CelebaTagger.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import six\n",
    "import tensorflow_datasets as tfds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mDownloading and preparing dataset Unknown size (download: Unknown size, generated: Unknown size, total: Unknown size) to ~/tensorflow_datasets/celeb_a/2.0.1...\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "                                                                      \u001b[A\n",
      "Shuffling ~/tensorflow_datasets/celeb_a/2.0.1.incomplete0P3IXC/celeb_a-test.tfrecord*...:   0%|          | 0/19962 [00:00<?, ? examples/s]\u001b[A\n",
      "Shuffling ~/tensorflow_datasets/celeb_a/2.0.1.incomplete0P3IXC/celeb_a-test.tfrecord*...:  48%|████▊     | 9667/19962 [00:00<00:00, 96659.72 examples/s]\u001b[A\n",
      "Shuffling ~/tensorflow_datasets/celeb_a/2.0.1.incomplete0P3IXC/celeb_a-test.tfrecord*...: 100%|█████████▉| 19871/19962 [00:00<00:00, 99813.45 examples/s]\u001b[A\n",
      "                                                                                                                                                         \u001b[A\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mDataset celeb_a downloaded and prepared to ~/tensorflow_datasets/celeb_a/2.0.1. Subsequent calls will reuse this data.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "celeba = tfds.load('celeb_a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLASSES = [\n",
    "    '5_o_Clock_Shadow',\n",
    "    'Arched_Eyebrows',\n",
    "    'Attractive',\n",
    "    'Bags_Under_Eyes',\n",
    "    'Bald',\n",
    "    'Bangs',\n",
    "    'Big_Lips',\n",
    "    'Big_Nose',\n",
    "    'Black_Hair',\n",
    "    'Blond_Hair',\n",
    "    'Blurry',\n",
    "    'Brown_Hair',\n",
    "    'Bushy_Eyebrows',\n",
    "    'Chubby',\n",
    "    'Double_Chin',\n",
    "    'Eyeglasses',\n",
    "    'Goatee',\n",
    "    'Gray_Hair',\n",
    "    'Heavy_Makeup',\n",
    "    'High_Cheekbones',\n",
    "    'Male',\n",
    "    'Mouth_Slightly_Open',\n",
    "    'Mustache',\n",
    "    'Narrow_Eyes',\n",
    "    'No_Beard',\n",
    "    'Oval_Face',\n",
    "    'Pale_Skin',\n",
    "    'Pointy_Nose',\n",
    "    'Receding_Hairline',\n",
    "    'Rosy_Cheeks',\n",
    "    'Sideburns',\n",
    "    'Smiling',\n",
    "    'Straight_Hair',\n",
    "    'Wavy_Hair',\n",
    "    'Wearing_Earrings',\n",
    "    'Wearing_Hat',\n",
    "    'Wearing_Lipstick',\n",
    "    'Wearing_Necklace',\n",
    "    'Wearing_Necktie',\n",
    "    'Young']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(e):\n",
    "    res = e['image']\n",
    "    res = tf.image.resize_with_pad(res, 128, 128)\n",
    "    res = tf.cast(res, tf.float32) / 255. - 0.5\n",
    "    \n",
    "    labels = []\n",
    "    for c in CLASSES:\n",
    "        labels.append(tf.cast(e['attributes'][c], tf.float32))\n",
    "    labels = tf.stack(labels)\n",
    "    return res, labels\n",
    "\n",
    "ds_train = celeba['train'].map(preprocess).shuffle(10000).batch(64, drop_remainder=True)\n",
    "ds_test = celeba['validation'].map(preprocess).batch(64, drop_remainder=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1ba2cd0460>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for e in celeba['train'].take(1):\n",
    "    pass\n",
    "plt.imshow(e['image'].numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CelebaModel(tf.keras.Model):\n",
    "    def __init__(self, num_layers=5, channels=128, channel_mult=[]):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.num_layers = num_layers\n",
    "        self.channels = channels\n",
    "        \n",
    "        self.initial_block = tf.keras.models.Sequential()\n",
    "        self.initial_block.add(tf.keras.layers.Conv2D(channels, (3, 3)))\n",
    "        self.initial_block.add(tf.keras.layers.Activation('relu'))\n",
    "        self.initial_block.add(tf.keras.layers.LayerNormalization())\n",
    "        \n",
    "        self.blocks = tf.keras.models.Sequential()\n",
    "        for l in range(num_layers):\n",
    "            mult = 1\n",
    "            if channel_mult:\n",
    "                mult = channel_mult[min(l, len(channel_mult) - 1)]\n",
    "            block = tf.keras.models.Sequential()\n",
    "            block.add(tf.keras.layers.Conv2D(channels * mult, (3, 3)))\n",
    "            block.add(tf.keras.layers.Activation('relu'))\n",
    "            block.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
    "            block.add(tf.keras.layers.LayerNormalization())\n",
    "            block.add(tf.keras.layers.Dropout(0.2))\n",
    "            self.blocks.add(block)\n",
    "            \n",
    "        self.outer = tf.keras.layers.GlobalAveragePooling2D()\n",
    "        self.dnn = tf.keras.models.Sequential()\n",
    "        self.dnn.add(tf.keras.layers.Dense(64))\n",
    "        self.dnn.add(tf.keras.layers.Activation('relu'))\n",
    "        self.dnn.add(tf.keras.layers.Dense(len(CLASSES)))\n",
    "        \n",
    "        \n",
    "    def call(self, inputs, training=False):\n",
    "        flow = inputs\n",
    "        flow = self.initial_block(flow)\n",
    "        flow = self.blocks(flow, training=training)\n",
    "        flow = self.outer(flow)\n",
    "        flow = self.dnn(flow, training=training)\n",
    "        return flow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = CelebaModel(num_layers=5, channels=64, channel_mult=[1, 1, 2, 2, 2])\n",
    "model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
    "              optimizer=tf.keras.optimizers.Adam(3e-4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(ds_train, epochs=10, validation_data=ds_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = []\n",
    "preds = []\n",
    "for e, l in ds_test:\n",
    "    labels.extend(l.numpy())\n",
    "    preds.extend(model.predict(e, verbose=False))\n",
    "labels = np.array(labels)\n",
    "preds = np.array(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC for 5_o_Clock_Shadow: 0.970\n",
      "AUC for Arched_Eyebrows: 0.920\n",
      "AUC for Attractive: 0.908\n",
      "AUC for Bags_Under_Eyes: 0.882\n",
      "AUC for Bald: 0.994\n",
      "AUC for Bangs: 0.988\n",
      "AUC for Big_Lips: 0.795\n",
      "AUC for Big_Nose: 0.883\n",
      "AUC for Black_Hair: 0.961\n",
      "AUC for Blond_Hair: 0.984\n",
      "AUC for Blurry: 0.964\n",
      "AUC for Brown_Hair: 0.910\n",
      "AUC for Bushy_Eyebrows: 0.950\n",
      "AUC for Chubby: 0.956\n",
      "AUC for Double_Chin: 0.965\n",
      "AUC for Eyeglasses: 0.999\n",
      "AUC for Goatee: 0.986\n",
      "AUC for Gray_Hair: 0.987\n",
      "AUC for Heavy_Makeup: 0.981\n",
      "AUC for High_Cheekbones: 0.954\n",
      "AUC for Male: 0.999\n",
      "AUC for Mouth_Slightly_Open: 0.985\n",
      "AUC for Mustache: 0.977\n",
      "AUC for Narrow_Eyes: 0.938\n",
      "AUC for No_Beard: 0.989\n",
      "AUC for Oval_Face: 0.736\n",
      "AUC for Pale_Skin: 0.959\n",
      "AUC for Pointy_Nose: 0.801\n",
      "AUC for Receding_Hairline: 0.952\n",
      "AUC for Rosy_Cheeks: 0.959\n",
      "AUC for Sideburns: 0.988\n",
      "AUC for Smiling: 0.984\n",
      "AUC for Straight_Hair: 0.862\n",
      "AUC for Wavy_Hair: 0.915\n",
      "AUC for Wearing_Earrings: 0.947\n",
      "AUC for Wearing_Hat: 0.995\n",
      "AUC for Wearing_Lipstick: 0.985\n",
      "AUC for Wearing_Necklace: 0.838\n",
      "AUC for Wearing_Necktie: 0.977\n",
      "AUC for Young: 0.929\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "for i, c in enumerate(CLASSES):\n",
    "    print('AUC for %s: %.3f' % (c, roc_auc_score(labels[:, i], preds[:, i])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}