Fix GTA-UAV eval + training pipeline: full gallery, mutex sampler, per-sample mask

Six critical fixes to the caption-test training/eval stack:

1. **IndentationError blocker** (train_gtauav.py:765-766)
   Unparseable file — train-recall LOGGER.info block was orphaned outside
   its `if eval_every` guard. Wrapped in `if train_recall:` so val eval
   and Grad-CAM only run on eval epochs.

2. **Full satellite gallery in `_evaluate`**
   Old code assembled gallery from DataLoader batches (one random sat per
   drone), producing an incomplete gallery of size ≈ N_query instead of
   N_unique_sat. Metrics were inflated because retrieval was against a
   subset that always contained the target.
   New `GTAUAVSatGallery` / `GTAUAVDroneQuery` iterate all unique tiles
   and queries independently; full-gallery multi-match R@K + MRR.

3. **Per-sample caption mask** (`AsymmetricEncoder._fuse_with_mask`)
   Mixed batches (some samples have captions, some don't) previously
   encoded empty strings through DGTRS and mixed the noise output into
   every sample via scalar gate. New `encode_query`/`encode_gallery` use
   `torch.where` to fall back to pure image features for empty-caption
   samples. Training `forward()` routes through the same helper so
   training and eval share code.

4. **Symmetric InfoNCE as primary loss** (multi_infonce.InfoNCELoss)
   Switched gin default from `WeightedInfoNCELoss` (adaptive label
   smoothing — not the Game4Loc soft-IoU target it claimed) to the
   existing symmetric InfoNCE with q2g=0.6/g2q=0.4 weighting. Loss type
   now selectable via `cfg.loss_type ∈ {"symmetric", "weighted"}`.

5. **MutuallyExclusiveSampler** (new file)
   BatchSampler that greedily packs drones whose `sat_candidates` sets
   are pairwise disjoint within a batch. Eliminates false negatives from
   the semi-positive graph without needing soft-label losses.
   At bs=8 keeps 100% of 24,891 train entries; at bs=64 keeps 92.6%.
   `set_epoch()` for reproducibility + different batches per epoch.

6. **Temperature clamp [0.01, 0.1]** (both loss modules)
   Old tau_max=0.5 allowed the logit distribution to collapse into a
   near-uniform softmax. Tightened to the CLIP-standard range.

Also:
- Added `scripts/smoke_eval.py` / `scripts/smoke_train.py` for fast
  regression checks (eval in ~2 min, 2 train steps in ~1 min on RTX 4090).
- CLAUDE.md updated to reflect the new pipeline.

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

conf/gtauav_balanced.gin

@@ -1,8 +1,8 @@
 # GTA-UAV Balanced: Asymmetric DINOv3 (WEB+SAT) with L1/L2/L3 captions.
-# WeightedInfoNCE loss for GTA-UAV partial overlap handling.
+# Symmetric InfoNCE + MutuallyExclusiveSampler (no false negatives).
 # 10 epochs, MONA all 24 blocks, 1024-dim retrieval, hard negative bank.
 
-import src.losses.weighted_infonce
+import src.losses.multi_infonce
 
 # ---- Training ----
 TrainConfigGTAUAV.epochs = 10
@@ -26,11 +26,17 @@ TrainConfigGTAUAV.shared_encoder = False
 TrainConfigGTAUAV.gradient_checkpointing = True
 
 # ---- Loss ----
+TrainConfigGTAUAV.loss_type = "symmetric"
 TrainConfigGTAUAV.tau_init = 0.07
 TrainConfigGTAUAV.label_smoothing = 0.1
 TrainConfigGTAUAV.learnable_temperature = True
+TrainConfigGTAUAV.weight_q2g = 0.6
+TrainConfigGTAUAV.weight_g2q = 0.4
 TrainConfigGTAUAV.neg_bank_size = 4096
 
+# ---- Sampling ----
+TrainConfigGTAUAV.use_mutex_sampler = True
+
 # ---- Output ----
 TrainConfigGTAUAV.output_dir = "out/gtauav/with_text"
 
@@ -42,8 +48,11 @@ TrainConfigGTAUAV.gradcam_every = 5
 TrainConfigGTAUAV.use_profiler = False
 TrainConfigGTAUAV.log_grad_norms = True
 
-# ---- WeightedInfoNCE (gin-configurable) ----
-WeightedInfoNCELoss.temperature_init = 0.07
-WeightedInfoNCELoss.learnable_temperature = True
-WeightedInfoNCELoss.label_smoothing = 0.1
-WeightedInfoNCELoss.k = 5.0
+# ---- InfoNCELoss (gin-configurable) ----
+InfoNCELoss.temperature_init = 0.07
+InfoNCELoss.learnable_temperature = True
+InfoNCELoss.label_smoothing = 0.1
+InfoNCELoss.weight_q2g = 0.6
+InfoNCELoss.weight_g2q = 0.4
+InfoNCELoss.tau_min = 0.01
+InfoNCELoss.tau_max = 0.1