Spaces:
Runtime error
Runtime error
File size: 4,188 Bytes
3b96cb1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 |
# 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
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.')
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=[2048, 1024],
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.')
outputs = get_model_complexity_info(
model,
input_shape,
inputs=data['inputs'],
show_table=False,
show_arch=False)
result['flops'] = _format_size(outputs['flops'])
result['params'] = _format_size(outputs['params'])
result['compute_type'] = 'direct: randomly generate a picture'
return result
def main():
args = parse_args()
logger = MMLogger.get_instance(name='MMLogger')
result = inference(args, logger)
split_line = '=' * 30
ori_shape = result['ori_shape']
pad_shape = result['pad_shape']
flops = result['flops']
params = result['params']
compute_type = result['compute_type']
if pad_shape != ori_shape:
print(f'{split_line}\nUse size divisor set input shape '
f'from {ori_shape} to {pad_shape}')
print(f'{split_line}\nCompute type: {compute_type}\n'
f'Input shape: {pad_shape}\nFlops: {flops}\n'
f'Params: {params}\n{split_line}')
print('!!!Please be cautious if you use the results in papers. '
'You may need to check if all ops are supported and verify '
'that the flops computation is correct.')
if __name__ == '__main__':
main()
|