MobileNetV2网络结构如下，网络的详细讲解参考博客：MobileNet系列(2)：MobileNet-V2 网络详解
图1 MobileNet V2网络架构
从表格的网络结构可以看出，模型基本上就是堆叠倒残差结构 (bottleneck),然后通过 1x1的普通卷积核操作，紧接着是池化核为 7x7的平均池化下采样，最后通过 1x1卷积得到最终的输出。搭建该网络的关键是 倒残差结构，只要构建好 倒残差结构,就能很方便对网络进行搭建了。
; pytorch 网络搭建
在 model.py文件中，首先定义网络的基础组件。
在 mobilenet v2网络中卷积基本上都是通过： Conv+BN+ReLU6组成的。
卷积组件
Conv+BN+ReLU6
class ConvBNReLU(nn.Sequential):
def __init__(self,in_channel,out_channel,kernel_size,stride=1,groups=1):
padding=(kernel_size-1) // 2
super(ConvBNReLU,self).__init__(
nn.Conv2d(in_channel,out_channel,kernel_size,stride,padding,groups=groups,bias=False),
nn.BatchNorm2d(out_channel),
nn.ReLU6(inplace=True)
)
注意 groups=1表示构建的是普通的卷积，如果 groups等于in_channel,那么它就是 DW卷积。由于要使用 BN层，因此 bias是不使用的，设置为 False
倒残差结构
定义一个 InvertedResidual类，它继承与 nn.Moudle这个父类。倒残差结构网络图如下：
倒残差网络结构与普通的残差网络结构是类似的，
普通残差结构是两头粗中间细的结构，倒残差结构相反是两头细中间粗的结构。详见：MobileNet系列(2)：MobileNet-V2 网络详解，DW卷积的个数是个输入channel是一样的，每个DW卷积层只负责一个channel.所以经过 DW卷积后不改变channel的大小。
class InvertedResidual(nn.Module):
def __init__(self,in_channel,out_channel,stride,expand_ratio):
super(InvertResidual,self).__init__()
hidden_channel=in_channel*expand_ratio
self.use_shotcut = stride ==1 and in_channel==out_channel
layers= []
if expand_ratio !=1:
layers.append(ConvBNReLU(in_channel,hidden_channel,kernel_size=1))
layers.extend([
ConvBNReLU(hidden_channel,hidden_channel,stride=stride,groups=hidden_channel)
nn.Conv2d(hidden_channel,out_channel,kernel_size=1,bias=False)
nn.BatchNorm2d(out_channel)
])
self.conv=nn.Sequential(*layers)
def forward(self,x):
if self.use_shotcut:
return x+ self.conv(x)
else:
return self.conv(x)
MobileNet V2网络结构
定义 MobileNetV2类，继承 nn.Module, 完整网络搭建代码如下：
class MobileNetV2(nn.Module):
def __init__(self,num_classes=100,alpha=1.0,round_nearest=8):
super(MobileNetV2,self).__init__()
block=InvertedResidual
input_channel=_make_divisible(32*alpha,round_nearest)
last_channel=_make_divisible(1280*alpha,round_nearest)
inverted_residual_setting = [
[1,16,1,1],
[6,24,2,2],
[6,32,3,2],
[6,64,4,2],
[9,96,3,1],
[6,160,3,2],
[6,320,1,1]
]
features = []
features.append(ConvBNReLU(3,input_channel,stride=2))
for t,c,n,s in inverted_residual_setting:
output_channels= _make_divisible(c*alpha,round_nearest)
for i in range(n):
stride= s if i==0 else 1
features.append(block(input_channel,output_channel,stride,expand_ratio=t))
input_channel=output_channel
features.append(ConvBNReLU(input_channel,last_channel,1))
self.features=nn.Sequential(*features)
self.avgpool=nn.AdaptiveAvgPool2d((1,1))
self.classifier = nn.Sequential(
nn.Dropout(0.2),
nn.Linear(last_channel,num_classes)
)
for m in self.modules():
if isinstance(m,nn.Conv2d):
nn.init.kaiming_normal_(m.weight,mode='fan_out')
if m.bias is not None:
m.init.zeros_(m.bias)
elif isinstance(m,nn.BatchNorm2d):
nn.init.ones_(m.weight,0,0.01)
nn.init.zeros_(m.bias)
def forward(self,x):
x=self.features(x)
x=self.avgpool(x)
x=torch.flatten(x,1)
x=self.classifier(x)
return x
其中 _make_divisible函数l来源于 tensorflow官方实现的代码:
def _make_divisible(ch,divisor=8,min_ch=None):
"""
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
"""
if min_ch is None:
min_ch=divisor
new_ch=max(min_ch,int(ch+divisor/2)//divisor*divisor)
if new_ch <0.9 * ch:
new_ch +=divisor
return new_ch
模型训练
首先说下，如何去下载官方的预训练模型参数。比如下载mobilenet的预训练模型
import torchvision.models.mobilenet
点击 torchvision.models.mobilenet进入官方的函数定义中，这里有一个 model_urls,这个 url就是模型的预训练权重的下载链接：
model_urls= {
'mobilenet_v2':'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth'
}
复制模型 url到迅雷进行下载，下载后存在当前项目目录下，并命名： mobilenet_v2.pth
训练脚本
train.py
1. import python 包
import torch
import torch.nn as nn
from torchvision import transforms,datasets
import json
import os
import torch.optim as optim
from model import MobileNetV2
2. 数据准备
data_transform= {
"train": transforms.Compose([transforms.RandomResizeCrop(224),
transforms.RandomHorizontalFlip(),
transforms.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225])]),
"val":transforms.Compose([transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225])])
}
data_root = os.path.abspath(os.path.join(os.getcwd(),'../..'))
image_path=data_root +"/data_set/flower_data/"
train_dataset = datasets.ImageFolder(root=image_path + "train",transform=data_transform["train"])
train_num=len(train_dataset)
flower_list = train_dataset.class_to_idx
cla_dict =dict((val,key) for key,value in flower_list.items())
json_str=json.dumps(cla_dict,indent=4)
with open('class_indices.json','w') as json_file:
json_file.write(json_str)
bath_size=16
train_loader=torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,shuffle=True,
num_workers=0)
validate_data=datasets.ImageFolder(root=image_path + "val",
transform=data_transform["val"])
val_num=len(validate_dataset)
validate_loader=torch.utils.data.DataLoader(validate_dataset,
batch_size=batch_size,shuffle=False,
num_works=0)
3. 加载模型
net=MobileNetV2(num_classes=5)
model_weight_path="./mobilenet_v2.pth"
assert os.path.exists(model_weight_path), "file {} dose not exist.".format(model_weight_path)
pre_weights = torch.load(model_weight_path, map_location=device)
pre_dict=={k:v for k,v in pre_weights.items() if net.state_dict()[k].numel() == v.numel()}
missing_keys,unexpected_keys=net.load_state_dict(pre_dict,strict=False)
for param in net.features.parameters():
param.requires_grad=False
net.to(device)
4. 模型的训练
loss_function=nn.CrossEntropyLoss()
params=[p for p in net.parameters() if p.requires_grad]
optimizer=optim.Adam(params,lr=0.0001)
best_acc=0.0
save_path='./MobileNetV2.pth'
train_steps = len(train_loader)
for epoch in range(epochs):
net.train()
running_loss=0.0
train_bar=tqdm(train_loader)
for step,data in enumerate(train_bar):
images,labels=data
optimizer.zero_grad()
logits=net(images.to(device))
loss=loss_function(logits,labels.to(device))
loss.backward()
optimizer.step()
running_loss +=loss.item()
train_bar.desc="train epoch [{} / {}] loss:{:.3f}".format(epoch+1,epochs,loss)
net.eval()
acc=0.0
with torch.no_grad():
val_bar=tqdm(validate_loader)
for val_data in val_bar:
val_images,val_labels=val_data
outputs = net(val_images.to(device))
predict_y= torch.max(outputs,dim=1)[1]
acc += torch.eq(predict_y,val_labels.to(device)).sum().item()
val_bar.desc ="valid epoch [{}/{}]".format(epoch+1,epochs)
val_accurate = acc / val_num
print('[epoch %d] train_loss: %.3f val_accuracy: %.3f' %
(epoch + 1, running_loss / train_steps, val_accurate))
if val_accurate > best_acc:
best_acc = val_accurate
torch.save(net.state_dict(), save_path)
print('Finished Training')
Original: https://blog.csdn.net/weixin_38346042/article/details/125358925
Author: @BangBang
Title: Mobilenet系列(3):使用pytorch搭建MobileNetV2并基于迁移学习训练
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