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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import tempfile
from pathlib import Path
import torch
from mmengine import Config, DictAction
from mmengine.logging import MMLogger
from mmengine.model import revert_sync_batchnorm
from mmengine.registry import init_default_scope
from mmseg.models import BaseSegmentor
from mmseg.registry import MODELS
from mmseg.structures import SegDataSample
import os
import json
try:
from mmengine.analysis import get_model_complexity_info
from mmengine.analysis.print_helper import _format_size
except ImportError:
raise ImportError('Please upgrade mmengine >= 0.6.0 to use this script.')
from fvcore.nn import FlopCountAnalysis
def parse_args():
parser = argparse.ArgumentParser(
description='Get the FLOPs of a segmentor')
parser.add_argument('config', help='train config file path')
parser.add_argument(
'--shape',
type=int,
nargs='+',
default=[512, 512],
help='input image size')
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.')
args = parser.parse_args()
return args
def inference(args: argparse.Namespace, logger: MMLogger) -> dict:
config_name = Path(args.config)
if not config_name.exists():
logger.error(f'Config file {config_name} does not exist')
cfg: Config = Config.fromfile(config_name)
cfg.work_dir = tempfile.TemporaryDirectory().name
cfg.log_level = 'WARN'
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
init_default_scope(cfg.get('scope', 'mmseg'))
if len(args.shape) == 1:
input_shape = (3, args.shape[0], args.shape[0])
elif len(args.shape) == 2:
input_shape = (3, ) + tuple(args.shape)
else:
raise ValueError('invalid input shape')
result = {}
model: BaseSegmentor = MODELS.build(cfg.model)
if hasattr(model, 'auxiliary_head'):
model.auxiliary_head = None
if torch.cuda.is_available():
model.cuda()
model = revert_sync_batchnorm(model)
result['ori_shape'] = input_shape[-2:]
result['pad_shape'] = input_shape[-2:]
data_batch = {
'inputs': [torch.rand(input_shape)],
'data_samples': [SegDataSample(metainfo=result)]
}
data = model.data_preprocessor(data_batch)
model.eval()
if cfg.model.decode_head.type in ['MaskFormerHead', 'Mask2FormerHead']:
# TODO: Support MaskFormer and Mask2Former
raise NotImplementedError('MaskFormer and Mask2Former are not '
'supported yet.')
if hasattr(model, 'module'):
all_cfgs = model.module.backbone.all_cfgs
else:
all_cfgs = model.backbone.all_cfgs
stitch_results = {}
for cfg_id in all_cfgs:
if hasattr(model, 'module'):
model.module.backbone.reset_stitch_id(cfg_id)
else:
model.backbone.reset_stitch_id(cfg_id)
flops = FlopCountAnalysis(model, torch.randn([1]+list(input_shape)).cuda()).total()
stitch_results[cfg_id] = flops
save_dir = './model_flops'
if not os.path.exists(save_dir):
os.mkdir(save_dir)
config_name = args.config.split('/')[-1].split('.')[0]
with open(os.path.join(save_dir, f'snnet_flops_{config_name}.json'), 'w+') as f:
json.dump(stitch_results, f, indent=4)
def main():
args = parse_args()
logger = MMLogger.get_instance(name='MMLogger')
inference(args, logger)
if __name__ == '__main__':
main()
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