lie_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import read\n",
    "import torch\n",
    "from lietorch import SO3, SE3, LieGroupParameter\n",
    "\n",
    "import argparse\n",
    "import numpy as np\n",
    "import time\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "import torch.nn as nn\n",
    "import chamfer3D.dist_chamfer_3D\n",
    "# import pytorch3d\n",
    "from pdb import post_mortem, set_trace as bp\n",
    "\n",
    "import itertools\n",
    "from torch.autograd import Variable\n",
    "import random\n",
    "import torch.nn.functional as FF\n",
    "from sklearn.cluster import kmeans_plusplus\n",
    "import open3d as o3d\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_obj(obj_path,for_open_mesh=False):\n",
    "    with open(obj_path) as file:\n",
    "        flag = 0\n",
    "        points = []\n",
    "        normals = []\n",
    "        faces = []\n",
    "        while 1:\n",
    "            line = file.readline()\n",
    "            if not line:\n",
    "                break\n",
    "            strs = line.split(\" \")\n",
    "            if strs[0] =='o' and flag==0:\n",
    "                flag = 1\n",
    "                continue\n",
    "            elif strs[0]=='o' and flag==1:\n",
    "                break\n",
    "            if strs[0] =='v':\n",
    "                points.append((float(strs[1]),float(strs[2]),float(strs[3])))\n",
    "            \n",
    "            if strs[0] == 'vn':\n",
    "                normals.append((float(strs[1]),float(strs[2]),float(strs[3])))\n",
    "            if strs[0] =='f':\n",
    "                single_line_face = strs[1:]\n",
    "                \n",
    "#                 for sf in single_line_face:\n",
    "#                     face_index = sf.split('/')\n",
    "#                     faces.append((int(face_index[0]),int(face_index[1]),int(face_index[2])))\n",
    "#                     break\n",
    "                f_co = []\n",
    "                for sf in single_line_face:\n",
    "                    face_tmp = sf.split('/')[0]\n",
    "                    f_co.append(face_tmp)\n",
    "                if for_open_mesh == False:\n",
    "                    if len(f_co)==3:\n",
    "                        faces.append((int(f_co[0]),int(f_co[1]),int(f_co[2])))\n",
    "                    elif len(f_co)==4:\n",
    "                        faces.append((int(f_co[0]),int(f_co[1]),int(f_co[2])))\n",
    "                        faces.append((int(f_co[0]),int(f_co[1]),int(f_co[3])))\n",
    "                        faces.append((int(f_co[1]),int(f_co[2]),int(f_co[3])))\n",
    "                        faces.append((int(f_co[0]),int(f_co[3]),int(f_co[2])))\n",
    "                else:\n",
    "                    faces.append([int(ver) for ver in f_co])\n",
    "                \n",
    "    points = np.array(points)\n",
    "\n",
    "    normals = np.array(normals)\n",
    "    faces = np.array(faces)\n",
    "    return points,normals,faces\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LaplacianLoss(nn.Module):\n",
    "    def __init__(self, vertex, faces, average=False):\n",
    "        super(LaplacianLoss, self).__init__()\n",
    "        self.nv = vertex.shape[0]\n",
    "        self.nf = faces.shape[0]\n",
    "        self.average = average\n",
    "        laplacian = np.zeros([self.nv, self.nv]).astype(np.float32)\n",
    "\n",
    "        laplacian[faces[:, 0], faces[:, 1]] = -1\n",
    "        laplacian[faces[:, 1], faces[:, 0]] = -1\n",
    "        laplacian[faces[:, 1], faces[:, 2]] = -1\n",
    "        laplacian[faces[:, 2], faces[:, 1]] = -1\n",
    "        laplacian[faces[:, 2], faces[:, 0]] = -1\n",
    "        laplacian[faces[:, 0], faces[:, 2]] = -1\n",
    "\n",
    "        r, c = np.diag_indices(laplacian.shape[0])\n",
    "        laplacian[r, c] = -laplacian.sum(1)\n",
    "\n",
    "        \n",
    "        for i in range(self.nv):\n",
    "            if laplacian[i, i]!=0: \n",
    "                laplacian[i, :] /= laplacian[i, i]\n",
    "\n",
    "        self.register_buffer('laplacian', torch.from_numpy(laplacian))\n",
    "\n",
    "    def forward(self, x):\n",
    "        batch_size = x.shape[0]\n",
    "        x = torch.matmul(self.laplacian.cuda(), x)\n",
    "        dims = tuple(range(x.ndimension())[1:])\n",
    "        x = x.pow(2).sum(dims)\n",
    "        if self.average:\n",
    "            return x.sum() / batch_size\n",
    "        else:\n",
    "            return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ivenwu/miniconda3/envs/lietorch/lib/python3.7/site-packages/ipykernel_launcher.py:94: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "this is loss for epoch 0 : 10621.0302734375, 0.0 \n",
      "this is loss for epoch 1 : 10201.5625, 0.0 \n",
      "this is loss for epoch 2 : 9797.103515625, 0.0 \n",
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     ]
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     "text": [
      "this is loss for epoch 158 : 36.410213470458984, 0.0 \n",
      "this is loss for epoch 159 : 35.95897674560547, 0.0 \n",
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      "this is loss for epoch 619 : 6.817063808441162, 0.0 \n",
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      "this is loss for epoch 774 : 5.427468776702881, 0.0 \n",
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     "name": "stdout",
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      "this is loss for epoch 929 : 4.457954406738281, 0.0 \n",
      "this is loss for epoch 930 : 4.452788829803467, 0.0 \n",
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      "this is loss for epoch 1081 : 3.7454583644866943, 0.0 \n"
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    {
     "name": "stdout",
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     "text": [
      "this is loss for epoch 1082 : 3.7414398193359375, 0.0 \n",
      "this is loss for epoch 1083 : 3.7374393939971924, 0.0 \n",
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      "this is loss for epoch 1232 : 3.1723129749298096, 0.0 \n"
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    {
     "name": "stdout",
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      "this is loss for epoch 1233 : 3.1690967082977295, 0.0 \n",
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    {
     "name": "stdout",
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     "text": [
      "this is loss for epoch 1384 : 2.724700450897217, 0.0 \n",
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      "this is loss for epoch 1534 : 2.3608431816101074, 0.0 \n"
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    {
     "name": "stdout",
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      "this is loss for epoch 1535 : 2.358656167984009, 0.0 \n",
      "this is loss for epoch 1536 : 2.3564727306365967, 0.0 \n",
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      "this is loss for epoch 1685 : 2.0280299186706543, 0.0 \n"
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    {
     "name": "stdout",
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     "text": [
      "this is loss for epoch 1686 : 2.0260438919067383, 0.0 \n",
      "this is loss for epoch 1687 : 2.024301290512085, 0.0 \n",
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      "this is loss for epoch 1700 : 1.9986648559570312, 0.0 \n",
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      "this is loss for epoch 1702 : 1.9946975708007812, 0.0 \n",
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     "name": "stdout",
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      "this is loss for epoch 1836 : 1.738885521888733, 0.0 \n",
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      "this is loss for epoch 1985 : 1.5031527280807495, 0.0 \n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is loss for epoch 1986 : 1.502102017402649, 0.0 \n",
      "this is loss for epoch 1987 : 1.5004535913467407, 0.0 \n",
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      "this is loss for epoch 2001 : 1.479990005493164, 0.0 \n",
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      "this is loss for epoch 2135 : 1.3057891130447388, 0.0 \n"
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    {
     "name": "stdout",
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     "text": [
      "this is loss for epoch 2136 : 1.304447054862976, 0.0 \n",
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      "this is loss for epoch 2285 : 1.1522085666656494, 0.0 \n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is loss for epoch 2286 : 1.1512993574142456, 0.0 \n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "this is loss for epoch 2585 : 0.9072055816650391, 0.0 \n"
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      "this is loss for epoch 2885 : 0.7370171546936035, 0.0 \n"
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     "name": "stdout",
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      "this is loss for epoch 3185 : 0.6073840856552124, 0.0 \n"
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      "this is loss for epoch 3485 : 0.5015570521354675, 0.0 \n"
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      "this is loss for epoch 3634 : 0.45291757583618164, 0.0 \n"
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      "this is loss for epoch 3782 : 0.41648340225219727, 0.0 \n"
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     "name": "stdout",
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      "this is loss for epoch 4079 : 0.33840879797935486, 0.0 \n"
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     "name": "stdout",
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      "this is loss for epoch 4227 : 0.30759721994400024, 0.0 \n"
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     "name": "stdout",
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      "this is loss for epoch 4375 : 0.2812981605529785, 0.0 \n"
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      "this is loss for epoch 4672 : 0.24267423152923584, 0.0 \n"
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      "this is loss for epoch 4820 : 0.22110438346862793, 0.0 \n"
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      "this is loss for epoch 4886 : 0.2150619775056839, 0.0 \n",
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      "this is loss for epoch 4968 : 0.20636296272277832, 0.0 \n"
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      "this is loss for epoch 5264 : 0.17791590094566345, 0.0 \n"
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      "this is loss for epoch 5412 : 0.18066638708114624, 0.0 \n"
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      "this is loss for epoch 5461 : 0.16154174506664276, 0.0 \n",
      "this is loss for epoch 5462 : 0.1614464968442917, 0.0 \n",
      "this is loss for epoch 5463 : 0.16136720776557922, 0.0 \n",
      "this is loss for epoch 5464 : 0.16127413511276245, 0.0 \n",
      "this is loss for epoch 5465 : 0.16119542717933655, 0.0 \n",
      "this is loss for epoch 5466 : 0.16113094985485077, 0.0 \n",
      "this is loss for epoch 5467 : 0.16106081008911133, 0.0 \n",
      "this is loss for epoch 5468 : 0.16100606322288513, 0.0 \n",
      "this is loss for epoch 5469 : 0.16093412041664124, 0.0 \n",
      "this is loss for epoch 5470 : 0.16086415946483612, 0.0 \n",
      "this is loss for epoch 5471 : 0.1607910692691803, 0.0 \n",
      "this is loss for epoch 5472 : 0.16071876883506775, 0.0 \n",
      "this is loss for epoch 5473 : 0.16066835820674896, 0.0 \n",
      "this is loss for epoch 5474 : 0.16062745451927185, 0.0 \n",
      "this is loss for epoch 5475 : 0.1606290191411972, 0.0 \n",
      "this is loss for epoch 5476 : 0.16064278781414032, 0.0 \n",
      "this is loss for epoch 5477 : 0.1606760174036026, 0.0 \n",
      "this is loss for epoch 5478 : 0.16065196692943573, 0.0 \n",
      "this is loss for epoch 5479 : 0.1605776995420456, 0.0 \n",
      "this is loss for epoch 5480 : 0.16049376130104065, 0.0 \n",
      "this is loss for epoch 5481 : 0.16050569713115692, 0.0 \n",
      "this is loss for epoch 5482 : 0.16063161194324493, 0.0 \n",
      "this is loss for epoch 5483 : 0.1608135849237442, 0.0 \n",
      "this is loss for epoch 5484 : 0.1609961837530136, 0.0 \n",
      "this is loss for epoch 5485 : 0.1612430214881897, 0.0 \n",
      "this is loss for epoch 5486 : 0.16160163283348083, 0.0 \n",
      "this is loss for epoch 5487 : 0.16202311217784882, 0.0 \n",
      "this is loss for epoch 5488 : 0.16241972148418427, 0.0 \n",
      "this is loss for epoch 5489 : 0.16283836960792542, 0.0 \n",
      "this is loss for epoch 5490 : 0.1632183939218521, 0.0 \n",
      "this is loss for epoch 5491 : 0.16346342861652374, 0.0 \n",
      "this is loss for epoch 5492 : 0.1634495109319687, 0.0 \n",
      "this is loss for epoch 5493 : 0.16321150958538055, 0.0 \n",
      "this is loss for epoch 5494 : 0.16276328265666962, 0.0 \n",
      "this is loss for epoch 5495 : 0.16218559443950653, 0.0 \n",
      "this is loss for epoch 5496 : 0.16154778003692627, 0.0 \n",
      "this is loss for epoch 5497 : 0.16097500920295715, 0.0 \n",
      "this is loss for epoch 5498 : 0.16061320900917053, 0.0 \n",
      "this is loss for epoch 5499 : 0.16042548418045044, 0.0 \n",
      "this is loss for epoch 5500 : 0.16024087369441986, 0.0 \n",
      "this is loss for epoch 5501 : 0.1598491221666336, 0.0 \n",
      "this is loss for epoch 5502 : 0.15941207110881805, 0.0 \n",
      "this is loss for epoch 5503 : 0.15937648713588715, 0.0 \n",
      "this is loss for epoch 5504 : 0.15975558757781982, 0.0 \n",
      "this is loss for epoch 5505 : 0.15969736874103546, 0.0 \n",
      "this is loss for epoch 5506 : 0.15961439907550812, 0.0 \n",
      "this is loss for epoch 5507 : 0.1593044251203537, 0.0 \n",
      "this is loss for epoch 5508 : 0.15873989462852478, 0.0 \n",
      "this is loss for epoch 5509 : 0.15864431858062744, 0.0 \n",
      "this is loss for epoch 5510 : 0.1583690345287323, 0.0 \n",
      "this is loss for epoch 5511 : 0.1582677662372589, 0.0 \n",
      "this is loss for epoch 5512 : 0.1580512523651123, 0.0 \n",
      "this is loss for epoch 5513 : 0.15787500143051147, 0.0 \n",
      "this is loss for epoch 5514 : 0.15771017968654633, 0.0 \n",
      "this is loss for epoch 5515 : 0.15766434371471405, 0.0 \n",
      "this is loss for epoch 5516 : 0.15767259895801544, 0.0 \n",
      "this is loss for epoch 5517 : 0.15757624804973602, 0.0 \n",
      "this is loss for epoch 5518 : 0.15746931731700897, 0.0 \n",
      "this is loss for epoch 5519 : 0.1573219746351242, 0.0 \n",
      "this is loss for epoch 5520 : 0.15723514556884766, 0.0 \n",
      "this is loss for epoch 5521 : 0.15713118016719818, 0.0 \n",
      "this is loss for epoch 5522 : 0.1570454239845276, 0.0 \n",
      "this is loss for epoch 5523 : 0.1569097489118576, 0.0 \n",
      "this is loss for epoch 5524 : 0.15681640803813934, 0.0 \n",
      "this is loss for epoch 5525 : 0.1567596197128296, 0.0 \n",
      "this is loss for epoch 5526 : 0.15666747093200684, 0.0 \n",
      "this is loss for epoch 5527 : 0.15655246376991272, 0.0 \n",
      "this is loss for epoch 5528 : 0.15646764636039734, 0.0 \n",
      "this is loss for epoch 5529 : 0.1564207375049591, 0.0 \n",
      "this is loss for epoch 5530 : 0.1563417911529541, 0.0 \n",
      "this is loss for epoch 5531 : 0.15626496076583862, 0.0 \n",
      "this is loss for epoch 5532 : 0.15621022880077362, 0.0 \n",
      "this is loss for epoch 5533 : 0.15612559020519257, 0.0 \n",
      "this is loss for epoch 5534 : 0.1560349315404892, 0.0 \n",
      "this is loss for epoch 5535 : 0.15595829486846924, 0.0 \n",
      "this is loss for epoch 5536 : 0.15589992702007294, 0.0 \n",
      "this is loss for epoch 5537 : 0.15583983063697815, 0.0 \n",
      "this is loss for epoch 5538 : 0.15579700469970703, 0.0 \n",
      "this is loss for epoch 5539 : 0.1557883322238922, 0.0 \n",
      "this is loss for epoch 5540 : 0.15577749907970428, 0.0 \n",
      "this is loss for epoch 5541 : 0.1557789295911789, 0.0 \n",
      "this is loss for epoch 5542 : 0.15582863986492157, 0.0 \n",
      "this is loss for epoch 5543 : 0.15593646466732025, 0.0 \n",
      "this is loss for epoch 5544 : 0.15611307322978973, 0.0 \n",
      "this is loss for epoch 5545 : 0.1563730090856552, 0.0 \n",
      "this is loss for epoch 5546 : 0.15675263106822968, 0.0 \n",
      "this is loss for epoch 5547 : 0.1572977602481842, 0.0 \n",
      "this is loss for epoch 5548 : 0.15805284678936005, 0.0 \n",
      "this is loss for epoch 5549 : 0.15905265510082245, 0.0 \n",
      "this is loss for epoch 5550 : 0.16032405197620392, 0.0 \n",
      "this is loss for epoch 5551 : 0.16191083192825317, 0.0 \n",
      "this is loss for epoch 5552 : 0.16380934417247772, 0.0 \n",
      "this is loss for epoch 5553 : 0.1659674048423767, 0.0 \n",
      "this is loss for epoch 5554 : 0.16823779046535492, 0.0 \n",
      "this is loss for epoch 5555 : 0.17027421295642853, 0.0 \n",
      "this is loss for epoch 5556 : 0.1716577559709549, 0.0 \n",
      "this is loss for epoch 5557 : 0.17177143692970276, 0.0 \n",
      "this is loss for epoch 5558 : 0.17033949494361877, 0.0 \n",
      "this is loss for epoch 5559 : 0.16730429232120514, 0.0 \n",
      "this is loss for epoch 5560 : 0.16332612931728363, 0.0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is loss for epoch 5561 : 0.15936200320720673, 0.0 \n",
      "this is loss for epoch 5562 : 0.15642622113227844, 0.0 \n",
      "this is loss for epoch 5563 : 0.15505102276802063, 0.0 \n",
      "this is loss for epoch 5564 : 0.15517637133598328, 0.0 \n",
      "this is loss for epoch 5565 : 0.15629112720489502, 0.0 \n",
      "this is loss for epoch 5566 : 0.1576739400625229, 0.0 \n",
      "this is loss for epoch 5567 : 0.158675417304039, 0.0 \n",
      "this is loss for epoch 5568 : 0.15884384512901306, 0.0 \n",
      "this is loss for epoch 5569 : 0.15816648304462433, 0.0 \n",
      "this is loss for epoch 5570 : 0.1569076031446457, 0.0 \n",
      "this is loss for epoch 5571 : 0.15554200112819672, 0.0 \n",
      "this is loss for epoch 5572 : 0.15445521473884583, 0.0 \n",
      "this is loss for epoch 5573 : 0.15385417640209198, 0.0 \n",
      "this is loss for epoch 5574 : 0.15373791754245758, 0.0 \n",
      "this is loss for epoch 5575 : 0.15396051108837128, 0.0 \n",
      "this is loss for epoch 5576 : 0.15431438386440277, 0.0 \n",
      "this is loss for epoch 5577 : 0.15459579229354858, 0.0 \n",
      "this is loss for epoch 5578 : 0.15467020869255066, 0.0 \n",
      "this is loss for epoch 5579 : 0.1544947475194931, 0.0 \n",
      "this is loss for epoch 5580 : 0.1541183590888977, 0.0 \n",
      "this is loss for epoch 5581 : 0.15364490449428558, 0.0 \n",
      "this is loss for epoch 5582 : 0.15318790078163147, 0.0 \n",
      "this is loss for epoch 5583 : 0.1528359204530716, 0.0 \n",
      "this is loss for epoch 5584 : 0.1526252031326294, 0.0 \n",
      "this is loss for epoch 5585 : 0.15254998207092285, 0.0 \n",
      "this is loss for epoch 5586 : 0.15257222950458527, 0.0 \n",
      "this is loss for epoch 5587 : 0.15263982117176056, 0.0 \n",
      "this is loss for epoch 5588 : 0.15269705653190613, 0.0 \n",
      "this is loss for epoch 5589 : 0.15270185470581055, 0.0 \n",
      "this is loss for epoch 5590 : 0.1526346504688263, 0.0 \n",
      "this is loss for epoch 5591 : 0.15250034630298615, 0.0 \n",
      "this is loss for epoch 5592 : 0.15232092142105103, 0.0 \n",
      "this is loss for epoch 5593 : 0.1521257609128952, 0.0 \n",
      "this is loss for epoch 5594 : 0.15194164216518402, 0.0 \n",
      "this is loss for epoch 5595 : 0.15178711712360382, 0.0 \n",
      "this is loss for epoch 5596 : 0.15167057514190674, 0.0 \n",
      "this is loss for epoch 5597 : 0.15159158408641815, 0.0 \n",
      "this is loss for epoch 5598 : 0.15154339373111725, 0.0 \n",
      "this is loss for epoch 5599 : 0.15151548385620117, 0.0 \n",
      "this is loss for epoch 5600 : 0.1514964997768402, 0.0 \n",
      "this is loss for epoch 5601 : 0.1514795869588852, 0.0 \n",
      "this is loss for epoch 5602 : 0.15146557986736298, 0.0 \n",
      "this is loss for epoch 5603 : 0.15144455432891846, 0.0 \n",
      "this is loss for epoch 5604 : 0.15142977237701416, 0.0 \n",
      "this is loss for epoch 5605 : 0.15142959356307983, 0.0 \n",
      "this is loss for epoch 5606 : 0.1514519453048706, 0.0 \n",
      "this is loss for epoch 5607 : 0.1515161097049713, 0.0 \n",
      "this is loss for epoch 5608 : 0.15163694322109222, 0.0 \n",
      "this is loss for epoch 5609 : 0.15183523297309875, 0.0 \n",
      "this is loss for epoch 5610 : 0.15213754773139954, 0.0 \n",
      "this is loss for epoch 5611 : 0.15257754921913147, 0.0 \n",
      "this is loss for epoch 5612 : 0.1531684398651123, 0.0 \n",
      "this is loss for epoch 5613 : 0.15395110845565796, 0.0 \n",
      "this is loss for epoch 5614 : 0.15487022697925568, 0.0 \n",
      "this is loss for epoch 5615 : 0.15590935945510864, 0.0 \n",
      "this is loss for epoch 5616 : 0.15686623752117157, 0.0 \n",
      "this is loss for epoch 5617 : 0.15762178599834442, 0.0 \n",
      "this is loss for epoch 5618 : 0.15782921016216278, 0.0 \n",
      "this is loss for epoch 5619 : 0.15743562579154968, 0.0 \n",
      "this is loss for epoch 5620 : 0.15630310773849487, 0.0 \n",
      "this is loss for epoch 5621 : 0.15474839508533478, 0.0 \n",
      "this is loss for epoch 5622 : 0.15310591459274292, 0.0 \n",
      "this is loss for epoch 5623 : 0.15178707242012024, 0.0 \n",
      "this is loss for epoch 5624 : 0.1509932279586792, 0.0 \n",
      "this is loss for epoch 5625 : 0.1507623940706253, 0.0 \n",
      "this is loss for epoch 5626 : 0.1509215384721756, 0.0 \n",
      "this is loss for epoch 5627 : 0.15123669803142548, 0.0 \n",
      "this is loss for epoch 5628 : 0.15152764320373535, 0.0 \n",
      "this is loss for epoch 5629 : 0.15165650844573975, 0.0 \n",
      "this is loss for epoch 5630 : 0.15157531201839447, 0.0 \n",
      "this is loss for epoch 5631 : 0.1512944996356964, 0.0 \n",
      "this is loss for epoch 5632 : 0.15091949701309204, 0.0 \n",
      "this is loss for epoch 5633 : 0.15055201947689056, 0.0 \n",
      "this is loss for epoch 5634 : 0.15030452609062195, 0.0 \n",
      "this is loss for epoch 5635 : 0.1502138376235962, 0.0 \n",
      "this is loss for epoch 5636 : 0.15026067197322845, 0.0 \n",
      "this is loss for epoch 5637 : 0.15039698779582977, 0.0 \n",
      "this is loss for epoch 5638 : 0.15056614577770233, 0.0 \n",
      "this is loss for epoch 5639 : 0.1507420837879181, 0.0 \n",
      "this is loss for epoch 5640 : 0.1509096473455429, 0.0 \n",
      "this is loss for epoch 5641 : 0.1511082798242569, 0.0 \n",
      "this is loss for epoch 5642 : 0.15133027732372284, 0.0 \n",
      "this is loss for epoch 5643 : 0.15162433683872223, 0.0 \n",
      "this is loss for epoch 5644 : 0.1519814133644104, 0.0 \n",
      "this is loss for epoch 5645 : 0.15243062376976013, 0.0 \n",
      "this is loss for epoch 5646 : 0.1529362052679062, 0.0 \n",
      "this is loss for epoch 5647 : 0.1535048633813858, 0.0 \n",
      "this is loss for epoch 5648 : 0.15408384799957275, 0.0 \n",
      "this is loss for epoch 5649 : 0.15462437272071838, 0.0 \n",
      "this is loss for epoch 5650 : 0.15504024922847748, 0.0 \n",
      "this is loss for epoch 5651 : 0.15524256229400635, 0.0 \n",
      "this is loss for epoch 5652 : 0.1551440954208374, 0.0 \n",
      "this is loss for epoch 5653 : 0.15470628440380096, 0.0 \n",
      "this is loss for epoch 5654 : 0.15393273532390594, 0.0 \n",
      "this is loss for epoch 5655 : 0.15288469195365906, 0.0 \n",
      "this is loss for epoch 5656 : 0.15166477859020233, 0.0 \n",
      "this is loss for epoch 5657 : 0.15042182803153992, 0.0 \n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_18599/156675893.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    140\u001b[0m             \u001b[0;31m# bp()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0;31m# bp()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mloss2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m         \u001b[0;31m# print(\"this is loss for epoch {} : {} \".format(i,loss.detach().cpu().numpy()))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"this is loss for epoch {} : {}, {} \"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mloss\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss2\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lietorch/lib/python3.7/site-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    253\u001b[0m                 \u001b[0mcreate_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m                 inputs=inputs)\n\u001b[0;32m--> 255\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lietorch/lib/python3.7/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    147\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[1;32m    148\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m         allow_unreachable=True, accumulate_grad=True)  # allow_unreachable flag\n\u001b[0m\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    # parser = argparse.ArgumentParser()\n",
    "    # parser.add_argument('--old', default='frame_000001.obj')\n",
    "    # parser.add_argument('--target', default='frame_000002.obj')\n",
    "    # args = parser.parse_args()\n",
    "\n",
    "    # output_path = args.problem.replace('.g2o', '_rotavg.g2o')\n",
    "    # input_pose_graph = read_g2o(args.problem)\n",
    "\n",
    "    seed = 2000\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "\n",
    "    points_info,normals_info,face_info = read_obj('frame_000001.obj')\n",
    "    old_ones = np.ones((points_info.shape[0],1))\n",
    "    \n",
    "    \n",
    "    points_info = np.concatenate((points_info,old_ones),axis=1)\n",
    "    all_info_list = []\n",
    "    for i in range(20, 30):\n",
    "        path = \"frame_{:06d}.obj\".format(i+1)\n",
    "\n",
    "        tmp_target = read_obj(path)[0]\n",
    "        target_ones = np.ones((tmp_target.shape[0],1))\n",
    "        tmp_target_new = np.concatenate((tmp_target,target_ones),axis=1)\n",
    "        all_info_list.append(tmp_target_new)\n",
    "\n",
    "        \n",
    "    la_loss = LaplacianLoss(torch.tensor(points_info),torch.tensor(face_info-1),average=True)\n",
    "    all_info = np.stack(all_info_list)\n",
    "    old_info = points_info\n",
    "    target_info = all_info\n",
    "    # W,R= train(old_info=points_info,target_info=all_info)\n",
    "\n",
    "    N = points_info.shape[0]\n",
    "    F = target_info.shape[0]\n",
    "    B = 40  ### 这个肯定可以通过读pkl得到\n",
    "\n",
    "    center, indexes = kmeans_plusplus(old_info[:, :-1], n_clusters=B, random_state=0)\n",
    "    old_mesh = torch.tensor(old_info).float().cuda().detach()\n",
    "    # bp()\n",
    "    # ones = torch.ones(old_mesh.shape[0], 1).cuda()\n",
    "    # x = torch.cat([old_mesh, ones], dim=1)\n",
    "\n",
    "    center_t = torch.tensor(center).repeat(F, 1, 1).cuda()\n",
    "    target_mesh = torch.tensor(target_info, requires_grad=False).float().cuda().detach()\n",
    "\n",
    "    # bp()\n",
    "    # W = torch.randint(10,(N,B))\n",
    "    # W = torch.randn((N,B)).cuda()\n",
    "    # W = np.random.randint(10,size=(N,B))\n",
    "    # bp()\n",
    "    # W = SO3(torch.from_numpy(W).float().cuda())\n",
    "    # W = torch.zeros((N, B), dtype=torch.float, requires_grad=True, device=\"cuda\")\n",
    "    W = torch.randn((N, B), requires_grad=True, device=\"cuda\")\n",
    "    W2 = torch.randn((N, B), requires_grad=True, device=\"cuda\")\n",
    "    # W = torch.tensor(W0.float(), requires_grad=True, device=\"cuda\")\n",
    "    # W = W * 0\n",
    "    # bp()\n",
    "    # W = LieGroupParameter(W)\n",
    "    # bp()\n",
    "    # R = torch.randint(10,(B,3,3))\n",
    "\n",
    "    # bp()pcd.points = Vector3dVector(np_points)\n",
    "    # random quaternion\n",
    "   # bounding_box_max = old_mesh.max()\n",
    "    # bounding_box_min = old_mesh.min()\n",
    "    p1 = torch.randn((F, B, 7), requires_grad=True, device=\"cuda\")\n",
    "    # with torch.no_grad():\n",
    "    #     p1[:, :, 4:] = center_t\n",
    "    # W = W.cuda()\n",
    "    # p1 = p1.cuda()\n",
    "    # p1 = p1 / p1.norm(dim=-1, keepdim=True)\n",
    "    # create SO3 object from quaternion (differentiable w.r.t q)\n",
    "    \n",
    "    # 4x4 transformation matrix (differentiable w.r.t R)\n",
    "    \n",
    "\n",
    "    # W = torch.tensor(np.load('weight.npy')).float().cuda()\n",
    "    optimizer = optim.Adam([W,  p1],lr=1e-2)\n",
    "\n",
    "    chamLoss = chamfer3D.dist_chamfer_3D.chamfer_3DDist()\n",
    "    for i in range(10000):\n",
    "        # if i == 2000:\n",
    "        #     optimizer = optim.Adam([W, p1],lr=1e-3)\n",
    "        optimizer.zero_grad()\n",
    "        loss = 0\n",
    "        loss2 = 0\n",
    "        f_idxs = random.sample(range(F), F)\n",
    "        \n",
    "        for f in f_idxs:\n",
    "            T = torch.randn(B, 4, 4)\n",
    "            W1 = FF.softmax(W * 10)\n",
    "            W1 = (W1 / (W1.sum(1, keepdim=True).detach()))\n",
    "            # W3 = FF.softmax(W2)\n",
    "            # W3 = (W3 / (W3.sum(1, keepdim=True).detach()))\n",
    "            # dist= W3.unsqueeze(2) * old_mesh.unsqueeze(1).repeat(1, 45, 1)\n",
    "            # dist = dist.sum(0)\n",
    "            \n",
    "            for b in range(B):\n",
    "                # R = SO3.InitFromVec(p1[f, b])\n",
    "                # bp()\n",
    "                # bp()\n",
    "                # with torch.no_grad():\n",
    "                # temp = torch.cat([p1[f, b], dist[f][:-1]])\n",
    "                R = SE3.InitFromVec(p1[f, b])\n",
    "                # bp()\n",
    "                T[b] = R.matrix()\n",
    "            T = T.cuda()     \n",
    "            # bp()      \n",
    "            # bp() \n",
    "            # W1 = W\n",
    "            # W1 = (W - W.min()) / (W.max() - W.min())\n",
    "            \n",
    "            # bp()\n",
    "            # p1[:, :, 4:] = \n",
    "            # bp()\n",
    "            # ones = torch.ones(old_mesh.shape[0], 1).cuda()\n",
    "            # x = torch.cat([old_mesh, ones], dim=1)\n",
    "            x = old_mesh\n",
    "        \n",
    "        \n",
    "            bx = (T@x.T).permute(2, 0, 1)\n",
    "            wbx = W1.unsqueeze(2) * bx\n",
    "            # bp()\n",
    "            wbx =  wbx.permute((1,0,2))\n",
    "            # print(\"Wbx\", wbx.shape)\n",
    "            # wbx = wbx.sum(0, keepdim=True)[:, :, :-1]\n",
    "            wbx = wbx.sum(0, keepdim=True)\n",
    "            # loss_init = chamLoss(target_mesh[f][None],wbx)\n",
    "            # loss += loss_init[0].sum()\n",
    "\n",
    "#             bp()\n",
    "            loss += torch.sum((target_mesh[f]-wbx)**2)\n",
    "            # loss2 += la_loss(W1) * 100\n",
    "            # loss += torch.sum((target_mesh[f]-wbx)**2) +la_loss(W.detach().cpu())\n",
    "#             loss.backward()\n",
    "            # print(\"loss\",loss)\n",
    "            # bp()\n",
    "        # bp()\n",
    "        (loss + loss2).backward()\n",
    "        # print(\"this is loss for epoch {} : {} \".format(i,loss.detach().cpu().numpy()))\n",
    "        print(\"this is loss for epoch {} : {}, {} \".format(i,loss / F, loss2/F))\n",
    "        # print(\"p1: \", p1)\n",
    "        optimizer.step()\n",
    "\n",
    "\n",
    "    new_R = SE3.InitFromVec(p1)\n",
    "    R = new_R.matrix()\n",
    "\n",
    "    # W1 = (W - W.min()) / (W.max() - W.min())\n",
    "    # W1 = (W1 / (W1.sum(1, keepdim=True).detach()))\n",
    "    # bp()\n",
    "    # return R.detach(), W1.detach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if __name__ == '__main__':\n",
    "    seed = 2000\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "\n",
    "    points_info,normals_info,face_info = read_obj('frame_000001.obj')\n",
    "    old_ones = np.ones((points_info.shape[0],1))\n",
    "\n",
    "    points_info = np.concatenate((points_info,old_ones),axis=1)\n",
    "\n",
    "    old_info = points_info\n",
    "    \n",
    "    all_info_list = []\n",
    "    for i in range(80,90):\n",
    "        path = \"frame_0000{}.obj\".format(i+1)\n",
    "\n",
    "        tmp_target = read_obj(path)[0]\n",
    "        target_ones = np.ones((tmp_target.shape[0],1))\n",
    "        tmp_target_new = np.concatenate((tmp_target,target_ones),axis=1)\n",
    "        all_info_list.append(tmp_target_new)\n",
    "\n",
    "    all_info = np.stack(all_info_list)\n",
    "    old_info = points_info\n",
    "    target_info = all_info\n",
    "    # W,R= train(old_info=points_info,target_info=all_info)\n",
    "#     bp()\n",
    "    N = points_info.shape[0]\n",
    "    F = target_info.shape[0]\n",
    "    B = 40  ### 这个肯定可以通过读pkl得到\n",
    "\n",
    "    center, indexes = kmeans_plusplus(old_info[:, :-1], n_clusters=B, random_state=0)\n",
    "    old_mesh = torch.tensor(old_info).float().cuda().detach()\n",
    "    # bp()\n",
    "    # ones = torch.ones(old_mesh.shape[0], 1).cuda()\n",
    "    # x = torch.cat([old_mesh, ones], dim=1)\n",
    "\n",
    "    center_t = torch.tensor(center).repeat(F, 1, 1).cuda()\n",
    "    target_mesh = torch.tensor(target_info, requires_grad=False).float().cuda().detach()\n",
    "\n",
    "    \n",
    "    \n",
    "    p1 = torch.randn((F, B, 7), requires_grad=True, device=\"cuda\")\n",
    "\n",
    "#     W1 = FF.softmax(W * 10).clone().detach()\n",
    "#     W1 = (W1 / (W1.sum(1, keepdim=True).detach()))\n",
    "\n",
    "    optimizer = optim.Adam([p1],lr=1e-2)\n",
    "    chamLoss = chamfer3D.dist_chamfer_3D.chamfer_3DDist()\n",
    "#     W = torch.tensor(W)\n",
    "    \n",
    "#     W1 = np.load('weight.npy')\n",
    "    W1 = torch.tensor(W1).cuda()\n",
    "    la_loss = LaplacianLoss(torch.tensor(points_info),torch.tensor(face_info-1),average=True)\n",
    "    \n",
    "    for i in range(10000):\n",
    "        if i == 400:\n",
    "            optimizer = optim.Adam([p1],lr=1e-3)\n",
    "        optimizer.zero_grad()\n",
    "        loss = 0\n",
    "        f_idxs = random.sample(range(F), F)\n",
    "        \n",
    "        for f in f_idxs:\n",
    "            T = torch.randn(B, 4, 4)\n",
    "\n",
    "            for b in range(B):\n",
    "                # R = SO3.InitFromVec(p1[f, b])\n",
    "                # bp()\n",
    "                # bp()\n",
    "                # with torch.no_grad():\n",
    "                # temp = torch.cat([p1[f, b], dist[f][:-1]])\n",
    "                R = SE3.InitFromVec(p1[f, b])\n",
    "                # bp()\n",
    "                T[b] = R.matrix()\n",
    "            T = T.cuda()     \n",
    "        \n",
    "            x = old_mesh\n",
    "        \n",
    "        \n",
    "            bx = (T@x.T).permute(2, 0, 1)\n",
    "            wbx = W1.unsqueeze(2) * bx\n",
    "            # bp()\n",
    "            wbx =  wbx.permute((1,0,2))40\n",
    "            # print(\"Wbx\", wbx.shape)\n",
    "            # wbx = wbx.sum(0, keepdim=True)[:, :, :-1]\n",
    "            wbx = wbx.sum(0, keepdim=True)\n",
    "            # loss_init = chamLoss(target_mesh[f][None],wbx)\n",
    "            # loss += loss_init[0].sum()\n",
    "#             bp()\n",
    "            loss += torch.sum((target_mesh[f]-wbx)**2) \n",
    "#             print(loss)\n",
    "        # bp()\n",
    "        loss.backward()\n",
    "        # print(\"this is loss for epoch {} : {} \".format(i,loss.detach().cpu().numpy()))\n",
    "        print(\"this is loss for epoch {} : {} \".format(i,loss / F))\n",
    "        # print(\"p1: \", p1)\n",
    "        optimizer.step()\n",
    "\n",
    "\n",
    "    new_R = SE3.InitFromVec(p1)\n",
    "    R = new_R.matrix()\n",
    "\n",
    "    # W1 = (W - W.min()) / (W.max() - W.min())\n",
    "    # W1 = (W1 / (W1.sum(1, keepdim=True).detach()))\n",
    "    # bp()\n",
    "    # return R.detach(), W1.detach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "la_loss = LaplacianLoss(points_info,points_face-1,average=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.0052)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "la_loss(torch.tensor(wbx_result[-1][:, :-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccc = torch.tensor([[1,2,3],[4,5,6]])\n",
    "ccc.size(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3199, 4)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points_info.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "points_info,points_normal,points_face = read_obj('frame_000001.obj')\n",
    "# all_info_list = []\n",
    "\n",
    "# for i in range(1, 9):\n",
    "#     path = \"frame_00000{}.obj\".format(i+1)\n",
    "#     all_info_list.append(read_obj(path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(711, 3)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points_info.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8726,)"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points_face.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 45, 3, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R2 = R[:, :, :-1,:-1]\n",
    "R2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ivenwu/miniconda3/envs/lietorch/lib/python3.7/site-packages/ipykernel_launcher.py:2: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "W = torch.tensor(W).cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ivenwu/miniconda3/envs/lietorch/lib/python3.7/site-packages/ipykernel_launcher.py:3: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([8315, 45])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_mesh = torch.tensor(points_info).float().cuda()\n",
    "# W1 = (W - W.min()) / (W.max() - W.min())\n",
    "W1 = FF.softmax(W)\n",
    "W1 = (W1 / (W1.sum(1, keepdim=True).detach()))\n",
    "W1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "R = torch.tensor(R).cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for @: 'numpy.ndarray' and 'Tensor'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_30316/2019785926.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mW1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mR\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m@\u001b[0m\u001b[0mold_mesh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpermute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpermute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for @: 'numpy.ndarray' and 'Tensor'"
     ]
    }
   ],
   "source": [
    "(W1.unsqueeze(2) * (R[0]@old_mesh.T).permute(2, 0, 1)).permute(1, 0, 2).sum(0).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_R = SE3.InitFromVec(p1)\n",
    "R = new_R.matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "R1 = R.matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([8, 30, 4, 4])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_R = SE3.InitFromVec(p1)\n",
    "R = new_R.matrix()\n",
    "wbx_result = []\n",
    "for r in R:\n",
    "    bx = (r@old_mesh.T).permute(2, 0, 1)\n",
    "    # W = W.unsqueeze(1)\n",
    "    wbx = W1.unsqueeze(2) * bx\n",
    "\n",
    "    wbx =  wbx.permute((1,0,2))\n",
    "    wbx = wbx.sum(0).detach().cpu().numpy()\n",
    "    wbx_result.append(wbx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "wbx_result0 = wbx_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'detach'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_5191/840867176.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mwbx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwbx_result\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mwbx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'detach'"
     ]
    }
   ],
   "source": [
    "wbx = wbx_result.detach().cpu().numpy()[:, :-1]\n",
    "wbx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is index  0\n",
      "21.36908918353724\n",
      "this is index  1\n",
      "128.30545293720138\n",
      "this is index  2\n",
      "373.0977968274271\n",
      "this is index  3\n",
      "771.560108692853\n",
      "this is index  4\n",
      "1293.8557314639206\n",
      "this is index  5\n",
      "1870.6729752832866\n",
      "this is index  6\n",
      "2412.4298995824365\n",
      "this is index  7\n",
      "2813.369257814287\n"
     ]
    }
   ],
   "source": [
    "W3.unsqueeze(2) * old_mesh.unsqueeze(1).repeat(1, 45, 1)for wbx_index,single_wbx in enumerate(wbx_result):\n",
    "    # single_wbx = single_wbx.detach().cpu().numpy()\n",
    "    print(\"this is index \",wbx_index)\n",
    "    print(np.sum((all_info[wbx_index]-single_wbx)**2))\n",
    "    # for gt in all_info:\n",
    "    #     print(np.sum((gt-single_wbx)**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "W1 = W1.detach().cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "W1 = torch.tensor(W1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_list = []\n",
    "number_list = [0,0.2,0.4,0.6,0.8,1]\n",
    "for i in range(B):\n",
    "    color = random.sample(number_list,3)\n",
    "    color_list.append(color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_array = np.array(color_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_array = np.array([[0.8, 0.4, 0.2],\n",
    "       [0.6, 0.8, 1. ],\n",
    "       [0.6, 0.2, 0.4],\n",
    "       [1. , 0.6, 0.8],\n",
    "       [0.2, 0.4, 0. ],\n",
    "       [0.2, 0.4, 0.8],\n",
    "       [0.4, 1. , 0.6],\n",
    "       [1. , 0.4, 0. ],\n",
    "       [0.6, 0.2, 0. ],\n",
    "       [0.2, 1. , 0.4],\n",
    "       [0.6, 0.2, 0.4],\n",
    "       [0.6, 0.4, 0. ],\n",
    "       [0.4, 0.6, 0.8],\n",
    "       [0.8, 0. , 1. ],\n",
    "       [0.4, 0.6, 0.2],\n",
    "       [0.6, 1. , 0.2],\n",
    "       [0. , 0.2, 0.4],\n",
    "       [0.2, 0.8, 1. ],\n",
    "       [0.4, 0.2, 1. ],\n",
    "       [0.8, 0.4, 1. ],\n",
    "       [0.8, 1. , 0. ],\n",
    "       [1. , 0. , 0.6],\n",
    "       [0.2, 1. , 0. ],\n",
    "       [0.6, 0.2, 0.8],\n",
    "       [1. , 0.6, 0.4],\n",
    "       [0.4, 0. , 1. ],\n",
    "       [0.2, 1. , 0.4],\n",
    "       [0.2, 0.4, 0.8],\n",
    "       [1. , 0. , 0.8],\n",
    "       [1. , 0.8, 0.4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "W_new = W1.clone()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(3198.9993, device='cuda:0')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W_new.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'W_new' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_8854/1205028815.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmax_idx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m                 \u001b[0mW1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m         \u001b[0mW_new\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mW1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mW1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'W_new' is not defined"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    for i in range(W1.shape[0]):\n",
    "        max_idx = torch.topk(W1[i], 3)[1]\n",
    "        for j in range(B):\n",
    "            if j not in max_idx:\n",
    "                W1[i, j] = 0\n",
    "        W_new[i] = W1[i] / W1[i].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "W_c = W_new.detach().cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 30 is different from 40)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_32427/2784454989.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mW_c\u001b[0m\u001b[0;34m@\u001b[0m\u001b[0mcolor_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 30 is different from 40)"
     ]
    }
   ],
   "source": [
    "(W_c@color_array).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcd_test = o3d.geometry.PointCloud()\n",
    "pcd_test.points = o3d.utility.Vector3dVector(points_info)\n",
    "# pcd_test.colors = o3d.utility.Vector3dVector(W_c@color_array)\n",
    "pcd_test.normals = o3d.utility.Vector3dVector(points_normal)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "distances = pcd_test.compute_nearest_neighbor_distance()\n",
    "avg_dist = np.mean(distances)\n",
    "radius = avg_dist "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "bpa_mesh = o3d.geometry.TriangleMesh.create_from_point_cloud_ball_pivoting(\n",
    "pcd_test,o3d.utility.DoubleVector([radius, radius * 2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "bpa_mesh.triangles = o3d.utility.Vector3iVector(points_face-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8804, 3)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(bpa_mesh.triangles).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "o3d.visualization.draw_geometries([bpa_mesh])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(wbx_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import open3d as o3d\n",
    "pcd = o3d.geometry.PointCloud()\n",
    "# pcd2 = o3d.geometry.PointCloud()\n",
    "pcd.points = o3d.utility.Vector3dVector(wbx_result[-1][:, :-1])\n",
    "# pcd.points = o3d.utility.Vector3dVector(points_info)\n",
    "pcd.colors = o3d.utility.Vector3dVector(W_c@color_array)\n",
    "# pcd.normals = o3d.utility.Vector3dVector(normals_info)\n",
    "# pcd2.paint_uniform_color([0, 0, 1])\n",
    "o3d.visualization.draw_geometries([pcd])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_8854/3042664672.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mpcd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mo3d\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeometry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPointCloud\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# pcd2 = o3d.geometry.PointCloud()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mpcd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoints\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mo3d\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutility\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mVector3dVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoints_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;31m# pcd2.points = o3d.utility.Vector3dVector(eigvector_array)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mpcd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mo3d\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutility\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mVector3dVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mW_c\u001b[0m\u001b[0;34m@\u001b[0m\u001b[0mcolor_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import open3d as o3d\n",
    "pcd = o3d.geometry.PointCloud()\n",
    "# pcd2 = o3d.geometry.PointCloud()\n",
    "pcd.points = o3d.utility.Vector3dVector(points_info)\n",
    "# pcd2.points = o3d.utility.Vector3dVector(eigvector_array)\n",
    "pcd.colors = o3d.utility.Vector3dVector(W_c@color_array)\n",
    "# pcd2.paint_uniform_color([0, 0, 1])\n",
    "o3d.visualization.draw_geometries([pcd])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "W3 = W1[:, -2]\n",
    "# W2 = W3.detach().cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([8315, 3])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onet = torch.zeros((8315, 2)).cuda()\n",
    "# ff = torch.randn((8315,2)).cuda()\n",
    "W2 = torch.cat([W3.unsqueeze(1), onet], dim=1)\n",
    "W2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "W2 = W2.detach().cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8315,)"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "import openmesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh11 = openmesh.PolyMesh()\n",
    "vv = []\n",
    "for i in points_info:\n",
    "    vv.append(mesh11.add_vertex(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "for j in points_face:\n",
    "\n",
    "    if len(j) ==3:\n",
    "        mesh11.add_face([vv[j[0]-1],vv[j[1]-1],vv[j[2]-1]])\n",
    "    elif len(j) ==4:\n",
    "        mesh11.add_face([vv[j[0]-1],vv[j[1]-1],vv[j[2]-1],vv[j[3]-1]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "write_mesh(): incompatible function arguments. The following argument types are supported:\n    1. (filename: str, mesh: openmesh.TriMesh, binary: bool = False, msb: bool = False, lsb: bool = False, swap: bool = False, vertex_normal: bool = False, vertex_color: bool = False, vertex_tex_coord: bool = False, halfedge_tex_coord: bool = False, edge_color: bool = False, face_normal: bool = False, face_color: bool = False, color_alpha: bool = False, color_float: bool = False) -> None\n    2. (filename: str, mesh: openmesh.PolyMesh, binary: bool = False, msb: bool = False, lsb: bool = False, swap: bool = False, vertex_normal: bool = False, vertex_color: bool = False, vertex_tex_coord: bool = False, halfedge_tex_coord: bool = False, edge_color: bool = False, face_normal: bool = False, face_color: bool = False, color_alpha: bool = False, color_float: bool = False) -> None\n\nInvoked with: <openmesh.PolyMesh object at 0x7f2207309f30>, 'bunny1111.obj'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_5191/4196886075.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mopenmesh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_mesh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmesh11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"bunny1111.obj\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: write_mesh(): incompatible function arguments. The following argument types are supported:\n    1. (filename: str, mesh: openmesh.TriMesh, binary: bool = False, msb: bool = False, lsb: bool = False, swap: bool = False, vertex_normal: bool = False, vertex_color: bool = False, vertex_tex_coord: bool = False, halfedge_tex_coord: bool = False, edge_color: bool = False, face_normal: bool = False, face_color: bool = False, color_alpha: bool = False, color_float: bool = False) -> None\n    2. (filename: str, mesh: openmesh.PolyMesh, binary: bool = False, msb: bool = False, lsb: bool = False, swap: bool = False, vertex_normal: bool = False, vertex_color: bool = False, vertex_tex_coord: bool = False, halfedge_tex_coord: bool = False, edge_color: bool = False, face_normal: bool = False, face_color: bool = False, color_alpha: bool = False, color_float: bool = False) -> None\n\nInvoked with: <openmesh.PolyMesh object at 0x7f2207309f30>, 'bunny1111.obj'"
     ]
    }
   ],
   "source": [
    "openmesh.write_mesh(mesh11, \"bunny1111.obj\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8315"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points_face.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.0000e+00, -1.5586e-08,  1.6673e-08],\n",
       "        [-1.5586e-08,  1.0000e+00, -2.3843e-08],\n",
       "        [ 1.6673e-08, -2.3843e-08,  1.0000e+00]], device='cuda:0',\n",
       "       grad_fn=<MmBackward>)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R[0, 0, :-1, :-1]@(R[0, 0, :-1, :-1].T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.60822886,  2.6521692 ,  0.7332895 ],\n",
       "       [ 0.63643765,  2.6594446 ,  0.63895345],\n",
       "       [ 0.66882116,  2.567572  ,  0.6456163 ],\n",
       "       ...,\n",
       "       [-0.85560316, -1.5839747 , -0.34642002],\n",
       "       [-0.56195986,  1.6341548 ,  1.198669  ],\n",
       "       [ 0.34078336, -0.55604595, -0.81593436]], dtype=float32)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.vstack([wbx_result[3][:, :-1], Bone_location[3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45, 3)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Bone_location[3].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8315, 3)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wbx_result[3][:, :-1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([[1, 2], [1, 4], [1, 0],\n",
    "...               [10, 2], [10, 4], [10, 0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 2)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "center, indexes = kmeans_plusplus(old_mesh[:, :-1].detach().cpu().numpy(), n_clusters=10, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45, 3)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"R.npy\", R.detach().cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"W.npy\", W.detach().cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "W = np.load('W.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "R = np.load('R.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ 6.6551e-01,  5.5304e-01, -5.0125e-01,  7.7804e-01],\n",
       "         [-4.2060e-01,  8.3265e-01,  3.6026e-01, -3.1423e-01],\n",
       "         [ 6.1661e-01, -2.8933e-02,  7.8674e-01,  3.8223e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 9.9372e-01, -1.0652e-01, -3.4407e-02,  1.7831e+00],\n",
       "         [ 1.1189e-01,  9.5393e-01,  2.7840e-01,  2.1400e-01],\n",
       "         [ 3.1677e-03, -2.8050e-01,  9.5985e-01, -5.5811e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[-5.4936e-02,  3.2795e-01, -9.4310e-01,  5.8502e-01],\n",
       "         [-4.7454e-01,  8.2246e-01,  3.1365e-01, -9.7908e-01],\n",
       "         [ 8.7852e-01,  4.6477e-01,  1.1044e-01,  2.0641e+00],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 5.6366e-01,  6.3519e-01, -5.2803e-01, -5.1763e-01],\n",
       "         [-8.2434e-01,  4.7319e-01, -3.1073e-01,  6.6134e-01],\n",
       "         [ 5.2489e-02,  6.1043e-01,  7.9033e-01, -1.2407e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 7.7681e-01, -3.5558e-01,  5.1973e-01, -1.2656e+00],\n",
       "         [ 3.7012e-01,  9.2553e-01,  8.0025e-02,  3.2150e-01],\n",
       "         [-5.0948e-01,  1.3020e-01,  8.5057e-01,  1.2183e+00],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 9.6660e-01,  2.3507e-01, -1.0216e-01, -6.7492e-01],\n",
       "         [-1.7811e-01,  9.0264e-01,  3.9181e-01,  1.9369e+00],\n",
       "         [ 1.8431e-01, -3.6052e-01,  9.1436e-01, -5.2655e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 8.9896e-01, -2.0257e-01,  3.8838e-01,  1.7789e+00],\n",
       "         [ 2.4328e-01,  9.6822e-01, -5.8104e-02,  1.3645e+00],\n",
       "         [-3.6426e-01,  1.4672e-01,  9.1967e-01,  5.8979e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 8.4397e-01,  5.7972e-02, -5.3325e-01,  1.6885e+00],\n",
       "         [ 8.6141e-02,  9.6659e-01,  2.4141e-01, -6.0826e-01],\n",
       "         [ 5.2943e-01, -2.4968e-01,  8.1078e-01, -2.8051e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 9.2537e-01,  2.4766e-01, -2.8698e-01, -1.7401e-01],\n",
       "         [-2.4137e-01,  9.6872e-01,  5.7679e-02,  9.6804e-01],\n",
       "         [ 2.9229e-01,  1.5896e-02,  9.5620e-01, -1.3263e+00],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 5.5093e-01,  3.9342e-02,  8.3362e-01, -3.1969e-01],\n",
       "         [ 4.3725e-01,  8.3721e-01, -3.2848e-01,  8.5124e-01],\n",
       "         [-7.1084e-01,  5.4547e-01,  4.4404e-01,  1.1207e+00],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 3.7434e-01,  9.2447e-01,  7.2305e-02,  4.8286e-01],\n",
       "         [-5.2425e-01,  2.7531e-01, -8.0583e-01,  1.2931e+00],\n",
       "         [-7.6487e-01,  2.6375e-01,  5.8772e-01,  4.5957e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 3.8854e-01,  8.8925e-01,  2.4141e-01, -9.0160e-01],\n",
       "         [-9.0744e-01,  4.1476e-01, -6.7296e-02,  1.0694e+00],\n",
       "         [-1.5997e-01, -1.9292e-01,  9.6809e-01, -1.0656e+00],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 1.5536e-01, -2.0594e-01,  9.6615e-01, -1.8813e+00],\n",
       "         [ 7.8424e-01,  6.2042e-01,  6.1416e-03,  3.0330e-01],\n",
       "         [-6.0069e-01,  7.5675e-01,  2.5789e-01,  1.0863e+00],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 9.4349e-01, -1.2764e-01,  3.0582e-01, -2.9280e-01],\n",
       "         [ 1.6071e-01,  9.8330e-01, -8.5407e-02,  1.8943e+00],\n",
       "         [-2.8981e-01,  1.2973e-01,  9.4825e-01, -2.0536e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 9.6407e-01,  2.0729e-01,  1.6615e-01,  2.4664e-01],\n",
       "         [-1.8704e-01,  9.7377e-01, -1.2959e-01, -1.0072e+00],\n",
       "         [-1.8866e-01,  9.3861e-02,  9.7755e-01,  2.7008e-02],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 2.5403e-01, -9.4290e-01,  2.1541e-01, -8.7354e-01],\n",
       "         [ 3.0247e-01,  2.8899e-01,  9.0829e-01,  6.6242e-01],\n",
       "         [-9.1869e-01, -1.6558e-01,  3.5861e-01,  6.1400e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 9.8367e-01, -4.9258e-02, -1.7311e-01, -7.2214e-01],\n",
       "         [ 1.3252e-01,  8.4904e-01,  5.1143e-01, -1.5496e+00],\n",
       "         [ 1.2178e-01, -5.2602e-01,  8.4171e-01, -5.2281e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 9.9824e-01, -7.2869e-03,  5.8908e-02,  6.5592e-01],\n",
       "         [ 7.1866e-03,  9.9997e-01,  1.9137e-03,  1.3797e+00],\n",
       "         [-5.8921e-02, -1.4870e-03,  9.9826e-01,  5.1163e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 9.8219e-01, -9.4798e-02,  1.6222e-01, -9.2592e-01],\n",
       "         [ 1.8366e-01,  6.6648e-01, -7.2254e-01, -2.2719e-01],\n",
       "         [-3.9623e-02,  7.3947e-01,  6.7203e-01,  1.3585e+00],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 7.8321e-01, -4.2211e-01, -4.5651e-01, -4.6558e-01],\n",
       "         [ 1.4702e-01,  8.3913e-01, -5.2368e-01,  1.0003e+00],\n",
       "         [ 6.0412e-01,  3.4304e-01,  7.1928e-01, -8.9368e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 5.4991e-02,  9.5197e-01, -3.0121e-01, -1.2092e+00],\n",
       "         [-9.9838e-01,  4.8058e-02, -3.0383e-02,  1.0641e+00],\n",
       "         [-1.4448e-02,  3.0239e-01,  9.5307e-01,  1.6906e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 9.2196e-01, -2.3463e-01,  3.0812e-01,  2.6716e-01],\n",
       "         [ 3.4431e-01,  8.6081e-01, -3.7477e-01, -1.1699e+00],\n",
       "         [-1.7731e-01,  4.5161e-01,  8.7442e-01, -2.7637e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[-3.9541e-01, -8.7100e-01,  2.9156e-01,  1.6318e+00],\n",
       "         [ 8.1353e-01, -4.7946e-01, -3.2906e-01, -1.0769e-01],\n",
       "         [ 4.2640e-01,  1.0708e-01,  8.9817e-01, -1.6352e+00],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 9.9407e-01,  4.4692e-02, -9.9162e-02,  4.1413e-01],\n",
       "         [-7.1606e-02,  9.5515e-01, -2.8734e-01,  2.2514e-01],\n",
       "         [ 8.1872e-02,  2.9274e-01,  9.5268e-01,  2.0296e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 9.4692e-01,  1.7685e-01,  2.6846e-01, -9.4751e-01],\n",
       "         [-1.3612e-01,  9.7710e-01, -1.6354e-01, -3.4916e-01],\n",
       "         [-2.9123e-01,  1.1831e-01,  9.4931e-01,  9.4282e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 9.9342e-01,  1.1357e-01,  1.5066e-02, -1.2594e+00],\n",
       "         [-1.1101e-01,  9.2172e-01,  3.7164e-01, -1.1461e+00],\n",
       "         [ 2.8320e-02, -3.7086e-01,  9.2826e-01, -2.4004e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 7.8884e-01,  9.9247e-02, -6.0653e-01,  8.7797e-02],\n",
       "         [ 3.4365e-03,  9.8615e-01,  1.6583e-01,  2.1099e-01],\n",
       "         [ 6.1458e-01, -1.3290e-01,  7.7758e-01, -5.9753e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 9.5213e-01,  1.1002e-01,  2.8522e-01,  1.7260e-01],\n",
       "         [-1.1857e-01,  9.9286e-01,  1.2839e-02,  1.6968e+00],\n",
       "         [-2.8177e-01, -4.6043e-02,  9.5838e-01,  1.1597e+00],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 9.9117e-01,  1.1538e-01, -6.5324e-02,  9.2171e-02],\n",
       "         [-9.6278e-02,  9.6505e-01,  2.4373e-01,  1.6924e-01],\n",
       "         [ 9.1163e-02, -2.3529e-01,  9.6764e-01,  3.3434e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 9.3077e-01,  3.6309e-01,  4.2854e-02, -1.2028e-01],\n",
       "         [-3.1055e-01,  7.2328e-01,  6.1678e-01, -2.3026e-01],\n",
       "         [ 1.9295e-01, -5.8739e-01,  7.8597e-01, -4.6127e-02],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 9.8862e-01,  7.7940e-02,  1.2869e-01,  2.8935e-01],\n",
       "         [-1.0176e-01,  9.7642e-01,  1.9037e-01,  4.5099e-01],\n",
       "         [-1.1082e-01, -2.0130e-01,  9.7324e-01,  2.5375e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 2.1833e-01,  9.4669e-01,  2.3688e-01,  8.8545e-01],\n",
       "         [-6.3046e-01,  3.2211e-01, -7.0623e-01,  1.5689e+00],\n",
       "         [-7.4489e-01,  4.8511e-03,  6.6717e-01,  1.7809e+00],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 9.9740e-01, -2.6468e-02, -6.7020e-02, -6.8976e-01],\n",
       "         [-9.3034e-03,  8.7501e-01, -4.8402e-01,  8.2319e-01],\n",
       "         [ 7.1454e-02,  4.8339e-01,  8.7249e-01,  1.5807e+00],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 9.8303e-01,  1.2615e-01, -1.3319e-01,  1.4777e+00],\n",
       "         [-2.8250e-02,  8.2146e-01,  5.6956e-01, -4.9583e-01],\n",
       "         [ 1.8126e-01, -5.5613e-01,  8.1109e-01, -1.4516e+00],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 8.5611e-01,  5.0174e-01, -1.2380e-01,  4.2580e-01],\n",
       "         [-4.5400e-01,  8.4465e-01,  2.8366e-01, -4.7950e-01],\n",
       "         [ 2.4689e-01, -1.8664e-01,  9.5090e-01, -1.4198e+00],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 6.7347e-01,  6.3245e-01,  3.8269e-01, -8.5413e-01],\n",
       "         [-7.0222e-01,  7.0908e-01,  6.3921e-02,  9.6550e-01],\n",
       "         [-2.3093e-01, -3.1178e-01,  9.2166e-01, -8.8702e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[-2.6517e-01,  5.0341e-01,  8.2236e-01, -8.1465e-01],\n",
       "         [ 6.6103e-01,  7.1582e-01, -2.2504e-01,  3.0285e-02],\n",
       "         [-7.0195e-01,  4.8393e-01, -5.2258e-01,  3.2289e+00],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 6.6527e-01, -7.1699e-01, -2.0819e-01,  9.8987e-01],\n",
       "         [ 6.9201e-01,  6.9684e-01, -1.8852e-01, -6.7987e-02],\n",
       "         [ 2.8024e-01, -1.8653e-02,  9.5975e-01, -1.1600e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 8.9819e-01, -1.9276e-01, -3.9509e-01,  1.2070e-01],\n",
       "         [ 3.8609e-01,  7.7564e-01,  4.9932e-01, -7.3930e-01],\n",
       "         [ 2.1019e-01, -6.0102e-01,  7.7110e-01,  7.1425e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 2.6310e-01, -9.6465e-01,  1.5221e-02, -8.2205e-01],\n",
       "         [ 9.5737e-01,  2.6300e-01,  1.1947e-01,  3.6236e-01],\n",
       "         [-1.1925e-01, -1.6861e-02,  9.9272e-01,  4.3681e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 2.1212e-01, -4.4247e-01,  8.7134e-01, -2.5965e+00],\n",
       "         [ 5.9158e-01,  7.6783e-01,  2.4589e-01,  2.1493e+00],\n",
       "         [-7.7784e-01,  4.6331e-01,  4.2463e-01,  6.4380e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 3.7936e-01, -2.1185e-01,  9.0067e-01, -2.2181e+00],\n",
       "         [ 3.8914e-01,  9.1969e-01,  5.2419e-02,  9.4298e-01],\n",
       "         [-8.3944e-01,  3.3060e-01,  4.3133e-01,  1.7836e+00],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 9.2811e-01,  2.4032e-01,  2.8436e-01, -4.7806e-01],\n",
       "         [-2.6420e-03,  7.6801e-01, -6.4043e-01, -1.5733e-01],\n",
       "         [-3.7230e-01,  5.9364e-01,  7.1343e-01,  1.2954e+00],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 9.7558e-01, -9.7829e-02, -1.9668e-01, -3.3001e-01],\n",
       "         [ 1.1744e-01,  9.8893e-01,  9.0648e-02,  2.2720e+00],\n",
       "         [ 1.8563e-01, -1.1153e-01,  9.7627e-01,  4.6859e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]],\n",
       "\n",
       "        [[ 9.5406e-01,  2.4050e-01, -1.7871e-01, -8.9745e-01],\n",
       "         [-2.7506e-01,  9.3951e-01, -2.0410e-01,  2.3149e-01],\n",
       "         [ 1.1881e-01,  2.4388e-01,  9.6250e-01, -8.2991e-01],\n",
       "         [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]]],\n",
       "       device='cuda:0')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "R_tmp = R[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ivenwu/miniconda3/envs/lietorch/lib/python3.7/site-packages/ipykernel_launcher.py:3: UserWarning: torch.eig is deprecated in favor of torch.linalg.eig and will be removed in a future PyTorch release.\n",
      "torch.linalg.eig returns complex tensors of dtype cfloat or cdouble rather than real tensors mimicking complex tensors.\n",
      "L, _ = torch.eig(A)\n",
      "should be replaced with\n",
      "L_complex = torch.linalg.eigvals(A)\n",
      "and\n",
      "L, V = torch.eig(A, eigenvectors=True)\n",
      "should be replaced with\n",
      "L_complex, V_complex = torch.linalg.eig(A) (Triggered internally at  /opt/conda/conda-bld/pytorch_1623448224956/work/aten/src/ATen/native/BatchLinearAlgebra.cpp:2897.)\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "eigvector_list = []\n",
    "for i in range(R_tmp.shape[0]):\n",
    "    (evals,evecs) = torch.eig(torch.tensor(R_tmp[i]),eigenvectors=True)\n",
    "#     print(i)\n",
    "#     print(evals)\n",
    "#     print(evecs)\n",
    "    flag =  0\n",
    "    for val_index in range(len(evals)):\n",
    "        \n",
    "        if torch.abs(evals[val_index][0]-1)<1e-5 and torch.abs(evals[val_index][1])<1e-5:\n",
    "\n",
    "            scale_info = -100\n",
    "            if torch.abs(evecs[val_index][0])+torch.abs(evecs[val_index][1])+torch.abs(evecs[val_index][2])>1e-4:\n",
    "#                 print(evecs[val_index])\n",
    "#                 vector_wanted = (evecs[val_index]/torch.abs(evecs[val_index][-1]))[:-1]\n",
    "                vector_wanted = evecs[val_index][:-1]\n",
    "#                 flag = 1\n",
    "#                 if scale_info != -100:\n",
    "#                     vector_wanted = vector_wanted/torch.abs(scale_info)\n",
    "#                     eigvector_list.append(vector_wanted.cpu().detach().numpy())\n",
    "#                     break\n",
    "#             else:\n",
    "#                 scale_info = evecs[val_index][-1]\n",
    "#                 if flag==1:\n",
    "#                     vector_wanted = vector_wanted/torch.abs(scale_info)\n",
    "#                     eigvector_list.append(vector_wanted.cpu().detach().numpy())\n",
    "#                     break\n",
    "#                 eigvector_list.append(vector_wanted.cpu().detach().numpy())\n",
    "#                 break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "eigvector_list = []\n",
    "for i in range(R_tmp.shape[0]):\n",
    "\n",
    "    (evals,evecs) = torch.linalg.eig(R_tmp[i])\n",
    "    for j in range(4):\n",
    "        if torch.abs(evecs[j].real.max()) > 1e-5 and torch.abs(evecs[j].imag.max()) < 1e-5:\n",
    "            eigvector_list.append(evecs[j])\n",
    "    # print(evals, evecs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigvector_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 4])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R_tmp[i].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "eigvector_array = np.array(eigvector_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.08931750e+05, -9.44651125e+05,  1.12544150e+06],\n",
       "       [-3.24681225e+06, -1.43762031e+05,  8.98924188e+05],\n",
       "       [ 1.95427598e+04, -5.86003062e+05,  3.64042188e+05],\n",
       "       [-5.96894375e+05, -1.18165962e+06,  2.50963225e+06],\n",
       "       [-1.24324430e+05,  4.42932750e+06,  4.41323500e+06],\n",
       "       [ 1.31386288e+06, -3.10073656e+05, -5.37888375e+05],\n",
       "       [-1.26899762e+06,  9.39687400e+06,  7.66483850e+06],\n",
       "       [-9.65893750e+05,  0.00000000e+00,  3.28692266e+04],\n",
       "       [-2.63832363e+04, -5.67883438e+05,  6.76968875e+05],\n",
       "       [ 4.71411600e+06,  0.00000000e+00,  1.49484412e+06],\n",
       "       [ 9.45399625e+05, -1.65568438e+06,  2.87590175e+06],\n",
       "       [-5.24790875e+05,  1.72209975e+06,  1.08762620e+07],\n",
       "       [-8.25391094e+04,  3.47957688e+05,  2.88241406e+05],\n",
       "       [-1.15535425e+06,  7.71927500e+06,  5.02589250e+06],\n",
       "       [-9.30507812e+05, -2.15668350e+06,  3.12389625e+06],\n",
       "       [-2.89633725e+06, -4.90623450e+06,  6.42465350e+06],\n",
       "       [-8.08298650e+06, -9.63290750e+05, -1.64752312e+06],\n",
       "       [ 2.92674219e+04,  8.31555950e+06,  1.44393925e+06],\n",
       "       [-2.77627600e+06,  2.66241406e+05, -3.77330062e+05],\n",
       "       [ 7.28664250e+06,  0.00000000e+00,  4.28173800e+06],\n",
       "       [-4.35142219e+05, -5.17617719e+05,  4.24592400e+06],\n",
       "       [-9.34659500e+05,  3.17961750e+05, -5.13810844e+05],\n",
       "       [-5.15592219e+05,  1.72776500e+06,  9.40927800e+06],\n",
       "       [-2.23558475e+06,  3.02539625e+05,  5.02299156e+05],\n",
       "       [ 1.25983688e+06,  5.59919400e+06,  4.05355300e+06],\n",
       "       [-4.15642125e+06,  5.02174492e+04,  8.89088312e+05],\n",
       "       [ 1.06310238e+06,  0.00000000e+00, -1.14584500e+05],\n",
       "       [-2.37797125e+05,  6.15242350e+06,  3.49158500e+06],\n",
       "       [-1.90194438e+06, -4.01868625e+05, -6.20215375e+05],\n",
       "       [-1.10365738e+06, -8.48950547e+04, -4.39157750e+05],\n",
       "       [ 4.58805281e+05, -1.26987391e+05, -2.13043703e+05],\n",
       "       [ 5.78177000e+05,  1.00682181e+06,  2.13620150e+06],\n",
       "       [ 1.28120800e+06, -1.59121465e+04,  1.29697664e+05],\n",
       "       [-8.71960900e+06, -8.14515688e+05,  1.73726138e+06],\n",
       "       [-2.06972425e+06,  3.09868350e+06,  8.41076900e+06],\n",
       "       [-1.84705112e+06,  3.42714825e+06,  1.02133090e+07],\n",
       "       [ 7.85228594e+04,  0.00000000e+00,  1.04119785e+04],\n",
       "       [-1.60200801e+04, -4.90684258e+04,  1.98893734e+05],\n",
       "       [ 1.40588512e+06, -4.58220125e+05, -6.02139688e+05],\n",
       "       [-2.13344000e+05,  2.17366328e+05,  4.32314500e+06],\n",
       "       [-6.51071375e+05,  9.35442100e+06,  8.24355300e+06],\n",
       "       [-3.25663125e+05,  6.31103500e+06,  3.04792125e+06],\n",
       "       [-1.72779862e+06,  2.12339859e+05, -6.59835625e+05],\n",
       "       [-1.76740700e+06,  7.50004750e+06,  5.42418250e+06],\n",
       "       [-1.03313219e+05, -2.24781375e+05,  3.35404250e+05]], dtype=float32)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigvector_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1125441.5 -944651.1\n",
      "898924.2 -3246812.2\n",
      "364042.2 -586003.06\n",
      "2509632.2 -1181659.6\n",
      "4429327.5 -124324.43\n",
      "1313862.9 -537888.4\n",
      "9396874.0 -1268997.6\n",
      "32869.227 -965893.75\n",
      "676968.9 -567883.44\n",
      "4714116.0 0.0\n",
      "2875901.8 -1655684.4\n",
      "10876262.0 -524790.9\n",
      "347957.7 -82539.11\n",
      "7719275.0 -1155354.2\n",
      "3123896.2 -2156683.5\n",
      "6424653.5 -4906234.5\n",
      "-963290.75 -8082986.5\n",
      "8315559.5 29267.422\n",
      "266241.4 -2776276.0\n",
      "7286642.5 0.0\n",
      "4245924.0 -517617.72\n",
      "317961.75 -934659.5\n",
      "9409278.0 -515592.22\n",
      "502299.16 -2235584.8\n",
      "5599194.0 1259836.9\n",
      "889088.3 -4156421.2\n",
      "1063102.4 -114584.5\n",
      "6152423.5 -237797.12\n",
      "-401868.62 -1901944.4\n",
      "-84895.055 -1103657.4\n",
      "458805.28 -213043.7\n",
      "2136201.5 578177.0\n",
      "1281208.0 -15912.146\n",
      "1737261.4 -8719609.0\n",
      "8410769.0 -2069724.2\n",
      "10213309.0 -1847051.1\n",
      "78522.86 0.0\n",
      "198893.73 -49068.426\n",
      "1405885.1 -602139.7\n",
      "4323145.0 -213344.0\n",
      "9354421.0 -651071.4\n",
      "6311035.0 -325663.12\n",
      "212339.86 -1727798.6\n",
      "7500047.5 -1767407.0\n",
      "335404.25 -224781.38\n"
     ]
    }
   ],
   "source": [
    "for k in range(eigvector_array.shape[0]):\n",
    "    \n",
    "    print(eigvector_array[k].max(),eigvector_array[k].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "(evals,evecs) = torch.eig(R[0][1],eigenvectors=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.0000,  0.0000],\n",
       "        [ 0.9537,  0.3006],\n",
       "        [ 0.9537, -0.3006],\n",
       "        [ 1.0000,  0.0000]], device='cuda:0')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-9.2959e-01, -4.1160e-02,  2.5737e-01,  9.2959e-01],\n",
       "        [-6.2496e-02,  7.0572e-01,  0.0000e+00,  6.2496e-02],\n",
       "        [ 3.6326e-01,  1.6084e-02,  6.5861e-01, -3.6326e-01],\n",
       "        [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  2.8631e-07]], device='cuda:0')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evecs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.6655,  0.5530, -0.5012,  0.7780],\n",
       "        [-0.4206,  0.8327,  0.3603, -0.3142],\n",
       "        [ 0.6166, -0.0289,  0.7867,  0.3822],\n",
       "        [ 0.0000,  0.0000,  0.0000,  1.0000]], device='cuda:0')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([8, 45, 4, 4])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for j in range(45):\n",
    "    R[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(old_info,target_info, n_steps=1000, lr_init=0.2):\n",
    "    \"\"\" Riemannian Gradient Descent \"\"\"\n",
    "\n",
    "    N = points_info.shape[0]\n",
    "    F = target_info.shape[0]\n",
    "    B = 45  ### 这个肯定可以通过读pkl得到\n",
    "\n",
    "\n",
    "    old_mesh = torch.tensor(old_info).float().cuda().detach()\n",
    "    \n",
    "    # bp()\n",
    "    # ones = torch.ones(old_mesh.shape[0], 1).cuda()\n",
    "    # x = torch.cat([old_mesh, ones], dim=1)\n",
    "\n",
    "\n",
    "    target_mesh = torch.tensor(target_info, requires_grad=False).float().cuda().detach()\n",
    "\n",
    "    # bp()\n",
    "    # W = torch.randint(10,(N,B))\n",
    "    # W = torch.randn((N,B)).cuda()\n",
    "    # W = np.random.randint(10,size=(N,B))\n",
    "    # bp()\n",
    "    # W = SO3(torch.from_numpy(W).float().cuda())\n",
    "    # W = torch.zeros((N, B), dtype=torch.float, requires_grad=True, device=\"cuda\")\n",
    "    W = torch.randn((N, B), requires_grad=True, device=\"cuda\")\n",
    "    W2 = torch.randn((N, B), requires_grad=True, device=\"cuda\")\n",
    "    # W = torch.tensor(W0.float(), requires_grad=True, device=\"cuda\")\n",
    "    # W = W * 0\n",
    "    # bp()\n",
    "    # W = LieGroupParameter(W)\n",
    "    # bp()\n",
    "    # R = torch.randint(10,(B,3,3))laplacian = torch.zeros([self.nv, self.nv]).astype(np.float32)\n",
    "\n",
    "    # bp()\n",
    "    # random quaternion\n",
    "    p1 = torch.randn((F, B, 7), requires_grad=True, device=\"cuda\")\n",
    "    # W = W.cuda()\n",
    "    # p1 = p1.cuda()\n",
    "    # p1 = p1 / p1.norm(dim=-1, keepdim=True)\n",
    "    # create SO3 object from quaternion (differentiable w.r.t q)\n",
    "    \n",
    "    # 4x4 transformation matrix (differentiable w.r.t R)\n",
    "    \n",
    "\n",
    "\n",
    "    optimizer = optim.Adam([W, p1],lr=1e-3)\n",
    "    chamLoss = chamfer3D.dist_chamfer_3D.chamfer_3DDist()\n",
    "    for i in range(n_steps):\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss = 0\n",
    "        f_idxs = random.sample(range(F), F)\n",
    "        \n",
    "        for f in f_idxs:\n",
    "            T = torch.randn(B, 4, 4)\n",
    "            for b in range(B):\n",
    "                # R = SO3.InitFromVec(p1[f, b])\n",
    "                R = SE3.InitFromVec(p1[f, b])\n",
    "                # bp()\n",
    "                T[b] = R.matrix()\n",
    "            T = T.cuda()     \n",
    "            # bp()      \n",
    "            # bp() \n",
    "            # W1 = W\n",
    "            # W1 = (W - W.min()) / (W.max() - W.min())\n",
    "            W1 = FF.softmax(W*10)\n",
    "            W1 = (W1 / (W1.sum(1, keepdim=True).detach()))\n",
    "            W2[W1 < 0.1] = 0\n",
    "            \n",
    "            # ones = torch.ones(old_mesh.shape[0], 1).cuda()\n",
    "            # x = torch.cat([old_mesh, ones], dim=1)\n",
    "            x = old_mesh\n",
    "        \n",
    "        \n",
    "            bx = (T@x.T).permute(2, 0, 1)\n",
    "            wbx = W1.unsqueeze(2) * bx\n",
    "            # bp()\n",
    "            wbx =  wbx.permute((1,0,2))\n",
    "            # print(\"Wbx\", wbx.shape)\n",
    "            # wbx = wbx.sum(0, keepdim=True)[:, :, :-1]\n",
    "            wbx = wbx.sum(0, keepdim=True)\n",
    "            # loss_init = chamLoss(target_mesh[f][None],wbx)\n",
    "            # loss += loss_init[0].sum()\n",
    "\n",
    "            loss += torch.sum((target_mesh[f]-wbx)**2) + torch.sum(torch.abs(W))\n",
    "\n",
    "#             loss.backward()\n",
    "            print(\"loss\",loss)\n",
    "            # bp()\n",
    "        # bp()\n",
    "        loss.backward()\n",
    "        # print(\"this is loss for epoch {} : {} \".format(i,loss.detach().cpu().numpy()))\n",
    "        print(\"this is loss for epoch {} : {} \".format(i,loss / F))\n",
    "        # print(\"p1: \", p1)\n",
    "        optimizer.step()\n",
    "\n",
    "\n",
    "    new_R = SE3.InitFromVec(p1)\n",
    "    R = new_R.matrix()\n",
    "\n",
    "    # W1 = (W - W.min()) / (W.max() - W.min())\n",
    "    # W1 = (W1 / (W1.sum(1, keepdim=True).detach()))\n",
    "    # bp()\n",
    "    # return R.detach(), W1.detach()\n",
    "    return W.detach(),R.detach()"
   ]
  }
 ],
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