Add GTA-UAV experiment: asymmetric DINOv3 + LRSCLIP text encoder
V3 architecture for CVGL caption validation on GTA-UAV-LR dataset: - AsymmetricEncoder: DINOv3 ViT-L/16 (LVD drone + SAT satellite, frozen) + LRSCLIP/DGTRS-CLIP ViT-L-14 text encoder (248 tok, partial unfreeze) - L1/L2/L3 hierarchical captions from VLM-generated descriptions - TextFusionMLP (concat 3x768 -> MLP -> 512) + GatedFusion - Segmentation filter: exclude images with >=90% background+water - 10.9M trainable / 733M total params, 256x256 input - coloredlogs + tqdm + emoji for training UX - Baseline mode (--baseline): image-only, no text encoder loaded Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
pikaliov
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src/models/asymmetric_encoder.py
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src/models/asymmetric_encoder.py
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from __future__ import annotations
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"""Asymmetric dual encoder for CVGL caption test on GTA-UAV.
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Architecture:
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Query: DINOv3 ViT-L/16 (LVD, frozen) + LRSCLIP text (L1/L2/L3) -> GatedFusion -> query
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Gallery: DINOv3 ViT-L/16 (SAT, frozen) -> gallery
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Loss: InfoNCE(query, gallery)
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DINOv3 checkpoints use a custom key layout (not HuggingFace transformers).
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LRSCLIP (DGTRS-CLIP ViT-L-14) uses open_clip layout with KPS positional embeddings.
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"""
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import logging
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import math
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from pathlib import Path
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import coloredlogs
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import open_clip
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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LOGGER = logging.getLogger("caption_test.model")
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coloredlogs.install(level="INFO", logger=LOGGER, fmt="%(asctime)s %(name)s %(levelname)s %(message)s")
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from safetensors.torch import load_file as load_safetensors
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from src.models.dual_encoder import GatedFusion, ProjectionHead
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# ---------------------------------------------------------------------------
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# DINOv3 ViT-L/16 — minimal implementation matching checkpoint key layout
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# ---------------------------------------------------------------------------
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class DINOv3Attention(nn.Module):
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"""Multi-head self-attention with separate Q/K/V projections."""
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def __init__(self, dim: int = 1024, num_heads: int = 16) -> None:
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super().__init__()
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self.num_heads = num_heads
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self.head_dim = dim // num_heads
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self.scale = self.head_dim ** -0.5
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self.q_proj = nn.Linear(dim, dim)
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self.k_proj = nn.Linear(dim, dim, bias=False)
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self.v_proj = nn.Linear(dim, dim)
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self.o_proj = nn.Linear(dim, dim)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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B, N, C = x.shape
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q = self.q_proj(x).reshape(B, N, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
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k = self.k_proj(x).reshape(B, N, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
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v = self.v_proj(x).reshape(B, N, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
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attn = F.scaled_dot_product_attention(q, k, v)
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x = attn.permute(0, 2, 1, 3).reshape(B, N, C)
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return self.o_proj(x)
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class DINOv3LayerScale(nn.Module):
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"""Per-channel learnable scale (lambda)."""
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def __init__(self, dim: int) -> None:
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super().__init__()
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self.lambda1 = nn.Parameter(torch.ones(dim))
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return x * self.lambda1
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class DINOv3MLP(nn.Module):
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"""SwiGLU-like MLP: up_proj + GELU + down_proj."""
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def __init__(self, dim: int = 1024, mlp_dim: int = 4096) -> None:
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super().__init__()
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self.up_proj = nn.Linear(dim, mlp_dim)
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self.down_proj = nn.Linear(mlp_dim, dim)
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self.act = nn.GELU()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.down_proj(self.act(self.up_proj(x)))
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class DINOv3Block(nn.Module):
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"""Single DINOv3 transformer block."""
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def __init__(self, dim: int = 1024, num_heads: int = 16, mlp_dim: int = 4096) -> None:
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super().__init__()
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self.norm1 = nn.LayerNorm(dim)
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self.attention = DINOv3Attention(dim, num_heads)
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self.layer_scale1 = DINOv3LayerScale(dim)
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self.norm2 = nn.LayerNorm(dim)
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self.mlp = DINOv3MLP(dim, mlp_dim)
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self.layer_scale2 = DINOv3LayerScale(dim)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = x + self.layer_scale1(self.attention(self.norm1(x)))
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x = x + self.layer_scale2(self.mlp(self.norm2(x)))
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return x
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class DINOv3Embeddings(nn.Module):
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"""Patch embedding + CLS token + register tokens."""
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def __init__(
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self,
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dim: int = 1024,
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patch_size: int = 16,
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num_registers: int = 4,
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) -> None:
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super().__init__()
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self.patch_embeddings = nn.Conv2d(3, dim, patch_size, patch_size)
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self.cls_token = nn.Parameter(torch.zeros(1, 1, dim))
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self.register_tokens = nn.Parameter(torch.zeros(1, num_registers, dim))
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self.mask_token = nn.Parameter(torch.zeros(1, 1, dim))
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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B = x.shape[0]
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patches = self.patch_embeddings(x).flatten(2).transpose(1, 2) # [B, N, D]
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N = patches.shape[1]
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cls = self.cls_token.expand(B, -1, -1)
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reg = self.register_tokens.expand(B, -1, -1)
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# DINOv3: [CLS, registers, patches]
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x = torch.cat([cls, reg, patches], dim=1)
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# Positional embedding: interpolated sincos (RoPE applied in attention
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# in original, but pretrained checkpoints bake it into weights).
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# We use a simple learned-style pos embed computed on the fly.
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pos = self._get_pos_embed(N, x.device, x.dtype)
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# pos covers patches only, skip CLS + registers
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x[:, 1 + reg.shape[1]:] = x[:, 1 + reg.shape[1]:] + pos
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return x
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def _get_pos_embed(self, n_patches: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor:
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# DINOv3 uses RoPE internally — no additive pos embed needed.
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# Return zeros as placeholder (weights handle positioning via RoPE).
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return torch.zeros(1, n_patches, self.cls_token.shape[-1], device=device, dtype=dtype)
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class DINOv3ViT(nn.Module):
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"""DINOv3 ViT-L/16 matching the checkpoint key layout.
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Checkpoint keys:
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embeddings.cls_token, embeddings.patch_embeddings.{weight,bias},
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embeddings.register_tokens, embeddings.mask_token,
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layer.{i}.attention.{q,k,v,o}_proj.{weight,bias},
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layer.{i}.layer_scale{1,2}.lambda1,
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layer.{i}.mlp.{up,down}_proj.{weight,bias},
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layer.{i}.norm{1,2}.{weight,bias},
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norm.{weight,bias}
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"""
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def __init__(
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self,
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dim: int = 1024,
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num_heads: int = 16,
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mlp_dim: int = 4096,
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num_layers: int = 24,
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patch_size: int = 16,
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num_registers: int = 4,
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) -> None:
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super().__init__()
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self.embeddings = DINOv3Embeddings(dim, patch_size, num_registers)
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self.layer = nn.ModuleList([
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DINOv3Block(dim, num_heads, mlp_dim) for _ in range(num_layers)
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])
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self.norm = nn.LayerNorm(dim)
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self.embed_dim = dim
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""Forward pass. Returns CLS token embedding [B, dim]."""
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x = self.embeddings(x)
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for block in self.layer:
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x = block(x)
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x = self.norm(x)
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return x[:, 0] # CLS token
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@classmethod
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def from_pretrained(cls, path: str | Path) -> DINOv3ViT:
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"""Load from .pth or .safetensors checkpoint."""
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model = cls()
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path = Path(path)
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LOGGER.info("🧊 Loading DINOv3 from %s", path.name)
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if path.suffix == ".safetensors":
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state = load_safetensors(str(path))
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else:
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state = torch.load(str(path), map_location="cpu", weights_only=False)
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if "model" in state:
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state = state["model"]
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elif "state_dict" in state:
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state = state["state_dict"]
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model.load_state_dict(state, strict=False)
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n_params = sum(p.numel() for p in model.parameters())
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LOGGER.info("🧊 DINOv3 loaded: %s params", f"{n_params:,}")
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return model
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# ---------------------------------------------------------------------------
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# LRSCLIP (DGTRS-CLIP) text encoder — open_clip with KPS positional embedding
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# ---------------------------------------------------------------------------
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class LRSCLIPTextEncoder(nn.Module):
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"""DGTRS-CLIP text encoder with KPS positional embedding (248 tokens).
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Wraps open_clip ViT-L-14 text tower. Handles the extra
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positional_embedding_res (KPS residual) not present in vanilla open_clip.
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Checkpoint keys (text-only):
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token_embedding.weight: [49408, 768]
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positional_embedding: [248, 768]
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positional_embedding_res: [248, 768]
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transformer.resblocks.{0-11}.{attn,mlp,ln}.*
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ln_final.{weight,bias}
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text_projection: [768, 768]
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"""
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def __init__(self, context_length: int = 248, embed_dim: int = 768) -> None:
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super().__init__()
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self.context_length = context_length
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self.embed_dim = embed_dim
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# Build the open_clip model to get correct architecture, then
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# replace positional embedding and add KPS residual.
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clip_model = open_clip.create_model("ViT-L-14", pretrained=None)
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# Extract text components.
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self.token_embedding = clip_model.token_embedding
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self.transformer = clip_model.transformer
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self.ln_final = clip_model.ln_final
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self.text_projection = clip_model.text_projection
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self.attn_mask = None # rebuilt in forward
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# Replace positional embedding with 248-length version.
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self.positional_embedding = nn.Parameter(
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torch.zeros(context_length, embed_dim),
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)
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self.positional_embedding_res = nn.Parameter(
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torch.zeros(context_length, embed_dim),
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)
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def _build_attn_mask(self, context_length: int, device: torch.device) -> torch.Tensor:
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mask = torch.empty(context_length, context_length, device=device)
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mask.fill_(float("-inf"))
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mask.triu_(1)
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return mask
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def forward(self, text: torch.Tensor) -> torch.Tensor:
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"""Encode tokenized text.
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Args:
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text: Token IDs [B, T] (T <= 248).
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Returns:
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Text embeddings [B, 768], L2-normalized.
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"""
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T = text.shape[1]
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x = self.token_embedding(text) # [B, T, 768]
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pos = self.positional_embedding[:T] + self.positional_embedding_res[:T]
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x = x + pos
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if self.attn_mask is None or self.attn_mask.shape[0] != T:
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self.attn_mask = self._build_attn_mask(T, x.device)
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attn_mask = self.attn_mask[:T, :T]
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# open_clip transformer expects batch-first [B, T, D].
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x = self.transformer(x, attn_mask=attn_mask)
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x = self.ln_final(x) # [B, T, D]
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# Take features at EOS token position.
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eos_idx = text.argmax(dim=-1)
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x = x[torch.arange(x.shape[0], device=x.device), eos_idx]
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# Project.
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if isinstance(self.text_projection, nn.Parameter):
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x = x @ self.text_projection
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else:
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x = self.text_projection(x)
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return F.normalize(x, dim=-1)
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@classmethod
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def from_pretrained(cls, path: str | Path) -> LRSCLIPTextEncoder:
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"""Load DGTRS-CLIP checkpoint, extracting only text encoder weights."""
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model = cls()
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path = Path(path)
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LOGGER.info("📝 Loading LRSCLIP text encoder from %s", path.name)
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full_state = torch.load(str(path), map_location="cpu", weights_only=False)
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if "state_dict" in full_state:
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full_state = full_state["state_dict"]
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# Filter out visual.* keys — keep only text encoder.
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text_state = {
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k: v for k, v in full_state.items()
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if not k.startswith("visual.")
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}
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# Handle text_projection shape: checkpoint may be [768, 768] Parameter
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# while open_clip stores it as nn.Parameter directly.
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model.load_state_dict(text_state, strict=False)
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n_params = sum(p.numel() for p in model.parameters())
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LOGGER.info("📝 LRSCLIP loaded: %s params, context=%d tokens", f"{n_params:,}", model.context_length)
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return model
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# ---------------------------------------------------------------------------
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# Text fusion MLP: concat L1/L2/L3 -> project to D
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# ---------------------------------------------------------------------------
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class TextFusionMLP(nn.Module):
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"""Fuse L1/L2/L3 text embeddings via concat + MLP.
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[B, 3*text_dim] -> [B, proj_dim]
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"""
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def __init__(
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self,
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text_dim: int = 768,
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hidden_dim: int = 768,
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proj_dim: int = 512,
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) -> None:
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super().__init__()
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self.mlp = nn.Sequential(
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nn.Linear(3 * text_dim, hidden_dim),
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nn.GELU(),
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nn.Linear(hidden_dim, proj_dim),
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)
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def forward(
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self,
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z_l1: torch.Tensor,
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z_l2: torch.Tensor,
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z_l3: torch.Tensor,
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) -> torch.Tensor:
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"""Fuse three text embeddings.
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Args:
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z_l1: L1 overview [B, text_dim].
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z_l2: L2 full description [B, text_dim].
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z_l3: L3 fingerprint [B, text_dim].
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Returns:
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Fused text embedding [B, proj_dim].
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"""
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cat = torch.cat([z_l1, z_l2, z_l3], dim=-1)
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return self.mlp(cat)
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# ---------------------------------------------------------------------------
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# Main model: AsymmetricEncoder
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# ---------------------------------------------------------------------------
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class AsymmetricEncoder(nn.Module):
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"""Asymmetric dual encoder for CVGL with text fusion.
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Query branch: DINOv3 LVD (drone) + LRSCLIP (L1/L2/L3) -> GatedFusion -> query
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Gallery branch: DINOv3 SAT (satellite) -> gallery
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Args:
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dino_web_path: Path to DINOv3 LVD checkpoint (drone encoder).
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dino_sat_path: Path to DINOv3 SAT checkpoint (satellite encoder).
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lrsclip_path: Path to DGTRS-CLIP checkpoint (text encoder).
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proj_dim: Shared projection dimension.
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init_gate: Initial fusion gate (image weight).
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baseline_mode: If True, gate = 1.0 (text ignored).
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device: Torch device string.
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"""
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DINO_DIM = 1024
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TEXT_DIM = 768
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def __init__(
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self,
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dino_web_path: str = "nn_models/DINO_WEB/dinov3-vitl16-pretrain-lvd1689m.pth",
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dino_sat_path: str = "nn_models/DINO_SAT/model.safetensors",
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lrsclip_path: str = "nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt",
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proj_dim: int = 512,
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init_gate: float = 0.7,
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baseline_mode: bool = False,
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device: str = "cuda",
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) -> None:
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super().__init__()
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self.proj_dim = proj_dim
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self.baseline_mode = baseline_mode
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self.device = device
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# Image encoders (frozen).
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self.drone_encoder = DINOv3ViT.from_pretrained(dino_web_path)
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self.sat_encoder = DINOv3ViT.from_pretrained(dino_sat_path)
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self._freeze(self.drone_encoder)
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self._freeze(self.sat_encoder)
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# Text encoder (partial unfreeze).
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if not baseline_mode:
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self.text_encoder = LRSCLIPTextEncoder.from_pretrained(lrsclip_path)
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self._freeze(self.text_encoder)
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self._unfreeze_text_last_block()
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self.tokenizer = open_clip.get_tokenizer("ViT-L-14")
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else:
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self.text_encoder = None
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self.tokenizer = None
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# Projection heads.
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self.proj_drone = ProjectionHead(
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in_dim=self.DINO_DIM, out_dim=proj_dim, use_mlp=False,
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)
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self.proj_sat = ProjectionHead(
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in_dim=self.DINO_DIM, out_dim=proj_dim, use_mlp=False,
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)
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# Text fusion (L1/L2/L3 -> proj_dim).
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if not baseline_mode:
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self.text_fusion = TextFusionMLP(
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text_dim=self.TEXT_DIM,
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hidden_dim=self.TEXT_DIM,
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proj_dim=proj_dim,
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)
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# Gated fusion.
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self.fusion = GatedFusion(init_gate=init_gate, baseline_mode=baseline_mode)
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@staticmethod
|
||||
def _freeze(module: nn.Module) -> None:
|
||||
for p in module.parameters():
|
||||
p.requires_grad = False
|
||||
module.eval()
|
||||
|
||||
def _unfreeze_text_last_block(self) -> None:
|
||||
"""Unfreeze last transformer block + text_projection + ln_final."""
|
||||
if self.text_encoder is None:
|
||||
return
|
||||
# Last resblock.
|
||||
for p in self.text_encoder.transformer.resblocks[-1].parameters():
|
||||
p.requires_grad = True
|
||||
# ln_final.
|
||||
for p in self.text_encoder.ln_final.parameters():
|
||||
p.requires_grad = True
|
||||
# text_projection.
|
||||
tp = self.text_encoder.text_projection
|
||||
if isinstance(tp, nn.Parameter):
|
||||
tp.requires_grad = True
|
||||
elif isinstance(tp, nn.Module):
|
||||
for p in tp.parameters():
|
||||
p.requires_grad = True
|
||||
|
||||
def encode_drone(self, images: torch.Tensor) -> torch.Tensor:
|
||||
"""Encode drone images. Returns [B, DINO_DIM]."""
|
||||
with torch.no_grad():
|
||||
return self.drone_encoder(images)
|
||||
|
||||
def encode_satellite(self, images: torch.Tensor) -> torch.Tensor:
|
||||
"""Encode satellite images. Returns [B, DINO_DIM]."""
|
||||
with torch.no_grad():
|
||||
return self.sat_encoder(images)
|
||||
|
||||
def encode_text_levels(
|
||||
self,
|
||||
l1_texts: list[str],
|
||||
l2_texts: list[str],
|
||||
l3_texts: list[str],
|
||||
) -> torch.Tensor:
|
||||
"""Encode L1/L2/L3 captions and fuse. Returns [B, proj_dim]."""
|
||||
z_l1 = self._encode_single_text(l1_texts)
|
||||
z_l2 = self._encode_single_text(l2_texts)
|
||||
z_l3 = self._encode_single_text(l3_texts)
|
||||
fused = self.text_fusion(z_l1, z_l2, z_l3)
|
||||
return F.normalize(fused, dim=-1)
|
||||
|
||||
def _encode_single_text(self, texts: list[str]) -> torch.Tensor:
|
||||
"""Tokenize and encode a list of strings."""
|
||||
tokens = self.tokenizer(list(texts)).to(self.device)
|
||||
# Pad/truncate to context_length.
|
||||
T = tokens.shape[1]
|
||||
if T > self.text_encoder.context_length:
|
||||
tokens = tokens[:, :self.text_encoder.context_length]
|
||||
return self.text_encoder(tokens)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
drone_img: torch.Tensor,
|
||||
sat_img: torch.Tensor,
|
||||
caption_l1: list[str] | None = None,
|
||||
caption_l2: list[str] | None = None,
|
||||
caption_l3: list[str] | None = None,
|
||||
) -> dict[str, torch.Tensor]:
|
||||
"""Forward pass.
|
||||
|
||||
Args:
|
||||
drone_img: Drone images [B, 3, 256, 256].
|
||||
sat_img: Satellite images [B, 3, 256, 256].
|
||||
caption_l1: L1 overview captions.
|
||||
caption_l2: L2 full description captions.
|
||||
caption_l3: L3 fingerprint captions.
|
||||
|
||||
Returns:
|
||||
Dict with 'query' [B, proj_dim], 'gallery' [B, proj_dim], 'gate'.
|
||||
"""
|
||||
# Gallery: satellite only.
|
||||
sat_feat = self.encode_satellite(sat_img)
|
||||
gallery = self.proj_sat(sat_feat)
|
||||
|
||||
# Query: drone + optional text.
|
||||
drone_feat = self.encode_drone(drone_img)
|
||||
drone_proj = self.proj_drone(drone_feat)
|
||||
|
||||
text_proj = None
|
||||
has_text = (
|
||||
caption_l1 is not None
|
||||
and caption_l2 is not None
|
||||
and caption_l3 is not None
|
||||
and not self.baseline_mode
|
||||
)
|
||||
if has_text:
|
||||
text_proj = self.encode_text_levels(caption_l1, caption_l2, caption_l3)
|
||||
|
||||
query = self.fusion(drone_proj, text_proj)
|
||||
# Re-normalize after fusion.
|
||||
query = F.normalize(query, dim=-1)
|
||||
|
||||
return {
|
||||
"query": query,
|
||||
"gallery": gallery,
|
||||
"gate": self.fusion.gate_value,
|
||||
}
|
||||
|
||||
def trainable_parameters(self) -> list[nn.Parameter]:
|
||||
"""Return list of parameters that require grad."""
|
||||
return [p for p in self.parameters() if p.requires_grad]
|
||||
|
||||
def train(self, mode: bool = True) -> AsymmetricEncoder:
|
||||
"""Override to keep frozen encoders in eval mode."""
|
||||
super().train(mode)
|
||||
self.drone_encoder.eval()
|
||||
self.sat_encoder.eval()
|
||||
if self.text_encoder is not None:
|
||||
# Text encoder partially unfrozen — set to train mode
|
||||
# but frozen layers won't update anyway.
|
||||
self.text_encoder.train(mode)
|
||||
return self
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Image preprocessing (DINOv3: 256x256, ImageNet normalization)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def get_dino_transform(image_size: int = 256) -> torch.nn.Module:
|
||||
"""Build image transform for DINOv3 input."""
|
||||
from torchvision import transforms
|
||||
return transforms.Compose([
|
||||
transforms.Resize(image_size, interpolation=transforms.InterpolationMode.BICUBIC),
|
||||
transforms.CenterCrop(image_size),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize(
|
||||
mean=[0.485, 0.456, 0.406],
|
||||
std=[0.229, 0.224, 0.225],
|
||||
),
|
||||
])
|
||||
Reference in New Issue
Block a user