model.py

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

# This class MUST match the architecture of the model you saved in the .pth file.
# For this example, we assume 3 output classes (e.g., cat, dog, bird).
# And input images of size 3x224x224 (3 channels, 224x224 pixels).

class SimpleCNN(nn.Module):
    def __init__(self, num_classes=3):
        super(SimpleCNN, self).__init__()
        # Conv Layer 1
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, padding=1)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        # Conv Layer 2
        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding=1)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        # Flatten the layer
        # Image size starts at 224x224, after two pools -> 224/2 -> 112/2 -> 56x56
        self.fc1 = nn.Linear(32 * 56 * 56, 128)
        self.relu3 = nn.ReLU()
        self.fc2 = nn.Linear(128, num_classes)

    def forward(self, x):
        x = self.pool1(self.relu1(self.conv1(x)))
        x = self.pool2(self.relu2(self.conv2(x)))
        # Flatten the output for the fully connected layers
        x = x.view(-1, 32 * 56 * 56)
        x = self.relu3(self.fc1(x))
        x = self.fc2(x)
        return x