Datasets:
Tasks:
Image-to-3D
Modalities:
Tabular
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
File size: 2,942 Bytes
150d753 | 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 | """High-level API: load stratified metadata + LayeredDepth-Syn rows."""
from __future__ import annotations
from pathlib import Path
from typing import Dict, Iterator, List, Optional, Sequence
from .preprocess import DEFAULT_LAYER_IDS, preprocess_sample
from .stratified_sampling import build_epoch_order, load_manifest, split_order_by_rank
BASE_DATASET = "princeton-vl/LayeredDepth-Syn"
def load_metadata(repo_id: str, *, split: str = "train", token: str | None = None):
from datasets import load_dataset
return load_dataset(repo_id, split=split, token=token)
def load_base_dataset(*, split: str = "train", cache_dir: str | None = None, streaming: bool = False):
from datasets import load_dataset
kwargs = {"split": split, "streaming": streaming}
if cache_dir:
kwargs["cache_dir"] = cache_dir
return load_dataset(BASE_DATASET, **kwargs)
def iter_stratified_epoch(
*,
manifest_path: str | Path,
cache_dir: str | None = None,
seed: int = 42,
epoch: int = 0,
rank: int = 0,
world_size: int = 1,
layer_ids: Sequence[int] = DEFAULT_LAYER_IDS,
selected_layer_ids: Sequence[int] | None = None,
) -> Iterator[dict]:
"""Yield preprocessed samples in stratified epoch order."""
buckets, batch_mix = load_manifest(manifest_path)
order = build_epoch_order(buckets, batch_mix, seed=seed, epoch=epoch)
order = split_order_by_rank(order, rank, world_size)
base = load_base_dataset(split="train", cache_dir=cache_dir, streaming=False)
for row_index in order:
row = base[int(row_index)]
sample = preprocess_sample(
row,
layer_ids=layer_ids,
selected_layer_ids=selected_layer_ids,
)
sample["row_index"] = int(row_index)
sample["epoch"] = int(epoch)
yield sample
def iter_from_hub_metadata(
repo_id: str,
*,
cache_dir: str | None = None,
seed: int = 42,
epoch: int = 0,
rank: int = 0,
world_size: int = 1,
token: str | None = None,
) -> Iterator[dict]:
"""Load metadata from HF hub repo, then stream preprocessed base rows."""
meta = load_metadata(repo_id, token=token)
buckets: Dict[str, List[int]] = {str(i): [] for i in range(1, 5)}
for row in meta:
buckets[str(row["bucket"])].append(int(row["row_index"]))
batch_mix = None
manifest_file = Path(__file__).resolve().parents[1] / "metadata" / "bucket_manifest.json"
if manifest_file.is_file():
_, batch_mix = load_manifest(manifest_file)
order = build_epoch_order(buckets, batch_mix, seed=seed, epoch=epoch)
order = split_order_by_rank(order, rank, world_size)
base = load_base_dataset(split="train", cache_dir=cache_dir, streaming=False)
for row_index in order:
sample = preprocess_sample(base[int(row_index)])
sample["row_index"] = int(row_index)
sample["epoch"] = int(epoch)
yield sample
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