HeadPose_ResNet50_Tutorial_Gluon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Head Pose Estimator (Gluon)\n",
    "\n",
    "Tatsuya J. Arai @araitats\n",
    "02/28/2018\n",
    "\n",
    "\n",
    "## Introduction\n",
    "\n",
    "In this notebook, the details of construction of convolutional neural network based head pose estimator are described. \n",
    "\n",
    "## Overview \n",
    "\n",
    "When there is a face photo presented in front of you, your human eyes can immediately recognize what direction the person in the photo is looking at (e.g. either facing straight up to the camera or looking at somewhere else).  The direction is defined as the head pose. A convolutional neural network model is trained with thousands of such face photos and their corresponding head pose labels, the model will be able to estimate various head poses when a new batch of face images are presented. In this notebook, the head pose is categorized into nine classes (i.e. the combinations of looking down, straight, and up (tilt angles) and looking right, middle and left (pan angles)).\n",
    "    \n",
    "## Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.7.12 (default, Nov 20 2017, 18:23:56) \n",
      "[GCC 5.4.0 20160609]\n"
     ]
    }
   ],
   "source": [
    "import mxnet as mx\n",
    "## Python version\n",
    "print(sys.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.7.12 (default, Nov 20 2017, 18:23:56)  [GCC 5.4.0 20160609]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1.0\n"
     ]
    }
   ],
   "source": [
    "print(mx.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Color shift in HSV space  \n",
    "\n",
    "``shiftHSV`` shifts colors in a randomly selected input image.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def shiftHSV(im, h_shift_lim=(-180, 180),\n",
    "                 s_shift_lim=(-255, 255),\n",
    "                 v_shift_lim=(-255, 255), drop_p=0.5):\n",
    "    if np.random.random() < drop_p:\n",
    "        im = cv2.cvtColor(im, cv2.COLOR_BGR2HSV)\n",
    "        h, s, v = cv2.split(im)\n",
    "        h_shift = np.random.uniform(h_shift_lim[0], h_shift_lim[1])\n",
    "        h = cv2.add(h, h_shift) \n",
    "        s_shift = np.random.uniform(s_shift_lim[0], s_shift_lim[1])\n",
    "        s = cv2.add(s, s_shift)\n",
    "        v_shift = np.random.uniform(v_shift_lim[0], v_shift_lim[1])\n",
    "        v = cv2.add(v, v_shift)\n",
    "        im = cv2.merge((h, s, v))\n",
    "        im = cv2.cvtColor(im, cv2.COLOR_HSV2BGR)\n",
    "        im = np.uint8(im) \n",
    "        im = np.float32(im)\n",
    "    return im"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obtain a pre-trained ResNet Model from model zoo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNetV1(\n",
      "  (features): HybridSequential(\n",
      "    (0): Conv2D(3 -> 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "    (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=64)\n",
      "    (2): Activation(relu)\n",
      "    (3): MaxPool2D(size=(3, 3), stride=(2, 2), padding=(1, 1), ceil_mode=False)\n",
      "    (4): HybridSequential(\n",
      "      (0): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(64 -> 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=64)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=64)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(64 -> 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "        )\n",
      "        (downsample): HybridSequential(\n",
      "          (0): Conv2D(64 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "        )\n",
      "      )\n",
      "      (1): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(256 -> 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=64)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=64)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(64 -> 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "        )\n",
      "      )\n",
      "      (2): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(256 -> 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=64)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=64)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(64 -> 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (5): HybridSequential(\n",
      "      (0): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(256 -> 128, kernel_size=(1, 1), stride=(2, 2))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=128)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=128)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "        )\n",
      "        (downsample): HybridSequential(\n",
      "          (0): Conv2D(256 -> 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "        )\n",
      "      )\n",
      "      (1): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(512 -> 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=128)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=128)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "        )\n",
      "      )\n",
      "      (2): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(512 -> 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=128)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=128)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "        )\n",
      "      )\n",
      "      (3): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(512 -> 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=128)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=128)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (6): HybridSequential(\n",
      "      (0): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(512 -> 256, kernel_size=(1, 1), stride=(2, 2))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=1024)\n",
      "        )\n",
      "        (downsample): HybridSequential(\n",
      "          (0): Conv2D(512 -> 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=1024)\n",
      "        )\n",
      "      )\n",
      "      (1): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(1024 -> 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=1024)\n",
      "        )\n",
      "      )\n",
      "      (2): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(1024 -> 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=1024)\n",
      "        )\n",
      "      )\n",
      "      (3): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(1024 -> 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=1024)\n",
      "        )\n",
      "      )\n",
      "      (4): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(1024 -> 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=1024)\n",
      "        )\n",
      "      )\n",
      "      (5): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(1024 -> 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=1024)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (7): HybridSequential(\n",
      "      (0): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(1024 -> 512, kernel_size=(1, 1), stride=(2, 2))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(512 -> 2048, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=2048)\n",
      "        )\n",
      "        (downsample): HybridSequential(\n",
      "          (0): Conv2D(1024 -> 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=2048)\n",
      "        )\n",
      "      )\n",
      "      (1): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(2048 -> 512, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(512 -> 2048, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=2048)\n",
      "        )\n",
      "      )\n",
      "      (2): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(2048 -> 512, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(512 -> 2048, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=2048)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (8): GlobalAvgPool2D(size=(1, 1), stride=(1, 1), padding=(0, 0), ceil_mode=True)\n",
      "  )\n",
      "  (output): Dense(2048 -> 1000, linear)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "from mxnet.gluon.model_zoo.vision import resnet50_v1\n",
    "pretrained_net = resnet50_v1(pretrained=True, prefix = 'headpose_')\n",
    "print(pretrained_net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load preprocessed datasets\n",
    "### Dataset \n",
    "\n",
    "\n",
    "Original Data: http://www-prima.inrialpes.fr/perso/Gourier/Faces/HPDatabase.html\n",
    "\n",
    "> N. Gourier, D. Hall, J. L. Crowley,\n",
    "> Estimating Face Orientation from Robust Detection of Salient Facial Features,\n",
    "> *Proceedings of Pointing 2004, ICPR, International Workshop on Visual Observation of Deictic Gestures*, Cambridge, UK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have to preprocess the dataset using ``preprocessingDataset_py2.py``. This may take some time. \n",
    "\n",
    "> python2 preprocessingDataset_py2.py --num-data-aug 15 --aspect-ratio 1\n",
    "\n",
    "Preprocessed Data: (6.7 GB (Aspect Ratio, 1:1) or 5.0 GB (Aspect Ratio, 16:9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "trn_im, test_im, trn_output, test_output = pickle.load(open( \"HeadPoseData_trn_test_x15_py2.pkl\", \"rb\" ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((33480, 3, 84, 84), (1674, 3, 84, 84))\n",
      "((33480, 2), (1674, 2))\n"
     ]
    }
   ],
   "source": [
    "print(trn_im.shape, test_im.shape)\n",
    "print(trn_output.shape, test_output.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification of Head Pose"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make mirror images (data augmentation)\n",
    "\n",
    "Head-pose images were flipped along the horizontal axis and the signs of corresponding head-pose pan angles were changed accordingly. The mirror image procedure effectively doubles the size of training data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trn_im_mirror = trn_im[:,:,:,::-1]\n",
    "trn_output_mirror = np.zeros(trn_output.shape)\n",
    "# Tilt\n",
    "trn_output_mirror[:,0] = trn_output[:,0] \n",
    "# Pan\n",
    "trn_output_mirror[:,1] = trn_output[:,1] * -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([-0.33333334, -1.        ], dtype=float32), array([-0.33333334,  1.        ]))\n"
     ]
    }
   ],
   "source": [
    "im_idx = 200\n",
    "print(trn_output[im_idx,:], trn_output_mirror[im_idx,:]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trn_im = np.concatenate((trn_im, trn_im_mirror), axis = 0) \n",
    "trn_output = np.concatenate((trn_output, trn_output_mirror), axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((66960, 3, 84, 84), (66960, 2))\n"
     ]
    }
   ],
   "source": [
    "print(trn_im.shape, trn_output.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From (normalized) angles to angle classes (Tilts and Pans)\n",
    "\n",
    "xxx_output[:,0] and xxx_output[i0,1] contain normalized agnle data (from -90 degrees to 90 degrees -> from -1.0 to 1.0) in tilt and pan directions, respectively. This process is to convert the normalized angle into one of three angle classes in each direction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold Angles -19.47, 19.47\n"
     ]
    }
   ],
   "source": [
    "n_grid = 3\n",
    "angles_thrshld = [np.arcsin(float(a) * 2 / n_grid - 1)/np.pi * 180 / 90 for a in range(1,n_grid)]\n",
    "print(\"Threshold Angles \" +  (', ').join([\"{:.2f}\".format(a * 90) for a in angles_thrshld]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Head pose is classified into 9 categories (the combinations of 3 tilt and 3 pan classes). The head pose contained within +/- 19.5 degrees in tilt and pan angles is labeled as a center position (i.e. Grid Class of 4, Tilt Class of 1 and Pan Class of 1). The threshold angles, +/- 19.5 degrees split a semicircle (i.e. the distance between sin(-90 degrees) and sin(90 degrees)) into three equal arch lengths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def angles2Cat(angles_thrshld, angl_input):\n",
    "    # angl_input: Normalized angle -90 - 90 -> -1.0 - 1.0\n",
    "    angles_cat_temp = angles_thrshld + [angl_input]\n",
    "    return np.argmin(np.multiply(sorted(angles_cat_temp)-angl_input,sorted(angles_cat_temp)-angl_input))\n",
    "\n",
    "### Dataset ###\n",
    "trn_tilt_cls = []\n",
    "trn_pan_cls = []\n",
    "for i0 in range(trn_output.shape[0]):\n",
    "    trn_tilt_cls += [angles2Cat(angles_thrshld, trn_output[i0,0])]\n",
    "    trn_pan_cls += [angles2Cat(angles_thrshld, trn_output[i0,1])]\n",
    "\n",
    "test_tilt_cls = []\n",
    "test_pan_cls = []\n",
    "for i0 in range(test_output.shape[0]):\n",
    "    test_tilt_cls += [angles2Cat(angles_thrshld, test_output[i0,0])]\n",
    "    test_pan_cls += [angles2Cat(angles_thrshld, test_output[i0,1])]\n",
    "\n",
    "    \n",
    "np_trn_tilt_cls = np.asarray(trn_tilt_cls)\n",
    "np_test_tilt_cls = np.asarray(test_tilt_cls)\n",
    "np_trn_pan_cls = np.asarray(trn_pan_cls)\n",
    "np_test_pan_cls = np.asarray(test_pan_cls)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From angles classes to 9 head pose classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((66960,), (66960,))\n"
     ]
    }
   ],
   "source": [
    "np_trn_grid_cls = np_trn_pan_cls * n_grid + np_trn_tilt_cls\n",
    "np_test_grid_cls = np_test_pan_cls * n_grid + np_test_tilt_cls\n",
    "\n",
    "print(np_trn_grid_cls.shape, np_trn_grid_cls.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shift colors in the training data (additional data augmentation)\n",
    "\n",
    "The color shift procedure would simulate the changes in the lighting condition, skin tone, and background."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i0 in range(trn_im.shape[0]):\n",
    "    im_temp = trn_im[i0,:,:,:]\n",
    "    im_temp = np.transpose(im_temp, (1,2,0)) * 255 #transposing and restoring the color\n",
    "    im_temp = shiftHSV(im_temp,\n",
    "                       h_shift_lim=(-0.1, 0.1),\n",
    "                       s_shift_lim=(-0.1, 0.1),\n",
    "                       v_shift_lim=(-0.1, 0.1))\n",
    "    im_temp = np.transpose(im_temp, (2,0,1)) / 255 #transposing and restoring the color\n",
    "    trn_im[i0,:,:,:] = im_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b0el0OHPmDOfPnyfLMnq9HnEct96TFrjzhaDQFQbaBK6GWKC1dtnsS4tIHMeE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\nPn6bQzMAAAABSURBVBbc7Mi5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f748f33b3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "im_idx = 390\n",
    "n_panels = 4\n",
    "for i in range(n_panels):\n",
    "    i0 = i + im_idx\n",
    "    plt.subplot(1,n_panels,i+1)\n",
    "    im_temp = trn_im[i0,:,:,:]\n",
    "    im_temp = np.transpose(im_temp, (1,2,0))\n",
    "    ## BGR -> RGB \n",
    "    plt.imshow(im_temp[:,:,[2,1,0]])\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modify the ResNet 50 model from model zoo \n",
    "\n",
    "The number of output classes is modified to 9 to match our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNetV1(\n",
      "  (features): HybridSequential(\n",
      "    (0): Conv2D(3 -> 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "    (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=64)\n",
      "    (2): Activation(relu)\n",
      "    (3): MaxPool2D(size=(3, 3), stride=(2, 2), padding=(1, 1), ceil_mode=False)\n",
      "    (4): HybridSequential(\n",
      "      (0): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(64 -> 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=64)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=64)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(64 -> 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "        )\n",
      "        (downsample): HybridSequential(\n",
      "          (0): Conv2D(64 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "        )\n",
      "      )\n",
      "      (1): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(256 -> 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=64)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=64)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(64 -> 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "        )\n",
      "      )\n",
      "      (2): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(256 -> 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=64)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=64)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(64 -> 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (5): HybridSequential(\n",
      "      (0): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(256 -> 128, kernel_size=(1, 1), stride=(2, 2))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=128)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=128)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "        )\n",
      "        (downsample): HybridSequential(\n",
      "          (0): Conv2D(256 -> 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "        )\n",
      "      )\n",
      "      (1): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(512 -> 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=128)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=128)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "        )\n",
      "      )\n",
      "      (2): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(512 -> 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=128)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=128)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "        )\n",
      "      )\n",
      "      (3): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(512 -> 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=128)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=128)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (6): HybridSequential(\n",
      "      (0): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(512 -> 256, kernel_size=(1, 1), stride=(2, 2))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=1024)\n",
      "        )\n",
      "        (downsample): HybridSequential(\n",
      "          (0): Conv2D(512 -> 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=1024)\n",
      "        )\n",
      "      )\n",
      "      (1): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(1024 -> 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=1024)\n",
      "        )\n",
      "      )\n",
      "      (2): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(1024 -> 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=1024)\n",
      "        )\n",
      "      )\n",
      "      (3): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(1024 -> 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=1024)\n",
      "        )\n",
      "      )\n",
      "      (4): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(1024 -> 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=1024)\n",
      "        )\n",
      "      )\n",
      "      (5): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(1024 -> 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=256)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=1024)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (7): HybridSequential(\n",
      "      (0): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(1024 -> 512, kernel_size=(1, 1), stride=(2, 2))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(512 -> 2048, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=2048)\n",
      "        )\n",
      "        (downsample): HybridSequential(\n",
      "          (0): Conv2D(1024 -> 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=2048)\n",
      "        )\n",
      "      )\n",
      "      (1): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(2048 -> 512, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(512 -> 2048, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=2048)\n",
      "        )\n",
      "      )\n",
      "      (2): BottleneckV1(\n",
      "        (body): HybridSequential(\n",
      "          (0): Conv2D(2048 -> 512, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "          (2): Activation(relu)\n",
      "          (3): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=512)\n",
      "          (5): Activation(relu)\n",
      "          (6): Conv2D(512 -> 2048, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (7): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, momentum=0.9, axis=1, in_channels=2048)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (8): GlobalAvgPool2D(size=(1, 1), stride=(1, 1), padding=(0, 0), ceil_mode=True)\n",
      "  )\n",
      "  (output): Dense(2048 -> 9, linear)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "net = resnet50_v1(classes=9, prefix='headpose_')\n",
    "net.collect_params().initialize()\n",
    "net.features = pretrained_net.features\n",
    "from mxnet import init\n",
    "#net.output.initialize(init.Xavier(rnd_type='gaussian', factor_type=\"in\", magnitude=2)) # MXNet 1.1.0\n",
    "net.initialize(init.Xavier(rnd_type='gaussian', factor_type=\"in\", magnitude=2)) # MXNet 0.12.1\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Training Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from mxnet import autograd\n",
    "from mxnet import nd\n",
    "\n",
    "# Accuracy Evaluation\n",
    "def eval_acc(data_iter, net, ctx):\n",
    "    acc = mx.metric.Accuracy()\n",
    "    for i, (data, label) in enumerate(data_iter):\n",
    "        data = data.as_in_context(ctx)\n",
    "        label = label.as_in_context(ctx)\n",
    "\n",
    "        output = net(data)\n",
    "        pred = nd.argmax(output, axis=1)\n",
    "        acc.update(preds=pred, labels=label)\n",
    "    return acc.get()[1]\n",
    "\n",
    "# Training Loop\n",
    "def train_util(net, train_iter, validation_iter, loss_fn, trainer, ctx, epochs, batch_size):\n",
    "    metric = mx.metric.create(['acc'])\n",
    "    lst_val_acc = []\n",
    "    lst_trn_acc = []\n",
    "    best_accuracy = 0\n",
    "    for epoch in range(epochs):\n",
    "        for i, (data, label) in enumerate(train_iter):\n",
    "            # ensure context            \n",
    "            data = data.as_in_context(ctx)\n",
    "            label = label.as_in_context(ctx)\n",
    "            \n",
    "            with autograd.record():\n",
    "                output = net(data)\n",
    "                loss = loss_fn(output, label)\n",
    "\n",
    "            loss.backward()\n",
    "            trainer.step(data.shape[0])\n",
    "\n",
    "        train_acc = eval_acc(train_iter, net, ctx)\n",
    "        validation_acc = eval_acc(validation_iter, net, ctx)\n",
    "\n",
    "        lst_trn_acc += [train_acc]\n",
    "        lst_val_acc += [validation_acc]\n",
    "                \n",
    "        ### Save checkpoint weights\n",
    "        #\n",
    "        # We save the model in the symbolic format (i.e. XXX.json and XXX.params)\n",
    "        #\n",
    "        \n",
    "        # Export .json and .params files\n",
    "        # chkpt-XX-symbol.json does not come with softmax layer at the end. \n",
    "        net.export('chkpt-{}'.format(epoch)) \n",
    "        # Overwrite .json with the one with softmax\n",
    "        net_with_softmax = net(mx.sym.var('data'))\n",
    "        net_with_softmax = mx.sym.SoftmaxOutput(data=net_with_softmax, name=\"softmax\")\n",
    "        net_with_softmax.save('chkpt-{}-symbol.json'.format(epoch)) \n",
    "        print(\"Epoch %s | training_acc %s | val_acc %s \" % (epoch, train_acc, validation_acc))\n",
    "        \n",
    "        if validation_acc > best_accuracy:\n",
    "            # A network with the best validation accuracy is returned.  \n",
    "            net_best = net\n",
    "            net_with_softmax_best = net_with_softmax\n",
    "            best_accuracy = validation_acc\n",
    "        \n",
    "    return lst_trn_acc, lst_val_acc, net_best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fine-tune the model\n",
    "\n",
    "We run the training. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train(net, ctx, batch_size=64, epochs=10, learning_rate=0.0005):\n",
    "    # Making Gluon iterators\n",
    "    train_iter = mx.gluon.data.DataLoader(mx.gluon.data.ArrayDataset((trn_im.astype(np.float32)-0.5) *2, np_trn_grid_cls),\n",
    "                                                batch_size=batch_size, shuffle=True, last_batch='discard')\n",
    "    test_iter = mx.gluon.data.DataLoader(mx.gluon.data.ArrayDataset((test_im.astype(np.float32)-0.5) *2 , np_test_grid_cls),\n",
    "                                                batch_size=batch_size, shuffle=True, last_batch='discard')\n",
    "\n",
    "    net.collect_params().reset_ctx(ctx)\n",
    "    net.hybridize()\n",
    "    \n",
    "    loss = gluon.loss.SoftmaxCrossEntropyLoss()\n",
    "    trainer = gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': learning_rate})\n",
    "    \n",
    "    return train_util(net, train_iter, test_iter, loss, trainer, ctx, epochs, batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 | training_acc 0.97866873805 | val_acc 0.795673076923 \n",
      "Epoch 1 | training_acc 0.979953393881 | val_acc 0.798677884615 \n",
      "Epoch 2 | training_acc 0.985316085086 | val_acc 0.783052884615 \n",
      "Epoch 3 | training_acc 0.988363408222 | val_acc 0.795072115385 \n",
      "Epoch 4 | training_acc 0.987258006692 | val_acc 0.785456730769 \n"
     ]
    }
   ],
   "source": [
    "from mxnet import gluon\n",
    "import mxnet as mx\n",
    "ctx = mx.gpu()\n",
    "lst_trn_acc, lst_val_acc, net_gluon = train(net, ctx, batch_size=64, epochs=5, learning_rate=0.0005)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the model in the serial (Gluon) format\n",
    "\n",
    "We can also save the model in the serial (Gluon) format. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "Save net (Gluon model) in the serial format  -> Pass the weights to another serialized model. \n",
    "'''\n",
    "\n",
    "net_gluon.save_params('net_gluon.params')\n",
    "net_gluon2 = resnet50_v1(classes=9, prefix='headpose_')\n",
    "net_gluon2.load_params('net_gluon.params', ctx=mx.cpu())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning Curve\n",
    "\n",
    "The plot shows the changes in validation accuracy during the trining. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f748c401510>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f748c663910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(lst_val_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Take a peek at validation results\n",
    "\n",
    "Let's load a model and make inference results of validation data.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make iterators for the modular model artifact from nparrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 300\n",
    "\n",
    "trn_iter_grid = mx.io.NDArrayIter((trn_im.astype(np.float32) -0.5) * 2, np_trn_grid_cls, batch_size, shuffle=True)\n",
    "test_iter_grid = mx.io.NDArrayIter((test_im.astype(np.float32)-0.5) * 2, np_test_grid_cls, batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load modular model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_model(s_fname, p_fname):\n",
    "    \"\"\"\n",
    "    Load model checkpoint from file.\n",
    "    :return: (arg_params, aux_params)\n",
    "    arg_params : dict of str to NDArray\n",
    "        Model parameter, dict of name to NDArray of net's weights.\n",
    "    aux_params : dict of str to NDArray\n",
    "        Model parameter, dict of name to NDArray of net's auxiliary states.\n",
    "    \"\"\"\n",
    "    symbol = mx.symbol.load(s_fname)\n",
    "    save_dict = mx.nd.load(p_fname)\n",
    "    arg_params = {}\n",
    "    aux_params = {}\n",
    "    for k, v in save_dict.items():\n",
    "        tp, name = k.split(':', 1)\n",
    "        if tp == 'arg':\n",
    "            arg_params[name] = v\n",
    "        if tp == 'aux':\n",
    "            aux_params[name] = v\n",
    "    return symbol, arg_params, aux_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 31,
     "metadata": {},
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   ],
   "source": [
    "model_symbol = 'chkpt-3-symbol.json'\n",
    "model_params = 'chkpt-3-0000.params'\n",
    "\n",
    "sym, arg_params, aux_params = load_model(model_symbol, model_params)\n",
    "\n",
    "ctx = [mx.gpu(i) for i in range(4)] # USE GPU to look up validation results \n",
    "net2 = mx.mod.Module(symbol=sym,context=ctx)\n",
    "\n",
    "shape = {\"data\": (batch_size, trn_im.shape[1], trn_im.shape[2], trn_im.shape[3])}\n",
    "mx.viz.plot_network(sym, shape=shape)\n",
    "\n",
    "### You may notice that Gluon-ResNet-50 does not have a batch norm layer right after the input layer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net2.bind(data_shapes=[trn_iter_grid.provide_data[0]], label_shapes=[trn_iter_grid.provide_label[0]])\n",
    "net2.set_params(arg_params, aux_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Infer validation data iterator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1674, 9)\n"
     ]
    }
   ],
   "source": [
    "preds = net2.predict(test_iter_grid).asnumpy()\n",
    "print(preds.shape)\n",
    "### Head Pose Prediction (9 classes)\n",
    "pred_cls = []\n",
    "\n",
    "for idx in range(preds.shape[0]):\n",
    "    pred_cls += [int(preds[idx].argmax())]\n",
    "\n",
    "### Tilt Prediction (3 classes)\n",
    "pred_tilt = pred_cls % max(np_trn_tilt_cls + 1)\n",
    "### Pan Prediction (3 classes)\n",
    "pred_pan = pred_cls // max(np_trn_tilt_cls + 1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion matrix\n",
    "\n",
    "Confusion matrix is one of the useful ways to visualize the validation accuracy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "lst_angl_lbl = ['<< -19.5 dgrs', '-19.5 to 19.5 dgrs', '>> 19.5 dgrs']\n",
    "lst_angl_lbl_tilt = ['<< -19.5 dgrs (Down)', '-19.5 to 19.5 dgrs', '>> 19.5 dgrs (Up)']\n",
    "lst_angl_lbl_pan = ['<< -19.5 dgrs (Your Right)', '-19.5 to 19.5 dgrs', '>> 19.5 dgrs (Your Left)']\n",
    "\n",
    "cm_grid = confusion_matrix(y_true=np_test_grid_cls,  # True class for test-set.\n",
    "                      y_pred=pred_cls)  # Predicted class.\n",
    "\n",
    "cm_tilt = confusion_matrix(y_true=np_test_tilt_cls,  # True class for test-set.\n",
    "                      y_pred=pred_tilt)  # Predicted class.\n",
    "\n",
    "cm_pan = confusion_matrix(y_true=np_test_pan_cls,  # True class for test-set.\n",
    "                      y_pred=pred_pan)  # Predicted class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion matrix helper function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.3f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Head pose prediction in the 9 classes\n",
    "### Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[144  30   0   6   0   0   0   0   0]\n",
      " [ 18 201  45   0   4   2   0   0   0]\n",
      " [  0   4 161   0   0  15   0   0   0]\n",
      " [  2   0   0 111   2   0  11   0   0]\n",
      " [  0   1   0  13 108  30   3   5   2]\n",
      " [  0   0   1   0  14 102   0   0   9]\n",
      " [  0   0   0   6   0   0 149  25   0]\n",
      " [  0   0   0   0   4   0  29 196  41]\n",
      " [  0   0   0   0   0   3   0  19 158]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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9WkT6OG2RIvKFiPwlIn+KyONO+yPOvORVIpIghXFu9oaqLgTyJgzhCIYNu49xzZCZtH59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\nx3t1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f748c5acf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute confusion matrix Head Pose \n",
    "cnf_matrix = cm_grid\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=[\"Grid Class\" + str(i) for i in range(max(np_trn_grid_cls + 1))],\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "plt.rc('figure', figsize=(10.0, 5.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalized confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized confusion matrix\n",
      "[[ 0.8   0.17  0.    0.03  0.    0.    0.    0.    0.  ]\n",
      " [ 0.07  0.74  0.17  0.    0.01  0.01  0.    0.    0.  ]\n",
      " [ 0.    0.02  0.89  0.    0.    0.08  0.    0.    0.  ]\n",
      " [ 0.02  0.    0.    0.88  0.02  0.    0.09  0.    0.  ]\n",
      " [ 0.    0.01  0.    0.08  0.67  0.19  0.02  0.03  0.01]\n",
      " [ 0.    0.    0.01  0.    0.11  0.81  0.    0.    0.07]\n",
      " [ 0.    0.    0.    0.03  0.    0.    0.83  0.14  0.  ]\n",
      " [ 0.    0.    0.    0.    0.01  0.    0.11  0.73  0.15]\n",
      " [ 0.    0.    0.    0.    0.    0.02  0.    0.11  0.88]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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x2v6CvPWqNqze6iCpiPMDCG3ZkIBmdVkWm1Camj+MMz+fzRs38PfHn+XTZV+T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\nnZfjOI6Tcvw/PIJlDmP6l+AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f751b274950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=[\"Grid Class\" + str(i) for i in range(9)], normalize=True,\n",
    "                      title='Normalized confusion matrix')\n",
    "\n",
    "plt.rc('figure', figsize=(10.0, 5.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Head pose prediction in Tilt (Pitch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[429  57   0]\n",
      " [ 63 519 120]\n",
      " [  0  37 449]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.883  0.117  0.   ]\n",
      " [ 0.09   0.739  0.171]\n",
      " [ 0.     0.076  0.924]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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HSdPAzxJJ80h6KJdeL6egIZOkP+SS4dOSbqor+eYS4z8kDQWOlrRL/hwjJD05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\nRQoEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f748c356390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jorMge/0WjrrtHQ6+4S2G9mhKg1rV2PjLb/zlhc/49/kHMe6anixf9ws5O7yH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7486b87d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute confusion matrix Tilt (Pitch)\n",
    "cnf_matrix = cm_tilt\n",
    "np.set_printoptions(precision=3)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=lst_angl_lbl,\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "# Plot normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=lst_angl_lbl, normalize=True,\n",
    "                      title='Normalized confusion matrix')\n",
    "\n",
    "plt.rc('figure', figsize=(5.0, 4.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Head pose prediction in Pan (Yaw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[603  27   0]\n",
      " [  4 380  30]\n",
      " [  0  13 617]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.957  0.043  0.   ]\n",
      " [ 0.01   0.918  0.072]\n",
      " [ 0.     0.021  0.979]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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vsAWn9urBTddfzTbbduSvPSvlFawx6lTdDbAQ2NPM5kqqC3wk6X/AhcB/zOxZ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\nDFhJKvIGh/VoUQ3tNIAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7486abe190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kowV8j6QJwPmS+sd2TJb0/trKUh66duvOtzO+Yeb337Ns2TKGDhlMr959i5Tp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\nIgdVM7Mq+gdHwhWxWCArTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7486ad7650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute confusion matrix Pan (Yaw)\n",
    "cnf_matrix = cm_pan\n",
    "np.set_printoptions(precision=3)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=lst_angl_lbl,\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "# Plot normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=lst_angl_lbl, normalize=True,\n",
    "                      title='Normalized confusion matrix')\n",
    "\n",
    "plt.rc('figure', figsize=(5.0, 4.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of false predictions: 344\n",
      "Validation Accuracy: 0.795\n"
     ]
    }
   ],
   "source": [
    "i_test_count = 0\n",
    "\n",
    "for idx in range(np_test_tilt_cls.shape[0]):\n",
    "    if abs(np_test_tilt_cls[idx] - pred_tilt[idx]) + abs(np_test_pan_cls[idx] - pred_pan[idx]) >= 1:\n",
    "        i_test_count += 1\n",
    "\n",
    "print(\"The number of false predictions: {}\".format(i_test_count))\n",
    "print(\"Validation Accuracy: {:.3f}\".format(1 - float(i_test_count)/np_test_tilt_cls.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Predict a head pose of an arbitrary image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/mxnet/module/base_module.py:65: UserWarning: Data provided by label_shapes don't match names specified by label_names ([] vs. ['softmax_label'])\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "dshape = [('data', (1,trn_im.shape[1], trn_im.shape[2], trn_im.shape[3]))]\n",
    "\n",
    "ctx = mx.cpu() # USE CPU to predict... \n",
    "net2 = mx.mod.Module(symbol=sym,context=ctx)\n",
    "net2.bind(for_training=False, data_shapes=dshape)\n",
    "net2.set_params(arg_params, aux_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load, crop, and resize a head image "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aDHlPDG9ZjKibVbxWrTW27x1hVIZRBt03lTmZLfDXbzAajfjyl79M09R85rOv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\nFG7plm5pRf8fpZgn8ae7PUIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f748c1ab3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(360, 360, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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AdQp4HuHz6w+Gi4oYuaxDWSK1W0RWumHickDHfP7EKQBAk1qS/3+f/HMAwCvveXU2trAU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\nNhUlba+HTMUL65ax+OgXg3fAOnCcqagE6cyceT1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AH5btOwB8BsATAH4XQPVaz28X13Ev\ngIfkM/pDAAf28+cD4N8A+AqARwD8JoDqlfp8UuZeQsIUIpF7CQlTiPTDT0iYQqQffkLCFCL98BMS\nphDph5+QMIVIP/yEhClE+uEnJEwh0g8/IWEK8f8DP6Gpz8U93gwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f751c9e4150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(84, 84, 3)\n",
      "(-0.97647058823529409, 1.0)\n"
     ]
    }
   ],
   "source": [
    "###\n",
    "im = cv2.imread(\"testIMs/\" + \"IMG_1247.JPG\")\n",
    "im_true = im.copy()\n",
    "\n",
    "if trn_im.shape[2] == trn_im.shape[3]:\n",
    "    ### Aspect Ratio 1:1\n",
    "    crop_uly = 62\n",
    "    crop_height = 360\n",
    "    crop_ulx = 100\n",
    "    crop_width = 360\n",
    "    im = im[crop_uly:crop_uly + crop_height, crop_ulx:crop_ulx + crop_width]\n",
    "    im_crop = im.copy()\n",
    "    plt.imshow(im_crop[:,:,::-1])\n",
    "    plt.show()\n",
    "    print(im.shape)\n",
    "\n",
    "    im = cv2.resize(im, (trn_im.shape[3], trn_im.shape[2]))\n",
    "    plt.imshow(im[:,:,::-1])\n",
    "    plt.show()\n",
    "    print(im.shape)\n",
    "else:\n",
    "    ### Aspect Ratio 16:9\n",
    "    crop_uly = 62\n",
    "    crop_height = 360\n",
    "    crop_ulx = 0\n",
    "    crop_width = 640\n",
    "    im = im[crop_uly:crop_uly + crop_height, crop_ulx:crop_ulx + crop_width]\n",
    "    im_crop = im.copy()\n",
    "    plt.imshow(im_crop[:,:,::-1])\n",
    "    plt.show()\n",
    "    print(im.shape)\n",
    "\n",
    "    im = cv2.resize(im, (trn_im.shape[3], trn_im.shape[2]))\n",
    "    plt.imshow(im[:,:,::-1])\n",
    "    plt.show()\n",
    "    print(im.shape)\n",
    "    \n",
    "im = np.swapaxes(im, 0, 2)\n",
    "im = np.swapaxes(im, 1, 2)\n",
    "im = (im[np.newaxis, :] / 255.0 - 0.5) * 2\n",
    "print(np.min(im), np.max(im))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3, 84, 84)\n"
     ]
    }
   ],
   "source": [
    "print(im.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict a head pose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  1.131e-05   1.212e-08   3.173e-08   1.000e+00   9.886e-07   6.803e-08\n",
      "    9.365e-07   1.450e-08   9.858e-09]]\n",
      "Predicted Class: 3, 100.0%\n"
     ]
    }
   ],
   "source": [
    "from collections import namedtuple\n",
    "Batch = namedtuple('Batch', ['data'])\n",
    "net2.forward(Batch([mx.nd.array(im)]))\n",
    "prob = net2.get_outputs()[0].asnumpy()\n",
    "pred = prob.argmax()\n",
    "print(prob)\n",
    "print(\"Predicted Class: {}, {:.1f}%\".format(pred, prob[0][pred] * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display a head pose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V6Eopa3anOX8ZeIsxvCrcl37zO1L4nXeauCWxQijAazP+xgTeELAqdJzKb1qfoPCl3xSw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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f748409f750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_grid_cls = 9\n",
    "n_tilt_cls = 3\n",
    "\n",
    "### Tilt Prediction\n",
    "pred_tilt_pic = pred % n_tilt_cls\n",
    "### Pan Prediction\n",
    "pred_pan_pic = pred // n_tilt_cls\n",
    "\n",
    "extent = 0, im_true.shape[1]-1, im_true.shape[0]-1, 0\n",
    "Panel_Pred = np.zeros((n_tilt_cls, n_tilt_cls))\n",
    "Panel_Pred[pred_tilt_pic, pred_pan_pic] = 1\n",
    "Panel_Pred = np.fliplr(Panel_Pred)\n",
    "Panel_Pred = np.flipud(Panel_Pred)\n",
    "plt.imshow(im_true[:,:,[2,1,0]], extent=extent)\n",
    "plt.imshow(Panel_Pred, cmap=plt.cm.Blues, alpha=.2, interpolation='nearest', extent=extent)\n",
    "plt.axis('off')\n",
    "arrw_mg = 100\n",
    "arrw_x_rad = 1 * (prob[0][0] + prob[0][1] + prob[0][2] - prob[0][6] -prob[0][7] - prob[0][8]) * 90 * np.pi / 180. \n",
    "arrw_y_rad = 1 * (prob[0][0] + prob[0][3] + prob[0][6] - prob[0][2] -prob[0][5] - prob[0][8]) * 90 * np.pi / 180.\n",
    "plt.arrow(im_true.shape[1]//2, im_true.shape[0]//2, \n",
    "          np.sin(arrw_x_rad) * arrw_mg, np.sin(arrw_y_rad) * arrw_mg, \n",
    "          head_width=10, head_length=10, fc='b', ec='b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## End"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}