Three related architecture changes, driven by a cost/simplicity trade-off:
1. **Shared encoder**: one DINOv3 LVD-1689M (WEB) processes both drone
and satellite images. Previously asymmetric — separate WEB (drone) and
SAT-493M (satellite) encoders. Saves ~303M frozen params and halves
VRAM for the image tower. Expected to lose some satellite-domain
inductive bias; MONA adapters pick up the slack.
2. **MONA in last 12/24 blocks**: adapters injected only in the top half
of the ViT. The lowest 12 blocks keep their pretrained features
untouched. Trainable MONA count drops from 14.0M (48 adapters × 2
encoders) to 3.5M (24 adapters × 1 encoder).
3. **No DINO_SAT**: `nn_models/DINO_SAT` is no longer loaded by the
default config. It stays on disk and the path param is kept for
backward compat with asymmetric checkpoints.
Parameter counts (with text fusion + LoRA + gates):
Before: 17.6M trainable / 733M total (2.35%)
After: 7.06M trainable / 434M total (1.63%)
Also fixes a pre-existing resume bug: checkpoints now record
`shared_encoder`, `baseline_mode`, `mona_bottleneck`, `mona_last_n_blocks`
so `AsymmetricEncoder.load_checkpoint` can rebuild the right architecture.
Old checkpoints still load (missing keys fall back to asymmetric defaults
via `ckpt.get(..., <default>)`).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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>
New `hard_mining_k` parameter on InfoNCELoss. When >0 and queue is non-empty,
each query row keeps only its K highest-similarity queue entries (via
`torch.topk`) as negatives, instead of the full queue. Fully vectorized —
no Python loop, no extra forward pass.
Rationale: the memory bank grows to 4096 detached gallery embeddings, but
most are easy negatives that contribute almost nothing to the gradient.
Hard mining focuses compute on the small subset that actually shapes the
decision boundary. +2-3% R@1 in similar contrastive setups.
Edge cases:
- K=0: mining disabled, full queue used (original behavior).
- K >= queue size: falls back to full queue (e.g. warmup when queue is small).
- Queue empty: in-batch only, no changes.
Default in `gtauav_balanced.gin`: K=512 (1/8 of queue). Smoke-train updated
to exercise the full memory-bank + mining path.
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:
- Asymmetric DINOv3: WEB (drone) + SAT (satellite) with separate MONA
- MONA on all 24 blocks per encoder (was last 12)
- Remove projection, native 1024-dim retrieval space (was 512)
- Total: 748M params, 17.6M trainable (2.35%)
Hard negative memory bank:
- MoCo-style FIFO queue of 4096 detached gallery embeddings
- Each batch: B in-batch + Q queue negatives in InfoNCE
- Queue updated after each forward pass
Training config:
- batch_size=8, grad_accum=8, effective_batch=64
- eval_every=1 (eval + train recall every epoch)
- Max bs=24 with grad checkpointing on RTX 4090
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- DINOv3: checkpoint each of 24 transformer blocks (recompute on backward)
- DGTRS-CLIP: checkpoint each of 12 transformer blocks
- Enables batch_size=24 on RTX 4090 (was 8 without checkpointing)
- Peak VRAM: 20.3 GB at bs=24 (was OOM at bs=16 before)
- ~20-30% slower per step, but 3x more in-batch negatives (23 vs 7)
- Enabled by default (gradient_checkpointing=True in config)
- Update README with VRAM benchmarks and checkpointing docs
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Single DINOv3 WEB for both drone and satellite branches (shared_encoder=True default)
- One set of MONA adapters instead of two: 7M trainable vs 14M
- Total params: 438M (was 748M), trainable: 10.6M (was 17.6M)
- Asymmetric mode still available via shared_encoder=False
- Add gradient accumulation (grad_accum_steps, --grad-accum CLI flag)
- Update model summary in README
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- New config field grad_accum_steps (default=1, no change in behavior)
- Loss scaled by 1/accum, optimizer step every N micro-batches
- Scheduler counts optimizer steps (not micro-batches)
- CLI flag --grad-accum for override
- Document gradient accumulation and in-batch negatives in README
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Use gin.configurable(module=...) to prevent __main__ vs module name clash
- Remove `import src.training.train_gtauav` from gin files (already loaded)
- Use short selector names (TrainConfigGTAUAV) in all gin configs
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add unified experiment tracker (W&B + TensorBoard) with graceful fallback
- Add gradient norm monitoring per param group (MONA, LoRA, MLP, gates, tau)
- Add Grad-CAM visualization for DINOv3 drone/satellite encoders
- Add PyTorch Profiler wrapper + torchinfo model summary
- Add gin-config support to train_gtauav.py with CLI overrides
- Add v3 gin configs: gtauav_balanced, gtauav_baseline, gtauav_text_heavy, gtauav_image_heavy
- Generate metric plots every epoch (not just on eval)
- Set default epochs to 10
- Update README and CLAUDE.md with new tooling and usage docs
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
