计算模型FLOPs和参数量
在pytorch环境下，有两个计算FLOPs和参数量的包thop和ptflops，结果基本是一致的。
thop
参考https://github.com/Lyken17/pytorch-OpCounter
安装方法：pip install thop
使用方法：
from torchvision.models import resnet18
from thop import profile
model = resnet18()
input = torch.randn(1, 3, 224, 224) #模型输入的形状,batch_size=1
flops, params = profile(model, inputs=(input, ))
print(flops/1e9,params/1e6) #flops单位G，para单位M
用来测试3d resnet18的FLOPs：
model =C3D_Hash_Model(48)
input = torch.randn(1, 3,10, 112, 112) #视频取10帧
flops, params = profile(model, inputs=(input, ))
print(flops/1e9,params/1e6)
ptflops
参考https://github.com/sovrasov/flops-counter.pytorch
安装方法：pip install pthflops
或者 pip install git+https://github.com/sovrasov/flops-counter.pytorch.git
使用方法：
import torchvision.models as models
import torch
from ptflops import get_model_complexity_info
with torch.cuda.device(0):
net = models.resnet18()
flops, params = get_model_complexity_info(net, (3, 224, 224), as_strings=True, print_per_layer_stat=True) #不用写batch_size大小，默认batch_size=1
print('Flops: ' + flops)
print('Params: ' + params)
用来测试3d resnet18的FLOPs：
import torch
from ptflops.flops_counter import get_model_complexity_info
with torch.cuda.device(0):
net = C3D_Hash_Model(48)
flops, params = get_model_complexity_info(net, (3,10, 112, 112), as_strings=True, print_per_layer_stat=True)
print('Flops: ' + flops)
print('Params: ' + params)
如果安装ptflops出问题，可以直接到https://github.com/sovrasov/flops-counter.pytorch.git下载代码，然后直接把目录ptflops复制到项目代码中，通过from ptflops.flops_counter import get_model_complexity_info来调用函数计算FLOPs
python计时程序运行时间
import time
time_start=time.time()
#在这里运行模型
time_end=time.time()
print('totally cost',time_end-time_start)
相关于3d resnet的FLOPs的论文or博客：
1、似乎是3d resnet的原始论文
ConvNet Architecture Search for Spatiotemporal Feature Learning
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2、github上的3d resnet代码 https://github.com/kenshohara/3D-ResNets-PyTorch
对应的论文：Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
奇怪的是在原论文里3d resnet18的FLOPs是19.3，参数量是33.2
而用thop和ptflops计算github上的3d resnet18的FLOPs是8左右，参数量是33.2
此外，以下的计算结果得到是两倍的帧会带来两倍的flops：
