Acquisition of model parameters (Parameters) and calculation amount (FLOPs) [using thop]

Tips: For some open source codes, there is no code for calculating network parameters and calculations. Here is a general method to obtain the parameter amount and calculation amount of the network. Use thop to get it quickly

1 Model parameters and computation

Amount of parameters #paramsThat is, how many parameters are included in the network model, which has nothing to do with the input data, but mainly has a relationship with the structure of the model; its main influence on the model operation is the required memory or video memory

Computation Amount #FLOPsFLOPs (Floating point operations, the number of floating point operations) are usually used to represent the amount of calculation, which mainly measures the complexity of the algorithm/model. In the paper, GFLOPs are generally used to represent, 1GFLOPs=10^9 FLOPs;

2 install thop

pip install thop

3 sample code

#### (such as calculating the parameter amount and calculation amount of the following network) ####

import torch
import torch.nn as nn

# SENet
class SELayer(nn.Module):
    def __init__(self, channel, reduction=16):
        super(SELayer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(channel, channel // reduction, bias=False),
            nn.ReLU(inplace=True),
            nn.Linear(channel // reduction, channel, bias=False),
            nn.Sigmoid()
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        return x * y.expand_as(x)

#### (thop code, you can optimize it by yourself) ####

import thop
if __name__ == '__main__':
    # 输入1 channel
    model = SELayer(channel=64)
    # (1, 64, 640, 640) 输入的图片尺寸
    x = torch.randn(1, 64, 640, 640)
    flops, params = thop.profile(model, inputs=(x,))
    print("%s | %s | %s" % ("Model", "Params(M)", "FLOPs(G)"))
    print("------|-----------|------")
    print("%s | %.7f | %.7f" % ("模型  ", params / (1000 ** 2), flops / (1000 ** 3)))

4 Result display