hf_job_sam3d.py

#!/usr/bin/env python3
"""
SAM 3D Body Inference Job - Extract 3D pose and keypoints
Outputs: Vertices, keypoints 2D/3D, camera params, bboxes
"""
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
from PIL import Image
import cv2
import json
import time

# SAM 3D Body imports
sam_repo = Path(__file__).parent.parent / "sam-3d-body"
if str(sam_repo) not in sys.path:
    sys.path.insert(0, str(sam_repo))
from sam_3d_body import load_sam_3d_body, SAM3DBodyEstimator

os.environ['PYOPENGL_PLATFORM'] = 'osmesa'


def process_batch(batch):
    """Process batch of images with SAM 3D Body"""
    images = batch['image']
    image_paths = batch.get('image_path', [f'img_{i:06d}' for i in range(len(images))])
    
    results_list = []
    
    for idx, image_pil in enumerate(images):
        image_id = Path(image_paths[idx]).stem if image_paths[idx] else f'img_{idx:06d}'
        img_width, img_height = image_pil.size
        
        # Convert to BGR
        image_rgb = np.array(image_pil.convert('RGB'))
        image_bgr = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2BGR)
        
        # Process with SAM 3D Body
        with torch.inference_mode():
            outputs = teacher.process_one_image(image_bgr)
        
        if not outputs:
            results_list.append({
                'image_id': image_id,
                'num_humans': 0,
                'data': None
            })
            continue
        
        # Collect all humans data
        humans_data = []
        for human_idx, pred in enumerate(outputs):
            human_data = {
                'vertices': pred.get('pred_vertices').astype(np.float32).tolist() if pred.get('pred_vertices') is not None else None,
                'cam_t': pred.get('pred_cam_t').astype(np.float32).tolist() if pred.get('pred_cam_t') is not None else None,
                'focal_length': float(pred.get('focal_length')) if pred.get('focal_length') is not None else None,
                'keypoints_2d': pred.get('pred_keypoints_2d').astype(np.float32).tolist() if pred.get('pred_keypoints_2d') is not None else None,
                'keypoints_3d': pred.get('pred_keypoints_3d').astype(np.float32).tolist() if pred.get('pred_keypoints_3d') is not None else None,
                'bbox': pred.get('bbox').tolist() if pred.get('bbox') is not None else None
            }
            humans_data.append(human_data)
        
        results_list.append({
            'image_id': image_id,
            'num_humans': len(humans_data),
            'data': json.dumps(humans_data)
        })
    
    return {
        'image_id': [r['image_id'] for r in results_list],
        'num_humans': [r['num_humans'] for r in results_list],
        'sam3d_data': [r['data'] for r in results_list]
    }


def main():
    global teacher
    
    logger.info("="*60)
    logger.info("SAM 3D Body Inference")
    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('--checkpoint', type=str, default='checkpoints/sam-3d-body-dinov3/model.ckpt')
    ap.add_argument('--mhr-path', type=str, default='checkpoints/sam-3d-body-dinov3/assets/mhr_model.pt')
    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)
    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}")
    
    # Load model
    logger.info("Loading SAM 3D Body...")
    model, model_cfg = load_sam_3d_body(args.checkpoint, device=device, mhr_path=args.mhr_path)
    model.eval()
    
    teacher = SAM3DBodyEstimator(
        sam_3d_body_model=model,
        model_cfg=model_cfg,
        human_detector=None,
        human_segmentor=None,
        fov_estimator=None,
    )
    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
    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")


if __name__ == '__main__':
    main()