1.方法
torchstat.stat：计算Pytorch模型的FLOPs、模型参数量、MAdd、模型显存占用量
thop：工具包仅支持FLOPs和参数量的计算
ptflops：统计 参数量 和 FLOPs
torchsummary：用来计算网络的计算参数等信息
下载不下来用
-i http://pypi.douban.com/simple --trusted-host pypi.douban.com
2.计算换算
FLOPs是floating point operations的缩写（s表复数），意指浮点运算数，理解为计算量，用以衡量算法/模型复杂度。
MACs 每秒执行的定点乘累加操作次数的缩写，它是衡量计算机定点处理能力的量，这个量经常用在那些需要大量定点乘法累加运算的科学运算中，记为MACs。
- 一个 MFLOPS (megaFLOPS) 等于每秒1百万 (=10^6) 次的浮点运算，
- 一个 GFLOPS (gigaFLOPS) 等于每秒10亿 (=10^9) 次的浮点运算，
- 一个 TFLOPS (teraFLOPS) 等于每秒1万亿 (=10^12) 次的浮点运算，
- 一个 PFLOPS (petaFLOPS) 等于每秒1千万亿 (=10^15) 次的浮点运算
- GMAC=0.5GFLOPs
3.具体代码
代码1：
def shufflenet_1x(num_classes=10):
return ShuffleNetV2(1, num_classes)
model=shufflenet_1x()
#a=torch.randn(1,3,224,224)
# print(model(a))
"""通过torchstat.stat 可以查看网络模型的参数量和计算复杂度FLOPs"""
from torchstat import stat
# stat(model,(3,224,224))
#=======================================================================================================================================================
#Total params: 561,706
#-------------------------------------------------------------------------------------------------------------------------------------------------------
#Total memory: 6.88MB
#Total MAdd: 79.01MMAdd
#Total Flops: 39.96MFlops
#Total MemR+W: 14.08MB
"""thop工具包仅支持FLOPs和参数量的计算"""
# from thop import profile
# from thop import clever_format
# input=torch.randn(1,3,224,224)
# flops, params = profile(model, inputs=(input, ))
# print(flops, params) # 46388784.0 561706.0
# flops, params = clever_format([flops, params], "%.3f")
# print(flops, params) # 46.389M 561.706K
"""ptflops统计 参数量 和 FLOPs"""
from ptflops import get_model_complexity_info
macs, params = get_model_complexity_info(model, (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))
#Computational complexity: 0.05 GMac
#Number of parameters: 1.26 M
"""torchsummary 用来计算网络的计算参数等信息"""
from torchsummary import summary
model2 = ShuffleNetV2(scale=1.5, in_channels=3, c_tag=0.5, num_classes=10, activation=nn.ReLU,SE=False, residual=False)
a=torch.randn(1,3,224,224)
summary(model2.cuda(),input_size=(3,224,224))
# ================================================================
#Total params: 2,489,770
#Trainable params: 2,489,770
#Non-trainable params: 0
#----------------------------------------------------------------
#Input size (MB): 0.57
#Forward/backward pass size (MB): 62.77
#Params size (MB): 9.50
#Estimated Total Size (MB): 72.84
#----------------------------------------------------------------
代码2：
from nanodet.model.arch import build_model
from nanodet.util import cfg, load_config
from torchvision.models import resnet50
import torch
config = 'D:/pycharm/nanodet-main/config/nanodet-m.yml'
load_config(cfg, config)
model = build_model(cfg.model)
model1 = resnet50()
"""通过torchstat.stat 可以查看网络模型的参数量和计算复杂度FLOPs"""
from thop import profile
input = torch.randn(1,3,224,224)
flops,params = profile(model,inputs=(input,))
print('the flops is {}G,the params is {}M'.format(round(flops/(10**9),2), round(params/(10**6),2))) # 4111514624.0 25557032.0 res50
