model.py

import torch
import torch.nn as nn
import torch.nn.functional as F

class LargeNet(nn.Module):
    def __init__(self):
        super(LargeNet, self).__init__()
        self.name = "large"
        self.conv1 = nn.Conv2d(3, 5, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(5, 10, 5)
        self.fc1 = nn.Linear(10 * 29 * 29, 32)
        self.fc2 = nn.Linear(32, 7)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 10 * 29 * 29)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        x = x.squeeze(1)  # Flatten to [batch_size]
        return x

def load_model(model_path, device='cpu'):
    """Load the trained model from saved weights"""
    model = LargeNet()
    state_dict = torch.load(model_path, map_location=device)
    model.load_state_dict(state_dict)
    model.to(device)
    model.eval()
    return model