File size: 6,160 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 | #!/usr/bin/env python3
"""Build MVEB CompCars subset from a precomputed train/test image split JSON.
Input JSON format:
{
"train": ["1/1103/2011/xxx.jpg", ...],
"test": ["2/2201/2013/yyy.jpg", ...]
}
Output (when executed from MVEB root):
./MVEB-train/CompCars/{query.parquet,candidate.parquet,media-*.parquet,README.md}
./MVEB-test/CompCars/{query.parquet,candidate.parquet,media-*.parquet,README.md}
"""
from __future__ import annotations
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 = "Find a car of the same model, regardless of color."
CANDIDATE_INSTRUCTION = "You are a helpful assistant."
CANDIDATE_TEXT = "Find a car of the same model, regardless of color."
def _make_annotations(rel_paths: List[str]) -> Tuple[List[dict], List[dict]]:
"""Create query/candidate annotations compatible with pack_media_parquet."""
candidate_rows: List[dict] = []
inst_to_ids: Dict[str, List[str]] = {}
for idx, rel_path in enumerate(rel_paths):
parts = rel_path.split("/")
if len(parts) < 4:
raise ValueError(
f"Invalid CompCars relative path: {rel_path!r}. "
"Expected at least make/model/year/file.jpg"
)
inst_id = "_".join(parts[:3])
row = {
"id": rel_path,
"instance_id": inst_id,
"image_path": rel_path,
"instruction": CANDIDATE_INSTRUCTION,
"text": CANDIDATE_TEXT,
}
candidate_rows.append(row)
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:
# Skip single-image identities for query set, aligned with converter/comp_cars.py
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, "CompCars")
if overwrite and out_dir.exists():
shutil.rmtree(out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
unique_paths = sorted(set(rel_paths))
query_rows, candidate_rows = _make_annotations(unique_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="CompCars",
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
default_split_json = script_dir / "train_test_split.json"
if not default_split_json.exists():
# Backward-compatible filename used in some local copies.
default_split_json = script_dir / "train_test_split.json"
parser = argparse.ArgumentParser(description="Process CompCars split JSON to MVEB parquet format.")
parser.add_argument(
"--split-json",
type=Path,
default=default_split_json,
help="JSON containing train/test relative image paths.",
)
parser.add_argument(
"--image-root",
type=Path,
default=root_dir / "source" / "compcars" / "data" / "image",
help="Root directory of raw CompCars images (contains make/model/year).",
)
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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