29a09349e7
- 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>
51 lines
1.5 KiB
Plaintext
51 lines
1.5 KiB
Plaintext
# GTA-UAV Balanced: GatedFusion with L1/L2/L3 captions on both branches.
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# query = sigma(alpha) * drone + (1-sigma(alpha)) * text -> InfoNCE vs gallery
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# 20 epochs, DINOv3 + DGTRS-CLIP, MONA + LoRA adapters.
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import src.losses.multi_infonce
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import src.training.train_gtauav
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# ---- Training ----
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TrainConfigGTAUAV.epochs = 10
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TrainConfigGTAUAV.batch_size = 8
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TrainConfigGTAUAV.num_workers = 4
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TrainConfigGTAUAV.learning_rate = 1e-4
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TrainConfigGTAUAV.text_lr_factor = 0.1
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TrainConfigGTAUAV.weight_decay = 1e-4
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TrainConfigGTAUAV.grad_clip = 1.0
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TrainConfigGTAUAV.use_amp = True
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TrainConfigGTAUAV.eval_every = 2
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TrainConfigGTAUAV.warmup_epochs = 2
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TrainConfigGTAUAV.seed = 42
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TrainConfigGTAUAV.device = "cuda"
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# ---- Model ----
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TrainConfigGTAUAV.init_gate = 0.7
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TrainConfigGTAUAV.baseline_mode = False
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# ---- Loss ----
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TrainConfigGTAUAV.tau_init = 0.07
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TrainConfigGTAUAV.label_smoothing = 0.1
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TrainConfigGTAUAV.weight_q2g = 0.6
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TrainConfigGTAUAV.weight_g2q = 0.4
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TrainConfigGTAUAV.learnable_temperature = True
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# ---- Output ----
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TrainConfigGTAUAV.output_dir = "out/gtauav/with_text"
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# ---- Tracking ----
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TrainConfigGTAUAV.use_wandb = False
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TrainConfigGTAUAV.use_tb = True
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TrainConfigGTAUAV.use_gradcam = True
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TrainConfigGTAUAV.gradcam_every = 5
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TrainConfigGTAUAV.use_profiler = False
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TrainConfigGTAUAV.log_grad_norms = True
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# ---- InfoNCE Loss (gin-configurable) ----
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InfoNCELoss.temperature_init = 0.07
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InfoNCELoss.temperature_final = 0.01
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InfoNCELoss.label_smoothing = 0.1
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InfoNCELoss.weight_q2g = 0.6
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InfoNCELoss.weight_g2q = 0.4
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InfoNCELoss.learnable_temperature = True
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