16.1 ResNet网络结构设计
################################################################################################################
#ResNet
################################################################################################################
import torch
from torch import nn
from torch.nn import functional as F
from torchsummary import summary
class Residual(nn.Module):
def __init__(self, input_channels,num_channels,use_1x1conv=False,strides=1):
super().__init__()
self.conv1=nn.Conv2d(input_channels,num_channels,kernel_size=3,padding=1,stride=strides)
self.conv2=nn.Conv2d(num_channels,num_channels,kernel_size=3,padding=1)
if use_1x1conv:
self.conv3=nn.Conv2d(input_channels,num_channels,kernel_size=1,stride=strides)
else:
self.conv3=None
self.bn1=nn.BatchNorm2d(num_channels)
self.bn2=nn.BatchNorm2d(num_channels)
def forward(self,X):
Y=F.relu(self.bn1(self.conv1(X)))
Y=self.bn2(self.conv2(Y))
if self.conv3:
X=self.conv3(X)
Y=Y+X #f(x)=x+g(x)
return F.relu(Y)
def resnet_block(input_channels,num_channels,num_residuals,first_block=False):
blk=[]
#首先对齐residual的第一个层,需要做下采样
#网络第一个阶段通常不做下采样，也不调整通道数。
for i in range(num_residuals):
if i==0 and not first_block:
blk.append(Residual(input_channels,num_channels,use_1x1conv=True,strides=2))#这个是除了第一个模块之后需要执行的
else:
blk.append(Residual(num_channels,num_channels))
return blk
b1=nn.Sequential(nn.Conv2d(1,64,kernel_size=7,stride=2,padding=3),
nn.BatchNorm2d(64),nn.ReLU(),
nn.MaxPool2d(kernel_size=3,stride=2,padding=1))
b2=nn.Sequential(*resnet_block(64,64,2,first_block=True))
b3=nn.Sequential(*resnet_block(64,128,2))
b4=nn.Sequential(*resnet_block(128,256,2))
b5=nn.Sequential(*resnet_block(256,512,2))
device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
model=nn.Sequential(b1,b2,b3,b4,b5,nn.AdaptiveAvgPool2d((1,1)),nn.Flatten(),nn.Linear(512,10)).to(device)
summary(model,input_size=(1,224,224),batch_size=64)
16.2 ResNet网络实现Fashion-Mnist分类
################################################################################################################
#ResNet
################################################################################################################
import torch
import torchvision
from torch import nn
import matplotlib.pyplot as plt
from torchvision.transforms import transforms
from torch.utils.data import DataLoader
from tqdm import tqdm
from sklearn.metrics import accuracy_score
from torch.nn import functional as F
plt.rcParams['font.family']=['Times New Roman']
class Reshape(torch.nn.Module):
def forward(self,x):
return x.view(-1,1,28,28)#[bs,1,28,28]
def plot_metrics(train_loss_list, train_acc_list, test_acc_list, title='Training Curve'):
epochs = range(1, len(train_loss_list) + 1)
plt.figure(figsize=(4, 3))
plt.plot(epochs, train_loss_list, label='Train Loss')
plt.plot(epochs, train_acc_list, label='Train Acc',linestyle='--')
plt.plot(epochs, test_acc_list, label='Test Acc', linestyle='--')
plt.xlabel('Epoch')
plt.ylabel('Value')
plt.title(title)
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()
def train_model(model,train_data,test_data,num_epochs):
train_loss_list = []
train_acc_list = []
test_acc_list = []
for epoch in range(num_epochs):
total_loss=0
total_acc_sample=0
total_samples=0
loop=tqdm(train_data,desc=f"EPOCHS[{epoch+1}/{num_epochs}]")
for X,y in loop:
#X=X.reshape(X.shape[0],-1)
#print(X.shape)
X=X.to(device)
y=y.to(device)
y_hat=model(X)
loss=CEloss(y_hat,y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
#loss累加
total_loss+=loss.item()*X.shape[0]
y_pred=y_hat.argmax(dim=1).detach().cpu().numpy()
y_true=y.detach().cpu().numpy()
total_acc_sample+=accuracy_score(y_pred,y_true)*X.shape[0]#保存样本数
total_samples+=X.shape[0]
test_acc_samples=0
test_samples=0
for X,y in test_data:
X=X.to(device)
y=y.to(device)
#X=X.reshape(X.shape[0],-1)
y_hat=model(X)
y_pred=y_hat.argmax(dim=1).detach().cpu().numpy()
y_true=y.detach().cpu().numpy()
test_acc_samples+=accuracy_score(y_pred,y_true)*X.shape[0]#保存样本数
test_samples+=X.shape[0]
avg_train_loss=total_loss/total_samples
avg_train_acc=total_acc_sample/total_samples
avg_test_acc=test_acc_samples/test_samples
train_loss_list.append(avg_train_loss)
train_acc_list.append(avg_train_acc)
test_acc_list.append(avg_test_acc)
print(f"Epoch {epoch+1}: Loss: {avg_train_loss:.4f},Trian Accuracy: {avg_train_acc:.4f},test Accuracy: {avg_test_acc:.4f}")
plot_metrics(train_loss_list, train_acc_list, test_acc_list)
return model
def init_weights(m):
if type(m) == nn.Linear or type(m) == nn.Conv2d:
nn.init.xavier_uniform_(m.weight)
################################################################################################################
#ResNet
################################################################################################################
class Residual(nn.Module):
def __init__(self, input_channels,num_channels,use_1x1conv=False,strides=1):
super().__init__()
self.conv1=nn.Conv2d(input_channels,num_channels,kernel_size=3,padding=1,stride=strides)
self.conv2=nn.Conv2d(num_channels,num_channels,kernel_size=3,padding=1)
if use_1x1conv:
self.conv3=nn.Conv2d(input_channels,num_channels,kernel_size=1,stride=strides)
else:
self.conv3=None
self.bn1=nn.BatchNorm2d(num_channels)
self.bn2=nn.BatchNorm2d(num_channels)
def forward(self,X):
Y=F.relu(self.bn1(self.conv1(X)))
Y=self.bn2(self.conv2(Y))
if self.conv3:
X=self.conv3(X)
Y=Y+X #f(x)=x+g(x)
return F.relu(Y)
def resnet_block(input_channels,num_channels,num_residuals,first_block=False):
blk=[]
#首先对齐residual的第一个层,需要做下采样
#网络第一个阶段通常不做下采样，也不调整通道数。
for i in range(num_residuals):
if i==0 and not first_block:
blk.append(Residual(input_channels,num_channels,use_1x1conv=True,strides=2))#这个是除了第一个模块之后需要执行的
else:
blk.append(Residual(num_channels,num_channels))
return blk
b1=nn.Sequential(nn.Conv2d(1,64,kernel_size=7,stride=2,padding=3),
nn.BatchNorm2d(64),nn.ReLU(),
nn.MaxPool2d(kernel_size=3,stride=2,padding=1))
b2=nn.Sequential(*resnet_block(64,64,2,first_block=True))
b3=nn.Sequential(*resnet_block(64,128,2))
b4=nn.Sequential(*resnet_block(128,256,2))
b5=nn.Sequential(*resnet_block(256,512,2))
################################################################################################################
transforms=transforms.Compose([transforms.Resize(96),transforms.ToTensor(),transforms.Normalize((0.5,),(0.5,))])#第一个是mean,第二个是std
train_img=torchvision.datasets.FashionMNIST(root="./data",train=True,transform=transforms,download=True)
test_img=torchvision.datasets.FashionMNIST(root="./data",train=False,transform=transforms,download=True)
train_data=DataLoader(train_img,batch_size=128,num_workers=4,shuffle=True)
test_data=DataLoader(test_img,batch_size=128,num_workers=4,shuffle=False)
################################################################################################################
device=torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
model=nn.Sequential(b1,b2,b3,b4,b5,nn.AdaptiveAvgPool2d((1,1)),nn.Flatten(),nn.Linear(512,10)).to(device)
model.apply(init_weights)
optimizer=torch.optim.SGD(model.parameters(),lr=0.01,momentum=0.9)
CEloss=nn.CrossEntropyLoss()
model=train_model(model,train_data,test_data,num_epochs=15)
################################################################################################################
