Flops counter for convolutional networks in pytorch framework
该脚本旨在计算卷积神经网络中理论乘积运算的数量。 它还可以计算参数的数量并打印给定网络的每层计算成本，使用起来非常简单。
安装
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| pip install --upgrade git+https://github.com/sovrasov/flops-counter.pytorch.git
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测试
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| import torchvision.models as models
import torch
from ptflops import get_model_complexity_info
with torch.cuda.device(0):
net = models.densenet161()
macs, params = get_model_complexity_info(net, (3, 224, 224), as_strings=True,
print_per_layer_stat=True, verbose=True)
print('{:<30} {:<8}'.format('Computational complexity: ', macs))
print('{:<30} {:<8}'.format('Number of parameters: ', params))
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torchvision的部分结果
| Model |
Input Resolution |
Params(M) |
MACs(G) |
Top-1 error |
Top-5 error |
| alexnet |
224x224 |
61.1 |
0.72 |
43.45 |
20.91 |
| vgg11 |
224x224 |
132.86 |
7.63 |
30.98 |
11.37 |
| vgg13 |
224x224 |
133.05 |
11.34 |
30.07 |
10.75 |
| vgg16 |
224x224 |
138.36 |
15.5 |
28.41 |
9.62 |
| vgg19 |
224x224 |
143.67 |
19.67 |
27.62 |
9.12 |
| vgg11_bn |
224x224 |
132.87 |
7.64 |
29.62 |
10.19 |
Reference
https://github.com/sovrasov/flops-counter.pytorch
