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86 lines
2.4 KiB
86 lines
2.4 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('--bz', default=32, type=int, help='test config file path')
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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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# Since we only care about the forward speed of the network
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cfg.model.pretrained=None
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cfg.model.test_cfg.flip_test=False
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cfg.model.test_cfg.use_udp=False
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cfg.model.test_cfg.post_process='none'
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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=args.bz,
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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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model = MMDataParallel(model, device_ids=[0])
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model.eval()
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# get the example data
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for i, data in enumerate(data_loader):
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break
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# the first several iterations may be very slow so skip them
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num_warmup = 100
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inference_times = 100
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with torch.no_grad():
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start_time = time.perf_counter()
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for i in range(num_warmup):
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torch.cuda.synchronize()
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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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print(f'warmup cost {elapsed} time')
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start_time = time.perf_counter()
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for i in range(inference_times):
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torch.cuda.synchronize()
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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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fps = args.bz * inference_times / elapsed
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print(f'the fps is {fps}')
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if __name__ == '__main__':
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main()
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