168 lines
6.7 KiB
Python
168 lines
6.7 KiB
Python
from utils import *
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class Model_Vehicle_CLS_2_1(nn.Module):
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def __init__(self, num_classes=3):
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super(Model_Vehicle_CLS_2_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, dilation=1, bias=False),
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nn.BatchNorm2d(16),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding=1, dilation=1, bias=False),
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nn.BatchNorm2d(32),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1, dilation=1, bias=False),
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nn.BatchNorm2d(64),
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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=64, out_channels=128, kernel_size=3, padding=1, dilation=1, bias=False),
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nn.BatchNorm2d(128),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1, dilation=1, bias=False),
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nn.BatchNorm2d(256),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1, dilation=1, bias=False),
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nn.BatchNorm2d(512),
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nn.ReLU(inplace=True),
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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 = self.conv1(x)
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x = 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_2_2(nn.Module):
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def __init__(self, num_classes=3):
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super(Model_Vehicle_CLS_2_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=3, padding=1, dilation=1, bias=False),
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nn.BatchNorm2d(16),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding=2, dilation=2, bias=False),
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nn.BatchNorm2d(32),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=5, dilation=5, bias=False),
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nn.BatchNorm2d(64),
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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=64, out_channels=128, kernel_size=3, padding=1, dilation=1, bias=False),
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nn.BatchNorm2d(128),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=2, dilation=2, bias=False),
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nn.BatchNorm2d(256),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=5, dilation=5, bias=False),
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nn.BatchNorm2d(512),
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nn.ReLU(inplace=True),
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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 = self.conv1(x)
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x = 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_2_3(nn.Module):
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def __init__(self, num_classes=3):
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super(Model_Vehicle_CLS_2_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=3, padding=1, dilation=1, bias=False),
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nn.BatchNorm2d(16),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding=3, dilation=3, bias=False),
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nn.BatchNorm2d(32),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=5, dilation=5, bias=False),
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nn.BatchNorm2d(64),
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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=64, out_channels=128, kernel_size=3, padding=1, dilation=1, bias=False),
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nn.BatchNorm2d(128),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=3, dilation=3, bias=False),
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nn.BatchNorm2d(256),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=5, dilation=5, bias=False),
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nn.BatchNorm2d(512),
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nn.ReLU(inplace=True),
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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 = self.conv1(x)
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x = 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_2_4(nn.Module):
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def __init__(self, num_classes=3):
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super(Model_Vehicle_CLS_2_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=3, padding=1, dilation=1, bias=False),
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nn.BatchNorm2d(16),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding=3, dilation=3, bias=False),
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nn.BatchNorm2d(32),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=7, dilation=7, bias=False),
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nn.BatchNorm2d(64),
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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=64, out_channels=128, kernel_size=3, padding=1, dilation=1, bias=False),
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nn.BatchNorm2d(128),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=3, dilation=3, bias=False),
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nn.BatchNorm2d(256),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=7, dilation=7, bias=False),
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nn.BatchNorm2d(512),
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nn.ReLU(inplace=True),
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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 = self.conv1(x)
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x = 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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if __name__ == "__main__":
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num_epochs = 61
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learning_rate = 1e-4
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batch_size = 256
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dilations = ["[[1, 1, 1], [1, 1, 1]]",
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"[[1, 2, 5], [1, 2, 5]]",
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"[[1, 3, 5], [1, 3, 5]]",
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"[[1, 3, 7], [1, 3, 7]]"]
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models = [
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Model_Vehicle_CLS_2_1,
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Model_Vehicle_CLS_2_2,
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Model_Vehicle_CLS_2_3,
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Model_Vehicle_CLS_2_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("Dilation: " + dilations[i])
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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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