Method get_sliced_cache

detrsmpl/data/data_structures/human_data.py:297–381

Slice the whole HumanData into pieces for HumanDataCacheWriter. Args: slice_size (int, optional): The length of each unit in HumanData cache. Defaults to 10. Returns: List: Two dicts for HumanDataCacheWriter.

(self, slice_size=10)

        np.savez_compressed(npz_path, **dict_to_dump)

    def get_sliced_cache(self, slice_size=10) -> List:
        """Slice the whole HumanData into pieces for HumanDataCacheWriter.

        Args:
            slice_size (int, optional):
                The length of each unit in HumanData cache.
                Defaults to 10.

        Returns:
            List:
                Two dicts for HumanDataCacheWriter.
                Init HumanDataCacheWriter by HumanDataCacheWriter(**Returns[0])
                and set data by
                human_data_cache_writer.update_sliced_dict(Returns[1]).
        """
        keypoints_info = {}
        non_sliced_data = {}
        sliced_data = {}
        slice_num = ceil(self.__data_len__ / slice_size)
        for slice_index in range(slice_num):
            sliced_data[str(slice_index)] = {}
        dim_dict = self.__get_slice_dim__()
        for key, dim in dim_dict.items():
            # no dim to slice
            if dim is None:
                if key.startswith('keypoints') and\
                        (key.endswith('_mask') or
                         key.endswith('_convention')):
                    keypoints_info[key] = self[key]
                else:
                    non_sliced_data[key] = self[key]
            elif isinstance(dim, dict):
                value_dict = self.get_raw_value(key)
                non_sliced_sub_dict = {}
                for sub_key in value_dict.keys():
                    sub_value = value_dict[sub_key]
                    if dim[sub_key] is None:
                        non_sliced_sub_dict[sub_key] = sub_value
                    else:
                        sub_dim = dim[sub_key]
                        for slice_index in range(slice_num):
                            slice_start = slice_index * slice_size
                            slice_end = min((slice_index + 1) * slice_size,
                                            self.__data_len__)
                            slice_range = slice(slice_start, slice_end)
                            sliced_sub_value = \
                                HumanData.__get_sliced_result__(
                                    sub_value, sub_dim, slice_range
                                )
                            if key not in sliced_data[str(slice_index)]:
                                sliced_data[str(slice_index)][key] = {}
                            sliced_data[str(slice_index)][key][sub_key] = \
                                sliced_sub_value
                if len(non_sliced_sub_dict) > 0:
                    non_sliced_data[key] = non_sliced_sub_dict
            else:
                value = self.get_raw_value(key)
                # slice as ndarray