VGG_CIFAR_10_with_PyTorch.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyPLRVNuBvzhYM+mL2dB8QfV",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/PSLeon24/Artificial_Intelligence/blob/main/VGG_CIFAR_10_with_PyTorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dj1KD6P6O-w3",
        "outputId": "735b9dc9-0b22-439f-e2ca-6134a9f673ce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "device is cuda\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth\n",
            "100%|██████████| 528M/528M [00:06<00:00, 82.8MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VGG(\n",
              "  (features): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU(inplace=True)\n",
              "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (3): ReLU(inplace=True)\n",
              "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (6): ReLU(inplace=True)\n",
              "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (8): ReLU(inplace=True)\n",
              "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (11): ReLU(inplace=True)\n",
              "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (13): ReLU(inplace=True)\n",
              "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (15): ReLU(inplace=True)\n",
              "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (18): ReLU(inplace=True)\n",
              "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (20): ReLU(inplace=True)\n",
              "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (22): ReLU(inplace=True)\n",
              "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (25): ReLU(inplace=True)\n",
              "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (27): ReLU(inplace=True)\n",
              "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (29): ReLU(inplace=True)\n",
              "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
              "  (classifier): Sequential(\n",
              "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
              "    (1): ReLU()\n",
              "    (2): Dropout(p=0.5, inplace=False)\n",
              "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
              "    (4): ReLU()\n",
              "    (5): Dropout(p=0.5, inplace=False)\n",
              "    (6): Linear(in_features=4096, out_features=10, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "from torchvision.models.vgg import vgg16\n",
        "\n",
        "print(\"device is cuda\") if torch.cuda.is_available() else print(\"device is cpu\")\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "model = vgg16(pretrained = True)\n",
        "\n",
        "fc = nn.Sequential( # define the classifier, fc stands for fully connected\n",
        "                    nn.Linear(512 * 7 * 7, 4096),\n",
        "                    nn.ReLU(),\n",
        "                    nn.Dropout(), # It is used to avoid overfitting\n",
        "                    nn.Linear(4096, 4096),\n",
        "                    nn.ReLU(),\n",
        "                    nn.Dropout(),\n",
        "                    nn.Linear(4096, 10),\n",
        "  )\n",
        "\n",
        "model.classifier = fc\n",
        "model.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tqdm\n",
        "\n",
        "from torchvision.datasets.cifar import CIFAR10\n",
        "from torchvision.transforms import Compose, ToTensor, Resize\n",
        "from torchvision.transforms import RandomHorizontalFlip, RandomCrop, Normalize\n",
        "from torch.utils.data.dataloader import DataLoader\n",
        "\n",
        "from torch.optim.adam import Adam\n",
        "\n",
        "transforms = Compose([\n",
        "    Resize(224),\n",
        "    RandomCrop((224, 224), padding = 4),\n",
        "    RandomHorizontalFlip(p = 0.5),\n",
        "    ToTensor(),\n",
        "    Normalize(mean = (0.4914, 0.4822, 0.4465), std = (0.247, 0.243, 0.261))\n",
        "])"
      ],
      "metadata": {
        "id": "u6jB73pGQAvg"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_data = CIFAR10(root = './', train = True, download = True, transform = transforms)\n",
        "test_data = CIFAR10(root = './', train = False, download = True, transform = transforms)\n",
        "\n",
        "train_loader = DataLoader(training_data, batch_size = 32, shuffle = True)\n",
        "test_loader = DataLoader(test_data, batch_size = 32, shuffle = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WAhcFkkXQsnV",
        "outputId": "bf913bfc-08db-4582-a532-8e86dfce9704"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 170498071/170498071 [00:12<00:00, 13169312.92it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./cifar-10-python.tar.gz to ./\n",
            "Files already downloaded and verified\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr = 1e-4\n",
        "optim = Adam(model.parameters(), lr = lr)\n",
        "\n",
        "for epoch in range(30):\n",
        "  iterator = tqdm.tqdm(train_loader) # print training log\n",
        "  for data, label in iterator:\n",
        "    optim.zero_grad() # weight initialization\n",
        "\n",
        "    preds = model(data.to(device))\n",
        "\n",
        "    loss = nn.CrossEntropyLoss()(preds, label.to(device))\n",
        "    loss.backward()\n",
        "    optim.step()\n",
        "\n",
        "    iterator.set_description(f\"epoch: {epoch + 1}, loss: {loss.item()}\")\n",
        "\n",
        "  torch.save(model.state_dict(), \"CIFAR_pretrained.pth\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "1svAnWsESwhg",
        "outputId": "1a600b0f-f5bf-41e7-d9cd-99b232b46448"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "epoch: 1, loss: 0.23573541641235352: 100%|██████████| 1563/1563 [13:40<00:00,  1.90it/s]\n",
            "epoch: 2, loss: 0.1997595876455307: 100%|██████████| 1563/1563 [13:32<00:00,  1.92it/s]\n",
            "epoch: 3, loss: 0.24917474389076233: 100%|██████████| 1563/1563 [13:30<00:00,  1.93it/s]\n",
            "epoch: 4, loss: 0.09359929710626602:  12%|█▏        | 180/1563 [01:33<11:56,  1.93it/s]\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-f6a20b119e68>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCrossEntropyLoss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m     \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0moptim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluation on the transfered model\n",
        "\n",
        "model.load_state_dict(torch.load(\"CIFAR_pretrained.pth\", map_location = device))\n",
        "\n",
        "num_corr = 0\n",
        "\n",
        "with torch.no_grad():\n",
        "  iterator = tqdm.tqdm(test_loader)\n",
        "  for data, label in iterator:\n",
        "    output = model(data.to(device))\n",
        "    preds = output.data.max(1)[1]\n",
        "    corr = preds.eq(label.to(device).data).sum().item()\n",
        "    num_corr += corr\n",
        "\n",
        "  print(f\"Accuracy: {num_corr / len(test_data)}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gYut5akZTbVc",
        "outputId": "39622656-7eb1-4241-d98b-ea5c033163d5"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 313/313 [01:04<00:00,  4.89it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9031%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TUMZa7Khei3z"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}