File size: 8,727 Bytes
e2b3733 2b883a1 e2b3733 18c1533 e2b3733 2b883a1 e2b3733 | 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 | """
T-REN HuggingFace model wrapper.
Usage:
from transformers import AutoModel
model = AutoModel.from_pretrained("savyak2/T-REN", trust_remote_code=True)
model.load_backbone("/path/to/dinov3/weights/")
# Or in one shot:
model = AutoModel.from_pretrained(
"savyak2/T-REN",
trust_remote_code=True,
dinov3_weights_dir="/path/to/dinov3/weights/",
)
outputs = model(pixel_values) # pixel_values: (B, 3, H, W) float in [0, 1]
"""
import numpy as np
import torch
from transformers import PreTrainedModel
from transformers.utils import logging
try:
from .configuration_tren import TRENConfig
from .model import FeatureExtractor, RegionEncoder, TextEncoder
except ImportError:
from configuration_tren import TRENConfig
from model import FeatureExtractor, RegionEncoder, TextEncoder
logger = logging.get_logger(__name__)
DINOV3_BACKBONE_FILENAME = "dinov3_vitl16_pretrain_lvd1689m-8aa4cbdd.pth"
DINOV3_HEAD_FILENAME = "dinov3_vitl16_dinotxt_vision_head_and_text_encoder-a442d8f5.pth"
def _build_cfg_dict(config: TRENConfig, dinov3_weights_dir: str = None) -> dict:
"""Convert TRENConfig into the dict format expected by existing model classes."""
return {
"pretrained": {
"feature_extractor": "dinov3_vitl16",
"text_encoder": "dinov3_vitl16",
},
"architecture": {
"patch_size": config.patch_size,
"hidden_dim": config.hidden_dim,
"text_embed_dim": config.text_embed_dim,
"num_decoder_layers": config.num_decoder_layers,
"num_attention_heads": config.num_attention_heads,
},
"parameters": {
"image_resolution": config.image_resolution,
"num_multiscale_regions": config.num_multiscale_regions,
"merging_iou_threshold": config.merging_iou_threshold,
"merging_similarity_threshold": config.merging_similarity_threshold,
},
# save_dir + exp_name join to give the directory containing DINOv3 weights.
# e.g. os.path.join("/path/to/dir", "", "filename.pth") -> "/path/to/dir/filename.pth"
"logging": {
"save_dir": dinov3_weights_dir or "",
"exp_name": "",
},
}
class TRENModel(PreTrainedModel):
"""
T-REN: Text-aligned Region Encoder Network.
Takes raw images and returns dense region tokens aligned to a shared
vision-language embedding space (DINOv3 / DINOtxt).
The trainable RegionEncoder weights are stored in this HF repo and loaded
automatically. The DINOv3 ViT-L/16 backbone (~2 GB) must be provided
separately via load_backbone().
DINOv3 weights needed in the same directory:
- dinov3_vitl16_pretrain_lvd1689m-8aa4cbdd.pth
- dinov3_vitl16_dinotxt_vision_head_and_text_encoder-a442d8f5.pth
"""
config_class = TRENConfig
base_model_prefix = "region_encoder"
def __init__(self, config: TRENConfig, dinov3_weights_dir: str = None):
super().__init__(config)
cfg = _build_cfg_dict(config)
# RegionEncoder: the trained T-REN head. HF saves/loads these weights.
self.region_encoder = RegionEncoder(cfg)
# Lazy placeholders — not registered as nn.Module submodules so they
# are excluded from HF save/load. _grid_points is computed on first
# forward() call to avoid meta-device issues during from_pretrained().
object.__setattr__(self, "_grid_points", None)
object.__setattr__(self, "_image_encoder", None)
object.__setattr__(self, "_text_encoder", None)
self.post_init()
if dinov3_weights_dir is not None:
self.load_backbone(dinov3_weights_dir)
def load_backbone(self, dinov3_weights_dir: str) -> None:
"""
Load the frozen DINOv3 image and text encoder backbones.
Args:
dinov3_weights_dir: Directory containing both DINOv3 weight files:
- dinov3_vitl16_pretrain_lvd1689m-8aa4cbdd.pth
- dinov3_vitl16_dinotxt_vision_head_and_text_encoder-a442d8f5.pth
"""
device = next(self.region_encoder.parameters()).device
cfg = _build_cfg_dict(self.config, dinov3_weights_dir)
logger.info("Loading DINOv3 image encoder...")
image_encoder = FeatureExtractor(cfg, device=str(device)).eval()
logger.info("Loading DINOv3 text encoder...")
text_encoder = TextEncoder(cfg, device=str(device)).eval()
object.__setattr__(self, "_image_encoder", image_encoder)
object.__setattr__(self, "_text_encoder", text_encoder)
def adapt_to_resolution(self, image_resolution: int) -> None:
"""
Interpolate the RegionEncoder's positional embeddings to a new spatial
resolution. Call this after from_pretrained() when running inference at
a resolution different from the training resolution (512px by default).
Args:
image_resolution: Target image resolution in pixels (e.g. 384).
Example::
model = AutoModel.from_pretrained("aryaaan12/T-REN", trust_remote_code=True)
model.load_backbone("/path/to/dinov3/weights/")
model.adapt_to_resolution(384) # eval at 384px instead of 512px
"""
if image_resolution == self.config.image_resolution:
return
saved_state = self.region_encoder.state_dict()
device = next(self.region_encoder.parameters()).device
ps = self.config.patch_size
num_patches = (image_resolution // ps) ** 2
C = self.region_encoder.feature_embeddings.shape[-1]
self.region_encoder.feature_embeddings = torch.nn.Parameter(
torch.zeros(num_patches, C, device=device)
)
self.region_encoder.load_state_dict_resolution_agnostic(saved_state)
self.region_encoder.to(device)
# Reset grid so it is rebuilt at the new resolution on the next forward().
object.__setattr__(self, "_grid_points", None)
logger.info(
f"Adapted positional embeddings: {self.config.image_resolution}px → {image_resolution}px"
)
def forward(
self,
pixel_values: torch.Tensor,
texts: list = None,
aggregate_tokens: bool = True,
) -> dict:
"""
Encode an image into region tokens.
Args:
pixel_values: Float tensor of shape (B, 3, H, W) in [0, 1].
texts: Optional list of text strings. When provided, text embeddings
are returned alongside region tokens for similarity scoring.
aggregate_tokens: Merge overlapping region tokens by mask IoU and
embedding cosine similarity (recommended for downstream use).
Returns:
dict with keys:
pred_tokens – (B, N, D) raw region feature tokens.
region_masks – (B, N, fH, fW) attention-derived region masks.
text_aligned_tokens – (B, N, D) tokens in the DINOtxt embedding space.
class_tokens – (B, D) image-level DINOv3 class tokens.
text_encodings – (T, D) text embeddings, only if texts is provided.
"""
if self._image_encoder is None:
raise RuntimeError(
"DINOv3 backbone not loaded. "
"Call model.load_backbone(dinov3_weights_dir=...) first, "
"or pass dinov3_weights_dir= to from_pretrained()."
)
device = pixel_values.device
# Build grid on first call (avoids meta-device issues during from_pretrained).
if self._grid_points is None:
res = self.config.image_resolution
ps = self.config.patch_size
coords = np.linspace(1, res - 2, res // ps, dtype=int)
object.__setattr__(self, "_grid_points",
torch.tensor([(y, x) for y in coords for x in coords]))
prompts = [self._grid_points.to(device) for _ in range(pixel_values.shape[0])]
with torch.no_grad():
backbone_out = self._image_encoder(pixel_values)
feature_maps = backbone_out["feature_maps"].to(device)
class_tokens = backbone_out["text_aligned_class_tokens"].to(device)
outputs = self.region_encoder(feature_maps, prompts, aggregate_tokens=aggregate_tokens)
outputs["class_tokens"] = class_tokens
if texts is not None:
if self._text_encoder is None:
raise RuntimeError("Text encoder not loaded. Call load_backbone() first.")
outputs["text_encodings"] = self._text_encoder(texts)
return outputs
|