本博文以 Dense Block 为例，Pytorch 为 DL 框架，最终计算模块参数量方法如下：
import torch import torch.nn as nn class Norm_Conv(nn.Module): def __init__(self,in_channel): super(Norm_Conv,self).__init__() self.layers = nn.Sequential( nn.Conv2d(in_channel,in_channel,3,1,1), nn.ReLU(True), nn.BatchNorm2d(in_channel), nn.Conv2d(in_channel,in_channel,3,1,1), nn.ReLU(True), nn.BatchNorm2d(in_channel), nn.Conv2d(in_channel,in_channel,3,1,1), nn.ReLU(True), nn.BatchNorm2d(in_channel)) def forward(self,input): out = self.layers(input) return out class DenseBlock_Norm(nn.Module): def __init__(self,in_channel): super(DenseBlock_Norm,self).__init__() self.first_layer = nn.Sequential(nn.Conv2d(in_channel,in_channel,3,1,1), nn.ReLU(True), nn.BatchNorm2d(in_channel)) self.second_layer = nn.Sequential(nn.Conv2d(in_channel*2,in_channel,3,1,1), nn.ReLU(True), nn.BatchNorm2d(in_channel)) self.third_layer = nn.Sequential( nn.Conv2d(in_channel*3,in_channel,3,1,1), nn.ReLU(True), nn.BatchNorm2d(in_channel)) def forward(self,input): output1 = self.first_layer(input) output2 = self.second_layer(torch.cat((output1,input),dim=1)) output3 = self.third_layer(torch.cat((input,output1,output2),dim=1)) return output3 def count_param(model): param_count = 0 for param in model.parameters(): param_count += param.view(-1).size()[0] return param_count # Get Parameter number of Network in_channel = 128 net1 = Norm_Conv(in_channel) print('Norm Conv parameter count is {}'.format(count_param(net1))) net2 = DenseBlock_Norm(in_channel) print('DenseBlock Norm parameter count is {}'.format(count_param(net2)))
最终结果如下
Norm Conv parameter count is 443520 DenseBlock Norm parameter count is 885888
