File size: 4,026 Bytes
6b1a775 | 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 | #!/usr/bin/env python3
"""
Add height/width columns to nastol-images-full dataset
"""
import argparse
import os
import logging
import sys
from pathlib import Path
logging.basicConfig(
level=logging.INFO,
format='[%(asctime)s] %(levelname)s: %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
stream=sys.stdout,
force=True
)
logger = logging.getLogger(__name__)
from datasets import load_dataset
from huggingface_hub import HfApi
import pyarrow as pa
import pyarrow.parquet as pq
def main():
ap = argparse.ArgumentParser()
ap.add_argument('--input-dataset', type=str, default='vlordier/nastol-images-full')
ap.add_argument('--output-dataset', type=str, default='vlordier/nastol-images-full')
ap.add_argument('--split', type=str, default='train')
ap.add_argument('--shard-index', type=int, default=0)
ap.add_argument('--num-shards', type=int, default=1)
ap.add_argument('--batch-size', type=int, default=1000)
args = ap.parse_args()
logger.info("="*60)
logger.info("Add Height/Width Columns to Dataset")
logger.info("="*60)
logger.info(f"Arguments: {vars(args)}")
token = os.environ.get('HF_TOKEN')
api = HfApi(token=token)
# Load dataset
logger.info(f"Loading {args.input_dataset}...")
ds = load_dataset(args.input_dataset, split=args.split, streaming=True)
if args.num_shards > 1:
ds = ds.shard(num_shards=args.num_shards, index=args.shard_index)
logger.info(f"Processing shard {args.shard_index+1}/{args.num_shards}")
# Process in batches
buffer = []
batch_count = 0
upload_count = 0
def flush_buffer():
nonlocal buffer, upload_count
if not buffer:
return
# Build columns
image_paths = [b['image_path'] for b in buffer]
images_bytes = [b['image'] for b in buffer]
heights = [b['height'] for b in buffer]
widths = [b['width'] for b in buffer]
table = pa.table({
'image_path': image_paths,
'image': images_bytes,
'height': heights,
'width': widths
})
# Write parquet
local_dir = Path('dimension_batches')
local_dir.mkdir(parents=True, exist_ok=True)
file_name = f"shard-{args.shard_index:03d}-batch-{upload_count:04d}.parquet"
local_path = local_dir / file_name
pq.write_table(table, local_path)
# Upload
path_in_repo = f"data/{file_name}"
logger.info(f"Uploading batch {upload_count} with {len(buffer)} images -> {path_in_repo}")
try:
api.upload_file(
path_or_fileobj=str(local_path),
path_in_repo=path_in_repo,
repo_id=args.output_dataset,
repo_type='dataset',
token=token
)
logger.info("✓ Uploaded")
except Exception as e:
logger.error(f"Upload failed: {e}")
buffer.clear()
upload_count += 1
logger.info("Processing images...")
for idx, sample in enumerate(ds):
image = sample['image']
image_path = sample.get('image_path', f'img_{idx:06d}')
# Get dimensions
width, height = image.size
# Store original image bytes
import io
buf = io.BytesIO()
image.save(buf, format='PNG')
image_bytes = buf.getvalue()
buffer.append({
'image_path': image_path,
'image': image_bytes,
'height': height,
'width': width
})
if len(buffer) >= args.batch_size:
flush_buffer()
batch_count += 1
logger.info(f"Processed {batch_count * args.batch_size} images")
# Final flush
flush_buffer()
logger.info(f"✓ Completed shard {args.shard_index}: {batch_count * args.batch_size + len(buffer)} images")
if __name__ == '__main__':
main()
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