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e1ced61 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 | """CLEVR structured-source adapter.
The adapter joins official ``CLEVR_<split>_questions.json`` rows to
``CLEVR_<split>_scenes.json`` by ``image_index``. Scene objects keep the
released attributes and coordinates while receiving stable ``object:<index>``
semantic IDs; relationship adjacency is rewritten to those IDs so deleting an
object never silently renumbers another.
The public no-images CLEVR archive is supported directly. When the source PNG
is absent, the adapter creates a deterministic fresh scene render from the
structured world. That fallback is fully machine generated and records its
origin in provenance; it never asks a vision-language model to reconstruct the
scene.
"""
from __future__ import annotations
import json
from collections.abc import Iterator, Mapping, Sequence
from pathlib import Path
from typing import Any
from ..dsl.ast import Program
from ..ingest.base import (
AnswerType,
ImageStore,
IngestError,
NormalizedItem,
Policy,
make_item,
)
from .base import (
AdapterError,
CertificateTier,
RawItem,
World,
extract_zip,
http_download,
store_images,
)
SOURCE = "clevr"
WORLD_SCHEMA = "clevr_world_v1"
_VALID_SPLITS = frozenset({"train", "val", "test"})
_COLORS = frozenset({"gray", "red", "blue", "green", "brown", "purple", "cyan", "yellow"})
_MATERIALS = frozenset({"rubber", "metal"})
_SHAPES = frozenset({"cube", "sphere", "cylinder"})
_SIZES = frozenset({"small", "large"})
_RELATIONS = frozenset({"left", "right", "front", "behind"})
class ClevrAdapter:
"""C1 adapter for official CLEVR scenes and native functional programs."""
def __init__(
self,
raw_dir: Path,
store: ImageStore,
*,
revision: str,
render_missing_images: bool = True,
) -> None:
self.raw_dir = Path(raw_dir)
self.store = store
self.revision = revision
self.render_missing_images = render_missing_images
@classmethod
def is_materialized(cls, raw_dir: Path, split: str) -> bool:
if split not in _VALID_SPLITS:
return False
return (
_find_split_file(Path(raw_dir), split, "questions", required=False) is not None
and _find_split_file(Path(raw_dir), split, "scenes", required=False) is not None
)
@classmethod
def materialize(
cls,
raw_dir: Path,
split: str,
*,
source_config: Mapping[str, Any],
expected_sha256: Mapping[str, str] | None = None,
) -> Path:
"""Materialize the official no-images archive when it is not present."""
raw_dir = Path(raw_dir)
if cls.is_materialized(raw_dir, split):
return raw_dir
archive_url = source_config.get("archive_url")
if not isinstance(archive_url, str) or not archive_url:
raise AdapterError(
f"{SOURCE}: missing {split} scenes/questions and no archive_url configured"
)
archive = raw_dir / "clevr-no-images.zip"
http_download(
archive_url,
archive,
expected_sha256=(expected_sha256 or {}).get("archive"),
)
extract_zip(archive, raw_dir)
if not cls.is_materialized(raw_dir, split):
raise AdapterError(f"{SOURCE}: archive does not contain the {split!r} split")
return raw_dir
def iter_base_items(self, split: str) -> Iterator[RawItem]:
if split not in _VALID_SPLITS:
raise AdapterError(f"{SOURCE}: unsupported split {split!r}")
question_path = _find_split_file(self.raw_dir, split, "questions")
scene_path = _find_split_file(self.raw_dir, split, "scenes")
if question_path is None or scene_path is None: # required=True above
raise AdapterError(f"{SOURCE}: split {split!r} is not materialized")
questions = _record_array(question_path, "questions")
scenes = _record_array(scene_path, "scenes")
scene_index: dict[int, Mapping[str, Any]] = {}
for scene in scenes:
image_index = _integer_field(scene, "image_index", context="scene")
if image_index in scene_index:
raise AdapterError(f"{SOURCE}: duplicate scene image_index {image_index}")
scene_index[image_index] = scene
indexed_questions = list(enumerate(questions))
indexed_questions.sort(
key=lambda pair: (
_sort_integer(pair[1].get("question_index"), pair[0]),
pair[0],
)
)
for source_position, question in indexed_questions:
image_index = _integer_field(
question,
"image_index",
context=f"question[{source_position}]",
)
matched_scene = scene_index.get(image_index)
if matched_scene is None:
raise AdapterError(
f"{SOURCE}: question[{source_position}] references missing scene {image_index}"
)
filename = question.get("image_filename") or matched_scene.get("image_filename")
if not isinstance(filename, str) or not filename:
raise AdapterError(f"{SOURCE}: question[{source_position}] has no image_filename")
image = _find_image(self.raw_dir, split, filename)
image_origin = "official_png"
if image is None:
if not self.render_missing_images:
raise AdapterError(f"{SOURCE}: image {filename!r} is absent")
from ..renderers.clevr import ClevrRenderer
assets = ClevrRenderer().render(
world_from_scene(matched_scene),
renderer_id="clevr_train_v1",
seed=image_index,
width=480,
height=320,
)
image_bytes = assets.rgba_png
image_origin = "fresh_scene_render"
else:
image_bytes = image.read_bytes()
question_index = _sort_integer(question.get("question_index"), source_position)
payload = {
"question": dict(question),
"scene": dict(matched_scene),
"image_origin": image_origin,
}
yield RawItem(
source=SOURCE,
split=split,
source_revision=self.revision,
native_id=str(question_index),
payload=payload,
images={"scene": image_bytes},
)
def normalize(self, raw: RawItem) -> NormalizedItem:
question_row = raw.payload.get("question")
scene = raw.payload.get("scene")
if not isinstance(question_row, Mapping) or not isinstance(scene, Mapping):
raise IngestError(f"{SOURCE}/{raw.native_id}: joined question/scene missing")
question = question_row.get("question")
answer = question_row.get("answer")
program = question_row.get("program")
if not isinstance(question, str) or not question.strip():
raise IngestError(f"{SOURCE}/{raw.native_id}: question missing/empty")
if not isinstance(answer, (str, int, bool)) or str(answer).strip() == "":
raise IngestError(f"{SOURCE}/{raw.native_id}: answer missing/empty")
if not isinstance(program, list) or not program:
raise IngestError(f"{SOURCE}/{raw.native_id}: native program missing/empty")
if not raw.images:
raise IngestError(f"{SOURCE}/{raw.native_id}: scene image missing")
paths, shas = store_images(self.store, raw.images)
answer_type, answer_canonical = _answer(answer)
policy: Policy = (
"c1_train_candidate" if raw.split == "train" else "c1_certified_eval_candidate"
)
return make_item(
source=SOURCE,
source_revision=raw.source_revision,
source_config="official",
source_split=raw.split,
source_native_id=raw.native_id,
question=question,
choices=(),
answer_raw=answer,
answer_canonical=answer_canonical,
answer_type=answer_type,
image_paths=paths,
image_sha256=shas,
policy=policy,
native_row=dict(question_row),
extra_provenance={
"program": program,
"scene": dict(scene),
"image_index": question_row.get("image_index"),
"image_filename": question_row.get("image_filename"),
"question_family_index": question_row.get("question_family_index"),
"image_origin": raw.payload.get("image_origin"),
},
)
def build_world(self, item: NormalizedItem) -> World:
scene = item.provenance.get("scene")
if not isinstance(scene, Mapping):
raise AdapterError(f"{SOURCE}/{item.source_native_id}: scene provenance missing")
return world_from_scene(scene)
def get_or_compile_program(self, item: NormalizedItem) -> Program:
from ..dsl.clevrdsl import compile_clevr
return compile_clevr(item, self.build_world(item))
def official_answer(self, item: NormalizedItem) -> str | int | bool:
return item.answer_canonical
def source_certificate_tier(self, item: NormalizedItem) -> CertificateTier:
return "C1_SOURCE_NATIVE"
def world_from_scene(scene: Mapping[str, Any]) -> World:
"""Normalize one official scene into a renderer/executor-ready world."""
raw_objects = scene.get("objects")
if not isinstance(raw_objects, list):
raise AdapterError(f"{SOURCE}: scene objects must be an array")
objects: list[dict[str, Any]] = []
for index, raw in enumerate(raw_objects):
if not isinstance(raw, Mapping):
raise AdapterError(f"{SOURCE}: scene object {index} is not an object")
color = _enum(raw, "color", _COLORS, index)
material = _enum(raw, "material", _MATERIALS, index)
shape = _enum(raw, "shape", _SHAPES, index)
size = _enum(raw, "size", _SIZES, index)
pixel_coords = _coords(raw.get("pixel_coords"), "pixel_coords", index, minimum=2)
coords_3d = _coords(raw.get("3d_coords"), "3d_coords", index, minimum=2)
rotation = raw.get("rotation", 0)
if isinstance(rotation, bool) or not isinstance(rotation, (int, float)):
raise AdapterError(f"{SOURCE}: object {index} rotation is not numeric")
objects.append(
{
"id": f"object:{index}",
"index": index,
"color": color,
"material": material,
"shape": shape,
"size": size,
"rotation": rotation,
"3d_coords": coords_3d,
"pixel_coords": pixel_coords,
}
)
relationships = _relationships(scene.get("relationships"), len(objects))
world: World = {
"world_schema": WORLD_SCHEMA,
"image_index": scene.get("image_index"),
"image_filename": scene.get("image_filename"),
"split": scene.get("split"),
"objects": objects,
"relationships": relationships,
"_node_visibility": {},
}
directions = scene.get("directions")
if isinstance(directions, Mapping):
world["directions"] = dict(directions)
return world
def _relationships(raw: Any, object_count: int) -> dict[str, dict[str, list[str]]]:
if raw is None:
raw = {}
if not isinstance(raw, Mapping):
raise AdapterError(f"{SOURCE}: scene relationships must be an object")
output: dict[str, dict[str, list[str]]] = {}
for relation in sorted(_RELATIONS):
adjacency = raw.get(relation, [[] for _ in range(object_count)])
relation_map: dict[str, list[str]] = {}
if isinstance(adjacency, list):
if len(adjacency) != object_count:
raise AdapterError(
f"{SOURCE}: {relation} adjacency has {len(adjacency)} rows, "
f"expected {object_count}"
)
for index, targets in enumerate(adjacency):
relation_map[f"object:{index}"] = _target_ids(
targets,
object_count,
relation,
index,
)
elif isinstance(adjacency, Mapping):
for index in range(object_count):
oid = f"object:{index}"
targets = adjacency.get(oid, adjacency.get(str(index), []))
relation_map[oid] = _target_ids(targets, object_count, relation, index)
else:
raise AdapterError(f"{SOURCE}: {relation} adjacency is malformed")
output[relation] = relation_map
return output
def _target_ids(raw: Any, count: int, relation: str, source: int) -> list[str]:
if not isinstance(raw, list):
raise AdapterError(f"{SOURCE}: {relation}[{source}] is not an array")
output: list[str] = []
for value in raw:
if isinstance(value, str) and value.startswith("object:"):
try:
index = int(value.split(":", 1)[1])
except ValueError as exc:
raise AdapterError(f"{SOURCE}: malformed object id {value!r}") from exc
elif isinstance(value, int) and not isinstance(value, bool):
index = value
else:
raise AdapterError(f"{SOURCE}: invalid {relation} target {value!r}")
if index < 0 or index >= count:
raise AdapterError(f"{SOURCE}: {relation} target {index} is out of range")
output.append(f"object:{index}")
return sorted(set(output), key=_object_index)
def _record_array(path: Path, key: str) -> list[Mapping[str, Any]]:
try:
payload = json.loads(path.read_text(encoding="utf-8"))
except (OSError, json.JSONDecodeError) as exc:
raise AdapterError(f"{SOURCE}: cannot read {path}: {exc}") from exc
records = payload.get(key) if isinstance(payload, Mapping) else None
if not isinstance(records, list) or any(not isinstance(row, Mapping) for row in records):
raise AdapterError(f"{SOURCE}: {path} has no valid {key!r} array")
return records
def _find_split_file(
raw_dir: Path,
split: str,
kind: str,
*,
required: bool = True,
) -> Path | None:
name = f"CLEVR_{split}_{kind}.json"
matches = sorted(path for path in raw_dir.rglob(name) if path.is_file())
if not matches:
if required:
raise AdapterError(f"{SOURCE}: cannot find {name} under {raw_dir}")
return None
if len(matches) > 1:
raise AdapterError(f"{SOURCE}: multiple files named {name}: {matches}")
return matches[0]
def _find_image(raw_dir: Path, split: str, filename: str) -> Path | None:
direct = (
raw_dir / "images" / split / filename,
raw_dir / "images" / filename,
raw_dir / filename,
)
for path in direct:
if path.is_file():
return path
matches = sorted(path for path in raw_dir.rglob(filename) if path.is_file())
if len(matches) > 1:
raise AdapterError(f"{SOURCE}: multiple images named {filename}: {matches}")
return matches[0] if matches else None
def _answer(answer: str | int | bool) -> tuple[AnswerType, str | int | bool]:
if isinstance(answer, bool):
return "boolean", "yes" if answer else "no"
text = str(answer).strip()
if text.lower() in {"yes", "no"}:
return "boolean", text.lower()
try:
return "integer", int(text)
except ValueError:
return "short_text", text
def _integer_field(row: Mapping[str, Any], key: str, *, context: str) -> int:
value = row.get(key)
if isinstance(value, bool) or not isinstance(value, int):
raise AdapterError(f"{SOURCE}: {context} {key} is not an integer")
return value
def _sort_integer(value: Any, fallback: int) -> int:
return value if isinstance(value, int) and not isinstance(value, bool) else fallback
def _enum(
row: Mapping[str, Any],
key: str,
allowed: frozenset[str],
index: int,
) -> str:
value = row.get(key)
if not isinstance(value, str) or value not in allowed:
raise AdapterError(f"{SOURCE}: object {index} has invalid {key} {value!r}")
return value
def _coords(raw: Any, name: str, index: int, *, minimum: int) -> list[int | float]:
if not isinstance(raw, Sequence) or isinstance(raw, (str, bytes)) or len(raw) < minimum:
raise AdapterError(f"{SOURCE}: object {index} {name} is malformed")
if any(isinstance(value, bool) or not isinstance(value, (int, float)) for value in raw):
raise AdapterError(f"{SOURCE}: object {index} {name} is not numeric")
return list(raw)
def _object_index(node_id: str) -> int:
return int(node_id.split(":", 1)[1])
__all__ = ["ClevrAdapter", "SOURCE", "WORLD_SCHEMA", "world_from_scene"]
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