安装基本库：
???????pip install ptflops
pip install --upgrade git+https://github.com/sovrasov/flops-counter.pytorch.git详细可参考： https://github.com/sovrasov/flops-counter.pytorch
代码如下：
import torchvision.models as models
import torch
from ptflops import get_model_complexity_info
with torch.cuda.device(0):
net = models.vgg16()
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))root@2c5bb9a7b477:/home/Semisup_cloudSemSeg_master# python Print_CNN_FLOPS_parameters.py
Warning: module Dropout is treated as a zero-op.
Warning: module VGG is treated as a zero-op.
VGG(
138.36 M, 100.000% Params, 15.5 GMac, 100.000% MACs,
(features): Sequential(
14.71 M, 10.635% Params, 15.38 GMac, 99.202% MACs,
(0): Conv2d(1.79 k, 0.001% Params, 89.92 MMac, 0.580% MACs, 3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(0, 0.000% Params, 3.21 MMac, 0.021% MACs, inplace=True)
(2): Conv2d(36.93 k, 0.027% Params, 1.85 GMac, 11.951% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): ReLU(0, 0.000% Params, 3.21 MMac, 0.021% MACs, inplace=True)
(4): MaxPool2d(0, 0.000% Params, 3.21 MMac, 0.021% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(5): Conv2d(73.86 k, 0.053% Params, 926.45 MMac, 5.976% MACs, 64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(6): ReLU(0, 0.000% Params, 1.61 MMac, 0.010% MACs, inplace=True)
(7): Conv2d(147.58 k, 0.107% Params, 1.85 GMac, 11.941% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(8): ReLU(0, 0.000% Params, 1.61 MMac, 0.010% MACs, inplace=True)
(9): MaxPool2d(0, 0.000% Params, 1.61 MMac, 0.010% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(10): Conv2d(295.17 k, 0.213% Params, 925.65 MMac, 5.971% MACs, 128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(0, 0.000% Params, 802.82 KMac, 0.005% MACs, inplace=True)
(12): Conv2d(590.08 k, 0.426% Params, 1.85 GMac, 11.936% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(13): ReLU(0, 0.000% Params, 802.82 KMac, 0.005% MACs, inplace=True)
(14): Conv2d(590.08 k, 0.426% Params, 1.85 GMac, 11.936% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): ReLU(0, 0.000% Params, 802.82 KMac, 0.005% MACs, inplace=True)
(16): MaxPool2d(0, 0.000% Params, 802.82 KMac, 0.005% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(17): Conv2d(1.18 M, 0.853% Params, 925.25 MMac, 5.968% MACs, 256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(18): ReLU(0, 0.000% Params, 401.41 KMac, 0.003% MACs, inplace=True)
(19): Conv2d(2.36 M, 1.706% Params, 1.85 GMac, 11.933% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(20): ReLU(0, 0.000% Params, 401.41 KMac, 0.003% MACs, inplace=True)
(21): Conv2d(2.36 M, 1.706% Params, 1.85 GMac, 11.933% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(22): ReLU(0, 0.000% Params, 401.41 KMac, 0.003% MACs, inplace=True)
(23): MaxPool2d(0, 0.000% Params, 401.41 KMac, 0.003% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(24): Conv2d(2.36 M, 1.706% Params, 462.52 MMac, 2.983% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(25): ReLU(0, 0.000% Params, 100.35 KMac, 0.001% MACs, inplace=True)
(26): Conv2d(2.36 M, 1.706% Params, 462.52 MMac, 2.983% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(27): ReLU(0, 0.000% Params, 100.35 KMac, 0.001% MACs, inplace=True)
(28): Conv2d(2.36 M, 1.706% Params, 462.52 MMac, 2.983% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(29): ReLU(0, 0.000% Params, 100.35 KMac, 0.001% MACs, inplace=True)
(30): MaxPool2d(0, 0.000% Params, 100.35 KMac, 0.001% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(avgpool): AdaptiveAvgPool2d(0, 0.000% Params, 25.09 KMac, 0.000% MACs, output_size=(7, 7))
(classifier): Sequential(
123.64 M, 89.365% Params, 123.65 MMac, 0.798% MACs,
(0): Linear(102.76 M, 74.275% Params, 102.76 MMac, 0.663% MACs, in_features=25088, out_features=4096, bias=True)
(1): ReLU(0, 0.000% Params, 4.1 KMac, 0.000% MACs, inplace=True)
(2): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.5, inplace=False)
(3): Linear(16.78 M, 12.129% Params, 16.78 MMac, 0.108% MACs, in_features=4096, out_features=4096, bias=True)
(4): ReLU(0, 0.000% Params, 4.1 KMac, 0.000% MACs, inplace=True)
(5): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.5, inplace=False)
(6): Linear(4.1 M, 2.961% Params, 4.1 MMac, 0.026% MACs, in_features=4096, out_features=1000, bias=True)
)
)
Computational complexity: 15.5 GMac
Number of parameters: 138.36 M
root@2c5bb9a7b477:/home/Semisup_cloudSemSeg_master#
