前向推理

def infer(self, mm_inputs, batch_size, max_output_length, ignore_eos, max_iters=None, **kwargs):
        input_texts = mm_inputs.input_texts
        image_path_list = mm_inputs.image_path
        video_path_list = mm_inputs.video_path
        path_list = image_path_list if image_path_list else video_path_list
        if len(input_texts) != len(image_path_list):
            raise RuntimeError("input_text length must equal input_images length")
        if not ENV.profiling_enable:
            if self.max_batch_size > 0:
                max_iters = math.ceil(len(mm_inputs.image_path) / self.max_batch_size)
            else:
                raise RuntimeError("f{self.max_batch_size} max_batch_size should > 0, please check")
        return super().infer(mm_inputs, batch_size, max_output_length, ignore_eos, max_iters=max_iters)

def precision_save(self, precision_inputs, **kwargs):
        all_input_texts = precision_inputs.all_input_texts
        all_generate_text_list = precision_inputs.all_generate_text_list
        image_file_list = precision_inputs.image_file_list
        video_file_list = precision_inputs.video_file_list
        file_list = image_file_list if image_file_list else video_file_list
        answer_pairs = {}
        if not file_list:
            raise ValueError("Both image_file_list and video_file_list are empty.")
        if len(all_input_texts) != len(file_list):
            raise ValueError(f"Mismatched lengths between \
                all_input_texts={all_input_texts} and file_list={file_list}")
        for text_index in range(len(all_input_texts)):
            image_answer_pairs[file_list[text_index]] = all_generate_text_list[text_index]
            image_answer_pairs = dict(sorted(answer_pairs.items()))
        super().precision_save(precision_inputs, answer_pairs=answer_pairs)