Question 7: The following code snippets represent different blocks in…

1import torch
2import torch.nn as nn
3
4class RegionProposal(nn.Module):
5    def __init__(self, in_channels):
6        super(RegionProposal, self).__init__()
7        self.conv = nn.Conv2d(in_channels, 256, kernel_size=3, stride=1, padding=1)
8        self.cls_layer = nn.Conv2d(256, 18, kernel_size=1)
9        self.reg_layer = nn.Conv2d(256, 36, kernel_size=1)
10
11    def forward(self, x):
12        features = torch.relu(self.conv(x))
13        cls_logits = self.cls_layer(features)
14        reg_deltas = self.reg_layer(features)
15        return cls_logits, reg_deltas

1import torchvision.models as models
2
3class FeatureExtractor(nn.Module):
4    def __init__(self):
5        super(FeatureExtractor, self).__init__()
6        vgg = models.vgg16(pretrained=True)
7        self.features = vgg.features  # Use pre-trained VGG16 convolutional layers
8
9    def forward(self, x):
10        return self.features(x)

1from torchvision.ops import roi_pool
2
3class ROIPooling(nn.Module):
4    def __init__(self, output_size=(7, 7)):
5        super(ROIPooling, self).__init__()
6        self.output_size = output_size
7
8    def forward(self, feature_map, proposals):
9        return roi_pool(feature_map, proposals, output_size=self.output_size)

1class FullyConnectedHead(nn.Module):
2    def __init__(self, in_features, num_classes):
3        super(FullyConnectedHead, self).__init__()
4        self.fc1 = nn.Linear(in_features, 4096)
5        self.fc2 = nn.Linear(4096, 4096)
6        self.cls_score = nn.Linear(4096, num_classes)
7        self.bbox_pred = nn.Linear(4096, num_classes * 4)  # 4 coordinates per class
8
9    def forward(self, x):
10        x = torch.relu(self.fc1(x))
11        x = torch.relu(self.fc2(x))
12        cls_logits = self.cls_score(x)
13        bbox_deltas = self.bbox_pred(x)
14        return cls_logits, bbox_deltas