Add task-aligned detector specialization pipeline

Author: Boris Peyriguere

complexity/generative/detection/__init__.py

@@ -21,6 +21,8 @@
 from .config import TRHashDetectorConfig
 from .data import (
     CocoDetectionDataset,
+    CocoVideoDetectionDataset,
+    HuggingFaceDetectionDataset,
     SyntheticShapesDataset,
     YoloDetectionDataset,
     collate_detection,
@@ -55,6 +57,8 @@
     "distribution_focal_loss",
     "quality_focal_loss",
     "CocoDetectionDataset",
+    "CocoVideoDetectionDataset",
+    "HuggingFaceDetectionDataset",
     "SyntheticShapesDataset",
     "YoloDetectionDataset",
     "collate_detection",

complexity/generative/detection/config.py

@@ -60,6 +60,21 @@ class TRHashDetectorConfig:
     quality_loss_weight: float = 1.0
     box_l1_weight: float = 0.25
     box_iou_weight: float = 1.0
+    level_adapters_enabled: bool = False
+    level_adapter_ratio: float = 0.25
+    class_level_hash_gate_enabled: bool = False
+    class_level_gate_temperature: float = 1.0
+    object_weighting_enabled: bool = False
+    object_weighting_beta: float = 0.999
+    object_weighting_max: float = 4.0
+    level_aux_loss_weight: float = 0.0
+    gate_calibration_loss_weight: float = 0.0
+    object_contrastive_loss_weight: float = 0.0
+    object_contrastive_temperature: float = 0.1
+    object_contrastive_dim: int = 64
+    video_motion_enabled: bool = False
+    video_motion_hidden_size: int = 64
+    video_motion_scale_init: float = 0.1
     dropout: float = 0.0
     layer_norm_eps: float = 1e-6
 
@@ -128,6 +143,30 @@ def __post_init__(self) -> None:
             raise ValueError("progressive_box_start must be in (0, 1]")
         if self.progressive_quality_start < 1.0:
             raise ValueError("progressive_quality_start must be at least 1")
+        if not 0.0 < self.level_adapter_ratio <= 1.0:
+            raise ValueError("level_adapter_ratio must be in (0, 1]")
+        if self.class_level_gate_temperature <= 0.0:
+            raise ValueError("class_level_gate_temperature must be positive")
+        if not 0.0 <= self.object_weighting_beta < 1.0:
+            raise ValueError("object_weighting_beta must be in [0, 1)")
+        if self.object_weighting_max < 1.0:
+            raise ValueError("object_weighting_max must be at least 1")
+        if min(
+            self.level_aux_loss_weight,
+            self.gate_calibration_loss_weight,
+            self.object_contrastive_loss_weight,
+        ) < 0.0:
+            raise ValueError("auxiliary loss weights must be non-negative")
+        if self.gate_calibration_loss_weight and not self.class_level_hash_gate_enabled:
+            raise ValueError("gate calibration requires class_level_hash_gate_enabled")
+        if self.object_contrastive_temperature <= 0.0:
+            raise ValueError("object_contrastive_temperature must be positive")
+        if self.object_contrastive_dim <= 0:
+            raise ValueError("object_contrastive_dim must be positive")
+        if self.video_motion_hidden_size <= 0:
+            raise ValueError("video_motion_hidden_size must be positive")
+        if self.video_motion_scale_init < 0.0:
+            raise ValueError("video_motion_scale_init must be non-negative")
 
     @property
     def grid_size(self) -> int: