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CIFAR10_playground.py Executable file
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import torch
import torchvision
import numpy as np
from tqdm import tqdm
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
################### 数据集初始化与读入 ###################
train_transform = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, padding=4),
transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2),
transforms.ToTensor()
])
train_dset = torchvision.datasets.CIFAR10(root='./CIFAR10',train=True,download=False,transform=train_transform)
test_dset = torchvision.datasets.CIFAR10(root='./CIFAR10',train=False,download=False,transform=transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(train_dset, batch_size=128, shuffle=True, num_workers=0)
test_loader = torch.utils.data.DataLoader(test_dset, batch_size=128, shuffle=False, num_workers=0)
#######################################################
################### 构建模型 ###################
class Net(nn.Module):
def __init__(self,act):
super(Net, self).__init__()
# 卷积层 (32x32x3的图像)
self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
# 卷积层(16x16x16)
self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
# 卷积层(8x8x32)
self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
# 最大池化层
self.pool = nn.MaxPool2d(2, 2)
# linear layer (64 * 4 * 4 -> 500)
self.fc1 = nn.Linear(64 * 4 * 4, 500)
# linear layer (500 -> 10)
self.fc2 = nn.Linear(500, 10)
if act == 'relu':
self.act = F.relu
elif act == 'tanh':
self.act = torch.tanh
elif act == 'sigmoid':
self.act = F.sigmoid
def forward(self, x):
# add sequence of convolutional and max pooling layers
x = self.pool(self.act(self.conv1(x)))
x = self.pool(self.act(self.conv2(x)))
x = self.pool(self.act(self.conv3(x)))
# flatten image input
x = x.view(-1, 64 * 4 * 4)
x = self.act(self.fc1(x))
x = self.fc2(x)
return x
#######################################################
################### 模型加入batchnorm ###################
class BnNet(nn.Module):
def __init__(self, act):
super(BnNet, self).__init__()
# 卷积层 (32x32x3的图像)
self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
self.bn1 = nn.BatchNorm2d(16)
# 卷积层(16x16x16)
self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
self.bn2 = nn.BatchNorm2d(32)
# 卷积层(8x8x32)
self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
self.bn3 = nn.BatchNorm2d(64)
# 最大池化层
self.pool = nn.MaxPool2d(2, 2)
# linear layer (64 * 4 * 4 -> 500)
self.fc1 = nn.Linear(64 * 4 * 4, 500)
self.bn4 = nn.BatchNorm1d(500)
# linear layer (500 -> 10)
self.fc2 = nn.Linear(500, 10)
if act == 'relu':
self.act = F.relu
elif act == 'tanh':
self.act = torch.tanh
elif act == 'sigmoid':
self.act = F.sigmoid
def forward(self, x):
# add sequence of convolutional and max pooling layers
x = self.pool(self.act(self.bn1(self.conv1(x))))
x = self.pool(self.act(self.bn2(self.conv2(x))))
x = self.pool(self.act(self.bn3(self.conv3(x))))
# flatten image input
x = x.view(-1, 64 * 4 * 4)
x = self.act(self.bn4(self.fc1(x)))
x = self.fc2(x)
return x
################### 构建模型 ###################
class DeepNet(nn.Module):
def __init__(self,act):
super(DeepNet, self).__init__()
################### 代码填空：请在此填补模型定义代码 ###################
# 卷积层 (32x32x3的图像)
self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
# 卷积层(32x32x16)
self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
# 卷积层(16x16x32)
self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
# 卷积层(16x16x64)
self.conv4 = nn.Conv2d(64, 128, 3, padding=1)
# 卷积层(8x8x128)
self.conv5 = nn.Conv2d(128, 256, 3, padding=1)
# 卷积层(8x8x256)
self.conv6 = nn.Conv2d(256, 512, 3, padding=1)
# 最大池化层
self.pool = nn.MaxPool2d(2, 2)
# 自适应平均池化层
self.apool = nn.AdaptiveAvgPool2d((1, 1))
# linear layer (512 -> 256)
self.fc1 = nn.Linear(512, 256)
# linear layer (256 -> 128)
self.fc2 = nn.Linear(256, 128)
# linear layer (128 -> 10)
self.fc3 = nn.Linear(128, 10)
if act == 'relu':
self.act = F.relu
elif act == 'tanh':
self.act = torch.tanh
elif act == 'sigmoid':
self.act = F.sigmoid
##################################################################
def forward(self, x):
# convolutional layers
################### 代码填空：请在此填补前向计算代码 ###################
x = self.pool(self.act(self.conv2(self.act(self.conv1(x)))))
x = self.pool(self.act(self.conv4(self.act(self.conv3(x)))))
x = self.apool(self.act(self.conv6(self.act(self.conv5(x)))))
# flatten image input
x = x.view(-1, 512)
x = self.act(self.fc1(x))
x = self.act(self.fc2(x))
x = self.fc3(x)
return x
##################################################################
class BnDeepNet(nn.Module):
def __init__(self,act):
super(BnDeepNet, self).__init__()
################### 代码填空：请在此填补模型定义代码 ###################
# 卷积层 (32x32x3的图像)
self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
self.bn1 = nn.BatchNorm2d(16)
# 卷积层(32x32x16)
self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
self.bn2 = nn.BatchNorm2d(32)
# 卷积层(16x16x32)
self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
self.bn3 = nn.BatchNorm2d(64)
# 卷积层(16x16x64)
self.conv4 = nn.Conv2d(64, 128, 3, padding=1)
self.bn4 = nn.BatchNorm2d(128)
# 卷积层(8x8x128)
self.conv5 = nn.Conv2d(128, 256, 3, padding=1)
self.bn5 = nn.BatchNorm2d(256)
# 卷积层(8x8x256)
self.conv6 = nn.Conv2d(256, 512, 3, padding=1)
self.bn6 = nn.BatchNorm2d(512)
# 最大池化层
self.pool = nn.MaxPool2d(2, 2)
# 自适应平均池化层
self.apool = nn.AdaptiveAvgPool2d((1, 1))
# linear layer (512 -> 256)
self.fc1 = nn.Linear(512, 256)
self.bn7 = nn.BatchNorm1d(256)
# linear layer (256 -> 128)
self.fc2 = nn.Linear(256, 128)
self.bn8 = nn.BatchNorm1d(128)
# linear layer (128 -> 10)
self.fc3 = nn.Linear(128, 10)
if act == 'relu':
self.act = F.relu
elif act == 'tanh':
self.act = torch.tanh
elif act == 'sigmoid':
self.act = F.sigmoid
###################################################################
def forward(self, x):
# convolutional layers
################### 代码填空：请在此填补前向计算代码 ###################
x = self.pool(self.act(self.bn2(self.conv2(self.act(self.bn1(self.conv1(x)))))))
x = self.pool(self.act(self.bn4(self.conv4(self.act(self.bn3(self.conv3(x)))))))
x = self.apool(self.act(self.bn6(self.conv6(self.act(self.bn5(self.conv5(x)))))))
# flatten image input
x = x.view(-1, 512)
x = self.act(self.bn7(self.fc1(x)))
x = self.act(self.bn8(self.fc2(x)))
x = self.fc3(x)
return x
##################################################################
################### 训练前准备 ###################
# model = Net('tanh')
# model = BnNet('relu')
model = DeepNet('tanh')
# model = BnDeepNet('relu')
device=torch.device("cuda")
model.to(device)
criterion = nn.CrossEntropyLoss()
optimizer_type = "SGD"
# optimizer_type = "Adam"
if optimizer_type == "SGD":
optimizer = optim.SGD(model.parameters(), lr=0.001)
elif optimizer_type == "Adam":
########## 代码填空：请在此填补Adam优化器计算代码, lr=0.0001 ###########
optimizer = optim.Adam(model.parameters(), lr = 0.0001)
##################################################################
n_epochs = 100
train_losses = []
valid_losses = []
accuracies = []
################################################
################### 训练+验证 ###################
for epoch in range(n_epochs):
train_loss = 0.0
valid_loss = 0.0
model.train()
for idx,(img,label) in tqdm(enumerate(train_loader)):
img, label=img.to(device), label.to(device)
optimizer.zero_grad()
output = model(img)
loss = criterion(output,label)
loss.backward()
optimizer.step()
train_loss += loss.item() * img.shape[0]
model.eval()
correct = 0
total = 0
for idx,(img,label) in tqdm(enumerate(test_loader)):
output = model(img)
loss = criterion(output, label)
valid_loss += loss.item() * img.shape[0]
_, predicted = torch.max(output.data, 1)
total += label.size(0)
correct += (predicted == label).sum().item()
train_loss = train_loss / len(train_dset)
valid_loss = valid_loss / len(test_dset)
train_losses.append(train_loss)
valid_losses.append(valid_loss)
accuracy = correct / total
accuracies.append(accuracy)
print(f"Epoch:{epoch}, Acc:{correct/total}, Train Loss:{train_loss}, Test Loss:{valid_loss}")
################################################
################### 曲线绘制 ###################
print("MAX ACC: ",np.max(accuracies))
plt.plot(range(n_epochs), train_losses, label='Train Loss')
plt.plot(range(n_epochs), valid_losses, label='Valid Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.savefig("Loss.png")
plt.clf()
# 绘制验证集准确率随epoch的变化曲线
plt.plot(range(n_epochs), accuracies, label='Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.savefig("Acc.png")
################################################