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| #!/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() | |