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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()
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