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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import copy
import os
import time
import torch
from mmengine import Config, DictAction
from mmengine.dist import get_world_size, init_dist
from mmengine.logging import MMLogger, print_log
from mmengine.registry import init_default_scope
from mmengine.runner import Runner, load_checkpoint
from mmengine.utils import mkdir_or_exist
from mmengine.utils.dl_utils import set_multi_processing
from mmyolo.registry import MODELS
# TODO: Refactoring and improving
def parse_args():
parser = argparse.ArgumentParser(description='MMYOLO benchmark a model')
parser.add_argument('config', help='test config file path')
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument(
'--repeat-num',
type=int,
default=1,
help='number of repeat times of measurement for averaging the results')
parser.add_argument(
'--max-iter', type=int, default=2000, help='num of max iter')
parser.add_argument(
'--log-interval', type=int, default=50, help='interval of logging')
parser.add_argument(
'--work-dir',
help='the directory to save the file containing '
'benchmark metrics')
parser.add_argument(
'--fuse-conv-bn',
action='store_true',
help='Whether to fuse conv and bn, this will slightly increase'
'the inference speed')
parser.add_argument(
'--cfg-options',
nargs='+',
action=DictAction,
help='override some settings in the used config, the key-value pair '
'in xxx=yyy format will be merged into config file. If the value to '
'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
'Note that the quotation marks are necessary and that no white space '
'is allowed.')
parser.add_argument(
'--launcher',
choices=['none', 'pytorch', 'slurm', 'mpi'],
default='none',
help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()
if 'LOCAL_RANK' not in os.environ:
os.environ['LOCAL_RANK'] = str(args.local_rank)
return args
def measure_inference_speed(cfg, checkpoint, max_iter, log_interval,
is_fuse_conv_bn):
env_cfg = cfg.get('env_cfg')
if env_cfg.get('cudnn_benchmark'):
torch.backends.cudnn.benchmark = True
mp_cfg: dict = env_cfg.get('mp_cfg', {})
set_multi_processing(**mp_cfg, distributed=cfg.distributed)
# Because multiple processes will occupy additional CPU resources,
# FPS statistics will be more unstable when num_workers is not 0.
# It is reasonable to set num_workers to 0.
dataloader_cfg = cfg.test_dataloader
dataloader_cfg['num_workers'] = 0
dataloader_cfg['batch_size'] = 1
dataloader_cfg['persistent_workers'] = False
data_loader = Runner.build_dataloader(dataloader_cfg)
# build the model and load checkpoint
model = MODELS.build(cfg.model)
load_checkpoint(model, checkpoint, map_location='cpu')
model = model.cuda()
model.eval()
# the first several iterations may be very slow so skip them
num_warmup = 5
pure_inf_time = 0
fps = 0
# benchmark with 2000 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.test_step(data)
torch.cuda.synchronize()
elapsed = time.perf_counter() - start_time
if i >= num_warmup:
pure_inf_time += elapsed
if (i + 1) % log_interval == 0:
fps = (i + 1 - num_warmup) / pure_inf_time
print_log(
f'Done image [{i + 1:<3}/ {max_iter}], '
f'fps: {fps:.1f} img / s, '
f'times per image: {1000 / fps:.1f} ms / img', 'current')
if (i + 1) == max_iter:
fps = (i + 1 - num_warmup) / pure_inf_time
print_log(
f'Overall fps: {fps:.1f} img / s, '
f'times per image: {1000 / fps:.1f} ms / img', 'current')
break
return fps
def repeat_measure_inference_speed(cfg,
checkpoint,
max_iter,
log_interval,
is_fuse_conv_bn,
repeat_num=1):
assert repeat_num >= 1
fps_list = []
for _ in range(repeat_num):
cp_cfg = copy.deepcopy(cfg)
fps_list.append(
measure_inference_speed(cp_cfg, checkpoint, max_iter, log_interval,
is_fuse_conv_bn))
if repeat_num > 1:
fps_list_ = [round(fps, 1) for fps in fps_list]
times_pre_image_list_ = [round(1000 / fps, 1) for fps in fps_list]
mean_fps_ = sum(fps_list_) / len(fps_list_)
mean_times_pre_image_ = sum(times_pre_image_list_) / len(
times_pre_image_list_)
print_log(
f'Overall fps: {fps_list_}[{mean_fps_:.1f}] img / s, '
f'times per image: '
f'{times_pre_image_list_}[{mean_times_pre_image_:.1f}] ms / img',
'current')
return fps_list
return fps_list[0]
# TODO: refactoring
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
init_default_scope(cfg.get('default_scope', 'mmyolo'))
distributed = False
if args.launcher != 'none':
init_dist(args.launcher, **cfg.get('env_cfg', {}).get('dist_cfg', {}))
distributed = True
assert get_world_size(
) == 1, 'Inference benchmark does not allow distributed multi-GPU'
cfg.distributed = distributed
log_file = None
if args.work_dir:
log_file = os.path.join(args.work_dir, 'benchmark.log')
mkdir_or_exist(args.work_dir)
MMLogger.get_instance('mmyolo', log_file=log_file, log_level='INFO')
repeat_measure_inference_speed(cfg, args.checkpoint, args.max_iter,
args.log_interval, args.fuse_conv_bn,
args.repeat_num)
if __name__ == '__main__':
main()
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