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import argparse |
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import tempfile |
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from pathlib import Path |
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import torch |
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from mmengine import Config, DictAction |
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from mmengine.logging import MMLogger |
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from mmengine.model import revert_sync_batchnorm |
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from mmengine.registry import init_default_scope |
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from mmseg.models import BaseSegmentor |
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from mmseg.registry import MODELS |
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from mmseg.structures import SegDataSample |
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try: |
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from mmengine.analysis import get_model_complexity_info |
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from mmengine.analysis.print_helper import _format_size |
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except ImportError: |
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raise ImportError('Please upgrade mmengine >= 0.6.0 to use this script.') |
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def parse_args(): |
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parser = argparse.ArgumentParser( |
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description='Get the FLOPs of a segmentor') |
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parser.add_argument('config', help='train config file path') |
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parser.add_argument( |
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'--shape', |
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type=int, |
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nargs='+', |
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default=[2048, 1024], |
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help='input image size') |
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parser.add_argument( |
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'--cfg-options', |
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nargs='+', |
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action=DictAction, |
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help='override some settings in the used config, the key-value pair ' |
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'in xxx=yyy format will be merged into config file. If the value to ' |
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'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' |
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'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' |
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'Note that the quotation marks are necessary and that no white space ' |
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'is allowed.') |
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args = parser.parse_args() |
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return args |
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def inference(args: argparse.Namespace, logger: MMLogger) -> dict: |
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config_name = Path(args.config) |
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if not config_name.exists(): |
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logger.error(f'Config file {config_name} does not exist') |
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cfg: Config = Config.fromfile(config_name) |
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cfg.work_dir = tempfile.TemporaryDirectory().name |
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cfg.log_level = 'WARN' |
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if args.cfg_options is not None: |
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cfg.merge_from_dict(args.cfg_options) |
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init_default_scope(cfg.get('scope', 'mmseg')) |
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if len(args.shape) == 1: |
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input_shape = (3, args.shape[0], args.shape[0]) |
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elif len(args.shape) == 2: |
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input_shape = (3, ) + tuple(args.shape) |
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else: |
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raise ValueError('invalid input shape') |
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result = {} |
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model: BaseSegmentor = MODELS.build(cfg.model) |
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if hasattr(model, 'auxiliary_head'): |
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model.auxiliary_head = None |
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if torch.cuda.is_available(): |
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model.cuda() |
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model = revert_sync_batchnorm(model) |
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result['ori_shape'] = input_shape[-2:] |
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result['pad_shape'] = input_shape[-2:] |
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data_batch = { |
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'inputs': [torch.rand(input_shape)], |
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'data_samples': [SegDataSample(metainfo=result)] |
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} |
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data = model.data_preprocessor(data_batch) |
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model.eval() |
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if cfg.model.decode_head.type in ['MaskFormerHead', 'Mask2FormerHead']: |
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raise NotImplementedError('MaskFormer and Mask2Former are not ' |
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'supported yet.') |
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outputs = get_model_complexity_info( |
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model, |
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input_shape, |
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inputs=data['inputs'], |
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show_table=False, |
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show_arch=False) |
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result['flops'] = _format_size(outputs['flops']) |
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result['params'] = _format_size(outputs['params']) |
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result['compute_type'] = 'direct: randomly generate a picture' |
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return result |
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def main(): |
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args = parse_args() |
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logger = MMLogger.get_instance(name='MMLogger') |
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result = inference(args, logger) |
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split_line = '=' * 30 |
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ori_shape = result['ori_shape'] |
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pad_shape = result['pad_shape'] |
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flops = result['flops'] |
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params = result['params'] |
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compute_type = result['compute_type'] |
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if pad_shape != ori_shape: |
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print(f'{split_line}\nUse size divisor set input shape ' |
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f'from {ori_shape} to {pad_shape}') |
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print(f'{split_line}\nCompute type: {compute_type}\n' |
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f'Input shape: {pad_shape}\nFlops: {flops}\n' |
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f'Params: {params}\n{split_line}') |
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print('!!!Please be cautious if you use the results in papers. ' |
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'You may need to check if all ops are supported and verify ' |
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'that the flops computation is correct.') |
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if __name__ == '__main__': |
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main() |
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