- Switch MONA from fp32 to bfloat16 (same exponent range as fp32, no underflow)
- fp16 causes NaN: gamma=1e-6 falls into subnormal range (min normal ~6.1e-5)
- bf16 min normal ~1.2e-38, so 1e-6 is safe
- RTX 4090 supports bf16 natively
- Document bf16 vs fp16 vs fp32 comparison in README
- Update model summary: 3.5M MONA (last 12 blocks), 5.6M total trainable
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- MONA fp16 causes NaN (gamma=1e-6 underflows in fp16 min subnormal ~6e-8)
- Revert MONA forward to fp32 with autocast(enabled=False), cast output back
- Fix loss CSV: save raw_loss before backward() (tensor consumed after backward)
- Verified: loss=3.78, no NaN, bs=48 peak=21.4 GB
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>
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>
- AsymmetricEncoder.save_checkpoint(): saves model_state + metadata
- AsymmetricEncoder.load_checkpoint(): rebuilds model with frozen backbones,
then loads trainable weights from checkpoint
- --resume flag restores optimizer, loss (learnable tau), and scheduler state
- Training continues from the saved epoch
Usage:
python -m src.training.train_gtauav --resume out/gtauav/with_text/ckpt_epoch004.pt
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
