def trt_perf_analysis(raw_df):
    """
    sperate raw dataframe to a list of dataframe
    compare tensorrt percision performance
    """
    # filter df by gpu, compare tensorrt and gpu
    # define default dataframe for gpu performance analysis
    gpu_df = raw_df.loc[raw_df['runtime_device'] == 'gpu']
    new_df = filter_df_merge(gpu_df, "precision")

    # calculate qps diff percentile
    infer_fp32 = "inference_time(ms)_precision_fp32"
    infer_fp16 = "inference_time(ms)_precision_fp16"
    infer_int8 = "inference_time(ms)_precision_int8"
    new_df["fp32_fp16_diff"] = new_df[[infer_fp32, infer_fp16]].apply(
        lambda x: (float(x[infer_fp16]) - float(x[infer_fp32])) / float(x[infer_fp32]),
        axis=1)
    new_df["fp32_gpu_diff"] = new_df[["inference_time(ms)", infer_fp32]].apply(
        lambda x: (float(x[infer_fp32]) - float(x[infer_fp32])) / float(x["inference_time(ms)"]),
        axis=1)
    new_df["fp16_int8_diff"] = new_df[[infer_fp16, infer_int8]].apply(
        lambda x: (float(x[infer_int8]) - float(x[infer_fp16])) / float(x[infer_fp16]),
        axis=1)

    return new_df

    sperate raw dataframe to a list of dataframe
    compare tensorrt percision performance
    
deploy/benchmark/log_parser_excel.py