44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
from utils import *
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import ipdb
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class Model_Vehicle_CLS_1_2(nn.Module):
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def __init__(self, num_classes=3):
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super(Model_Vehicle_CLS_1_2, self).__init__()
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self.conv1 = nn.Conv2d(in_channels=3, out_channels=128, kernel_size=3, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(128)
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self.conv2 = nn.Conv2d(in_channels=128, out_channels=512, kernel_size=3, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(512)
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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.bn1(self.conv1(x)))
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x = F.relu(self.bn2(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_Haze_Removal_1_2(nn.Module):
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def __init__(self):
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super(Model_Haze_Removal_1_2, self).__init__()
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self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(16)
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self.conv2 = nn.Conv2d(in_channels=16, out_channels=48, kernel_size=5, padding=2, bias=False)
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self.bn2 = nn.BatchNorm2d(48)
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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.bn1(self.conv1(x)))
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x = F.relu(self.bn2(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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model = Model_Vehicle_CLS_1_2()
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train_Vehicle_CLS(model=model, learning_rate=4e-4, batch_size=64)
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model = Model_Haze_Removal_1_2()
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train_Haze_Removal(model=model, learning_rate=5e-3, batch_size=16)
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