{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "---\n",
    "execute:\n",
    "  enabled: false\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- d2l:prose id=image-classification-dataset-md-1 fw=all -->\n",
    "\n",
    "# The Image Classification Dataset\n",
    "\n",
    "\n",
    "One widely used benchmark for image classification is [MNIST](https://en.wikipedia.org/wiki/MNIST_database) [@LeCun.Bottou.Bengio.ea.1998], a dataset of 70,000 handwritten-digit images ($28 \\times 28$ pixels, 10 classes). MNIST shaped a generation of machine learning research, but today even simple models exceed 95% accuracy (and a linear classifier already tops 90%), so differences between strong and weak models are hard to see. To compare models meaningfully we need a dataset where a linear baseline is clearly outpaced by a richer one.\n",
    "\n",
    "We therefore use **Fashion-MNIST** [@Xiao.Rasul.Vollgraf.2017], a drop-in replacement released in 2017. It has exactly the same structure (60,000 training and 10,000 test images of $28 \\times 28$ grayscale pixels, in 10 classes) but the classes are clothing categories (t-shirt, trouser, pullover, and so on) that are harder to tell apart, which makes accuracy differences between models clearly visible. For large-scale experiments the standard benchmark is ImageNet [@Deng.Dong.Socher.ea.2009] (1.2 million images, 1000 classes), but it is too large to keep our examples interactive; Fashion-MNIST teaches the same lessons at a fraction of the compute cost."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "image-classification-dataset-the-image-classification-dataset"
   },
   "source": [
    "%matplotlib inline\n",
    "from d2l import jax as d2l\n",
    "import jax\n",
    "from jax import numpy as jnp\n",
    "import numpy as np\n",
    "import time\n",
    "import tensorflow as tf\n",
    "\n",
    "d2l.use_svg_display()"
   ],
   "execution_count": 1,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- d2l:prose id=image-classification-dataset-md-2 fw=all -->\n",
    "\n",
    "## Loading the Dataset\n",
    "\n",
    "The library provides a preprocessed version of Fashion-MNIST that we can download and read into memory."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "image-classification-dataset-loading-the-dataset-1"
   },
   "source": [
    "class FashionMNIST(d2l.DataModule):\n",
    "    \"\"\"The Fashion-MNIST dataset.\"\"\"\n",
    "    def __init__(self, batch_size=64, resize=(28, 28)):\n",
    "        super().__init__()\n",
    "        self.save_hyperparameters()\n",
    "        self.train, self.val = tf.keras.datasets.fashion_mnist.load_data()"
   ],
   "execution_count": 2,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- d2l:prose id=image-classification-dataset-md-3 fw=all -->\n",
    "\n",
    "Fashion-MNIST consists of images from 10 categories, each represented\n",
    "by 6000 images in the training dataset and by 1000 in the test dataset.\n",
    "A *test dataset* is used for evaluating model performance (it must not be used for training).\n",
    "Consequently the training set and the test set\n",
    "contain 60,000 and 10,000 images, respectively."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "image-classification-dataset-loading-the-dataset-2"
   },
   "source": [
    "data = FashionMNIST(resize=(32, 32))\n",
    "len(data.train[0]), len(data.val[0])"
   ],
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(60000, 10000)"
     },
     "metadata": {},
     "execution_count": 3
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- d2l:prose id=image-classification-dataset-md-4 fw=all -->\n",
    "\n",
    "We instantiated the dataset with `resize=(32, 32)`, so each image is delivered as a single-channel tensor of spatial size $32 \\times 32$. One subtlety matters here: where the channel axis lives. There are two conventions, *channel-first* $c \\times h \\times w$ ($c$ color channels, then height and width) and *channel-last* $h \\times w \\times c$. The `get_dataloader` method below produces the appropriate layout; we confirm the per-image shape once the loader is in place, below. Here $c = 1$ since the images are grayscale; most photographs have $c = 3$ channels (red, green, blue).\n",
    "\n",
    "\n",
    "The categories of Fashion-MNIST have human-understandable names. \n",
    "The following convenience method converts between numeric labels and their names."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "image-classification-dataset-loading-the-dataset-4"
   },
   "source": [
    "@d2l.add_to_class(FashionMNIST)\n",
    "def text_labels(self, indices):\n",
    "    \"\"\"Return text labels.\"\"\"\n",
    "    labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n",
    "              'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n",
    "    return [labels[int(i)] for i in indices]"
   ],
   "execution_count": 4,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- d2l:prose id=image-classification-dataset-md-5 fw=all -->\n",
    "\n",
    "## Reading a Minibatch\n",
    "\n",
    "To make our life easier when reading from the training and test sets,\n",
    "we use the built-in data iterator rather than creating one from scratch.\n",
    "Recall that at each iteration, a data iterator\n",
    "reads a minibatch of data with size `batch_size`.\n",
    "We also randomly shuffle the examples for the training data iterator."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "image-classification-dataset-reading-a-minibatch-1"
   },
   "source": [
    "@d2l.add_to_class(FashionMNIST)\n",
    "def get_dataloader(self, train):\n",
    "    data = self.train if train else self.val\n",
    "    process = lambda X, y: (tf.expand_dims(X, axis=3) / 255,\n",
    "                            tf.cast(y, dtype='int32'))\n",
    "    resize_fn = lambda X, y: (tf.image.resize_with_pad(X, *self.resize), y)\n",
    "    shuffle_buf = len(data[0]) if train else 1\n",
    "    # `drop_remainder=train` keeps every training minibatch the same\n",
    "    # shape, so JAX does not retrace the `@jax.jit`'d step function for\n",
    "    # a smaller last batch.\n",
    "    dataset = (tf.data.Dataset.from_tensor_slices(process(*data)).shuffle(\n",
    "        shuffle_buf).batch(self.batch_size, drop_remainder=train).map(\n",
    "            resize_fn))\n",
    "    return d2l.TensorFlowDataLoader(dataset)"
   ],
   "execution_count": 5,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- d2l:prose id=image-classification-dataset-md-6 fw=all -->\n",
    "\n",
    "Now that the loader is defined, let us read one image and confirm where the channel axis lands."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "image-classification-dataset-loading-the-dataset-3"
   },
   "source": [
    "X, y = next(iter(data.train_dataloader()))\n",
    "X[0].shape  # channel-last: (height, width, channels)"
   ],
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(32, 32, 1)"
     },
     "metadata": {},
     "execution_count": 6
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- d2l:prose id=image-classification-dataset-md-7 fw=all -->\n",
    "\n",
    "Here is the same batch again, now at batch granularity: the first axis is the batch dimension (64 images per step by default), followed by the per-image shape we just confirmed, and the labels arrive as a matching vector of 64 integers."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "image-classification-dataset-reading-a-minibatch-2"
   },
   "source": [
    "X, y = next(iter(data.train_dataloader()))\n",
    "print(X.shape, X.dtype, y.shape, y.dtype)"
   ],
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(64, 32, 32, 1) float32 (64,) int32\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- d2l:prose id=image-classification-dataset-md-8 fw=all -->\n",
    "\n",
    "Let us time one full pass through the training set. A single pass takes seconds, not minutes; loading is not the bottleneck. For the convolutional networks of the coming chapters, a single forward and backward pass over a minibatch takes far longer than the corresponding I/O. (For the tiny linear models of *this* chapter the gap is much smaller; try the first exercise below.) If loading *were* slower than training, you would overlap I/O with compute via prefetching or raise the number of loader workers."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "image-classification-dataset-reading-a-minibatch-3"
   },
   "source": [
    "tic = time.time()\n",
    "for X, y in data.train_dataloader():\n",
    "    continue\n",
    "f'{time.time() - tic:.2f} sec'"
   ],
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "'0.75 sec'"
     },
     "metadata": {},
     "execution_count": 8
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- d2l:prose id=image-classification-dataset-md-9 fw=all -->\n",
    "\n",
    "## Visualization\n",
    "\n",
    "We will often be using the Fashion-MNIST dataset. The `d2l` library provides a convenience function `show_images` that lays out a list of images in a grid with optional per-image titles.\n",
    "\n",
    "Let's put it to good use. In general, it is a good idea to visualize and inspect data that you are training on. \n",
    "Humans are very good at spotting oddities, so visualization is a cheap safeguard against errors in the design of experiments. Here are the images and their corresponding labels (in text)\n",
    "for the first few examples in the training dataset."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "image-classification-dataset-visualization-2"
   },
   "source": [
    "@d2l.add_to_class(FashionMNIST)\n",
    "def visualize(self, batch, nrows=1, ncols=8, labels=None):\n",
    "    X, y = batch\n",
    "    if not labels:\n",
    "        labels = self.text_labels(y)\n",
    "    d2l.show_images(jnp.squeeze(X), nrows, ncols, titles=labels)\n",
    "\n",
    "batch = next(iter(data.val_dataloader()))\n",
    "data.visualize(batch)"
   ],
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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    "<!-- d2l:prose id=image-classification-dataset-md-10 fw=all -->\n",
    "\n",
    "We are now ready to work with the Fashion-MNIST dataset in the sections that follow.\n",
    "\n",
    "## Summary\n",
    "\n",
    "We now have a slightly more realistic dataset to use for classification. Fashion-MNIST is an apparel classification dataset consisting of images representing 10 categories. We will use this dataset in subsequent sections and chapters to evaluate various network designs, from a simple linear model to advanced residual networks. As we commonly do with images, we read them as a tensor of shape (batch size, number of channels, height, width). For now, we only have one channel as the images are grayscale (the visualization above uses a false color palette for improved visibility). A well-implemented data iterator keeps loading fast, so that training speed is set by the model rather than by I/O.\n",
    "\n",
    "\n",
    "## Exercises\n",
    "\n",
    "1. Time one full training epoch at `batch_size` of 1, 16, 64, 256, and 1024. Plot throughput (images per second) against `batch_size`. Why does throughput rise with batch size up to a point and then plateau?\n",
    "1. Set `num_workers=0` (single-threaded loading) and compare against the default multi-worker setting. Under what conditions does increasing `num_workers` stop helping?\n",
    "1. PyTorch stores tensors in channel-first order $(c, h, w)$, while TensorFlow and JAX use channel-last $(h, w, c)$. Read the `get_dataloader` implementations for all four frameworks. Which step introduces the channel dimension, and where does the layout differ?"
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   "source": [
    "<!-- d2l:prose id=image-classification-dataset-md-11 fw=jax -->\n",
    "\n",
    "[Discussions](https://d2l.discourse.group/t/17980)"
   ]
  }
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