DDRNet for Semantic Segmentation

configs/ddrnet_segmentation_model.yaml

@@ -0,0 +1,45 @@
+# DDRNet-23-slim model for segmentation
+# Refer to here for optimal hyperparameters for this model: https://github.com/Deci-AI/super-gradients/blob/4797c974c7c445d12e2575c468848d9c3e04becd/src/super_gradients/recipes/cityscapes_ddrnet.yaml#L4
+
+model:
+  name: ddrnet_segmentation
+  predefined_model:
+    name: DDRNetSegmentationModel
+    params:
+      task: binary
+      backbone_params:
+        use_aux_heads: True # set to False to disable auxiliary heads (for export)
+        variant: '23-slim'
+
+loader:
+  params:
+    dataset_name: coco_test
+
+trainer:
+  preprocessing:
+    train_image_size: [&height 256, &width 320]
+    keep_aspect_ratio: False
+    normalize:
+      active: True
+
+  batch_size: 4
+  epochs: &epochs 500
+  num_workers: 4
+  validation_interval: 10
+  num_log_images: 8
+
+  callbacks:
+    - name: TestOnTrainEnd
+    - name: ExportOnTrainEnd
+
+  optimizer:
+    name: SGD
+    params:
+      lr: 0.01
+      momentum: 0.9
+      weight_decay: 0.0005
+
+  scheduler:
+    name: CosineAnnealingLR
+    params:
+      T_max: *epochs

luxonis_train/models/predefined_models/__init__.py

@@ -1,5 +1,6 @@
 from .base_predefined_model import BasePredefinedModel
 from .classification_model import ClassificationModel
+from .ddrnet_segmentation_model import DDRNetSegmentationModel
 from .detection_model import DetectionModel
 from .keypoint_detection_model import KeypointDetectionModel
 from .segmentation_model import SegmentationModel
@@ -10,4 +11,5 @@
     "DetectionModel",
     "KeypointDetectionModel",
     "ClassificationModel",
+    "DDRNetSegmentationModel",
 ]

luxonis_train/models/predefined_models/ddrnet_segmentation_model.py

@@ -0,0 +1,77 @@
+from dataclasses import dataclass, field
+
+from luxonis_train.utils.config import (
+    LossModuleConfig,
+    ModelNodeConfig,
+)
+from luxonis_train.utils.types import Kwargs
+
+from .segmentation_model import SegmentationModel
+
+
+@dataclass
+class DDRNetSegmentationModel(SegmentationModel):
+    backbone: str = "DDRNet"
+    aux_head_params: Kwargs = field(default_factory=dict)
+
+    @property
+    def nodes(self) -> list[ModelNodeConfig]:
+        self.head_params.update({"attach_index": -1})
+
+        self.aux_head_params.update({"attach_index": -2})
+
+        node_list = [
+            ModelNodeConfig(
+                name=self.backbone,
+                alias="ddrnet_backbone",
+                freezing=self.backbone_params.pop("freezing", {}),
+                params=self.backbone_params,
+            ),
+            ModelNodeConfig(
+                name="DDRNetSegmentationHead",
+                alias="segmentation_head",
+                inputs=["ddrnet_backbone"],
+                freezing=self.head_params.pop("freezing", {}),
+                params=self.head_params,
+                task=self.task_name,
+            ),
+        ]
+        if self.backbone_params.get("use_aux_heads", False):
+            node_list.append(
+                ModelNodeConfig(
+                    name="DDRNetSegmentationHead",
+                    alias="aux_segmentation_head",
+                    inputs=["ddrnet_backbone"],
+                    freezing=self.aux_head_params.pop("freezing", {}),
+                    params=self.aux_head_params,
+                    task=self.task_name,
+                )
+            )
+        return node_list
+
+    @property
+    def losses(self) -> list[LossModuleConfig]:
+        loss_list = [
+            LossModuleConfig(
+                name="BCEWithLogitsLoss"
+                if self.task == "binary"
+                else "CrossEntropyLoss",
+                alias="segmentation_loss",
+                attached_to="segmentation_head",
+                params=self.loss_params,
+                weight=1.0,
+            ),
+        ]
+        if self.backbone_params.get("use_aux_heads", False):
+            loss_list.append(
+                LossModuleConfig(
+                    name="BCEWithLogitsLoss"
+                    if self.task == "binary"
+                    else "CrossEntropyLoss",
+                    alias="aux_segmentation_loss",
+                    attached_to="aux_segmentation_head",
+                    params=self.loss_params,
+                    weight=0.4,
+                )
+            )
+        return loss_list

luxonis_train/nodes/backbones/__init__.py

@@ -1,4 +1,5 @@
 from .contextspatial import ContextSpatial
+from .ddrnet import DDRNet
 from .efficientnet import EfficientNet
 from .efficientrep import EfficientRep
 from .micronet import MicroNet
@@ -18,4 +19,5 @@
     "ReXNetV1_lite",
     "RepVGG",
     "ResNet",
+    "DDRNet",
 ]

luxonis_train/nodes/backbones/ddrnet/__init__.py

@@ -0,0 +1,3 @@
+from .ddrnet import DDRNet
+
+__all__ = ["DDRNet"]