完成实验四

2024-01-12 02:27:03 +08:00
parent 7fbb893223
commit feca24347a
15 changed files with 4588 additions and 7 deletions
--- a/Lab4/code/1.3.py
+++ b/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()