# Copyright (c) OpenMMLab. All rights reserved.

import argparse

import time

import torch

from mmcv import Config

from mmcv.parallel import MMDataParallel

from mmcv.runner import load_checkpoint, wrap_fp16_model

from mmseg.datasets import build_dataloader, build_dataset

from mmseg.models import build_segmentor

def parse_args():

    parser = argparse.ArgumentParser(description='MMSeg benchmark a model')

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

    parser.add_argument('checkpoint', help='checkpoint file')

    parser.add_argument(

        '--log-interval', type=int, default=50, help='interval of logging')

    args = parser.parse_args()

    return args

def main():

    args = parse_args()

    cfg = Config.fromfile(args.config)

    # set cudnn_benchmark

    torch.backends.cudnn.benchmark = False

    cfg.model.pretrained = None

    cfg.data.test.test_mode = True

    # build the dataloader

    # TODO: support multiple images per gpu (only minor changes are needed)

    dataset = build_dataset(cfg.data.test)

    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

    cfg.model.train_cfg = None

    model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg'))

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

    if fp16_cfg is not None:

        wrap_fp16_model(model)

    load_checkpoint(model, args.checkpoint, map_location='cpu')

    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

    total_iters = 200

    # benchmark with 200 image 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, rescale=True, **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 image [{i + 1:<3}/ {total_iters}], '

                      f'fps: {fps:.2f} img / s')

        if (i + 1) == total_iters:

            fps = (i + 1 - num_warmup) / pure_inf_time

            print(f'Overall fps: {fps:.2f} img / s')

            break

if __name__ == '__main__':

    main()