Unsupervised-OBIC-Pytorch/model.py at master · CVEO/Unsupervised-OBIC-Pytorch · GitHub

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import torch.nn as nn


class MyNet(nn.Module):
    def __init__(self, inp_dim, mod_dim1, mod_dim2):
        super(MyNet, self).__init__()

        self.seq = nn.Sequential(
            nn.Conv2d(inp_dim, mod_dim1, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(mod_dim1),
            nn.ReLU(inplace=True),

            nn.Conv2d(mod_dim1, mod_dim2, kernel_size=1, stride=1, padding=0),
            nn.BatchNorm2d(mod_dim2),
            nn.ReLU(inplace=True),

            nn.Conv2d(mod_dim2, mod_dim1, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(mod_dim1),
            nn.ReLU(inplace=True),

            nn.Conv2d(mod_dim1, mod_dim2, kernel_size=1, stride=1, padding=0),
            nn.BatchNorm2d(mod_dim2),
        )

    def forward(self, x):
        return self.seq(x)

class DeepNet(nn.Module):
    def __init__(self, inp_dim, classes):
        super(DeepNet, self).__init__()

        self.seq = nn.Sequential(
            nn.Conv2d(inp_dim, 64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2, stride=2),

            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2, stride=2),

            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),

            nn.Upsample(scale_factor=2, mode='nearest'),
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),


            nn.Upsample(scale_factor=2, mode='nearest'),
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),

            nn.Conv2d(64, classes, kernel_size=1, stride=1),
            nn.BatchNorm2d(classes),
        )

    def forward(self, x):
        return self.seq(x)


if __name__ == '__main__':
    import torch
    from torchsummary import summary
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # PyTorch v0.4.0
    model = DeepNet(3, 32).to(device)
    #summary(model, input_size=(3, 512, 512))