model_inference.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to validate model checkpoints, we run inference on the validation part of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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     "status": "ok",
     "timestamp": 1645384461101,
     "user": {
      "displayName": "Evgeny Kovalev",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "import albumentations as A\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "from PIL import Image\n",
    "from sklearn.metrics import accuracy_score, mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import callbacks, losses, metrics, models, optimizers\n",
    "from tensorflow.keras.applications.vgg16 import VGG16\n",
    "from tensorflow.keras.layers import BatchNormalization, Conv2D, Dense, Dropout, GlobalAveragePooling2D, MaxPooling2D\n",
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.utils import plot_model, Sequence\n",
    "from tqdm import tqdm\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')\n",
    "\n",
    "def extract_target(file_name):\n",
    "    age, gender = file_name.split('_')[:2]\n",
    "    age, gender = float(age), int(gender)\n",
    "    return age, gender\n",
    "\n",
    "IMG_DIR = '/content/drive/MyDrive/age_gender_estimation/train_val'\n",
    "\n",
    "train_img_names, val_img_names = train_test_split(os.listdir(IMG_DIR), test_size=0.25, random_state=42, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1645384462726,
     "user": {
      "displayName": "Evgeny Kovalev",
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    "id": "m-NcyH-0ffdk"
   },
   "outputs": [],
   "source": [
    "def run_inference(weights_path):\n",
    "    model = load_model(weights_path)\n",
    "    ages_correct, genders_correct = [], []\n",
    "    ages_predicted, genders_predicted = [], []\n",
    "    for img_name in tqdm(val_img_names):\n",
    "        img = np.array(Image.open(os.path.join(IMG_DIR, img_name))) / 255\n",
    "        img = img.reshape(1, 128, 128, 3)\n",
    "        age, gender = extract_target(img_name)\n",
    "        ages_correct.append(age), genders_correct.append(gender)\n",
    "        model_pred = model.predict(img)\n",
    "        age_pred = model_pred[0][0][0]\n",
    "        gender_pred = model_pred[1][0][0]\n",
    "        ages_predicted.append(age_pred), genders_predicted.append(round(gender_pred))\n",
    "    print(f'\\nAccuracy: {accuracy_score(genders_correct, genders_predicted)}')\n",
    "    print(f'MAE: {mean_absolute_error(ages_correct, ages_predicted)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 221112,
     "status": "ok",
     "timestamp": 1645384689901,
     "user": {
      "displayName": "Evgeny Kovalev",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1250/1250 [03:39<00:00,  5.70it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 0.8544\n",
      "MAE: 7.970671821308136\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "run_inference('/content/drive/MyDrive/age_gender_estimation/models/age_gender_A.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 294585,
     "status": "ok",
     "timestamp": 1645384994138,
     "user": {
      "displayName": "Evgeny Kovalev",
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    },
    "id": "y_Yq_JbVhpNf",
    "outputId": "08fc8066-3c2b-4502-9e27-d998f28ccbb8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1250/1250 [04:52<00:00,  4.27it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 0.896\n",
      "MAE: 5.932304301548005\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "run_inference('/content/drive/MyDrive/age_gender_estimation/models/age_gender_B.h5')"
   ]
  }
 ],
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