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"""R6 battery: cross-view predictable (content) subspace projection.
Views of the same scene share content and differ in style. Directions that
maximize between-scene over within-scene variance are estimated from
within-modality orbit structure alone (no pairs anywhere), then states are
projected onto the top content directions. Hidden pairs are used only to
score the cross-modal effect.
Note the contrast with population whitening, which was destructive: the
generalized eigenproblem whitens the within-scene (view-noise) covariance,
not the total covariance, so directions where redescriptions of the same
scene agree are amplified rather than equalized away.
"""
from __future__ import annotations
import argparse
import json
import numpy as np
import torch
import torch.nn.functional as F
from scipy.linalg import eigh
from .common import read_json, write_json
from .io import load_feature_pair, select_rows
from .manifold_gate import all_transposition_delta_mse, standardize_relation
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--manifest", default="artifacts/manifest.json")
parser.add_argument("--vision", default="artifacts/vision.pt")
parser.add_argument("--text", default="artifacts/text.pt")
parser.add_argument("--text-orbits", default="artifacts/text_orbits_qwen0p5b.pt")
parser.add_argument("--vg-vision", default="artifacts/vg_5k/vision_features.pt")
parser.add_argument("--vg-text", default="artifacts/vg_5k/text_features.pt")
parser.add_argument(
"--vg-ground-truth", default="artifacts/vg_5k/ground_truth.private.jsonl"
)
parser.add_argument("--samples", type=int, default=512)
parser.add_argument("--dims", default="8,16,32,64,128,256")
parser.add_argument("--shrinkage", type=float, default=0.05)
parser.add_argument(
"--output", default="artifacts/manifold_gate/content_projection.json"
)
return parser.parse_args()
def content_directions(
views: torch.Tensor, shrinkage: float
) -> tuple[np.ndarray, np.ndarray]:
"""Generalized eigenvectors of between-scene vs within-scene covariance.
views: [scenes, views_per_scene, dim], any consistent preprocessing.
Returns (mean, directions) with directions sorted by decreasing ratio.
"""
flat = views.reshape(-1, views.shape[-1]).double().numpy()
mean = flat.mean(0)
scene_means = views.double().mean(1).numpy()
within = views.double().numpy() - scene_means[:, None, :]
within = within.reshape(-1, views.shape[-1])
sigma_within = within.T @ within / max(len(within) - 1, 1)
centered_means = scene_means - mean
sigma_between = (
centered_means.T @ centered_means / max(len(centered_means) - 1, 1)
)
trace_scale = np.trace(sigma_within) / len(sigma_within)
regularized = sigma_within + shrinkage * trace_scale * np.eye(len(sigma_within))
values, vectors = eigh(sigma_between, regularized)
order = np.argsort(values)[::-1]
return mean, vectors[:, order]
def project(
states: torch.Tensor, mean: np.ndarray, directions: np.ndarray, dims: int
) -> torch.Tensor:
basis = torch.from_numpy(directions[:, :dims]).double()
centered = states.double() - torch.from_numpy(mean).double()
return F.normalize(centered @ basis, dim=-1)
def relation_spearman(text_states: torch.Tensor, visual_states: torch.Tensor) -> float:
text_relation = text_states @ text_states.T
visual_relation = visual_states @ visual_states.T
mask = ~torch.eye(len(text_relation), dtype=torch.bool)
t, v = text_relation[mask], visual_relation[mask]
ranks = torch.stack(
[t.argsort().argsort().double(), v.argsort().argsort().double()]
)
return float(torch.corrcoef(ranks)[0, 1])
def improving_fraction(
text_states: torch.Tensor, visual_states: torch.Tensor
) -> float:
text_field, _, _ = standardize_relation(text_states @ text_states.T)
visual_field, _, _ = standardize_relation(visual_states @ visual_states.T)
delta = all_transposition_delta_mse(text_field, visual_field)
upper = torch.triu(torch.ones_like(delta, dtype=torch.bool), diagonal=1)
return float((delta[upper] < 0).double().mean())
def flickr_battery(args: argparse.Namespace, dims: list[int]) -> dict:
manifest = read_json(args.manifest)
vision, _, vision_lookup, _ = load_feature_pair(args.vision, args.text)
orbits = torch.load(args.text_orbits, map_location="cpu", weights_only=False)
lookup = {int(row): i for i, row in enumerate(orbits["rows"])}
features = F.normalize(orbits["features"].double(), dim=-1)
train_rows = [int(r) for r in manifest["text_only_train"]]
test_rows = manifest["test"][: args.samples]
train_views = features[[lookup[r] for r in train_rows]]
mean, directions = content_directions(train_views, args.shrinkage)
visual = select_rows(vision["features"], vision_lookup, test_rows).double()
visual = F.normalize(visual, dim=-1)
test_views = features[[lookup[int(r)] for r in test_rows]]
orbit_mean = F.normalize(test_views.mean(1), dim=-1)
single = test_views[:, 0]
report = {
"baseline_orbit_mean": {
"spearman": relation_spearman(orbit_mean, visual),
"improving_fraction": improving_fraction(orbit_mean, visual),
},
"baseline_single": {
"spearman": relation_spearman(F.normalize(single, dim=-1), visual),
"improving_fraction": improving_fraction(
F.normalize(single, dim=-1), visual
),
},
"projected": {},
}
for k in dims:
projected_mean = project(test_views.mean(1), mean, directions, k)
projected_single = project(single, mean, directions, k)
report["projected"][k] = {
"orbit_mean_spearman": relation_spearman(projected_mean, visual),
"orbit_mean_improving_fraction": improving_fraction(
projected_mean, visual
),
"single_spearman": relation_spearman(projected_single, visual),
}
return report
def vg_battery(args: argparse.Namespace, dims: list[int]) -> dict:
vision = torch.load(args.vg_vision, map_location="cpu", weights_only=False)
text = torch.load(args.vg_text, map_location="cpu", weights_only=False)
pairs = [
json.loads(line)
for line in open(args.vg_ground_truth, encoding="utf-8")
if line.strip()
]
vision_index = {node: i for i, node in enumerate(vision["node_ids"])}
text_index = {node: i for i, node in enumerate(text["node_ids"])}
vision_order = [vision_index[p["vision_node_id"]] for p in pairs]
text_order = [text_index[p["text_node_id"]] for p in pairs]
visual_views = F.normalize(vision["region_features"].double(), dim=-1)[
vision_order
]
text_views = F.normalize(text["region_features"].double(), dim=-1)[text_order]
visual_mean, visual_directions = content_directions(visual_views, args.shrinkage)
text_mean, text_directions = content_directions(text_views, args.shrinkage)
generator = torch.Generator().manual_seed(0)
subset = torch.randperm(len(visual_views), generator=generator)[: args.samples]
visual_node = F.normalize(visual_views[subset].mean(1), dim=-1)
text_node = F.normalize(text_views[subset].mean(1), dim=-1)
report = {
"baseline": {
"spearman": relation_spearman(text_node, visual_node),
"improving_fraction": improving_fraction(text_node, visual_node),
},
"projected": {},
}
for k in dims:
projected_text = project(
text_views[subset].mean(1), text_mean, text_directions, k
)
projected_visual = project(
visual_views[subset].mean(1), visual_mean, visual_directions, k
)
report["projected"][k] = {
"both_sides_spearman": relation_spearman(projected_text, projected_visual),
"both_sides_improving_fraction": improving_fraction(
projected_text, projected_visual
),
"text_only_spearman": relation_spearman(projected_text, visual_node),
}
return report
def main() -> None:
args = parse_args()
dims = [int(d) for d in args.dims.split(",")]
report = {
"protocol": (
"Content directions maximize between-scene over within-scene "
"variance of view states, fitted per modality on unpaired "
"orbit structure only. Hidden pairs score the effect."
),
"flickr": flickr_battery(args, dims),
"vg_region_closed": vg_battery(args, dims),
}
write_json(args.output, report)
print(json.dumps(report, indent=2))
if __name__ == "__main__":
main()
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