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"""Is Visual Genome capped, or is our part correspondence the problem?
Visual Genome's cross-modal anchor bound is 0.336 against a within-modality
ceiling of 0.805 for text and 0.994 for vision. Both sides identify their own
scenes; they disagree about which scenes resemble each other. That leaves two
very different explanations and they call for opposite responses.
Either the two encoders would agree if only their parts were put in
correspondence -- our spectral segments and the annotators' phrases carve the
image differently, and the aggregation has to guess which goes with which --
or they would not, because a DINOv2 patch descriptor and a written phrase
simply do not covary over what makes two regions alike.
Visual Genome settles it, because each region box is index-aligned with its own
description. Encode the crop inside each box and the phrase describing it, and
the parts are in correspondence **by annotation**. If the cross-modal bound
jumps, part correspondence is the whole problem and it is an engineering target.
If it stays near 0.336, the encoders do not agree at the part level either, and
no aggregation will rescue this corpus.
DIAGNOSTIC ONLY. Box-to-phrase alignment is cross-modal supervision that a
deployed system would not have. This measures a ceiling, it is not a method.
"""
from __future__ import annotations
import argparse
import json
import re
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from scipy.optimize import linear_sum_assignment
from tqdm import tqdm
from transformers import AutoImageProcessor, AutoModel
from .common import seed_everything, write_json
from .natural_families import load_phrases
from .natural_pipeline import content_directions, word_vectors
from .synth_cc_battery import moment_field
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--vg-dir", default="artifacts/vg_5k")
parser.add_argument("--image-cache", default="/tmp/yurenh2-worldalign-vg-images")
parser.add_argument("--model", default="facebook/dinov2-base")
parser.add_argument("--crop-size", type=int, default=126)
parser.add_argument("--samples", type=int, default=256)
parser.add_argument("--fit-scenes", type=int, default=900)
parser.add_argument("--word-vectors", type=int, default=128)
parser.add_argument("--min-count", type=int, default=60)
parser.add_argument("--max-vocabulary", type=int, default=600)
parser.add_argument("--context-window", type=int, default=2)
parser.add_argument("--keep", type=int, default=64)
parser.add_argument("--shrinkage", type=float, default=0.05)
parser.add_argument("--weight-power", type=float, default=0.5)
parser.add_argument("--batch-size", type=int, default=64)
parser.add_argument("--device", default="cuda:3")
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--output", default="artifacts/vg_5k/region_oracle.json")
return parser.parse_args()
def standardise(matrix: np.ndarray) -> np.ndarray:
mask = ~np.eye(len(matrix), dtype=bool)
values = matrix[mask]
out = (matrix - values.mean()) / values.std()
np.fill_diagonal(out, 0.0)
return out
def anchor_bound(visual: np.ndarray, text: np.ndarray, repeats: int = 5) -> float:
size = len(visual)
scores = []
for repeat in range(repeats):
generator = np.random.default_rng(repeat)
shuffle = generator.permutation(size)
half = size // 2
anchors, probe = shuffle[:half], shuffle[half:]
order = generator.permutation(len(probe))
def profile(field, rows):
block = field[np.ix_(rows, anchors)]
block = block - block.mean(1, keepdims=True)
return block / block.std(1, keepdims=True).clip(1e-9)
similarity = profile(visual, probe) @ profile(text, probe[order]).T / half
_, columns = linear_sum_assignment(-similarity)
scores.append(float((order[columns] == np.arange(len(probe))).mean()))
return float(np.mean(scores))
@torch.inference_mode()
def main() -> None:
args = parse_args()
seed_everything(args.seed)
vg_dir = Path(args.vg_dir)
truth = [json.loads(line) for line in
(vg_dir / "ground_truth.private.jsonl").read_text().splitlines() if line.strip()]
vision = {json.loads(line)["node_id"]: json.loads(line) for line in
(vg_dir / "vision_nodes.private.jsonl").read_text().splitlines() if line.strip()}
records = {json.loads(line)["node_id"]: json.loads(line) for line in
(vg_dir / "text_nodes.jsonl").read_text().splitlines() if line.strip()}
pairs = [p for p in truth
if p["vision_node_id"] in vision and p["text_node_id"] in records]
needed = pairs[: args.samples + args.fit_scenes]
print(f"{len(needed)} scenes", flush=True)
processor = AutoImageProcessor.from_pretrained(args.model)
model = AutoModel.from_pretrained(args.model, torch_dtype=torch.float32).to(args.device)
model.eval()
mean = torch.tensor(processor.image_mean).view(3, 1, 1)
std = torch.tensor(processor.image_std).view(3, 1, 1)
# one crop per annotated region, in the order the phrases are given
crops, owners = [], []
for index, pair in enumerate(tqdm(needed, desc="crop")):
record = vision[pair["vision_node_id"]]
phrases = records[pair["text_node_id"]]["region_closed"]
path = Path(args.image_cache, f"{record['source_image_id']}.jpg")
if not path.exists():
continue
with Image.open(path) as handle:
picture = handle.convert("RGB")
width, height = picture.size
for position, region in enumerate(record["regions"][: len(phrases)]):
x0 = max(0, min(int(region["x"]), width - 2))
y0 = max(0, min(int(region["y"]), height - 2))
x1 = max(x0 + 2, min(int(region["x"] + region["width"]), width))
y1 = max(y0 + 2, min(int(region["y"] + region["height"]), height))
patch = picture.crop((x0, y0, x1, y1)).resize(
(args.crop_size, args.crop_size), Image.BILINEAR
)
crops.append(torch.from_numpy(np.asarray(patch).copy())
.permute(2, 0, 1).float() / 255.0)
owners.append((index, position))
features = []
for start in tqdm(range(0, len(crops), args.batch_size), desc="encode"):
batch = torch.stack(crops[start : start + args.batch_size])
normalised = ((batch - mean) / std).to(args.device)
hidden = model(pixel_values=normalised, return_dict=True).last_hidden_state
features.append(hidden[:, 0].float().cpu().numpy())
features = np.concatenate(features)
print(f"{len(features)} region crops encoded", flush=True)
vectors = word_vectors(load_phrases(vg_dir / "text_nodes.jsonl", "region_closed"), args)
def phrase_vector(phrase: str) -> np.ndarray | None:
hits = [vectors[t] for t in re.findall(r"[a-z]+", phrase.lower()) if t in vectors]
return np.mean(hits, axis=0) if hits else None
# collect per scene, keeping only regions whose phrase encodes -- so the
# two sides carry exactly the same parts in exactly the same order
vision_parts: dict[int, list] = {}
text_parts: dict[int, list] = {}
for (index, position), feature in zip(owners, features):
phrase = records[needed[index]["text_node_id"]]["region_closed"][position]
encoded = phrase_vector(phrase)
if encoded is None:
continue
vision_parts.setdefault(index, []).append(feature.astype(np.float64))
text_parts.setdefault(index, []).append(encoded)
usable = [i for i in sorted(vision_parts) if len(vision_parts[i]) >= 4]
evaluated = [i for i in usable if i < args.samples][: args.samples]
fit = [i for i in usable if i >= args.samples]
print(f"{len(evaluated)} evaluated, {len(fit)} for fitting", flush=True)
results = {}
for name, parts in (("vision", vision_parts), ("text", text_parts)):
fitted = content_directions([np.stack(parts[i]) for i in fit], args.shrinkage)
centre, basis, values = fitted
width = min(args.keep, basis.shape[1])
scale = values[:width] ** args.weight_power
results[name] = [
F.normalize(torch.tensor(
(np.stack(parts[i]) - centre) @ basis[:, :width] * scale,
dtype=torch.float32), dim=-1)
for i in evaluated
]
visual_field = standardise(moment_field(results["vision"]).double().numpy())
text_field = standardise(moment_field(results["text"]).double().numpy())
mask = ~np.eye(len(evaluated), dtype=bool)
summary = {
"protocol": (
"DIAGNOSTIC, NOT A METHOD. Each annotated region box is encoded as a "
"crop and paired with its own description, so the two modalities "
"carry the same parts in the same order. That box-to-phrase link is "
"cross-modal supervision a deployed system would not have."
),
"scenes": len(evaluated),
"mean_parts": float(np.mean([len(vision_parts[i]) for i in evaluated])),
"correlation": float(np.corrcoef(visual_field[mask], text_field[mask])[0, 1]),
"anchor_bound_with_part_oracle": anchor_bound(visual_field, text_field),
"anchor_bound_unsupervised_reference": 0.336,
"within_modality_ceiling_text": 0.805,
"reading": (
"A large jump means part correspondence is the whole problem and is "
"an engineering target. Staying near 0.336 means the two encoders "
"do not covary at the part level either, and no aggregation "
"rescues this corpus."
),
}
print(json.dumps(summary, indent=2))
write_json(args.output, summary)
if __name__ == "__main__":
main()
|
