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Diffstat (limited to 'worldalign/tier0_pipeline.py')
| -rw-r--r-- | worldalign/tier0_pipeline.py | 310 |
1 files changed, 310 insertions, 0 deletions
diff --git a/worldalign/tier0_pipeline.py b/worldalign/tier0_pipeline.py new file mode 100644 index 0000000..778e20f --- /dev/null +++ b/worldalign/tier0_pipeline.py @@ -0,0 +1,310 @@ +"""Tier 0 pipeline: unpaired corpora to aligned relation fields. + +Consolidates the components that produced the synthetic world's +end-to-end result, each of which was developed and measured separately: + +- watershed object extraction, which splits touching ring members that + connected components merge (exact object count 74.2% to 100%); +- Gestalt appearance grouping, which assembles members into groups by + shared colour, size, and shape rather than by a distance threshold + (exact group count 71.9% to 90.2%); +- size classes from radial extent under one-dimensional k-means per + member count, because area confounds size with shape and the classes + are gap-separated rather than equally populated (52.7% to 87.9%); +- text factor families from mutual exclusivity within a group phrase; +- the cross-modal value correspondence from marginal frequency rank. + +Nothing crosses modalities except the frequency ranking, and the two +corpora it reads are disjoint: no instance appears on both sides. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +import torch +import torch.nn.functional as F +from scipy import ndimage +from scipy.cluster.hierarchy import fcluster, linkage +from sklearn.cluster import KMeans +from tqdm import tqdm + +from .common import read_json, seed_everything, write_json +from .synth_cc_battery import moment_field +from .synth_set_battery import parse_group_phrases +from .synth_towers import load_image +from .tier0_dictionary import partition_text_vocabulary, text_token_statistics + +NUMBER_TO_COUNT = {"two": 2, "three": 3, "four": 4} +SINGULAR = ("a", "an") + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--data-dir", default="artifacts/synth_v1") + parser.add_argument("--split", choices=["val", "test"], default="test") + parser.add_argument("--samples", type=int, default=256) + parser.add_argument("--offset", type=int, default=0) + parser.add_argument("--fit-scenes", type=int, default=1500) + parser.add_argument("--peak-distance", type=int, default=5) + parser.add_argument("--group-threshold", type=float, default=0.35) + parser.add_argument("--views", type=int, default=0, help="0 uses manifest.") + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--output", required=True) + parser.add_argument("--states-output", default="") + return parser.parse_args() + + +def extract_objects(image: torch.Tensor, peak_distance: int) -> list[dict]: + """Foreground objects, splitting touching ones by distance watershed.""" + array = image.permute(1, 2, 0).numpy() + background = np.median(array.reshape(-1, 3), axis=0) + foreground = np.abs(array - background).sum(-1) > 0.12 + distance = ndimage.distance_transform_edt(foreground) + window = 2 * peak_distance + 1 + peaks = ( + distance >= ndimage.maximum_filter(distance, size=window) - 1e-9 + ) & (distance > 1.0) + labels, count = ndimage.label(peaks) + if count == 0: + labels, count = ndimage.label(foreground) + else: + while True: + grown = ndimage.grey_dilation(labels, size=3) + take = (labels == 0) & foreground & (grown > 0) + if not take.any(): + break + labels = np.where(take, grown, labels) + count = labels.max() + objects = [] + for index in range(1, count + 1): + mask = labels == index + area = float(mask.sum()) + if area < 6: + continue + ys, xs = np.nonzero(mask) + centred_y, centred_x = ys - ys.mean(), xs - xs.mean() + covariance = np.cov(np.stack([centred_x, centred_y])) + 1e-6 * np.eye(2) + eigenvalues = np.linalg.eigvalsh(covariance) + eroded = ndimage.binary_erosion(mask) + perimeter = max(float((mask & ~eroded).sum()), 1.0) + box = (xs.max() - xs.min() + 1) * (ys.max() - ys.min() + 1) + objects.append( + { + "rgb": array[mask].mean(0), + "area": area, + "extent": float(np.hypot(centred_y, centred_x).max()), + "shape": np.array( + [ + 4 * np.pi * area / perimeter**2, + 1 - eigenvalues[0] / eigenvalues[1], + area / max(box, 1), + ] + ), + } + ) + return objects + + +def appearance(item: dict) -> np.ndarray: + return np.concatenate( + [item["rgb"] * 3.0, [np.log(item["area"] + 1e-6) * 0.6], item["shape"]] + ) + + +def group_objects(objects: list[dict], threshold: float) -> list[dict]: + """Members of one group share appearance; group by similarity.""" + if not objects: + return [] + if len(objects) == 1: + labels = np.array([0]) + else: + features = np.stack([appearance(item) for item in objects]) + labels = fcluster(linkage(features, "complete"), threshold, "distance") + buckets: dict[int, list[dict]] = {} + for item, label in zip(objects, labels): + buckets.setdefault(int(label), []).append(item) + return [ + { + "rgb": np.mean([item["rgb"] for item in members], axis=0), + "members": len(members), + "extent": float(np.mean([item["extent"] for item in members])), + } + for members in buckets.values() + ] + + +class VisionCoder: + """Colour classes and size classes fitted on the vision corpus alone.""" + + def __init__(self, groups: list[dict], classes: int, seed: int) -> None: + colours = np.stack([group["rgb"] for group in groups]).astype(np.float64) + self.colour_model = KMeans(classes, n_init=10, random_state=seed).fit(colours) + frequency = Counter(self.colour_model.labels_.tolist()) + self.colour_rank = { + label: rank for rank, (label, _) in enumerate(frequency.most_common()) + } + by_count: dict[int, list[float]] = defaultdict(list) + for group in groups: + by_count[min(group["members"], 4)].append(group["extent"]) + self.size_models = {} + for count, extents in by_count.items(): + model = KMeans(3, n_init=10, random_state=seed).fit( + np.asarray(extents, dtype=np.float64)[:, None] + ) + order = np.argsort(model.cluster_centers_[:, 0]) + self.size_models[count] = ( + model, + {int(label): rank for rank, label in enumerate(order)}, + ) + + def encode(self, groups: list[dict], classes: int) -> torch.Tensor: + if not groups: + groups = [{"rgb": np.zeros(3), "members": 1, "extent": 1.0}] + colours = self.colour_model.predict( + np.stack([group["rgb"] for group in groups]).astype(np.float64) + ) + vectors = [] + for group, colour in zip(groups, colours): + model, order = self.size_models[min(group["members"], 4)] + size = order[ + int(model.predict(np.array([[group["extent"]]], dtype=np.float64))[0]) + ] + vectors.append( + factor_vector(self.colour_rank[int(colour)], group["members"], size, classes) + ) + return F.normalize(torch.stack(vectors), dim=-1) + + +def factor_vector(colour: int, count: int, size: int, classes: int) -> torch.Tensor: + vector = torch.zeros(classes + 4 + 3) + vector[colour] = 1.0 + vector[classes + min(count - 1, 3)] = 1.0 + vector[classes + 4 + size] = 1.0 + return vector + + +def encode_caption(caption: str, colour_rank: dict[str, int], classes: int) -> torch.Tensor: + vectors = [] + for phrase in parse_group_phrases(caption): + tokens = phrase.split() + colour = next((colour_rank[t] for t in tokens if t in colour_rank), 0) + count = ( + 1 + if any(token in SINGULAR for token in tokens) + else next( + (NUMBER_TO_COUNT[t] for t in tokens if t in NUMBER_TO_COUNT), 1 + ) + ) + size = 0 if "small" in tokens else (2 if "large" in tokens else 1) + vectors.append(factor_vector(colour, count, size, classes)) + if not vectors: + vectors = [factor_vector(0, 1, 1, classes)] + return F.normalize(torch.stack(vectors), dim=-1) + + +def moment_state(states: torch.Tensor) -> torch.Tensor: + first = states.mean(0) + second = (states[:, :, None] * states[:, None, :]).mean(0).flatten() + return torch.cat([first, second]) + + +def main() -> None: + args = parse_args() + seed_everything(args.seed) + manifest = read_json(Path(args.data_dir, "manifest.json")) + captions = read_json(Path(args.data_dir, "captions.json"))["captions"] + image_dir = Path(manifest["image_dir"]) + views = args.views or manifest["visual_views"] + rows = manifest[args.split][args.offset : args.offset + args.samples] + + families = partition_text_vocabulary( + text_token_statistics(captions, manifest["text_only_train"]) + ) + colour_words = families["colour_words"] + classes = len(colour_words) + colour_rank = {word: rank for rank, word in enumerate(colour_words)} + + fit_groups = [ + group + for row in tqdm( + manifest["vision_only_train"][: args.fit_scenes], desc="fit vision" + ) + for group in group_objects( + extract_objects( + load_image(image_dir / f"scene{row:06d}_v0.png"), args.peak_distance + ), + args.group_threshold, + ) + ] + coder = VisionCoder(fit_groups, classes, args.seed) + + per_view_fields = [] + view_states = [] + for view in range(views): + sets = [ + coder.encode( + group_objects( + extract_objects( + load_image(image_dir / f"scene{row:06d}_v{view}.png"), + args.peak_distance, + ), + args.group_threshold, + ), + classes, + ) + for row in tqdm(rows, desc=f"encode view {view}") + ] + per_view_fields.append(moment_field(sets)) + if view == 0: + view_states = [moment_state(item) for item in sets] + visual_field = torch.stack(per_view_fields).mean(0) + + text_sets = [encode_caption(captions[row][0], colour_rank, classes) for row in rows] + text_field = moment_field(text_sets) + + mask = ~np.eye(len(rows), 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, "rows": rows}, + args.output, + ) + if args.states_output: + torch.save( + { + "vision_states": torch.stack(view_states), + "text_states": torch.stack([moment_state(item) for item in text_sets]), + "rows": rows, + }, + args.states_output, + ) + summary = { + "data_dir": args.data_dir, + "split": args.split, + "samples": len(rows), + "colour_classes": classes, + "text_families": { + key: families[key] for key in ("count_words", "colour_words", "size_words") + }, + "field_correlation_at_truth": correlation, + "note": ( + "The dictionary is derived from disjoint corpora; the " + "correlation is a diagnostic computed with hidden pairs and " + "never used by the pipeline." + ), + } + print(json.dumps({"field_correlation_at_truth": correlation})) + write_json(args.output.replace(".pt", ".json"), summary) + print(f"Wrote {args.output}") + + +if __name__ == "__main__": + main() |