kornia_lightning_mnist_gpu.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/edgarriba/kornia-examples/blob/master/kornia_lightning_mnist_gpu.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x83-rnVKT8Wo",
        "colab_type": "text"
      },
      "source": [
        "# Kornia and PyTorch Lightning GPU data augmentation demo\n",
        "\n",
        "In this tutorial we show how one can combine both Kornia and PyTorch Lightning to perform data augmentation to train a model using CPUs and GPUs in batch mode without additional effort.\n",
        "\n",
        "\n",
        "Enjoy the example !"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iCsre0XmawoR",
        "colab_type": "text"
      },
      "source": [
        "## Install Kornia and PyTorch Lightning\n",
        "Next, we install Kornia and PyTorch Lightning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dEeUzX_5aLrX",
        "colab_type": "code",
        "outputId": "753ec5d4-8af7-43a1-814b-e57eeaaa71b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 855
        }
      },
      "source": [
        "! pip install git+git://github.com/PyTorchLightning/pytorch-lightning.git@master --upgrade\n",
        "! pip install kornia -q"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+git://github.com/PyTorchLightning/pytorch-lightning.git@master\n",
            "  Cloning git://github.com/PyTorchLightning/pytorch-lightning.git (to revision master) to /tmp/pip-req-build-t2r5dn0h\n",
            "  Running command git clone -q git://github.com/PyTorchLightning/pytorch-lightning.git /tmp/pip-req-build-t2r5dn0h\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied, skipping upgrade: tensorboard>=1.14 in /usr/local/lib/python3.6/dist-packages (from pytorch-lightning==0.6.1.dev0) (1.15.0)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.16.4 in /usr/local/lib/python3.6/dist-packages (from pytorch-lightning==0.6.1.dev0) (1.17.5)\n",
            "Collecting tqdm>=4.35.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/47/55/fd9170ba08a1a64a18a7f8a18f088037316f2a41be04d2fe6ece5a653e8f/tqdm-4.43.0-py2.py3-none-any.whl (59kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 1.9MB/s \n",
            "\u001b[?25hCollecting future>=0.17.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/45/0b/38b06fd9b92dc2b68d58b75f900e97884c45bedd2ff83203d933cf5851c9/future-0.18.2.tar.gz (829kB)\n",
            "\u001b[K     |████████████████████████████████| 829kB 4.1MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: torch>=1.1 in /usr/local/lib/python3.6/dist-packages (from pytorch-lightning==0.6.1.dev0) (1.4.0)\n",
            "Requirement already satisfied, skipping upgrade: protobuf>=3.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch-lightning==0.6.1.dev0) (3.10.0)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch-lightning==0.6.1.dev0) (1.12.0)\n",
            "Requirement already satisfied, skipping upgrade: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch-lightning==0.6.1.dev0) (45.1.0)\n",
            "Requirement already satisfied, skipping upgrade: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch-lightning==0.6.1.dev0) (1.0.0)\n",
            "Requirement already satisfied, skipping upgrade: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch-lightning==0.6.1.dev0) (3.2.1)\n",
            "Requirement already satisfied, skipping upgrade: absl-py>=0.4 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch-lightning==0.6.1.dev0) (0.9.0)\n",
            "Requirement already satisfied, skipping upgrade: grpcio>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch-lightning==0.6.1.dev0) (1.27.1)\n",
            "Requirement already satisfied, skipping upgrade: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch-lightning==0.6.1.dev0) (0.34.2)\n",
            "Building wheels for collected packages: pytorch-lightning\n",
            "  Building wheel for pytorch-lightning (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pytorch-lightning: filename=pytorch_lightning-0.6.1.dev0-cp36-none-any.whl size=148144 sha256=ddf0b060e51c4a4ca30ec9a1f09820e6870b98a648e781a22f0ba1c9b603d345\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-ft44bq0c/wheels/10/69/8d/aa7539d71fd3f79eec266a45eae3206c704f37fcf4ff976e0d\n",
            "Successfully built pytorch-lightning\n",
            "Building wheels for collected packages: future\n",
            "  Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for future: filename=future-0.18.2-cp36-none-any.whl size=491057 sha256=38234a7b66dfd3e5ccc036f2ce4970e0a52a45b7a2f5dcfc34dc0c3049685305\n",
            "  Stored in directory: /root/.cache/pip/wheels/8b/99/a0/81daf51dcd359a9377b110a8a886b3895921802d2fc1b2397e\n",
            "Successfully built future\n",
            "Installing collected packages: tqdm, future, pytorch-lightning\n",
            "  Found existing installation: tqdm 4.28.1\n",
            "    Uninstalling tqdm-4.28.1:\n",
            "      Successfully uninstalled tqdm-4.28.1\n",
            "  Found existing installation: future 0.16.0\n",
            "    Uninstalling future-0.16.0:\n",
            "      Successfully uninstalled future-0.16.0\n",
            "Successfully installed future-0.18.2 pytorch-lightning-0.6.1.dev0 tqdm-4.43.0\n"
          ],
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            "\u001b[?25h"
          ],
          "name": "stdout"
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LXGKqIZuTLMs",
        "colab_type": "text"
      },
      "source": [
        "## Define lightning model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aDagOcKyZ_qh",
        "colab_type": "code",
        "outputId": "f4a8c000-1e42-4f37-81b7-5cb6ea0f5431",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "import os\n",
        "\n",
        "import torch\n",
        "from torch.nn import functional as F\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision.datasets import MNIST\n",
        "\n",
        "import pytorch_lightning as pl\n",
        "import kornia as K\n",
        "\n",
        "class CoolSystem(pl.LightningModule):\n",
        "\n",
        "    def __init__(self):\n",
        "        super(CoolSystem, self).__init__()\n",
        "        # not the best model...\n",
        "        self.l1 = torch.nn.Linear(28 * 28, 10)\n",
        "\n",
        "        self.transform = torch.nn.Sequential(\n",
        "            K.augmentation.RandomRectangleErasing((.05, .1), (.3, 1/.3)),\n",
        "            K.augmentation.RandomRotation((-15., 15.))\n",
        "        )\n",
        "    \n",
        "        self.pil_to_tensor = lambda x: K.image_to_tensor(x)\n",
        "\n",
        "    def forward(self, x):\n",
        "        return torch.relu(self.l1(x.view(x.size(0), -1)))\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        # REQUIRED\n",
        "        x, y = batch\n",
        "        x_aug = self.transform(x)  # => we perform GPU/Batched data augmentation\n",
        "        y_hat = self.forward(x_aug)\n",
        "        loss = F.cross_entropy(y_hat, y)\n",
        "        tensorboard_logs = {'train_loss': loss}\n",
        "        return {'loss': loss, 'log': tensorboard_logs}\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        # OPTIONAL\n",
        "        x, y = batch\n",
        "        y_hat = self.forward(x)\n",
        "        return {'val_loss': F.cross_entropy(y_hat, y)}\n",
        "\n",
        "    def validation_end(self, outputs):\n",
        "        # OPTIONAL\n",
        "        avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()\n",
        "        tensorboard_logs = {'val_loss': avg_loss}\n",
        "        return {'avg_val_loss': avg_loss, 'log': tensorboard_logs}\n",
        "        \n",
        "    def test_step(self, batch, batch_idx):\n",
        "        # OPTIONAL\n",
        "        x, y = batch\n",
        "        y_hat = self.forward(x)\n",
        "        return {'test_loss': F.cross_entropy(y_hat, y)}\n",
        "\n",
        "    def test_end(self, outputs):\n",
        "        # OPTIONAL\n",
        "        avg_loss = torch.stack([x['test_loss'] for x in outputs]).mean()\n",
        "        tensorboard_logs = {'test_loss': avg_loss}\n",
        "        return {'avg_test_loss': avg_loss, 'log': tensorboard_logs}\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        # REQUIRED\n",
        "        # can return multiple optimizers and learning_rate schedulers\n",
        "        # (LBFGS it is automatically supported, no need for closure function)\n",
        "        return torch.optim.Adam(self.parameters(), lr=0.0004)\n",
        "\n",
        "    def prepare_data(self):\n",
        "        MNIST(os.getcwd(), train=True, download=True, transform=self.pil_to_tensor)\n",
        "        MNIST(os.getcwd(), train=False, download=True, transform=self.pil_to_tensor)\n",
        "\n",
        "    def train_dataloader(self):\n",
        "        # REQUIRED\n",
        "        dataset = MNIST(os.getcwd(), train=True, download=False, transform=self.pil_to_tensor)\n",
        "        loader = DataLoader(dataset, batch_size=32)\n",
        "        return loader\n",
        "\n",
        "    def val_dataloader(self):\n",
        "        dataset = MNIST(os.getcwd(), train=True, download=False, transform=self.pil_to_tensor)\n",
        "        loader = DataLoader(dataset, batch_size=32)\n",
        "        return loader\n",
        "\n",
        "    def test_dataloader(self):\n",
        "        dataset = MNIST(os.getcwd(), train=False, download=False, transform=self.pil_to_tensor)\n",
        "        loader = DataLoader(dataset, batch_size=32)\n",
        "        return loader"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pytorch_lightning/core/decorators.py:13: UserWarning: data_loader decorator deprecated in 0.6.1. Will remove 0.8.0\n",
            "  warnings.warn(w)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yyLkgogITNtj",
        "colab_type": "text"
      },
      "source": [
        "## Run training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kr8cql-aaKnC",
        "colab_type": "code",
        "outputId": "bd235be7-6ddc-41ff-bdb8-1dc07623828b",
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            "cb1d3f99e1fd4d0f9313028475272254"
          ]
        }
      },
      "source": [
        "from pytorch_lightning import Trainer\n",
        "\n",
        "model = CoolSystem()\n",
        "\n",
        "# most basic trainer, uses good defaults\n",
        "trainer = Trainer()\n",
        "trainer.fit(model)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:root:\n",
            "  | Name        | Type                   | Params\n",
            "---------------------------------------------------\n",
            "0 | l1          | Linear                 | 7 K   \n",
            "1 | transform   | Sequential             | 0     \n",
            "2 | transform.0 | RandomRectangleErasing | 0     \n",
            "3 | transform.1 | RandomRotation         | 0     \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to /content/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
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        {
          "output_type": "stream",
          "text": [
            "Extracting /content/MNIST/raw/train-images-idx3-ubyte.gz to /content/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to /content/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "stream",
          "text": [
            "Extracting /content/MNIST/raw/train-labels-idx1-ubyte.gz to /content/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to /content/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
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        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Extracting /content/MNIST/raw/t10k-images-idx3-ubyte.gz to /content/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to /content/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
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          "text": [
            "Extracting /content/MNIST/raw/t10k-labels-idx1-ubyte.gz to /content/MNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "stream",
          "text": [
            "\r\n"
          ],
          "name": "stdout"
        },
        {
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        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/training_io.py:358: UserWarning: Did not find hyperparameters at model.hparams. Saving checkpoint without hyperparameters\n",
            "  \"Did not find hyperparameters at model.hparams. Saving checkpoint without\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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        {
          "output_type": "execute_result",
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    {
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      "source": [
        "trainer.test(model)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:root:\n",
            "  | Name        | Type                   | Params\n",
            "---------------------------------------------------\n",
            "0 | l1          | Linear                 | 7 K   \n",
            "1 | transform   | Sequential             | 0     \n",
            "2 | transform.0 | RandomRectangleErasing | 0     \n",
            "3 | transform.1 | RandomRotation         | 0     \n",
            "INFO:root:Model and Trainer restored from checkpoint: /content/lightning_logs/version_0/checkpoints/_ckpt_epoch_3.ckpt\n"
          ],
          "name": "stderr"
        },
        {
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  ]
}