Update to remove type in recipe

openvinotoolkit · Apr 29, 2024 · ec44eb9 · ec44eb9
1 parent c9a8562
commit ec44eb9
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Showing 7 changed files with 5 additions and 40 deletions.
diff --git a/src/otx/algo/detection/heads/yolox_head.py b/src/otx/algo/detection/heads/yolox_head.py
@@ -148,7 +148,7 @@ def __init__(
         self.train_cfg = train_cfg
 
         if self.train_cfg:
-            self.assigner = SimOTAAssigner(center_radius=2.5)
+            self.assigner = SimOTAAssigner(**self.train_cfg["assigner"])
             # YOLOX does not support sampling
             self.sampler = PseudoSampler()  # type: ignore[no-untyped-call]
 

diff --git a/src/otx/algo/detection/mmconfigs/yolox_l.yaml b/src/otx/algo/detection/mmconfigs/yolox_l.yaml
@@ -1,17 +1,13 @@
 load_from: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth
 train_cfg:
   assigner:
-    type: SimOTAAssigner
     center_radius: 2.5
 test_cfg:
   score_thr: 0.01
   nms:
-    type: nms
     iou_threshold: 0.65
-  max_per_img: 100
-type: YOLOX
+  max_per_img: 10
 data_preprocessor:
-  type: DetDataPreprocessor
   non_blocking: true
   mean:
     - 0.0
@@ -25,23 +21,20 @@ data_preprocessor:
   bgr_to_rgb: false
   pad_size_divisor: 32
 backbone:
-  type: CSPDarknet
   deepen_factor: 1.0
   widen_factor: 1.0
   out_indices:
     - 2
     - 3
     - 4
 neck:
-  type: YOLOXPAFPN
   in_channels:
     - 256
     - 512
     - 1024
   out_channels: 256
   num_csp_blocks: 3
 bbox_head:
-  type: YOLOXHead
   num_classes: 80
   in_channels: 256
   feat_channels: 256
diff --git a/src/otx/algo/detection/mmconfigs/yolox_s.yaml b/src/otx/algo/detection/mmconfigs/yolox_s.yaml
@@ -1,17 +1,13 @@
 load_from: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_s_8x8_300e_coco/yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth
 train_cfg:
   assigner:
-    type: SimOTAAssigner
     center_radius: 2.5
 test_cfg:
   score_thr: 0.01
   nms:
-    type: nms
     iou_threshold: 0.65
-  max_per_img: 100
-type: YOLOX
+  max_per_img: 10
 data_preprocessor:
-  type: DetDataPreprocessor
   non_blocking: true
   mean:
     - 0.0
@@ -25,23 +21,20 @@ data_preprocessor:
   bgr_to_rgb: false
   pad_size_divisor: 32
 backbone:
-  type: CSPDarknet
   deepen_factor: 0.33
   widen_factor: 0.5
   out_indices:
     - 2
     - 3
     - 4
 neck:
-  type: YOLOXPAFPN
   in_channels:
     - 128
     - 256
     - 512
   out_channels: 128
   num_csp_blocks: 4
 bbox_head:
-  type: YOLOXHead
   num_classes: 80
   in_channels: 128
   feat_channels: 128
diff --git a/src/otx/algo/detection/mmconfigs/yolox_tiny.yaml b/src/otx/algo/detection/mmconfigs/yolox_tiny.yaml
@@ -1,17 +1,13 @@
 load_from: https://storage.openvinotoolkit.org/repositories/openvino_training_extensions/models/object_detection/v2/yolox_tiny_8x8.pth
 train_cfg:
   assigner:
-    type: SimOTAAssigner
     center_radius: 2.5
 test_cfg:
   score_thr: 0.01
   nms:
-    type: nms
     iou_threshold: 0.65
-  max_per_img: 100
-type: YOLOX
+  max_per_img: 10
 data_preprocessor:
-  type: DetDataPreprocessor
   non_blocking: true
   mean:
     - 123.675
@@ -25,23 +21,20 @@ data_preprocessor:
   bgr_to_rgb: false
   pad_size_divisor: 32
 backbone:
-  type: CSPDarknet
   deepen_factor: 0.33
   widen_factor: 0.375
   out_indices:
     - 2
     - 3
     - 4
 neck:
-  type: YOLOXPAFPN
   in_channels:
     - 96
     - 192
     - 384
   out_channels: 96
   num_csp_blocks: 1
 bbox_head:
-  type: YOLOXHead
   num_classes: 80
   in_channels: 96
   feat_channels: 96
diff --git a/src/otx/algo/detection/mmconfigs/yolox_x.yaml b/src/otx/algo/detection/mmconfigs/yolox_x.yaml
@@ -1,17 +1,13 @@
 load_from: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_x_8x8_300e_coco/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth
 train_cfg:
   assigner:
-    type: SimOTAAssigner
     center_radius: 2.5
 test_cfg:
   score_thr: 0.01
   nms:
-    type: nms
     iou_threshold: 0.65
-  max_per_img: 100
-type: YOLOX
+  max_per_img: 10
 data_preprocessor:
-  type: DetDataPreprocessor
   non_blocking: true
   mean:
     - 0.0
@@ -25,23 +21,20 @@ data_preprocessor:
   bgr_to_rgb: false
   pad_size_divisor: 32
 backbone:
-  type: CSPDarknet
   deepen_factor: 1.33
   widen_factor: 1.25
   out_indices:
     - 2
     - 3
     - 4
 neck:
-  type: YOLOXPAFPN
   in_channels:
     - 320
     - 640
     - 1280
   out_channels: 320
   num_csp_blocks: 4
 bbox_head:
-  type: YOLOXHead
   num_classes: 80
   in_channels: 320
   feat_channels: 320
diff --git a/src/otx/algo/detection/yolox.py b/src/otx/algo/detection/yolox.py
@@ -47,25 +47,21 @@ class YOLOX(SingleStageDetector):
 
     def build_backbone(self, cfg: ConfigDict | dict) -> nn.Module:
         """Build backbone."""
-        cfg.pop("type")  # TODO (sungchul): remove `type` in recipe
         return CSPDarknet(**cfg)
 
     def build_neck(self, cfg: ConfigDict | dict) -> nn.Module:
         """Build neck."""
-        cfg.pop("type")  # TODO (sungchul): remove `type` in recipe
         return YOLOXPAFPN(**cfg)
 
     def build_bbox_head(self, cfg: ConfigDict | dict) -> nn.Module:
         """Build bbox head."""
-        cfg.pop("type")  # TODO (sungchul): remove `type` in recipe
         return YOLOXHead(**cfg)
 
     def build_det_data_preprocessor(self, cfg: ConfigDict | dict) -> nn.Module:
         """Build DetDataPreprocessor.
 
         TODO (sungchul): DetDataPreprocessor will be removed.
         """
-        cfg.pop("type")  # TODO (sungchul): remove `type` in recipe
         return DetDataPreprocessor(**cfg)
 
 
@@ -104,7 +100,6 @@ def _create_model(self) -> nn.Module:
 
         config = deepcopy(self.config)
         self.classification_layers = self.get_classification_layers(config, "model.")
-        config.pop("type")  # TODO (sungchul): remove `type` in recipe
         detector = YOLOX(**convert_conf_to_mmconfig_dict(config))
         detector.init_weights()
         if self.load_from is not None:
@@ -215,7 +210,6 @@ def get_classification_layers(
             Normally it is related with background classes.
         """
         sample_config = deepcopy(config)
-        sample_config.pop("type")  # TODO (sungchul): remove `type` in recipe
         modify_num_classes(sample_config, 5)
         sample_model_dict = YOLOX(**convert_conf_to_mmconfig_dict(sample_config)).state_dict()
 

diff --git a/tests/unit/algo/detection/heads/test_yolox_head.py b/tests/unit/algo/detection/heads/test_yolox_head.py
@@ -49,7 +49,6 @@ def test_loss_by_feat(self):
         train_cfg = Config(
             {
                 "assigner": {
-                    "type": "SimOTAAssigner",
                     "center_radius": 2.5,
                     "candidate_topk": 10,
                     "iou_weight": 3.0,