最近需要对深度学习模型进行部署,因此需要对模型进行压缩,博主取舍了很多大佬的博文并亲测有效,分享笔记邀大家共同学习讨论

前言

深度学习剪枝(Pruning)是一种用于减少神经网络模型大小、减少计算量和提高推理效率的技术，通过去除神经网络中的冗余连接(权重)或节点(神经元)，从而实现模型的稀疏化。
深度学习剪枝(Pruning)具有以下几个好处：1. 模型压缩和存储节省；2. 计算资源节省；3. 加速推理速度；4. 防止过拟合。
“假剪枝”(Fake Pruning)是一种剪枝算法的称呼，它在剪枝过程中并不真正删除权重或节点，而是通过一些技巧将它们置零或禁用，以模拟剪枝的效果，不少优秀的论文就采用了"假剪枝"策略，尽管可以在一定程度上提高模型的推理速度，但假剪枝算法没有真正减少模型的大小，博主将通过讲解一个小案例，简洁易懂的说明一种对"假剪枝"卷积层进行真正的剪枝的的方法。

卷积层剪枝

可以先将最后的完整代码拷贝到自己的py文件中，然后按照博主的思路学习如何将置零卷积核进行真实剪枝：

1. 初始化卷积层，并查看卷积层权重

   # 示例使用一个具有3个输入通道和5个输出通道的卷积层
   conv = nn.Conv2d(3, 5, 3)
   print("原始卷积层权重：")
   print(conv.weight.data)
   print(conv.weight.size())
   print("原始卷积层偏置：")
   print(conv.bias.data)
   print(conv.bias.size())
   

2. 通过随机函数让部分卷积核权重置为0，模拟完成了假剪枝。

   # remove_zero_kernels方法内的代码
   weight = conv_layer.weight.data
   # 卷积核个数
   num_kernels = weight.size(0)
   # 随机对部分卷积置0
   pruned = torch.ones(num_kernels, 1, 1, 1)
   # 选择随着置0的卷积序号
   random_int = random.randint(1, num_kernels-1)
   for i in range(random_int):pruned[i, 0, 0, 0] = 0
   conv_layer.weight.data = weight * pruned
   weight = conv_layer.weight.data
   bias = conv_layer.bias.data
   

3. 保存未被剪枝的卷积核的权重和偏置

   # 计算每个卷积核的L2范数,目的是为了检查卷积核的所有位置是不是都置0了
   norms = torch.norm(weight.view(num_kernels, -1), dim=1)
   zero_kernel_indices = torch.nonzero(norms==0).squeeze()
   print(zero_kernel_indices)
   # 移除L2范数为零的卷积核
   new_weight = torch.stack([weight[i, :, :, :] for i in range(num_kernels) if i not in zero_kernel_indices])
   new_bias = torch.stack([bias[i] for i in range(num_kernels) if i not in zero_kernel_indices])
   

4. 构建新的卷积层，用来替换此前的卷积层，完成置零卷积核的真实剪枝

   # 构建新的卷积层
   if zero_kernel_indices.numel() > 0:# 输入channelin_channels = weight.size(1)# 输出channelout_channels = new_weight.size(0)# 卷积核大小kernel_size = weight.size(2)# 步长stride = conv_layer.stridepadding = conv_layer.paddingdilation = conv_layer.dilationgroups = conv_layer.groupsnew_conv_layer = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups)new_conv_layer.weight.data = new_weightnew_conv_layer.bias.data = new_bias
   else:new_conv_layer = conv_layer
   

完整代码

import torch
import torch.nn as nn
import randomdef remove_zero_kernels(conv_layer):# 卷积核权重weight = conv_layer.weight.data# 卷积核个数num_kernels = weight.size(0)# 随机对部分卷积置0pruned = torch.ones(num_kernels, 1, 1, 1)# 选择随着置0的卷积序号random_int = random.randint(1, num_kernels-1)for i in range(random_int):pruned[i, 0, 0, 0] = 0conv_layer.weight.data = weight * prunedweight = conv_layer.weight.databias = conv_layer.bias.data# 计算每个卷积核的L2范数，目的是为了检查卷积核的所有位置是不是都置0了norms = torch.norm(weight.view(num_kernels, -1), dim=1)zero_kernel_indices = torch.nonzero(norms==0).squeeze()print(zero_kernel_indices)# 移除L2范数为零的卷积核new_weight = torch.stack([weight[i, :, :, :] for i in range(num_kernels) if i not in zero_kernel_indices])new_bias = torch.stack([bias[i] for i in range(num_kernels) if i not in zero_kernel_indices])# 构建新的卷积层if zero_kernel_indices.numel() > 0:# 输入channelin_channels = weight.size(1)# 输出channelout_channels = new_weight.size(0)# 卷积核大小kernel_size = weight.size(2)# 步长stride = conv_layer.stridepadding = conv_layer.paddingdilation = conv_layer.dilationgroups = conv_layer.groupsnew_conv_layer = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups)new_conv_layer.weight.data = new_weightnew_conv_layer.bias.data = new_biaselse:new_conv_layer = conv_layerreturn new_conv_layer# 示例使用一个具有3个输入通道和5个输出通道的卷积层
conv = nn.Conv2d(3, 5, 3)
# print("原始卷积层权重：")
# print(conv.weight.data)
# print(conv.weight.size())
# print("原始卷积层偏置：")
# print(conv.bias.data)
# print(conv.bias.size())# 将置零的卷积核移除
new_conv = remove_zero_kernels(conv)
# print("原始卷积层权重：")
# print(new_conv.weight.data)
# print(new_conv.weight.size())
# print("原始卷积层偏置：")
# print(new_conv.bias.data)
# print(new_conv.bias.size())

总结

博主的思路就是用卷积层中保留的(未被剪枝)权重初始化一个新的卷积层，这样就将假剪枝的置零卷积核真实的除去，有没有研究这方面的读者可以给博主分享其他的方法，共同进步。