|
|
|
import argparse |
|
from functools import partial |
|
|
|
import torch |
|
|
|
from mmpose.apis.inference import init_pose_model |
|
|
|
try: |
|
from mmcv.cnn import get_model_complexity_info |
|
except ImportError: |
|
raise ImportError('Please upgrade mmcv to >0.6.2') |
|
|
|
|
|
def parse_args(): |
|
parser = argparse.ArgumentParser(description='Train a recognizer') |
|
parser.add_argument('config', help='train config file path') |
|
parser.add_argument( |
|
'--shape', |
|
type=int, |
|
nargs='+', |
|
default=[256, 192], |
|
help='input image size') |
|
parser.add_argument( |
|
'--input-constructor', |
|
'-c', |
|
type=str, |
|
choices=['none', 'batch'], |
|
default='none', |
|
help='If specified, it takes a callable method that generates ' |
|
'input. Otherwise, it will generate a random tensor with ' |
|
'input shape to calculate FLOPs.') |
|
parser.add_argument( |
|
'--batch-size', '-b', type=int, default=1, help='input batch size') |
|
parser.add_argument( |
|
'--not-print-per-layer-stat', |
|
'-n', |
|
action='store_true', |
|
help='Whether to print complexity information' |
|
'for each layer in a model') |
|
args = parser.parse_args() |
|
return args |
|
|
|
|
|
def batch_constructor(flops_model, batch_size, input_shape): |
|
"""Generate a batch of tensors to the model.""" |
|
batch = {} |
|
|
|
img = torch.ones(()).new_empty( |
|
(batch_size, *input_shape), |
|
dtype=next(flops_model.parameters()).dtype, |
|
device=next(flops_model.parameters()).device) |
|
|
|
batch['img'] = img |
|
return batch |
|
|
|
|
|
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 = init_pose_model(args.config) |
|
|
|
if args.input_constructor == 'batch': |
|
input_constructor = partial(batch_constructor, model, args.batch_size) |
|
else: |
|
input_constructor = None |
|
|
|
if args.input_constructor == 'batch': |
|
input_constructor = partial(batch_constructor, model, args.batch_size) |
|
else: |
|
input_constructor = None |
|
|
|
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_model_complexity_info( |
|
model, |
|
input_shape, |
|
input_constructor=input_constructor, |
|
print_per_layer_stat=(not args.not_print_per_layer_stat)) |
|
split_line = '=' * 30 |
|
input_shape = (args.batch_size, ) + input_shape |
|
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.') |
|
|
|
|
|
if __name__ == '__main__': |
|
main() |
|
|
