{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision import datasets, transforms\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "!cp -r /content/drive/MyDrive/Dataset_clean /content/\n",
        "\n",
        "#Verify structure\n",
        "for split in ['train', 'val', 'test']:\n",
        "    path = f'/content/Dataset_clean/{split}'\n",
        "    classes = os.listdir(path)\n",
        "    total = sum(len(os.listdir(os.path.join(path, c))) for c in classes)\n",
        "    print(f'{split}: {total} images | classes: {classes}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sE1F3em-V5go",
        "outputId": "2c73a9a6-a198-468c-a2cc-253b2de7cc3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nG2bh66rQ56G"
      },
      "outputs": [],
      "source": [
        "class EyeCNN(nn.Module):\n",
        "    def __init__(self, num_classes=2):\n",
        "        super(EyeCNN, self).__init__()\n",
        "        self.conv_layers = nn.Sequential(\n",
        "            nn.Conv2d(3, 32, 3, 1, 1),\n",
        "            nn.BatchNorm2d(32),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(2, 2),\n",
        "\n",
        "            nn.Conv2d(32, 64, 3, 1, 1),\n",
        "            nn.BatchNorm2d(64),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(2, 2),\n",
        "\n",
        "            nn.Conv2d(64, 128, 3, 1, 1),\n",
        "            nn.BatchNorm2d(128),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(2, 2),\n",
        "\n",
        "            nn.Conv2d(128, 256, 3, 1, 1),\n",
        "            nn.BatchNorm2d(256),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(2, 2)\n",
        "        )\n",
        "\n",
        "        self.fc_layers = nn.Sequential(\n",
        "            nn.AdaptiveAvgPool2d((1, 1)),\n",
        "            nn.Flatten(),\n",
        "            nn.Linear(256, 512),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(0.35),\n",
        "            nn.Linear(512, num_classes)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv_layers(x)\n",
        "        x = self.fc_layers(x)\n",
        "        return x"
      ]
    }
  ]
}