mmpretrain/tools/analysis_tools/get_flops.py

62 lines
1.9 KiB
Python

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
from mmengine.analysis import get_model_complexity_info
from mmpretrain import get_model
def parse_args():
parser = argparse.ArgumentParser(description='Get model flops and params')
parser.add_argument('config', help='config file path')
parser.add_argument(
'--shape',
type=int,
nargs='+',
default=[224, 224],
help='input image size')
args = parser.parse_args()
return args
def main():
args = parse_args()
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')
model = get_model(args.config)
model.eval()
if hasattr(model, 'extract_feat'):
model.forward = model.extract_feat
else:
raise NotImplementedError(
'FLOPs counter is currently not currently supported with {}'.
format(model.__class__.__name__))
analysis_results = get_model_complexity_info(
model,
input_shape,
)
flops = analysis_results['flops_str']
params = analysis_results['params_str']
activations = analysis_results['activations_str']
out_table = analysis_results['out_table']
out_arch = analysis_results['out_arch']
print(out_arch)
print(out_table)
split_line = '=' * 30
print(f'{split_line}\nInput shape: {input_shape}\n'
f'Flops: {flops}\nParams: {params}\n'
f'Activation: {activations}\n{split_line}')
print('!!!Only the backbone network is counted in FLOPs analysis.')
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()