#!/usr/bin/env bash

# Copyright (c) OpenMMLab. All rights reserved.

import argparse

import time

import torch

from mmcv import Config

from mmcv.cnn import fuse_conv_bn

from mmcv.parallel import MMDataParallel

from mmcv.runner.fp16_utils import wrap_fp16_model

from mmpose.datasets import build_dataloader, build_dataset

from mmpose.models import build_posenet

def parse_args():

    parser = argparse.ArgumentParser(

        description='MMPose benchmark a recognizer')

    parser.add_argument('config', help='test config file path')

    parser.add_argument(

        '--log-interval', default=10, help='interval of logging')

    parser.add_argument(

        '--fuse-conv-bn',

        action='store_true',

        help='Whether to fuse conv and bn, this will slightly increase'

        'the inference speed')

    args = parser.parse_args()

    return args

def main():

    args = parse_args()

    cfg = Config.fromfile(args.config)

    # set cudnn_benchmark

    if cfg.get('cudnn_benchmark', False):

        torch.backends.cudnn.benchmark = True

    # build the dataloader

    dataset = build_dataset(cfg.data.val)

    data_loader = build_dataloader(

        dataset,

        samples_per_gpu=1,

        workers_per_gpu=cfg.data.workers_per_gpu,

        dist=False,

        shuffle=False)

    # build the model and load checkpoint

    model = build_posenet(cfg.model)

    fp16_cfg = cfg.get('fp16', None)

    if fp16_cfg is not None:

        wrap_fp16_model(model)

    if args.fuse_conv_bn:

        model = fuse_conv_bn(model)

    model = MMDataParallel(model, device_ids=[0])

    # the first several iterations may be very slow so skip them

    num_warmup = 5

    pure_inf_time = 0

    # benchmark with total batch and take the average

    for i, data in enumerate(data_loader):

        torch.cuda.synchronize()

        start_time = time.perf_counter()

        with torch.no_grad():

            model(return_loss=False, **data)

        torch.cuda.synchronize()

        elapsed = time.perf_counter() - start_time

        if i >= num_warmup:

            pure_inf_time += elapsed

            if (i + 1) % args.log_interval == 0:

                its = (i + 1 - num_warmup) / pure_inf_time

                print(f'Done item [{i + 1:<3}],  {its:.2f} items / s')

    print(f'Overall average: {its:.2f} items / s')

    print(f'Total time: {pure_inf_time:.2f} s')

if __name__ == '__main__':

    main()