Method evaluate

mbench/__init__.py:321–429

Run evaluation on motion data by first rendering to videos and then evaluating Args: evaluation_path: Path containing dimension folders with motion files name: Name for this evaluation run dimension_list: List of dimensions to evaluate (i

(self, evaluation_path: str, name: str, 
                dimension_list: List[str] = None, 
                device_id: int = 0, **kwargs)

        return cur_full_info_path

    def evaluate(self, evaluation_path: str, name: str, 
                dimension_list: List[str] = None, 
                device_id: int = 0, **kwargs) -> Dict[str, Any]:
        """
        Run evaluation on motion data by first rendering to videos and then evaluating
        
        Args:
            evaluation_path: Path containing dimension folders with motion files
            name: Name for this evaluation run
            dimension_list: List of dimensions to evaluate (if None, evaluates all)
            device_id: CUDA device ID for rendering
            **kwargs: Additional arguments passed to evaluation functions
            
        Returns:
            Dictionary containing evaluation results for each dimension
        """
        results_dict = {}
        
        if dimension_list is None:
            dimension_list = self.build_full_dimension_list()
        
        # First, render motions to intermediate videos for video-based dimensions
        # if "Motion_Condition_Consistency" in dimension_list or "Motion_Generalizability" in dimension_list:
        #     print("Rendering motions to intermediate videos...")
        self.render_motions_to_videos(evaluation_path, dimension_list, name, device_id)
        
        # Build the full info JSON file using both videos and pt files
        print("Building evaluation metadata...")
        cur_full_info_path = self.build_full_info_json(
            evaluation_path, name, dimension_list, **kwargs
        )
        
        # Evaluate each dimension
        print("Running evaluations...")
        per_motion_registry: Dict[str, Dict[str, Any]] = {}

        for dimension in dimension_list:
            try:
                # Import the dimension module
                category = self.get_category(dimension)
                dimension_lower = dimension.lower()
                dimension_module = importlib.import_module(f'mbench.{category}')
                evaluate_func = getattr(dimension_module, f'compute_{dimension_lower}')
                
                print(f'Evaluating dimension: {dimension}')
                results = evaluate_func(cur_full_info_path, self.device, **kwargs)
                results_dict[dimension] = results

                per_motion_entries = []
                if isinstance(results, dict):
                    per_motion_entries = results.get("per_motion") or []
                for entry in per_motion_entries:
                    motion_id = entry.get("id")
                    if motion_id is None:
                        continue
                    motion_key = str(motion_id)
                    base_record = per_motion_registry.setdefault(
                        motion_key,