models.py

import torch.nn as nn
from torchvision.models import resnet18, ResNet18_Weights
import torch.nn.functional as F
import torch

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.feature_extractor = resnet18(weights=ResNet18_Weights)
        in_channels = self.feature_extractor.fc.in_features
        self.feature_extractor.fc = nn.Identity()
        # Output is a vector of dimension 1 + 4
        # 1 for probability of belonging to any class
        # 4 for bounding box of object that is presented (if no object is presented i. e. the probability < a threshold, any 4 numbers)
        self.fc_prob = nn.Sequential(
            nn.Linear(in_channels, 512),
            nn.Linear(512, 1)
        )
        self.fc_bbox = nn.Sequential(
            nn.Linear(in_channels, 512),
            nn.Linear(512, 4)
        )
    
    def forward(self, x):
        pred_prob = torch.sigmoid(self.fc_prob(self.feature_extractor(x)))

        pred_bbox = self.fc_bbox(self.feature_extractor(x))

        return (pred_prob, pred_bbox)