pytorch实现 分组卷积 深度可分离卷积

• 常规卷积：

 

常规卷积需要的参数量：Cin​×K×K×Cout​

• 分组卷积：

分组卷积需要的计算量 ： Cin​×K×K×Cout​ / g

• 深度可分离卷积(Depthwise Separable Convolution):

需要的计算量：Cin​×K×K+Cout​×1×1

 

实现：

import torch
from torchsummary import  summary
import torch.nn as nn

'''
实现分组卷积demo
'''
class CRNN(nn.Module):
    def __init__(self,in_channel,out_channel,group):
        super(CRNN,self).__init__()
        self.conv = nn.Conv2d(in_channels=in_channel,
                              out_channels=out_channel,
                              kernel_size=3,
                              stride=1,
                              padding=1,
                              groups=group,
                              bias=False)
    def forward(self,input):
        out = self.conv(input)
        return out


'''
深度可分离卷积demo
'''
class DEPTHWISECONV(nn.Module):
    def __init__(self,in_ch,out_ch):
        super(DEPTHWISECONV, self).__init__()
        self.depth_conv = nn.Conv2d(in_channels=in_ch,
                                    out_channels=in_ch,
                                    kernel_size=3,
                                    stride=1,
                                    padding=1,
                                    groups=in_ch)
        self.point_conv = nn.Conv2d(in_channels=in_ch,
                                    out_channels=out_ch,
                                    kernel_size=1,
                                    stride=1,
                                    padding=0,
                                    groups=1)
    def forward(self,input):
        out = self.depth_conv(input)
        out = self.point_conv(out)
        return out



if __name__=='__main__':

    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    conv = CRNN(3,6,1).to(device)

    print(summary(conv,input_size=(3,32,32)))

    dp = DEPTHWISECONV(3,6).to(device)
    print(summary(dp,input_size=(3,32,32)))

reference:https://blog.csdn.net/weixin_30793735/article/details/88915612