File size: 5,484 Bytes
44a7ed3 | 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 | #!/usr/bin/env python3
"""Build MVEB Inshop subset from a precomputed train/test split JSON."""
from __future__ import annotations
import os
import argparse
import json
import shutil
import sys
from pathlib import Path
from typing import Dict, List, Tuple
_SCRIPTS_ROOT = Path(__file__).resolve().parent.parent
if str(_SCRIPTS_ROOT) not in sys.path:
sys.path.insert(0, str(_SCRIPTS_ROOT))
from pack_media_parquet import pack_dataset_with_media, resolve_split_output_dir
QUERY_INSTRUCTION = "Represent the image with the following text."
QUERY_TEXT = "Retrieve all images that the same outfit from different views"
CANDIDATE_INSTRUCTION = "You are a helpful assistant."
CANDIDATE_TEXT = ""
def _instance_id_from_path(rel_path: str) -> str:
"""Extract item id from Inshop path like img/.../id_00000001/xx.jpg."""
parts = rel_path.split("/")
for part in parts:
if part.startswith("id_"):
return part
if len(parts) >= 2:
return parts[-2]
raise ValueError(f"Invalid Inshop path: {rel_path}")
def _make_annotations(rel_paths: List[str]) -> Tuple[List[dict], List[dict]]:
candidate_rows: List[dict] = []
inst_to_ids: Dict[str, List[str]] = {}
for idx, rel_path in enumerate(sorted(set(rel_paths))):
inst_id = _instance_id_from_path(rel_path)
candidate_rows.append(
{
"id": rel_path,
"instance_id": inst_id,
"image_path": rel_path,
"instruction": CANDIDATE_INSTRUCTION,
"text": CANDIDATE_TEXT,
}
)
inst_to_ids.setdefault(inst_id, []).append(rel_path)
query_rows: List[dict] = []
for row in candidate_rows:
pos_ids = [x for x in inst_to_ids[row["instance_id"]] if x != row["id"]]
if not pos_ids:
continue
query_rows.append(
{
"id": row["id"],
"instance_id": row["instance_id"],
"image_path": row["image_path"],
"instruction": QUERY_INSTRUCTION,
"text": QUERY_TEXT,
"pos_ids": pos_ids,
}
)
return query_rows, candidate_rows
def _process_one_split(
split_name: str,
rel_paths: List[str],
image_root: Path,
output_root: Path,
overwrite: bool,
media_rows_per_shard: int,
row_group_size: int,
num_workers: int,
) -> None:
out_dir = resolve_split_output_dir(output_root, split_name, "Inshop")
if overwrite and out_dir.exists():
shutil.rmtree(out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
query_rows, candidate_rows = _make_annotations(rel_paths)
stats = pack_dataset_with_media(
query_annotations=query_rows,
candidate_annotations=candidate_rows,
image_dir=str(image_root),
output_dir=str(out_dir),
media_rows_per_shard=media_rows_per_shard,
row_group_size=row_group_size,
num_workers=num_workers,
dataset_name="Inshop",
data_split=split_name,
write_subset_readme=True,
show_progress=True,
)
print(
f"[{split_name}] done: media={stats['num_media']}, "
f"query={stats['num_query']}, candidate={stats['num_candidate']}, "
f"shards={stats['num_shards']} -> {out_dir}"
)
def main() -> None:
script_dir = Path(__file__).resolve().parent
root_dir = script_dir.parent.parent.parent
parser = argparse.ArgumentParser(description="Process Inshop split JSON to MVEB parquet format.")
parser.add_argument(
"--split-json",
type=Path,
default=script_dir / "train_test_split.json",
help="JSON containing train/test relative image paths.",
)
parser.add_argument(
"--image-root",
type=Path,
default=root_dir / "source" / "inshop",
help="Root directory containing img_highres/.",
)
parser.add_argument(
"--output-root",
type=Path,
default=root_dir,
help="Output MVEB root directory (contains train/ and test/).",
)
parser.add_argument("--overwrite", action="store_true", help="Delete existing output split dir before writing.")
parser.add_argument("--media-rows-per-shard", type=int, default=5000)
parser.add_argument("--row-group-size", type=int, default=100)
parser.add_argument("--num-workers", type=int, default=1)
args = parser.parse_args()
if not args.split_json.exists():
raise FileNotFoundError(f"split json not found: {args.split_json}")
if not args.image_root.exists():
raise FileNotFoundError(f"image root not found: {args.image_root}")
with args.split_json.open("r", encoding="utf-8") as f:
split_data = json.load(f)
for key in ("train", "test"):
if key not in split_data or not isinstance(split_data[key], list):
raise ValueError(f"split json must contain key {key!r} with a list value")
_process_one_split(
"train",
split_data["train"],
args.image_root,
args.output_root,
args.overwrite,
args.media_rows_per_shard,
args.row_group_size,
args.num_workers,
)
_process_one_split(
"test",
split_data["test"],
args.image_root,
args.output_root,
args.overwrite,
args.media_rows_per_shard,
args.row_group_size,
args.num_workers,
)
if __name__ == "__main__":
main()
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