Lightning_Datamodules.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TirendazAcademy/PyTorch-Lightning-Tutorials/blob/main/Lightning_Datamodules.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing the necessary libraries"
      ],
      "metadata": {
        "id": "R9d35E28wmc_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install lightning"
      ],
      "metadata": {
        "id": "V6HiUJ4xwhlu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "cSOVSOy7wPRh"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "import lightning as L\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "from torch import nn\n",
        "from torch.utils.data import DataLoader, random_split\n",
        "from torchmetrics.functional import accuracy\n",
        "from torchvision import transforms\n",
        "\n",
        "# Note - you must have torchvision installed for this example\n",
        "from torchvision.datasets import CIFAR10, MNIST\n",
        "\n",
        "PATH_DATASETS = os.environ.get(\"PATH_DATASETS\", \".\")\n",
        "BATCH_SIZE = 256 if torch.cuda.is_available() else 64"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"torch version:\",torch.__version__)\n",
        "print(\"pytorch ligthening version:\",L.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O3BbsY1xw_gk",
        "outputId": "e13f4d51-3a6d-4b77-af00-1ebca1dadbab"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch version: 2.0.0+cu118\n",
            "pytorch ligthening version: 2.0.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Defining The MNISTDataModule\n",
        "\n",
        "Let’s go over each function in the class below and talk about what they’re doing:\n",
        "\n",
        "1. `__init__`\n",
        "\n",
        "- Takes in a `data_dir` arg that points to where you have downloaded/wish to download the MNIST dataset.\n",
        "- Defines a transform that will be applied across train, val, and test dataset splits.\n",
        "- Defines default `self.dims`.\n",
        "\n",
        "2. `prepare_data`\n",
        "\n",
        "- This is where we can download the dataset. We point to our desired dataset and ask torchvision’s MNIST dataset class to download if the dataset isn’t found there.\n",
        "- Note we do not make any state assignments in this function (i.e. self.something = ...)\n",
        "\n",
        "3. `setup`\n",
        "\n",
        "- Loads in data from file and prepares PyTorch tensor datasets for each split (train, val, test).\n",
        "- Setup expects a ‘stage’ arg which is used to separate logic for ‘fit’ and ‘test’.\n",
        "- If you don’t mind loading all your datasets at once, you can set up a condition to allow for both ‘fit’ related setup and ‘test’ related setup to run whenever None is passed to stage.\n",
        "- Note this runs across all GPUs and it is safe to make state assignments here\n",
        "\n",
        "4. `x_dataloader`\n",
        "\n",
        "- `train_dataloader()`, `val_dataloader()`, and `test_dataloader()` all return PyTorch DataLoader instances that are created by wrapping their respective datasets that we prepared in setup()\n"
      ],
      "metadata": {
        "id": "nKrDhA_jyX8r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class MNISTDataModule(L.LightningDataModule):\n",
        "    def __init__(self, data_dir: str = PATH_DATASETS):\n",
        "        super().__init__()\n",
        "        self.data_dir = data_dir\n",
        "        self.transform = transforms.Compose(\n",
        "            [\n",
        "                transforms.ToTensor(),\n",
        "                transforms.Normalize((0.1307,), (0.3081,)),\n",
        "            ]\n",
        "        )\n",
        "\n",
        "        self.dims = (1, 28, 28)\n",
        "        self.num_classes = 10\n",
        "\n",
        "    def prepare_data(self):\n",
        "        # download\n",
        "        MNIST(self.data_dir, train=True, download=True)\n",
        "        MNIST(self.data_dir, train=False, download=True)\n",
        "\n",
        "    def setup(self, stage=None):\n",
        "        # Assign train/val datasets for use in dataloaders\n",
        "        if stage == \"fit\" or stage is None:\n",
        "            mnist_full = MNIST(self.data_dir, train=True, transform=self.transform)\n",
        "            self.mnist_train, self.mnist_val = random_split(mnist_full, [55000, 5000])\n",
        "\n",
        "        # Assign test dataset for use in dataloader(s)\n",
        "        if stage == \"test\" or stage is None:\n",
        "            self.mnist_test = MNIST(self.data_dir, train=False, transform=self.transform)\n",
        "\n",
        "    def train_dataloader(self):\n",
        "        return DataLoader(self.mnist_train, batch_size=BATCH_SIZE)\n",
        "\n",
        "    def val_dataloader(self):\n",
        "        return DataLoader(self.mnist_val, batch_size=BATCH_SIZE)\n",
        "\n",
        "    def test_dataloader(self):\n",
        "        return DataLoader(self.mnist_test, batch_size=BATCH_SIZE)"
      ],
      "metadata": {
        "id": "swG9n_lSziNu"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Defining the dataset agnostic LitModel"
      ],
      "metadata": {
        "id": "PLB_NngF0HZb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class LitModel(L.LightningModule):\n",
        "    def __init__(self, channels, width, height, num_classes, hidden_size=64, learning_rate=2e-4):\n",
        "        super().__init__()\n",
        "\n",
        "        # We take in input dimensions as parameters and use those to dynamically build model.\n",
        "        self.channels = channels\n",
        "        self.width = width\n",
        "        self.height = height\n",
        "        self.num_classes = num_classes\n",
        "        self.hidden_size = hidden_size\n",
        "        self.learning_rate = learning_rate\n",
        "\n",
        "        self.model = nn.Sequential(\n",
        "            nn.Flatten(),\n",
        "            nn.Linear(channels * width * height, hidden_size),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(0.1),\n",
        "            nn.Linear(hidden_size, hidden_size),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(0.1),\n",
        "            nn.Linear(hidden_size, num_classes),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.model(x)\n",
        "        return F.log_softmax(x, dim=1)\n",
        "\n",
        "    def training_step(self, batch):\n",
        "        x, y = batch\n",
        "        logits = self(x)\n",
        "        loss = F.nll_loss(logits, y)\n",
        "        return loss\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        x, y = batch\n",
        "        logits = self(x)\n",
        "        loss = F.nll_loss(logits, y)\n",
        "        preds = torch.argmax(logits, dim=1)\n",
        "        acc = accuracy(preds, y, task=\"multiclass\", num_classes=10)\n",
        "        self.log(\"val_loss\", loss, prog_bar=True)\n",
        "        self.log(\"val_acc\", acc, prog_bar=True)\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n",
        "        return optimizer"
      ],
      "metadata": {
        "id": "tTyJmhzC0ELC"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training the LitModel using the MNISTDataModule"
      ],
      "metadata": {
        "id": "5OwZauUt0YBb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Init DataModule\n",
        "dm = MNISTDataModule()\n",
        "# Init model from datamodule's attributes\n",
        "model = LitModel(*dm.dims, dm.num_classes)\n",
        "# Init trainer\n",
        "trainer = L.Trainer(\n",
        "    max_epochs=3,\n",
        "    accelerator=\"auto\",\n",
        "    devices=1,\n",
        ")\n",
        "# Pass the datamodule as arg to trainer.fit to override model hooks :)\n",
        "trainer.fit(model, dm)"
      ],
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        "outputId": "e6112d56-33b9-4006-a4b3-238ec2fd068a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO: GPU available: True (cuda), used: True\n",
            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO: TPU available: False, using: 0 TPU cores\n",
            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO: IPU available: False, using: 0 IPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO: HPU available: False, using: 0 HPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "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 ./MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 9912422/9912422 [00:00<00:00, 263185719.18it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/train-images-idx3-ubyte.gz to ./MNIST/raw\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
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          "name": "stdout",
          "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 ./MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 28881/28881 [00:00<00:00, 23599394.86it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/train-labels-idx1-ubyte.gz to ./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 ./MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1648877/1648877 [00:00<00:00, 71571592.34it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/t10k-images-idx3-ubyte.gz to ./MNIST/raw\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
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          "text": [
            "\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 ./MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4542/4542 [00:00<00:00, 21309316.30it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MNIST/raw\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING: Missing logger folder: /content/lightning_logs\n",
            "WARNING:lightning.pytorch.loggers.tensorboard:Missing logger folder: /content/lightning_logs\n",
            "INFO: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:lightning.pytorch.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO: \n",
            "  | Name  | Type       | Params\n",
            "-------------------------------------\n",
            "0 | model | Sequential | 55.1 K\n",
            "-------------------------------------\n",
            "55.1 K    Trainable params\n",
            "0         Non-trainable params\n",
            "55.1 K    Total params\n",
            "0.220     Total estimated model params size (MB)\n",
            "INFO:lightning.pytorch.callbacks.model_summary:\n",
            "  | Name  | Type       | Params\n",
            "-------------------------------------\n",
            "0 | model | Sequential | 55.1 K\n",
            "-------------------------------------\n",
            "55.1 K    Trainable params\n",
            "0         Non-trainable params\n",
            "55.1 K    Total params\n",
            "0.220     Total estimated model params size (MB)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Sanity Checking: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        {
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          "data": {
            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
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              "Validation: 0it [00:00, ?it/s]"
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              "Validation: 0it [00:00, ?it/s]"
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          "text": [
            "INFO: `Trainer.fit` stopped: `max_epochs=3` reached.\n",
            "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=3` reached.\n"
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    {
      "cell_type": "markdown",
      "source": [
        "# Defining the CIFAR10 DataModule"
      ],
      "metadata": {
        "id": "x6l-uukU0gbq"
      }
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    {
      "cell_type": "code",
      "source": [
        "class CIFAR10DataModule(L.LightningDataModule):\n",
        "    def __init__(self, data_dir: str = \"./\"):\n",
        "        super().__init__()\n",
        "        self.data_dir = data_dir\n",
        "        self.transform = transforms.Compose(\n",
        "            [\n",
        "                transforms.ToTensor(),\n",
        "                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
        "            ]\n",
        "        )\n",
        "\n",
        "        self.dims = (3, 32, 32)\n",
        "        self.num_classes = 10\n",
        "\n",
        "    def prepare_data(self):\n",
        "        # download\n",
        "        CIFAR10(self.data_dir, train=True, download=True)\n",
        "        CIFAR10(self.data_dir, train=False, download=True)\n",
        "\n",
        "    def setup(self, stage=None):\n",
        "        # Assign train/val datasets for use in dataloaders\n",
        "        if stage == \"fit\" or stage is None:\n",
        "            cifar_full = CIFAR10(self.data_dir, train=True, transform=self.transform)\n",
        "            self.cifar_train, self.cifar_val = random_split(cifar_full, [45000, 5000])\n",
        "\n",
        "        # Assign test dataset for use in dataloader(s)\n",
        "        if stage == \"test\" or stage is None:\n",
        "            self.cifar_test = CIFAR10(self.data_dir, train=False, transform=self.transform)\n",
        "\n",
        "    def train_dataloader(self):\n",
        "        return DataLoader(self.cifar_train, batch_size=BATCH_SIZE)\n",
        "\n",
        "    def val_dataloader(self):\n",
        "        return DataLoader(self.cifar_val, batch_size=BATCH_SIZE)\n",
        "\n",
        "    def test_dataloader(self):\n",
        "        return DataLoader(self.cifar_test, batch_size=BATCH_SIZE)"
      ],
      "metadata": {
        "id": "vId6-glo0hoO"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dm = CIFAR10DataModule()\n",
        "model = LitModel(*dm.dims, dm.num_classes, hidden_size=256)\n",
        "trainer = L.Trainer(\n",
        "    max_epochs=5,\n",
        "    accelerator=\"auto\",\n",
        "    devices=1,\n",
        ")\n",
        "trainer.fit(model, dm)"
      ],
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        "outputId": "043db4df-0f42-4b3a-9e48-babc6d825c2d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO: GPU available: True (cuda), used: True\n",
            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO: TPU available: False, using: 0 TPU cores\n",
            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO: IPU available: False, using: 0 IPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO: HPU available: False, using: 0 HPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
          ]
        },
        {
          "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 [00:06<00:00, 27701580.82it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./cifar-10-python.tar.gz to ./\n",
            "Files already downloaded and verified\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:lightning.pytorch.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO: \n",
            "  | Name  | Type       | Params\n",
            "-------------------------------------\n",
            "0 | model | Sequential | 855 K \n",
            "-------------------------------------\n",
            "855 K     Trainable params\n",
            "0         Non-trainable params\n",
            "855 K     Total params\n",
            "3.420     Total estimated model params size (MB)\n",
            "INFO:lightning.pytorch.callbacks.model_summary:\n",
            "  | Name  | Type       | Params\n",
            "-------------------------------------\n",
            "0 | model | Sequential | 855 K \n",
            "-------------------------------------\n",
            "855 K     Trainable params\n",
            "0         Non-trainable params\n",
            "855 K     Total params\n",
            "3.420     Total estimated model params size (MB)\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "Sanity Checking: 0it [00:00, ?it/s]"
            ],
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              "Validation: 0it [00:00, ?it/s]"
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              "Validation: 0it [00:00, ?it/s]"
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              "model_id": "cc1d0bfcf4a24ddcb02a2ab1b1704501"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO: `Trainer.fit` stopped: `max_epochs=5` reached.\n",
            "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=5` reached.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Resource\n",
        "- [PyTorch Lightning DataModules](https://lightning.ai/docs/pytorch/stable/notebooks/lightning_examples/datamodules.html)\n",
        "\n",
        "\n",
        "🔗 Let's connect [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [Twitter](http://twitter.com/tirendazacademy) | [Instagram](https://www.instagram.com/tirendazacademy) | [GitHub](http://github.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) | [Kaggle](https://www.kaggle.com/tirendazacademy) 😎"
      ],
      "metadata": {
        "id": "JyISc2m80_ZT"
      }
    }
  ]
}