SupervisedLearning/mlp_lightning_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "280eb483",
   "metadata": {},
   "source": [
    "## Pytorch Lightning MLP Demo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3b78f6c",
   "metadata": {},
   "source": [
    "We demonstrate the Multilayer Preceptron (MLP). Nearly a duplicate to our MLP demo [here](https://github.com/izzajalandoni/Deep-Learning-Helper/blob/main/BuildingBlocks/mlp_demo.ipynb), except we will be utilizing Pytorch Lightning for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "05d9d0b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1588c098",
   "metadata": {},
   "source": [
    "### Preparing our Dataset\n",
    "\n",
    "We use the same cifar10 dataset we used in our Hugging Face datasets demo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "087c063b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Reusing dataset cifar10 (/home/izza/.cache/huggingface/datasets/cifar10/plain_text/1.0.0/447d6ec4733dddd1ce3bb577c7166b986eaa4c538dcd9e805ba61f35674a9de4)\n",
      "Reusing dataset cifar10 (/home/izza/.cache/huggingface/datasets/cifar10/plain_text/1.0.0/447d6ec4733dddd1ce3bb577c7166b986eaa4c538dcd9e805ba61f35674a9de4)\n"
     ]
    }
   ],
   "source": [
    "import datasets\n",
    "\n",
    "trainset = datasets.load_dataset('cifar10', split='train')\n",
    "testset = datasets.load_dataset('cifar10', split='test')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3737cc32",
   "metadata": {},
   "source": [
    "Preprocess our input before passing them to our dataloader.\n",
    "\n",
    "**Note:** Unlike the previous demo, we do not remove `label` because we need this as ground truth during training. We simply transform our PIL image to tensor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e7453eb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import transforms\n",
    "\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor()\n",
    "])\n",
    "\n",
    "def transform_input(data):\n",
    "    data['img'] = [transform(image.convert('RGB')) for image in data['img']]\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cacd57b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainset.set_transform(transform=transform_input)\n",
    "testset.set_transform(transform=transform_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "07a76e76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'img': tensor([[[0.6980, 0.6980, 0.6980,  ..., 0.6667, 0.6588, 0.6471],\n",
       "          [0.7059, 0.7020, 0.7059,  ..., 0.6784, 0.6706, 0.6588],\n",
       "          [0.6941, 0.6941, 0.6980,  ..., 0.6706, 0.6627, 0.6549],\n",
       "          ...,\n",
       "          [0.4392, 0.4431, 0.4471,  ..., 0.3922, 0.3843, 0.3961],\n",
       "          [0.4392, 0.4392, 0.4431,  ..., 0.4000, 0.4000, 0.4000],\n",
       "          [0.4039, 0.3922, 0.4039,  ..., 0.3608, 0.3647, 0.3569]],\n",
       " \n",
       "         [[0.6902, 0.6902, 0.6902,  ..., 0.6588, 0.6510, 0.6392],\n",
       "          [0.6980, 0.6941, 0.6980,  ..., 0.6706, 0.6627, 0.6510],\n",
       "          [0.6863, 0.6863, 0.6902,  ..., 0.6627, 0.6549, 0.6471],\n",
       "          ...,\n",
       "          [0.4196, 0.4275, 0.4314,  ..., 0.3804, 0.3686, 0.3725],\n",
       "          [0.4000, 0.4039, 0.4039,  ..., 0.3725, 0.3647, 0.3608],\n",
       "          [0.3765, 0.3647, 0.3725,  ..., 0.3294, 0.3373, 0.3294]],\n",
       " \n",
       "         [[0.7412, 0.7412, 0.7412,  ..., 0.7059, 0.6941, 0.6824],\n",
       "          [0.7490, 0.7451, 0.7490,  ..., 0.7137, 0.7059, 0.6941],\n",
       "          [0.7373, 0.7373, 0.7412,  ..., 0.7059, 0.6980, 0.6902],\n",
       "          ...,\n",
       "          [0.4196, 0.4235, 0.4314,  ..., 0.3686, 0.3647, 0.3725],\n",
       "          [0.3961, 0.4000, 0.4039,  ..., 0.3647, 0.3569, 0.3569],\n",
       "          [0.3608, 0.3529, 0.3686,  ..., 0.3137, 0.3137, 0.3020]]]),\n",
       " 'label': 0}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9453d069",
   "metadata": {},
   "source": [
    "Pass our data to torch DataLoader() function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e58e117e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=4)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c8ba6886",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([4, 3, 32, 32])\n"
     ]
    }
   ],
   "source": [
    "batch = next(iter(testloader))\n",
    "print(batch['img'].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c15eedfd",
   "metadata": {},
   "source": [
    "### Utility Functions\n",
    "\n",
    "Display function, accuracy calculation that we might use later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a733f616",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "labels = {0:\"airplane\", 1:\"automobile\", 2:\"bird\", 3:\"cat\", 4:\"deer\",\n",
    "          5:\"dog\", 6:\"frog\", 7:\"horse\", 8:\"ship\", 9:\"truck\"}\n",
    "\n",
    "def display_outputs(x, y_p, y):\n",
    "    num_outputs = x.shape[0]\n",
    "    y_p = torch.argmax(y_p, dim=1)\n",
    "    \n",
    "    for i in range(num_outputs):\n",
    "        image = x[i].permute((1,2,0))\n",
    "        plt.imshow(image)\n",
    "        plt.show()\n",
    "        print(\"Ground Truth: {}[{}], Predicted: {}[{}]\".format(\n",
    "            labels[y[i].item()], y[i], labels[y_p[i].item()], y_p[i]))\n",
    "        \n",
    "def accuracy(y_p, y):\n",
    "    y_p = torch.argmax(y_p, dim=1) # We get top class\n",
    "    acc = y_p.eq(y).sum()          # Compare it with ground truth\n",
    "    acc = acc/y.shape[0]           # Get acc for the batch\n",
    "    return acc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ad451f0",
   "metadata": {},
   "source": [
    "### Preparing our Model\n",
    "\n",
    "We create our MLP model. We will use Pytorch Lightning for this.\n",
    "\n",
    "Learn more about NN building blocks [here](https://pytorch.org/docs/stable/nn.html). Notes on pytorch lightning callbacks [here](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.callbacks.Callback.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2c896d5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pytorch_lightning as pl\n",
    "import torch.nn as nn\n",
    "import torchmetrics as tm\n",
    "\n",
    "class MLP(pl.LightningModule):\n",
    "    def __init__(self, input_dim, num_class):\n",
    "        super().__init__()\n",
    "        self.input_dim = input_dim\n",
    "        self.num_class = num_class\n",
    "        \n",
    "        self.fc1 = nn.Linear(self.input_dim, 128)\n",
    "        self.fc2 = nn.Linear(128,64)\n",
    "        self.fc3 = nn.Linear(64, self.num_class)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.criterion = nn.CrossEntropyLoss()\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = x.view(x.shape[0], -1)\n",
    "        x = self.relu(self.fc1(x))\n",
    "        x = self.relu(self.fc2(x))\n",
    "        return self.fc3(x)\n",
    "    \n",
    "    def display(self, x, y_p, y):\n",
    "        num_outputs = x.shape[0]\n",
    "        y_p = torch.argmax(y_p, dim=1)\n",
    "\n",
    "        for i in range(num_outputs):\n",
    "            image = x[i].permute((1,2,0))\n",
    "            plt.imshow(image)\n",
    "            plt.show()\n",
    "            print(\"Ground Truth: {}[{}], Predicted: {}[{}]\".format(\n",
    "                labels[y[i].item()], y[i], labels[y_p[i].item()], y_p[i]))\n",
    "    \n",
    "    def training_step(self, batch, batch_idx):\n",
    "        x,y = batch['img'], batch['label']\n",
    "        y_p = self.forward(x)\n",
    "        loss = self.criterion(y_p, y)\n",
    "        \n",
    "        # Since this is per batch, we can sum correct predictions first\n",
    "        acc = torch.argmax(y_p, dim=1).eq(y).sum()\n",
    "        acc = acc/y.shape[0]\n",
    "        \n",
    "        return {\"loss\": loss, \"acc\": acc}\n",
    "    \n",
    "    def training_epoch_end(self, outputs):\n",
    "        epoch_loss = torch.stack([out['loss'] for out in outputs]).mean()\n",
    "        epoch_acc = torch.stack([out['acc'] for out in outputs]).mean()\n",
    "        \n",
    "        print(\"Epoch {}, Loss: {}, Accuracy: {}\".format(self.current_epoch, epoch_loss, epoch_acc))\n",
    "    \n",
    "    def test_step(self, batch, batch_idx):\n",
    "        x,y = batch['img'], batch['label']\n",
    "        y_p = self.forward(x)\n",
    "        \n",
    "        if batch_idx == 0:\n",
    "            self.display(x, y_p, y)\n",
    "        \n",
    "        # Calculate accuracy\n",
    "        acc = torch.argmax(y_p, dim=1).eq(y).sum()\n",
    "        acc = acc/y.shape[0]\n",
    "        return {\"acc\": acc}\n",
    "    \n",
    "    def test_epoch_end(self, outputs):\n",
    "        test_acc = torch.stack([out['acc'] for out in outputs]).mean()\n",
    "        print(\"Test Accuracy: {}\".format(test_acc))\n",
    "    \n",
    "    def configure_optimizers(self):\n",
    "        return torch.optim.Adam(self.parameters())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b656738",
   "metadata": {},
   "source": [
    "### Main Program\n",
    "\n",
    "We need to get some details about our data that we will use for our model configs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1170a489",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input size: 3072, num_class: 10\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "b,c,h,w = batch['img'].shape\n",
    "input_dim = c*h*w\n",
    "num_class = len(np.unique(testset[:]['label']))\n",
    "\n",
    "print(\"input size: {}, num_class: {}\".format(input_dim, num_class))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5b8ef70",
   "metadata": {},
   "source": [
    "With pytorch lightning, our main code becomes cleaner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f8bb190a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "\n",
      "  | Name      | Type             | Params\n",
      "-----------------------------------------------\n",
      "0 | fc1       | Linear           | 393 K \n",
      "1 | fc2       | Linear           | 8.3 K \n",
      "2 | fc3       | Linear           | 650   \n",
      "3 | relu      | ReLU             | 0     \n",
      "4 | criterion | CrossEntropyLoss | 0     \n",
      "-----------------------------------------------\n",
      "402 K     Trainable params\n",
      "0         Non-trainable params\n",
      "402 K     Total params\n",
      "1.609     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "278c3adfc5ff42c28c7cde335b29b9d0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0, Loss: 1.8849409818649292, Accuracy: 0.3097600042819977\n",
      "Epoch 1, Loss: 1.7790148258209229, Accuracy: 0.35468000173568726\n",
      "Epoch 2, Loss: 1.7473351955413818, Accuracy: 0.36381998658180237\n",
      "Epoch 3, Loss: 1.7267974615097046, Accuracy: 0.37327998876571655\n",
      "Epoch 4, Loss: 1.712096095085144, Accuracy: 0.3776000142097473\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 5\n",
    "\n",
    "model = MLP(input_dim, num_class)\n",
    "trainer = pl.Trainer(devices=\"auto\", accelerator=\"auto\",\n",
    "                    max_epochs=num_epochs)\n",
    "trainer.fit(model, trainloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "55f6d555",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb4ecc4fabea4228b989eb6a6452ddcb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ground Truth: cat[3], Predicted: cat[3]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ground Truth: ship[8], Predicted: ship[8]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ground Truth: ship[8], Predicted: ship[8]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ground Truth: airplane[0], Predicted: ship[8]\n",
      "Test Accuracy: 0.39629998803138733\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{}]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.test(model, testloader)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1989a149",
   "metadata": {},
   "source": [
    "This is for demo purposes. You may try to improve accuracy by experimenting on different parameters, or increasing training epochs.\n",
    "\n",
    "For more info about pytorch lightning, please visit [this link](https://www.pytorchlightning.ai/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39a88bc5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dl_course",
   "language": "python",
   "name": "dl_course"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}