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>
- scripts/make_split.py: merges cross-area train+test (33,708 pairs),
shuffles with seed=42, splits 80/20
- meta/train_80.json (26,966) + meta/test_20.json (6,742)
- After seg filter: 24,891 train / 6,252 test
- Default paths in train_gtauav.py updated to use new split
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
