CNN_with_Lightning_V1.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/CNN_with_Lightning_V1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ed-SPXdMhcNn"
      },
      "outputs": [],
      "source": [
        "!pip install lightning -q "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import torch\n",
        "from torch.nn import functional as F\n",
        "from torch.utils.data import DataLoader,random_split\n",
        "from torchvision.datasets import MNIST\n",
        "from torchvision import transforms\n",
        "import pytorch_lightning as pl\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import torchvision"
      ],
      "metadata": {
        "id": "Vggwi4jXhqr-"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MnistDataModule(pl.LightningDataModule):\n",
        "    def __init__(self):\n",
        "        #this is the init function where we will defnine the architecture\n",
        "        super().__init__()\n",
        "\n",
        "    def prepare_data(self):     \n",
        "        # This contains the manupulation on data that needs to be done only once such as downloading it\n",
        "\n",
        "        #download the MNIST dataset\n",
        "        MNIST(os.getcwd(), train=True, download =True)\n",
        "        MNIST(os.getcwd(), train=False, download =True)\n",
        "        \n",
        "        # See here I have set download to false as it is already downloaded\n",
        "        mnist_train=MNIST(os.getcwd(), train=True, download =False,transform=transforms.ToTensor())\n",
        "  \n",
        "        #dividing into validation and training set\n",
        "        self.train_set, self.val_set= random_split(mnist_train,[55000,5000])\n",
        "    \n",
        "    def train_dataloader(self):\n",
        "        # REQUIRED\n",
        "        # This is an essential function. Needs to be included in the code              \n",
        "        return DataLoader(self.train_set, batch_size=128, num_workers=32)\n",
        "        \n",
        "    def val_dataloader(self):\n",
        "        # OPTIONAL\n",
        "        #loading validation dataset\n",
        "        return DataLoader(self.val_set, batch_size=128,num_workers=32)\n",
        "\n",
        "    def test_dataloader(self):\n",
        "        # OPTIONAL\n",
        "        # loading test dataset\n",
        "        return DataLoader(MNIST(os.getcwd(), train=False, download=False, transform=transforms.ToTensor()), batch_size=128,num_workers=32)"
      ],
      "metadata": {
        "id": "S0V9J-V0hyrd"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#defining the model\n",
        "class NN(pl.LightningModule):\n",
        "    def __init__(self):\n",
        "        #this is the init function where we will defnine the architecture\n",
        "        super().__init__()\n",
        "        \n",
        "        self.layer1 = torch.nn.Sequential(\n",
        "            torch.nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1),\n",
        "            torch.nn.ReLU(),\n",
        "            torch.nn.MaxPool2d(kernel_size=2, stride=2),\n",
        "            torch.nn.Dropout(p=0.5))\n",
        "        self.layer2 = torch.nn.Sequential(\n",
        "            torch.nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),\n",
        "            torch.nn.ReLU(),\n",
        "            torch.nn.MaxPool2d(kernel_size=2, stride=2),\n",
        "            torch.nn.Dropout(p=0.5))\n",
        "        self.layer3 = torch.nn.Sequential(\n",
        "            torch.nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),\n",
        "            torch.nn.ReLU(),\n",
        "            torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=1),\n",
        "            torch.nn.Dropout(p=0.5))\n",
        "\n",
        "        self.fc1 = torch.nn.Linear(4 * 4 * 128, 625, bias=True)\n",
        "        torch.nn.init.xavier_uniform(self.fc1.weight)\n",
        "        self.layer4 = torch.nn.Sequential(\n",
        "            self.fc1,\n",
        "            torch.nn.ReLU(),\n",
        "            torch.nn.Dropout(p=0.5))\n",
        "        self.dense1_bn = torch.nn.BatchNorm1d(625)\n",
        "        self.fc2 = torch.nn.Linear(625, 10, bias=True)\n",
        "        self.dense2_bn = torch.nn.BatchNorm1d(10)\n",
        "        torch.nn.init.xavier_uniform_(self.fc2.weight) # initialize parameters\n",
        "\n",
        "    def forward(self,x):\n",
        "        # evaluating the batch data as it moves forward in the netowrk\n",
        "        out = self.layer1(x)\n",
        "        out = self.layer2(out)\n",
        "        out = self.layer3(out)\n",
        "        out = out.view(out.size(0), -1)   # Flatten them for FC\n",
        "        # out = self.fc1(out)\n",
        "        out = self.dense1_bn(self.fc1(out))\n",
        "        \n",
        "        # out = self.fc2(out)\n",
        "        out = self.dense2_bn(self.fc2(out))\n",
        "        # print(out.shape)\n",
        "        return F.softmax(out,dim=1)\n",
        "    \n",
        "    def training_step(self,batch,batch_idx):\n",
        "        #REQUIRED\n",
        "        # extracting input and output from the batch\n",
        "        x, labels=batch\n",
        "        pred=self.forward(x)\n",
        "\n",
        "        correct=pred.argmax(dim=1).eq(labels).sum().item()\n",
        "        total=len(labels)\n",
        "        #calculating the loss\n",
        "        train_loss = F.cross_entropy(pred, labels)\n",
        "\n",
        "        #logs\n",
        "        logs={\"train_loss\": train_loss}\n",
        "\n",
        "        output={\n",
        "            #REQUIRED: It ie required for us to return \"loss\"\n",
        "            \"loss\": train_loss,\n",
        "            #optional for logging purposes\n",
        "            \"log\": logs,\n",
        "            \"correct\": correct,\n",
        "            \"total\": total\n",
        "        }\n",
        "        return output\n",
        "\n",
        "    def validation_step(self, batch, batch_nb):\n",
        "        # OPTIONAL\n",
        "        # The code that runs as we forward pass a validation batch\n",
        "        x, y = batch\n",
        "        y_hat = self(x)\n",
        "        correct=y_hat.argmax(dim=1).eq(y).sum().item()\n",
        "        total=len(y)\n",
        "        return {'val_loss': F.cross_entropy(y_hat, y),\"correct\": correct,\"total\": total}\n",
        "\n",
        "    def test_step(self, batch, batch_nb):\n",
        "        # OPTIONAL\n",
        "        # The code that runs as we forward pass a test batch\n",
        "\n",
        "        x, y = batch\n",
        "        y_hat = self(x)\n",
        "        correct=y_hat.argmax(dim=1).eq(y).sum().item()\n",
        "        total=len(y)\n",
        "        # returning the batch_dictionary\n",
        "        return {'test_loss': F.cross_entropy(y_hat, y),\n",
        "                \"correct\": correct,\n",
        "                \"total\": total}\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        # REQUIRED\n",
        "        # Can return multiple optimizers and learning_rate schedulers\n",
        "        return torch.optim.Adam(self.parameters(), lr=0.0005)"
      ],
      "metadata": {
        "id": "1tSERIwBhttv"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dm = MnistDataModule()\n",
        "model = NN()\n",
        "trainer = pl.Trainer(fast_dev_run=True, accelerator=\"auto\", devices=\"auto\")\n",
        "trainer.fit(model, dm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 936,
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        "outputId": "2c62355c-d53b-4c7d-ae73-04fdb615ecd0"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-4-ab1896426550>:24: UserWarning: nn.init.xavier_uniform is now deprecated in favor of nn.init.xavier_uniform_.\n",
            "  torch.nn.init.xavier_uniform(self.fc1.weight)\n",
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:Running in `fast_dev_run` mode: will run the requested loop using 1 batch(es). Logging and checkpointing is suppressed.\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 /content/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 9912422/9912422 [00:00<00:00, 87262901.35it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /content/MNIST/raw/train-images-idx3-ubyte.gz to /content/MNIST/raw\n",
            "\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 /content/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 28881/28881 [00:00<00:00, 25680664.37it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /content/MNIST/raw/train-labels-idx1-ubyte.gz to /content/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 /content/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1648877/1648877 [00:00<00:00, 25077566.89it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /content/MNIST/raw/t10k-images-idx3-ubyte.gz to /content/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 /content/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4542/4542 [00:00<00:00, 22545004.46it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /content/MNIST/raw/t10k-labels-idx1-ubyte.gz to /content/MNIST/raw\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\n",
            "  | Name      | Type        | Params\n",
            "------------------------------------------\n",
            "0 | layer1    | Sequential  | 320   \n",
            "1 | layer2    | Sequential  | 18.5 K\n",
            "2 | layer3    | Sequential  | 73.9 K\n",
            "3 | fc1       | Linear      | 1.3 M \n",
            "4 | layer4    | Sequential  | 1.3 M \n",
            "5 | dense1_bn | BatchNorm1d | 1.2 K \n",
            "6 | fc2       | Linear      | 6.3 K \n",
            "7 | dense2_bn | BatchNorm1d | 20    \n",
            "------------------------------------------\n",
            "1.4 M     Trainable params\n",
            "0         Non-trainable params\n",
            "1.4 M     Total params\n",
            "5.523     Total estimated model params size (MB)\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:561: UserWarning: This DataLoader will create 32 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/fit_loop.py:280: PossibleUserWarning: The number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
            "  rank_zero_warn(\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
            ],
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            "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_steps=1` reached.\n"
          ]
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        "trainer = pl.Trainer(max_epochs=10, accelerator=\"auto\", devices=\"auto\")\n",
        "trainer.fit(model, dm)\n",
        "trainer.validate(model, dm)\n",
        "trainer.test(model, dm)"
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      },
      "execution_count": 6,
      "outputs": [
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            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "WARNING:pytorch_lightning.loggers.tensorboard:Missing logger folder: /content/lightning_logs\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\n",
            "  | Name      | Type        | Params\n",
            "------------------------------------------\n",
            "0 | layer1    | Sequential  | 320   \n",
            "1 | layer2    | Sequential  | 18.5 K\n",
            "2 | layer3    | Sequential  | 73.9 K\n",
            "3 | fc1       | Linear      | 1.3 M \n",
            "4 | layer4    | Sequential  | 1.3 M \n",
            "5 | dense1_bn | BatchNorm1d | 1.2 K \n",
            "6 | fc2       | Linear      | 6.3 K \n",
            "7 | dense2_bn | BatchNorm1d | 20    \n",
            "------------------------------------------\n",
            "1.4 M     Trainable params\n",
            "0         Non-trainable params\n",
            "1.4 M     Total params\n",
            "5.523     Total estimated model params size (MB)\n"
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              "Sanity Checking: 0it [00:00, ?it/s]"
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            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:561: UserWarning: This DataLoader will create 32 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
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            "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=10` reached.\n",
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              "[{}]"
            ]
          },
          "metadata": {},
          "execution_count": 6
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    {
      "cell_type": "markdown",
      "source": [
        "# Resource\n",
        "\n",
        "- [TensorBoard with PyTorch Lightning](https://learnopencv.com/tensorboard-with-pytorch-lightning/)\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": "X7Z_fvH5kwhe"
      }
    }
  ]
}