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82 lines
2.3 KiB
82 lines
2.3 KiB
#!/usr/bin/env bash
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# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import time
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import torch
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from mmcv import Config
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from mmcv.cnn import fuse_conv_bn
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from mmcv.parallel import MMDataParallel
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from mmcv.runner.fp16_utils import wrap_fp16_model
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from mmpose.datasets import build_dataloader, build_dataset
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from mmpose.models import build_posenet
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def parse_args():
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parser = argparse.ArgumentParser(
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description='MMPose benchmark a recognizer')
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parser.add_argument('config', help='test config file path')
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parser.add_argument(
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'--log-interval', default=10, help='interval of logging')
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parser.add_argument(
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'--fuse-conv-bn',
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action='store_true',
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help='Whether to fuse conv and bn, this will slightly increase'
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'the inference speed')
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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cfg = Config.fromfile(args.config)
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# set cudnn_benchmark
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if cfg.get('cudnn_benchmark', False):
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torch.backends.cudnn.benchmark = True
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# build the dataloader
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dataset = build_dataset(cfg.data.val)
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data_loader = build_dataloader(
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dataset,
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samples_per_gpu=1,
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workers_per_gpu=cfg.data.workers_per_gpu,
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dist=False,
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shuffle=False)
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# build the model and load checkpoint
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model = build_posenet(cfg.model)
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fp16_cfg = cfg.get('fp16', None)
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if fp16_cfg is not None:
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wrap_fp16_model(model)
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if args.fuse_conv_bn:
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model = fuse_conv_bn(model)
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model = MMDataParallel(model, device_ids=[0])
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# the first several iterations may be very slow so skip them
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num_warmup = 5
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pure_inf_time = 0
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# benchmark with total batch and take the average
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for i, data in enumerate(data_loader):
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torch.cuda.synchronize()
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start_time = time.perf_counter()
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with torch.no_grad():
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model(return_loss=False, **data)
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torch.cuda.synchronize()
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elapsed = time.perf_counter() - start_time
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if i >= num_warmup:
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pure_inf_time += elapsed
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if (i + 1) % args.log_interval == 0:
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its = (i + 1 - num_warmup) / pure_inf_time
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print(f'Done item [{i + 1:<3}], {its:.2f} items / s')
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print(f'Overall average: {its:.2f} items / s')
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print(f'Total time: {pure_inf_time:.2f} s')
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if __name__ == '__main__':
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main()
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