74 lines
3.0 KiB
Python
74 lines
3.0 KiB
Python
from utils import *
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import ipdb
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class My_Conv2d(nn.Module):
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def __init__(self, in_channels:int, out_channels:int, kernel_size:int, padding:int=0, bias=True):
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super(My_Conv2d, self).__init__()
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self.has_bias = bias
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.kernel_size = kernel_size
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self.padding = padding
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self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels, kernel_size, kernel_size))
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nn.init.xavier_uniform_(self.weight)
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if self.has_bias:
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self.bias = nn.Parameter(torch.zeros(out_channels, requires_grad=True, dtype=torch.float32))
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def forward(self, x):
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batch_size, _, input_height, input_width = x.shape
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if self.padding > 0:
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x = F.pad(x, (self.padding, self.padding, self.padding, self.padding))
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x = F.unfold(x, kernel_size=self.kernel_size)
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x = x.permute(0, 2, 1).contiguous()
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weight_unfold = self.weight.view(self.out_channels, -1).t()
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x = torch.matmul(x, weight_unfold)
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if self.has_bias:
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x += self.bias
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output_height = input_height + 2 * self.padding - self.kernel_size + 1
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output_width = input_width + 2 * self.padding - self.kernel_size + 1
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x = x.view(batch_size, output_height, output_width, self.out_channels).permute(0, 3, 1, 2).contiguous()
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return x
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class Model_Vehicle_CLS_1_1(nn.Module):
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def __init__(self, num_classes=3):
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super(Model_Vehicle_CLS_1_1, self).__init__()
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self.conv1 = My_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 = My_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_1(nn.Module):
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def __init__(self):
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super(Model_Haze_Removal_1_1, self).__init__()
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self.conv1 = My_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 = My_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 = My_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_1()
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train_Vehicle_CLS(model=model, learning_rate=4e-4, batch_size=256)
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model = Model_Haze_Removal_1_1()
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train_Haze_Removal(model=model, learning_rate=5e-3, batch_size=16)
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