CIFAR_data_display.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import random_split\n",
    "from torchvision.datasets.utils import download_url\n",
    "import os\n",
    "from torchvision.datasets import ImageFolder\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_url = \"https://s3.amazonaws.com/fast-ai-imageclas/cifar10.tgz\"\n",
    "download_url(dataset_url, '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract from archive\n",
    "with tarfile.open('./cifar10.tgz', 'r:gz') as tar:\n",
    "    tar.extractall(path='./data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "transform = transforms.Compose(\n",
    "    [transforms.ToTensor(),\n",
    "     transforms.Resize(32)])\n",
    "\n",
    "batch_size = 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['test', 'train']\n",
      "['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n"
     ]
    }
   ],
   "source": [
    "data_dir = './data/cifar10'\n",
    "\n",
    "print(os.listdir(data_dir))\n",
    "classes = os.listdir(data_dir + \"/train\")\n",
    "print(classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "trainset = ImageFolder(data_dir+'/train', transform=transform)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=2)\n",
    "\n",
    "testset = ImageFolder(data_dir+'/test', transform=transform)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=2)\n",
    "\n",
    "# classes = ('plane', 'car', 'bird', 'cat',\n",
    "#            'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature batch shape: torch.Size([128, 3, 32, 32])\n",
      "Labels batch shape: torch.Size([128])\n",
      "torch.Size([3, 32, 32])\n",
      "Feature batch shape: torch.Size([128, 3, 32, 32])\n",
      "Labels batch shape: torch.Size([128])\n",
      "torch.Size([3, 32, 32])\n"
     ]
    }
   ],
   "source": [
    "train_features, train_labels = next(iter(trainloader))\n",
    "print(f\"Feature batch shape: {train_features.size()}\")\n",
    "print(f\"Labels batch shape: {train_labels.size()}\")\n",
    "img = train_features[0]\n",
    "label = train_labels[0]\n",
    "print(img.shape)\n",
    "# print(train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1bdfad37c10>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img.swapaxes(0,1).swapaxes(1,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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