app/model.py

import torch
import torch.nn as nn
import torchvision.models as models


class CatvsDogResNet50(nn.Module):
    def __init__(self, freeze_backbone: bool = True):
        super().__init__()
        self.backbone = models.resnet50(pretrained=True)

        if freeze_backbone:
            for param in self.backbone.parameters():
                param.requires_grad = False

        num_ftrs = self.backbone.fc.in_features
        self.backbone.fc = nn.Sequential(
            nn.Dropout(0.5),
            nn.Linear(num_ftrs, 1),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.backbone(x)