46b1208891
- New config field grad_accum_steps (default=1, no change in behavior) - Loss scaled by 1/accum, optimizer step every N micro-batches - Scheduler counts optimizer steps (not micro-batches) - CLI flag --grad-accum for override - Document gradient accumulation and in-batch negatives in README Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
463 lines
21 KiB
Markdown
463 lines
21 KiB
Markdown
# Caption Quality Test for Cross-View Geo-Localization
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Validate whether generated text captions improve retrieval R@1 in cross-view
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geo-localization (drone-to-satellite).
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## Architecture
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### Overview
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Asymmetric dual-encoder with domain-specific frozen backbones and hierarchical
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text fusion for drone-to-satellite image retrieval.
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```
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┌─────────────────────────────── QUERY BRANCH ─────────────────────────────────┐
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│ │
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│ drone_img ──► DINOv3 ViT-L/16 LVD (frozen, 303M) ──► CLS token │
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│ [B,3,256,256] + MONA adapters (7M trainable) d_img [B,1024] │
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│ (2 per block × 24 blocks, bn=64) │ │
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│ │ │
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│ L1 (overview) ──► DGTRS-CLIP ViT-L-14 ──► z₁ [B,768] ─┐ │
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│ L2 (full desc) ──► (frozen, 124M) ──► z₂ [B,768] ─┼─ cat ──[B,2304] │
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│ L3 (fingerprint) ──► + LoRA r=4 (147K) ──► z₃ [B,768] ─┘ │ │
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│ (248 tok, KPS pos.) TextFusionMLP (shared, 3.4M) │
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│ Linear→GELU→Linear │
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│ │ │
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│ d_txt [B,1024] │
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│ │ │
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│ q = σ(α_q)·d_img + (1−σ(α_q))·d_txt GatedFusion_q │
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│ │ │
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│ q̂ = q / ‖q‖₂ ──► query [B,1024] │
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└───────────────────────────────────────────────────────────────────────────────┘
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┌────────────────────────────── GALLERY BRANCH ────────────────────────────────┐
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│ │
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│ sat_img ──► DINOv3 ViT-L/16 SAT (frozen, 303M) ──► CLS token │
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│ [B,3,256,256] + MONA adapters (7M trainable) s_img [B,1024] │
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│ (2 per block × 24 blocks, bn=64) │ │
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│ │ │
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│ sat_L1 ──► DGTRS-CLIP ──► z₁ [768] ─┐ │
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│ sat_L2 ──► (shared encoder)──► z₂ [768] ─┼─ cat ──► TextFusionMLP (shared) │
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│ sat_L3 ──► + LoRA ──► z₃ [768] ─┘ │ │
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│ s_txt [B,1024] │
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│ │ │
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│ g = σ(α_g)·s_img + (1−σ(α_g))·s_txt GatedFusion_g │
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│ │ │
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│ ĝ = g / ‖g‖₂ ──► gallery [B,1024] │
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└───────────────────────────────────────────────────────────────────────────────┘
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┌────────────────────────────── RETRIEVAL ──────────────────────────────────────┐
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│ │
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│ similarity = q̂ · ĝᵀ / τ (τ learnable, init=0.07, clamp [0.01, 0.5]) │
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│ loss = 0.6·CE(q→g) + 0.4·CE(g→q) (label_smoothing=0.1) │
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│ │
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│ Retrieval space: 1024-dim (DINOv3 native, no projection layers) │
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│ TextFusionMLP shared between query and gallery branches │
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│ For sat images without captions: s_txt=None → g = s_img (gate passthrough) │
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│ BASELINE: σ(α) = 1.0 for both branches (text disabled, DGTRS not loaded) │
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└───────────────────────────────────────────────────────────────────────────────┘
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┌──────────────────────────── DIAGNOSTICS PIPELINE ────────────────────────────┐
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│ │
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│ Per-batch ──► CSV (train_batches.csv) ──► TensorBoard / W&B scalars │
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│ Per-epoch ──► CSV (train.csv, val.csv) ──► Seaborn plots (PNG) │
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│ Every N epochs ──► Grad-CAM heatmaps (drone + satellite DINOv3 last block) │
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│ Epoch 0 (opt) ──► PyTorch Profiler (Chrome trace + CUDA timeline) │
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│ Per-50-batch ──► Gradient norms per group (MONA, LoRA, MLP, gates, τ) │
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│ On init ──► torchinfo model summary (model_summary.txt) │
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└───────────────────────────────────────────────────────────────────────────────┘
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```
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### Text hierarchy (L1 / L2 / L3)
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Each drone image has a VLM-generated caption (Qwen3-VL) split into 3 levels:
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| Level | Name | Content | Typical length |
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|-------|------|---------|----------------|
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| **L1** | Overview | First sentence of P1: land-cover summary with class percentages | 15–30 tokens |
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| **L2** | Full description | Complete P1 (inventory) + P2 (spatial layout with 5+ zones) | 100–200 tokens |
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| **L3** | Fingerprint | P3: unique landmarks and spatial signature for matching | 20–50 tokens |
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All three levels are encoded by a **single DGTRS-CLIP ViT-L-14** text encoder
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(248-token context via KPS positional embedding, 768-dim output)
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adapted with **LoRA** (rank=4 on Q/V in all 12 blocks).
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### Text fusion (shared MLP for both branches)
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```math
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\mathbf{z}_{\text{text}} = \text{MLP}\bigl([\mathbf{z}_1 \;;\; \mathbf{z}_2 \;;\; \mathbf{z}_3]\bigr)
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```
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where `[z₁ ; z₂ ; z₃] ∈ ℝ^{B×2304}` is the concatenation of three 768-dim DGTRS-CLIP embeddings, and
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```math
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\text{MLP}: \text{Linear}(2304, 1024) \to \text{GELU} \to \text{Linear}(1024, 1024), \quad \mathbf{z}_{\text{text}} \in \mathbb{R}^{B \times 1024}
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```
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### Gated fusion (separate gates for query and gallery)
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```math
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\mathbf{q} = \sigma(\alpha_q) \cdot \mathbf{d}_{\text{img}} + \bigl(1 - \sigma(\alpha_q)\bigr) \cdot \mathbf{d}_{\text{txt}} \qquad \text{(query branch)}
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```
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```math
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\mathbf{g} = \sigma(\alpha_g) \cdot \mathbf{s}_{\text{img}} + \bigl(1 - \sigma(\alpha_g)\bigr) \cdot \mathbf{s}_{\text{txt}} \qquad \text{(gallery branch)}
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```
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- `α_q, α_g` — separate learnable scalars in logit-space, init `σ(α) ≈ 0.7`
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- `σ` — sigmoid function
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- `d_img, s_img ∈ ℝ^{B×1024}` — DINOv3+MONA image embeddings
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- `d_txt, s_txt ∈ ℝ^{B×1024}` — fused text embeddings
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- For satellite images without captions: `s_txt = None → g = s_img`
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### Adaptation methods
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| Method | Applied to | Where | Params |
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|--------|-----------|-------|--------|
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| **MONA** (CVPR 2025) | DINOv3 ViT-L/16 (drone + sat) | After MSA and MLP in each of 24 blocks | 7.0M per encoder |
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| **LoRA** (rank=4) | DGTRS-CLIP text encoder | Q and V projections in all 12 blocks | 147K |
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**MONA adapter** (per block):
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```math
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\mathbf{x} \leftarrow \mathbf{x} + \text{Up}_{64 \to 1024}\!\Bigl(\text{GELU}\bigl(\text{MonaOp}\bigl(\text{Down}_{1024 \to 64}(\hat{\mathbf{x}})\bigr)\bigr)\Bigr)
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```
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where `x̂ = γ · LN(x) + γₓ · x` (scaled LayerNorm, `γ` init `10⁻⁶`, `γₓ` init `1`)
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```math
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\text{MonaOp}(\mathbf{x}) = \frac{\text{DWConv}_{3 \times 3}(\mathbf{x}) + \text{DWConv}_{5 \times 5}(\mathbf{x}) + \text{DWConv}_{7 \times 7}(\mathbf{x})}{3} + \mathbf{x}
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```
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**LoRA** (per attention layer):
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```math
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\mathbf{Q}' = \mathbf{Q} + \frac{\alpha}{r} \cdot \mathbf{x} \mathbf{A}_Q^T \mathbf{B}_Q^T, \qquad \mathbf{V}' = \mathbf{V} + \frac{\alpha}{r} \cdot \mathbf{x} \mathbf{A}_V^T \mathbf{B}_V^T
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```
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where `A ∈ ℝ^{r×d}`, `B ∈ ℝ^{d×r}`, `r = 4`
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### Pair formation and negative sampling
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The dataset provides only **positive pairs** — each entry maps one drone image to its
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matching satellite crop(s). Negative pairs are **not** stored explicitly; instead, they
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are constructed automatically inside each training batch via the InfoNCE loss:
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```
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Batch (B = 8):
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drone_0 ↔ sat_0 ← positive (from dataset)
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drone_1 ↔ sat_1 ← positive
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...
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drone_7 ↔ sat_7 ← positive
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Similarity matrix B×B:
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sat_0 sat_1 ... sat_7
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q_0 [ pos neg ... neg ] ← CE target = 0
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q_1 [ neg pos ... neg ] ← CE target = 1
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...
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q_7 [ neg neg ... pos ] ← CE target = 7
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```
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- **Positives:** diagonal `sim[i, i]` — correct drone-satellite pair
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- **Negatives:** off-diagonal `sim[i, j≠i]` — other satellite images in the batch (in-batch negatives)
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- At `batch_size=8`: 1 positive + 7 in-batch negatives per query
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- No hard-negative mining — negatives are random within the batch
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- Larger batch → more negatives → harder contrastive task
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Each drone image may have multiple satellite candidates (with distance-based weights).
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At training time, one satellite crop is sampled per drone (weighted if semi-positive).
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### Loss function
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Symmetric InfoNCE with learnable temperature (CLIP-style `logit_scale`):
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```math
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\mathcal{L} = w_{q \to g} \cdot \mathcal{L}_{q \to g} + w_{g \to q} \cdot \mathcal{L}_{g \to q}
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```
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```math
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\mathcal{L}_{q \to g} = \text{CrossEntropy}\!\left(\frac{\hat{\mathbf{q}} \cdot \hat{\mathbf{g}}^T}{\tau},\; \text{targets}\right), \qquad \mathcal{L}_{g \to q} = \text{CrossEntropy}\!\left(\frac{\hat{\mathbf{g}} \cdot \hat{\mathbf{q}}^T}{\tau},\; \text{targets}\right)
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```
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- `τ = 1 / exp(logit_scale)` — learnable scalar, clamped `τ ∈ [0.01, 0.5]`, init `τ₀ = 0.07`
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- `w_{q→g} = 0.6`, `w_{g→q} = 0.4`
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- `targets = [0, 1, 2, ..., B−1]` — positives on diagonal
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- label smoothing `= 0.1`
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Loss and adapters run in **fp32** (AMP autocast disabled) to prevent gradient overflow.
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### Gradient accumulation
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With `batch_size=8` on a 24 GB GPU, VRAM is the bottleneck. Gradient accumulation
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emulates a larger effective batch without extra memory:
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```
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effective_batch_size = batch_size × grad_accum_steps
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```
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| Setting | `batch_size` | `grad_accum_steps` | Effective batch | In-batch negatives |
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|---------|:---:|:---:|:---:|:---:|
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| Default | 8 | 1 | 8 | 7 |
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| Recommended | 8 | 8 | 64 | 7 per micro-batch |
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**Note:** gradient accumulation averages gradients across micro-batches, but each
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micro-batch still only sees `batch_size` in-batch negatives. To increase the number
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of negatives per forward pass, increase `batch_size` directly (requires more VRAM).
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```bash
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# Example: effective batch of 64 with 8 accumulation steps
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python -m src.training.train_gtauav --config conf/gtauav_balanced.gin \
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--filter-meta meta/seg_filter.json --grad-accum 8
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```
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### Metrics
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| Metric | Formula | Direction |
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|--------|---------|-----------|
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| **R@K** (Recall at K) | fraction of queries where correct gallery is in top-K | drone → satellite (primary) |
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| **Delta R@1** | R@1(with_text) − R@1(baseline) | higher = text helps |
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Reported: R@1, R@5, R@10 for both q→g and g→q directions.
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### Optimizer & scheduler
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```
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Optimizer: AdamW
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- MONA adapters, TextFusionMLP, gate α_q, gate α_g, logit_scale:
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lr = 1e-4, weight_decay = 1e-4
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- LoRA adapters (DGTRS-CLIP text encoder):
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lr = 1e-5 (10× lower, --text-lr-factor 0.1)
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Scheduler: Linear warmup (2 epochs) + cosine annealing
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- Per-step (not per-epoch)
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- warmup: lr linearly ramps from 0 to lr_max over warmup_steps
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- cosine: lr decays from lr_max to 0 over remaining steps
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Gradient clipping: max_norm = 1.0
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Mixed precision: AMP fp16 for model forward, fp32 for loss
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```
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### Augmentations
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| Transform | Drone (train) | Satellite (train) | Eval |
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|-----------|:---:|:---:|:---:|
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| RandomResizedCrop(256, scale=0.7–1.0) | ✓ | ✓ | — |
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| Resize(256) + CenterCrop(256) | — | — | ✓ |
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| RandomHorizontalFlip(0.5) | ✓ | ✓ | — |
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| RandomRotation(15°) | ✓ | — | — |
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| ColorJitter(0.3, 0.3, 0.2, 0.1) | ✓ | ✓ | — |
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| RandomGrayscale(0.05) | ✓ | ✓ | — |
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| GaussianBlur(k=3, σ=0.1–2.0) | ✓ | — | — |
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| ImageNet Normalize | ✓ | ✓ | ✓ |
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### Model summary
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| Component | Params | Trainable | Notes |
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|-----------|--------|-----------|-------|
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| DINOv3 ViT-L/16 LVD (drone) | 303M | frozen | backbone weights frozen |
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| MONA adapters (drone) | 7.0M | 7.0M | 2 per block × 24 blocks, bottleneck=64 |
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| DINOv3 ViT-L/16 SAT (satellite) | 303M | frozen | backbone weights frozen |
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| MONA adapters (satellite) | 7.0M | 7.0M | 2 per block × 24 blocks, bottleneck=64 |
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| DGTRS-CLIP ViT-L-14 (text) | 124M | frozen | backbone weights frozen |
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| LoRA adapters (text) | 147K | 147K | Q+V, rank=4, 12 blocks |
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| TextFusionMLP (shared) | 3.4M | 3.4M | Linear(2304,1024) + GELU + Linear(1024,1024) |
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| GatedFusion α_q + α_g | 2 | 2 | separate gate scalars |
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| logit_scale | 1 | 1 | learnable temperature |
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| **Total** | **748M** | **17.6M (2.35%)** | retrieval dim = 1024 |
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## Experiments
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### V3 — GTA-UAV + DINOv3 + DGTRS-CLIP (active)
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**Dataset:** GTA-UAV-LR (33K drone + 14K satellite, GTA V synthetic)
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- RGB: `/home/servml/Документы/datasets/GTA-UAV-LR/`
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- Captions: `/home/servml/Документы/datasets/GTA-UAV-LR-captions/` (40K JSON, 3-paragraph VLM)
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- Segmentation: `/home/servml/Документы/datasets/GTA-UAV-LR-aug/` (17 classes)
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- Seg filter: 37,498 passed / 10,905 excluded (>=90% background+water)
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- Split: 80/20 random (26,966 train / 6,742 test → 24,891/6,252 after seg filter)
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### V2 — UAV-GeoLoc + GeoRSCLIP (legacy)
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Single backbone with template captions.
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```
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Query: drone_img + caption -> GeoRSCLIP -> GatedFusion -> query
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Gallery: sat_img -> GeoRSCLIP -> gallery
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```
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**Dataset:** UAV-GeoLoc Terrain split (206K train queries)
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## Structure
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```
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caption-test/
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├── conf/ # Gin configs
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│ ├── gtauav_balanced.gin # GTA-UAV with text (10 epochs, v3)
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│ ├── gtauav_baseline.gin # GTA-UAV baseline, no text (v3)
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│ ├── gtauav_text_heavy.gin # GTA-UAV text-heavy gate=0.3 (v3)
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│ ├── gtauav_image_heavy.gin # GTA-UAV image-heavy gate=0.9 (v3)
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│ ├── balanced.gin # UAV-GeoLoc with text (v2)
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│ ├── baseline_no_text.gin # UAV-GeoLoc baseline (v2)
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│ └── text_heavy.gin # UAV-GeoLoc text-heavy (v2)
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├── nn_models/ # Pre-trained checkpoints (v3, gitignored)
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│ ├── DINO_WEB/ # DINOv3 ViT-L/16 LVD-1689M (.pth)
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│ ├── DINO_SAT/ # DINOv3 ViT-L/16 SAT-493M (.safetensors)
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│ └── LRSCLIP/ # DGTRS-CLIP ViT-L-14 (.pt)
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├── meta/ # Generated metadata
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│ ├── train_80.json # 80% train split (26,966 pairs)
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│ ├── test_20.json # 20% test split (6,742 pairs)
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│ └── seg_filter.json # Segmentation filter results
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├── scripts/
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│ ├── make_split.py # Create 80/20 train/test split
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│ ├── filter_segmentation.py # Exclude 90%+ background/water images
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│ ├── compare_runs.py # Delta R@1 comparison report
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│ └── generate_captions.py # Offline caption generation
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├── src/
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│ ├── datasets/
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│ │ ├── gtauav_dataset.py # GTA-UAV-LR loader + L1/L2/L3 parsing (v3)
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│ │ └── visloc_with_captions.py # UAV-GeoLoc loader (v2)
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│ ├── models/
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│ │ ├── dgtrs/ # Official DGTRS-CLIP text encoder (Apache-2.0)
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│ │ │ ├── model.py # DGTRSTextEncoder, build_model, tokenize
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│ │ │ ├── simple_tokenizer.py # BPE tokenizer (248 tokens)
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│ │ │ └── bpe_simple_vocab_16e6.txt.gz
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│ │ ├── asymmetric_encoder.py # DINOv3 + DGTRS + GatedFusion (v3)
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│ │ └── dual_encoder.py # GeoRSCLIP + GatedFusion (v2)
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│ ├── losses/
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│ │ └── multi_infonce.py # InfoNCE with learnable temperature
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│ ├── training/
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│ │ ├── train_gtauav.py # Training loop GTA-UAV (v3)
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│ │ ├── train.py # Training loop UAV-GeoLoc (v2)
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│ │ ├── trackers.py # Unified tracker: W&B + TensorBoard
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│ │ ├── grad_monitor.py # Gradient norm monitoring per group
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│ │ ├── gradcam.py # Grad-CAM visualization for DINOv3
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│ │ ├── profiling.py # PyTorch Profiler + torchinfo summary
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│ │ └── plot_metrics.py # Seaborn/matplotlib metric plots
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│ └── eval/
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│ └── evaluate.py # R@K metrics, Delta R@1
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└── checkpoints/ # GeoRSCLIP RS5M_ViT-B-32.pt (v2)
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```
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## Prerequisites
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```
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torch>=2.0
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safetensors
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coloredlogs
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tqdm
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ftfy
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regex
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gin-config
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Pillow
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numpy
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pandas
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matplotlib
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seaborn
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```
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### Optional (for extended diagnostics)
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```
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wandb # Weights & Biases experiment tracking
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torchinfo # Model summary tables
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tensorboard # TensorBoard logging (included with torch)
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```
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## Workflow (V3 — GTA-UAV)
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### 1. Create 80/20 split and filter segmentation
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```bash
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python -m scripts.make_split --output-dir meta
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python -m scripts.filter_segmentation --output meta/seg_filter.json
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```
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### 2. Train with gin configs (recommended)
|
||
|
||
```bash
|
||
# Baseline (no text, 10 epochs)
|
||
python -m src.training.train_gtauav --config conf/gtauav_baseline.gin \
|
||
--filter-meta meta/seg_filter.json
|
||
|
||
# With captions (L1/L2/L3, 10 epochs)
|
||
python -m src.training.train_gtauav --config conf/gtauav_balanced.gin \
|
||
--filter-meta meta/seg_filter.json
|
||
|
||
# Text-heavy (gate=0.3, 70% text weight)
|
||
python -m src.training.train_gtauav --config conf/gtauav_text_heavy.gin \
|
||
--filter-meta meta/seg_filter.json
|
||
```
|
||
|
||
### 3. Train without gin (CLI-only)
|
||
|
||
```bash
|
||
python -m src.training.train_gtauav --baseline --filter-meta meta/seg_filter.json
|
||
python -m src.training.train_gtauav --filter-meta meta/seg_filter.json
|
||
```
|
||
|
||
### 4. Enable diagnostics
|
||
|
||
```bash
|
||
# W&B + Grad-CAM + PyTorch Profiler
|
||
python -m src.training.train_gtauav --config conf/gtauav_balanced.gin \
|
||
--filter-meta meta/seg_filter.json --wandb --gradcam --profile
|
||
|
||
# Gin parameter overrides from CLI
|
||
python -m src.training.train_gtauav --config conf/gtauav_balanced.gin \
|
||
--filter-meta meta/seg_filter.json \
|
||
--gin-param 'TrainConfigGTAUAV.batch_size=16' 'TrainConfigGTAUAV.epochs=20'
|
||
```
|
||
|
||
CLI flags (`--wandb`, `--gradcam`, `--profile`, `--epochs`, etc.) take priority over gin config.
|
||
|
||
### 5. Resume from checkpoint
|
||
|
||
```bash
|
||
python -m src.training.train_gtauav --resume out/gtauav/with_text/ckpt_epoch004.pt \
|
||
--filter-meta meta/seg_filter.json
|
||
```
|
||
|
||
### 6. Compare and get verdict
|
||
|
||
```bash
|
||
python -m scripts.compare_runs \
|
||
--baseline_report out/gtauav/baseline/eval_report.json \
|
||
--full_report out/gtauav/with_text/eval_report.json \
|
||
--output out/gtauav/comparison.md
|
||
```
|
||
|
||
### 7. View TensorBoard
|
||
|
||
```bash
|
||
tensorboard --logdir out/gtauav/with_text/tb_logs
|
||
```
|
||
|
||
## Diagnostics & Visualization
|
||
|
||
| Tool | Flag | Output | Description |
|
||
|------|------|--------|-------------|
|
||
| **TensorBoard** | `--use-tb` (default on) | `{out}/tb_logs/` | Scalars, histograms, images |
|
||
| **W&B** | `--wandb` | cloud | Full experiment tracking, Grad-CAM images |
|
||
| **Grad-CAM** | `--gradcam` | `{out}/gradcam/` | DINOv3 attention heatmaps (drone + satellite) |
|
||
| **PyTorch Profiler** | `--profile` | `{out}/profiler/` | Chrome trace, CUDA timeline, memory |
|
||
| **torchinfo** | auto | `{out}/model_summary.txt` | Layer-by-layer parameter table |
|
||
| **Gradient norms** | `--log-grad-norms` (default on) | TB/W&B | Per-group: MONA, LoRA, MLP, gates, tau |
|
||
| **CSV (per-batch)** | auto | `{out}/logs/train_batches.csv` | Loss, tau, gates, lr for every batch |
|
||
| **CSV (per-epoch)** | auto | `{out}/logs/train.csv, val.csv` | Epoch averages + seaborn PNG plots |
|
||
|
||
## Decision rule
|
||
|
||
| Delta R@1 (drone→satellite) | Verdict |
|
||
|---|---|
|
||
| >= +3% | **PASS** — captions informative, proceed to NADEZHDA teacher |
|
||
| +1% to +3% | MARGINAL — add VLM refinement, re-run |
|
||
| 0 to +1% | WEAK — redesign caption pipeline |
|
||
| < 0 | HARMFUL — critical bug |
|
||
|
||
## Code style
|
||
|
||
- `from __future__ import annotations` everywhere
|
||
- Type hints on all signatures
|
||
- Google-style docstrings
|
||
- English-only comments
|