Point_Cloud_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s5sQUgiRYfQ9"
   },
   "source": [
    "# 0. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I2GfjfD6YfRF"
   },
   "source": [
    "## 0.1 Google Drive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YllKDiXlYfRR",
    "outputId": "0d81d80b-de68-4fdc-d6c0-5ad2b5d2806c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
      "/content/drive/My Drive/Project\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')\n",
    "%cd 'drive/My Drive/Project/'\n",
    "#%ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SbwAXE_7YfSd"
   },
   "source": [
    "## 0.2 Modules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sjBpjZRIl_FJ"
   },
   "source": [
    "### 0.2.1 Install these modules on Colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5NUZmx7Ll-SC",
    "outputId": "5b57fe82-1085-458a-e643-5037761f50dd"
   },
   "outputs": [],
   "source": [
    "!pip install open3d\n",
    "!pip install geomloss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jUBtmahJmD1N"
   },
   "source": [
    "### 0.2.2 Import Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "VHTCHjE5YfSf"
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.parallel\n",
    "import torch.utils.data\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import Dataset\n",
    "#import torch.utils.dlpack as dlpack\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 3D libraries\n",
    "import open3d as op3\n",
    "from geomloss import SamplesLoss\n",
    "\n",
    "# Notebook\n",
    "from tqdm import notebook\n",
    "from IPython.display import clear_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zUTyPGdXEqiK"
   },
   "source": [
    "## 0.3 Devices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "piJXHGSoYfSh"
   },
   "source": [
    "### 0.3.1 CUDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yVZg2rN0YfSi",
    "outputId": "1ddcc606-4429-431a-a3e1-45222e70d6c4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thu Dec 17 09:39:32 2020       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 455.45.01    Driver Version: 418.67       CUDA Version: 10.1     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  Tesla V100-SXM2...  Off  | 00000000:00:04.0 Off |                    0 |\n",
      "| N/A   33C    P0    23W / 300W |     10MiB / 16130MiB |      0%      Default |\n",
      "|                               |                      |                 ERR! |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|  No running processes found                                                 |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "# CUDA\n",
    "device = 'cpu'\n",
    "gpu_info = !nvidia-smi\n",
    "gpu_info = '\\n'.join(gpu_info)\n",
    "if gpu_info.find('failed') >= 0:\n",
    "  print('GPU accelerator not available.')\n",
    "else:\n",
    "  device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "  print(gpu_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e6RMwoKlEVlg"
   },
   "source": [
    "### 0.3.2 CPU\n",
    "Make sure the number of workers in the dataset matches the number of CPU cores! Google Colab provides 4 CPUs with 8 Cores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZXyMv-8Avohu",
    "outputId": "6222dc1f-b447-42f6-e1e7-4bf18637e030"
   },
   "outputs": [],
   "source": [
    "!cat /proc/cpuinfo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xJCznvzyYfSl"
   },
   "source": [
    "# 1. Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5Rksx7g1lVbW"
   },
   "source": [
    "## 1.1 Define Dataset Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "Ie0yUM3VYfTQ"
   },
   "outputs": [],
   "source": [
    "class KITTI360Dataset(Dataset):\n",
    "    def __init__(self, sequence_path='data.npy', cam2world_path='cam0_to_world.txt',\n",
    "                 num_points=4096, box_size=[20,15,20], num_classes=38, frame_step=5, frame_start=None, frame_end=None,\n",
    "                 estimate_normals=True, seed=29):\n",
    "        super().__init__()\n",
    "        self.num_points = num_points\n",
    "        self.box_size = box_size\n",
    "        self.radius = 0.5*(self.box_size[0]**2 + self.box_size[1]**2 + self.box_size[2]**2)**(1/2) # Compute radius for the box to normalize data\n",
    "        self.num_classes = num_classes\n",
    "        self.frame_step = frame_step\n",
    "        self.frame_start = frame_start\n",
    "        self.frame_end = frame_end\n",
    "        self.estimate_normals = estimate_normals\n",
    "        self.seed = seed\n",
    "        \n",
    "        # Read and process xyzrgbl data for the pointcloud\n",
    "        sequence_xyzrgbl = np.load(sequence_path) # xyzrgbl, N*7\n",
    "        self.sequence_xyzrgb = sequence_xyzrgbl[:,0:6] # xyzrgb, N*6\n",
    "        self.sequence_labels = sequence_xyzrgbl[:,6:7]  # l, N*1\n",
    "        self.rgb_means = np.mean(sequence_xyzrgbl[:,3:6], axis=0)\n",
    "\n",
    "        # Read and process Cam2World data\n",
    "        cam2world_data = np.loadtxt(cam2world_path) #\n",
    "        if self.frame_start:\n",
    "            cam2world_data = cam2world_data[self.frame_start:self.frame_end:self.frame_step,:] # skips the frames to speed up training\n",
    "        else:\n",
    "            cam2world_data = cam2world_data[0:cam2world_data.shape[0]:self.frame_step,:] # skips the frames to speed up training\n",
    "        self.cam2world = cam2world_data[:,1:].reshape(cam2world_data.shape[0], 4, 4)\n",
    "        self.cam2world_inv = np.array([np.linalg.inv(x) for x in self.cam2world])\n",
    "\n",
    "        # Create PointClouds\n",
    "        self.pcd_rgb = op3.geometry.PointCloud( op3.utility.Vector3dVector(self.sequence_xyzrgb[:,:3]))\n",
    "        self.pcd_rgb.colors = op3.utility.Vector3dVector(self.sequence_xyzrgb[:,3:6])\n",
    "        \n",
    "        col2rgb = np.concatenate((self.sequence_labels/self.num_classes, self.sequence_labels/self.num_classes,self.sequence_labels/self.num_classes), axis=1)\n",
    "        self.pcd_l = op3.geometry.PointCloud( op3.utility.Vector3dVector(self.sequence_xyzrgb[:,:3]))\n",
    "        self.pcd_l.colors = op3.utility.Vector3dVector(col2rgb)\n",
    "\n",
    "        self.box = op3.geometry.AxisAlignedBoundingBox.create_from_points(self.pcd_rgb.points)\n",
    "\n",
    "        self.frame_idxs = np.arange(self.cam2world.shape[0])\n",
    "        print(\"Total of {} frames in dataset.\".format(self.cam2world.shape[0]))\n",
    "\n",
    "    def __getitem__(self, frame_time):\n",
    "        frame_pose = self.cam2world[frame_time]\n",
    "        frame_pose_inv = self.cam2world_inv[frame_time]\n",
    "\n",
    "        vol = op3.geometry.OrientedBoundingBox.create_from_axis_aligned_bounding_box(self.box)\n",
    "        vol.extent = np.array(self.box_size)\n",
    "        vol.center = frame_pose[:3,3]\n",
    "        vol.rotate(frame_pose[:3,:3], vol.center)\n",
    "\n",
    "        frame_pcd_rgb = self.pcd_rgb.crop(vol)\n",
    "        frame_pcd_l = self.pcd_l.crop(vol)\n",
    "        frame_pcd_rgb.rotate(-frame_pose_inv[:3,:3], frame_pose_inv[:3,3])\n",
    "\n",
    "        xyz = np.asarray(frame_pcd_rgb.points)\n",
    "        rgb = np.asarray(frame_pcd_rgb.colors)\n",
    "        labels = np.asarray(frame_pcd_l.colors)[:,0:1]*self.num_classes\n",
    "        labels = labels.astype(int)\n",
    "\n",
    "        center = frame_pcd_rgb.get_center()\n",
    "\n",
    "        if self.estimate_normals:\n",
    "            # Estimate normals\n",
    "            frame_pcd_rgb.estimate_normals( search_param = op3.geometry.KDTreeSearchParamHybrid(radius=0.3, max_nn=6) )\n",
    "            frame_pcd_rgb.orient_normals_to_align_with_direction()\n",
    "            normals = np.asarray(frame_pcd_rgb.normals)\n",
    "\n",
    "        # Sample points\n",
    "        np.random.seed(self.seed)\n",
    "        sampled_idxs = np.random.choice(np.arange(0,xyz.shape[0]), self.num_points, replace=False)\n",
    "        sampled_xyz = xyz[sampled_idxs, :]  # num_points * 3\n",
    "        sampled_rgb = rgb[sampled_idxs, :]  # num_points *3\n",
    "        sampled_labels = labels[sampled_idxs, :]  # num_points * 1\n",
    "\n",
    "        # Center and normalize\n",
    "        if self.estimate_normals:\n",
    "            sampled_points = np.zeros((self.num_points, 9))  # num_points * 9 xyzrgbnnn\n",
    "            sampled_points[:, 6:9] = normals[sampled_idxs, 0:3]\n",
    "        else:\n",
    "            sampled_points = np.zeros((self.num_points, 6))  # num_points * 6 xyzrgb\n",
    "        sampled_points[:, 0] = (sampled_xyz[:, 0] - center[0]) / self.radius\n",
    "        sampled_points[:, 1] = (sampled_xyz[:, 1] - center[1]) / self.radius\n",
    "        sampled_points[:, 2] = (sampled_xyz[:, 2] - center[2]) / self.radius\n",
    "        sampled_points[:, 3] = sampled_rgb[:, 0] - self.rgb_means[0] # r data for the points\n",
    "        sampled_points[:, 4] = sampled_rgb[:, 1] - self.rgb_means[1] # g data for the points\n",
    "        sampled_points[:, 5] = sampled_rgb[:, 2] - self.rgb_means[2] # b data for the points\n",
    "\n",
    "        return sampled_points, sampled_labels\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.frame_idxs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RtMh3g80YfTS"
   },
   "source": [
    "## 1.2 Load Dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Mg2kNGL2zHIs",
    "outputId": "89fb0a03-e5d3-45d5-ed0c-39744f0410a6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 40 frames in dataset.\n"
     ]
    }
   ],
   "source": [
    "sequence_path = 'mini_dataset/2013_05_28_drive_0000_005249_005900_mini_train.npy'\n",
    "cam2world_path = 'mini_dataset/cam0_to_world.txt'\n",
    "num_classes = 38\n",
    "num_channels = 9 # set 9 for xyzrgb with normals, set 6 for xyzrgb, set 3 for xyz only\n",
    "num_points = 4096 #4096 #16384\n",
    "box_size = [20,15,20]\n",
    "batch_size = 1\n",
    "frame_start = 5250\n",
    "frame_end = 5410\n",
    "frame_step = 4\n",
    "num_workers = 4\n",
    "\n",
    "train_dataset = KITTI360Dataset(sequence_path, cam2world_path, num_points=num_points, \n",
    "                                box_size=box_size, frame_step=frame_step, frame_start=frame_start, \n",
    "                                frame_end=frame_end, estimate_normals=True)\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=False, \n",
    "                                           num_workers=num_workers, pin_memory=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XN13EKjdYfTU"
   },
   "source": [
    "# 2. Define Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "cbHnQ5__YfTV"
   },
   "outputs": [],
   "source": [
    "def get_radius(voxel_size, multiplier=1.15):\n",
    "    \"\"\"\"\n",
    "    Calculates a radius to bound points within a box of side\n",
    "    length voxel_size\n",
    "    \"\"\"\"\n",
    "    radius = multiplier*0.5*((3)**(1/2))*voxel_size \n",
    "    return radius\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",
    "def index2data(points, idx):\n",
    "    \"\"\"\n",
    "    Input:\n",
    "        points: points to sample from, [B, N, C]\n",
    "        idx: sample index, [B, S, npoints] where npoints is number of points in sample\n",
    "    Output:\n",
    "        new_points:, sampled data, [B, S, npoints, C]\n",
    "    \"\"\"\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 CentroidSampler(xyz, voxel_size):\n",
    "    \"\"\"\n",
    "    Input:\n",
    "        xyz: point cloud, [B, N, 3]\n",
    "        npoints: number of centroids to draw\n",
    "    Output:\n",
    "        centroids_xyz: sampled centroids xyz, [B, npoints, 3]\n",
    "    \"\"\"\n",
    "    B, N, C = xyz.shape\n",
    "    points_xyz = xyz.clone().detach().squeeze(0).cpu().numpy()\n",
    "    pcd = op3.geometry.PointCloud( op3.utility.Vector3dVector(points_xyz) )\n",
    "    pcd = pcd.voxel_down_sample(voxel_size)\n",
    "    centroids_xyz = torch.tensor( np.asarray(pcd.points), dtype=torch.float).to(device).unsqueeze(0)\n",
    "\n",
    "    return centroids_xyz\n",
    "\n",
    "def GroupPoints(radius, num_sample, points_xyz, centroids):\n",
    "    \"\"\"\n",
    "    Input:\n",
    "        radius: local region radius\n",
    "        num_sample: max sample number in local region\n",
    "        points_xyz: all points, [B, N, 3]\n",
    "        centroids: query points, [B, S, 3]\n",
    "    Output:\n",
    "        group_idx: grouped points index, [B, S, num_sample]\n",
    "    \"\"\"\n",
    "    device = points_xyz.device\n",
    "    B, N, C = points_xyz.shape\n",
    "    _, S, _ = centroids.shape\n",
    "    group_idx = torch.arange(N, dtype=torch.long).to(device).view(1, 1, N).repeat([B, S, 1])\n",
    "    sqrdists = square_distance(centroids, points_xyz)\n",
    "    group_idx[sqrdists > radius ** 2] = N\n",
    "    group_idx = group_idx.sort(dim=-1)[0][:, :, :num_sample]\n",
    "    group_first = group_idx[:, :, 0].view(B, S, 1).repeat([1, 1, num_sample])\n",
    "    mask = group_idx == N\n",
    "    group_idx[mask] = group_first[mask]\n",
    "    return group_idx\n",
    "\n",
    "      \n",
    "class SetAbstraction(nn.Module):\n",
    "    def __init__(self, voxel_size, radius_mult_list, num_sample_list, in_channel, mlp_list):\n",
    "        super(SetAbstraction, self).__init__()\n",
    "        self.radius_mult_list = radius_mult_list\n",
    "        self.radius_list = []\n",
    "        self.num_sample_list = num_sample_list\n",
    "        self.conv_blocks = nn.ModuleList()\n",
    "        self.bn_blocks = nn.ModuleList()\n",
    "        self.voxel_size = voxel_size\n",
    "        for i in range(len(radius_mult_list)):\n",
    "            self.radius_list.append(get_radius(voxel_size, radius_mult_list[i]))\n",
    "\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: points xyz data, [B, N, C]\n",
    "            points: points data, [B, N, D]\n",
    "        Output:\n",
    "            new_xyz: sampled points xyz, [B, S, C]\n",
    "            new_points_concat: sample points feature data, [B, S, D']\n",
    "        \"\"\"\n",
    "        B, N, C = xyz.shape\n",
    "        #S = self.npoints\n",
    "        centroids_xyz = CentroidSampler(xyz, self.voxel_size) # S is the number of centroids i.e. local regions\n",
    "        S = centroids_xyz.shape[1]\n",
    "        new_points_list = []\n",
    "        for i, radius in enumerate(self.radius_list):\n",
    "            K = self.num_sample_list[i]\n",
    "            group_idx = GroupPoints(radius, K, xyz, centroids_xyz)\n",
    "            grouped_xyz = index2data(xyz, group_idx)\n",
    "            grouped_xyz -= centroids_xyz.view(B, S, 1, C) # centroids_xyz [B, npoints, 3]\n",
    "            grouped_points = index2data(points, group_idx)\n",
    "            grouped_points = torch.cat([grouped_points, grouped_xyz], dim=-1)\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_points_concat = torch.cat(new_points_list, dim=1).permute(0, 2, 1) #torch.Size([4, 96, 1024])\n",
    "        return centroids_xyz, new_points_concat\n",
    "\n",
    "\n",
    "class FeaturePropagation(nn.Module):\n",
    "    def __init__(self, in_channel, mlp):\n",
    "        super(FeaturePropagation, 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, N, C]\n",
    "            xyz2: sampled input points position data, [B, S, C]\n",
    "            points1: input points data, [B, N, D]\n",
    "            points2: input points data, [B, S, D]\n",
    "        Output:\n",
    "            new_points: upsampled points data, [B, N, D']\n",
    "        \"\"\"\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 = np.empty([B,N,3], dtype=np.float)\n",
    "            idx = np.empty([B,N,3], dtype=np.long)\n",
    "            xyz2np = xyz2.clone().detach().cpu().numpy()\n",
    "            xyz1np = xyz1.clone().detach().cpu().numpy()\n",
    "\n",
    "            for i in range(B):\n",
    "                pcd = op3.geometry.PointCloud( op3.utility.Vector3dVector( xyz2np[i,:,0:3] ))\n",
    "                pcd_tree = op3.geometry.KDTreeFlann(pcd)\n",
    "                for j in range(N):\n",
    "                    [_, id, ds] = pcd_tree.search_knn_vector_3d(xyz1np[i,j,0:3], 3)\n",
    "                    idx[i,j,:] = np.asarray(id)\n",
    "                    dists[i,j,:] = np.asarray(ds)\n",
    "            \n",
    "            dists = torch.tensor(dists, dtype=torch.float).to(device)\n",
    "            idx = torch.tensor(idx, dtype=torch.long).to(device)\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(index2data(points2, idx) * weight.view(B, N, 3, 1), dim=2)\n",
    "\n",
    "        new_points = torch.cat([points1, interpolated_points], dim=-1)\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.permute(0, 2, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0l9zRqQ-YfTX"
   },
   "source": [
    "# 3. Define the Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0HOtRsDBFimw"
   },
   "source": [
    "## 3.1 PointNet++ Semantic Segmentation with 4-Layers of Multi Scale Grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "vdgGFydtYfTX"
   },
   "outputs": [],
   "source": [
    "class PointNet4MSG(nn.Module):\n",
    "    def __init__(self, num_classes, num_channels):\n",
    "        super(PointNet4MSG, self).__init__()\n",
    "        self.sa1 = SetAbstraction(0.04, [1.15, 1.3], [16, 32], num_channels, [[32, 32, 32], [32, 32, 64]])\n",
    "        self.sa2 = SetAbstraction(0.07, [1.15, 1.3], [16, 32], 32+64, [[64, 64, 128], [64, 96, 128]])\n",
    "        self.sa3 = SetAbstraction(0.09, [1.15, 1.3], [16, 32], 128+128, [[128, 196, 256], [128, 196, 256]])\n",
    "        self.sa4 = SetAbstraction(0.11, [1.15, 1.4], [10, 24], 256+256, [[256, 256, 512], [256, 384, 512]])\n",
    "        self.fp4 = FeaturePropagation(512+512+256+256, [256, 256])\n",
    "        self.fp3 = FeaturePropagation(128+128+256, [256, 256])\n",
    "        self.fp2 = FeaturePropagation(32+64+256, [256, 256])\n",
    "        self.fp1 = FeaturePropagation(256+num_channels, [256, 256, 256])\n",
    "        self.conv1 = nn.Conv1d(256, 256, 1)\n",
    "        self.conv2 = nn.Conv1d(256, num_classes, 1)\n",
    "        self.bn1 = nn.BatchNorm1d(256)\n",
    "        self.drop1 = nn.Dropout(0.5)\n",
    "\n",
    "    def forward(self, xyz):\n",
    "        l0_points = xyz\n",
    "        l0_xyz = xyz[:,:,0:3]\n",
    "\n",
    "        l1_xyz, l1_points = self.sa1(l0_xyz, l0_points)\n",
    "        l2_xyz, l2_points = self.sa2(l1_xyz, l1_points)\n",
    "        l3_xyz, l3_points = self.sa3(l2_xyz, l2_points)\n",
    "        l4_xyz, l4_points = self.sa4(l3_xyz, l3_points)\n",
    "\n",
    "        l3_points = self.fp4(l3_xyz, l4_xyz, l3_points, l4_points)\n",
    "        l2_points = self.fp3(l2_xyz, l3_xyz, l2_points, l3_points)\n",
    "        l1_points = self.fp2(l1_xyz, l2_xyz, l1_points, l2_points)\n",
    "        l0_points = self.fp1(l0_xyz, l1_xyz, l0_points, l1_points)\n",
    "\n",
    "        l0_points = l0_points.permute(0, 2, 1)\n",
    "        x = self.conv1(l0_points)\n",
    "        x = self.bn1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.drop1(x)\n",
    "\n",
    "        x = self.conv2(x)\n",
    "        x = F.relu(x)\n",
    "\n",
    "        x = F.log_softmax(x, dim=1)\n",
    "        x = x.permute(0, 2, 1)\n",
    "\n",
    "        return x, l4_points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kUTcwShDgA5T"
   },
   "source": [
    "## 3.2 PointNet++ Semantic Segmentation with 3-Layers of Multi Scale Grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Lz71gs-JgBLZ"
   },
   "outputs": [],
   "source": [
    "class PointNet3MSG(nn.Module):\n",
    "    def __init__(self, num_classes, num_channels):\n",
    "        super(PointNet3MSG, self).__init__()\n",
    "        self.sa1 = SetAbstraction(0.04, [1.15, 1.3], [16, 32], num_channels, [[32, 32, 32], [32, 32, 64]])\n",
    "        self.sa2 = SetAbstraction(0.07, [1.15, 1.3], [16, 32], 32+64, [[64, 64, 128], [64, 96, 128]])\n",
    "        self.sa3 = SetAbstraction(0.11, [1.15, 1.4], [16, 32], 128+128, [[128, 196, 256], [128, 196, 256]])\n",
    "        self.fp3 = FeaturePropagation(128+128+256+256, [256, 256])\n",
    "        self.fp2 = FeaturePropagation(32+64+256, [256, 256])\n",
    "        self.fp1 = FeaturePropagation(256+num_channels, [256, 256, 256])\n",
    "        self.conv1 = nn.Conv1d(256, 256, 1)\n",
    "        self.conv2 = nn.Conv1d(256, num_classes, 1)\n",
    "        self.bn1 = nn.BatchNorm1d(256)\n",
    "        self.drop1 = nn.Dropout(0.5)\n",
    "\n",
    "    def forward(self, xyz):\n",
    "        l0_points = xyz\n",
    "        l0_xyz = xyz[:,:,0:3]\n",
    "\n",
    "        l1_xyz, l1_points = self.sa1(l0_xyz, l0_points)\n",
    "        l2_xyz, l2_points = self.sa2(l1_xyz, l1_points)\n",
    "        l3_xyz, l3_points = self.sa3(l2_xyz, l2_points)\n",
    "\n",
    "        l2_points = self.fp3(l2_xyz, l3_xyz, l2_points, l3_points)\n",
    "        l1_points = self.fp2(l1_xyz, l2_xyz, l1_points, l2_points)\n",
    "        l0_points = self.fp1(l0_xyz, l1_xyz, l0_points, l1_points)\n",
    "\n",
    "        l0_points = l0_points.permute(0, 2, 1)\n",
    "        x = self.conv1(l0_points)\n",
    "        x = self.bn1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.drop1(x)\n",
    "\n",
    "        x = self.conv2(x)\n",
    "        x = F.relu(x)\n",
    "\n",
    "        x = F.log_softmax(x, dim=1)\n",
    "        x = x.permute(0, 2, 1)\n",
    "\n",
    "        return x, l3_points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yR5rjVvNgBWY"
   },
   "source": [
    "## 3.3 PointNet++ Semantic Segmentation with 2-Layers of Multi Scale Grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oNHf0PZZgBhb"
   },
   "outputs": [],
   "source": [
    "class PointNet2MSG(nn.Module):\n",
    "    def __init__(self, num_classes, num_channels):\n",
    "        super(PointNet2MSG, self).__init__()\n",
    "        self.sa1 = SetAbstraction(0.04, [1.15, 1.3], [16, 32], num_channels, [[32, 32, 32], [32, 32, 64]])\n",
    "        self.sa2 = SetAbstraction(0.11, [1.15, 1.3], [16, 32], 32+64, [[64, 64, 128], [64, 96, 128]])\n",
    "        self.fp2 = FeaturePropagation(32+64+256, [256, 256])\n",
    "        self.fp1 = FeaturePropagation(256+num_channels, [256, 256, 256])\n",
    "        self.conv1 = nn.Conv1d(256, 256, 1)\n",
    "        self.conv2 = nn.Conv1d(256, num_classes, 1)\n",
    "        self.bn1 = nn.BatchNorm1d(256)\n",
    "        self.drop1 = nn.Dropout(0.5)\n",
    "\n",
    "    def forward(self, xyz):\n",
    "        l0_points = xyz\n",
    "        l0_xyz = xyz[:,:,0:3]\n",
    "\n",
    "        l1_xyz, l1_points = self.sa1(l0_xyz, l0_points)\n",
    "        l2_xyz, l2_points = self.sa2(l1_xyz, l1_points)\n",
    "\n",
    "        l1_points = self.fp2(l1_xyz, l2_xyz, l1_points, l2_points)\n",
    "        l0_points = self.fp1(l0_xyz, l1_xyz, l0_points, l1_points)\n",
    "\n",
    "        l0_points = l0_points.permute(0, 2, 1)\n",
    "        x = self.conv1(l0_points)\n",
    "        x = self.bn1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.drop1(x)\n",
    "\n",
    "        x = self.conv2(x)\n",
    "        x = F.relu(x)\n",
    "\n",
    "        x = F.log_softmax(x, dim=1)\n",
    "        x = x.permute(0, 2, 1)\n",
    "\n",
    "        return x, l2_points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z0skAAZEF3vc"
   },
   "source": [
    "## 3.5 VAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2bb55I21JA30"
   },
   "outputs": [],
   "source": [
    "class Abstraction(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Abstraction, self).__init__()\n",
    "        self.sa1 = SetAbstraction(1024, [0.08, 0.1], [16, 32], 9, [[32, 32, 32], [32, 32, 64]], 0.03)\n",
    "        self.sa2 = SetAbstraction(256, [0.10, 0.14], [16, 32], 32+64, [[64, 64, 128], [64, 96, 128]], 0.06)\n",
    "        self.sa3 = SetAbstraction(128, [0.14, 0.2], [16, 32], 128+128, [[128, 196, 256], [128, 196, 256]], 0.08) #0.045 gets 600 pts\n",
    "        self.sa4 = SetAbstraction(64, [0.2, 0.5], [16, 32], 256+256, [[256, 256, 512], [256, 384, 512]], 0.11)\n",
    "\n",
    "    def forward(self, l0_xyz, l0_points):\n",
    "        batch_size = l0_xyz.shape[0]\n",
    "        l1_xyz, l1_points = self.sa1(l0_xyz, l0_points)\n",
    "        l2_xyz, l2_points = self.sa2(l1_xyz, l1_points)\n",
    "        l3_xyz, l3_points = self.sa3(l2_xyz, l2_points)\n",
    "        l4_xyz, l4_points = self.sa4(l3_xyz, l3_points)\n",
    "\n",
    "        return l0_xyz, l0_points, l1_xyz, l1_points, l2_xyz, l2_points, l3_xyz, l3_points, l4_xyz, l4_points\n",
    "\n",
    "\n",
    "class Propagation(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Propagation, self).__init__()\n",
    "        self.fp4 = FeaturePropagation(512+512+256+256, [256, 256])\n",
    "        self.fp3 = FeaturePropagation(128+128+256, [256, 256])\n",
    "        self.fp2 = FeaturePropagation(32+64+256, [256, 256])\n",
    "        self.fp1 = FeaturePropagation(256+9, [256, 256, 256])\n",
    "\n",
    "    def forward(self, l0_xyz, l0_points, l1_xyz, l1_points, l2_xyz, l2_points, l3_xyz, l3_points, l4_xyz, l4_points):\n",
    "        l3_points = self.fp4(l3_xyz, l4_xyz, l3_points, l4_points)\n",
    "        l2_points = self.fp3(l2_xyz, l3_xyz, l2_points, l3_points)\n",
    "        l1_points = self.fp2(l1_xyz, l2_xyz, l1_points, l2_points)\n",
    "        l0_points = self.fp1(l0_xyz, l1_xyz, l0_points, l1_points)\n",
    "\n",
    "        return l0_points\n",
    "\n",
    "class Encoder(nn.Module):\n",
    "    def __init__(self, hdim, zdim):\n",
    "        ''' in: [xyz|l4_points] size [B, N', C'], out: [B, 64, zdim]=[B, ]'''\n",
    "        super(Encoder, self).__init__()\n",
    "        self.hdim = hdim\n",
    "        self.zdim = zdim\n",
    "        # torch.Size([4, 64, 3]) torch.Size([4, 64, 1024])\n",
    "        #self.conv1 = nn.Conv1d(3+1024, 256, 1)\n",
    "        self.conv1 = nn.Conv1d(1024, 256, 1)\n",
    "        self.conv2 = nn.Conv1d(256, hdim, 1)\n",
    "        self.bn1 = nn.BatchNorm1d(256)\n",
    "        self.mu = nn.Linear(hdim*64, zdim)\n",
    "        self.logvar = nn.Linear(hdim*64, zdim)\n",
    "\n",
    "    def forward(self, points):\n",
    "        batch_size = points.shape[0]\n",
    "\n",
    "        points = points.permute(0, 2, 1) # [4, 3+1024, 64]\n",
    "        x = self.conv1(points)\n",
    "        x = F.relu(x)\n",
    "        x = self.bn1(x)\n",
    "        x = self.conv2(x) # [4, hdim, 64]\n",
    "        h = x.permute(0, 2, 1) # [4, 64, hdim]\n",
    "\n",
    "        h = h.contiguous().view(batch_size, -1)\n",
    "        \n",
    "        #hr = h.clone().detach()\n",
    "        mu = self.mu(h)\n",
    "        logvar = self.logvar(h)\n",
    "        return mu, logvar\n",
    "\n",
    "\n",
    "class Decoder(nn.Module):\n",
    "    def __init__(self, hdim, zdim):\n",
    "        super(Decoder, self).__init__()\n",
    "        self.hdim = hdim\n",
    "        self.zdim = zdim\n",
    "        self.fc0 = nn.Linear(zdim, hdim*64)\n",
    "        #self.fc1 = nn.Linear(256*64, num_points*6)\n",
    "        #self.conv1 = nn.Conv1d(hdim, 128, kernel_size=1)#nn.Linear(hdim*64, num_points*6)\n",
    "        #self.conv2 = nn.Conv1d(128, 256, kernel_size=1)\n",
    "        self.convt1 = nn.ConvTranspose1d(64, 256, kernel_size=1) # 63\n",
    "        self.convt2 = nn.ConvTranspose1d(256, num_points, kernel_size=1)\n",
    "        #self.drop = nn.Dropout(0.5)\n",
    "\n",
    "    def forward(self, z):\n",
    "        batch_size = z.shape[0]\n",
    "        h = self.fc0(z)\n",
    "        h = h.reshape([batch_size, self.hdim, 64])\n",
    "        h = h.permute(0, 2, 1)\n",
    "        h = self.convt1(h)\n",
    "        h = F.leaky_relu(h, 0.2)\n",
    "        points = self.convt2(h)\n",
    "        # h = F.relu(h)\n",
    "        # h = h.contiguous().view(batch_size, -1)\n",
    "        # points = self.fc1(h)\n",
    "        # points = points.reshape([batch_size, num_points, 6])\n",
    "        return points\n",
    "\n",
    "class VAE(nn.Module):\n",
    "    def __init__(self, num_classes, hdim, zdim):\n",
    "        super(VAE, self).__init__()\n",
    "        self.zdim = zdim\n",
    "        self.abst = Abstraction()\n",
    "        self.prop = Propagation()\n",
    "        self.conv1 = nn.Conv1d(256, 256, 1)\n",
    "        self.conv2 = nn.Conv1d(256, num_classes, 1)\n",
    "        self.bn1 = nn.BatchNorm1d(256)\n",
    "        self.drop1 = nn.Dropout(0.5)\n",
    "        self.enc = Encoder(hdim, zdim)\n",
    "        self.dec = Decoder(hdim, zdim)\n",
    "\n",
    "    def semseg(self, l0_points):\n",
    "        l0_points = l0_points.permute(0, 2, 1)\n",
    "        x = self.conv1(l0_points)\n",
    "        x = self.bn1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.drop1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = F.relu(x)\n",
    "        x = F.log_softmax(x, dim=1)\n",
    "        x = x.permute(0, 2, 1)\n",
    "\n",
    "        return x\n",
    "\n",
    "    def reparameterize(self, mu, logvar):\n",
    "      if self.training:\n",
    "        std = torch.exp(0.5*logvar)\n",
    "        eps = torch.randn_like(std)\n",
    "        z = mu + std*eps\n",
    "      else:\n",
    "        z = mu\n",
    "      return z\n",
    "\n",
    "    def forward(self, xyz):\n",
    "        l0_points = xyz\n",
    "        l0_xyz = xyz[:,:,0:3]\n",
    "        \n",
    "        # Abstraction, Propagation, Semantic Segmentation\n",
    "        l0_xyz, l0_points, l1_xyz, l1_points, l2_xyz, l2_points, l3_xyz, l3_points, l4_xyz, l4_points = self.abst(l0_xyz, l0_points)\n",
    "        semseg_points = self.prop(l0_xyz, l0_points, l1_xyz, l1_points, l2_xyz, l2_points, l3_xyz, l3_points, l4_xyz, l4_points)\n",
    "        scores = self.semseg(semseg_points)\n",
    "\n",
    "        # VAE\n",
    "        #mu, logvar = self.enc( torch.cat([l4_xyz, l4_points], dim=-1) )\n",
    "        mu, logvar = self.enc(l4_points)\n",
    "        z = self.reparameterize(mu, logvar)\n",
    "        points = self.dec(z)\n",
    "        \n",
    "        return scores, points, mu, logvar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7OdPJDGPYfTZ"
   },
   "source": [
    "# 4. Train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-egEMYlvIQii"
   },
   "source": [
    "## 4.1 Training Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "TdnJZXvpL8Ws"
   },
   "outputs": [],
   "source": [
    "# Path for saving the model and optimizer states\n",
    "# file name C for number of channels, L for number of MSG layers\n",
    "root_path = 'checkpoints/'\n",
    "model_name = 'Point_Cloud_Project'\n",
    "file_name_end = '_'+str(num_channels)+'C_4L_' + str(num_points)\n",
    "file_format = '.pth'\n",
    "save_path = root_path + model_name + file_name_end + file_format\n",
    "\n",
    "# Set optimization parameters\n",
    "num_epochs = 50\n",
    "lr = 0.01\n",
    "momentum = 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 37
    },
    "id": "6Gqu7KyzcbHZ",
    "outputId": "50ef0161-8cfa-4a65-f218-d8883487f7cb"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth'"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KziRRGAsYfTb"
   },
   "source": [
    "## 4.2 Define Network and Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "hMnbvrtqYfTb"
   },
   "outputs": [],
   "source": [
    "# PointNet++ with 4 MSG Layers\n",
    "classifier = PointNet4MSG(num_classes, num_channels).to(device)\n",
    "optimizer = torch.optim.SGD(classifier.parameters(), lr=lr, momentum=momentum)\n",
    "\n",
    "# PointNet++ with 3 MSG Layers\n",
    "# classifier = PointNet3MSG(num_classes, num_channels).to(device)\n",
    "# optimizer = torch.optim.SGD(classifier.parameters(), lr=lr, momentum=momentum)\n",
    "\n",
    "# PointNet++ with 2 MSG Layers\n",
    "# classifier = PointNet2MSG(num_classes, num_channels).to(device)\n",
    "# optimizer = torch.optim.SGD(classifier.parameters(), lr=lr, momentum=momentum)\n",
    "\n",
    "# PointNet++ VAE\n",
    "#classifier = VAE(num_classes, num_channels, 6, 30).to(device)\n",
    "#optimizer = torch.optim.Adam(classifier.parameters(), lr=0.01, weight_decay=1e-4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tdEeeZI4YfTc"
   },
   "source": [
    "## 4.3 Define the Loss and Train Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WrvdKak1g96T"
   },
   "source": [
    "### 4.3.1 Define Loss Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V6gdbDacKwTn"
   },
   "source": [
    "#### 4.3.1.1 Chamfer Reconstruction Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5q_HixJdLwDL"
   },
   "outputs": [],
   "source": [
    "def ChamferDistance(p1, p2):\n",
    "    '''\n",
    "    Calculate Chamfer Distance between two point sets\n",
    "    :param p1: size[B, N, D]\n",
    "    :param p2: size[B, M, D]\n",
    "    :return: sum of all batches of Chamfer Distance of two point sets\n",
    "    '''\n",
    "    assert p1.size(0) == p2.size(0) and p1.size(2) == p2.size(2)\n",
    "    p1 = p1.unsqueeze(1)\n",
    "    p2 = p2.unsqueeze(1)\n",
    "    p1 = p1.repeat(1, p2.size(2), 1, 1)\n",
    "    p1 = p1.transpose(1, 2)\n",
    "    p2 = p2.repeat(1, p1.size(1), 1, 1)\n",
    "    dist = torch.add(p1, torch.neg(p2))\n",
    "    dist = torch.norm(dist, 2, dim=3)\n",
    "    dist = torch.min(dist, dim=2)[0]\n",
    "    dist = torch.sum(dist)\n",
    "    return dist\n",
    "\n",
    "def ChamferLoss(xhat, x, p, phat, mu, logvar, kl_weight=10):\n",
    "    CE = F.nll_loss(xhat, x)\n",
    "    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), 1).mean()\n",
    "    dist = ChamferDistance(p, phat)*20/x.shape[0]\n",
    "    loss = (CE + kl_weight*KLD + dist)\n",
    "    #print(CE.item(), KLD.item(), dist, x.shape[0])\n",
    "    return loss, CE.item(), KLD.item(), dist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5Of1uHOuLIa4"
   },
   "source": [
    "#### 4.3.1.2 Sinkhorn Reconstruction Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZvpG68SKf9jT"
   },
   "outputs": [],
   "source": [
    "def SinkhornLoss(xhat, x, p, phat, mu, logvar, kl_weight=0.1):\n",
    "    CE = F.nll_loss(xhat, x)\n",
    "    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), 1).mean()\n",
    "\n",
    "    SK = SamplesLoss(\"sinkhorn\", p=1, blur=.0001, backend='tensorized')\n",
    "    dist = 0\n",
    "    for i in range(batch_size):\n",
    "        dist += SK(p[i], phat[i])\n",
    "\n",
    "    loss = (CE + kl_weight*KLD + dist)\n",
    "    return loss, CE.item(), KLD.item(), dist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IxxPxva4Mn1e"
   },
   "source": [
    "#### 4.3.1.3 Hybrid Chamfer & Sinkhorn Reconstruction Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EQDII0-PMvHY"
   },
   "outputs": [],
   "source": [
    "def HybridLoss(xhat, x, p, phat, mu, logvar, kl_weight=0.1):\n",
    "    CE = F.nll_loss(xhat, x)\n",
    "    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), 1).mean()\n",
    "\n",
    "    SK = SamplesLoss(\"sinkhorn\", p=1, blur=.0001, backend='tensorized')\n",
    "    sinkhorn = 0\n",
    "    for i in range(batch_size):\n",
    "        sinkhorn += SK(p[i], phat[i])\n",
    "    chamfer = ChamferDistance(p, phat)*20/x.shape[0]\n",
    "    dist = sinkhorn + chamfer\n",
    "\n",
    "    loss = (CE + kl_weight*KLD + dist)\n",
    "    return loss, CE.item(), KLD.item(), dist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hwMU0TtSdukT"
   },
   "source": [
    "### 4.3.2 Define the Training Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "5UUf8c5lYfTc"
   },
   "outputs": [],
   "source": [
    "def train(classifier, optimizer, train_loader, num_epochs, file_name_end, load_checkpoint=None):\n",
    "    print(\"Number of training samples: \", len(train_dataset))\n",
    "\n",
    "    if load_checkpoint:\n",
    "        checkpoint = torch.load(load_checkpoint)\n",
    "        classifier.load_state_dict(checkpoint['model_state_dict'])\n",
    "        optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
    "\n",
    "    classifier.train()\n",
    "    num_batches = len(train_loader)\n",
    "    num_points_in_epoch = num_batches * batch_size * num_points\n",
    "    max_accuracy = 0\n",
    "    mean_loss_list = []\n",
    "    accuracy_list = []\n",
    "\n",
    "    for epoch in range(1,num_epochs+1):\n",
    "        print('---------- EPOCH %d of %s ----------' % (epoch, num_epochs))\n",
    "        \n",
    "        total_correct = 0\n",
    "        loss_sum = 0\n",
    "        for i, data in notebook.tqdm(enumerate(train_loader), total=len(train_loader)):\n",
    "            points, target = data\n",
    "            points, target = points.float().to(device), target.long().to(device)\n",
    "            optimizer.zero_grad()\n",
    "            scores, trans_feat = classifier(points)\n",
    "            scores = scores.contiguous().view(-1, num_classes)\n",
    "            target = target.view(-1, 1)[:, 0]\n",
    "            loss = F.nll_loss(scores, target)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            # if i % 4 == 0:\n",
    "            #     optimizer.step()\n",
    "            #     optimizer.zero_grad()\n",
    "            predictions = scores.cpu().data.max(-1)[1].numpy()\n",
    "            correct = np.sum(predictions == target.cpu().data.numpy())\n",
    "            total_correct += correct\n",
    "            loss_sum += loss.item()\n",
    "\n",
    "        accuracy = total_correct / num_points_in_epoch\n",
    "        mean_loss_list.append(loss_sum / num_batches)\n",
    "        accuracy_list.append(accuracy)\n",
    "        print('Mean loss: %f' % (loss_sum / num_batches))\n",
    "        print('Accuracy: %f' % (100*accuracy))\n",
    "        if accuracy > max_accuracy:\n",
    "            max_accuracy = accuracy\n",
    "            print('Saving at %s' % save_path)\n",
    "            state = {\n",
    "                'model_state_dict': classifier.state_dict(),\n",
    "                'optimizer_state_dict': optimizer.state_dict(),\n",
    "            }\n",
    "            torch.save(state, save_path)\n",
    "            print('Save complete.\\n')\n",
    "        #if epoch != 0 and epoch % (num_epochs-1) == 0: clear_output(wait=True)\n",
    "\n",
    "    np.save('graphs/training_mean_loss' + file_name_end, mean_loss_list)\n",
    "    np.save('graphs/training_accuracy' + file_name_end, accuracy_list)\n",
    "    return mean_loss_list, accuracy_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B39x-3P_IWey"
   },
   "source": [
    "## 4.4 Start Training"
   ]
  },
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    },
    "id": "_p8FcsSFP8-x",
    "outputId": "5635a41c-2712-4826-e7b8-247a0bfbb5c4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training samples:  40\n",
      "---------- EPOCH 1 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "15a51af825084641ac4c6912c36307b2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 2.100306\n",
      "Accuracy: 46.804810\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 2 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "58521590818547e0ad995035628387a1",
       "version_major": 2,
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      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 1.231253\n",
      "Accuracy: 65.675659\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 3 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "31ebe2d8eabe474e8390c339a4a23cf8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 1.024314\n",
      "Accuracy: 70.454102\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 4 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b646c6f1a6f14767a61df70d88b41360",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.989438\n",
      "Accuracy: 72.014160\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 5 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e0b22f2d4ebe48a9951e49017b27c03b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.952229\n",
      "Accuracy: 72.271118\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 6 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc24ea2fad9a4ad0a13e23e12efeb956",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.954667\n",
      "Accuracy: 71.223145\n",
      "---------- EPOCH 7 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd99c1087d304cc698f45d5ef266426c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.874485\n",
      "Accuracy: 73.234253\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 8 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d7b29e49bd441868eeec6d6888d3e7a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.801394\n",
      "Accuracy: 75.698242\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 9 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d315a08aa8b44ed8f04da7b53eeb43b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.737396\n",
      "Accuracy: 77.370605\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 10 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b18c2d0b471644dab3cc2a54b6216011",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.697829\n",
      "Accuracy: 78.201904\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 11 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "46c3f5371a52455da14f8468a96a876a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.631449\n",
      "Accuracy: 79.695435\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 12 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3b692233c9aa4b9a92ba007c21440225",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.599447\n",
      "Accuracy: 81.005859\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 13 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b1d04693861d46c8853b0295cb2f74ac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.546903\n",
      "Accuracy: 82.165527\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 14 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e64a8db335b443fa03fde9eb5669f7f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.517503\n",
      "Accuracy: 82.966309\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 15 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab00b970c1ed4e14b075841da0e73354",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.494214\n",
      "Accuracy: 83.190918\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 16 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c5f5366097df442e92f74723f75bab5c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.508447\n",
      "Accuracy: 82.608032\n",
      "---------- EPOCH 17 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8515c701f6184249987309af21c44bfb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.489233\n",
      "Accuracy: 83.084717\n",
      "---------- EPOCH 18 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "33100bd8a86142ae910f551ed76d63b8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.463163\n",
      "Accuracy: 84.746704\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 19 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "efa81e49c4424c4ca430544fac90114b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.443771\n",
      "Accuracy: 85.694580\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 20 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "468c1195fe11499bb8e9787a01d214b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.404574\n",
      "Accuracy: 86.450195\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 21 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3441c29ec1ef4bfbbb8d36ecc573a04a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.379131\n",
      "Accuracy: 88.161621\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 22 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e837a8123ade41ecb465ae9722e181eb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.361318\n",
      "Accuracy: 88.491821\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 23 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "889b041610e44013b8e99051d8a4ff43",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.354418\n",
      "Accuracy: 88.614502\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 24 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa7dc301d3084c7bb72414c5d3adb96b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.330255\n",
      "Accuracy: 89.420776\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 25 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f2a96938d5a44c99bbfe058aa8bfb690",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.371446\n",
      "Accuracy: 87.842407\n",
      "---------- EPOCH 26 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c4581238580e45489c0e2fb0c4ec6b7d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.330198\n",
      "Accuracy: 89.447021\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 27 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1021b010beaa4471bf4b29123c839f2f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.322773\n",
      "Accuracy: 89.360352\n",
      "---------- EPOCH 28 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b1d73e4e3d404bb58c8aa4351801a164",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.324592\n",
      "Accuracy: 89.411011\n",
      "---------- EPOCH 29 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "11d05b2c9a90485b95f45a0950eb3c53",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.292924\n",
      "Accuracy: 90.302734\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 30 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3dc11280b4024ed5a48c20afffdd70a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.286104\n",
      "Accuracy: 90.613403\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 31 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c6a25c4599e5401bb4281926850200cb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.281550\n",
      "Accuracy: 90.599976\n",
      "---------- EPOCH 32 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "51a8e849d32d4da99c4d1b9490a283c8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.269163\n",
      "Accuracy: 90.885620\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 33 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7eef4b83335a4367a8282f573d19fda8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.246912\n",
      "Accuracy: 91.665039\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 34 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3cfb07405a1d4f849c5dffb8588c3c16",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.230123\n",
      "Accuracy: 92.109375\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 35 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a7f685c85d2f4c689548af5b122c4005",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.222178\n",
      "Accuracy: 92.349854\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 36 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c8326d54640e46c4849326b3784b434b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.243873\n",
      "Accuracy: 91.542969\n",
      "---------- EPOCH 37 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "de832249a1a64d15bd5fa1423927e192",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.221350\n",
      "Accuracy: 92.369995\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 38 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e10a55c11107483fabd99e3a6c3ecd46",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.223463\n",
      "Accuracy: 92.229004\n",
      "---------- EPOCH 39 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "40a9c36471584880aad944f01719ec8d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.213562\n",
      "Accuracy: 92.611694\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 40 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f2844e752685431390b51d8e82db09c7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.208789\n",
      "Accuracy: 92.805786\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 41 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cd94f71941b34ac0b631da3339fc691d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.209887\n",
      "Accuracy: 92.666016\n",
      "---------- EPOCH 42 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1dfc981bbeab49578c713eae114c852f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.205613\n",
      "Accuracy: 92.787476\n",
      "---------- EPOCH 43 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "502ebfa9ce7f424ebab5a81e7119ba1d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.192110\n",
      "Accuracy: 93.238525\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 44 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e031fcdc735f42a3860ae878f0b4338c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.187262\n",
      "Accuracy: 93.456421\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 45 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9d68954df4224b7ca0bb50705ff5a14c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.193117\n",
      "Accuracy: 93.252563\n",
      "---------- EPOCH 46 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "69a855ffcf4249f39ffdb9953c0606ee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.186871\n",
      "Accuracy: 93.428955\n",
      "---------- EPOCH 47 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df2f5d57b4524b11ae8aea8247d6f637",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.184997\n",
      "Accuracy: 93.356934\n",
      "---------- EPOCH 48 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7428753b195a4ade87455e643337595a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.177452\n",
      "Accuracy: 93.718262\n",
      "Saving at checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth\n",
      "Save complete.\n",
      "\n",
      "---------- EPOCH 49 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e7bea0f052904aa6ad42434e4e712e3c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.185509\n",
      "Accuracy: 93.451538\n",
      "---------- EPOCH 50 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a756d7ef2884d70a4304156fbf72ec0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 0.182074\n",
      "Accuracy: 93.530273\n",
      "---------- EPOCH 51 of 100 ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "975f4eae421f44949c1079e3a9211ace",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-50-4caf4cff6468>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmean_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_epochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_name_end\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[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Max Accuracy: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maccuracy\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[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Min Mean Loss: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean_loss\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<ipython-input-46-8ed63daa3772>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(classifier, optimizer, train_loader, num_epochs, file_name_end, load_checkpoint)\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0mtotal_correct\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[1;32m     20\u001b[0m         \u001b[0mloss_sum\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---> 21\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnotebook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\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[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m             \u001b[0mpoints\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m             \u001b[0mpoints\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpoints\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tqdm/notebook.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    214\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtqdm_notebook\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    216\u001b[0m                 \u001b[0;31m# return super(tqdm...) will not catch exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tqdm/std.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1102\u001b[0m                 fp_write=getattr(self.fp, 'write', sys.stderr.write))\n\u001b[1;32m   1103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1104\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\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   1105\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1106\u001b[0m             \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    433\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    434\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\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--> 435\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\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    436\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1066\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1067\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shutdown\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tasks_outstanding\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-> 1068\u001b[0;31m             \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_data\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   1069\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tasks_outstanding\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1070\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_get_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1022\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1023\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_alive\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[0m\n\u001b[0;32m-> 1024\u001b[0;31m                 \u001b[0msuccess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_get_data\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   1025\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0msuccess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1026\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_try_get_data\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    870\u001b[0m         \u001b[0;31m#   (bool: whether successfully get data, any: data if successful else None)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    871\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 872\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data_queue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\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    873\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    874\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/queue.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    171\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mremaining\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m                         \u001b[0;32mraise\u001b[0m \u001b[0mEmpty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_empty\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremaining\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    174\u001b[0m             \u001b[0mitem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\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    175\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_full\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotify\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/usr/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    297\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 299\u001b[0;31m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\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    300\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "mean_loss, accuracy = train(classifier, optimizer, train_loader, num_epochs, file_name_end)\n",
    "print('Max Accuracy: ', 100*max(accuracy))\n",
    "print('Min Mean Loss: ', min(mean_loss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z7knezqAd3ut"
   },
   "source": [
    "## 4.5 Plot Accuracy and Mean Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "q1TNi5Jri_si",
    "outputId": "0a579736-64dd-4542-dc71-3ce21efa3082"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(mean_loss, label='Mean Loss')\n",
    "plt.plot(accuracy, label='Accuracy %')\n",
    "plt.legend()\n",
    "plt.ylim((0,1))\n",
    "plt.title('Accuracy of per point predictions')\n",
    "plt.xlabel('Epochs');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UoZRKUVEfbmM"
   },
   "source": [
    "# 5. Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-MEaRtLWtuNm"
   },
   "source": [
    "## 5.1 Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Pn2n3rA2tuNm",
    "outputId": "8ba6c560-5ce3-4a93-8961-fe509b9a0632"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 33 frames in dataset.\n"
     ]
    }
   ],
   "source": [
    "sequence_path = 'mini_dataset/2013_05_28_drive_0000_006154_006400_mini_validation.npy'\n",
    "cam2world_path = 'mini_dataset/cam0_to_world.txt'\n",
    "frame_start = 5660\n",
    "frame_end = 5790\n",
    "box_size = [15,15,20]\n",
    "\n",
    "val_dataset = KITTI360Dataset(sequence_path, cam2world_path, num_points=num_points,\n",
    "                              box_size=box_size, frame_step=frame_step, frame_start=frame_start, \n",
    "                              frame_end=frame_end, estimate_normals=True)\n",
    "val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False, \n",
    "                                         num_workers=num_workers, pin_memory=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9GED0oWZtuNm"
   },
   "source": [
    "## 5.2 Define Validation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "McUEjCz-tuNm"
   },
   "outputs": [],
   "source": [
    "def validation(classifier, val_loader, file_name_end, load_checkpoint=None):\n",
    "    print(\"Number of validation samples: \", len(val_loader))\n",
    "\n",
    "    if load_checkpoint:\n",
    "        checkpoint = torch.load(load_checkpoint)\n",
    "        classifier.load_state_dict(checkpoint['model_state_dict'])\n",
    "\n",
    "    mean_loss = []\n",
    "    accuracy_list = []\n",
    "    class_num_points = {x:0 for x in range(num_classes)}\n",
    "    true_positive = {x:0 for x in range(num_classes)}\n",
    "    false_positive = {x:0 for x in range(num_classes)}\n",
    "    false_negative = {x:0 for x in range(num_classes)}\n",
    "    class_accuracy = {x:0 for x in range(num_classes)}\n",
    "    class_mIoU = {x:0 for x in range(num_classes)}\n",
    "\n",
    "    print('---------- VALIDATION ----------')\n",
    "    \n",
    "    num_batches = len(val_loader)\n",
    "    total_correct = 0\n",
    "    total_points = 0\n",
    "    loss_sum = 0\n",
    "    with torch.no_grad():\n",
    "        classifier.eval()\n",
    "        for i, data in notebook.tqdm(enumerate(val_loader), total=len(val_loader)):\n",
    "            points, target = data\n",
    "            points, target = points.float().to(device), target.long().to(device)\n",
    "            scores, trans_feat = classifier(points)\n",
    "            scores = scores.contiguous().view(-1, num_classes)\n",
    "            batch_label = target.view(-1, 1)[:, 0].cpu().data.numpy()\n",
    "            target = target.view(-1, 1)[:, 0]\n",
    "            loss = F.nll_loss(scores, target)\n",
    "            predictions = scores.cpu().data.max(-1)[1].numpy()\n",
    "            correct = np.sum(predictions == batch_label)\n",
    "            total_correct += correct\n",
    "            total_points += (batch_size * num_points)\n",
    "            loss_sum += loss.item()\n",
    "            \n",
    "            for j in range(num_classes):\n",
    "                class_num_points[j] += (target[target==j]).shape[0]\n",
    "                true_positive[j] += np.sum(batch_label[batch_label==j] == predictions[batch_label==j])\n",
    "                false_positive[j] += np.sum(batch_label[predictions==j] != predictions[predictions==j])\n",
    "                false_negative[j] += np.sum(batch_label[batch_label==j] != predictions[batch_label==j])\n",
    "\n",
    "    for i in range(num_classes):\n",
    "        if class_num_points[i] != 0:\n",
    "            class_accuracy[i] = true_positive[i]/class_num_points[i]\n",
    "            class_mIoU[i] = true_positive[i]/(true_positive[i] + false_positive[i] + false_negative[i])\n",
    "\n",
    "    accuracy = total_correct / total_points\n",
    "    mean_loss.append(loss_sum / num_batches)\n",
    "    accuracy_list.append(accuracy)\n",
    "    print('Mean loss: %f' % (loss_sum / num_batches))\n",
    "    print('Accuracy: %f' % (100*accuracy))\n",
    "\n",
    "    #np.save('val_mean_loss' + file_name_end, mean_loss)\n",
    "    #np.save('val_accuracy' + file_name_end, accuracy_list)\n",
    "    #np.save('val_class_accuracy' + file_name_end, class_accuracy)\n",
    "    #np.save('val_class_mIoU' + file_name_end, class_mIoU)\n",
    "    return mean_loss, accuracy_list, class_accuracy, class_mIoU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "thDAeI4TtuNm"
   },
   "source": [
    "## 5.3 Start Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 134,
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      "c7a32fb55a94407586a2d4153b57eb3a",
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    },
    "id": "AoDSuRJd87O6",
    "outputId": "493a7d73-823e-43e8-9ba4-48a7fab5d83d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of validation samples:  33\n",
      "---------- VALIDATION ----------\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      "Mean loss: 1.753065\n",
      "Accuracy: 67.844460\n"
     ]
    }
   ],
   "source": [
    "val_mean_loss, val_accuracy, val_class_accuracy, val_class_mIoU = validation(classifier, val_loader, file_name_end, save_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bSQmANfqHv_l"
   },
   "source": [
    "## 5.4 Save Validation Class Accuracy and mIoU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "ScY3Gdo_MLBT"
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "with open('results/val_class_accuracy' + file_name_end + '.csv', 'w', newline='') as csvfile:\n",
    "    fieldnames = ['id', 'accuracy']\n",
    "    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n",
    "\n",
    "    writer.writeheader()\n",
    "    for i in range(num_classes):\n",
    "        writer.writerow({'id':i, 'accuracy':val_class_accuracy[i]})\n",
    "\n",
    "with open('results/val_class_mIoU' + file_name_end + '.csv', 'w', newline='') as csvfile:\n",
    "    fieldnames = ['id', 'mIoU']\n",
    "    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n",
    "\n",
    "    writer.writeheader()\n",
    "    for i in range(num_classes):\n",
    "        writer.writerow({'id':i, 'mIoU':val_class_mIoU[i]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 37
    },
    "id": "mVLw7n1lnz2A",
    "outputId": "5e1c4109-b7c2-48ee-c62f-10483af21c5a"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'checkpoints/Test_Point_Cloud_Project_9C_4L_4096.pth'"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5molXNzWQOxp"
   },
   "source": [
    "# 6. Make Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pazLFrOhqwdX"
   },
   "outputs": [],
   "source": [
    "for i, data in enumerate(val_loader):\n",
    "    points, target = data\n",
    "    points, target = points.float().to(device), target.long().to(device)\n",
    "    classifier.eval()\n",
    "    scores, trans_feat = classifier(points)\n",
    "    scores = scores.contiguous().view(-1, num_classes)\n",
    "    batch_label = target.view(-1, 1)[:, 0].cpu().data.numpy()\n",
    "    predictions = scores.cpu().data.max(-1)[1].numpy()\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zeS9M8ZTrbTG"
   },
   "outputs": [],
   "source": [
    "np.save('predictions', predictions)\n",
    "np.save('points', points.detach().cpu().numpy())\n",
    "np.save('target', target.detach().cpu().numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VDHEOjSyJ97p"
   },
   "source": [
    "# 7. Generating Point Clouds with VAE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BpFFiJO_Kh6J"
   },
   "source": [
    "## 7.1 Define Network and Load Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bosGzhZzP9t7"
   },
   "outputs": [],
   "source": [
    "#checkpoint = torch.load('checkpoints/VAE4096.pth', map_location=torch.device('cpu'))\n",
    "checkpoint = torch.load('checkpoints/VAE4096.pth')\n",
    "classifier.load_state_dict(checkpoint['model_state_dict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "N7yH_F8kPK-q"
   },
   "outputs": [],
   "source": [
    "N = 4\n",
    "classifier.eval()\n",
    "\n",
    "# Sample z~p(z)\n",
    "z = torch.randn((N, classifier.enc.zdim)).to(device)\n",
    "\n",
    "# Decode\n",
    "sample = classifier.dec(z).detach().cpu().numpy()\n",
    "\n",
    "np.save('reconstruction-23', sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "YJjts6MYtuNn"
   },
   "outputs": [],
   "source": []
  }
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