DeepLearningForSelfDrivingCars/model.py at master · ManajitPal/DeepLearningForSelfDrivingCars · GitHub

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import torch.nn as nn

class NetworkDense(nn.Module):

    def __init__(self):
        super(NetworkDense, self).__init__()
        self.conv_layers = nn.Sequential(
            nn.Conv2d(3, 24, 5, stride=2),
            nn.ELU(),
            nn.Conv2d(24, 36, 5, stride=2),
            nn.ELU(),
            nn.Conv2d(36, 48, 5, stride=2),
            nn.ELU(),
            nn.Conv2d(48, 64, 3),
            nn.ELU(),
            nn.Conv2d(64, 64, 3),
            nn.Dropout(0.25)
        )
        self.linear_layers = nn.Sequential(
            nn.Linear(in_features=64 * 2 * 33, out_features=100),
            nn.ELU(),
            nn.Linear(in_features=100, out_features=50),
            nn.ELU(),
            nn.Linear(in_features=50, out_features=10),
            nn.Linear(in_features=10, out_features=1)
        )

    def forward(self, input):
        input = input.view(input.size(0), 3, 70, 320)
        output = self.conv_layers(input)
        print(output.shape)
        output = output.view(output.size(0), -1)
        output = self.linear_layers(output)
        return output


class NetworkLight(nn.Module):

    def __init__(self):
        super(NetworkLight, self).__init__()
        self.conv_layers = nn.Sequential(
            nn.Conv2d(3, 24, 3, stride=2),
            nn.ELU(),
            nn.Conv2d(24, 48, 3, stride=2),
            nn.MaxPool2d(4, stride=4),
            nn.Dropout(p=0.25)
        )
        self.linear_layers = nn.Sequential(
            nn.Linear(in_features=48*4*19, out_features=50),
            nn.ELU(),
            nn.Linear(in_features=50, out_features=10),
            nn.Linear(in_features=10, out_features=1)
        )


    def forward(self, input):
        input = input.view(input.size(0), 3, 70, 320)
        output = self.conv_layers(input)
        print(output.shape)
        output = output.view(output.size(0), -1)
        output = self.linear_layers(output)
        return output