2-Data.ipynb

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    "<div>\n",
    "<img src=\"https://discuss.pytorch.org/uploads/default/original/2X/3/35226d9fbc661ced1c5d17e374638389178c3176.png\" width=\"400\" style=\"margin: 50px auto; display: block; position: relative; left: -30px;\" />\n",
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   "source": [
    "<!--NAVIGATION-->\n",
    "# [< Basics](3-Basics.ipynb) | Data | [Autograd >](3-Autograd.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data\n",
    "\n",
    "In this notebook, we will see how to deal with data in PyTorch using Dataset and DataLoaders. \n",
    "\n",
    "### Table of Contents\n",
    "\n",
    "#### 1. [Dataset](#Dataset)\n",
    "#### 2. [Dataloader](#Dataloader)\n",
    "#### 3. [A Real Example](#A-real-example:-Alien-vs-Predator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To work with data, PyTorch provides a Dataset class that can be subclassed.  \n",
    "A dataset is an object that can be queried with an index and that will return the corresponding sample.  \n",
    "\n",
    "It should implement two functions:\n",
    "- `__len__` : this should return the size of the dataset\n",
    "- `__getitem__` : this should return one sample from the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "<img src=\"figures/dataset.png\" width=\"600\" style=\"margin-left: auto;margin-right: auto;display: block;\" />\n",
    "</p>\n"
   ]
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  {
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   "source": [
    "class DummyDataset(Dataset):\n",
    "    def __init__(self):\n",
    "        self.data = torch.rand(10, 2)\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        sample = self.data[index]\n",
    "        label = sample[0] > sample[1]\n",
    "        return (sample, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = DummyDataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When indexed, the dataset returns tuple (train data, class label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataloader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `DataLoader` is a PyTorch utility class to iterate over the dataset.  \n",
    "It allows multi-process data loading, automatic batching, shuffling and more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = DataLoader(dataset, batch_size=5, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for sample, label in loader:\n",
    "    print(sample, label, sep=\"\\n\")\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use multiple workers to load data in parallel: \n",
    "Find how in the [PyTorch doc](https://pytorch.org/docs/master/index.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = DataLoader(dataset, batch_size=5, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A real example: Alien vs Predator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Install the dependencies, download the dataset and place it in data/alien-vs-predator folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget -q https://raw.githubusercontent.com/theevann/amld-pytorch-workshop/master/binder/requirements.txt -O requirements.txt\n",
    "!pip install -qr requirements.txt\n",
    "\n",
    "!mkdir -p data\n",
    "!curl -Lo alien-vs-predator.zip \"https://docs.google.com/uc?export=download&id=1hct3PjRf14ZBp83ob3f6Uo_0mqrT9FGZ\"\n",
    "!unzip -oq alien-vs-predator.zip -d data/\n",
    "!rm alien-vs-predator.zip\n",
    "!ls -l data/alien-vs-predator/\n",
    "\n",
    "# for PIL.Image\n",
    "!pip install --no-cache-dir -I Pillow==7.1.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset is located in data/alien-vs-predator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!tree -nd ./data/alien-vs-predator  # This command will not work on colab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each directory contains images of the corresponding class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "\n",
    "img_predator = Image.open(\"./data/alien-vs-predator/train/predator/10.jpg\").convert('RGB')\n",
    "img_alien = Image.open(\"./data/alien-vs-predator/train/alien/10.jpg\").convert('RGB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_alien"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_predator"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background-color:lightblue;padding:1rem;border-radius: 0.015rem 0.015rem 0.03rem 0.03rem;\">\n",
    "<h3 style=\"display: inline; font-weight:bold\">Your turn!</h3>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below is implementing a Dataset class for these images.  \n",
    "It loads all the image paths and add it in the `img_instance` variable along with a label.  \n",
    "The alien class has label 0 while the predator class has label 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code is incomplete: you need to fill the `__len__` and `__get_item__` functions.\n",
    "\n",
    "You can use this snippet to load an image from a `path`:\n",
    "```python\n",
    "with open(path, 'rb') as f:\n",
    "    img = Image.open(f).convert('RGB')\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from PIL import Image\n",
    "\n",
    "\n",
    "class AlienPredatorDataset(Dataset):\n",
    "    def __init__(self, root, split):\n",
    "        self.root = root\n",
    "        self.split = split\n",
    "        \n",
    "        # Load and save all image paths\n",
    "        self.img_instances = []\n",
    "        \n",
    "        for img_path in Path(root, split, \"alien\").glob(\"*.jpg\"):\n",
    "            self.img_instances.append((img_path, 0))\n",
    "            \n",
    "        for img_path in Path(root, split, \"predator\").glob(\"*.jpg\"):\n",
    "            self.img_instances.append((img_path, 1))\n",
    "    \n",
    "    \n",
    "    def __len__(self):\n",
    "        return # YOUR TURN\n",
    "    \n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        # YOUR TURN\n",
    "        return (img, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = AlienPredatorDataset(\"./data/alien-vs-predator/\", \"train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[0]  # Here again it returns a tuple (image, class label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[1][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we get PIL images that are of different sizes.  \n",
    "To create proper PyTorch batches, we need to input **tensors** that have the **same size**.  \n",
    "To do so, we will use Torchvision transforms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Torchvision's transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision.transforms import ToTensor, RandomCrop\n",
    "\n",
    "crop_transform = RandomCrop(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = dataset[0][0]\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "crop_transform(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision.transforms import Compose\n",
    "\n",
    "all_transforms = Compose((\n",
    "    RandomCrop(100),\n",
    "    ToTensor(),\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_transforms(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_transforms(img).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's apply it to our dataset !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = DataLoader(dataset, batch_size=5, shuffle=True) # workers\n",
    "\n",
    "for sample, label in loader:\n",
    "    print(sample.shape, label)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using torchvision ImageFolder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Torchvision provides many more useful classes to deal with images.\n",
    "\n",
    "Specifically, as image classification is a pretty common computer vision task, torchvision provides a dataset named `ImageFolder` that loads images given a folder (the subfolders are splitting the different classes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision.datasets import ImageFolder\n",
    "dataset = ImageFolder(root=\"./data/alien-vs-predator/train\", transform=all_transforms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "# [< Basics](3-Basics.ipynb) | Data | [Autograd >](3-Autograd.ipynb)"
   ]
  }
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