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模型压缩（一）通道剪枝-BN层

论文：https://arxiv.org/pdf/1708.06519.pdf

BN层中缩放因子γ与卷积层中的每个通道关联起来。在训练过程中对这些比例因子进行稀疏正则化，以自动识别不重要的通道。缩放因子值较小的通道(橙色)将被修剪(左侧)。剪枝后，获得了紧凑的模型(右侧)，然后对其进行微调，以达到与正常训练的全网络相当(甚至更高)的精度。

BN层原理：

归一化化后，BN层服从正态分布，当γ,β趋于0时，经过阈值分离，输出为0，与之连接的卷积层输入为0。

剪枝流程：

剪枝原理：

在BN层网络中加入稀疏因子，训练使得BN层稀疏化，对稀疏训练的后的模型中所有BN层权重进行统计排序，获取指定保留BN层数量即取得排序后权重阈值thres。遍历模型中的BN层权重，制作各层mask（权重>thres值为1，权重<thres值为0）。剪枝操作，根据各层的mask构建新模型结构（各层保留的通道数），获取BN层权重*mask非零值的索引，非零索引对应的原始conv层、BN层、linear层各通道的权重、偏置等值赋值给新模型各层。加载剪枝后模型，进行fine-tune。

如下实现一个简单的网络剪枝。

1、自定义一个网络

对网络进行

import torch
import torch.nn as nn
import numpy as np

class net(nn.Module):
    def __init__(self,cfg=None):
        super(net, self).__init__()
        if cfg:
            self.features=self.make_layer(cfg)
            self.linear = nn.Linear(cfg[2], 2)
        else:
            layers=[]
            layers+=[nn.Conv2d(3,64,7,2,1,bias=False),
                nn.BatchNorm2d(64),
                nn.ReLU(inplace=True)]

            layers += [
                nn.Conv2d(64,128,3,2,1,bias=False),
                nn.BatchNorm2d(128),
                nn.ReLU(inplace=True)
            ]
            layers += [
                nn.Conv2d(128, 256, 3, 2, 1,bias=False),
                nn.BatchNorm2d(256),
                nn.ReLU(inplace=True)
            ]
            layers += [nn.AvgPool2d(2)]
            self.features=nn.Sequential(*layers)
            self.linear=nn.Linear(256,2)

    def make_layer(self,cfg):
        layers=[]
        layers += [nn.Conv2d(3, cfg[0], 7, 2, 1, bias=False),
                   nn.BatchNorm2d(cfg[0]),
                   nn.ReLU(inplace=True)]

        layers += [
            nn.Conv2d(cfg[0], cfg[1], 3, 2, 1, bias=False),
            nn.BatchNorm2d(cfg[1]),
            nn.ReLU(inplace=True)
        ]
        layers += [
            nn.Conv2d(cfg[1], cfg[2], 3, 2, 1, bias=False),
            nn.BatchNorm2d(cfg[2]),
            nn.ReLU(inplace=True)
        ]
        layers += [nn.AvgPool2d(2)]
        return nn.Sequential(*layers)

    def forward(self,x):
        x=self.features(x)
        # print(x.shape)
        x=x.view(x.size(0),-1)
        x=self.linear(x)
        return x

网络参数信息：

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1              [1, 64, 8, 8]           9,408
       BatchNorm2d-2              [1, 64, 8, 8]             128
              ReLU-3              [1, 64, 8, 8]               0
            Conv2d-4             [1, 128, 4, 4]          73,728
       BatchNorm2d-5             [1, 128, 4, 4]             256
              ReLU-6             [1, 128, 4, 4]               0
            Conv2d-7             [1, 256, 2, 2]         294,912
       BatchNorm2d-8             [1, 256, 2, 2]             512
              ReLU-9             [1, 256, 2, 2]               0
        AvgPool2d-10             [1, 256, 1, 1]               0
           Linear-11                     [1, 2]             514
================================================================
Total params: 379,458
Trainable params: 379,458
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.17
Params size (MB): 1.45
Estimated Total Size (MB): 1.62

2、稀疏训练

在BN层中各权重加入稀疏因子。

def updateBN(model,s=0.0001):
    for m in model.modules():
        if isinstance(m,nn.BatchNorm2d):
            m.weight.grad.data.add_(s*torch.sign(m.weight.data))

if __name__=="__main__":
    model=net()
    # from torchsummary import summary
    # print(summary(model,(3,20,20),1))
    # x = torch.rand((1, 3, 20, 20))
    # print(model(x))
    optimer=torch.optim.Adam(model.parameters())
    loss_fn=torch.nn.CrossEntropyLoss()
    for e in range(100):
        x = torch.rand((1, 3, 20, 20))
        y=torch.tensor(np.random.randint(0,2,(1))).long()
        out=model(x)
        loss=loss_fn(out,y)
        optimer.zero_grad()
        loss.backward()
        #BN权重稀疏化
        updateBN(model)
        optimer.step()
    torch.save(model.state_dict(),"net.pth")

3、剪枝

稀疏训练后的模型，解析。

import net
import torch
import torch.nn as nn
import numpy as np

model = net.net()
#加载稀疏训练的模型
model.load_state_dict(torch.load("net.pth"))
total = 0  # 统计所有BN层的参数量
for m in model.modules():
    if isinstance(m, nn.BatchNorm2d):
        # print(m.weight.data.shape[0])  # 每个BN层权重w参数量：64/128/256
        # print(m.weight.data)
        total += m.weight.data.shape[0]

print("所有BN层总weight数量：",total)

bn_data=torch.zeros(total)
index=0
for m in model.modules():
    #将各个BN层的参数值拷贝到bn中
    if isinstance(m,nn.BatchNorm2d):
        size=m.weight.data.shape[0]
        bn_data[index:(index+size)]=m.weight.data.abs().clone()
        index=size
#对bn中的weight值排序
data,id=torch.sort(bn_data)
percent=0.7#保留70%的BN层通道数
thresh_index=int(total*percent)
thresh=data[thresh_index]#取bn排序后的第thresh_index索引值为bn权重的截断阈值

#制作mask
pruned_num=0#统计BN层剪枝通道数
cfg=[]#统计保存通道数
cfg_mask=[]#BN层权重矩阵，剪枝的通道记为0，未剪枝通道记为1

for k,m in enumerate(model.modules()):
    if isinstance(m,nn.BatchNorm2d):
        weight_copy=m.weight.data.abs().clone()
        # print(weight_copy)
        mask=weight_copy.gt(thresh).float()#阈值分离权重
        # print(mask)
        # exit()
        pruned_num+=mask.shape[0]-torch.sum(mask)#
        # print(pruned_num)
        m.weight.data.mul_(mask)#更新BN层的权重，剪枝通道的权重值为0
        m.bias.data.mul_(mask)

        cfg.append(int(torch.sum(mask)))#记录未被剪枝的通道数量
        cfg_mask.append(mask.clone())
        print("layer index:{:d}\t total channel:{:d}\t remaining channel:{:d}".format(k,mask.shape[0],int(torch.sum(mask))))
    elif isinstance(m,nn.AvgPool2d):
        cfg.append("A")

pruned_ratio=pruned_num/total
print("剪枝通道占比：",pruned_ratio)
print(cfg)
newmodel=net.net(cfg)
# print(newmodel)
# from torchsummary import summary
# print(summary(newmodel,(3,20,20),1))

layer_id_in_cfg=0#层
start_mask=torch.ones(3)
end_mask=cfg_mask[layer_id_in_cfg]#第一个BN层对应的mask
# print(cfg_mask)
# print(end_mask)

for(m0,m1)in zip(model.modules(),newmodel.modules()):#以最少的为准
    if isinstance(m0,nn.BatchNorm2d):
        # idx1=np.squeeze(np.argwhere(np.asarray(end_mask.numpy())))#获得mask中非零索引即未被减掉的序号
        # print(idx1)
        # exit()
        # idx1=np.array([1])
        # # print(idx1)
        if idx1.size==1:
            idx1=np.resize(idx1,(1,))
            # print(idx1)
        # exit()
        #将旧模型的参数值拷贝到新模型中
        m1.weight.data=m0.weight.data[idx1.tolist()].clone()
        m1.bias.data=m0.bias.data[idx1.tolist()].clone()
        m1.running_mean=m0.running_mean[idx1.tolist()].clone()
        m1.running_var = m0.running_var[idx1.tolist()].clone()

        layer_id_in_cfg+=1#下一个mask
        start_mask=end_mask.clone()
        if layer_id_in_cfg<len(cfg_mask):
            end_mask=cfg_mask[layer_id_in_cfg]
    elif isinstance(m0,nn.Conv2d):#输入
        idx0=np.squeeze(np.argwhere(np.asarray(start_mask.numpy())))#输入非0索引
        idx1=np.squeeze(np.argwhere(np.asarray(end_mask.numpy())))#输出非0索引

        if idx0.size==1:
            idx0=np.resize(idx0,(1,))
        if idx1.size==1:
            idx1=np.resize(idx1,(1,))

        w1=m0.weight.data[:,idx0.tolist(),:,:].clone()
        w1=w1[idx1.tolist(),:,:,:].clone()
        m1.weight.data=w1.clone()
    elif isinstance(m0,nn.Linear):
        idx0 = np.squeeze(np.argwhere(np.asarray(start_mask.numpy())))  # 输入非0索引
        if idx0.size==1:
            idx0=np.resize(idx0,(1,))

        m1.weight.data=m0.weight.data[:,idx0].clone()
        m1.bias.data=m0.bias.data.clone()

torch.save(newmodel.state_dict(),"prune_net.pth")
print(newmodel)

新模型结构：

所有BN层总weight数量： 448
layer index:3     total channel:64     remaining channel:29
layer index:6     total channel:128     remaining channel:56
layer index:9     total channel:256     remaining channel:75
剪枝通道占比： tensor(0.6429)
[29, 56, 75, 'A']
net(
  (features): Sequential(
    (0): Conv2d(3, 29, kernel_size=(7, 7), stride=(2, 2), padding=(1, 1), bias=False)
    (1): BatchNorm2d(29, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace=True)
    (3): Conv2d(29, 56, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
    (4): BatchNorm2d(56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU(inplace=True)
    (6): Conv2d(56, 75, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
    (7): BatchNorm2d(75, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (8): ReLU(inplace=True)
    (9): AvgPool2d(kernel_size=2, stride=2, padding=0)
  )
  (linear): Linear(in_features=75, out_features=2, bias=True)
)
----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1              [1, 29, 8, 8]           4,263
       BatchNorm2d-2              [1, 29, 8, 8]              58
              ReLU-3              [1, 29, 8, 8]               0
            Conv2d-4              [1, 56, 4, 4]          14,616
       BatchNorm2d-5              [1, 56, 4, 4]             112
              ReLU-6              [1, 56, 4, 4]               0
            Conv2d-7              [1, 75, 2, 2]          37,800
       BatchNorm2d-8              [1, 75, 2, 2]             150
              ReLU-9              [1, 75, 2, 2]               0
        AvgPool2d-10              [1, 75, 1, 1]               0
           Linear-11                     [1, 2]             152
================================================================
Total params: 57,151
Trainable params: 57,151
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.07
Params size (MB): 0.22
Estimated Total Size (MB): 0.29
----------------------------------------------------------------

模型大小由1.45m压缩到230k，压缩率：84%

4、fine-tune训练

newmodel.load_state_dict(torch.load("prune_net.pth"))
#
optimer=torch.optim.Adam(model.parameters())
loss_fn=torch.nn.CrossEntropyLoss()
for e in range(100):
    x = torch.rand((1, 3, 20, 20))
    y=torch.tensor(np.random.randint(0,2,(1))).long()
    out=newmodel(x)
    loss=loss_fn(out,y)
    optimer.zero_grad()
    loss.backward()
    optimer.step()
torch.save(newmodel.state_dict(),"prune_net.pth")

以上过程仅供参考。

参考：GitHub - foolwood/pytorch-slimming: Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.

Network Slimming——有效的通道剪枝方法（Channel Pruning）_Law-Yao的博客-CSDN博客_通道剪枝算法

标签： 剪枝 人工智能 深度学习
本文转载自: https://blog.csdn.net/m0_37264397/article/details/126157647
版权归原作者 HySmiley 所有， 如有侵权，请联系我们删除。
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