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ce8f665 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 | """Minimal model-loading code for the 7 VFM baselines in the paper."""
from __future__ import annotations
import sys
from pathlib import Path
from typing import Callable
import torch
import torch.nn as nn
from torchvision import transforms
from transformers import AutoConfig, AutoImageProcessor, AutoModel
ROOT = Path(__file__).resolve().parent
WEIGHTS_DIR = ROOT / "weights"
MODEL_SPECS = {
"metacliplin": {
"paper_name": "MetaCLIP-Linear",
"checkpoint": "metacliplin0.pth",
"hf_model": "facebook/metaclip-h14-fullcc2.5b",
"feature_dim": 1280,
"image_size": 224,
"pooler_output": True,
},
"metaclip2lin": {
"paper_name": "MetaCLIP2-Linear",
"checkpoint": "metaclip2lin0.pth",
"hf_model": "facebook/metaclip-2-worldwide-giant",
"feature_dim": 1280,
"image_size": 224,
"pooler_output": True,
},
"sigliplin": {
"paper_name": "SigLIP-Linear",
"checkpoint": "sigliplin0.pth",
"hf_model": "google/siglip-large-patch16-384",
"feature_dim": 1024,
"image_size": 384,
"pooler_output": True,
},
"siglip2lin": {
"paper_name": "SigLIP2-Linear",
"checkpoint": "siglip2lin0.pth",
"hf_model": "google/siglip2-giant-opt-patch16-384",
"feature_dim": 1536,
"image_size": 384,
"pooler_output": True,
},
"pelin": {
"paper_name": "PE-CLIP-Linear",
"checkpoint": "pelin0.pth",
"feature_dim": 1024,
"image_size": 336,
"pooler_output": False,
},
"dinov2lin": {
"paper_name": "DINOv2-Linear",
"checkpoint": "dinov2lin0.pth",
"feature_dim": 1024,
"pooler_output": False,
},
"dinov3lin": {
"paper_name": "DINOv3-Linear",
"checkpoint": "dinov3lin0.pth",
"hf_model": "facebook/dinov3-vit7b16-pretrain-lvd1689m",
"feature_dim": 4096,
"pooler_output": False,
},
}
ALIASES = {
"MetaCLIP-Linear": "metacliplin",
"MetaCLIP2-Linear": "metaclip2lin",
"SigLIP-Linear": "sigliplin",
"SigLIP2-Linear": "siglip2lin",
"PE-CLIP-Linear": "pelin",
"DINOv2-Linear": "dinov2lin",
"DINOv3-Linear": "dinov3lin",
}
def canonical_model_name(name: str) -> str:
if name in MODEL_SPECS:
return name
if name in ALIASES:
return ALIASES[name]
raise KeyError(f"Unknown model: {name}")
def default_checkpoint_path(model_name: str) -> Path:
model_name = canonical_model_name(model_name)
return WEIGHTS_DIR / MODEL_SPECS[model_name]["checkpoint"]
def _resolve_device(device: str | torch.device | None = None) -> torch.device:
if device is None:
return torch.device("cuda" if torch.cuda.is_available() else "cpu")
return torch.device(device)
def _load_checkpoint(checkpoint_path: str | Path) -> dict:
checkpoint = torch.load(str(checkpoint_path), map_location="cpu", weights_only=False)
if isinstance(checkpoint, dict):
for key in ("state_dict", "model", "model_state_dict"):
if key in checkpoint and isinstance(checkpoint[key], dict):
checkpoint = checkpoint[key]
break
normalized = {}
for key, value in checkpoint.items():
normalized[key[7:] if key.startswith("module.") else key] = value
return normalized
def _infer_feature_dim(state_dict: dict, default_dim: int) -> int:
head_weight = state_dict.get("head.weight")
if isinstance(head_weight, torch.Tensor) and head_weight.ndim == 2:
return int(head_weight.shape[1])
return default_dim
def _load_image_processor(model_name: str):
try:
return AutoImageProcessor.from_pretrained(model_name, local_files_only=True)
except Exception:
try:
return AutoImageProcessor.from_pretrained(model_name)
except Exception:
return None
def _load_backbone(model_name: str):
try:
return AutoModel.from_pretrained(model_name, local_files_only=True)
except Exception:
config = AutoConfig.from_pretrained(model_name)
return AutoModel.from_config(config)
class _PoolerLinearModel(nn.Module):
def __init__(self, backbone: nn.Module, feature_dim: int):
super().__init__()
self.backbone = backbone
self.head = nn.Linear(feature_dim, 2)
def forward(self, x):
with torch.no_grad():
outputs = self.backbone(x)
features = outputs.pooler_output.float()
return self.head(features)
class _ClsTokenLinearModel(nn.Module):
def __init__(self, backbone: nn.Module, feature_dim: int):
super().__init__()
self.backbone = backbone
self.head = nn.Linear(feature_dim, 2)
def forward(self, x):
with torch.no_grad():
outputs = self.backbone(x)
features = outputs.last_hidden_state[:, 0].float()
return self.head(features)
class _PELinearModel(nn.Module):
def __init__(self, backbone: nn.Module, feature_dim: int):
super().__init__()
self.backbone = backbone
self.head = nn.Linear(feature_dim, 2)
def forward(self, x):
with torch.no_grad():
features = self.backbone(x)
if isinstance(features, torch.Tensor):
features = features.float()
return self.head(features)
def _finalize_model(model: nn.Module, state_dict: dict, device=None) -> nn.Module:
model.load_state_dict(state_dict, strict=False)
model.to(_resolve_device(device))
model.eval()
return model
def _build_clip_transform(image_size: int, image_processor=None):
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
if image_processor is not None:
mean = getattr(image_processor, "image_mean", mean)
std = getattr(image_processor, "image_std", std)
return transforms.Compose(
[
transforms.Resize(image_size, interpolation=transforms.InterpolationMode.BICUBIC),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std),
]
)
def _build_dino_transform():
return transforms.Compose(
[
transforms.Resize(256, interpolation=transforms.InterpolationMode.BICUBIC),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
)
def load_metacliplin(checkpoint_path: str | Path | None = None, device=None):
spec = MODEL_SPECS["metacliplin"]
checkpoint_path = checkpoint_path or default_checkpoint_path("metacliplin")
state_dict = _load_checkpoint(checkpoint_path)
feature_dim = _infer_feature_dim(state_dict, spec["feature_dim"])
image_processor = _load_image_processor(spec["hf_model"])
backbone = _load_backbone(spec["hf_model"])
model = _PoolerLinearModel(backbone.vision_model, feature_dim)
model = _finalize_model(model, state_dict, device=device)
return model, _build_clip_transform(spec["image_size"], image_processor)
def load_metaclip2lin(checkpoint_path: str | Path | None = None, device=None):
spec = MODEL_SPECS["metaclip2lin"]
checkpoint_path = checkpoint_path or default_checkpoint_path("metaclip2lin")
state_dict = _load_checkpoint(checkpoint_path)
feature_dim = _infer_feature_dim(state_dict, spec["feature_dim"])
image_processor = _load_image_processor(spec["hf_model"])
backbone = _load_backbone(spec["hf_model"])
model = _PoolerLinearModel(backbone.vision_model, feature_dim)
model = _finalize_model(model, state_dict, device=device)
return model, _build_clip_transform(spec["image_size"], image_processor)
def load_sigliplin(checkpoint_path: str | Path | None = None, device=None):
spec = MODEL_SPECS["sigliplin"]
checkpoint_path = checkpoint_path or default_checkpoint_path("sigliplin")
state_dict = _load_checkpoint(checkpoint_path)
feature_dim = _infer_feature_dim(state_dict, spec["feature_dim"])
image_processor = _load_image_processor(spec["hf_model"])
backbone = _load_backbone(spec["hf_model"])
model = _PoolerLinearModel(backbone.vision_model, feature_dim)
model = _finalize_model(model, state_dict, device=device)
return model, _build_clip_transform(spec["image_size"], image_processor)
def load_siglip2lin(checkpoint_path: str | Path | None = None, device=None):
spec = MODEL_SPECS["siglip2lin"]
checkpoint_path = checkpoint_path or default_checkpoint_path("siglip2lin")
state_dict = _load_checkpoint(checkpoint_path)
feature_dim = _infer_feature_dim(state_dict, spec["feature_dim"])
image_processor = _load_image_processor(spec["hf_model"])
backbone = _load_backbone(spec["hf_model"])
model = _PoolerLinearModel(backbone.vision_model, feature_dim)
model = _finalize_model(model, state_dict, device=device)
return model, _build_clip_transform(spec["image_size"], image_processor)
def load_dinov2lin(checkpoint_path: str | Path | None = None, device=None):
checkpoint_path = checkpoint_path or default_checkpoint_path("dinov2lin")
state_dict = _load_checkpoint(checkpoint_path)
feature_dim = _infer_feature_dim(state_dict, MODEL_SPECS["dinov2lin"]["feature_dim"])
if feature_dim == 1536:
candidates = ["facebook/dinov2-giant", "facebook/dinov2-large"]
elif feature_dim == 1024:
candidates = ["facebook/dinov2-large", "facebook/dinov2-base"]
elif feature_dim == 768:
candidates = ["facebook/dinov2-base", "facebook/dinov2-small"]
else:
candidates = ["facebook/dinov2-large"]
last_error = None
backbone = None
for candidate in candidates:
try:
backbone = _load_backbone(candidate)
break
except Exception as exc:
last_error = exc
if backbone is None:
raise RuntimeError(f"Failed to load DINOv2 backbone: {last_error}")
model = _ClsTokenLinearModel(backbone, feature_dim)
model = _finalize_model(model, state_dict, device=device)
return model, _build_dino_transform()
def load_dinov3lin(checkpoint_path: str | Path | None = None, device=None):
checkpoint_path = checkpoint_path or default_checkpoint_path("dinov3lin")
state_dict = _load_checkpoint(checkpoint_path)
feature_dim = _infer_feature_dim(state_dict, MODEL_SPECS["dinov3lin"]["feature_dim"])
backbone = _load_backbone(MODEL_SPECS["dinov3lin"]["hf_model"])
model = _ClsTokenLinearModel(backbone, feature_dim)
model = _finalize_model(model, state_dict, device=device)
return model, _build_dino_transform()
def load_pelin(checkpoint_path: str | Path | None = None, device=None):
checkpoint_path = checkpoint_path or default_checkpoint_path("pelin")
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
import core.vision_encoder.pe as pe
import core.vision_encoder.transforms as pe_transforms
state_dict = _load_checkpoint(checkpoint_path)
feature_dim = _infer_feature_dim(state_dict, MODEL_SPECS["pelin"]["feature_dim"])
clip_model = pe.CLIP.from_config("PE-Core-L14-336", pretrained=False)
model = _PELinearModel(clip_model.visual, feature_dim)
model = _finalize_model(model, state_dict, device=device)
return model, pe_transforms.get_image_transform(MODEL_SPECS["pelin"]["image_size"])
LOADERS: dict[str, Callable] = {
"metacliplin": load_metacliplin,
"metaclip2lin": load_metaclip2lin,
"sigliplin": load_sigliplin,
"siglip2lin": load_siglip2lin,
"pelin": load_pelin,
"dinov2lin": load_dinov2lin,
"dinov3lin": load_dinov3lin,
}
def load_model(model_name: str, checkpoint_path: str | Path | None = None, device=None):
model_name = canonical_model_name(model_name)
return LOADERS[model_name](checkpoint_path=checkpoint_path, device=device)
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