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"""Natural-data field pipeline: continuous states and content projection.
Produces the field correlation reported for Visual Genome. Two choices
carry most of it, both measured. States stay continuous -- quantising
segments and phrases into class codes costs more than half the signal,
because relation fields need scene similarity and nothing else. And each
side is projected onto the directions that separate scenes rather than
parts, fitted per modality on scenes outside the evaluated set.
Vision states come from `natural_objects`; language states are
positive-PMI context vectors of the corpus, averaged per phrase. Hidden
pairs are read only to report the correlation.
"""
from __future__ import annotations
import argparse
import json
import re
from collections import Counter
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
from scipy.linalg import eigh
from sklearn.decomposition import TruncatedSVD
from .common import read_json, seed_everything, write_json
from .natural_families import load_phrases, statistics
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("--objects", default="artifacts/vg_5k/natural_objects.pt")
parser.add_argument("--samples", type=int, default=256)
parser.add_argument("--fit-scenes", type=int, default=1200)
parser.add_argument("--word-vectors", type=int, default=48)
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=8)
parser.add_argument("--shrinkage", type=float, default=0.05)
parser.add_argument("--views", type=int, default=1)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--output", default="artifacts/vg_5k/natural_pipeline.pt")
return parser.parse_args()
def word_vectors(
phrases: list[list[str]], args: argparse.Namespace
) -> dict[str, np.ndarray]:
"""Positive-PMI context vectors, reduced by truncated SVD."""
counts = Counter(token for tokens in phrases for token in set(tokens))
vocabulary = [
word
for word, count in counts.most_common(args.max_vocabulary)
if count >= args.min_count
]
_, _, context = statistics(phrases, set(vocabulary), args.context_window)
features = sorted({key for word in vocabulary for key in context[word]})
index = {key: position for position, key in enumerate(features)}
matrix = np.zeros((len(vocabulary), len(features)))
for row, word in enumerate(vocabulary):
for key, value in context[word].items():
matrix[row, index[key]] = value
total = matrix.sum()
expected = matrix.sum(1, keepdims=True) * matrix.sum(0, keepdims=True) / total
pmi = np.log(np.maximum(matrix, 1e-9) / np.maximum(expected, 1e-9))
pmi[matrix == 0] = 0.0
embedding = TruncatedSVD(args.word_vectors, random_state=args.seed).fit_transform(
np.maximum(pmi, 0.0)
)
embedding /= np.linalg.norm(embedding, axis=1, keepdims=True).clip(1e-9)
return {word: embedding[row] for row, word in enumerate(vocabulary)}
def content_directions(
sets: list[np.ndarray], shrinkage: float
) -> tuple[np.ndarray, np.ndarray]:
"""Directions separating scenes more than they separate parts."""
means = np.stack([item.mean(0) for item in sets])
centre = means.mean(0)
within = np.concatenate([item - item.mean(0, keepdims=True) for item in sets])
scatter_within = within.T @ within / max(len(within) - 1, 1)
centred = means - centre
scatter_between = centred.T @ centred / max(len(centred) - 1, 1)
trace = np.trace(scatter_within) / len(scatter_within)
values, vectors = eigh(
scatter_between, scatter_within + shrinkage * trace * np.eye(len(scatter_within))
)
return centre, vectors[:, np.argsort(values)[::-1]]
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(encoding="utf-8")
.splitlines()
if line.strip()
]
records = {
json.loads(line)["node_id"]: json.loads(line)
for line in (vg_dir / "text_nodes.jsonl").read_text(encoding="utf-8").splitlines()
if line.strip()
}
state = torch.load(args.objects, map_location="cpu", weights_only=False)
per_view = state.get("view_segments") or [state["segments"]]
views = min(args.views, len(per_view))
index = {node: position for position, node in enumerate(state["node_ids"])}
vectors = word_vectors(load_phrases(vg_dir / "text_nodes.jsonl", "region_closed"), args)
def language_state(node: str) -> np.ndarray | None:
rows = []
for phrase in records[node]["region_closed"]:
hits = [vectors[token] for token in re.findall(r"[a-z]+", phrase.lower())
if token in vectors]
if hits:
rows.append(np.mean(hits, axis=0))
return np.stack(rows) if rows else None
def vision_state(node: str, view: int) -> np.ndarray | None:
segments = per_view[view][index[node]]
return (
np.stack([item["feature"] for item in segments]).astype(np.float64)
if segments
else None
)
pairs = [
pair
for pair in truth
if pair["vision_node_id"] in index and pair["text_node_id"] in records
]
fit = pairs[args.samples : args.samples + args.fit_scenes]
language_fit = [language_state(p["text_node_id"]) for p in fit]
language_fit = [item for item in language_fit if item is not None and len(item) > 1]
vision_fit = [vision_state(p["vision_node_id"], 0) for p in fit]
vision_fit = [item for item in vision_fit if item is not None and len(item) > 1]
language_centre, language_basis = content_directions(language_fit, args.shrinkage)
vision_centre, vision_basis = content_directions(vision_fit, args.shrinkage)
evaluated = [
pair for pair in pairs[: args.samples]
if language_state(pair["text_node_id"]) is not None
]
keep = args.keep
def project(raw: np.ndarray, centre: np.ndarray, basis: np.ndarray) -> torch.Tensor:
width = min(keep, basis.shape[1])
return F.normalize(
torch.tensor((raw - centre) @ basis[:, :width], dtype=torch.float32), dim=-1
)
text_sets = [
project(language_state(p["text_node_id"]), language_centre, language_basis)
for p in evaluated
]
view_fields = []
for view in range(views):
sets = [
project(vision_state(p["vision_node_id"], view), vision_centre, vision_basis)
for p in evaluated
]
view_fields.append(moment_field(sets))
visual_field = torch.stack(view_fields).mean(0)
text_field = moment_field(text_sets)
mask = ~np.eye(len(evaluated), dtype=bool)
correlation = float(
np.corrcoef(
visual_field.double().numpy()[mask], text_field.double().numpy()[mask]
)[0, 1]
)
torch.save(
{"visual_field": visual_field, "text_field": text_field,
"nodes": [p["vision_node_id"] for p in evaluated]},
args.output,
)
summary = {
"protocol": (
"Continuous states, content projection fitted per modality on "
"scenes outside the evaluated set. Hidden pairs are read only "
"for the correlation."
),
"samples": len(evaluated),
"views": views,
"kept_directions": keep,
"field_correlation_at_truth": correlation,
"recovery_threshold": 0.9,
}
print(json.dumps(summary))
write_json(str(args.output).replace(".pt", ".json"), summary)
if __name__ == "__main__":
main()
|
