model.py

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torchvision import models
from PIL import Image
import os
import random


class ResNet50(nn.Module):
    def __init__(self):
        super(ResNet50, self).__init__()
        self.resnet = models.resnet50(pretrained=True)
        for param in self.resnet.parameters():
            param.requires_grad = False
        self.resnet.fc = nn.Sequential(
            nn.Linear(2048, 2)
        )

    def forward(self, x):
        x = self.resnet(x)
        return x