40 lines
2.0 KiB
Plaintext
40 lines
2.0 KiB
Plaintext
# GTA-UAV Balanced (SOFIA-Tiny backbone): SOFIA v7.1 student trained from scratch
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# с двухуровневой text fusion:
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# 1. Mid-level: Text-FiLM в SAT и UAV heads (модулирует feature map перед GGeM/CHP).
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# 2. Late-level: GatedFusion на дескрипторах (как в DINOv3/StripNet вариантах).
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#
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# Trainable (~5-7M):
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# - SOFIA backbone (Tiny, ~5M, from scratch — нет pretrained)
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# - SOFIA heads (SatHead GGeM+BN+Linear, UAVHead AltitudeFiLM+CHP+BN+Linear, +Text-FiLM)
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# - DGTRS-CLIP LoRA (rank=4, ~147K)
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# - TextFusionMLP (3*768 -> 1024 -> 1024, ~3.4M, shared)
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# - Gates α_q, α_g + learnable τ
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#
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# Altitude (drone_height метры) подаётся в UAVHead.AltitudeFiLM из dataset meta CSV.
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# Для sat — altitude=None → FiLM passthrough (γ=1, β=0).
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#
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# Note: SOFIA from scratch — нужно больше эпох или warmup, чем frozen DINOv3/StripNet.
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# Mamba-2 backend (mamba_ssm) даёт 2-8x speedup vs torch fallback.
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include 'conf/gtauav_balanced.gin'
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# ---- Backbone ----
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TrainConfigGTAUAV.backbone = "sofia"
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TrainConfigGTAUAV.sofia_preset = "Tiny"
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TrainConfigGTAUAV.sofia_d_descriptor = 1024
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TrainConfigGTAUAV.sofia_use_text_film_uav = True
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TrainConfigGTAUAV.sofia_use_text_film_sat = True
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TrainConfigGTAUAV.sofia_lora_rank = 4
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# Mamba-1 used for Tiny (Mamba-2 torch fallback has a pre-existing reshape bug
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# with channels not divisible by default headdim; switch to "mamba2" for M/L
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# presets where channels % 64 == 0 OR install mamba_ssm CUDA kernels).
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TrainConfigGTAUAV.sofia_mamba_variant = "mamba1"
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TrainConfigGTAUAV.sofia_mamba_backend = "auto" # mamba_ssm if installed else torch fallback
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# ---- Training overrides ----
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TrainConfigGTAUAV.gradient_checkpointing = False # SOFIA from-scratch — keep activations live
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TrainConfigGTAUAV.shared_encoder = True # ignored by SOFIA but kept for logging compat
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# ---- Output ----
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TrainConfigGTAUAV.output_dir = "out/gtauav/with_text_sofia"
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