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