Batches assembled from visually-similar drone queries pressure the model to
learn finer discriminative features. Random mutex batches average ~0.26
pairwise cosine similarity in query embedding space; DSS batches average
~0.71 — confirming the lookalikes grouping works as intended.
Algorithm per batch:
1. Pick a random seed drone from the remaining pool.
2. Rank the entire remaining pool by cosine similarity to the seed.
3. Walk the ranking in descending order; add items whose sat_candidates
don't collide with the batch's already-claimed set.
4. Drop the seed if no valid batch can be assembled (rare mutex deadlock).
Inherits MutuallyExclusiveSampler semantics — no false negatives. Degrades
gracefully to mutex-only when no embeddings are set (warmup epochs, or if
`sampler_type="mutex"` is chosen).
Integration in `train_gtauav.py`:
- New `_embed_drone_queries` helper: model.encode_query forwarded over
GTAUAVDroneQuery, returns [N, D] CPU tensor. ~13s per 1024 queries on
a 4090 → ~5 min for the full 25K train set.
- Epoch loop re-embeds every `dss_reembed_every` epochs after a `dss_warmup_epochs`
warmup (first epochs use mutex-only since untrained embeddings aren't
informative for kNN).
- Config: `sampler_type` ∈ {"mutex", "dss"}. Default flipped to "dss".
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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>
PROBLEM: GTA-UAV has overlapping satellite crops (partial IoU).
Standard InfoNCE with diagonal targets treated valid matches as negatives.
R@K checked only diagonal — missed valid matches, artificially low recall.
FIXES:
1. WeightedInfoNCE loss (src/losses/weighted_infonce.py):
- Per-sample adaptive label smoothing from positive_weights (IoU)
- Higher weight → sharper target, lower → softer (semi-positive tolerance)
- Based on Game4Loc reference implementation
2. Multi-match R@K evaluation:
- Uses dataset.get_all_valid_sat_names() to get ALL valid matches per query
- R@K counts hit if ANY valid satellite is in top-K (not just diagonal)
- AP computed as MRR over first valid match
3. Dataset returns positive_weight per sample:
- Sampled satellite weight passed to loss for adaptive smoothing
- All valid satellite candidates exposed for evaluation
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Architecture changes:
- Removed proj_drone/proj_sat (1024→512): retrieval space is now
DINOv3 native 1024-dim, no information loss from projection
- TextFusionMLP: 2304→1024→1024 (was 2304→768→512), shared between branches
- Gallery branch now uses satellite captions (L1/L2/L3) via shared TextFusionMLP
- Two separate GatedFusion gates: α_q (query) and α_g (gallery)
- For sat images without captions (~57%): gate passes image features through
Dataset changes:
- GTAUAVDataset now loads satellite captions from caption index
- collate_gtauav_batch includes sat_caption_l1/l2/l3
Training loop:
- Passes satellite captions to model forward
- Logs both gate_q and gate_g values
11.1M trainable / 734M total (1.51%)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
