evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install keras_cv_attention_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import evals\n",
    "from tensorflow import keras\n",
    "import tensorflow as tf\n",
    "import IJB_evals\n",
    "import matplotlib.pyplot as plt\n",
    "import keras_cv_attention_models\n",
    "import GhostFaceNets, GhostFaceNets_with_Bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
     ]
    }
   ],
   "source": [
    "gpus = tf.config.experimental.list_physical_devices(\"GPU\")\n",
    "for gpu in gpus:\n",
    "    tf.config.experimental.set_memory_growth(gpu, True)\n",
    "print(tf.config.list_physical_devices('GPU'))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2 ways to load the model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stride 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Either\n",
    "# basic_model = keras.models.load_model('checkpoints/ghostnetv1_w1.3_s2.h5', compile=False)\n",
    "# basic_model = keras.models.load_model('checkpoints/GhostFaceNet_W1.3_S1_ArcFace.h5', compile=False)\n",
    "# basic_model = keras.models.load_model('checkpoints/GhostFaceNet_W1.3_S2_ArcFace.h5', compile=False)\n",
    "# basic_model = keras.models.load_model('checkpoints/GN_W1.3_S1_ArcFace_epoch46.h5', compile=False)\n",
    "basic_model = keras.models.load_model('checkpoints/GN_W1.3_S2_ArcFace_epoch48.h5', compile=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#If the above did not work, then you need to build the model then load the weights. e.g.,\n",
    "basic_model = GhostFaceNets_with_Bias.buildin_models(\"ghostnetv1\", dropout=0, emb_shape=512, output_layer='GDC', bn_momentum=0.9, bn_epsilon=1e-5)\n",
    "basic_model = GhostFaceNets_with_Bias.add_l2_regularizer_2_model(basic_model, weight_decay=5e-4, apply_to_batch_normal=False)\n",
    "basic_model = GhostFaceNets_with_Bias.replace_ReLU_with_PReLU(basic_model, target_activation='PReLU')\n",
    "\n",
    "basic_model.load_weights('checkpoints/ghostnetv1_w1.3_s2.h5')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stride 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Either\n",
    "basic_model = keras.models.load_model('checkpoints/ghostnetv1_w1.3_s1.h5', compile=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#If the above did not work, then you need to build the model then load the weights. e.g.,\n",
    "basic_model = GhostFaceNets_with_Bias.buildin_models(\"ghostnetv1\", dropout=0, emb_shape=512, output_layer='GDC', bn_momentum=0.9, bn_epsilon=1e-5, scale=True, use_bias=True, strides=1)\n",
    "basic_model = GhostFaceNets_with_Bias.add_l2_regularizer_2_model(basic_model, weight_decay=5e-4, apply_to_batch_normal=False)\n",
    "basic_model = GhostFaceNets_with_Bias.replace_ReLU_with_PReLU(basic_model, target_activation='PReLU')\n",
    "\n",
    "basic_model.load_weights('checkpoints/ghostnetv1_w1.3_s1.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating akka2: 100%|██████████| 1/1 [00:02<00:00,  2.56s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ">>>> akka2 evaluation max accuracy: 0.600000, thresh: 0.468323, previous max accuracy: 0.000000, PCA accuray = 0.700000 ± 0.331662\n",
      "Incorrectly classified pairs (threshold=0.46832263469696045):\n",
      "Pair 1: same pair, distance=0.3212725520133972\n",
      "Pair 2: same pair, distance=0.3118765950202942\n",
      "Pair 9: same pair, distance=0.3784198760986328\n",
      "Pair 10: same pair, distance=0.030923159793019295\n",
      "Pair 12: same pair, distance=0.3212725520133972\n",
      "Pair 17: same pair, distance=0.015077505260705948\n",
      "Pair 18: different pair, distance=0.46832263469696045\n",
      "Pair 19: same pair, distance=0.011098723858594894\n",
      ">>>> Improved = 0.600000\n"
     ]
    }
   ],
   "source": [
    "# ee = evals.eval_callback(basic_model, 'datasets/faces_emore/lfw.bin', batch_size=256, flip=True, PCA_acc=True)\n",
    "# ee.on_epoch_end(0)\n",
    "ee = evals.eval_callback(basic_model, 'datasets/faces_emore2/akka2.bin', batch_size=256, flip=True, PCA_acc=True)\n",
    "ee.on_epoch_end(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ee = evals.eval_callback(basic_model, 'datasets/faces_emore/lfw.bin', batch_size=256, flip=True, PCA_acc=False)\n",
    "# ee.on_epoch_end(0)\n",
    "ee = evals.eval_callback(basic_model, 'datasets/faces_emore2/aa.bin', batch_size=256, flip=True, PCA_acc=True)\n",
    "ee.on_epoch_end(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ee = evals.eval_callback(basic_model, 'datasets/faces_emore/vgg2_fp.bin', batch_size=256, flip=True, PCA_acc=False)\n",
    "ee.on_epoch_end(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ee = evals.eval_callback(basic_model, 'datasets/faces_emore/cfp_ff.bin', batch_size=256, flip=True, PCA_acc=False)\n",
    "ee.on_epoch_end(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ee = evals.eval_callback(basic_model, 'datasets/faces_emore/cfp_fp.bin', batch_size=256, flip=True, PCA_acc=False)\n",
    "ee.on_epoch_end(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ee = evals.eval_callback(basic_model, 'datasets/faces_emore/calfw.bin', batch_size=256, flip=True, PCA_acc=False)\n",
    "ee.on_epoch_end(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ee = evals.eval_callback(basic_model, 'datasets/faces_emore/cplfw.bin', batch_size=256, flip=True, PCA_acc=False)\n",
    "ee.on_epoch_end(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ee = evals.eval_callback(basic_model, 'datasets/faces_emore/agedb_30.bin', batch_size=256, flip=True, PCA_acc=False)\n",
    "ee.on_epoch_end(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tt = IJB_evals.IJB_test(lambda imgs: basic_model((tf.cast(imgs, \"float32\") - 127.5) * 0.0078125).numpy(), data_path='path_to_IJB_Dataset/ijb-testsuite/ijb', subset='IJBB', batch_size=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = tt.run_model_test_single()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "IJB_evals.plot_roc_and_calculate_tpr([score], names=[basic_model.name + \"_IJBB\"], label=tt.label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tt = IJB_evals.IJB_test(lambda imgs: basic_model((tf.cast(imgs, \"float32\") - 127.5) * 0.0078125).numpy(), data_path='C:/Users/mohda/Downloads/ijb-testsuite/ijb', subset='IJBC', batch_size=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = tt.run_model_test_single()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "IJB_evals.plot_roc_and_calculate_tpr([score], names=[basic_model.name + \"_IJBC\"], label=tt.label)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot face quality distribution using norm value of feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc = tf.norm(ee.embs, axis=1).numpy()\n",
    "_ = plt.hist(cc, bins=512, alpha=0.5, label='agedb_30 quality')\n",
    "cc = tf.norm(tt.embs, axis=1).numpy()\n",
    "_ = plt.hist(cc, bins=512, alpha=0.5, label='IJBC quality')\n",
    "plt.legend()\n",
    "plt.tight_layout()"
   ]
  }
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