{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "oQJ4kzmr0QFb" }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "id": "sDAuhGSA0dze" }, "source": [ "# Read and Write Images as TFRecords\n", "\n", "Tensorflow-Transformers makes it easy to read and write tfrecords in and from any data type.\n", "Here we will see how we can make use of it to write and read images as tfrecords" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "c4x5NBTg0eaW" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "4SYpRIzc0fxm" }, "outputs": [], "source": [ "import json\n", "import glob\n", "import numpy as np \n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", "import math\n", "from tf_transformers.data import TFWriter, TFReader\n", "from datasets import load_dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cqx86-bB0gNh" }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "id": "E-vLJ5M9003N" }, "source": [ "## Load CelebA dataset from HF" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 315, "referenced_widgets": [ "836376a41735471dbf95b16b08342d8d", "51f45e88fd66414587501b0dafbbdc89", "e586b23e00fd4a3d9bc53737e6284bc2", "d9ca2afedc04491aa7b7677df89117ff", "452037daee604d719b28367e9a1f7110", "c632f10b38ac45ee86f47f7542e0ce46", "4d7563a5397e4237ada04f6bca9d2319", "84e45737df9c43d192379240bb4c14fc", "6569e36ddac84f6c962cd6707d593f0f", "34472bd117e0412da284837c5041c568", "8c8338d4473a4f2ca1405657be98ace8", "c7cd0b4b47ad4bae8500bbf584434d3a", "6d280a7e45674ba4a98423f763d763bf", "cfe43b6f59ae4cf492595cd70577f8c0", "58bfa0430449416797ea5dd69f1c0f14", "b655bae8189f444eb0ce8b43ed1a8295", "86c1a3dfa8994f7e81ba92f15f7a9ead", "07ea3e4ee2a9407f9f020a7842eb4a6c", "e985bb632072405f9a751b28e188cf23", "4f924c32e4a54285aaedf365234ef14e", "76304919b9b442f8ba50a712c628c29f", "f58561e6599b4070b35b85676c2fdbfc", "762b19366aa145c19716861105edef64", "4d4066daf83848ab8c0fee96314d13ea", "85b954c48a084b9084ab3ecd770a16e5", "740e995f84e745dea02d7eed5626b3c4", "0b66c484e1364b1a8baf728e5874b5d5", "b417e16ebdd44215b9b73d0a7a129276", "fe10a9676233483b871c315d8e872035", "7ce489f589f4412c99902f97a3f8859c", "f31e6948e0c44c6398898369e07e713f", "a26c4fbe69174069ab98b57600e76ee3", "36061e6469d24e0fb21cab5ff0310d11", "275a67c3aec34aecac01ef88fb0d6e42", "c2f978c6f947409c9bd66b55303b600d", "cadd19345a6341ce92441ba8f8b8262e", "f8c8e2342ea94769a4312e4268cd127e", "b38c9fea09364934a5af9bb07bedad1b", "8f84e971a86044468d29b388b6e31a6c", "38d22f1f07f84810b13a6b588120bb74", "52230587cff74e27a31b6a2dc14e4c6c", "b8b7473fd14041c88d67e72c490193ad", "499c5c6a217547069eb20e7796645df6", "6026392b2f154af58868232d52bad25f", "7f7b841fe2a44ac890b9cfa6c411756d", "18b094566c474bc19ee2d076a0238066", "04f57d2225614db9aa81748c22cfa7b5", "c4a3a703ce2d403db7b1e92fbe0a1874", "818d8d0f4bc6486bae0a8a3dcd8d1622", "7b4a70133afa44059356b42f1b7acc81", "49d189e9f4034288a763d52d00470073", "04c3ae6f4e7e4acb94eb7bf927acdaef", "a4f0ece850ba46d0ad562ab44ec108bc", "8093c58c12104435ad67e54c527c5894", "54494243505246b8ab2dbb01d3e13b1c", "b1f283f2340149b9aa9fb2296eff3312", "df9c3fdcf08c460ab272d63eac605a70", "afc707e0144e4625bc188f284a3c6923", "f4d59f4ac71b4165a47af633c54e65fc", "5962e1d214374e7ab529545d397ed689", "a3e912ac14f44e6eb9cace7c073c097f", "1cbb3ff9f07e4224afdd32d0c8d0878a", "566592c616144ea288e1994a72af5477", "bfef7a88d0fc47018fe426c898ce359e", "120b1fd6be3e43fbbe62aa7362082cb6", "05f8eb9c34fa46adb6506fad2204f9fc", "a3bd6d36cf1445a4866b2af47cae0821", "16901721fb394a8388bb45c47234dd9d", "d6555447fe414edbbd62b58a9f54385a", "2b3247123e984feb995f79026bff8d14", "ffe3f8703fe04a00871c83a39735ac86", "79f317abafcf4b0fae7ada867ea64a9f", "caf1938cf18f4ee08ec48c36e82c33a8", "1049b4f6772e4049aaa1d89e54cf5bda", "f0c0e1a93c234b16aac03e54adbc4b26", "be68f84c4f4f404681079e6cf1a82cef", "1682d04ba34d4c73a339df4e3d25dd04", "ab1a055c23e94d28b61c331a8e8759f6", "960800bdd14f4f9bac78cbe1c93f9445", "c31d5105071243799eaff0794d829019", "3665df09187d4785b6cb5ca2da211e76", "dc65ec87091a47939515c5c453495dc2", "27148daa533b4475b1fabd39b87d669d", "bc0429d647664aa79a2f09fac8b890b3", "a5d3958c163a480bbbacdce4d52500b4", "b8e082a7de6c4b0f8be797a93bdffa90", "a388c5de0d6d43f5ac9c89cdb26120a7", "2ae1b689437747778b799d7b2263e75c" ] }, "id": "PTKNfSUd01dU", "outputId": "63cbdf61-8846-4465-aad2-b50751ae89cc" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "836376a41735471dbf95b16b08342d8d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| | 0.00/667 [00:00" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def display_images(images, cols=5):\n", " \"\"\"Display given images and their labels in a grid.\"\"\"\n", " rows = int(math.ceil(len(images) / cols))\n", " fig = plt.figure()\n", " fig.set_size_inches(cols * 3, rows * 3)\n", " for i, (image) in enumerate(images):\n", " plt.subplot(rows, cols, i + 1)\n", " plt.axis('off')\n", " plt.imshow(image)\n", " plt.title(i)\n", "\n", "NUM_IMAGES = 16\n", "# Extract each images individually for plot\n", "batch_images = [im.numpy() for im in item['image']]\n", "display_images(batch_images)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "OJKOISIW85fj" }, "outputs": [], "source": [] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "images_tfrecords.ipynb", "provenance": [] }, "jupytext": { "formats": "ipynb,md:myst" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 1 }