Question 12: Consider the following code snippets used in a multiscal…

1import torch
2import torch.nn as nn
3
4class DepthNetwork(nn.Module):
5    def __init__(self):
6        super(DepthNetwork, self).__init__()
7        self.encoder = nn.Sequential(
8            nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),
9            nn.ReLU(),
10            nn.Conv2d(64, 128, kernel_size=5, stride=2, padding=2),
11            nn.ReLU()
12        )
13        self.fc = nn.Sequential(
14            nn.Linear(128 * 28 * 28, 1024),
15            nn.ReLU(),
16            nn.Linear(1024, 128 * 56 * 56)
17        )
18    def forward(self, x):
19        x = self.encoder(x)
20        x = x.view(x.size(0), -1)
21        x = self.fc(x)
22        x = x.view(x.size(0), 128, 56, 56)
23        return x

1import torch
2import torch.nn as nn
3
4class DepthRefinementNetwork(nn.Module):
5    def __init__(self):
6        super(DepthRefinementNetwork, self).__init__()
7        self.refinement = nn.Sequential(
8            nn.Conv2d(131, 64, kernel_size=3, padding=1),
9            nn.ReLU(),
10            nn.Conv2d(64, 1, kernel_size=3, padding=1)
11        )
12
13    def forward(self, coarse_depth, rgb):
14        x = torch.cat((coarse_depth, rgb), dim=1)  # Concatenate coarse depth and RGB
15        x = self.refinement(x)
16        return x