File size: 3,868 Bytes
e3cb0cb | 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 | """Extract images from ULVR_all parquet files into Monet training layout."""
import argparse
import json
import multiprocessing as mp
import os
import pyarrow.parquet as pq
def build_mapping(jsonl_path: str) -> dict:
mapping = {}
with open(jsonl_path) as f:
for line in f:
d = json.loads(line)
sid = d["metadata"]["sample_id"]
input_path = None
inter_paths = []
for msg in d["data"]:
if msg["role"] == "user":
for c in msg["content"]:
if c["type"] == "image":
input_path = c["image"]
elif msg["role"] == "assistant":
for c in msg["content"]:
if c["type"] == "image":
inter_paths.append(c["image"])
mapping[sid] = (input_path, inter_paths)
return mapping
def process_parquet(args):
parq_path, mapping = args
written = 0
skipped = 0
out_root = process_parquet.out_root
f = pq.ParquetFile(parq_path)
cols = [
"id",
"input_image",
"intermediate_image_1",
"intermediate_image_2",
"intermediate_image_3",
]
for batch in f.iter_batches(batch_size=256, columns=cols):
df = batch.to_pandas()
for _, row in df.iterrows():
sid = row["id"]
if sid not in mapping:
skipped += 1
continue
input_path, inter_paths = mapping[sid]
img = row["input_image"]
if img is not None and img.get("bytes"):
out = os.path.join(out_root, input_path)
os.makedirs(os.path.dirname(out), exist_ok=True)
if not os.path.exists(out) or os.path.getsize(out) != len(img["bytes"]):
with open(out, "wb") as fp:
fp.write(img["bytes"])
written += 1
for i, inter_path in enumerate(inter_paths, start=1):
col = f"intermediate_image_{i}"
img = row.get(col)
if img is None or not hasattr(img, "get"):
continue
b = img.get("bytes")
if not b:
continue
out = os.path.join(out_root, inter_path)
os.makedirs(os.path.dirname(out), exist_ok=True)
if not os.path.exists(out) or os.path.getsize(out) != len(b):
with open(out, "wb") as fp:
fp.write(b)
written += 1
return parq_path, written, skipped
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--jsonl", required=True)
parser.add_argument("--parquet-dir", required=True)
parser.add_argument("--out-root", required=True)
parser.add_argument("--workers", type=int, default=8)
args = parser.parse_args()
mapping = build_mapping(args.jsonl)
print(f"Need to extract images for {len(mapping)} samples", flush=True)
files = sorted(
os.path.join(args.parquet_dir, f)
for f in os.listdir(args.parquet_dir)
if f.endswith(".parquet")
)
if len(files) != 26:
print(f"WARNING: expected 26 parquet shards, found {len(files)}", flush=True)
print(f"{len(files)} parquet files", flush=True)
process_parquet.out_root = args.out_root
pool_args = [(p, mapping) for p in files]
nproc = min(args.workers, len(files))
with mp.Pool(nproc) as pool:
for parq_path, written, skipped in pool.imap_unordered(process_parquet, pool_args):
print(
f" done {os.path.basename(parq_path)}: written={written}, skipped={skipped}",
flush=True,
)
print("All done.", flush=True)
if __name__ == "__main__":
main()
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