", 1, 0, 10, use_sawtooth=True)
+    state_dict = scheduler.state_dict()
+
+    def save_lr(engine):
+        lrs.append(optimizer.param_groups[0]["lr"])
+
+    trainer = Engine(lambda engine, batch: None)
+    trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)
+    trainer.add_event_handler(Events.ITERATION_COMPLETED, save_lr)
+    lr_values_in_cycle = [1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1]
+    for _ in range(2):
+        lrs = []
+        trainer.run([0] * 10, max_epochs=2)
+        assert lrs == pytest.approx([*lr_values_in_cycle, *lr_values_in_cycle])
+        scheduler.load_state_dict(state_dict)
+
+    optimizer = torch.optim.SGD([tensor], lr=0)
+    scheduler = LinearCyclicalScheduler(optimizer, "lr", 1, 0, 10, cycle_mult=2, use_sawtooth=True)
+    state_dict = scheduler.state_dict()
+
+    trainer = Engine(lambda engine, batch: None)
+    trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)
+    trainer.add_event_handler(Events.ITERATION_COMPLETED, save_lr)
+
+    for _ in range(2):
+        lrs = []
+        trainer.run([0] * 10, max_epochs=3)
+
+        assert lrs == list(
+            map(
+                pytest.approx,
+                [
+                    # Cycle 1
+                    1.0,
+                    0.9,
+                    0.8,
+                    0.7,
+                    0.6,
+                    0.5,
+                    0.4,
+                    0.3,
+                    0.2,
+                    0.1,
+                    # Cycle 2
+                    1.0,
+                    0.95,
+                    0.9,
+                    0.85,
+                    0.8,
+                    0.75,
+                    0.7,
+                    0.65,
+                    0.6,
+                    0.55,
+                    0.5,
+                    0.45,
+                    0.4,
+                    0.35,
+                    0.3,
+                    0.25,
+                    0.2,
+                    0.15,
+                    0.1,
+                    0.05,
+                ],
+            )
+        )
+        scheduler.load_state_dict(state_dict)
+
+
 def test_linear_scheduler_cycle_size_two():
     tensor = torch.zeros([1], requires_grad=True)
     optimizer = torch.optim.SGD([tensor], lr=0)