PointNet2_xyz.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "be8RFvYTsfYE",
    "tags": []
   },
   "source": [
    "# Data Load\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 15376,
     "status": "ok",
     "timestamp": 1674079742236,
     "user": {
      "displayName": "SUKIDA Tan",
      "userId": "04234593673225449814"
     },
     "user_tz": -60
    },
    "id": "0J4zJlrCqSb4"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import random\n",
    "import os\n",
    "import torch\n",
    "import scipy.spatial.distance\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import transforms, utils\n",
    "\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "\n",
    "import matplotlib.pylab as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "# from google.colab import drive\n",
    "import tensorflow as tf\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8394,
     "status": "ok",
     "timestamp": 1677260843122,
     "user": {
      "displayName": "SUKIDA Tan",
      "userId": "04234593673225449814"
     },
     "user_tz": -60
    },
    "id": "wOHAa2Ku_ci8",
    "outputId": "1c64c450-9b00-4473-c57b-e6e7396dccd0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1     19125652\n",
      "5      6440977\n",
      "2      3448050\n",
      "12     1979855\n",
      "16     1911652\n",
      "17     1746219\n",
      "6       628964\n",
      "3       288073\n",
      "14      242680\n",
      "13       24642\n",
      "Name: cla, dtype: int64\n",
      "1     2869200\n",
      "2      826645\n",
      "5      746775\n",
      "12     378061\n",
      "16     366536\n",
      "17     209118\n",
      "3      111354\n",
      "15      35457\n",
      "11      22421\n",
      "9        7342\n",
      "13       1932\n",
      "6        1319\n",
      "14        731\n",
      "Name: cla, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "lxyz = pd.read_csv('./data/training/labelledLocalCRS/DEBY_LOD2_4959323/DEBY_LOD2_4959322.xyz', sep=\" \", header=0)\n",
    "lxyz.columns = [\"x\", \"y\", \"z\", \"cla\"]\n",
    "print(lxyz.cla.value_counts())\n",
    "\n",
    "lxyz_test = pd.read_csv('./data/test/labelledLocalCRS/DEBY_LOD2_4959422/DEBY_LOD2_4959422.xyz', sep=\" \", header=0)\n",
    "lxyz_test.columns = [\"x\", \"y\", \"z\", \"cla\"]\n",
    "print(lxyz_test.cla.value_counts())\n",
    "\n",
    "############## !! Here you can choose whether to use large dataset ###############"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 10506,
     "status": "ok",
     "timestamp": 1674079752704,
     "user": {
      "displayName": "SUKIDA Tan",
      "userId": "04234593673225449814"
     },
     "user_tz": -60
    },
    "id": "wOHAa2Ku_ci8",
    "outputId": "aa11cb6d-3ffe-434d-da6c-309d1d541783"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1     36429131\n",
      "16     7566216\n",
      "11     7534665\n",
      "5      7056168\n",
      "2      6351914\n",
      "12     5938935\n",
      "15     3797046\n",
      "17     3600867\n",
      "7      1393392\n",
      "3       691885\n",
      "6       475590\n",
      "10      419409\n",
      "14      304608\n",
      "8       220774\n",
      "13       49393\n",
      "9        29398\n",
      "Name: cla, dtype: int64\n",
      "1     19125652\n",
      "5      6440977\n",
      "2      3448050\n",
      "12     1979855\n",
      "16     1911652\n",
      "17     1746219\n",
      "6       628964\n",
      "3       288073\n",
      "14      242680\n",
      "13       24642\n",
      "Name: cla, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# lxyz = pd.read_csv('./data/training/labelledLocalCRS/DEBY_LOD2_4959322/DEBY_LOD2_4959322.xyz', sep=\" \", header=0)\n",
    "# lxyz.columns = [\"x\", \"y\", \"z\", \"cla\"]\n",
    "lxyz_1 = pd.read_csv('./data/training/labelledLocalCRS/DEBY_LOD2_4906981/DEBY_LOD2_4906981.xyz', sep=\" \", header=0)\n",
    "lxyz_1.columns = [\"x\", \"y\", \"z\", \"cla\"]\n",
    "lxyz_2 = pd.read_csv('./data/training/labelledLocalCRS/DEBY_LOD2_4959322/DEBY_LOD2_4959322.xyz', sep=\" \", header=0)\n",
    "lxyz_2.columns = [\"x\", \"y\", \"z\", \"cla\"]\n",
    "lxyz_3 = pd.read_csv('./data/training/labelledLocalCRS/DEBY_LOD2_4959460/DEBY_LOD2_4959460.xyz', sep=\" \", header=0)\n",
    "lxyz_3.columns = [\"x\", \"y\", \"z\", \"cla\"]\n",
    "lxyz_4 = pd.read_csv('./data/training/labelledLocalCRS/DEBY_LOD2_4959462/DEBY_LOD2_4959462.xyz', sep=\" \", header=0)\n",
    "lxyz_4.columns = [\"x\", \"y\", \"z\", \"cla\"]\n",
    "lxyz = pd.concat([lxyz_1, lxyz_2, lxyz_3, lxyz_4], axis = 0)\n",
    "print(lxyz.cla.value_counts())\n",
    "\n",
    "lxyz_test = pd.read_csv('./data/training/labelledLocalCRS/DEBY_LOD2_4959323/DEBY_LOD2_4959323.xyz', sep=\" \", header=0)\n",
    "lxyz_test.columns = [\"x\", \"y\", \"z\", \"cla\"]\n",
    "print(lxyz_test.cla.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Sample Points & Merge class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1     40674984\n",
      "11    13473600\n",
      "13    11216476\n",
      "5      7531758\n",
      "2      6656522\n",
      "7      1393392\n",
      "3       691885\n",
      "8       220774\n",
      "Name: cla, dtype: int64\n",
      "1     19125652\n",
      "5      7069941\n",
      "2      3690730\n",
      "13     3682513\n",
      "11     1979855\n",
      "3       288073\n",
      "Name: cla, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "lclass = lxyz['cla'].replace({6:5,9:1,15:1,10:1,12:11,17:13,16:13,14:2,4:1})\n",
    "print(lclass.value_counts())\n",
    "\n",
    "lclass_test = lxyz_test['cla'].replace({6:5,9:1,15:1,10:1,12:11,17:13,16:13,14:2,4:1})\n",
    "print(lclass_test.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    40674984\n",
      "6    13473600\n",
      "5    11216476\n",
      "4     7531758\n",
      "2     6656522\n",
      "7     1393392\n",
      "3      691885\n",
      "8      220774\n",
      "Name: cla, dtype: int64\n",
      "1    19125652\n",
      "4     7069941\n",
      "2     3690730\n",
      "5     3682513\n",
      "6     1979855\n",
      "3      288073\n",
      "Name: cla, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "lclass = lclass.replace({5:4,13:5,11:6})\n",
    "print(lclass.value_counts())\n",
    "# 1: wall\n",
    "# 2: window\n",
    "# 3: door\n",
    "# 4: molding\n",
    "# 5: other\n",
    "# 6: terrian\n",
    "# 7: column\n",
    "# 8: arch\n",
    "\n",
    "lclass_test = lclass_test.replace({5:4,13:5,11:6})\n",
    "print(lclass_test.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    40674984\n",
      "5    13473600\n",
      "4    11216476\n",
      "3     7531758\n",
      "1     6656522\n",
      "6     1393392\n",
      "2      691885\n",
      "7      220774\n",
      "Name: cla, dtype: int64\n",
      "0    19125652\n",
      "3     7069941\n",
      "1     3690730\n",
      "4     3682513\n",
      "5     1979855\n",
      "2      288073\n",
      "Name: cla, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "lclass = lclass.replace({1:0,2:1,3:2,4:3,5:4,6:5,7:6,8:7})\n",
    "print(lclass.value_counts())\n",
    "# 0: wall\n",
    "# 1: window\n",
    "# 2: door\n",
    "# 3: molding\n",
    "# 4: other\n",
    "# 5: terrian\n",
    "# 6: column\n",
    "# 7: arch\n",
    "\n",
    "lclass_test = lclass_test.replace({1:0,2:1,3:2,4:3,5:4,6:5,7:6,8:7})\n",
    "print(lclass_test.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "lxyz = lxyz.drop('cla',axis = 1) \n",
    "lxyz_test = lxyz_test.drop('cla',axis = 1) \n",
    "\n",
    "lxyz = lxyz.to_numpy().reshape(-1,3)\n",
    "lxyz_test = lxyz_test.to_numpy().reshape(-1,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jupyter environment detected. Enabling Open3D WebVisualizer.\n",
      "[Open3D INFO] WebRTC GUI backend enabled.\n",
      "[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.\n"
     ]
    }
   ],
   "source": [
    "import open3d as o3d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "######################### using open3d for point processing ################\n",
    "device = o3d.core.Device(\"CPU:0\")\n",
    "dtype = o3d.core.float32\n",
    "pcd = o3d.t.geometry.PointCloud(device)\n",
    "pcd.point.positions = o3d.core.Tensor(lxyz, dtype, device)\n",
    "pcd.point.labels = o3d.core.Tensor(lclass.to_numpy().reshape(-1,1), o3d.core.int32, device)\n",
    "# o3d.visualization.draw([pcd])\n",
    "downpcd = pcd.voxel_down_sample(voxel_size = 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "lxyz_train = downpcd.point.positions.numpy()\n",
    "lclass_train = downpcd.point.labels.numpy()\n",
    "\n",
    "pcd_train = o3d.geometry.PointCloud()\n",
    "pcd_train.points = o3d.utility.Vector3dVector(lxyz_train)\n",
    "\n",
    "# color\n",
    "colors = plt.get_cmap(\"tab20\")(np.array(lclass_train).reshape(-1) / 17.0)\n",
    "colors = colors[:,0:3]\n",
    "pcd_train.colors=o3d.utility.Vector3dVector(colors)\n",
    "# o3d.visualization.draw_geometries([pcd_train], window_name='test the color',width=800,height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14623737\n"
     ]
    }
   ],
   "source": [
    "print(len(lxyz_train)) # see how many points after simplified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcd_test = o3d.t.geometry.PointCloud(device)\n",
    "pcd_test.point.positions = o3d.core.Tensor(lxyz_test, dtype, device)\n",
    "pcd_test.point.labels = o3d.core.Tensor(lclass_test.to_numpy().reshape(-1,1), o3d.core.int32, device)\n",
    "# o3d.visualization.draw([pcd])\n",
    "downpcd_test = pcd_test.voxel_down_sample(voxel_size = 0.05)\n",
    "\n",
    "lxyz_test = downpcd_test.point.positions.numpy()\n",
    "lclass_test = downpcd_test.point.labels.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcd_test = o3d.geometry.PointCloud()\n",
    "pcd_test.points = o3d.utility.Vector3dVector(lxyz_test)\n",
    "\n",
    "# color\n",
    "colors = plt.get_cmap(\"tab20\")(np.array(lclass_test).reshape(-1) / 17.0)\n",
    "colors = colors[:,0:3]\n",
    "pcd_test.colors=o3d.utility.Vector3dVector(colors)\n",
    "# o3d.visualization.draw_geometries([pcd_test], window_name='test the color',width=800,height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Add Features （ IF NOT KEEP AS X Y Z）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "lxyz_train = pd.DataFrame(lxyz_train)\n",
    "lxyz_train.columns = [\"x\", \"y\", \"z\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "lxyz_test = pd.DataFrame(lxyz_test)\n",
    "lxyz_test.columns = [\"x\", \"y\", \"z\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "########## Normalization For Each Column ##########\n",
    "def normalize_point_cloud(pcd):\n",
    "    centroid = np.mean(pcd, axis=0) # calculation the center of point cloud\n",
    "    pcd = pcd - centroid # put the center as origin\n",
    "    m = np.max(np.sqrt(np.sum(pcd ** 2, axis=1))) # calculation the longest vector\n",
    "    pcd_normalized = pcd / m # normalization to (-1, 1)\n",
    "    return pcd_normalized\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_lxyz_train = normalize_point_cloud(lxyz_train)\n",
    "norm_lxyz_test = normalize_point_cloud(lxyz_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "lclass_train = pd.DataFrame(lclass_train, columns = ['cla'], dtype = int)\n",
    "lclass_test = pd.DataFrame(lclass_test, columns = ['cla'], dtype = int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cla\n",
      "0      1663662\n",
      "4      1070326\n",
      "3       714129\n",
      "1       499659\n",
      "5       239910\n",
      "2        34572\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(lclass_test.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          cla\n",
      "0           0\n",
      "1           0\n",
      "2           0\n",
      "3           0\n",
      "4           0\n",
      "...       ...\n",
      "14623732    4\n",
      "14623733    4\n",
      "14623734    4\n",
      "14623735    4\n",
      "14623736    4\n",
      "\n",
      "[14623737 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(lclass_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         cla\n",
      "0          1\n",
      "1          1\n",
      "2          1\n",
      "3          1\n",
      "4          1\n",
      "...      ...\n",
      "4222253    4\n",
      "4222254    4\n",
      "4222255    4\n",
      "4222256    4\n",
      "4222257    4\n",
      "\n",
      "[4222258 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(lclass_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 x         y         z\n",
      "0        -0.193227 -0.330674 -0.012098\n",
      "1        -0.193227 -0.329890 -0.019932\n",
      "2        -0.193227 -0.329890 -0.059102\n",
      "3        -0.193227 -0.329890 -0.005830\n",
      "4        -0.193227 -0.330674 -0.010531\n",
      "...            ...       ...       ...\n",
      "14623732 -0.035762 -0.011043 -0.002697\n",
      "14623733 -0.035762 -0.011827 -0.002697\n",
      "14623734  0.162440  0.087666 -0.127258\n",
      "14623735 -0.058481  0.086883  0.135183\n",
      "14623736 -0.060048  0.082182  0.134400\n",
      "\n",
      "[14623737 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "print(norm_lxyz_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                x         y         z\n",
      "0       -0.137393 -0.555251 -0.032752\n",
      "1       -0.137393 -0.550621  0.070270\n",
      "2       -0.137393 -0.555251  0.066797\n",
      "3       -0.137393 -0.551779  0.080688\n",
      "4       -0.137393 -0.540203 -0.083683\n",
      "...           ...       ...       ...\n",
      "4222253  0.178617  0.339530  0.071427\n",
      "4222254  0.178617  0.338372  0.071427\n",
      "4222255  0.178617  0.339530  0.072585\n",
      "4222256  0.177459  0.338372  0.071427\n",
      "4222257  0.177459  0.339530  0.071427\n",
      "\n",
      "[4222258 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "print(norm_lxyz_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Data Spilt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "lxyz_train = pd.concat([norm_lxyz_train, lclass_train], axis = 1)\n",
    "lxyz_test = pd.concat([norm_lxyz_test, lclass_test], axis = 1)\n",
    "# print(lxyz_test.cla.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 x         y         z  cla\n",
      "0        -0.193227 -0.330674 -0.012098    0\n",
      "1        -0.193227 -0.329890 -0.019932    0\n",
      "2        -0.193227 -0.329890 -0.059102    0\n",
      "3        -0.193227 -0.329890 -0.005830    0\n",
      "4        -0.193227 -0.330674 -0.010531    0\n",
      "...            ...       ...       ...  ...\n",
      "14623732 -0.035762 -0.011043 -0.002697    4\n",
      "14623733 -0.035762 -0.011827 -0.002697    4\n",
      "14623734  0.162440  0.087666 -0.127258    4\n",
      "14623735 -0.058481  0.086883  0.135183    4\n",
      "14623736 -0.060048  0.082182  0.134400    4\n",
      "\n",
      "[14623737 rows x 4 columns]\n",
      "                x         y         z  cla\n",
      "0       -0.137393 -0.555251 -0.032752    1\n",
      "1       -0.137393 -0.550621  0.070270    1\n",
      "2       -0.137393 -0.555251  0.066797    1\n",
      "3       -0.137393 -0.551779  0.080688    1\n",
      "4       -0.137393 -0.540203 -0.083683    1\n",
      "...           ...       ...       ...  ...\n",
      "4222253  0.178617  0.339530  0.071427    4\n",
      "4222254  0.178617  0.338372  0.071427    4\n",
      "4222255  0.178617  0.339530  0.072585    4\n",
      "4222256  0.177459  0.338372  0.071427    4\n",
      "4222257  0.177459  0.339530  0.071427    4\n",
      "\n",
      "[4222258 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "print(lxyz_train)\n",
    "print(lxyz_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2139,
     "status": "ok",
     "timestamp": 1674079754823,
     "user": {
      "displayName": "SUKIDA Tan",
      "userId": "04234593673225449814"
     },
     "user_tz": -60
    },
    "id": "ey4Eh_U10ET5",
    "outputId": "d4aedcb5-0aef-465c-b3ce-f0fd69af7890"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5971\n",
      "1407\n",
      "1160\n",
      "3720\n",
      "1607\n",
      "187\n",
      "37\n",
      "189\n",
      "0    6114304\n",
      "4    3809280\n",
      "1    1645568\n",
      "3    1440768\n",
      "5    1187840\n",
      "6     193536\n",
      "2     191488\n",
      "7      37888\n",
      "Name: cla, dtype: int64\n",
      "---------\n",
      "487\n",
      "1624\n",
      "697\n",
      "1045\n",
      "234\n",
      "33\n"
     ]
    }
   ],
   "source": [
    "def spilt_data(lxyz):\n",
    "  class_uniques = lxyz['cla'].unique() #把一列所有取值提出来成为一个Array \n",
    "  re = []\n",
    "  for column in class_uniques:\n",
    "    temp_data = lxyz[lxyz['cla'].isin([column])] #按照列的某个值取一个dataframe\n",
    "    batch_num = int(len(temp_data)/1024)\n",
    "    print(batch_num)\n",
    "    temp_data = temp_data[0:batch_num*1024]\n",
    "    re.append(temp_data)\n",
    "  re = pd.concat(re)\n",
    "  return re\n",
    "\n",
    "lxyz_train = spilt_data(lxyz_train)\n",
    "print(lxyz_train.cla.value_counts())\n",
    "print('---------')\n",
    "lxyz_test = spilt_data(lxyz_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                x         y         z  cla\n",
      "0       -0.193227 -0.330674 -0.012098    0\n",
      "1       -0.193227 -0.329890 -0.019932    0\n",
      "2       -0.193227 -0.329890 -0.059102    0\n",
      "3       -0.193227 -0.329890 -0.005830    0\n",
      "4       -0.193227 -0.330674 -0.010531    0\n",
      "...           ...       ...       ...  ...\n",
      "4980460  0.259583  0.289785 -0.046567    6\n",
      "4980461  0.259583  0.289785 -0.069286    6\n",
      "4980462  0.259583  0.289785 -0.086521    6\n",
      "4980463  0.259583  0.290569 -0.039517    6\n",
      "4980464  0.259583  0.289785 -0.061452    6\n",
      "\n",
      "[14620672 rows x 4 columns]\n",
      "                x         y         z  cla\n",
      "0       -0.137393 -0.555251 -0.032752    1\n",
      "1       -0.137393 -0.550621  0.070270    1\n",
      "2       -0.137393 -0.555251  0.066797    1\n",
      "3       -0.137393 -0.551779  0.080688    1\n",
      "4       -0.137393 -0.540203 -0.083683    1\n",
      "...           ...       ...       ...  ...\n",
      "3821331 -0.154756  0.753931 -0.110307    2\n",
      "3821332 -0.154756  0.753931 -0.109149    2\n",
      "3821333 -0.154756  0.753931 -0.077896    2\n",
      "3821334 -0.154756  0.753931 -0.105677    2\n",
      "3821335 -0.154756  0.757403 -0.085999    2\n",
      "\n",
      "[4218880 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "print(lxyz_train)\n",
    "print(lxyz_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sSRH6VAGf7Xw",
    "tags": []
   },
   "source": [
    "# Dataset Preparation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "executionInfo": {
     "elapsed": 77,
     "status": "ok",
     "timestamp": 1674079787517,
     "user": {
      "displayName": "SUKIDA Tan",
      "userId": "04234593673225449814"
     },
     "user_tz": -60
    },
    "id": "n23Bk90QSKqq"
   },
   "outputs": [],
   "source": [
    "class ToTensor(object):\n",
    "    def __call__(self, pointcloud):\n",
    "        assert len(pointcloud.shape)==2\n",
    "        pointcloud = pointcloud.to_numpy()\n",
    "        return torch.from_numpy(pointcloud)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "executionInfo": {
     "elapsed": 77,
     "status": "ok",
     "timestamp": 1674079787518,
     "user": {
      "displayName": "SUKIDA Tan",
      "userId": "04234593673225449814"
     },
     "user_tz": -60
    },
    "id": "JYsFkTHqfLRp"
   },
   "outputs": [],
   "source": [
    "def default_transforms():\n",
    "    return transforms.Compose([\n",
    "                                # Normalize(),\n",
    "                                ToTensor()\n",
    "                              ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 885,
     "status": "ok",
     "timestamp": 1674079788327,
     "user": {
      "displayName": "SUKIDA Tan",
      "userId": "04234593673225449814"
     },
     "user_tz": -60
    },
    "id": "Z-Ur3QdPfLX1",
    "outputId": "a7977d44-a242-4069-d2be-eb22720ec8bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14620672\n",
      "4218880\n",
      "4120\n",
      "14278\n",
      "4120\n",
      "type of :  torch.Size([1024, 4])\n",
      "tensor([[-0.1397, -0.5691,  0.2381,  1.0000],\n",
      "        [-0.1397, -0.5553,  0.1652,  1.0000],\n",
      "        [-0.1397, -0.5564,  0.1976,  1.0000],\n",
      "        ...,\n",
      "        [-0.1409, -0.5529,  0.1988,  1.0000],\n",
      "        [-0.1409, -0.5657,  0.2358,  1.0000],\n",
      "        [-0.1409, -0.5634,  0.0668,  1.0000]], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "train_transforms = default_transforms()\n",
    "batch_num_test = int(len(lxyz_test)/1024)\n",
    "batch_num_train = int(len(lxyz_train)/1024)\n",
    "_train = lxyz_train[0:(1024*batch_num_train)]\n",
    "_test = lxyz_test[0:(1024*batch_num_test)]\n",
    "print(len(_train))\n",
    "print(len(_test))\n",
    "print(batch_num_test)\n",
    "print(batch_num_train)\n",
    "\n",
    "data_test_temp = np.split(_test,batch_num_test)\n",
    "data_train_temp = np.split(_train,batch_num_train)\n",
    "\n",
    "data_test = [ train_transforms(test) for test in data_test_temp]\n",
    "data_train = [ train_transforms(train) for train in data_train_temp]\n",
    "\n",
    "print(len(data_test))\n",
    "print('type of : ', data_test[2].size())\n",
    "print(data_test[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-0.1397, -0.1397, -0.1397,  ..., -0.1409, -0.1409, -0.1409],\n",
      "       dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "print(data_test[2][:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14278\n",
      "10708\n",
      "3570\n"
     ]
    }
   ],
   "source": [
    "len_total = len(data_train)\n",
    "print(len_total)\n",
    "train_size = int(len_total*0.75)\n",
    "test_size = len_total - train_size\n",
    "print(train_size)\n",
    "print(test_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3570\n",
      "type of :  torch.Size([1024, 4])\n",
      "tensor([[-0.7667, -0.3025,  0.0545,  0.0000],\n",
      "        [-0.7667, -0.2986,  0.0169,  0.0000],\n",
      "        [-0.7667, -0.3001,  0.0655,  0.0000],\n",
      "        ...,\n",
      "        [-0.7667, -0.3048,  0.0843,  0.0000],\n",
      "        [-0.7667, -0.3056,  0.0224,  0.0000],\n",
      "        [-0.7667, -0.3048, -0.0074,  0.0000]], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "data_train, data_valid = torch.utils.data.random_split(data_train, [train_size, test_size])\n",
    "print(len(data_valid))\n",
    "print('type of : ', data_valid[2].size())\n",
    "print(data_valid[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "executionInfo": {
     "elapsed": 65,
     "status": "ok",
     "timestamp": 1674079788359,
     "user": {
      "displayName": "SUKIDA Tan",
      "userId": "04234593673225449814"
     },
     "user_tz": -60
    },
    "id": "hNZb5f-K1lrh"
   },
   "outputs": [],
   "source": [
    "train_loader = DataLoader(dataset=data_train, batch_size= 4, shuffle=True)\n",
    "valid_loader = DataLoader(dataset=data_valid, batch_size= 4, shuffle=True)\n",
    "test_loader = DataLoader(dataset=data_test, batch_size = 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N0FRlJOh2EuO",
    "tags": []
   },
   "source": [
    "# Model Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from time import time\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def timeit(tag, t):\n",
    "    print(\"{}: {}s\".format(tag, time() - t))\n",
    "    return time()\n",
    "\n",
    "def pc_normalize(pc):\n",
    "    l = pc.shape[0]\n",
    "    centroid = np.mean(pc, axis=0)\n",
    "    pc = pc - centroid\n",
    "    m = np.max(np.sqrt(np.sum(pc**2, axis=1)))\n",
    "    pc = pc / m\n",
    "    return pc\n",
    "\n",
    "def square_distance(src, dst):\n",
    "    \"\"\"\n",
    "    Calculate Euclid distance between each two points.\n",
    "\n",
    "    src^T * dst = xn * xm + yn * ym + zn * zm；\n",
    "    sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn;\n",
    "    sum(dst^2, dim=-1) = xm*xm + ym*ym + zm*zm;\n",
    "    dist = (xn - xm)^2 + (yn - ym)^2 + (zn - zm)^2\n",
    "         = sum(src**2,dim=-1)+sum(dst**2,dim=-1)-2*src^T*dst\n",
    "\n",
    "    Input:\n",
    "        src: source points, [B, N, C]\n",
    "        dst: target points, [B, M, C]\n",
    "    Output:\n",
    "        dist: per-point square distance, [B, N, M]\n",
    "    \"\"\"\n",
    "    B, N, _ = src.shape\n",
    "    _, M, _ = dst.shape\n",
    "    dist = -2 * torch.matmul(src, dst.permute(0, 2, 1))\n",
    "    dist += torch.sum(src ** 2, -1).view(B, N, 1)\n",
    "    dist += torch.sum(dst ** 2, -1).view(B, 1, M)\n",
    "    return dist\n",
    "\n",
    "\n",
    "def index_points(points, idx):\n",
    "    \"\"\"\n",
    "\n",
    "    Input:\n",
    "        points: input points data, [B, N, C]\n",
    "        idx: sample index data, [B, S]\n",
    "    Return:\n",
    "        new_points:, indexed points data, [B, S, C]\n",
    "    \"\"\"\n",
    "    device = points.device\n",
    "    B = points.shape[0]\n",
    "    view_shape = list(idx.shape)\n",
    "    view_shape[1:] = [1] * (len(view_shape) - 1)\n",
    "    repeat_shape = list(idx.shape)\n",
    "    repeat_shape[0] = 1\n",
    "    batch_indices = torch.arange(B, dtype=torch.long).to(device).view(view_shape).repeat(repeat_shape)\n",
    "    new_points = points[batch_indices, idx, :]\n",
    "    return new_points\n",
    "\n",
    "\n",
    "def farthest_point_sample(xyz, npoint):\n",
    "    \"\"\"\n",
    "    Input:\n",
    "        xyz: pointcloud data, [B, N, 3]\n",
    "        npoint: number of samples\n",
    "    Return:\n",
    "        centroids: sampled pointcloud index, [B, npoint]\n",
    "    \"\"\"\n",
    "    device = xyz.device\n",
    "    B, N, C = xyz.shape\n",
    "    centroids = torch.zeros(B, npoint, dtype=torch.long).to(device)\n",
    "    distance = torch.ones(B, N).to(device) * 1e10\n",
    "    farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device)\n",
    "    batch_indices = torch.arange(B, dtype=torch.long).to(device)\n",
    "    for i in range(npoint):\n",
    "        centroids[:, i] = farthest\n",
    "        centroid = xyz[batch_indices, farthest, :].view(B, 1, 3)\n",
    "        dist = torch.sum((xyz - centroid) ** 2, -1)\n",
    "        mask = dist < distance\n",
    "        distance[mask] = dist[mask]\n",
    "        farthest = torch.max(distance, -1)[1]\n",
    "    return centroids\n",
    "\n",
    "\n",
    "def query_ball_point(radius, nsample, xyz, new_xyz):\n",
    "    \"\"\"\n",
    "    Input:\n",
    "        radius: local region radius\n",
    "        nsample: max sample number in local region\n",
    "        xyz: all points, [B, N, 3]\n",
    "        new_xyz: query points, [B, S, 3]\n",
    "    Return:\n",
    "        group_idx: grouped points index, [B, S, nsample]\n",
    "    \"\"\"\n",
    "    device = xyz.device\n",
    "    B, N, C = xyz.shape\n",
    "    _, S, _ = new_xyz.shape\n",
    "    group_idx = torch.arange(N, dtype=torch.long).to(device).view(1, 1, N).repeat([B, S, 1])\n",
    "    sqrdists = square_distance(new_xyz, xyz)\n",
    "    group_idx[sqrdists > radius ** 2] = N\n",
    "    group_idx = group_idx.sort(dim=-1)[0][:, :, :nsample]\n",
    "    group_first = group_idx[:, :, 0].view(B, S, 1).repeat([1, 1, nsample])\n",
    "    mask = group_idx == N\n",
    "    group_idx[mask] = group_first[mask]\n",
    "    return group_idx\n",
    "\n",
    "\n",
    "def sample_and_group(npoint, radius, nsample, xyz, points, returnfps=False):\n",
    "    \"\"\"\n",
    "    Input:\n",
    "        npoint:\n",
    "        radius:\n",
    "        nsample:\n",
    "        xyz: input points position data, [B, N, 3]\n",
    "        points: input points data, [B, N, D]\n",
    "    Return:\n",
    "        new_xyz: sampled points position data, [B, npoint, nsample, 3]\n",
    "        new_points: sampled points data, [B, npoint, nsample, 3+D]\n",
    "    \"\"\"\n",
    "    B, N, C = xyz.shape\n",
    "    S = npoint\n",
    "    fps_idx = farthest_point_sample(xyz, npoint) # [B, npoint, C]\n",
    "    new_xyz = index_points(xyz, fps_idx)\n",
    "    idx = query_ball_point(radius, nsample, xyz, new_xyz)\n",
    "    grouped_xyz = index_points(xyz, idx) # [B, npoint, nsample, C]\n",
    "    grouped_xyz_norm = grouped_xyz - new_xyz.view(B, S, 1, C)\n",
    "\n",
    "    if points is not None:\n",
    "        grouped_points = index_points(points, idx)\n",
    "        new_points = torch.cat([grouped_xyz_norm, grouped_points], dim=-1) # [B, npoint, nsample, C+D]\n",
    "    else:\n",
    "        new_points = grouped_xyz_norm\n",
    "    if returnfps:\n",
    "        return new_xyz, new_points, grouped_xyz, fps_idx\n",
    "    else:\n",
    "        return new_xyz, new_points\n",
    "\n",
    "\n",
    "def sample_and_group_all(xyz, points):\n",
    "    \"\"\"\n",
    "    Input:\n",
    "        xyz: input points position data, [B, N, 3]\n",
    "        points: input points data, [B, N, D]\n",
    "    Return:\n",
    "        new_xyz: sampled points position data, [B, 1, 3]\n",
    "        new_points: sampled points data, [B, 1, N, 3+D]\n",
    "    \"\"\"\n",
    "    device = xyz.device\n",
    "    B, N, C = xyz.shape\n",
    "    new_xyz = torch.zeros(B, 1, C).to(device)\n",
    "    grouped_xyz = xyz.view(B, 1, N, C)\n",
    "    if points is not None:\n",
    "        new_points = torch.cat([grouped_xyz, points.view(B, 1, N, -1)], dim=-1)\n",
    "    else:\n",
    "        new_points = grouped_xyz\n",
    "    return new_xyz, new_points\n",
    "\n",
    "\n",
    "class PointNetSetAbstraction(nn.Module):\n",
    "    def __init__(self, npoint, radius, nsample, in_channel, mlp, group_all):\n",
    "        super(PointNetSetAbstraction, self).__init__()\n",
    "        self.npoint = npoint\n",
    "        self.radius = radius\n",
    "        self.nsample = nsample\n",
    "        self.mlp_convs = nn.ModuleList()\n",
    "        self.mlp_bns = nn.ModuleList()\n",
    "        last_channel = in_channel\n",
    "        for out_channel in mlp:\n",
    "            self.mlp_convs.append(nn.Conv2d(last_channel, out_channel, 1))\n",
    "            self.mlp_bns.append(nn.BatchNorm2d(out_channel))\n",
    "            last_channel = out_channel\n",
    "        self.group_all = group_all\n",
    "\n",
    "    def forward(self, xyz, points):\n",
    "        \"\"\"\n",
    "        Input:\n",
    "            xyz: input points position data, [B, C, N]\n",
    "            points: input points data, [B, D, N]\n",
    "        Return:\n",
    "            new_xyz: sampled points position data, [B, C, S]\n",
    "            new_points_concat: sample points feature data, [B, D', S]\n",
    "        \"\"\"\n",
    "        xyz = xyz.permute(0, 2, 1)\n",
    "        if points is not None:\n",
    "            points = points.permute(0, 2, 1)\n",
    "\n",
    "        if self.group_all:\n",
    "            new_xyz, new_points = sample_and_group_all(xyz, points)\n",
    "        else:\n",
    "            new_xyz, new_points = sample_and_group(self.npoint, self.radius, self.nsample, xyz, points)\n",
    "        # new_xyz: sampled points position data, [B, npoint, C]\n",
    "        # new_points: sampled points data, [B, npoint, nsample, C+D]\n",
    "        new_points = new_points.permute(0, 3, 2, 1) # [B, C+D, nsample,npoint]\n",
    "        for i, conv in enumerate(self.mlp_convs):\n",
    "            bn = self.mlp_bns[i]\n",
    "            new_points =  F.relu(bn(conv(new_points)))\n",
    "\n",
    "        new_points = torch.max(new_points, 2)[0]\n",
    "        new_xyz = new_xyz.permute(0, 2, 1)\n",
    "        return new_xyz, new_points\n",
    "\n",
    "\n",
    "class PointNetSetAbstractionMsg(nn.Module):\n",
    "    def __init__(self, npoint, radius_list, nsample_list, in_channel, mlp_list):\n",
    "        super(PointNetSetAbstractionMsg, self).__init__()\n",
    "        self.npoint = npoint\n",
    "        self.radius_list = radius_list\n",
    "        self.nsample_list = nsample_list\n",
    "        self.conv_blocks = nn.ModuleList()\n",
    "        self.bn_blocks = nn.ModuleList()\n",
    "        for i in range(len(mlp_list)):\n",
    "            convs = nn.ModuleList()\n",
    "            bns = nn.ModuleList()\n",
    "            last_channel = in_channel + 3\n",
    "            for out_channel in mlp_list[i]:\n",
    "                convs.append(nn.Conv2d(last_channel, out_channel, 1))\n",
    "                bns.append(nn.BatchNorm2d(out_channel))\n",
    "                last_channel = out_channel\n",
    "            self.conv_blocks.append(convs)\n",
    "            self.bn_blocks.append(bns)\n",
    "\n",
    "    def forward(self, xyz, points):\n",
    "        \"\"\"\n",
    "        Input:\n",
    "            xyz: input points position data, [B, C, N]\n",
    "            points: input points data, [B, D, N]\n",
    "        Return:\n",
    "            new_xyz: sampled points position data, [B, C, S]\n",
    "            new_points_concat: sample points feature data, [B, D', S]\n",
    "        \"\"\"\n",
    "        xyz = xyz.permute(0, 2, 1)\n",
    "        if points is not None:\n",
    "            points = points.permute(0, 2, 1)\n",
    "\n",
    "        B, N, C = xyz.shape\n",
    "        S = self.npoint\n",
    "        new_xyz = index_points(xyz, farthest_point_sample(xyz, S))\n",
    "        new_points_list = []\n",
    "        for i, radius in enumerate(self.radius_list):\n",
    "            K = self.nsample_list[i]\n",
    "            group_idx = query_ball_point(radius, K, xyz, new_xyz)\n",
    "            grouped_xyz = index_points(xyz, group_idx)\n",
    "            grouped_xyz -= new_xyz.view(B, S, 1, C)\n",
    "            if points is not None:\n",
    "                grouped_points = index_points(points, group_idx)\n",
    "                grouped_points = torch.cat([grouped_points, grouped_xyz], dim=-1)\n",
    "            else:\n",
    "                grouped_points = grouped_xyz\n",
    "\n",
    "            grouped_points = grouped_points.permute(0, 3, 2, 1)  # [B, D, K, S]\n",
    "            for j in range(len(self.conv_blocks[i])):\n",
    "                conv = self.conv_blocks[i][j]\n",
    "                bn = self.bn_blocks[i][j]\n",
    "                grouped_points =  F.relu(bn(conv(grouped_points)))\n",
    "            new_points = torch.max(grouped_points, 2)[0]  # [B, D', S]\n",
    "            new_points_list.append(new_points)\n",
    "\n",
    "        new_xyz = new_xyz.permute(0, 2, 1)\n",
    "        new_points_concat = torch.cat(new_points_list, dim=1)\n",
    "        return new_xyz, new_points_concat\n",
    "\n",
    "\n",
    "class PointNetFeaturePropagation(nn.Module):\n",
    "    def __init__(self, in_channel, mlp):\n",
    "        super(PointNetFeaturePropagation, self).__init__()\n",
    "        self.mlp_convs = nn.ModuleList()\n",
    "        self.mlp_bns = nn.ModuleList()\n",
    "        last_channel = in_channel\n",
    "        for out_channel in mlp:\n",
    "            self.mlp_convs.append(nn.Conv1d(last_channel, out_channel, 1))\n",
    "            self.mlp_bns.append(nn.BatchNorm1d(out_channel))\n",
    "            last_channel = out_channel\n",
    "\n",
    "    def forward(self, xyz1, xyz2, points1, points2):\n",
    "        \"\"\"\n",
    "        Input:\n",
    "            xyz1: input points position data, [B, C, N]\n",
    "            xyz2: sampled input points position data, [B, C, S]\n",
    "            points1: input points data, [B, D, N]\n",
    "            points2: input points data, [B, D, S]\n",
    "        Return:\n",
    "            new_points: upsampled points data, [B, D', N]\n",
    "        \"\"\"\n",
    "        xyz1 = xyz1.permute(0, 2, 1)\n",
    "        xyz2 = xyz2.permute(0, 2, 1)\n",
    "\n",
    "        points2 = points2.permute(0, 2, 1)\n",
    "        B, N, C = xyz1.shape\n",
    "        _, S, _ = xyz2.shape\n",
    "\n",
    "        if S == 1:\n",
    "            interpolated_points = points2.repeat(1, N, 1)\n",
    "        else:\n",
    "            dists = square_distance(xyz1, xyz2)\n",
    "            dists, idx = dists.sort(dim=-1)\n",
    "            dists, idx = dists[:, :, :3], idx[:, :, :3]  # [B, N, 3]\n",
    "\n",
    "            dist_recip = 1.0 / (dists + 1e-8)\n",
    "            norm = torch.sum(dist_recip, dim=2, keepdim=True)\n",
    "            weight = dist_recip / norm\n",
    "            interpolated_points = torch.sum(index_points(points2, idx) * weight.view(B, N, 3, 1), dim=2)\n",
    "\n",
    "        if points1 is not None:\n",
    "            points1 = points1.permute(0, 2, 1)\n",
    "            new_points = torch.cat([points1, interpolated_points], dim=-1)\n",
    "        else:\n",
    "            new_points = interpolated_points\n",
    "\n",
    "        new_points = new_points.permute(0, 2, 1)\n",
    "        for i, conv in enumerate(self.mlp_convs):\n",
    "            bn = self.mlp_bns[i]\n",
    "            new_points = F.relu(bn(conv(new_points)))\n",
    "        return new_points\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "executionInfo": {
     "elapsed": 1042,
     "status": "ok",
     "timestamp": 1674079810838,
     "user": {
      "displayName": "SUKIDA Tan",
      "userId": "04234593673225449814"
     },
     "user_tz": -60
    },
    "id": "6A7mwh0Y2MxG",
    "tags": []
   },
   "outputs": [],
   "source": [
    "class get_model(nn.Module):\n",
    "    def __init__(self,num_class,normal_channel=True):\n",
    "        super(get_model, self).__init__()\n",
    "        in_channel = 3 if normal_channel else 0\n",
    "        self.normal_channel = normal_channel\n",
    "        self.sa1 = PointNetSetAbstractionMsg(512, [0.1, 0.2, 0.4], [16, 32, 128], in_channel,[[32, 32, 64], [64, 64, 128], [64, 96, 128]])\n",
    "        self.sa2 = PointNetSetAbstractionMsg(128, [0.2, 0.4, 0.8], [32, 64, 128], 320,[[64, 64, 128], [128, 128, 256], [128, 128, 256]])\n",
    "        self.sa3 = PointNetSetAbstraction(None, None, None, 640 + 3, [256, 512, 1024], True)\n",
    "        self.fc1 = nn.Linear(1024, 512)\n",
    "        self.bn1 = nn.BatchNorm1d(512)\n",
    "        self.drop1 = nn.Dropout(0.4)\n",
    "        self.fc2 = nn.Linear(512, 256)\n",
    "        self.bn2 = nn.BatchNorm1d(256)\n",
    "        self.drop2 = nn.Dropout(0.5)\n",
    "        self.fc3 = nn.Linear(256, num_class)\n",
    "\n",
    "    def forward(self, xyz):\n",
    "        B, _, _ = xyz.shape\n",
    "        if self.normal_channel:\n",
    "            norm = xyz[:, 3:, :]\n",
    "            xyz = xyz[:, :3, :]\n",
    "        else:\n",
    "            norm = None\n",
    "        l1_xyz, l1_points = self.sa1(xyz, norm)\n",
    "        l2_xyz, l2_points = self.sa2(l1_xyz, l1_points)\n",
    "        l3_xyz, l3_points = self.sa3(l2_xyz, l2_points)\n",
    "        x = l3_points.view(B, 1024)\n",
    "        x = self.drop1(F.relu(self.bn1(self.fc1(x))))\n",
    "        x = self.drop2(F.relu(self.bn2(self.fc2(x))))\n",
    "        x = self.fc3(x)\n",
    "        x = F.log_softmax(x, -1)\n",
    "\n",
    "\n",
    "        return x,l3_points\n",
    "\n",
    "\n",
    "class get_loss(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(get_loss, self).__init__()\n",
    "\n",
    "    def forward(self, pred, target, trans_feat):\n",
    "        total_loss = F.nll_loss(pred, target)\n",
    "\n",
    "        return total_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "CUDA_LAUNCH_BLOCKING=\"1\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GnM8yu2G2XVF"
   },
   "source": [
    "# Training\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 290,
     "status": "ok",
     "timestamp": 1674079813770,
     "user": {
      "displayName": "SUKIDA Tan",
      "userId": "04234593673225449814"
     },
     "user_tz": -60
    },
    "id": "v2_wk24U2cc-",
    "outputId": "cc19c979-2e04-4161-cafd-b53c425b0a71"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cpu')\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inplace_relu(m):\n",
    "    classname = m.__class__.__name__\n",
    "    if classname.find('ReLU') != -1:\n",
    "        m.inplace=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = get_model(8, normal_channel=False)\n",
    "criterion = get_loss()\n",
    "classifier.apply(inplace_relu)\n",
    "\n",
    "classifier = classifier.to(device)\n",
    "criterion = criterion.to(device)\n",
    "\n",
    "optimizer = torch.optim.Adam(classifier.parameters(), lr=0.001)\n",
    "\n",
    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(model, loader, num_class=8):\n",
    "    mean_correct = []\n",
    "    class_acc = np.zeros((num_class, 3))\n",
    "    classifier = model.eval()\n",
    "\n",
    "    for j, data in tqdm(enumerate(loader), total=len(loader)):\n",
    "\n",
    "        target = data[:,1,3]\n",
    "        target = target.type(torch.LongTensor) \n",
    "        points, target = data[:,:,0:3].to(device).float(), target.to(device)\n",
    "\n",
    "        points = points.transpose(2, 1)\n",
    "        pred, _ = classifier(points)\n",
    "        pred_choice = pred.data.max(1)[1]\n",
    "\n",
    "        for cat in np.unique(target.cpu()):\n",
    "            classacc = pred_choice[target == cat].eq(target[target == cat].long().data).cpu().sum()\n",
    "            class_acc[cat, 0] += classacc.item() / float(points[target == cat].size()[0])\n",
    "            class_acc[cat, 1] += 1\n",
    "\n",
    "        correct = pred_choice.eq(target.long().data).cpu().sum()\n",
    "        mean_correct.append(correct.item() / float(points.size()[0]))\n",
    "\n",
    "    class_acc[:, 2] = class_acc[:, 0] / class_acc[:, 1]\n",
    "    class_acc = np.mean(class_acc[:, 2])\n",
    "    instance_acc = np.mean(mean_correct)\n",
    "\n",
    "    return instance_acc, class_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "executionInfo": {
     "elapsed": 288,
     "status": "ok",
     "timestamp": 1674079819088,
     "user": {
      "displayName": "SUKIDA Tan",
      "userId": "04234593673225449814"
     },
     "user_tz": -60
    },
    "id": "XLE6uDz2VfGd"
   },
   "outputs": [],
   "source": [
    "def train(classifier, criterion, train_loader, optimizer, scheduler, val_loader=None, start_epoch = 0, epochs=10, save=True):\n",
    "    \n",
    "    global_epoch = 0\n",
    "    global_step = 0\n",
    "    best_instance_acc = 0.0\n",
    "    best_class_acc = 0.0\n",
    "    \n",
    "    for epoch in range(start_epoch, epochs):\n",
    "        print('Epoch %d (%d/%s):' % (global_epoch + 1, epoch + 1, epochs))\n",
    "        mean_correct = []\n",
    "        classifier = classifier.train()\n",
    "\n",
    "        scheduler.step()\n",
    "        for batch_id, data in tqdm(enumerate(train_loader, 0), total=len(train_loader), smoothing=0.9):\n",
    "            optimizer.zero_grad()\n",
    "            \n",
    "            target = data[:,1,3]\n",
    "            target = target.type(torch.LongTensor) \n",
    "            points, target = data[:,:,0:3].to(device).float(), target.to(device)\n",
    "            points = points.transpose(2, 1)\n",
    "\n",
    "            pred, trans_feat = classifier(points)\n",
    "            loss = criterion(pred, target.long(), trans_feat)\n",
    "            pred_choice = pred.data.max(1)[1]\n",
    "\n",
    "            correct = pred_choice.eq(target.long().data).cpu().sum()\n",
    "            mean_correct.append(correct.item() / float(points.size()[0]))\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            global_step += 1\n",
    "\n",
    "        train_instance_acc = np.mean(mean_correct)\n",
    "        print('Train Instance Accuracy: %f' % train_instance_acc)\n",
    "\n",
    "        with torch.no_grad():\n",
    "            instance_acc, class_acc = test(classifier.eval(), val_loader, num_class=8)\n",
    "\n",
    "            if (instance_acc >= best_instance_acc):\n",
    "                best_instance_acc = instance_acc\n",
    "                best_epoch = epoch + 1\n",
    "\n",
    "            if (class_acc >= best_class_acc):\n",
    "                best_class_acc = class_acc\n",
    "            print('Test Instance Accuracy: %f, Class Accuracy: %f' % (instance_acc, class_acc))\n",
    "            print('Best Instance Accuracy: %f, Class Accuracy: %f' % (best_instance_acc, best_class_acc))\n",
    "\n",
    "            if (instance_acc >= best_instance_acc):\n",
    "                print('Save model...')\n",
    "                savepath = 'best_model_p2_xyz_ld.pth'\n",
    "                print('Saving at %s' % savepath)\n",
    "                state = {\n",
    "                    'epoch': best_epoch,\n",
    "                    'instance_acc': instance_acc,\n",
    "                    'class_acc': class_acc,\n",
    "                    'model_state_dict': classifier.state_dict(),\n",
    "                    'optimizer_state_dict': optimizer.state_dict(),\n",
    "                }\n",
    "                torch.save(state, savepath)\n",
    "            global_epoch += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"max_split_size_mb:512\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5476095,
     "status": "ok",
     "timestamp": 1674085297893,
     "user": {
      "displayName": "SUKIDA Tan",
      "userId": "04234593673225449814"
     },
     "user_tz": -60
    },
    "id": "EOYHr6IW2Qsh",
    "outputId": "703e8b98-e753-43bc-c3b8-ea719bf8ffff"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\Anaconda_py3.9\\lib\\site-packages\\torch\\optim\\lr_scheduler.py:138: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
      "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 (1/10):\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 2677/2677 [09:33<00:00,  4.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Instance Accuracy: 0.852260\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 893/893 [02:19<00:00,  6.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Instance Accuracy: 0.881579, Class Accuracy: 0.755104\n",
      "Best Instance Accuracy: 0.881579, Class Accuracy: 0.755104\n",
      "Save model...\n",
      "Saving at best_model_p2_xyz_ld.pth\n",
      "Epoch 2 (2/10):\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 2677/2677 [09:27<00:00,  4.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Instance Accuracy: 0.847777\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 893/893 [02:21<00:00,  6.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Instance Accuracy: 0.855263, Class Accuracy: 0.714567\n",
      "Best Instance Accuracy: 0.881579, Class Accuracy: 0.755104\n",
      "Epoch 3 (3/10):\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 2677/2677 [09:23<00:00,  4.75it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Instance Accuracy: 0.853754\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 893/893 [02:19<00:00,  6.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Instance Accuracy: 0.800392, Class Accuracy: 0.686476\n",
      "Best Instance Accuracy: 0.881579, Class Accuracy: 0.755104\n",
      "Epoch 4 (4/10):\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Instance Accuracy: 0.854128\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 893/893 [02:22<00:00,  6.28it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Instance Accuracy: 0.857503, Class Accuracy: 0.738144\n",
      "Best Instance Accuracy: 0.881579, Class Accuracy: 0.755104\n",
      "Epoch 5 (5/10):\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 2677/2677 [09:28<00:00,  4.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Instance Accuracy: 0.859638\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Instance Accuracy: 0.896697, Class Accuracy: 0.755747\n",
      "Best Instance Accuracy: 0.896697, Class Accuracy: 0.755747\n",
      "Save model...\n",
      "Saving at best_model_p2_xyz_ld.pth\n",
      "Epoch 6 (6/10):\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Instance Accuracy: 0.858610\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Instance Accuracy: 0.899496, Class Accuracy: 0.733037\n",
      "Best Instance Accuracy: 0.899496, Class Accuracy: 0.755747\n",
      "Save model...\n",
      "Saving at best_model_p2_xyz_ld.pth\n",
      "Epoch 7 (7/10):\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Instance Accuracy: 0.868603\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Instance Accuracy: 0.870661, Class Accuracy: 0.707328\n",
      "Best Instance Accuracy: 0.899496, Class Accuracy: 0.755747\n",
      "Epoch 8 (8/10):\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 2677/2677 [09:41<00:00,  4.60it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Instance Accuracy: 0.865054\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Instance Accuracy: 0.890538, Class Accuracy: 0.717440\n",
      "Best Instance Accuracy: 0.899496, Class Accuracy: 0.755747\n",
      "Epoch 9 (9/10):\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 2677/2677 [09:20<00:00,  4.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Instance Accuracy: 0.866642\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 893/893 [04:13<00:00,  3.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Instance Accuracy: 0.901456, Class Accuracy: 0.799170\n",
      "Best Instance Accuracy: 0.901456, Class Accuracy: 0.799170\n",
      "Save model...\n",
      "Saving at best_model_p2_xyz_ld.pth\n",
      "Epoch 10 (10/10):\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 2677/2677 [11:22<00:00,  3.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Instance Accuracy: 0.870471\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 893/893 [02:20<00:00,  6.34it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Instance Accuracy: 0.901456, Class Accuracy: 0.784578\n",
      "Best Instance Accuracy: 0.901456, Class Accuracy: 0.799170\n",
      "Save model...\n",
      "Saving at best_model_p2_xyz_ld.pth\n"
     ]
    }
   ],
   "source": [
    "train(classifier, criterion, train_loader, optimizer, scheduler, valid_loader,  save=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rdIlrnXh2UX9"
   },
   "source": [
    "# Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "executionInfo": {
     "elapsed": 269,
     "status": "ok",
     "timestamp": 1674085428035,
     "user": {
      "displayName": "SUKIDA Tan",
      "userId": "04234593673225449814"
     },
     "user_tz": -60
    },
    "id": "mNefMkzH2Wvw"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "h2u-0bwC2X_Y"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkpoint = torch.load('best_model_p2_xyz_ld.pth')\n",
    "classifier.load_state_dict(checkpoint['model_state_dict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(model, test_loader, num_class=8, vote_num=1):\n",
    "    mean_correct = []\n",
    "    classifier = model.eval()\n",
    "    class_acc = np.zeros((num_class, 3))\n",
    "    all_preds = []\n",
    "    all_labels = []\n",
    "\n",
    "    for j, data in tqdm(enumerate(test_loader), total=len(test_loader)):\n",
    "        \n",
    "        target = data[:,1,3]\n",
    "        target = target.type(torch.LongTensor) \n",
    "        points, target = data[:,:,0:3].to(device).float(), target.to(device)\n",
    "        points = points.transpose(2, 1)\n",
    "        vote_pool = torch.zeros(target.size()[0], num_class).cuda()\n",
    "\n",
    "        for _ in range(vote_num):\n",
    "            pred, _ = classifier(points)\n",
    "            vote_pool += pred\n",
    "        pred = vote_pool / vote_num\n",
    "        pred_choice = pred.data.max(1)[1]\n",
    "        all_preds += list(pred_choice.cpu().numpy())\n",
    "        all_labels += list(target.cpu().numpy())\n",
    "\n",
    "        for cat in np.unique(target.cpu()):\n",
    "            classacc = pred_choice[target == cat].eq(target[target == cat].long().data).cpu().sum()\n",
    "            class_acc[cat, 0] += classacc.item() / float(points[target == cat].size()[0])\n",
    "            class_acc[cat, 1] += 1\n",
    "        correct = pred_choice.eq(target.long().data).cpu().sum()\n",
    "        mean_correct.append(correct.item() / float(points.size()[0]))\n",
    "\n",
    "    \n",
    "    for i in range(num_class):\n",
    "        if class_acc[i,1]>0:\n",
    "            class_acc[i, 2] = class_acc[i, 0] / class_acc[i, 1]\n",
    "    class_acc = np.mean(class_acc[:, 2])\n",
    "    instance_acc = np.mean(mean_correct)\n",
    "    return instance_acc, class_acc, all_preds, all_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 1030/1030 [02:44<00:00,  6.25it/s]\n"
     ]
    }
   ],
   "source": [
    "ins_acc, cla_acc, all_preds, all_labels = test(classifier.eval(), test_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4620581681230393\n",
      "0.7536407766990292\n"
     ]
    }
   ],
   "source": [
    "print(cla_acc)\n",
    "print(ins_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 376,
     "status": "ok",
     "timestamp": 1674088910585,
     "user": {
      "displayName": "SUKIDA Tan",
      "userId": "04234593673225449814"
     },
     "user_tz": -60
    },
    "id": "LvqVmt0i2ds3",
    "outputId": "2f890904-33c1-49e0-c5a9-7ba544a7cef6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1624  487   33  697 1045  234]\n",
      "[1886  388   19  571  999  223   24   10]\n",
      "[[1336  161    3   80   37    0    4    3]\n",
      " [ 225  196    2   13   45    0    5    1]\n",
      " [  30    1    0    1    1    0    0    0]\n",
      " [ 154   23   11  470   36    0    0    3]\n",
      " [ 131    7    3    6  880    0   15    3]\n",
      " [  10    0    0    1    0  223    0    0]\n",
      " [   0    0    0    0    0    0    0    0]\n",
      " [   0    0    0    0    0    0    0    0]]\n",
      "0.7536407766990292\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(all_labels, all_preds);\n",
    "cm\n",
    "print(np.bincount(all_labels))\n",
    "print(np.bincount(all_preds))\n",
    "print(cm)\n",
    "\n",
    "correct_rate = np.diagonal(cm).sum()/cm.sum()\n",
    "print(correct_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 245,
     "status": "ok",
     "timestamp": 1674086880589,
     "user": {
      "displayName": "SUKIDA Tan",
      "userId": "04234593673225449814"
     },
     "user_tz": -60
    },
    "id": "jQryCKH4R1FW",
    "outputId": "528acf69-4698-4cea-be2a-ded3b34f69d9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'wall & balcony & ..': 0,\n",
       " 'window & blinds': 1,\n",
       " 'door': 2,\n",
       " 'molding & decoration': 3,\n",
       " 'ground surface & terrian': 4,\n",
       " 'roof & others': 5,\n",
       " 'column': 6,\n",
       " 'arch': 7}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes = {'wall & balcony & ..': 0,\n",
    "           'window & blinds': 1,\n",
    "           'door': 2,\n",
    "           'molding & decoration': 3,\n",
    "           'ground surface & terrian': 4,\n",
    "           'roof & others': 5,\n",
    "           'column': 6,\n",
    "           'arch': 7\n",
    "           }\n",
    "classes\n",
    "cla_test = {'wall & balcony & ..': 0,\n",
    "           'window & blinds': 1,\n",
    "           'door': 2,\n",
    "           'molding & decoration': 3,\n",
    "           'ground surface & terrian': 4,\n",
    "           'roof & others': 5,\n",
    "           'column': 6,\n",
    "           'arch': 7}\n",
    "cla_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "executionInfo": {
     "elapsed": 235,
     "status": "ok",
     "timestamp": 1674086267324,
     "user": {
      "displayName": "SUKIDA Tan",
      "userId": "04234593673225449814"
     },
     "user_tz": -60
    },
    "id": "MvIkJrRI2g8L"
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# function from https://deeplizard.com/learn/video/0LhiS6yu2qQ\n",
    "def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt), horizontalalignment=\"center\", color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 652
    },
    "executionInfo": {
     "elapsed": 830,
     "status": "ok",
     "timestamp": 1674086917326,
     "user": {
      "displayName": "SUKIDA Tan",
      "userId": "04234593673225449814"
     },
     "user_tz": -60
    },
    "id": "YJeGeH212i6h",
    "outputId": "72dd3fa4-5411-44bb-c876-8f1e7cb6d839"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\15597\\AppData\\Local\\Temp\\ipykernel_16716\\1214108877.py:8: RuntimeWarning: invalid value encountered in true_divide\n",
      "  cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized confusion matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plot_confusion_matrix(cm, list(cla_test.keys()), normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 580
    },
    "executionInfo": {
     "elapsed": 1205,
     "status": "ok",
     "timestamp": 1674086946985,
     "user": {
      "displayName": "SUKIDA Tan",
      "userId": "04234593673225449814"
     },
     "user_tz": -60
    },
    "id": "9FeHmyzh2mZq",
    "outputId": "8cb5c54e-31e6-448c-c9c5-5b8b2c48c0bf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plot_confusion_matrix(cm, list(cla_test.keys()), normalize=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "authorship_tag": "ABX9TyOps7S6O6rs3xgWiavpNy6T",
   "collapsed_sections": [
    "be8RFvYTsfYE",
    "sSRH6VAGf7Xw",
    "N0FRlJOh2EuO"
   ],
   "provenance": [],
   "toc_visible": true
  },
  "gpuClass": "standard",
  "kernelspec": {
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