da2d2ea90e
- 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>
55 lines
1.7 KiB
Plaintext
55 lines
1.7 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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# 10 epochs, DINOv3 + DGTRS-CLIP, MONA + LoRA adapters.
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#
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# NOTE: TrainConfigGTAUAV is registered by train_gtauav.py before gin parsing.
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# InfoNCELoss is registered via import below.
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import src.losses.multi_infonce
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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.grad_accum_steps = 1
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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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TrainConfigGTAUAV.shared_encoder = True
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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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