pikaliov
fa32b2e67f
Suppress spurious lr_scheduler.step() warning from PyTorch
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Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com >
2026-04-21 18:24:54 +03:00
pikaliov
517b87d3d8
Fix scheduler warning: use last_epoch instead of step() loop on resume
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Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com >
2026-04-21 18:23:17 +03:00
pikaliov
905b9867c8
Add 80/20 random split (replaces cross-area 46/54 split)
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- 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 >
2026-04-21 18:19:37 +03:00
pikaliov
44bce3096c
Add model save/load and --resume for training continuation
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- 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 >
2026-04-21 18:14:54 +03:00
pikaliov
3014a5def8
Update docs: training improvements (learnable temp, augmentations, warmup)
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Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com >
2026-04-21 18:08:06 +03:00
pikaliov
998d52cb57
Improve training: learnable temperature, per-group LR, warmup, augmentations
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Loss:
- Learnable temperature (CLIP-style logit_scale) with clamp [0.01, 0.5]
- Replaces fixed cosine schedule (still available via --no-learnable-temp)
- Default tau_init=0.07
Optimizer:
- Per-group LR: projections 1e-4, text encoder 1e-5 (10x lower)
- Learnable temperature included in projection param group
Scheduler:
- Linear warmup (2 epochs default) + cosine annealing
- Per-step scheduling (not per-epoch)
Augmentations (separate drone/satellite):
- Drone: RandomResizedCrop(0.7-1.0), HFlip, Rotation(15), ColorJitter,
RandomGrayscale(0.05), GaussianBlur
- Satellite: RandomResizedCrop(0.7-1.0), HFlip, ColorJitter, RandomGrayscale
- Eval: clean Resize+CenterCrop (no augmentation)
Dataset: supports separate drone_transform/sat_transform args
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com >
2026-04-21 18:07:17 +03:00
pikaliov
6ad9c4d149
Add GTA-UAV experiment: asymmetric DINOv3 + LRSCLIP text encoder
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V3 architecture for CVGL caption validation on GTA-UAV-LR dataset:
- AsymmetricEncoder: DINOv3 ViT-L/16 (LVD drone + SAT satellite, frozen)
+ LRSCLIP/DGTRS-CLIP ViT-L-14 text encoder (248 tok, partial unfreeze)
- L1/L2/L3 hierarchical captions from VLM-generated descriptions
- TextFusionMLP (concat 3x768 -> MLP -> 512) + GatedFusion
- Segmentation filter: exclude images with >=90% background+water
- 10.9M trainable / 733M total params, 256x256 input
- coloredlogs + tqdm + emoji for training UX
- Baseline mode (--baseline): image-only, no text encoder loaded
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com >
2026-04-21 17:54:27 +03:00
pikaliov
5da791801c
Update docs: target-size 512, dataset verification results
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- UAV-VisLoc processed at 512x512 (for segmentation/depth/normals)
- Dataset verified: 6744 drone, 74807 crops, median match 25.9m
- Known issue: 6 drones in route 06 outside satellite coverage
- Resize to model input size (224/256) in dataloader
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com >
2026-04-18 02:41:31 +03:00
pikaliov
abb3337f8d
Rewrite: GatedFusion architecture + UAV-GeoLoc dataset
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Architecture v2:
- Query branch: drone + text -> GatedFusion -> proj -> query_emb
- Gallery branch: satellite -> proj -> gallery_emb
- Single InfoNCE loss (asymmetric 0.6/0.4)
- GatedFusion: learnable gated addition (sigma(alpha)*img + (1-sigma(alpha))*text)
- Baseline mode: gate=1.0 (text ignored)
Dataset:
- UAV-GeoLoc loader with template captions from path metadata
- 27 terrain types with predefined features
- Random positive crop sampling per epoch
Configs: balanced (gate=0.7), baseline (no text), text_heavy (gate=0.3)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com >
2026-04-17 17:13:00 +03:00
2ce4017ebd
Initial commit: caption quality test on UAV-VisLoc
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Self-contained experimental track validating generated text captions
via retrieval R@1 lift on UAV-VisLoc.
Architecture: GeoRSCLIP ViT-B/32 dual encoder, 512-dim shared space.
Loss: 4-term InfoNCE (img-img + sat-cap + drone-cap + cap-cap)
with cosine temperature decay, PALW-like curriculum.
Metric: delta R@1 (with text - without text) >= +3% => PASS.
Gin-configured (balanced / baseline_no_text / text_heavy variants).
Follows NADEZHDA code style.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com >
2026-04-17 00:04:46 +03:00
