Pikaliov
2ce4017ebd
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>
55 lines
1.8 KiB
Plaintext
55 lines
1.8 KiB
Plaintext
# Balanced configuration — primary test setup.
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# L = 1.0 * L_img_img + 0.3 * L_sat_cap + 0.3 * L_drone_cap + 0.1 * L_cap_cap
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import src.datasets.visloc_with_captions
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import src.losses.multi_infonce
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import src.models.dual_encoder
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import src.training.train
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# ---- Dual encoder ----
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DualEncoderCaptionTest.variant = "ViT-B-32"
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DualEncoderCaptionTest.pretrained_path = "checkpoints/RS5M_ViT-B-32.pt"
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DualEncoderCaptionTest.unfreeze_mode = "last_block"
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DualEncoderCaptionTest.embed_dim = 512
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DualEncoderCaptionTest.use_mlp_heads = False
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DualEncoderCaptionTest.shared_image_head = True
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DualEncoderCaptionTest.device = "cuda"
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ProjectionHead.in_dim = 512
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ProjectionHead.out_dim = 512
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ProjectionHead.use_mlp = False
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# ---- Loss ----
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MultiTermInfoNCE.temperature_init = 0.1
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MultiTermInfoNCE.temperature_final = 0.01
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MultiTermInfoNCE.label_smoothing = 0.1
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MultiTermInfoNCE.asym_drone_to_sat = 0.6
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MultiTermInfoNCE.asym_sat_to_drone = 0.4
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MultiTermInfoNCE.warmup_epochs = 3
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MultiTermInfoNCE.text_ramp_epochs = 10
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MultiTermInfoNCE.lambda_ii = 1.0
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MultiTermInfoNCE.lambda_sc_max = 0.3
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MultiTermInfoNCE.lambda_dc_max = 0.3
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MultiTermInfoNCE.lambda_cc_max = 0.1
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# ---- Dataset ----
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VisLocCaptionDataset.caption_strategy = "hybrid"
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VisLocCaptionDataset.drop_caption_prob = 0.0
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VisLocCaptionDataset.seed = 42
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# ---- Training ----
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TrainConfig.train_manifest = "data/visloc_train.json"
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TrainConfig.val_manifest = "data/visloc_val.json"
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TrainConfig.image_root = "data/visloc/images"
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TrainConfig.output_dir = "out/caption_test/balanced"
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TrainConfig.epochs = 30
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TrainConfig.batch_size = 128
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TrainConfig.num_workers = 4
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TrainConfig.learning_rate = 1e-4
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TrainConfig.weight_decay = 1e-4
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TrainConfig.grad_clip = 1.0
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TrainConfig.use_amp = True
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TrainConfig.eval_every = 1
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TrainConfig.seed = 42
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TrainConfig.device = "cuda"
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