def process(self, inputs, outputs):
        """
        Args:
            inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).
                It is a list of dict. Each dict corresponds to an image and
                contains keys like "height", "width", "file_name", "image_id".
            outputs: the outputs of a COCO model. It is a list of dicts with key
                "instances" that contains :class:`Instances`.
                The :class:`Instances` object needs to have `densepose` field.
        """
        for input, output in zip(inputs, outputs):
            instances = output["instances"].to(self._cpu_device)
            if not instances.has("pred_densepose"):
                continue
            prediction_list = prediction_to_dict(
                instances,
                input["image_id"],
                self._embedder,
                self._metadata.class_to_mesh_name,
                self._storage is not None,
            )
            if self._storage is not None:
                for prediction_dict in prediction_list:
                    dict_to_store = {}
                    for field_name in self._storage.data_schema:
                        dict_to_store[field_name] = prediction_dict[field_name]
                    record_id = self._storage.put(dict_to_store)
                    prediction_dict["record_id"] = record_id
                    prediction_dict["rank"] = get_rank()
                    for field_name in self._storage.data_schema:
                        del prediction_dict[field_name]
            self._predictions.extend(prediction_list)

        Args:
            inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).
                It is a list of dict. Each dict corresponds to an image and
                contains keys like "height", "width", "file_name", "image_id".
            outputs: the outputs of a COCO model. It is a list of dicts with key
                "instances" that contains :class:`Instances`.
                The :class:`Instances` object needs to have `densepose` field.
        
EVA-02/det/projects/DensePose/densepose/evaluation/evaluator.py