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Download src/explicit_learning/sources/clevr.py from sungguk/visual-answerability: direct link, hf CLI and curl.
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https://huggingface.co/datasets/sungguk/visual-answerability/resolve/main/src/explicit_learning/sources/clevr.py
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17.1 kB
| """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 | |
| 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 | |
| ) | |
| 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"] | |