SequentialLR Scheduler

luxonis_train/models/luxonis_lightning.py

@@ -813,8 +813,32 @@ def configure_optimizers(
         }
         optimizer = OPTIMIZERS.get(cfg_optimizer.name)(**optim_params)
 
-        scheduler_params = cfg_scheduler.params | {"optimizer": optimizer}
-        scheduler = SCHEDULERS.get(cfg_scheduler.name)(**scheduler_params)
+        def get_scheduler(scheduler_cfg, optimizer):
+            scheduler_class = SCHEDULERS.get(
+                scheduler_cfg["name"]
+            )  # For dictionary access
+            scheduler_params = scheduler_cfg["params"] | {
+                "optimizer": optimizer
+            }  # Dictionary access for params
+            return scheduler_class(**scheduler_params)
+
+        if cfg_scheduler.name == "SequentialLR":
+            schedulers_list = [
+                get_scheduler(scheduler_cfg, optimizer)
+                for scheduler_cfg in cfg_scheduler.params["schedulers"]
+            ]
+
+            scheduler = torch.optim.lr_scheduler.SequentialLR(
+                optimizer,
+                schedulers=schedulers_list,
+                milestones=cfg_scheduler.params["milestones"],
+            )
+        else:
+            scheduler_class = SCHEDULERS.get(
+                cfg_scheduler.name
+            )  # Access as attribute for single scheduler
+            scheduler_params = cfg_scheduler.params | {"optimizer": optimizer}
+            scheduler = scheduler_class(**scheduler_params)
 
         return [optimizer], [scheduler]
 

tests/integration/test_scheduler.py

@@ -0,0 +1,69 @@
+import multiprocessing as mp
+
+import pytest
+from luxonis_ml.data import LuxonisDataset
+
+from luxonis_train.core import LuxonisModel
+
+
+def create_model_config():
+    return {
+        "trainer": {
+            "n_sanity_val_steps": 0,
+            "preprocessing": {"train_image_size": [32, 32]},
+            "epochs": 2,
+            "batch_size": 4,
+            "num_workers": mp.cpu_count(),
+        },
+        "loader": {
+            "name": "LuxonisLoaderTorch",
+            "train_view": "train",
+            "params": {"dataset_name": "coco_test"},
+        },
+        "model": {
+            "name": "detection_light_model",
+            "predefined_model": {
+                "name": "DetectionModel",
+                "params": {
+                    "variant": "light",
+                },
+            },
+        },
+    }
+
+
+def sequential_scheduler():
+    return {
+        "name": "SequentialLR",
+        "params": {
+            "schedulers": [
+                {
+                    "name": "LinearLR",
+                    "params": {"start_factor": 0.1, "total_iters": 10},
+                },
+                {
+                    "name": "CosineAnnealingLR",
+                    "params": {"T_max": 1, "eta_min": 0.01},
+                },
+            ],
+            "milestones": [1],
+        },
+    }
+
+
+def cosine_annealing_scheduler():
+    return {
+        "name": "CosineAnnealingLR",
+        "params": {"T_max": 2, "eta_min": 0.001},
+    }
+
+
+@pytest.mark.parametrize(
+    "scheduler_config", [sequential_scheduler(), cosine_annealing_scheduler()]
+)
+def test_scheduler(coco_dataset: LuxonisDataset, scheduler_config):
+    config = create_model_config()
+    config["trainer"]["scheduler"] = scheduler_config
+    opts = {"loader.params.dataset_name": coco_dataset.dataset_name}
+    model = LuxonisModel(config, opts)
+    model.train()