Add StripNet backbone option with Conv-MONA adaptation

StripNet-small (Strip-R-CNN, HVision-NKU) as alternative image encoder to
DINOv3 ViT-L/16. ~28M params (10x smaller). Output 512-dim from stage 4
projected to 1024 to keep retrieval space compatible with DINOv3 configs.

- src/models/stripnet/: self-contained backbone (model.py, conv_mona.py).
  State-dict naming follows upstream Strip-R-CNN repo (conv_spatial1/2);
  ImageNet-1K pretrained head dropped on load.
- Conv-MONA: 2D adaptation of MONA (CVPR 2025) for CNN blocks. BN → 1x1
  Down(C->bn) → multi-scale DWConv {3,5,7} mean → +residual → GELU →
  1x1 Up(bn->C) with channel-wise layer scale γ init 1e-6. Two adapters
  per StripNet Block (post-attn, post-mlp); injected into deepest N stages.
- StripNetEncoder: GAP + Linear(512->1024). Overrides train() to keep
  frozen BatchNorm stats stable across mode flips.
- AsymmetricEncoder: new `backbone="stripnet"` option (always shared).
- TrainConfigGTAUAV: backbone, stripnet_path, stripnet_mona_last_n_stages.
- conf/gtauav_balanced_stripnet.gin + gtauav_baseline_stripnet.gin.

Smoke test: forward [2,3,256,256] -> [2,1024]. Trainable: 1.2M baseline
(8.27%), 4.76M with text (3.35% of 142M).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/models/asymmetric_encoder.py

@@ -28,6 +28,8 @@ from safetensors.torch import load_file as load_safetensors
 from src.models.adapters import inject_lora_into_dgtrs, inject_mona_into_dinov3
 from src.models.dgtrs.model import DGTRSTextEncoder, load_dgtrs_text_encoder, tokenize_dgtrs
 from src.models.dual_encoder import GatedFusion, ProjectionHead
+from src.models.stripnet import inject_conv_mona_into_stripnet
+from src.models.stripnet_encoder import StripNetEncoder
 
 
 # ---------------------------------------------------------------------------
@@ -302,15 +304,30 @@ class AsymmetricEncoder(nn.Module):
         mona_last_n_blocks: int = 24,
         lora_rank: int = 4,
         device: str = "cuda",
+        backbone: str = "dinov3",
+        stripnet_path: str = "nn_models/STRIPNET/stripnet_s.pth",
+        stripnet_mona_last_n_stages: int = 2,
     ) -> None:
         super().__init__()
-        self.embed_dim = self.DINO_DIM  # native 1024, no projection
+        self.embed_dim = self.DINO_DIM  # native 1024 (StripNet projects 512 -> 1024)
         self.baseline_mode = baseline_mode
         self.shared_encoder = shared_encoder
+        self.backbone = backbone
         self.device = device
 
         # Image encoder(s) (frozen + MONA adapters).
-        if shared_encoder:
+        if backbone == "stripnet":
+            # StripNet always operates as shared encoder (one CNN for both branches).
+            self.shared_encoder = True
+            self.image_encoder = StripNetEncoder(checkpoint_path=stripnet_path, out_dim=self.DINO_DIM)
+            self._freeze(self.image_encoder.backbone)
+            inject_conv_mona_into_stripnet(
+                self.image_encoder.backbone,
+                bottleneck=mona_bottleneck,
+                last_n_stages=stripnet_mona_last_n_stages,
+            )
+            LOGGER.info("StripNet backbone: shared encoder, projection 512 -> %d", self.DINO_DIM)
+        elif shared_encoder:
             self.image_encoder = DINOv3ViT.from_pretrained(dino_web_path)
             self._freeze(self.image_encoder)
             inject_mona_into_dinov3(self.image_encoder, bottleneck=mona_bottleneck, last_n_blocks=mona_last_n_blocks)