Chapter07/ch7_nb2_generate_and_parse_tfrecords.ipynb

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   "source": [
    "<p style=\"border: 1px solid #e7692c; border-left: 15px solid #e7692c; padding: 10px; text-align:justify;\">\n",
    "    <strong style=\"color: #e7692c\">Tip.</strong> <a style=\"color: #000000;\" href=\"https://nbviewer.jupyter.org/github/PacktPublishing/Hands-On-Computer-Vision-with-TensorFlow-2/blob/master/Chapter07/ch7_nb2_generate_and_parse_tfrecords.ipynb\" title=\"View with Jupyter Online\">Click here to view this notebook on <code>nbviewer.jupyter.org</code></a>. \n",
    "    <br/>These notebooks are better read there, as Github default viewer ignores some of the formatting and interactive content.\n",
    "    </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"font-size: 1em; padding: 0; margin: 0;\">\n",
    "    <tr style=\"vertical-align: top; padding: 0; margin: 0;background-color: #ffffff\">\n",
    "        <td style=\"vertical-align: top; padding: 0; margin: 0; padding-right: 15px;\">\n",
    "    <p style=\"background: #363636; color:#ffffff; text-align:justify; padding: 10px 25px;\">\n",
    "        <strong style=\"font-size: 1.0em;\"><span style=\"font-size: 1.2em;\"><span style=\"color: #e7692c;\">Hands-on</span> Computer Vision with TensorFlow 2</span><br/>by <em>Eliot Andres</em> & <em>Benjamin Planche</em> (Packt Pub.)</strong><br/><br/>\n",
    "        <strong>> Chapter 7: Training on Complex and Scarce Datasets</strong><br/>\n",
    "    </p>\n",
    "\n",
    "<h1 style=\"width: 100%; text-align: left; padding: 0px 25px;\"><small style=\"color: #e7692c;\">\n",
    "    Notebook 2:</small><br/>Generating and Parsing TFRecords</h1>\n",
    "<br/>\n",
    "<p style=\"border-left: 15px solid #363636; text-align:justify; padding: 0 10px;\">\n",
    "    Pursuing our journey into data optimization, we will dedicate the following notebook to <em><strong>TFRecord</em></strong>, TensorFlow format to persist entire datasets.\n",
    "    <br/><br/>\n",
    "    We will demonstrate how <em>TFRecords</em> files can be created and parsed.\n",
    "</p>\n",
    "<br/>\n",
    "<p style=\"border-left: 15px solid #e7692c; padding: 0 10px; text-align:justify;\">\n",
    "    <strong style=\"color: #e7692c;\">Tip.</strong> The notebooks shared on this git repository illustrate some notions from the book \"<em><strong>Hands-on Computer Vision with TensorFlow 2</strong></em>\" written by Eliot Andres and Benjamin Planche, published by Packt. If you enjoyed the insights shared here, <a href=\"https://www.amazon.com/Hands-Computer-Vision-TensorFlow-processing/dp/1788830644\" title=\"Learn more about the book!\"><strong>please consider acquiring the book!</strong></a>\n",
    "<br/><br/>\n",
    "The book provides further guidance for those eager to learn about computer vision and to harness the power of TensorFlow 2 and Keras to build efficient recognition systems for object detection, segmentation, video processing, smartphone applications, and more.</p>\n",
    "        </td>\n",
    "        <td style=\"vertical-align: top; padding: 0; margin: 0; width: 280px;\">\n",
    "    <a href=\"https://www.amazon.com/Hands-Computer-Vision-TensorFlow-processing/dp/1788830644\" title=\"Learn more about the book!\" target=\"_blank\">\n",
    "        <img src=\"../banner_images/book_cover.png\" width=280>\n",
    "    </a>\n",
    "    <p style=\"background: #e7692c; color:#ffffff; padding: 10px; text-align:justify;\"><strong>Leverage deep learning to create powerful image processing apps with TensorFlow 2 and Keras. <br/></strong>Get the book for more insights!</p>\n",
    "    <ul style=\"height: 32px; white-space: nowrap; text-align: center; margin: 0px; padding: 0px; padding-top: 10px;\">\n",
    "    <li style=\"display: block;height: 100%;float: left;vertical-align: middle;margin: 0 25px 10px;padding: 0px;\">\n",
    "        <a href=\"https://www.amazon.com/Hands-Computer-Vision-TensorFlow-processing/dp/1788830644\" title=\"Get the book on Amazon (paperback or Kindle version)!\" target=\"_blank\">\n",
    "        <img style=\"vertical-align: middle; max-width: 72px; max-height: 32px;\" src=\"../banner_images/logo_amazon.png\" width=\"75px\">\n",
    "        </a>\n",
    "    </li>\n",
    "    <li style=\"display: inline-block;height: 100%;vertical-align: middle;float: right;margin: -5px 25px 10px;padding: 0px;\">\n",
    "        <a href=\"https://www.packtpub.com/application-development/hands-computer-vision-tensorflow-2\" title=\"Get your Packt book (paperback, PDF, ePUB, or MOBI version)!\" target=\"_blank\">\n",
    "        <img style=\"vertical-align: middle; max-width: 72px; max-height: 32px;\" src=\"../banner_images/logo_packt.png\" width=\"75px\">\n",
    "        </a>\n",
    "    </li>\n",
    "    </ul>\n",
    "        </td>\n",
    "        </tr>\n",
    "        </table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing TFRecords\n",
    "\n",
    "In the book, we introduce how *TFRecord* files can be used to store in one binary archive complete training datasets, to more efficiently reuse and iterate over them afterwards. In the following section, we will demonstrate how `tf.data` and `tf.train` APIs can be used to process a dataset into a _TFRecord_ file.\n",
    "\n",
    "### Preparing our Dataset\n",
    "\n",
    "To illustrate this notebook, we will reuse the `rock-paper-scissors` dataset[$^1$](#ref) (kindly provided by [Laurence Moroney](http://laurencemoroney.com)) from `tensorflow-datasets` as example (c.f. Notebook [6-3](../Chapter06/ch6_nb3_improve_image_quality_with_dae.ipynb)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tfds.core.DatasetInfo(\n",
      "    name='rock_paper_scissors',\n",
      "    version=1.0.0,\n",
      "    description='Images of hands playing rock, paper, scissor game.',\n",
      "    urls=['http://laurencemoroney.com/rock-paper-scissors-dataset'],\n",
      "    features=FeaturesDict({\n",
      "        'image': Image(shape=(300, 300, 3), dtype=tf.uint8),\n",
      "        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=3)\n",
      "    },\n",
      "    total_num_examples=2892,\n",
      "    splits={\n",
      "        'test': <tfds.core.SplitInfo num_examples=372>,\n",
      "        'train': <tfds.core.SplitInfo num_examples=2520>\n",
      "    },\n",
      "    supervised_keys=('image', 'label'),\n",
      "    citation='\"\"\"\n",
      "        @ONLINE {rps,\n",
      "        author = \"Laurence Moroney\",\n",
      "        title = \"Rock, Paper, Scissors Dataset\",\n",
      "        month = \"feb\",\n",
      "        year = \"2019\",\n",
      "        url = \"http://laurencemoroney.com/rock-paper-scissors-dataset\"\n",
      "        }\n",
      "        \n",
      "    \"\"\"',\n",
      "    redistribution_info=,\n",
      ")\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import tensorflow_datasets as tfds\n",
    "\n",
    "hands_builder = tfds.builder(\"rock_paper_scissors\")\n",
    "hands_builder.download_and_prepare()\n",
    "\n",
    "print(hands_builder.info)\n",
    "\n",
    "# Instantiating the dataset we will convert into a TFRecord:\n",
    "hands_dataset = hands_builder.as_dataset(split=tfds.Split.TEST)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the sake of the demonstration, we will assume we are planning to train several different models for rock/paper/scissors classifition on lower-quality images, e.g., $64 \\times 64$ versions. Therefore, we want to prepare a _TFRecord_ file containing all the above dataset, with resized images. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<MapDataset shapes: {image: (64, 64, 3), label: ()}, types: {image: tf.uint8, label: tf.int64}>\n"
     ]
    }
   ],
   "source": [
    "input_size = (64, 64)\n",
    "\n",
    "def prepare_sample(sample):\n",
    "    sample['image'] = tf.image.resize(sample['image'], input_size)\n",
    "    # The above operation return float values. We cast them back to uint8:\n",
    "    sample['image'] = tf.cast(sample['image'], tf.uint8)\n",
    "    return sample\n",
    "\n",
    "hands_dataset = hands_dataset.map(prepare_sample)\n",
    "print(hands_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Note:*** Remember from the previous [notebook](.ch7_nb1_set_up_efficient_input_pipelines_with_tf_data.ipynb) that such input pipelines can be automatically optimized by TensorFlow (even though we won't really need the performance gain here):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "hands_dataset = hands_dataset.prefetch(1)\n",
    "\n",
    "options = tf.data.Options()\n",
    "options.experimental_optimization.map_fusion = True\n",
    "options.experimental_optimization.map_parallelization = True\n",
    "options.experimental_optimization.noop_elimination = True\n",
    "options.experimental_optimization.apply_default_optimizations = True\n",
    "options.experimental_autotune = True\n",
    "hands_dataset = hands_dataset.with_options(options)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us have a quick look at the dataset we want to serialize:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image labels: ['scissors' 'scissors' 'rock' 'rock' 'scissors']\n"
     ]
    },
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      "text/plain": [
       "<Figure size 1800x360 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plot_utils import plot_image_grid\n",
    "num_show = 5\n",
    "\n",
    "class_labels = np.asarray(hands_builder.info.features[\"label\"].names)\n",
    "\n",
    "hands_dataset_iter = hands_dataset.batch(num_show).skip(1).__iter__()\n",
    "# ^ `.skip(1)` is called to skip the 1st batch, which is a bit \"bland\" in this dataset.\n",
    "# We will use the 2nd batch for our illustrations, as it has more diverse hands...\n",
    "batch = next(hands_dataset_iter)\n",
    "\n",
    "fig = plot_image_grid([batch['image'].numpy()], transpose=True)\n",
    "fig.show()\n",
    "print(\"Image labels: {}\".format(class_labels[batch['label'].numpy()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Serializing Data Samples\n",
    "\n",
    "We presented how TFRecords files are composed of serialized [`tf.train.Example`](https://www.tensorflow.org/api_docs/python/tf/train/Example) instances, themselves composed of [`tf.train.Feature`](https://www.tensorflow.org/api_docs/python/tf/train/Feature) objects containing the actual data.\n",
    "\n",
    "`tf.train.Feature` can contain an element either as a list of double/float values, a list of int/bool values, or a list of bytes. The following functions are usually presented to convert tensors into `Feature` instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Snippet from https://www.tensorflow.org/alpha/tutorials/load_data/tf_records\n",
    "\n",
    "def _bytes_feature(value):\n",
    "    \"\"\"Returns a bytes_list from a string / byte.\"\"\"\n",
    "    if isinstance(value, type(tf.constant(0))):\n",
    "        value = value.numpy() # BytesList won't unpack a string from an EagerTensor.\n",
    "    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n",
    "\n",
    "def _float_feature(value):\n",
    "    \"\"\"Returns a float_list from a float / double.\"\"\"\n",
    "    return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\n",
    "\n",
    "def _int64_feature(value):\n",
    "    \"\"\"Returns an int64_list from a bool / enum / int / uint.\"\"\"\n",
    "    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is all fine, but how can we convert complex data structures like images into `Feature` instances? Well, when stored as JPEG or PNG files, **images are serialized as list of bytes** (compressed or not). We can do the same to transform them into features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _image_as_bytes_feature(image):\n",
    "    \"\"\"Returns a bytes_list from an image tensor.\"\"\"\n",
    "    \n",
    "    if image.dtype != tf.uint8:\n",
    "        # `tf.io.encode_jpeg``requires tf.unit8 input images, with values between\n",
    "        # 0 and 255. We do the conversion with the following function, if needed:\n",
    "        image = tf.image.convert_image_dtype(image, tf.uint8, saturate=True)\n",
    "    \n",
    "    # We convert the image tensor back into a byte list...\n",
    "    image_string = tf.io.encode_jpeg(image, quality=90)\n",
    "    \n",
    "    # ... and then into a Feature:\n",
    "    return _bytes_feature(image_string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A set of `Feature` instances corresponding to one training sample (e.g., an image and its label) can be wrapped into an `Example` object and serialized as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_sample_to_example(sample):\n",
    "    \"\"\" Convert image + label sample into Example for serialization.\"\"\"\n",
    "    \n",
    "    # We convert our elements into serialized features:\n",
    "    features = tf.train.Features(feature={\n",
    "        'image': _image_as_bytes_feature(sample['image']),\n",
    "        'label': _int64_feature(sample['label']),\n",
    "    })\n",
    "    \n",
    "    # Then we wrap them into an Example:\n",
    "    example = tf.train.Example(features=features)\n",
    "    return example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is what a serialized `Example` containing image + label looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features {\n",
      "  feature {\n",
      "    key: \"image\"\n",
      "    value {\n",
      "      bytes_list {\n",
      "        value: 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      "      }\n",
      "    }\n",
      "  }\n",
      "  feature {\n",
      "    key: \"label\"\n",
      "    value {\n",
      "      int64_list {\n",
      "        value: 2\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sample = next(hands_dataset.__iter__())\n",
    "example = convert_sample_to_example(sample)\n",
    "print(example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To serialize the complete dataset and store it into a TFRecord file, we now only have to iterate over the input pipeline and write the `Example` outputs. To persist them all together according to the TFRecord format, `tf.io.TFRecordWriter` can be used:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset to TFRecords: \u001b[92mstart\u001b[0m.\n",
      "Dataset to TFRecords: \u001b[91mend\u001b[0m (372 examples serialized).\n"
     ]
    }
   ],
   "source": [
    "tfrecord_file = os.path.join('.', 'resized_rockpaperscissors.tfrecords')\n",
    "\n",
    "print(\"Dataset to TFRecords: \\033[92mstart\\033[0m.\")\n",
    "num_samples = 0\n",
    "with tf.io.TFRecordWriter(tfrecord_file) as writer:\n",
    "    for sample in hands_dataset:\n",
    "        example = convert_sample_to_example(sample)\n",
    "        writer.write(example.SerializeToString())\n",
    "        num_samples += 1\n",
    "print(\"Dataset to TFRecords: \\033[91mend\\033[0m ({} examples serialized).\".format(num_samples))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is it, we persisted the integrality of our validation dataset (372 samples) into a TFRecord file (weighing only 570KB)!\n",
    "\n",
    "## Parsing TFRecords\n",
    "\n",
    "Now, we will demonstrate how this TFRecord file can be parsed and iterated over with the `tf.data` API.\n",
    "\n",
    "Instantiating a `tf.data.Dataset` pipeline to iterate over serialized `Example` objects stored in TFRecord file(s) is just a matter of calling the following method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_from_tfrecord = tf.data.TFRecordDataset(tfrecord_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This `Dataset` instance returns serialized `Example` objects. We thus have to parse then. As explained in the book, we need to inform TensorFlow of the structure of the `Example` by defining a dictionary detailing each feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_example_to_sample(example):\n",
    "    \"\"\" Parse the serialized example into an image + feature sample.\"\"\"\n",
    "    \n",
    "    # We create a dictionary describing the features.  \n",
    "    features_description = {\n",
    "        'image': tf.io.FixedLenFeature([], tf.string),\n",
    "        'label': tf.io.FixedLenFeature([], tf.int64),\n",
    "    }\n",
    "\n",
    "    # We pass the example and descriptive dict to TF parsing function:\n",
    "    sample =  tf.io.parse_single_example(example, features_description)\n",
    "    \n",
    "    # Finally, we decode the sample's JPEG-encoded image string into an actual image:\n",
    "    sample['image'] = tf.io.decode_jpeg(sample['image'])\n",
    "    \n",
    "    return sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "hands_dataset = dataset_from_tfrecord.map(convert_example_to_sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us make sure we properly parsed the data and recovered our samples: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image labels: ['scissors' 'scissors' 'rock' 'rock' 'scissors']\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1800x360 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hands_dataset_iter = hands_dataset.batch(num_show).skip(1).__iter__()\n",
    "# ^ `.skip(1)` is called to skip the 1st batch, which is a bit \"bland\" in this dataset.\n",
    "# We will use the 2nd batch for our illustrations, as it has more diverse hands...\n",
    "batch = next(hands_dataset_iter)\n",
    "\n",
    "fig = plot_image_grid([batch['image'].numpy()], transpose=True)\n",
    "fig.show()\n",
    "print(\"Image labels: {}\".format(class_labels[batch['label'].numpy()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "With this brief notebook, we presented simple solutions to create and read TFRecord files for applications having to deal with large/fragmented datasets. These files can be especially useful when one wants to persist a specific version of a dataset (e.g., after splitting data between training/validation sets, or after applying some data transformations).\n",
    "\n",
    "This notebook was the last one specifically dedicated to `tf.data`. In the next [notebook](ch7_nb3_render_images_from_3d_models.ipynb), we will start exploring solutions to train robust models when training data is lacking."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref\"></a>\n",
    "#### References\n",
    "\n",
    "1. Moroney, L., 2019. Rock, Paper, Scissors Dataset. http://laurencemoroney.com/rock-paper-scissors-dataset (Accessed: 03.04.2019)."
   ]
  }
 ],
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   "display_name": "Python 3",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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