先说一个小知识，助于理解代码中各个层之间维度是怎么变换的。
卷积函数：一般只用来改变输入数据的维度，例如3维到16维。
Conv2d()
Conv2d(in_channels:int,out_channels:int,kernel_size:Union[int,tuple],stride=1,padding=o):
"""
:param in_channels: 输入的维度
:param out_channels: 通过卷积核之后，要输出的维度
:param kernel_size: 卷积核大小
:param stride: 移动步长
:param padding: 四周添多少个零
"""
一个小例子：
import torch
import torch.nn
# 定义一个16张照片，每个照片3个通道，大小是28*28
x= torch.randn(16,3,32,32)
# 改变照片的维度，从3维升到16维，卷积核大小是5
conv= torch.nn.Conv2d(3,16,kernel_size=5,stride=1,padding=0)
res=conv(x)
print(res.shape)
# torch.Size([16, 16, 28, 28])
# 维度升到16维，因为卷积核大小是5，步长是1，所以照片的大小缩小了，变成28
卷积神经网络实战之ResNet18：
下面放一个ResNet18的一个示意图，
ResNet18主要是在层与层之间，加入了一个短接层，可以每隔k个层，进行一次短接。网络层的层数不是 越深就越好。
ResNet18就是，如果在原先的基础上再加上k层，如果有小优化，则保留，如果比原先结果还差，那就利用短接层，直接跳过。
ResNet18的构造如下：
ResNet18(
(conv1): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(3, 3))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(blk1): ResBlk(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(extra): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2))
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(blk2): ResBlk(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(extra): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2))
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(blk3): ResBlk(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(extra): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2))
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(blk4): ResBlk(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(extra): Sequential()
)
(outlayer): Linear(in_features=512, out_features=10, bias=True)
)
程序运行前，先启动visdom,如果没有配置好visdom环境的，先百度安装好visdom环境
- 1.使用快捷键win+r,在输入框输出cmd,然后在命令行窗口里输入
python -m visdom.server，启动visdom
代码实战
定义一个名为
resnet.py的文件，代码如下
import torch
from torch import nn
from torch.nn import functional as F
# 定义两个卷积层 + 一个短接层
class ResBlk(nn.Module):
def __init__(self,ch_in,ch_out,stride=1):
super(ResBlk, self).__init__()
# 两个卷积层
self.conv1=nn.Conv2d(ch_in,ch_out,kernel_size=3,stride=stride,padding=1)
self.bn1=nn.BatchNorm2d(ch_out)
self.conv2=nn.Conv2d(ch_out,ch_out,kernel_size=3,stride=1,padding=1)
self.bn2=nn.BatchNorm2d(ch_out)
# 短接层
self.extra=nn.Sequential()
if ch_out != ch_in:
self.extra=nn.Sequential(
nn.Conv2d(ch_in,ch_out,kernel_size=1,stride=stride),
nn.BatchNorm2d(ch_out)
)
def forward(self,x):
"""
:param x: [b,ch,h,w]
:return:
"""
out=F.relu(self.bn1(self.conv1(x)))
out=self.bn2(self.conv2(out))
# 短接层
# element-wise add: [b,ch_in,h,w]=>[b,ch_out,h,w]
out=self.extra(x)+out
return out
class ResNet18(nn.Module):
def __init__(self):
super(ResNet18, self).__init__()
# 定义预处理层
self.conv1=nn.Sequential(
nn.Conv2d(3,64,kernel_size=3,stride=3,padding=0),
nn.BatchNorm2d(64)
)
# 定义堆叠ResBlock层
# followed 4 blocks
# [b,64,h,w]-->[b,128,h,w]
self.blk1=ResBlk(64,128,stride=2)
# [b,128,h,w]-->[b,256,h,w]
self.blk2=ResBlk(128,256,stride=2)
# [b,256,h,w]-->[b,512,h,w]
self.blk3=ResBlk(256,512,stride=2)
# [b,512,h,w]-->[b,512,h,w]
self.blk4=ResBlk(512,512,stride=2)
# 定义全连接层
self.outlayer=nn.Linear(512,10)
def forward(self,x):
"""
:param x:
:return:
"""
# 1.预处理层
x=F.relu(self.conv1(x))
# 2. 堆叠ResBlock层：channel会慢慢的增加， 长和宽会慢慢的减少
# [b,64,h,w]-->[b,512,h,w]
x=self.blk1(x)
x=self.blk2(x)
x=self.blk3(x)
x=self.blk4(x)
# print("after conv:",x.shape) # [b,512,2,2]
# 不管原先什么后面两个维度是多少，都化成[1,1]，
# [b,512,1,1]
x=F.adaptive_avg_pool2d(x,[1,1])
# print("after pool2d:",x.shape) # [b,512,1,1]
# 将[b,512,1,1]打平成[b,512*1*1]
x=x.view(x.size(0),-1)
# 3.放到全连接层，进行打平
# [b,512]-->[b,10]
x=self.outlayer(x)
return x
def main():
blk=ResBlk(64,128,stride=2)
temp=torch.randn(2,64,32,32)
out=blk(temp)
# print('block:',out.shape)
x=torch.randn(2,3,32,32)
model=ResNet()
out=model(x)
# print("resnet:",out.shape)
if __name__ == '__main__':
main()
定义一个名为
main.py的文件，代码如下
import torch
from torchvision import datasets
from torchvision import transforms
from torch.utils.data import DataLoader
from torch import nn,optim
from visdom import Visdom
# from lenet5 import Lenet5
from resnet import ResNet18
import time
def main():
batch_siz=32
cifar_train = datasets.CIFAR10('cifar',True,transform=transforms.Compose([
transforms.Resize((32,32)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]),download=True)
cifar_train=DataLoader(cifar_train,batch_size=batch_siz,shuffle=True)
cifar_test = datasets.CIFAR10('cifar',False,transform=transforms.Compose([
transforms.Resize((32,32)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]),download=True)
cifar_test=DataLoader(cifar_test,batch_size=batch_siz,shuffle=True)
x,label = iter(cifar_train).next()
print('x:',x.shape,'label:',label.shape)
# 指定运行到cpu //GPU
device=torch.device('cpu')
# model = Lenet5().to(device)
model = ResNet18().to(device)
# 调用损失函数use Cross Entropy loss交叉熵
# 分类问题使用CrossEntropyLoss比MSELoss更合适
criteon = nn.CrossEntropyLoss().to(device)
# 定义一个优化器
optimizer=optim.Adam(model.parameters(),lr=1e-3)
print(model)
viz=Visdom()
viz.line([0.],[0.],win="loss",opts=dict(title='Lenet5 Loss'))
viz.line([0.],[0.],win="acc",opts=dict(title='Lenet5 Acc'))
# 训练train
for epoch in range(1000):
# 变成train模式
model.train()
# barchidx:下标，x:[b,3,32,32],label:[b]
str_time=time.time()
for barchidx,(x,label) in enumerate(cifar_train):
# 将x，label放在gpu上
x,label=x.to(device),label.to(device)
# logits:[b,10]
# label:[b]
logits = model(x)
loss = criteon(logits,label)
# viz.line([loss.item()],[barchidx],win='loss',update='append')
# backprop
optimizer.zero_grad()
loss.backward()
optimizer.step()
# print(barchidx)
end_time=time.time()
print('第 {} 次训练用时: {}'.format(epoch,(end_time-str_time)))
viz.line([loss.item()],[epoch],win='loss',update='append')
print(epoch,'loss:',loss.item())
# 变成测试模式
model.eval()
with torch.no_grad():
# 测试test
# 正确的数目
total_correct=0
total_num=0
for x,label in cifar_test:
# 将x，label放在gpu上
x,label=x.to(device),label.to(device)
# [b,10]
logits=model(x)
# [b]
pred=logits.argmax(dim=1)
# [b] = [b'] 统计相等个数
total_correct+=pred.eq(label).float().sum().item()
total_num+=x.size(0)
acc=total_correct/total_num
print(epoch,'acc:',acc)
print("------------------------------")
viz.line([acc],[epoch],win='acc',update='append')
# viz.images(x.view(-1, 3, 32, 32), win='x')
if __name__ == '__main__':
main()
测试结果
ResNet跑起来太费劲了，需要用GPU跑，但是我的电脑不支持GPU，头都大了，用cpu跑二十多分钟学习一次，头都大了。
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