|
|
|
|
|
import argparse |
|
|
from functools import partial |
|
|
|
|
|
import mmcv_custom |
|
|
import mmseg_custom |
|
|
import numpy as np |
|
|
import torch |
|
|
from mmcv import Config, DictAction |
|
|
from mmseg.models import build_segmentor |
|
|
|
|
|
try: |
|
|
from mmcv.cnn import get_model_complexity_info |
|
|
from mmcv.cnn.utils.flops_counter import flops_to_string, params_to_string |
|
|
except ImportError: |
|
|
raise ImportError('Please upgrade mmcv to >0.6.2') |
|
|
|
|
|
|
|
|
def parse_args(): |
|
|
parser = argparse.ArgumentParser(description='Train a detector') |
|
|
parser.add_argument('config', help='train config file path') |
|
|
parser.add_argument( |
|
|
'--shape', |
|
|
type=int, |
|
|
nargs='+', |
|
|
default=[512, 2048], |
|
|
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.') |
|
|
parser.add_argument( |
|
|
'--size-divisor', |
|
|
type=int, |
|
|
default=32, |
|
|
help='Pad the input image, the minimum size that is divisible ' |
|
|
'by size_divisor, -1 means do not pad the image.') |
|
|
args = parser.parse_args() |
|
|
return args |
|
|
|
|
|
|
|
|
def dcnv3_flops(n, k, c): |
|
|
return 5 * n * k * c |
|
|
|
|
|
|
|
|
def get_flops(model, input_shape): |
|
|
flops, params = get_model_complexity_info(model, input_shape, as_strings=False) |
|
|
|
|
|
backbone = model.backbone |
|
|
backbone_name = type(backbone).__name__ |
|
|
_, H, W = input_shape |
|
|
|
|
|
temp = 0 |
|
|
if 'InternImage' in backbone_name: |
|
|
depths = backbone.depths |
|
|
for idx, depth in enumerate(depths): |
|
|
channels = backbone.channels * (2 ** idx) |
|
|
h = H / (4 * (2 ** idx)) |
|
|
w = W / (4 * (2 ** idx)) |
|
|
temp += depth * dcnv3_flops(n=h*w, k=3*3, c=channels) |
|
|
|
|
|
flops = flops + temp |
|
|
return flops_to_string(flops), params_to_string(params) |
|
|
|
|
|
|
|
|
if __name__ == '__main__': |
|
|
|
|
|
args = parse_args() |
|
|
|
|
|
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') |
|
|
orig_shape = (3, h, w) |
|
|
divisor = args.size_divisor |
|
|
if divisor > 0: |
|
|
h = int(np.ceil(h / divisor)) * divisor |
|
|
w = int(np.ceil(w / divisor)) * divisor |
|
|
|
|
|
input_shape = (3, h, w) |
|
|
|
|
|
cfg = Config.fromfile(args.config) |
|
|
if args.cfg_options is not None: |
|
|
cfg.merge_from_dict(args.cfg_options) |
|
|
|
|
|
model = build_segmentor( |
|
|
cfg.model, |
|
|
train_cfg=cfg.get('train_cfg'), |
|
|
test_cfg=cfg.get('test_cfg')) |
|
|
|
|
|
if torch.cuda.is_available(): |
|
|
model.cuda() |
|
|
model.eval() |
|
|
if hasattr(model, 'forward_dummy'): |
|
|
model.forward = model.forward_dummy |
|
|
else: |
|
|
raise NotImplementedError( |
|
|
'FLOPs counter is currently not currently supported with {}'. |
|
|
format(model.__class__.__name__)) |
|
|
|
|
|
flops, params = get_flops(model, input_shape) |
|
|
split_line = '=' * 30 |
|
|
|
|
|
if divisor > 0 and \ |
|
|
input_shape != orig_shape: |
|
|
print(f'{split_line}\nUse size divisor set input shape ' |
|
|
f'from {orig_shape} to {input_shape}\n') |
|
|
print(f'{split_line}\nInput shape: {input_shape}\n' |
|
|
f'Flops: {flops}\nParams: {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.') |
|
|
|
