#!/usr/bin/env python3 """Read LocateAnything records and image bytes with Megatron-Energon 7.4.""" from __future__ import annotations import argparse import json from pathlib import Path from typing import Any, Mapping, Sequence from megatron.energon import ( Cooker, DefaultTaskEncoder, FileStore, Sample, WorkerConfig, basic_sample_keys, cooker, edataclass, get_savable_loader, get_train_dataset, stateless, ) @edataclass class LocateAnythingSample(Sample): """One spatial annotation and its encoded image bytes.""" record: dict[str, Any] image: bytes def _record(sample: Mapping[str, Any]) -> dict[str, Any]: payload = sample.get("json") if isinstance(payload, Mapping): record = dict(payload) elif isinstance(payload, (bytes, bytearray, str)): record = json.loads(payload) else: raise ValueError( "Energon sample does not contain a mapping- or JSON-valued 'json' field" ) if not isinstance(record, dict): raise ValueError("LocateAnything record must be a JSON object") if set(record) != {"_source", "image", "query", "task_type"}: raise ValueError("LocateAnything record schema mismatch") return record @stateless def cook_locate_anything( sample: dict[str, Any], **aux: FileStore ) -> LocateAnythingSample: record = _record(sample) image = record["image"] source = image["source"] member_name = image["path"] try: store = aux[source] except KeyError as error: raise ValueError(f"annotation names unknown auxiliary source {source!r}") from error encoded = store.get(member_name, sample) return LocateAnythingSample( **basic_sample_keys(sample), record=record, image=encoded, ) class LocateAnythingTaskEncoder( DefaultTaskEncoder[ LocateAnythingSample, LocateAnythingSample, LocateAnythingSample, LocateAnythingSample, ] ): """Minimal encoder; subclass its packing methods for model token budgets.""" decoder = None def __init__(self) -> None: self.cookers = [Cooker(cook_locate_anything)] super().__init__() @stateless def select_samples_to_pack( self, samples: list[LocateAnythingSample] ) -> list[list[LocateAnythingSample]]: return [[sample] for sample in samples] @stateless def pack_selected_samples( self, samples: list[LocateAnythingSample] ) -> LocateAnythingSample: if len(samples) != 1: raise ValueError("the example encoder only supports singleton packs") return samples[0] def _parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument( "metadataset", type=Path, help="Root, dataset, or view-level metadataset.yaml.", ) parser.add_argument("--samples", type=int, default=3) parser.add_argument("--workers", type=int, default=0) parser.add_argument("--rank", type=int, default=0) parser.add_argument("--world-size", type=int, default=1) return parser def main(argv: Sequence[str] | None = None) -> int: args = _parser().parse_args(argv) worker_config = WorkerConfig( rank=args.rank, world_size=args.world_size, num_workers=args.workers, ) dataset = get_train_dataset( args.metadataset, split_part="train", worker_config=worker_config, batch_size=None, max_samples_per_sequence=1, shuffle_over_epochs_multiplier=1, shuffle_buffer_size=None, task_encoder=LocateAnythingTaskEncoder(), repeat=False, ) loader = get_savable_loader(dataset) for index, sample in enumerate(loader): print( json.dumps( { "sample": index, "sample_id": sample.record["_source"]["sample_id"], "task_type": sample.record["task_type"], "image_bytes": len(sample.image), }, ensure_ascii=False, sort_keys=True, ) ) if index + 1 >= args.samples: break return 0 if __name__ == "__main__": raise SystemExit(main())