Upload hf_job_nsfw.py with huggingface_hub

hf_job_nsfw.py

@@ -0,0 +1,179 @@
+#!/usr/bin/env python3
+"""
+NSFW Classification Job - Process images with EraX YOLO
+Outputs: Per-image NSFW detections with bboxes and confidence scores
+"""
+import argparse
+import os
+from pathlib import Path
+import warnings
+warnings.filterwarnings('ignore')
+import logging
+import sys
+
+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__)
+
+import numpy as np
+import torch
+from datasets import load_dataset, Dataset as HFDataset, Features, Value, Sequence
+from PIL import Image
+import json
+from huggingface_hub import snapshot_download
+from ultralytics import YOLO
+
+
+def process_batch(batch):
+    """Process batch of images with NSFW detection"""
+    images = batch['image']
+    image_paths = batch.get('image_path', [f'img_{i:06d}' for i in range(len(images))])
+    
+    results_list = []
+    
+    # Convert all images to numpy arrays
+    crops = []
+    for image_pil in images:
+        image_rgb = np.array(image_pil.convert('RGB'))
+        crops.append(image_rgb)
+    
+    # Batch inference
+    if crops:
+        try:
+            yolo_results = model(crops, conf=0.2, iou=0.3, verbose=False)
+            
+            for idx, result in enumerate(yolo_results):
+                image_id = Path(image_paths[idx]).stem if image_paths[idx] else f'img_{idx:06d}'
+                img_width, img_height = images[idx].size
+                
+                detections = []
+                if result.boxes:
+                    for box in result.boxes:
+                        class_id = int(box.cls.item())
+                        confidence = box.conf.item()
+                        class_names = ['anus', 'make_love', 'nipple', 'penis', 'vagina']
+                        class_name = class_names[class_id] if class_id < len(class_names) else f'class_{class_id}'
+                        
+                        x1, y1, x2, y2 = box.xyxy[0].tolist()
+                        
+                        detections.append({
+                            'class': class_name,
+                            'confidence': confidence,
+                            'bbox': [x1, y1, x2, y2]
+                        })
+                
+                if not detections:
+                    detections = [{'class': 'safe', 'confidence': 1.0, 'bbox': [0, 0, img_width, img_height]}]
+                
+                results_list.append({
+                    'image_id': image_id,
+                    'detections': detections,
+                    'num_detections': len(detections)
+                })
+        except Exception as e:
+            logger.error(f"NSFW batch failed: {e}")
+            # Fallback: mark all as safe
+            for idx, image_path in enumerate(image_paths):
+                image_id = Path(image_path).stem if image_path else f'img_{idx:06d}'
+                img_width, img_height = images[idx].size
+                results_list.append({
+                    'image_id': image_id,
+                    'detections': [{'class': 'safe', 'confidence': 1.0, 'bbox': [0, 0, img_width, img_height]}],
+                    'num_detections': 1
+                })
+    
+    return {
+        'image_id': [r['image_id'] for r in results_list],
+        'detections_json': [json.dumps(r['detections']) for r in results_list],
+        'num_detections': [r['num_detections'] for r in results_list]
+    }
+
+
+def main():
+    global model
+    
+    logger.info("="*60)
+    logger.info("NSFW Classification with EraX YOLO")
+    logger.info("="*60)
+    
+    ap = argparse.ArgumentParser()
+    ap.add_argument('--input-dataset', type=str, required=True)
+    ap.add_argument('--output-dataset', type=str, required=True)
+    ap.add_argument('--split', type=str, default='train')
+    ap.add_argument('--batch-size', type=int, default=8)
+    ap.add_argument('--shard-index', type=int, default=0)
+    ap.add_argument('--num-shards', type=int, default=1)
+    args = ap.parse_args()
+    
+    logger.info(f"Arguments: {vars(args)}")
+    
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    logger.info(f"Using device: {device}")
+    
+    # Download and load NSFW model
+    logger.info("Downloading EraX-NSFW model...")
+    snapshot_download(repo_id="erax-ai/EraX-NSFW-V1.0", local_dir="./", force_download=False)
+    
+    logger.info("Loading YOLO model...")
+    model = YOLO('erax_nsfw_yolo11m.pt')
+    logger.info("✓ Model loaded")
+    
+    # Load dataset
+    logger.info(f"Loading dataset {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"Using shard {args.shard_index+1}/{args.num_shards}")
+    
+    # Process with batching
+    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) % 100 == 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'),
+        'detections_json': Value('string'),
+        'num_detections': Value('int32')
+    })
+    
+    output_ds = HFDataset.from_dict({
+        'image_id': [r['image_id'] for r in results],
+        'detections_json': [r['detections_json'] for r in results],
+        'num_detections': [r['num_detections'] 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")
+
+
+if __name__ == '__main__':
+    main()