model.py

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


class MRNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.pretrained_model = models.alexnet(pretrained=True)
        self.pooling_layer = nn.AdaptiveAvgPool2d(1)
        self.classifer = nn.Linear(256, 2)

    def forward(self, x):
        x = torch.squeeze(x, dim=0) 
        features = self.pretrained_model.features(x)
        pooled_features = self.pooling_layer(features)
        pooled_features = pooled_features.view(pooled_features.size(0), -1)
        flattened_features = torch.max(pooled_features, 0, keepdim=True)[0]
        output = self.classifer(flattened_features)
        return output