# 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 mmaction.datasets import build_dataloader, build_dataset

from mmaction.models import build_model

def parse_args():

    parser = argparse.ArgumentParser(

        description='MMAction2 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

    cfg.model.backbone.pretrained = None

    cfg.data.test.test_mode = True

    # build the dataloader

    dataset = build_dataset(cfg.data.test, dict(test_mode=True))

    data_loader = build_dataloader(

        dataset,

        videos_per_gpu=1,

        workers_per_gpu=cfg.data.workers_per_gpu,

        persistent_workers=cfg.data.get('persistent_workers', False),

        dist=False,

        shuffle=False)

    # build the model and load checkpoint

    model = build_model(

        cfg.model, train_cfg=None, test_cfg=cfg.get('test_cfg'))

    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])

    model.eval()

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

    num_warmup = 5

    pure_inf_time = 0

    # benchmark with 2000 video 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:

                fps = (i + 1 - num_warmup) / pure_inf_time

                print(

                    f'Done video [{i + 1:<3}/ 2000], fps: {fps:.1f} video / s')

        if (i + 1) == 200:

            pure_inf_time += elapsed

            fps = (i + 1 - num_warmup) / pure_inf_time

            print(f'Overall fps: {fps:.1f} video / s')

            break

if __name__ == '__main__':

    main()