commit 6d7f5d5c232670a5d6562710198d37d03c4f8321
Author: ClF3 <smh66ccff@outlook.com>
Date:   Wed Oct 16 10:08:07 2024 +0800

    first commit

diff --git a/CIFAR10_playground.py b/CIFAR10_playground.py
new file mode 100755
index 0000000..7df1dc8
--- /dev/null
+++ b/CIFAR10_playground.py
@@ -0,0 +1,296 @@
+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")
+################################################