benchmark_synchronization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5f0ca283",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jupyter environment detected. Enabling Open3D WebVisualizer.\n",
      "[Open3D INFO] WebRTC GUI backend enabled.\n",
      "[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.\n"
     ]
    }
   ],
   "source": [
    "# Copyright (c) Meta Platforms, Inc. and affiliates.\n",
    "#\n",
    "# This source code is licensed under the license found in the\n",
    "# LICENSE file in the root directory of this source tree.\n",
    "import os\n",
    "import random\n",
    "\n",
    "import pytorch3d\n",
    "import torch\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from syncmatch.models.synchronization import (\n",
    "    camera_chaining,\n",
    "    camera_synchronization,\n",
    "    camera_synchronization_eig,\n",
    ")\n",
    "from syncmatch.utils.metrics import evaluate_multiview_Rt\n",
    "from syncmatch.utils.transformations import (\n",
    "    SE3_inverse,\n",
    "    get_relative_Rt,\n",
    "    make_Rt,\n",
    "    random_Rt,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "906e0393-1e47-46cb-aa04-284f73cae763",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_N_views(batch, N):\n",
    "    Rs = pytorch3d.transforms.random_rotations(batch * N)\n",
    "    Rs = Rs.view(batch, N, 3, 3).contiguous()\n",
    "    ts = torch.randn(batch, N, 3) * 10\n",
    "    return make_Rt(Rs, ts)\n",
    "\n",
    "\n",
    "def generate_pairwise_dictionary(P, noise_R=0, noise_t=0, dropout=0.0):\n",
    "    batch_size, N, _, _ = P.shape\n",
    "\n",
    "    pw_P, conf = {}, {}\n",
    "    for i in range(N):\n",
    "        for j in range(i + 1, N):\n",
    "            rel_P = get_relative_Rt(P[:, i], P[:, j])\n",
    "            if noise_R > 0 or noise_t > 0:\n",
    "                noise_P = random_Rt(batch_size, noise_R, noise_t).to(rel_P)\n",
    "                rel_P = torch.bmm(noise_P, rel_P)\n",
    "\n",
    "            if torch.rand(1).item() < dropout and abs(i - j) > 1:\n",
    "                rel_P[:, :3, :3] = torch.eye(3).to(rel_P)\n",
    "                rel_P[:, :3, 3] = 0\n",
    "                conf[(i, j)] = torch.zeros(batch_size).to(P)\n",
    "            else:\n",
    "                pw_P[(i, j)] = rel_P\n",
    "                conf[(i, j)] = torch.ones(batch_size).to(P)\n",
    "\n",
    "    return pw_P, conf\n",
    "\n",
    "\n",
    "def benchmark_sync_method(sync_method, batch_size, N, noise_R, noise_t, dropout):\n",
    "    # Generate N views\n",
    "    gt_P = generate_N_views(batch_size, N)\n",
    "    gt_P = gt_P.cuda().float()\n",
    "    pw_P, conf = generate_pairwise_dictionary(gt_P, noise_R, noise_t, dropout)\n",
    "\n",
    "    start = torch.cuda.Event(enable_timing=True)\n",
    "    end = torch.cuda.Event(enable_timing=True)\n",
    "\n",
    "    start.record()\n",
    "    pr_P = sync_method(pw_P, conf, N)\n",
    "    end.record()\n",
    "\n",
    "    # Waits for everything to finish running\n",
    "    torch.cuda.synchronize()\n",
    "    elapsed_time = start.elapsed_time(end)\n",
    "\n",
    "    error_P = evaluate_multiview_Rt(pr_P, gt_P)\n",
    "    error_R = error_P[\"vp-error_R\"].mean()\n",
    "    error_t = error_P[\"vp-error_t\"].mean()\n",
    "\n",
    "    return elapsed_time, error_R, error_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "17b7f927",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================================================================================================\n",
      "method                         | drop |  N  | noise R | noise t | time (ms) | error R | error t |\n",
      "-------------------------------------------------------------------------------------------------\n",
      "Ours                           | 0.00 |   4 |   0 deg |  0.00 m |      2.91 |    0.01 |    0.00 |\n",
      "Ours                           | 0.00 |   4 |   0 deg |  0.05 m |      2.67 |    0.01 |    1.93 |\n",
      "Ours                           | 0.00 |   4 |   0 deg |  0.10 m |      2.68 |    0.01 |    3.97 |\n",
      "Ours                           | 0.00 |   4 |   0 deg |  0.15 m |      2.68 |    0.01 |    5.79 |\n",
      "Ours                           | 0.00 |   4 |   0 deg |  0.20 m |      2.77 |    0.01 |    7.62 |\n",
      "Ours                           | 0.00 |   4 |   0 deg |  0.25 m |      2.80 |    0.01 |    8.82 |\n",
      "Ours                           | 0.00 |   4 |   0 deg |  0.30 m |      2.77 |    0.01 |   11.71 |\n",
      "Gojcic et al.                  | 0.00 |   4 |   0 deg |  0.00 m |      4.07 |    0.02 |    0.00 |\n",
      "Gojcic et al.                  | 0.00 |   4 |   0 deg |  0.05 m |      4.09 |    0.01 |    1.86 |\n",
      "Gojcic et al.                  | 0.00 |   4 |   0 deg |  0.10 m |      4.00 |    0.02 |    3.87 |\n",
      "Gojcic et al.                  | 0.00 |   4 |   0 deg |  0.15 m |      4.13 |    0.01 |    5.52 |\n",
      "Gojcic et al.                  | 0.00 |   4 |   0 deg |  0.20 m |      4.19 |    0.01 |    7.14 |\n",
      "Gojcic et al.                  | 0.00 |   4 |   0 deg |  0.25 m |      4.06 |    0.01 |    9.89 |\n",
      "Gojcic et al.                  | 0.00 |   4 |   0 deg |  0.30 m |      4.00 |    0.01 |   10.40 |\n",
      "Naive Sync.                    | 0.00 |   4 |   0 deg |  0.00 m |      0.14 |    0.01 |    0.00 |\n",
      "Naive Sync.                    | 0.00 |   4 |   0 deg |  0.05 m |      0.15 |    0.01 |    3.04 |\n",
      "Naive Sync.                    | 0.00 |   4 |   0 deg |  0.10 m |      0.16 |    0.02 |    6.98 |\n",
      "Naive Sync.                    | 0.00 |   4 |   0 deg |  0.15 m |      0.16 |    0.01 |    9.70 |\n",
      "Naive Sync.                    | 0.00 |   4 |   0 deg |  0.20 m |      0.16 |    0.02 |   12.78 |\n",
      "Naive Sync.                    | 0.00 |   4 |   0 deg |  0.25 m |      0.18 |    0.01 |   16.62 |\n",
      "Naive Sync.                    | 0.00 |   4 |   0 deg |  0.30 m |      0.17 |    0.01 |   20.58 |\n",
      "Ours                           | 0.00 |   8 |   0 deg |  0.00 m |      5.67 |    0.01 |    0.00 |\n",
      "Ours                           | 0.00 |   8 |   0 deg |  0.05 m |      5.66 |    0.01 |    1.42 |\n",
      "Ours                           | 0.00 |   8 |   0 deg |  0.10 m |      5.65 |    0.01 |    3.00 |\n",
      "Ours                           | 0.00 |   8 |   0 deg |  0.15 m |      5.66 |    0.02 |    4.14 |\n",
      "Ours                           | 0.00 |   8 |   0 deg |  0.20 m |      5.70 |    0.01 |    6.29 |\n",
      "Ours                           | 0.00 |   8 |   0 deg |  0.25 m |      6.08 |    0.01 |    7.27 |\n",
      "Ours                           | 0.00 |   8 |   0 deg |  0.30 m |      5.59 |    0.01 |    9.43 |\n",
      "Gojcic et al.                  | 0.00 |   8 |   0 deg |  0.00 m |      9.22 |    0.01 |    0.00 |\n",
      "Gojcic et al.                  | 0.00 |   8 |   0 deg |  0.05 m |      9.57 |    0.01 |    1.58 |\n",
      "Gojcic et al.                  | 0.00 |   8 |   0 deg |  0.10 m |      9.49 |    0.01 |    3.21 |\n",
      "Gojcic et al.                  | 0.00 |   8 |   0 deg |  0.15 m |      9.51 |    0.01 |    4.62 |\n",
      "Gojcic et al.                  | 0.00 |   8 |   0 deg |  0.20 m |      9.34 |    0.01 |    6.09 |\n",
      "Gojcic et al.                  | 0.00 |   8 |   0 deg |  0.25 m |      9.29 |    0.01 |    7.21 |\n",
      "Gojcic et al.                  | 0.00 |   8 |   0 deg |  0.30 m |      9.77 |    0.01 |    9.25 |\n",
      "Naive Sync.                    | 0.00 |   8 |   0 deg |  0.00 m |      0.20 |    0.02 |    0.00 |\n",
      "Naive Sync.                    | 0.00 |   8 |   0 deg |  0.05 m |      0.24 |    0.01 |    5.86 |\n",
      "Naive Sync.                    | 0.00 |   8 |   0 deg |  0.10 m |      0.24 |    0.01 |   10.16 |\n",
      "Naive Sync.                    | 0.00 |   8 |   0 deg |  0.15 m |      0.23 |    0.01 |   16.04 |\n",
      "Naive Sync.                    | 0.00 |   8 |   0 deg |  0.20 m |      0.24 |    0.01 |   20.52 |\n",
      "Naive Sync.                    | 0.00 |   8 |   0 deg |  0.25 m |      0.23 |    0.01 |   25.61 |\n",
      "Naive Sync.                    | 0.00 |   8 |   0 deg |  0.30 m |      0.24 |    0.01 |   27.34 |\n",
      "Ours                           | 0.00 |  16 |   0 deg |  0.00 m |     17.92 |    0.01 |    0.00 |\n",
      "Ours                           | 0.00 |  16 |   0 deg |  0.05 m |     17.62 |    0.01 |    1.10 |\n",
      "Ours                           | 0.00 |  16 |   0 deg |  0.10 m |     18.05 |    0.01 |    2.10 |\n",
      "Ours                           | 0.00 |  16 |   0 deg |  0.15 m |     17.85 |    0.01 |    3.33 |\n",
      "Ours                           | 0.00 |  16 |   0 deg |  0.20 m |     17.87 |    0.01 |    4.58 |\n",
      "Ours                           | 0.00 |  16 |   0 deg |  0.25 m |     17.66 |    0.01 |    5.48 |\n",
      "Ours                           | 0.00 |  16 |   0 deg |  0.30 m |     18.05 |    0.01 |    6.45 |\n",
      "Gojcic et al.                  | 0.00 |  16 |   0 deg |  0.00 m |    136.76 |    0.01 |    0.00 |\n",
      "Gojcic et al.                  | 0.00 |  16 |   0 deg |  0.05 m |    139.29 |    0.01 |    1.09 |\n",
      "Gojcic et al.                  | 0.00 |  16 |   0 deg |  0.10 m |    137.64 |    0.01 |    2.27 |\n",
      "Gojcic et al.                  | 0.00 |  16 |   0 deg |  0.15 m |    137.85 |    0.01 |    3.69 |\n",
      "Gojcic et al.                  | 0.00 |  16 |   0 deg |  0.20 m |    137.73 |    0.01 |    4.48 |\n",
      "Gojcic et al.                  | 0.00 |  16 |   0 deg |  0.25 m |    137.32 |    0.01 |    5.42 |\n",
      "Gojcic et al.                  | 0.00 |  16 |   0 deg |  0.30 m |    135.83 |    0.01 |    6.86 |\n",
      "Naive Sync.                    | 0.00 |  16 |   0 deg |  0.00 m |      0.34 |    0.01 |    0.00 |\n",
      "Naive Sync.                    | 0.00 |  16 |   0 deg |  0.05 m |      0.37 |    0.01 |    6.54 |\n",
      "Naive Sync.                    | 0.00 |  16 |   0 deg |  0.10 m |      0.38 |    0.02 |   15.84 |\n",
      "Naive Sync.                    | 0.00 |  16 |   0 deg |  0.15 m |      0.38 |    0.01 |   24.11 |\n",
      "Naive Sync.                    | 0.00 |  16 |   0 deg |  0.20 m |      0.38 |    0.01 |   28.56 |\n",
      "Naive Sync.                    | 0.00 |  16 |   0 deg |  0.25 m |      0.37 |    0.01 |   34.85 |\n",
      "Naive Sync.                    | 0.00 |  16 |   0 deg |  0.30 m |      0.38 |    0.02 |   42.49 |\n",
      "Ours                           | 0.00 |  32 |   0 deg |  0.00 m |     65.62 |    0.01 |    0.00 |\n",
      "Ours                           | 0.00 |  32 |   0 deg |  0.05 m |     67.40 |    0.01 |    0.82 |\n",
      "Ours                           | 0.00 |  32 |   0 deg |  0.10 m |     67.30 |    0.01 |    1.61 |\n",
      "Ours                           | 0.00 |  32 |   0 deg |  0.15 m |     67.13 |    0.01 |    2.43 |\n",
      "Ours                           | 0.00 |  32 |   0 deg |  0.20 m |     66.39 |    0.01 |    3.26 |\n",
      "Ours                           | 0.00 |  32 |   0 deg |  0.25 m |     67.11 |    0.01 |    4.09 |\n",
      "Ours                           | 0.00 |  32 |   0 deg |  0.30 m |     67.40 |    0.02 |    5.03 |\n",
      "Gojcic et al.                  | 0.00 |  32 |   0 deg |  0.00 m |    289.04 |    0.01 |    0.00 |\n",
      "Gojcic et al.                  | 0.00 |  32 |   0 deg |  0.05 m |    286.45 |    0.01 |    0.78 |\n",
      "Gojcic et al.                  | 0.00 |  32 |   0 deg |  0.10 m |    286.03 |    0.01 |    1.59 |\n",
      "Gojcic et al.                  | 0.00 |  32 |   0 deg |  0.15 m |    287.52 |    0.01 |    2.45 |\n",
      "Gojcic et al.                  | 0.00 |  32 |   0 deg |  0.20 m |    291.38 |    0.01 |    3.28 |\n",
      "Gojcic et al.                  | 0.00 |  32 |   0 deg |  0.25 m |    289.20 |    0.02 |    4.04 |\n",
      "Gojcic et al.                  | 0.00 |  32 |   0 deg |  0.30 m |    284.53 |    0.01 |    4.75 |\n",
      "Naive Sync.                    | 0.00 |  32 |   0 deg |  0.00 m |      0.57 |    0.01 |    0.00 |\n",
      "Naive Sync.                    | 0.00 |  32 |   0 deg |  0.05 m |      0.66 |    0.02 |    9.11 |\n",
      "Naive Sync.                    | 0.00 |  32 |   0 deg |  0.10 m |      0.65 |    0.01 |   19.46 |\n",
      "Naive Sync.                    | 0.00 |  32 |   0 deg |  0.15 m |      0.65 |    0.01 |   34.08 |\n",
      "Naive Sync.                    | 0.00 |  32 |   0 deg |  0.20 m |      0.66 |    0.01 |   40.15 |\n",
      "Naive Sync.                    | 0.00 |  32 |   0 deg |  0.25 m |      0.64 |    0.02 |   50.65 |\n",
      "Naive Sync.                    | 0.00 |  32 |   0 deg |  0.30 m |      0.64 |    0.01 |   64.98 |\n",
      "Ours                           | 0.00 |  64 |   0 deg |  0.00 m |    263.12 |    0.01 |    0.00 |\n",
      "Ours                           | 0.00 |  64 |   0 deg |  0.05 m |    268.28 |    0.01 |    0.57 |\n",
      "Ours                           | 0.00 |  64 |   0 deg |  0.10 m |    269.28 |    0.01 |    1.12 |\n",
      "Ours                           | 0.00 |  64 |   0 deg |  0.15 m |    267.38 |    0.01 |    1.66 |\n",
      "Ours                           | 0.00 |  64 |   0 deg |  0.20 m |    269.96 |    0.01 |    2.30 |\n",
      "Ours                           | 0.00 |  64 |   0 deg |  0.25 m |    265.83 |    0.01 |    2.78 |\n",
      "Ours                           | 0.00 |  64 |   0 deg |  0.30 m |    268.91 |    0.01 |    3.38 |\n",
      "Gojcic et al.                  | 0.00 |  64 |   0 deg |  0.00 m |    864.38 |    0.01 |    0.00 |\n",
      "Gojcic et al.                  | 0.00 |  64 |   0 deg |  0.05 m |    847.66 |    0.01 |    0.57 |\n",
      "Gojcic et al.                  | 0.00 |  64 |   0 deg |  0.10 m |   1047.27 |    0.02 |    1.13 |\n",
      "Gojcic et al.                  | 0.00 |  64 |   0 deg |  0.15 m |    854.06 |    0.01 |    1.66 |\n",
      "Gojcic et al.                  | 0.00 |  64 |   0 deg |  0.20 m |    846.94 |    0.01 |    2.27 |\n",
      "Gojcic et al.                  | 0.00 |  64 |   0 deg |  0.25 m |    858.60 |    0.01 |    2.94 |\n",
      "Gojcic et al.                  | 0.00 |  64 |   0 deg |  0.30 m |    846.58 |    0.02 |    3.38 |\n",
      "Naive Sync.                    | 0.00 |  64 |   0 deg |  0.00 m |      1.07 |    0.00 |    0.00 |\n",
      "Naive Sync.                    | 0.00 |  64 |   0 deg |  0.05 m |      1.17 |    0.01 |   14.28 |\n",
      "Naive Sync.                    | 0.00 |  64 |   0 deg |  0.10 m |      1.16 |    0.01 |   33.59 |\n",
      "Naive Sync.                    | 0.00 |  64 |   0 deg |  0.15 m |      1.19 |    0.01 |   36.60 |\n",
      "Naive Sync.                    | 0.00 |  64 |   0 deg |  0.20 m |      1.16 |    0.00 |   48.83 |\n",
      "Naive Sync.                    | 0.00 |  64 |   0 deg |  0.25 m |      1.15 |    0.01 |   72.64 |\n",
      "Naive Sync.                    | 0.00 |  64 |   0 deg |  0.30 m |      1.16 |    0.01 |   90.22 |\n",
      "=================================================================================================\n",
      "Ours                           | 0.00 |   4 |   0 deg |  0.00 m |      0.00 |    0.01 |    0.00 |\n",
      "Ours                           | 0.00 |   4 |   5 deg |  0.00 m |      0.00 |    2.15 |   44.99 |\n",
      "Ours                           | 0.00 |   4 |  10 deg |  0.00 m |      0.00 |    4.23 |   76.17 |\n",
      "Ours                           | 0.00 |   4 |  15 deg |  0.00 m |      0.00 |    6.05 |  128.32 |\n",
      "Ours                           | 0.00 |   4 |  20 deg |  0.00 m |      0.00 |    7.35 |  191.26 |\n",
      "Ours                           | 0.00 |   4 |  25 deg |  0.00 m |      0.00 |   12.43 |  245.79 |\n",
      "Ours                           | 0.00 |   4 |  30 deg |  0.00 m |      0.00 |   13.42 |  356.28 |\n",
      "Gojcic et al.                  | 0.00 |   4 |   0 deg |  0.00 m |      0.00 |    0.01 |    0.00 |\n",
      "Gojcic et al.                  | 0.00 |   4 |   5 deg |  0.00 m |      0.00 |    2.02 |   42.58 |\n",
      "Gojcic et al.                  | 0.00 |   4 |  10 deg |  0.00 m |      0.00 |    4.15 |  102.73 |\n",
      "Gojcic et al.                  | 0.00 |   4 |  15 deg |  0.00 m |      0.00 |    6.47 |  123.57 |\n",
      "Gojcic et al.                  | 0.00 |   4 |  20 deg |  0.00 m |      0.00 |    8.08 |  185.69 |\n",
      "Gojcic et al.                  | 0.00 |   4 |  25 deg |  0.00 m |      0.00 |   10.76 |  250.40 |\n",
      "Gojcic et al.                  | 0.00 |   4 |  30 deg |  0.00 m |      0.00 |   12.22 |  283.49 |\n",
      "Naive Sync.                    | 0.00 |   4 |   0 deg |  0.00 m |      0.00 |    0.01 |    0.00 |\n",
      "Naive Sync.                    | 0.00 |   4 |   5 deg |  0.00 m |      0.00 |    3.57 |   82.53 |\n",
      "Naive Sync.                    | 0.00 |   4 |  10 deg |  0.00 m |      0.00 |    7.23 |  141.13 |\n",
      "Naive Sync.                    | 0.00 |   4 |  15 deg |  0.00 m |      0.00 |   11.67 |  180.05 |\n",
      "Naive Sync.                    | 0.00 |   4 |  20 deg |  0.00 m |      0.00 |   14.16 |  302.84 |\n",
      "Naive Sync.                    | 0.00 |   4 |  25 deg |  0.00 m |      0.00 |   17.64 |  369.63 |\n",
      "Naive Sync.                    | 0.00 |   4 |  30 deg |  0.00 m |      0.00 |   21.47 |  461.55 |\n",
      "Ours                           | 0.00 |   8 |   0 deg |  0.00 m |      0.01 |    0.01 |    0.00 |\n",
      "Ours                           | 0.00 |   8 |   5 deg |  0.00 m |      0.01 |    1.64 |   38.39 |\n",
      "Ours                           | 0.00 |   8 |  10 deg |  0.00 m |      0.01 |    3.05 |   79.88 |\n",
      "Ours                           | 0.00 |   8 |  15 deg |  0.00 m |      0.01 |    4.48 |  116.28 |\n",
      "Ours                           | 0.00 |   8 |  20 deg |  0.00 m |      0.01 |    5.87 |  138.87 |\n",
      "Ours                           | 0.00 |   8 |  25 deg |  0.00 m |      0.01 |    8.25 |  195.49 |\n",
      "Ours                           | 0.00 |   8 |  30 deg |  0.00 m |      0.01 |    9.62 |  204.95 |\n",
      "Gojcic et al.                  | 0.00 |   8 |   0 deg |  0.00 m |      0.01 |    0.01 |    0.02 |\n",
      "Gojcic et al.                  | 0.00 |   8 |   5 deg |  0.00 m |      0.01 |    1.58 |   37.08 |\n",
      "Gojcic et al.                  | 0.00 |   8 |  10 deg |  0.00 m |      0.01 |    2.98 |   65.49 |\n",
      "Gojcic et al.                  | 0.00 |   8 |  15 deg |  0.00 m |      0.01 |    4.64 |   95.01 |\n",
      "Gojcic et al.                  | 0.00 |   8 |  20 deg |  0.00 m |      0.01 |    5.82 |  138.33 |\n",
      "Gojcic et al.                  | 0.00 |   8 |  25 deg |  0.00 m |      0.01 |    7.66 |  189.56 |\n",
      "Gojcic et al.                  | 0.00 |   8 |  30 deg |  0.00 m |      0.01 |    9.01 |  206.23 |\n",
      "Naive Sync.                    | 0.00 |   8 |   0 deg |  0.00 m |      0.00 |    0.01 |    0.00 |\n",
      "Naive Sync.                    | 0.00 |   8 |   5 deg |  0.00 m |      0.00 |    5.56 |  135.59 |\n",
      "Naive Sync.                    | 0.00 |   8 |  10 deg |  0.00 m |      0.00 |   10.73 |  224.90 |\n",
      "Naive Sync.                    | 0.00 |   8 |  15 deg |  0.00 m |      0.00 |   14.39 |  350.04 |\n",
      "Naive Sync.                    | 0.00 |   8 |  20 deg |  0.00 m |      0.00 |   22.48 |  503.97 |\n",
      "Naive Sync.                    | 0.00 |   8 |  25 deg |  0.00 m |      0.00 |   25.72 |  618.37 |\n",
      "Naive Sync.                    | 0.00 |   8 |  30 deg |  0.00 m |      0.00 |   34.31 |  667.58 |\n",
      "Ours                           | 0.00 |  16 |   0 deg |  0.00 m |      0.02 |    0.01 |    0.00 |\n",
      "Ours                           | 0.00 |  16 |   5 deg |  0.00 m |      0.02 |    1.18 |   26.54 |\n",
      "Ours                           | 0.00 |  16 |  10 deg |  0.00 m |      0.02 |    2.29 |   53.08 |\n",
      "Ours                           | 0.00 |  16 |  15 deg |  0.00 m |      0.02 |    3.54 |   82.98 |\n",
      "Ours                           | 0.00 |  16 |  20 deg |  0.00 m |      0.02 |    4.42 |  103.05 |\n",
      "Ours                           | 0.00 |  16 |  25 deg |  0.00 m |      0.02 |    5.68 |  142.26 |\n",
      "Ours                           | 0.00 |  16 |  30 deg |  0.00 m |      0.02 |    7.84 |  179.46 |\n",
      "Gojcic et al.                  | 0.00 |  16 |   0 deg |  0.00 m |      0.14 |    0.01 |    0.00 |\n",
      "Gojcic et al.                  | 0.00 |  16 |   5 deg |  0.00 m |      0.14 |    1.19 |   26.75 |\n",
      "Gojcic et al.                  | 0.00 |  16 |  10 deg |  0.00 m |      0.14 |    2.41 |   50.88 |\n",
      "Gojcic et al.                  | 0.00 |  16 |  15 deg |  0.00 m |      0.14 |    3.53 |   84.32 |\n",
      "Gojcic et al.                  | 0.00 |  16 |  20 deg |  0.00 m |      0.14 |    4.30 |  110.30 |\n",
      "Gojcic et al.                  | 0.00 |  16 |  25 deg |  0.00 m |      0.14 |    5.70 |  133.77 |\n",
      "Gojcic et al.                  | 0.00 |  16 |  30 deg |  0.00 m |      0.14 |    6.89 |  165.50 |\n",
      "Naive Sync.                    | 0.00 |  16 |   0 deg |  0.00 m |      0.00 |    0.01 |    0.00 |\n",
      "Naive Sync.                    | 0.00 |  16 |   5 deg |  0.00 m |      0.00 |    7.24 |  191.24 |\n",
      "Naive Sync.                    | 0.00 |  16 |  10 deg |  0.00 m |      0.00 |   14.30 |  345.52 |\n",
      "Naive Sync.                    | 0.00 |  16 |  15 deg |  0.00 m |      0.00 |   19.51 |  534.89 |\n",
      "Naive Sync.                    | 0.00 |  16 |  20 deg |  0.00 m |      0.00 |   29.00 |  665.32 |\n",
      "Naive Sync.                    | 0.00 |  16 |  25 deg |  0.00 m |      0.00 |   38.33 |  797.43 |\n",
      "Naive Sync.                    | 0.00 |  16 |  30 deg |  0.00 m |      0.00 |   46.37 |  919.45 |\n",
      "Ours                           | 0.00 |  32 |   0 deg |  0.00 m |      0.07 |    0.01 |    0.00 |\n",
      "Ours                           | 0.00 |  32 |   5 deg |  0.00 m |      0.07 |    0.80 |   19.09 |\n",
      "Ours                           | 0.00 |  32 |  10 deg |  0.00 m |      0.07 |    1.66 |   39.54 |\n",
      "Ours                           | 0.00 |  32 |  15 deg |  0.00 m |      0.07 |    2.51 |   57.27 |\n",
      "Ours                           | 0.00 |  32 |  20 deg |  0.00 m |      0.07 |    3.23 |   78.75 |\n",
      "Ours                           | 0.00 |  32 |  25 deg |  0.00 m |      0.07 |    4.32 |   98.38 |\n",
      "Ours                           | 0.00 |  32 |  30 deg |  0.00 m |      0.07 |    5.09 |  119.40 |\n",
      "Gojcic et al.                  | 0.00 |  32 |   0 deg |  0.00 m |      0.29 |    0.01 |    0.00 |\n",
      "Gojcic et al.                  | 0.00 |  32 |   5 deg |  0.00 m |      0.30 |    0.81 |   18.91 |\n",
      "Gojcic et al.                  | 0.00 |  32 |  10 deg |  0.00 m |      0.30 |    1.56 |   36.66 |\n",
      "Gojcic et al.                  | 0.00 |  32 |  15 deg |  0.00 m |      0.30 |    2.47 |   55.06 |\n",
      "Gojcic et al.                  | 0.00 |  32 |  20 deg |  0.00 m |      0.30 |    3.27 |   79.84 |\n",
      "Gojcic et al.                  | 0.00 |  32 |  25 deg |  0.00 m |      0.30 |    4.04 |   94.81 |\n",
      "Gojcic et al.                  | 0.00 |  32 |  30 deg |  0.00 m |      0.29 |    4.93 |  116.99 |\n",
      "Naive Sync.                    | 0.00 |  32 |   0 deg |  0.00 m |      0.00 |    0.01 |    0.00 |\n",
      "Naive Sync.                    | 0.00 |  32 |   5 deg |  0.00 m |      0.00 |   10.88 |  260.01 |\n",
      "Naive Sync.                    | 0.00 |  32 |  10 deg |  0.00 m |      0.00 |   19.92 |  471.01 |\n",
      "Naive Sync.                    | 0.00 |  32 |  15 deg |  0.00 m |      0.00 |   32.40 |  623.47 |\n",
      "Naive Sync.                    | 0.00 |  32 |  20 deg |  0.00 m |      0.00 |   34.60 |  734.31 |\n",
      "Naive Sync.                    | 0.00 |  32 |  25 deg |  0.00 m |      0.00 |   52.87 |  995.37 |\n",
      "Naive Sync.                    | 0.00 |  32 |  30 deg |  0.00 m |      0.00 |   54.57 | 1264.49 |\n",
      "Ours                           | 0.00 |  64 |   0 deg |  0.00 m |      0.26 |    0.01 |    0.00 |\n",
      "Ours                           | 0.00 |  64 |   5 deg |  0.00 m |      0.27 |    0.57 |   14.04 |\n",
      "Ours                           | 0.00 |  64 |  10 deg |  0.00 m |      0.27 |    1.14 |   27.59 |\n",
      "Ours                           | 0.00 |  64 |  15 deg |  0.00 m |      0.27 |    1.68 |   39.42 |\n",
      "Ours                           | 0.00 |  64 |  20 deg |  0.00 m |      0.27 |    2.31 |   56.87 |\n",
      "Ours                           | 0.00 |  64 |  25 deg |  0.00 m |      0.27 |    2.95 |   72.38 |\n",
      "Ours                           | 0.00 |  64 |  30 deg |  0.00 m |      0.27 |    3.71 |   94.50 |\n",
      "Gojcic et al.                  | 0.00 |  64 |   0 deg |  0.00 m |      0.85 |    0.01 |    0.00 |\n",
      "Gojcic et al.                  | 0.00 |  64 |   5 deg |  0.00 m |      0.90 |    0.57 |   13.86 |\n",
      "Gojcic et al.                  | 0.00 |  64 |  10 deg |  0.00 m |      0.91 |    1.14 |   28.52 |\n",
      "Gojcic et al.                  | 0.00 |  64 |  15 deg |  0.00 m |      0.90 |    1.76 |   41.47 |\n",
      "Gojcic et al.                  | 0.00 |  64 |  20 deg |  0.00 m |      1.09 |    2.40 |   59.64 |\n",
      "Gojcic et al.                  | 0.00 |  64 |  25 deg |  0.00 m |      0.90 |    2.94 |   73.83 |\n",
      "Gojcic et al.                  | 0.00 |  64 |  30 deg |  0.00 m |      0.90 |    3.64 |   94.26 |\n",
      "Naive Sync.                    | 0.00 |  64 |   0 deg |  0.00 m |      0.00 |    0.01 |    0.00 |\n",
      "Naive Sync.                    | 0.00 |  64 |   5 deg |  0.00 m |      0.00 |   14.94 |  319.39 |\n",
      "Naive Sync.                    | 0.00 |  64 |  10 deg |  0.00 m |      0.00 |   27.17 |  620.26 |\n",
      "Naive Sync.                    | 0.00 |  64 |  15 deg |  0.00 m |      0.00 |   47.59 |  825.03 |\n",
      "Naive Sync.                    | 0.00 |  64 |  20 deg |  0.00 m |      0.00 |   48.82 | 1053.19 |\n",
      "Naive Sync.                    | 0.00 |  64 |  25 deg |  0.00 m |      0.00 |   75.68 | 1215.35 |\n",
      "Naive Sync.                    | 0.00 |  64 |  30 deg |  0.00 m |      0.00 |   79.11 | 1304.92 |\n",
      "=================================================================================================\n"
     ]
    }
   ],
   "source": [
    "# BLOCK FOR TRANSLATION ERROR\n",
    "batch_size = 8\n",
    "print(\"=\" * 97)\n",
    "print(\n",
    "    f\"{'method':30s} | drop |  N  | noise R | noise t | time (ms) |\",\n",
    "    \"error R | error t |\",\n",
    ")\n",
    "print(\"-\" * 97)\n",
    "\n",
    "N = 4\n",
    "dropout = 0\n",
    "\n",
    "result_dict = {}\n",
    "\n",
    "d_R = 0\n",
    "d_t = 0\n",
    "\n",
    "d_t_range = np.array([0, 0.05, 0.1, 0.15, 0.20, 0.25, 0.30])\n",
    "d_R_range = np.array([0, 5, 10, 15, 20, 25, 30])\n",
    "\n",
    "result_dict = {\"method\": [], \"Noise (cm)\": [], \"Noise (deg)\": [], \"Error (deg)\": [], \"Error (cm)\": []}\n",
    "    \n",
    "\n",
    "methods = {\n",
    "    \"Ours\": camera_synchronization,\n",
    "    \"Gojcic et al.\": camera_synchronization_eig,\n",
    "    \"Naive Sync.\": camera_chaining,\n",
    "}\n",
    "\n",
    "result_dict_t = {\"method\": [], \"Noise (cm)\": [], \"Noise (deg)\": [], \"Error (deg)\": [], \"Error (cm)\": [], \"Num Views\": []}\n",
    "for N in [4, 8, 16, 32, 64]:\n",
    "    for method in methods:\n",
    "        for d_t in d_t_range:\n",
    "            for d_R in [0, ]:\n",
    "                benchmark_sync_method(methods[method], batch_size, N, d_R, d_t, dropout)\n",
    "                time, errR, errt = [], [], []\n",
    "                num_iterations = 1\n",
    "\n",
    "                for i in range(num_iterations):\n",
    "                    time_i, errR_i, errt_i = benchmark_sync_method(\n",
    "                        methods[method], batch_size, N, d_R, d_t, dropout\n",
    "                    )\n",
    "                    time.append(time_i)\n",
    "                    errR.append(errR_i.item())\n",
    "                    errt.append(errt_i.item())\n",
    "\n",
    "                    result_dict_t[\"method\"].append(method)\n",
    "                    result_dict_t[\"Noise (deg)\"].append(d_R)\n",
    "                    result_dict_t[\"Noise (cm)\"].append(d_t * 100)\n",
    "                    result_dict_t[\"Error (deg)\"].append(errR_i.item())\n",
    "                    result_dict_t[\"Error (cm)\"].append(errt_i.item())\n",
    "\n",
    "                time = np.array(time)\n",
    "                errR = np.array(errR)\n",
    "                errt = np.array(errt)\n",
    "\n",
    "                print(\n",
    "                    f\"{method:30s} | {dropout:4.2f} | {N:3d} |\",\n",
    "                    f\"{d_R:3d} deg | {d_t:5.2f} m |\",\n",
    "                    f\"{time.mean():9.2f} | {errR.mean():7.2f} | {errt.mean():7.2f} |\",\n",
    "            )\n",
    "print(\"=\" * 97)\n",
    "\n",
    "\n",
    "result_dict_R = {\"method\": [], \"Noise (cm)\": [], \"Noise (deg)\": [], \"Error (deg)\": [], \"Error (cm)\": [], \"Num Views\": [], \"Time (s)\": []}\n",
    "for N in [4, 8, 16, 32, 64]:\n",
    "    for method in methods:\n",
    "        d_t = 0\n",
    "        for d_R in d_R_range:\n",
    "            benchmark_sync_method(methods[method], batch_size, N, d_R, d_t, dropout)\n",
    "            time, errR, errt = [], [], []\n",
    "            num_iterations = 1\n",
    "\n",
    "            for i in range(num_iterations):\n",
    "                time_i, errR_i, errt_i = benchmark_sync_method(\n",
    "                    methods[method], batch_size, N, d_R, d_t, dropout\n",
    "                )\n",
    "                time.append(time_i / 1000.0)\n",
    "                errR.append(errR_i.item())\n",
    "                errt.append(errt_i.item())\n",
    "\n",
    "                result_dict_R[\"method\"].append(method)\n",
    "                result_dict_R[\"Num Views\"].append(N)\n",
    "                result_dict_R[\"Noise (deg)\"].append(d_R)\n",
    "                result_dict_R[\"Noise (cm)\"].append(d_t * 100)\n",
    "                result_dict_R[\"Error (deg)\"].append(errR_i.item())\n",
    "                result_dict_R[\"Error (cm)\"].append(errt_i.item())\n",
    "                result_dict_R[\"Time (s)\"].append(time_i / 1000)\n",
    "\n",
    "            time = np.array(time)\n",
    "            errR = np.array(errR)\n",
    "            errt = np.array(errt)\n",
    "\n",
    "            print(\n",
    "                f\"{method:30s} | {dropout:4.2f} | {N:3d} |\",\n",
    "                f\"{d_R:3d} deg | {d_t:5.2f} m |\",\n",
    "                f\"{time.mean():9.2f} | {errR.mean():7.2f} | {errt.mean():7.2f} |\",\n",
    "            )\n",
    "print(\"=\" * 97)\n",
    "\n",
    "del result_dict_t[\"Num Views\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "0a938f9a-5afe-4e0c-878d-83b25b3da942",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## import pandas \n",
    "\n",
    "font_scale = 2.5\n",
    "linewidth = font_scale * 2\n",
    "markersize = font_scale * 6\n",
    "\n",
    "\n",
    "sns.set(font_scale=font_scale)\n",
    "sns.set_style(\n",
    "    \"ticks\",\n",
    "    {\n",
    "        \"ytick.left\": False,\n",
    "        \"font.family\": [\"serif\"],\n",
    "        \"font.serif\": [\"Times New Roman\"],\n",
    "    }\n",
    ")\n",
    "\n",
    "\n",
    "dft = pandas.DataFrame(result_dict_t)\n",
    "dfR = pandas.DataFrame(result_dict_R)\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(30, 6))\n",
    "fig.tight_layout()\n",
    "\n",
    "ax = sns.lineplot(ax=axes[0], x=\"Noise (deg)\", y=\"Error (deg)\", hue=\"method\", data=dfR, markers=True, style=\"method\", err_style=\"bars\", estimator=np.mean, ci=90, linewidth=linewidth,markersize=markersize)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "\n",
    "lgnd = ax.legend(\n",
    "    handles=handles[0:], labels=labels[0:], \n",
    "    prop={'size': font_scale * 8},\n",
    "    loc=\"upper left\",\n",
    ")\n",
    "\n",
    "\n",
    "for lh in lgnd.legendHandles:\n",
    "    lh.set_markersize(markersize)\n",
    "    lh.set_linewidth(linewidth)\n",
    "    \n",
    "ax = sns.lineplot(ax=axes[1], x=\"Noise (cm)\", y=\"Error (cm)\", hue=\"method\", data=dft, markers=True, style=\"method\", err_style=\"bars\", estimator=np.mean, ci=90, legend=False, linewidth=linewidth,markersize=markersize)\n",
    "ax = sns.lineplot(ax=axes[2], x=\"Num Views\", y=\"Time (s)\", hue=\"method\", data=dfR, markers=True, style=\"method\", err_style=\"bars\", estimator=np.mean, ci=90, legend=False, linewidth=linewidth,markersize=markersize)\n",
    "ax.set_xscale('log', base=2)\n",
    "# ax.set_yscale('log', base=10)\n",
    "\n",
    "\n",
    "title_scale = 12\n",
    "title_yoff = 1.08\n",
    "axes[0].set_title(\"Rotation Perturbation\", fontsize=font_scale * title_scale, y=title_yoff)\n",
    "axes[1].set_title(\"Translation Perturbation\", fontsize=font_scale * title_scale, y=title_yoff)\n",
    "axes[2].set_title(\"Runtime\", fontsize=font_scale * title_scale, y=title_yoff)\n",
    "\n",
    "plt.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "\n",
    "axes[0].yaxis.grid(True, color=\"gainsboro\")\n",
    "axes[1].yaxis.grid(True, color=\"gainsboro\")\n",
    "axes[2].yaxis.grid(True, color=\"gainsboro\")\n",
    "\n",
    "axes[0].set_xlim(0, 30)\n",
    "axes[1].set_xlim(0, 30)\n",
    "axes[2].set_xlim(2**2, 2**6.1)\n",
    "axes[0].set_ylim(0, 80)\n",
    "axes[1].set_ylim(0, 80)\n",
    "axes[2].set_ylim(0, 1)\n",
    "axes[0].yaxis.labelpad = 10\n",
    "axes[1].yaxis.labelpad = 10\n",
    "axes[2].yaxis.labelpad = 10\n",
    "\n",
    "axes[0].xaxis.labelpad = 6\n",
    "axes[1].xaxis.labelpad = 6\n",
    "axes[2].xaxis.labelpad = 6\n",
    "        \n",
    "\n",
    "\n",
    "sns.despine(top=True, right=True, left=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6.8 64-bit",
   "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": "3.6.8"
  },
  "vscode": {
   "interpreter": {
    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}