Function load_pose_data

mbench/pose_quality.py:54–91

Load pose data from evaluation file. Args: evaluation_file: Path to the evaluation file (.npy or .pt) device: Target device require_pose: If True, raises an error if 'pose' key is missing require_vertices: If True, raises an error if 'vertices' key is mi

(evaluation_file: str, device: str, require_pose: bool = False, require_vertices: bool = False)

def load_pose_data(evaluation_file: str, device: str, require_pose: bool = False, require_vertices: bool = False):
    """
    Load pose data from evaluation file.
    
    Args:
        evaluation_file: Path to the evaluation file (.npy or .pt)
        device: Target device
        require_pose: If True, raises an error if 'pose' key is missing
        require_vertices: If True, raises an error if 'vertices' key is missing
        
    Returns:
        Dictionary with available data ('joints', 'pose', 'vertices')
        
    Raises:
        ValueError: If required keys are missing
    """
    import numpy as np
    
    if evaluation_file.endswith('.npy'):
        # .npy files only contain raw joints
        if require_pose or require_vertices:
            raise ValueError(
                f"File {evaluation_file} is .npy format (raw joints only). "
                f"Pose_Quality and Body_Penetration require SMPLify-processed .pt files with 'pose' and 'vertices'. "
                f"Please run SMPLify rendering first."
            )
        joints = np.load(evaluation_file)
        return {'joints': torch.from_numpy(joints).float().to(device)}
    else:
        # .pt files from SMPLify have pose, joints, vertices
        data = torch.load(evaluation_file, map_location=device, weights_only=False)
        
        if require_pose and 'pose' not in data:
            raise ValueError(f"File {evaluation_file} missing required 'pose' key")
        if require_vertices and 'vertices' not in data:
            raise ValueError(f"File {evaluation_file} missing required 'vertices' key")
            
        return data

def compute_pose_quality(full_info_path: str, device: str, **kwargs):
    prompt_dict_ls = load_dimension_info(full_info_path, dimension='Pose_Quality')