Knowledge Distillation Example.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "2zon_4BD9Jg0"
      },
      "source": [
        "# Knowledge Distillation Example\n",
        "\n",
        "This notebook demonstrates how to use the knowledge distillation technique to train a student model to mimic a teacher model. The teacher is a larger convolutional neural network while the student is a simple DNN with only 2 fully connected layers."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "ogsNzVtwRiSD",
        "outputId": "935f988e-55dc-4b9d-80e9-7aead7034152"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting torchmetrics\n",
            "  Downloading torchmetrics-1.2.0-py3-none-any.whl (805 kB)\n",
            "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 805.2/805.2 kB 13.4 MB/s eta 0:00:00\n",
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            "Installing collected packages: lightning-utilities, torchmetrics\n",
            "Successfully installed lightning-utilities-0.10.0 torchmetrics-1.2.0\n",
            "Collecting torchview\n",
            "  Downloading torchview-0.2.6-py3-none-any.whl (25 kB)\n",
            "Installing collected packages: torchview\n",
            "Successfully installed torchview-0.2.6\n"
          ]
        }
      ],
      "source": [
        "%%bash\n",
        "pip install torchmetrics\n",
        "pip install torchview"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0F2n-uqC5v39",
        "outputId": "48eb04c6-0d61-4501-aaf1-c567ba0def64"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 9912422/9912422 [00:00<00:00, 170735128.92it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting data/MNIST/raw/train-images-idx3-ubyte.gz to data/MNIST/raw\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 28881/28881 [00:00<00:00, 99946942.10it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting data/MNIST/raw/train-labels-idx1-ubyte.gz to data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 1648877/1648877 [00:00<00:00, 31489401.97it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting data/MNIST/raw/t10k-images-idx3-ubyte.gz to data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 4542/4542 [00:00<00:00, 21922357.62it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting data/MNIST/raw/t10k-labels-idx1-ubyte.gz to data/MNIST/raw\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torchvision import datasets, transforms\n",
        "from torchmetrics import Accuracy\n",
        "from torchview import draw_graph\n",
        "from tqdm.auto import tqdm, trange\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "\n",
        "def count_parameters(model):\n",
        "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "\n",
        "\n",
        "# Define transformations\n",
        "transform = transforms.Compose([\n",
        "    transforms.ToTensor(),  # Convert image to tensor\n",
        "])\n",
        "\n",
        "# Define datasets\n",
        "datasets_kwargs = {'download': True, 'transform': transform}\n",
        "train_dataset = datasets.MNIST('data/', train=True, **datasets_kwargs)\n",
        "test_dataset = datasets.MNIST('data/', train=False, **datasets_kwargs)\n",
        "\n",
        "# Define dataloaders\n",
        "dataloader_kwargs = {'batch_size': 256, 'shuffle': True}\n",
        "train_loader = torch.utils.data.DataLoader(train_dataset, **dataloader_kwargs)\n",
        "test_loader = torch.utils.data.DataLoader(test_dataset, **dataloader_kwargs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "YpJuu3FQ9Iqa"
      },
      "outputs": [],
      "source": [
        "# Specify the models\n",
        "class Teacher(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Teacher, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(1, 16, 3, padding=1)  # 28x28x1 -> 28x28x16\n",
        "        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)  # 28x28x16 -> 28x28x32\n",
        "        self.conv3 = nn.Conv2d(32, 64, 3, padding=1)  # 28x28x32 -> 28x28x64\n",
        "\n",
        "        self.fc1 = nn.LazyLinear(512)\n",
        "        self.fc2 = nn.LazyLinear(10)\n",
        "\n",
        "        self.pool = nn.MaxPool2d(2, 2)  # 28x28x64 -> 14x14x64\n",
        "\n",
        "    def forward(self, x, temperature=1.0):\n",
        "        # Convolutional layers\n",
        "        x = F.gelu(self.conv1(x))  # 28x28x1 -> 28x28x16\n",
        "        x = self.pool(F.gelu(self.conv2(x)))  # 28x28x16 -> 14x14x32\n",
        "        x = self.pool(F.gelu(self.conv3(x)))  # 14x14x32 -> 7x7x64\n",
        "\n",
        "        # Fully connected layers\n",
        "        x = torch.flatten(x, 1)\n",
        "        x = F.gelu(self.fc1(x))  # 3136 -> 512\n",
        "        x = self.fc2(x)  # 512 -> 10\n",
        "        x /= temperature\n",
        "        x = F.softmax(x, dim=1)  # 10 -> 10\n",
        "        return x\n",
        "\n",
        "\n",
        "class Student(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Student, self).__init__()\n",
        "        self.fc1 = nn.Linear(28 * 28, 768)\n",
        "        self.fc2 = nn.LazyLinear(10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # Fully connected layers\n",
        "        x = torch.flatten(x, 1)\n",
        "        x = F.gelu(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "        x = F.softmax(x, dim=1)\n",
        "        return x\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aZ-i7ZqVEG0z"
      },
      "source": [
        "# Teacher Model Training"
      ]
    },
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            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.\n",
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      "source": [
        "# Train the teacher like a normal model\n",
        "teacher = Teacher().to(device)\n",
        "teacher_optimizer = torch.optim.AdamW(teacher.parameters(), lr=1e-3)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# Training loop\n",
        "n_epochs = 10\n",
        "hist = []\n",
        "best_epoch = 0\n",
        "best_loss = float('inf')\n",
        "iterator = trange(n_epochs)\n",
        "for epoch in iterator:\n",
        "    loss_i = 0.0\n",
        "    for i, (x_train, y_train) in tqdm(enumerate(train_loader, 0), total=len(train_loader)):\n",
        "        # Move data to device\n",
        "        x_train, y_train = x_train.to(device), y_train.to(device)\n",
        "\n",
        "        # Forward pass\n",
        "        teacher_optimizer.zero_grad()\n",
        "        y_hat = teacher.forward(x_train)\n",
        "        loss = criterion(y_hat, y_train)\n",
        "\n",
        "        # Backward pass\n",
        "        loss.backward()\n",
        "        teacher_optimizer.step()\n",
        "        loss_i += loss.item()\n",
        "\n",
        "\n",
        "# Model arch\n",
        "display(draw_graph(teacher, input_size=x_train.shape, depth=1).visual_graph)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
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        "id": "dUXgguAQGAkE",
        "outputId": "7ba559a3-af6e-4c4f-ebe7-ddfd884fa61f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "52eb42ec4c054c6581d12e9a6c27a90d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/40 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy = 99.08%\n"
          ]
        }
      ],
      "source": [
        "with torch.inference_mode():\n",
        "    accuracy = Accuracy(task='multiclass', num_classes=10)\n",
        "    for x_test, y_test in tqdm(test_loader, total=len(test_loader)):\n",
        "        # Move data to device\n",
        "        x_test, y_test = x_test.to(device), y_test.to(device)\n",
        "\n",
        "        # Forward pass\n",
        "        y_hat = teacher.forward(x_test)\n",
        "        _, predicted = torch.max(y_hat.data, 1)\n",
        "        accuracy(y_hat.cpu(), y_test.cpu())\n",
        "    teacher_acc = accuracy.compute()\n",
        "    print(f'Accuracy = {teacher_acc:.2%}')\n",
        "    accuracy.reset()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-0A8LrmrKB66"
      },
      "source": [
        "# Student Model Training"
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        "    accuracy.reset()\n",
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        "# Model arch\n",
        "display(draw_graph(student, input_size=x_train.shape, depth=1).visual_graph)"
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            "Accuracy = 97.87%\n"
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        "with torch.inference_mode():\n",
        "    accuracy = Accuracy(task='multiclass', num_classes=10)\n",
        "    for x_test, y_test in tqdm(test_loader, total=len(test_loader)):\n",
        "        # Move data to device\n",
        "        x_test, y_test = x_test.to(device), y_test.to(device)\n",
        "\n",
        "        # Forward pass\n",
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        "        accuracy(y_hat.cpu(), y_test.cpu())\n",
        "    student_acc = accuracy.compute()\n",
        "    print(f'Accuracy = {student_acc:.2%}')\n",
        "    accuracy.reset()"
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            "\n",
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        "print(f'Compression ratio = {count_parameters(teacher) / count_parameters(student):.1f}:1')\n",
        "print(f'    Teacher parameters = {count_parameters(teacher):,}')\n",
        "print(f'    Student parameters = {count_parameters(student):,}')\n",
        "print()\n",
        "print(f'Teacher Accuracy = {teacher_acc:.2%}')\n",
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