File size: 8,031 Bytes
f9306c2 3e67073 f9306c2 51bdf55 f9306c2 51bdf55 f9306c2 3e67073 f9306c2 3e67073 f9306c2 3e67073 f9306c2 3e67073 f9306c2 3e67073 f9306c2 3e67073 f9306c2 51bdf55 f9306c2 3e67073 51bdf55 f9306c2 51bdf55 f9306c2 51bdf55 f9306c2 3e67073 f9306c2 | 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 | """Embedding helpers for image records.
The default embedder is intentionally lightweight and deterministic. It gives the
project a testable local baseline while leaving room to plug in learned JEPA features.
"""
from __future__ import annotations
from dataclasses import dataclass
from importlib import import_module
from pathlib import Path
from typing import Any, Protocol
from PIL import Image, ImageDraw, ImageStat
@dataclass(frozen=True)
class PatchInterestMap:
"""Patch-level model interest scores for an image."""
scores: tuple[tuple[float, ...], ...]
image_size: tuple[int, int]
@property
def grid_size(self) -> tuple[int, int]:
"""Return ``(rows, columns)`` for the patch grid."""
return (len(self.scores), len(self.scores[0]) if self.scores else 0)
class ImageEmbedder(Protocol):
"""Protocol for objects that turn image paths into numeric vectors."""
def embed_image(self, image_path: Path) -> tuple[float, ...]:
"""Return an embedding vector for an image file."""
class ColorStatsEmbedder:
"""Embed images with normalized RGB mean and standard deviation features."""
def embed_image(self, image_path: Path) -> tuple[float, ...]:
"""Return six normalized color-statistics features for an image."""
with Image.open(image_path) as image:
rgb_image = image.convert("RGB")
stat = ImageStat.Stat(rgb_image)
means = tuple(channel / 255.0 for channel in stat.mean)
stddevs = tuple(channel / 255.0 for channel in stat.stddev)
return means + stddevs
class MissingImageError(RuntimeError):
"""Raised when a record cannot be embedded because no image path is available."""
class JepaDependencyError(RuntimeError):
"""Raised when optional JEPA dependencies are not installed."""
class IJepaImageEmbedder:
"""Embed images with a Hugging Face I-JEPA vision encoder."""
def __init__(
self,
*,
model_id: str = "facebook/ijepa_vith14_1k",
device: str | None = None,
) -> None:
"""Load the I-JEPA processor and model lazily at embedder construction time."""
self.model_id = model_id
self._torch = _import_optional("torch")
transformers = _import_optional("transformers")
_quiet_transformers_logging(transformers)
self._processor = transformers.AutoProcessor.from_pretrained(model_id)
self._model = transformers.AutoModel.from_pretrained(model_id)
self._device = device or ("cuda" if self._torch.cuda.is_available() else "cpu")
self._model.to(self._device)
self._model.eval()
def embed_image(self, image_path: Path) -> tuple[float, ...]:
"""Return a pooled I-JEPA feature vector for an image."""
rgb_image, outputs = self._encode_image(image_path)
rgb_image.close()
pooled = _mean_pool_features(outputs.last_hidden_state)
return tuple(float(value) for value in pooled.squeeze(0).detach().cpu().tolist())
def patch_interest_map(self, image_path: Path) -> PatchInterestMap:
"""Return normalized patch-interest scores from I-JEPA token activations."""
rgb_image, outputs = self._encode_image(image_path)
scores = _tokens_to_patch_scores(outputs.last_hidden_state, self._torch)
image_size = rgb_image.size
rgb_image.close()
return PatchInterestMap(scores=scores, image_size=image_size)
def render_patch_attention_overlay(
self,
image_path: Path,
*,
alpha: int = 135,
) -> Image.Image:
"""Render a heatmap overlay for the patches with strongest activations."""
interest_map = self.patch_interest_map(image_path)
return render_patch_interest_overlay(image_path, interest_map, alpha=alpha)
def _encode_image(self, image_path: Path) -> tuple[Image.Image, Any]:
with Image.open(image_path) as image:
rgb_image = image.convert("RGB")
encoded = self._processor(rgb_image, return_tensors="pt").to(self._model.device)
with self._torch.no_grad():
outputs = self._model(**encoded)
return rgb_image, outputs
def render_patch_interest_heatmap(
interest_map: PatchInterestMap,
*,
alpha: int = 135,
) -> Image.Image:
"""Render patch scores as a transparent red/yellow heatmap."""
width, height = interest_map.image_size
rows, columns = interest_map.grid_size
heatmap = Image.new("RGBA", (width, height), (0, 0, 0, 0))
if rows == 0 or columns == 0:
return heatmap
draw = ImageDraw.Draw(heatmap, "RGBA")
for row_index, row in enumerate(interest_map.scores):
for column_index, score in enumerate(row):
x0 = round(column_index * width / columns)
x1 = round((column_index + 1) * width / columns)
y0 = round(row_index * height / rows)
y1 = round((row_index + 1) * height / rows)
draw.rectangle((x0, y0, x1, y1), fill=(*_score_to_heat_color(score), alpha))
return heatmap
def render_patch_interest_overlay(
image_path: Path,
interest_map: PatchInterestMap,
*,
alpha: int = 135,
) -> Image.Image:
"""Overlay a patch-interest heatmap on top of the source image."""
with Image.open(image_path) as image:
base = image.convert("RGBA")
heatmap = render_patch_interest_heatmap(interest_map, alpha=alpha)
return Image.alpha_composite(base, heatmap)
def _mean_pool_features(features: Any) -> Any:
"""Pool token/time dimensions while preserving the final feature dimension."""
if features.ndim <= 2:
return features
return features.mean(dim=tuple(range(1, features.ndim - 1)))
def _tokens_to_patch_scores(features: Any, torch: Any) -> tuple[tuple[float, ...], ...]:
"""Convert model token features to a normalized square patch-score grid."""
token_features = features.squeeze(0).detach()
token_count = int(token_features.shape[0])
if _is_square(token_count - 1):
token_features = token_features[1:]
token_count -= 1
if not _is_square(token_count):
msg = f"Cannot infer a square patch grid from {token_count} visual tokens."
raise RuntimeError(msg)
scores = torch.linalg.vector_norm(token_features.float(), dim=-1)
min_score = scores.min()
score_range = scores.max() - min_score
if float(score_range.detach().cpu()) == 0.0:
normalized = torch.zeros_like(scores)
else:
normalized = (scores - min_score) / score_range
grid_width = int(token_count**0.5)
values = normalized.reshape(grid_width, grid_width).detach().cpu().tolist()
return tuple(tuple(float(value) for value in row) for row in values)
def _is_square(value: int) -> bool:
if value <= 0:
return False
root = int(value**0.5)
return root * root == value
def _score_to_heat_color(score: float) -> tuple[int, int, int]:
clamped = max(0.0, min(1.0, score))
red = 255
green = int(round(40 + 190 * clamped))
blue = int(round(30 * (1.0 - clamped)))
return (red, green, blue)
def embed_record_image(image_path: Path | None, embedder: ImageEmbedder) -> tuple[float, ...]:
"""Embed a record image or raise a clear error when the path is missing."""
if image_path is None:
raise MissingImageError("Record has no image path to embed.")
return embedder.embed_image(image_path)
def _import_optional(module_name: str) -> Any:
try:
return import_module(module_name)
except ImportError as error:
msg = (
"I-JEPA dependencies are missing. Install them with "
"`uv sync --extra ijepa --dev` (or `make sync-ijepa`)."
)
raise JepaDependencyError(msg) from error
def _quiet_transformers_logging(transformers: Any) -> None:
"""Reduce noisy dev-version Transformers compatibility logging."""
try:
transformers.logging.set_verbosity_error()
except AttributeError:
return
|