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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import tempfile
from pathlib import Path
import torch
from mmdet.registry import MODELS
from mmengine.analysis import get_model_complexity_info
from mmengine.config import Config, DictAction
from mmengine.logging import MMLogger
from mmengine.model import revert_sync_batchnorm
from mmengine.registry import init_default_scope
from mmyolo.utils import switch_to_deploy
def parse_args():
parser = argparse.ArgumentParser(description='Get a detector flops')
parser.add_argument('config', help='train config file path')
parser.add_argument(
'--shape',
type=int,
nargs='+',
default=[640, 640],
help='input image size')
parser.add_argument(
'--show-arch',
action='store_true',
help='whether return the statistics in the form of network layers')
parser.add_argument(
'--not-show-table',
action='store_true',
help='whether return the statistics in the form of table'),
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.')
return parser.parse_args()
def inference(args, logger):
config_name = Path(args.config)
if not config_name.exists():
logger.error(f'{config_name} not found.')
cfg = Config.fromfile(args.config)
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('default_scope', 'mmyolo'))
if len(args.shape) == 1:
h = w = args.shape[0]
elif len(args.shape) == 2:
h, w = args.shape
else:
raise ValueError('invalid input shape')
# model
model = MODELS.build(cfg.model)
if torch.cuda.is_available():
model.cuda()
model = revert_sync_batchnorm(model)
model.eval()
switch_to_deploy(model)
# input tensor
# automatically generate a input tensor with the given input_shape.
data_batch = {'inputs': [torch.rand(3, h, w)], 'batch_samples': [None]}
data = model.data_preprocessor(data_batch)
result = {'ori_shape': (h, w), 'pad_shape': data['inputs'].shape[-2:]}
outputs = get_model_complexity_info(
model,
input_shape=None,
inputs=data['inputs'], # the input tensor of the model
show_table=not args.not_show_table, # show the complexity table
show_arch=args.show_arch) # show the complexity arch
result['flops'] = outputs['flops_str']
result['params'] = outputs['params_str']
result['out_table'] = outputs['out_table']
result['out_arch'] = outputs['out_arch']
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']
print(result['out_table']) # print related information by table
print(result['out_arch']) # print related information by network layers
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}\n'
f'Input shape: {pad_shape}\nModel Flops: {flops}\n'
f'Model Parameters: {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()
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