Upload hf_job_sam3d.py with huggingface_hub

hf_job_sam3d.py

@@ -22,7 +22,8 @@ logger = logging.getLogger(__name__)
 
 import numpy as np
 import torch
-from datasets import load_dataset, Dataset as HFDataset, Features, Value
+from datasets import load_dataset
+from huggingface_hub import HfApi
 from PIL import Image
 import cv2
 import json
@@ -106,6 +107,8 @@ def main():
     ap.add_argument('--batch-size', type=int, default=4)
     ap.add_argument('--shard-index', type=int, default=0)
     ap.add_argument('--num-shards', type=int, default=1)
+    ap.add_argument('--max-images', type=int, default=8000, help='Limit number of images processed per shard')
+    ap.add_argument('--upload-interval', type=int, default=500, help='Upload partial results every N images')
     args = ap.parse_args()
     
     logger.info(f"Arguments: {vars(args)}")
@@ -135,48 +138,103 @@ def main():
         ds = ds.shard(num_shards=args.num_shards, index=args.shard_index)
         logger.info(f"Using shard {args.shard_index+1}/{args.num_shards}")
     
-    # Process
-    logger.info(f"Processing with batch_size={args.batch_size}")
-    
-    processed_ds = ds.map(
-        process_batch,
-        batched=True,
-        batch_size=args.batch_size,
-        remove_columns=ds.column_names
-    )
-    
-    # Collect results
-    results = []
-    for batch_idx, item in enumerate(processed_ds):
-        results.append(item)
-        
-        if (batch_idx + 1) % 50 == 0:
-            logger.info(f"Processed {batch_idx + 1} images")
-    
-    logger.info(f"✓ Processed {len(results)} images")
-    
-    # Create output dataset
-    features = Features({
-        'image_id': Value('string'),
-        'num_humans': Value('int32'),
-        'sam3d_data': Value('string')
-    })
-    
-    output_ds = HFDataset.from_dict({
-        'image_id': [r['image_id'] for r in results],
-        'num_humans': [r['num_humans'] for r in results],
-        'sam3d_data': [r['sam3d_data'] for r in results]
-    }, features=features)
-    
-    # Upload
-    logger.info(f"Uploading to {args.output_dataset}...")
-    output_ds.push_to_hub(
-        args.output_dataset,
-        split=args.split,
-        token=os.environ.get('HF_TOKEN'),
-        private=True
-    )
-    logger.info("✓ Upload complete")
+    # Prepare incremental upload
+    api = HfApi(token=os.environ.get('HF_TOKEN'))
+    token = os.environ.get('HF_TOKEN')
+    repo_id = args.output_dataset
+    
+    # Load existing image_ids (skip logic)
+    existing_ids = set()
+    try:
+        from datasets import load_dataset as _ld
+        existing = _ld(repo_id, split=args.split)
+        for r in existing:
+            existing_ids.add(r['image_id'])
+        logger.info(f"Loaded {len(existing_ids)} existing image_ids to skip")
+    except Exception:
+        logger.info("No existing output dataset found; starting fresh")
+    
+    # Processing loop with manual batching
+    logger.info(f"Processing with batch_size={args.batch_size}, max_images={args.max_images}, upload_interval={args.upload_interval}")
+    buffer = []  # list of dicts {image_id,num_humans,sam3d_data}
+    total_processed = 0
+    upload_index = 0
+    batch_images = []
+    batch_paths = []
+    
+    def flush_buffer():
+        nonlocal buffer, upload_index
+        if not buffer:
+            return
+        import pyarrow as pa
+        import pyarrow.parquet as pq
+        # Build columns
+        image_ids = [b['image_id'] for b in buffer]
+        num_humans = [b['num_humans'] for b in buffer]
+        sam3d_data = [b['sam3d_data'] for b in buffer]
+        table = pa.table({'image_id': image_ids, 'num_humans': num_humans, 'sam3d_data': sam3d_data})
+        file_name = f"batch-sh{args.shard_index}-u{upload_index:04d}.parquet"
+        local_dir = Path('sam3d_batches')
+        local_dir.mkdir(parents=True, exist_ok=True)
+        local_path = local_dir / file_name
+        pq.write_table(table, local_path)
+        path_in_repo = f"data/{file_name}"
+        logger.info(f"Uploading incremental batch {upload_index} 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=repo_id, repo_type='dataset', token=token)
+            logger.info("✓ Incremental upload committed")
+        except Exception as e:
+            logger.error(f"Incremental upload failed: {e}")
+        buffer.clear()
+        upload_index += 1
+    
+    # Iterate streaming dataset manually
+    current_batch_imgs = []
+    current_batch_paths = []
+    for idx, sample in enumerate(ds):
+        if idx >= args.max_images:
+            break
+        image = sample['image']
+        image_path = sample.get('image_path', None)
+        image_id = Path(image_path).stem if image_path else f"img_{idx:06d}"
+        if image_id in existing_ids:
+            continue
+        current_batch_imgs.append(image)
+        current_batch_paths.append(image_path)
+        # When batch full, process
+        if len(current_batch_imgs) == args.batch_size:
+            batch = {'image': current_batch_imgs, 'image_path': current_batch_paths}
+            batch_result = process_batch(batch)
+            for i in range(len(batch_result['image_id'])):
+                buffer.append({
+                    'image_id': batch_result['image_id'][i],
+                    'num_humans': batch_result['num_humans'][i],
+                    'sam3d_data': batch_result['sam3d_data'][i]
+                })
+                existing_ids.add(batch_result['image_id'][i])
+            total_processed += len(batch_result['image_id'])
+            current_batch_imgs = []
+            current_batch_paths = []
+            if total_processed % 50 == 0:
+                logger.info(f"Processed {total_processed} images")
+            if total_processed % args.upload_interval == 0:
+                flush_buffer()
+    # Process any leftover
+    if current_batch_imgs:
+        batch = {'image': current_batch_imgs, 'image_path': current_batch_paths}
+        batch_result = process_batch(batch)
+        for i in range(len(batch_result['image_id'])):
+            buffer.append({
+                'image_id': batch_result['image_id'][i],
+                'num_humans': batch_result['num_humans'][i],
+                'sam3d_data': batch_result['sam3d_data'][i]
+            })
+            existing_ids.add(batch_result['image_id'][i])
+        total_processed += len(batch_result['image_id'])
+    # Final flush
+    flush_buffer()
+    logger.info(f"✓ Finished shard processing with total images processed: {total_processed}")
+    logger.info("All incremental uploads done.")
 
 
 if __name__ == '__main__':