完成实验四

.gitignore

@@ -1,4 +1,4 @@
-dataset/
+raw/
 
 .vscode/
 

Lab3/网络优化实验.ipynb

@@ -1121,9 +1121,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:DeepLearningLab]",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "conda-env-DeepLearningLab-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {

Lab4/code/1.1.py

@@ -0,0 +1,73 @@
+from utils import *
+import ipdb
+
+
+class My_Conv2d(nn.Module):
+    def __init__(self, in_channels:int, out_channels:int, kernel_size:int, padding:int=0, bias=True):
+        super(My_Conv2d, self).__init__()
+        self.has_bias = bias
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.padding = padding
+        self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels, kernel_size, kernel_size))
+        nn.init.xavier_uniform_(self.weight)
+        if self.has_bias:
+            self.bias = nn.Parameter(torch.zeros(out_channels, requires_grad=True, dtype=torch.float32))
+
+    def forward(self, x):
+        batch_size, _, input_height, input_width = x.shape
+        if self.padding > 0:
+            x = F.pad(x, (self.padding, self.padding, self.padding, self.padding))
+        x = F.unfold(x, kernel_size=self.kernel_size)
+        x = x.permute(0, 2, 1).contiguous()
+        weight_unfold = self.weight.view(self.out_channels, -1).t()
+        x = torch.matmul(x, weight_unfold)
+        if self.has_bias:
+            x += self.bias
+        output_height = input_height + 2 * self.padding - self.kernel_size + 1
+        output_width = input_width + 2 * self.padding - self.kernel_size + 1
+        x = x.view(batch_size, output_height, output_width, self.out_channels).permute(0, 3, 1, 2).contiguous()
+        return x
+    
+
+class Model_Vehicle_CLS_1_1(nn.Module):
+    def __init__(self, num_classes=3):
+        super(Model_Vehicle_CLS_1_1, self).__init__()
+        self.conv1 = My_Conv2d(in_channels=3, out_channels=128, kernel_size=3, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(128)
+        self.conv2 = My_Conv2d(in_channels=128, out_channels=512, kernel_size=3, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(512)
+        self.fc = nn.Linear(in_features=512, out_features=num_classes)
+
+    def forward(self, x):
+        x = F.relu(self.bn1(self.conv1(x)))
+        x = F.relu(self.bn2(self.conv2(x)))
+        x = F.avg_pool2d(x, 32)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        return x
+    
+
+class Model_Haze_Removal_1_1(nn.Module):
+    def __init__(self):
+        super(Model_Haze_Removal_1_1, self).__init__()
+        self.conv1 = My_Conv2d(in_channels=3, out_channels=16, kernel_size=3, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(16)
+        self.conv2 = My_Conv2d(in_channels=16, out_channels=48, kernel_size=5, padding=2, bias=False)
+        self.bn2 = nn.BatchNorm2d(48)
+        self.conv3 = My_Conv2d(in_channels=48, out_channels=3, kernel_size=3, padding=1)
+
+    def forward(self, x):
+        x = F.relu(self.bn1(self.conv1(x)))
+        x = F.relu(self.bn2(self.conv2(x)))
+        x = self.conv3(x)
+        return x
+
+
+if __name__ == "__main__":
+    model = Model_Vehicle_CLS_1_1()
+    train_Vehicle_CLS(model=model, learning_rate=4e-4, batch_size=256)
+    
+    model = Model_Haze_Removal_1_1()
+    train_Haze_Removal(model=model, learning_rate=5e-3, batch_size=16)

Lab4/code/1.2.py

@@ -0,0 +1,43 @@
+from utils import *
+import ipdb
+
+class Model_Vehicle_CLS_1_2(nn.Module):
+    def __init__(self, num_classes=3):
+        super(Model_Vehicle_CLS_1_2, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels=3, out_channels=128, kernel_size=3, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(128)
+        self.conv2 = nn.Conv2d(in_channels=128, out_channels=512, kernel_size=3, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(512)
+        self.fc = nn.Linear(in_features=512, out_features=num_classes)
+
+    def forward(self, x):
+        x = F.relu(self.bn1(self.conv1(x)))
+        x = F.relu(self.bn2(self.conv2(x)))
+        x = F.avg_pool2d(x, 32)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        return x
+    
+
+class Model_Haze_Removal_1_2(nn.Module):
+    def __init__(self):
+        super(Model_Haze_Removal_1_2, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(16)
+        self.conv2 = nn.Conv2d(in_channels=16, out_channels=48, kernel_size=5, padding=2, bias=False)
+        self.bn2 = nn.BatchNorm2d(48)
+        self.conv3 = nn.Conv2d(in_channels=48, out_channels=3, kernel_size=3, padding=1)
+
+    def forward(self, x):
+        x = F.relu(self.bn1(self.conv1(x)))
+        x = F.relu(self.bn2(self.conv2(x)))
+        x = self.conv3(x)
+        return x
+
+
+if __name__ == "__main__":
+    model = Model_Vehicle_CLS_1_2()
+    train_Vehicle_CLS(model=model, learning_rate=4e-4, batch_size=64)
+
+    model = Model_Haze_Removal_1_2()
+    train_Haze_Removal(model=model, learning_rate=5e-3, batch_size=16)

Lab4/code/1.3.py

@@ -0,0 +1,215 @@
+from utils import *
+
+class Model_Vehicle_CLS_1_3_1(nn.Module):
+    def __init__(self, num_classes=3):
+        super(Model_Vehicle_CLS_1_3_1, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_channels=3, out_channels=512, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(512),
+        )
+        self.fc = nn.Linear(in_features=512, out_features=num_classes)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.avg_pool2d(x, 32)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        return x
+
+
+class Model_Vehicle_CLS_1_3_2(nn.Module):
+    def __init__(self, num_classes=3):
+        super(Model_Vehicle_CLS_1_3_2, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_channels=3, out_channels=128, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(128),
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_channels=128, out_channels=512, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(512),
+        )
+        self.fc = nn.Linear(in_features=512, out_features=num_classes, bias=False)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        x = F.avg_pool2d(x, 32)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        return x
+
+
+class Model_Vehicle_CLS_1_3_3(nn.Module):
+    def __init__(self, num_classes=3):
+        super(Model_Vehicle_CLS_1_3_3, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(64),
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_channels=64, out_channels=256, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(256),
+        )
+        self.conv3 = nn.Sequential(
+            nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(512),
+        )
+        self.fc = nn.Linear(in_features=512, out_features=num_classes)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        x = F.relu(self.conv3(x))
+        x = F.avg_pool2d(x, 32)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        return x
+
+
+class Model_Vehicle_CLS_1_3_4(nn.Module):
+    def __init__(self, num_classes=3):
+        super(Model_Vehicle_CLS_1_3_4, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(64),
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(128),
+        )
+        self.conv3 = nn.Sequential(
+            nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(256),
+        )
+        self.conv4 = nn.Sequential(
+            nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(512),
+        )
+        self.fc = nn.Linear(in_features=512, out_features=num_classes)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        x = F.relu(self.conv3(x))
+        x = F.relu(self.conv4(x))
+        x = F.avg_pool2d(x, 32)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        return x
+    
+
+class Model_Haze_Removal_1_3_1(nn.Module):
+    def __init__(self):
+        super(Model_Haze_Removal_1_3_1, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(16),
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_channels=16, out_channels=48, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(48),
+        )
+        self.conv3 = nn.Conv2d(in_channels=48, out_channels=3, kernel_size=3, padding=1)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        x = self.conv3(x)
+        return x
+
+
+class Model_Haze_Removal_1_3_2(nn.Module):
+    def __init__(self):
+        super(Model_Haze_Removal_1_3_2, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=5, padding=2, bias=False),
+            nn.BatchNorm2d(16),
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_channels=16, out_channels=48, kernel_size=5, padding=2, bias=False),
+            nn.BatchNorm2d(48),
+        )
+        self.conv3 = nn.Conv2d(in_channels=48, out_channels=3, kernel_size=5, padding=2)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        x = self.conv3(x)
+        return x
+
+
+class Model_Haze_Removal_1_3_3(nn.Module):
+    def __init__(self):
+        super(Model_Haze_Removal_1_3_3, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=7, padding=3, bias=False),
+            nn.BatchNorm2d(16),
+            nn.ReLU(inplace=True),
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_channels=16, out_channels=48, kernel_size=7, padding=3, bias=False),
+            nn.BatchNorm2d(48),
+            nn.ReLU(inplace=True),
+        )
+        self.conv3 = nn.Conv2d(in_channels=48, out_channels=3, kernel_size=7, padding=3)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        x = self.conv3(x)
+        return x
+
+
+class Model_Haze_Removal_1_3_4(nn.Module):
+    def __init__(self):
+        super(Model_Haze_Removal_1_3_4, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=9, padding=4, bias=False),
+            nn.BatchNorm2d(16),
+            nn.ReLU(inplace=True),
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_channels=16, out_channels=48, kernel_size=9, padding=4, bias=False),
+            nn.BatchNorm2d(48),
+            nn.ReLU(inplace=True),
+        )
+        self.conv3 = nn.Conv2d(in_channels=48, out_channels=3, kernel_size=9, padding=4)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        x = self.conv3(x)
+        return x
+    
+
+if __name__ == "__main__":
+    num_epochs = 61
+    learning_rate = 2e-4
+    batch_size = 256
+    models = [
+        Model_Vehicle_CLS_1_3_1, 
+        Model_Vehicle_CLS_1_3_2,
+        Model_Vehicle_CLS_1_3_3,
+        Model_Vehicle_CLS_1_3_4,
+    ]
+    for i in range(4):
+        model = models[i]()
+        print(f"卷积层层数={i + 1}")
+        train_loss, test_acc = train_Vehicle_CLS(model=model, learning_rate=learning_rate,
+                                                 batch_size=batch_size, num_epochs=num_epochs)
+        print()
+
+    num_epochs = 61
+    learning_rate = 8e-3
+    batch_size = 64
+    models = [
+        Model_Haze_Removal_1_3_1, 
+        Model_Haze_Removal_1_3_2, 
+        Model_Haze_Removal_1_3_3, 
+        Model_Haze_Removal_1_3_4, 
+    ]
+    for i in range(4):
+        model = models[i]()
+        print(f"卷积核大小={3 + 2 * i}")
+        train_loss, test_loss = train_Haze_Removal(model=model, learning_rate=learning_rate, batch_size=batch_size, num_epochs=num_epochs)
+        print()

Lab4/code/2.py

@@ -0,0 +1,168 @@
+from utils import *
+
+class Model_Vehicle_CLS_2_1(nn.Module):
+    def __init__(self, num_classes=3):
+        super(Model_Vehicle_CLS_2_1, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, padding=1, dilation=1, bias=False),
+            nn.BatchNorm2d(16),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding=1, dilation=1, bias=False),
+            nn.BatchNorm2d(32),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1, dilation=1, bias=False),
+            nn.BatchNorm2d(64),
+            nn.ReLU(inplace=True),
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1, dilation=1, bias=False),
+            nn.BatchNorm2d(128),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1, dilation=1, bias=False),
+            nn.BatchNorm2d(256),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1, dilation=1, bias=False),
+            nn.BatchNorm2d(512),
+            nn.ReLU(inplace=True),
+        )
+        self.fc = nn.Linear(in_features=512, out_features=num_classes)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = F.avg_pool2d(x, 32)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        return x
+
+
+class Model_Vehicle_CLS_2_2(nn.Module):
+    def __init__(self, num_classes=3):
+        super(Model_Vehicle_CLS_2_2, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, padding=1, dilation=1, bias=False),
+            nn.BatchNorm2d(16),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding=2, dilation=2, bias=False),
+            nn.BatchNorm2d(32),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=5, dilation=5, bias=False),
+            nn.BatchNorm2d(64),
+            nn.ReLU(inplace=True),
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1, dilation=1, bias=False),
+            nn.BatchNorm2d(128),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=2, dilation=2, bias=False),
+            nn.BatchNorm2d(256),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=5, dilation=5, bias=False),
+            nn.BatchNorm2d(512),
+            nn.ReLU(inplace=True),
+        )
+        self.fc = nn.Linear(in_features=512, out_features=num_classes)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = F.avg_pool2d(x, 32)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        return x
+
+
+class Model_Vehicle_CLS_2_3(nn.Module):
+    def __init__(self, num_classes=3):
+        super(Model_Vehicle_CLS_2_3, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, padding=1, dilation=1, bias=False),
+            nn.BatchNorm2d(16),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding=3, dilation=3, bias=False),
+            nn.BatchNorm2d(32),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=5, dilation=5, bias=False),
+            nn.BatchNorm2d(64),
+            nn.ReLU(inplace=True),
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1, dilation=1, bias=False),
+            nn.BatchNorm2d(128),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=3, dilation=3, bias=False),
+            nn.BatchNorm2d(256),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=5, dilation=5, bias=False),
+            nn.BatchNorm2d(512),
+            nn.ReLU(inplace=True),
+        )
+        self.fc = nn.Linear(in_features=512, out_features=num_classes)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = F.avg_pool2d(x, 32)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        return x
+
+
+class Model_Vehicle_CLS_2_4(nn.Module):
+    def __init__(self, num_classes=3):
+        super(Model_Vehicle_CLS_2_4, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, padding=1, dilation=1, bias=False),
+            nn.BatchNorm2d(16),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding=3, dilation=3, bias=False),
+            nn.BatchNorm2d(32),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=7, dilation=7, bias=False),
+            nn.BatchNorm2d(64),
+            nn.ReLU(inplace=True),
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1, dilation=1, bias=False),
+            nn.BatchNorm2d(128),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=3, dilation=3, bias=False),
+            nn.BatchNorm2d(256),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=7, dilation=7, bias=False),
+            nn.BatchNorm2d(512),
+            nn.ReLU(inplace=True),
+        )
+        self.fc = nn.Linear(in_features=512, out_features=num_classes)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = F.avg_pool2d(x, 32)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        return x
+    
+
+if __name__ == "__main__":
+    num_epochs = 61
+    learning_rate = 1e-4
+    batch_size = 256
+    dilations = ["[[1, 1, 1], [1, 1, 1]]", 
+                 "[[1, 2, 5], [1, 2, 5]]",
+                 "[[1, 3, 5], [1, 3, 5]]", 
+                 "[[1, 3, 7], [1, 3, 7]]"]
+    models = [
+        Model_Vehicle_CLS_2_1, 
+        Model_Vehicle_CLS_2_2, 
+        Model_Vehicle_CLS_2_3,
+        Model_Vehicle_CLS_2_4,
+    ]
+    for i in range(4):
+        model = models[i]()
+        print("Dilation: " + dilations[i])
+        train_loss, test_acc = train_Vehicle_CLS(model=model, learning_rate=learning_rate,
+                                                 batch_size=batch_size, num_epochs=num_epochs)
+        print()
+
+    

Lab4/code/3.py

@@ -0,0 +1,63 @@
+from utils import *
+
+class BasicResidualBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride=1):
+        super(BasicResidualBlock, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(out_channels)
+        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(out_channels)
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_channels != out_channels:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(out_channels)
+            )
+
+    def forward(self, x):
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = self.bn2(self.conv2(out))
+        out += self.shortcut(x)
+        out = F.relu(out)
+        return out
+
+
+class Model_Vehicle_CLS_3(nn.Module):
+    def __init__(self, num_classes=3):
+        super(Model_Vehicle_CLS_3, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(64),
+        )
+        self.conv2 = BasicResidualBlock(in_channels=64, out_channels=64)
+        self.conv3 = BasicResidualBlock(in_channels=64, out_channels=64)
+        self.conv4 = BasicResidualBlock(in_channels=64, out_channels=128, stride=2)
+        self.conv5 = BasicResidualBlock(in_channels=128, out_channels=128)
+        self.conv6 = BasicResidualBlock(in_channels=128, out_channels=256, stride=2)
+        self.conv7 = BasicResidualBlock(in_channels=256, out_channels=256)
+        self.conv8 = BasicResidualBlock(in_channels=256, out_channels=512, stride=2)
+        self.conv9 = BasicResidualBlock(in_channels=512, out_channels=512)
+        self.fc = nn.Linear(in_features=512, out_features=num_classes)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = self.conv2(x)
+        x = self.conv3(x)
+        x = self.conv4(x)
+        x = self.conv5(x)
+        x = self.conv6(x)
+        x = self.conv7(x)
+        x = self.conv8(x)
+        x = self.conv9(x)
+        x = F.avg_pool2d(x, 4)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        return x
+    
+
+if __name__ == "__main__":
+    num_epochs = 61
+    learning_rate = 15e-5
+    batch_size = 512
+    model = Model_Vehicle_CLS_3(num_classes=3)
+    train_loss, test_acc = train_Vehicle_CLS(model=model, learning_rate=learning_rate, batch_size=batch_size, num_epochs=num_epochs)

Lab4/code/utils.py

@@ -0,0 +1,197 @@
+import torch
+import torch.nn.functional as F
+from torch.utils.data import DataLoader, Dataset
+from torch import nn
+from torchvision import transforms
+from tqdm import tqdm
+import os
+import time
+from PIL import Image
+import pandas as pd
+
+class Vehicle(Dataset):
+    def __init__(self, root:str="../dataset", train:bool=True, transform=None):
+        root = os.path.join(root, "Vehicles")
+        csv_file = os.path.join(root, "train.csv" if train else "test.csv")
+        self.data = pd.read_csv(csv_file).to_numpy().tolist()
+        self.root = root
+        self.transform = transform
+
+    def __len__(self):
+        return len(self.data)
+    
+    def __getitem__(self, index):
+        img_name, label = self.data[index]
+        img_path = os.path.join(self.root, img_name)
+        image = Image.open(img_path)
+        label = int(label)
+        if self.transform:
+            image = self.transform(image)
+        return image, label
+    
+
+class Haze(Dataset):
+    def __init__(self, root:str="../dataset", train:bool=True, transform=None):
+        root = os.path.join(root, "Haze")
+        split_file = pd.read_csv(os.path.join(root, "split.csv")).to_numpy().tolist()
+        self.data = list()
+        for img, is_train in split_file:
+            if train and int(is_train) == 1:
+                self.data.append(img)
+            elif not train and int(is_train) == 0:
+                self.data.append(img)
+        self.root = root
+        self.transform = transform
+
+    def __len__(self):
+        return len(self.data)
+    
+    def __getitem__(self, index):
+        img_name = self.data[index]
+        img_path = os.path.join(self.root, "raw/haze", img_name)
+        ground_truth_path = os.path.join(self.root, "raw/no_haze", img_name)
+        image = Image.open(img_path)
+        ground_truth = Image.open(ground_truth_path)
+        if self.transform:
+            image = self.transform(image)
+            ground_truth = self.transform(ground_truth)
+        return image, ground_truth
+    
+
+def train_Vehicle_CLS(model:nn.Module, learning_rate=1e-3, batch_size=64, num_epochs=51):
+    num_classes = 3
+    device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+    transform = transforms.Compose(
+        [
+            transforms.ToTensor(),
+            transforms.Resize((32, 32), antialias=True),
+            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        ]
+    )
+    train_dataset = Vehicle(root="../dataset", train=True, transform=transform)
+    test_dataset = Vehicle(root="../dataset", train=False, transform=transform)
+    
+    train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=14, pin_memory=True)
+    test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, num_workers=14, pin_memory=True)
+
+    model = model.to(device)
+    criterion = nn.CrossEntropyLoss()
+    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+
+    train_loss = list()
+    test_acc = list()
+    for epoch in range(num_epochs):
+        model.train()
+        total_epoch_loss = 0
+        train_tik = time.time()
+        for index, (images, targets) in tqdm(enumerate(train_loader), total=len(train_loader)):
+            optimizer.zero_grad()
+
+            images = images.to(device)
+            targets = targets.to(device)
+            one_hot_targets = F.one_hot(targets, num_classes=num_classes).to(dtype=torch.float)
+
+            outputs = model(images)
+            loss = criterion(outputs, one_hot_targets)
+            total_epoch_loss += loss.item()
+
+            loss.backward()
+            optimizer.step()
+        train_tok = time.time()
+
+        model.eval()
+        with torch.no_grad():
+            total_epoch_acc = 0
+            test_tik = time.time()
+            for index, (image, targets) in tqdm(enumerate(test_loader), total=len(test_loader)):
+                image = image.to(device)
+                targets = targets.to(device)
+
+                outputs = model(image)
+                pred = F.softmax(outputs, dim=1)
+                total_epoch_acc += (pred.argmax(1) == targets).sum().item()
+            test_tok = time.time()
+
+        avg_epoch_acc = total_epoch_acc / len(test_dataset)
+        print(
+            f"Epoch [{epoch + 1}/{num_epochs}],",
+            f"Train Loss: {total_epoch_loss:.10f},",
+            f"Train Time: {1000 * (train_tok - train_tik):.2f}ms,",
+            f"Test Acc: {avg_epoch_acc * 100:.3f}%,",
+            f"Test Time: {1000 * (test_tok - test_tik):.2f}ms"
+        )
+        train_loss.append(total_epoch_loss)
+        test_acc.append(avg_epoch_acc)
+    print(f"最大显存使用量: {torch.cuda.max_memory_allocated() / (1024 * 1024):.2f}MiB")
+    torch.cuda.reset_peak_memory_stats()
+    return train_loss, test_acc
+
+
+def train_Haze_Removal(model:nn.Module, learning_rate=1e-3, batch_size=64, num_epochs=51):
+    num_epochs = 50
+    device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+    transform = transforms.Compose(
+        [
+            transforms.ToTensor(),
+            transforms.Resize((224, 224), antialias=True),
+        ]
+    )
+    train_dataset = Haze(root="../dataset", train=True, transform=transform)
+    test_dataset = Haze(root="../dataset", train=False, transform=transform)
+
+    train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=14, pin_memory=True)
+    test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, num_workers=14, pin_memory=True)
+
+    model = model.to(device)
+    criterion = nn.MSELoss()
+    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+
+    train_loss = list()
+    test_loss = list()
+    for epoch in range(num_epochs):
+        model.train()
+        total_epoch_train_loss = 0
+        train_tik = time.time()
+        for index, (images, ground_truth) in tqdm(enumerate(train_loader), total=len(train_loader)):
+            optimizer.zero_grad()
+
+            images = images.to(device)
+            ground_truth = ground_truth.to(device)
+
+            outputs = model(images)
+            loss = criterion(outputs, ground_truth)
+            total_epoch_train_loss += loss.item()
+
+            loss.backward()
+            optimizer.step()
+        train_tok = time.time()
+
+        model.eval()
+        with torch.no_grad():
+            total_epoch_test_loss = 0
+            test_tik = time.time()
+            for index, (image, ground_truth) in tqdm(enumerate(test_loader), total=len(test_loader)):
+                image = image.to(device)
+                ground_truth = ground_truth.to(device)
+
+                outputs = model(image)
+                loss = criterion(outputs, ground_truth)
+                total_epoch_test_loss += loss.item()
+            test_tok = time.time()
+
+        print(
+            f"Epoch [{epoch + 1}/{num_epochs}],",
+            f"Train Loss: {total_epoch_train_loss:.10f},",
+            f"Train Time: {1000 * (train_tok - train_tik):.2f}ms,",
+            f"Test Loss: {total_epoch_test_loss:.10f},",
+            f"Test Time: {1000 * (test_tok - test_tik):.2f}ms"
+        )
+        train_loss.append(total_epoch_train_loss)
+        test_loss.append(total_epoch_test_loss)
+    print(f"最大显存使用量: {torch.cuda.max_memory_allocated() / (1024 * 1024):.2f}MiB")
+    torch.cuda.reset_peak_memory_stats()
+    return train_loss, test_loss
+    
+    

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@@ -0,0 +1,521 @@
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Lab4/dataset/Haze/split_dataset.py

@@ -0,0 +1,16 @@
+import os
+import pandas as pd
+import random
+
+train_list = set()
+
+img_list = [i for i in os.listdir("raw/haze") if i.endswith(".jpg")]
+random.shuffle(img_list)
+for img in img_list[ : int(len(img_list) * 0.8)]:
+    train_list.add(img)
+img_list.sort()
+data = list()
+for img in img_list:
+    data.append([img, 1 if img in train_list else 0])
+
+pd.DataFrame(data=data, columns=["Image", "Train"]).to_csv("./split.csv", index=False)

Lab4/dataset/Vehicles/split_dataset.py

@@ -0,0 +1,26 @@
+import os
+import random
+import pandas as pd
+
+train_list = list()
+test_list = list()
+
+root_dir = "raw"
+class_index = 0
+for vehicle in os.listdir(root_dir):
+    img_list = [i for i in os.listdir(os.path.join(root_dir, vehicle)) if i.endswith(".jpg")]
+    random.shuffle(img_list)
+    split_num = int(len(img_list) * 0.8)
+    for img in img_list[0 : split_num]:
+        train_list.append([os.path.join(root_dir, vehicle, img), class_index])
+    for img in img_list[split_num : ]:
+        test_list.append([os.path.join(root_dir, vehicle, img), class_index])
+    class_index += 1
+
+train_list.sort()
+test_list.sort()
+
+pd.DataFrame(data=train_list, columns=["Vehicle", "Label"]).to_csv("./train.csv", index=False)
+pd.DataFrame(data=test_list, columns=["Vehicle", "Label"]).to_csv("./test.csv", index=False)
+
+

Lab4/dataset/Vehicles/test.csv

@@ -0,0 +1,273 @@
+Vehicle,Label
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requirements.txt

@@ -2,8 +2,9 @@ black
 ipdb
 jupyter
 numpy
-torch==2.1.0
-torchaudio==2.1.0
-torchvision==0.16.0
+torch
+torchaudio
+torchvision
 tqdm
-matplotlib
+matplotlib
+pandas