10.4.1 - ImageFolder1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Lab 10.4.1: Image Folder 1\r\n",
    "\r\n",
    "Edited By Steve Ive\r\n",
    "\r\n",
    "Here, we are going to learn about the ```ImageFolder``` of PyTorch's ```torchvision.datasets```, that allows us to add as a datasets with our custom data.\r\n",
    "\r\n",
    "Reference from\r\n",
    "\r\n",
    "https://github.com/deeplearningzerotoall/PyTorch/blob/master/lab-10_4_1_ImageFolder_1.ipynb"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Imports"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "import torchvision\r\n",
    "from torchvision import transforms"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "from matplotlib.pyplot import imshow\r\n",
    "%matplotlib inline"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Define torchvision.transforms propertise to create the datasets"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "trans = transforms.Compose([\r\n",
    "    transforms.Resize((64, 128))\r\n",
    "])\r\n",
    "\r\n",
    "train_data = torchvision.datasets.ImageFolder(root = 'catsanddogs/origin_data/', transform = trans)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Save the transformed images from origin_data to train_data"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "source": [
    "for num, value in enumerate(train_data):\r\n",
    "    data, label = value\r\n",
    "    print(num, data, label)\r\n",
    "\r\n",
    "    if(label == 0):\r\n",
    "        data.save('catsanddogs/train_data/cats/{}_{}.jpeg'.format(label, num))\r\n",
    "    else:\r\n",
    "        data.save('catsanddogs/train_data/dogs/{}_{}.jpeg'.format(label, num))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
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