classification_pointnet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "classification_pointnet",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DxIbxQUIXAjz",
        "outputId": "4b12d2f8-385d-4c76-fa8e-5a122bd6e889"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in links: https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py37_cu111_pyt1100/download.html\n",
            "Collecting pytorch3d\n",
            "  Downloading https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py37_cu111_pyt1100/pytorch3d-0.6.1-cp37-cp37m-linux_x86_64.whl (44.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 44.7 MB 1.2 MB/s \n",
            "\u001b[?25hCollecting fvcore\n",
            "  Downloading fvcore-0.1.5.post20220119.tar.gz (55 kB)\n",
            "\u001b[K     |████████████████████████████████| 55 kB 3.4 MB/s \n",
            "\u001b[?25hCollecting iopath\n",
            "  Downloading iopath-0.1.9-py3-none-any.whl (27 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from fvcore->pytorch3d) (1.19.5)\n",
            "Collecting yacs>=0.1.6\n",
            "  Downloading yacs-0.1.8-py3-none-any.whl (14 kB)\n",
            "Collecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 20.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from fvcore->pytorch3d) (4.62.3)\n",
            "Requirement already satisfied: termcolor>=1.1 in /usr/local/lib/python3.7/dist-packages (from fvcore->pytorch3d) (1.1.0)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from fvcore->pytorch3d) (7.1.2)\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from fvcore->pytorch3d) (0.8.9)\n",
            "Collecting portalocker\n",
            "  Downloading portalocker-2.3.2-py2.py3-none-any.whl (15 kB)\n",
            "Building wheels for collected packages: fvcore\n",
            "  Building wheel for fvcore (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fvcore: filename=fvcore-0.1.5.post20220119-py3-none-any.whl size=65267 sha256=ec031ebdd11230054d8739c9fee899bc1cb19815c7ca5db9c9ad0bc52dfbb1df\n",
            "  Stored in directory: /root/.cache/pip/wheels/f3/b8/eb/61ed840f80d7198725bc061872b6019a7b3e9db4dbadf68083\n",
            "Successfully built fvcore\n",
            "Installing collected packages: pyyaml, portalocker, yacs, iopath, fvcore, pytorch3d\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed fvcore-0.1.5.post20220119 iopath-0.1.9 portalocker-2.3.2 pytorch3d-0.6.1 pyyaml-6.0 yacs-0.1.8\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import sys\n",
        "import torch\n",
        "need_pytorch3d=False\n",
        "try:\n",
        "    import pytorch3d\n",
        "except ModuleNotFoundError:\n",
        "    need_pytorch3d=True\n",
        "if need_pytorch3d:\n",
        "    if torch.__version__.startswith(\"1.10.\") and sys.platform.startswith(\"linux\"):\n",
        "        # We try to install PyTorch3D via a released wheel.\n",
        "        pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
        "        version_str=\"\".join([\n",
        "            f\"py3{sys.version_info.minor}_cu\",\n",
        "            torch.version.cuda.replace(\".\",\"\"),\n",
        "            f\"_pyt{pyt_version_str}\"\n",
        "        ])\n",
        "        !pip install pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
        "    else:\n",
        "        # We try to install PyTorch3D from source.\n",
        "        !curl -LO https://github.com/NVIDIA/cub/archive/1.10.0.tar.gz\n",
        "        !tar xzf 1.10.0.tar.gz\n",
        "        os.environ[\"CUB_HOME\"] = os.getcwd() + \"/cub-1.10.0\"\n",
        "        !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/dymnmysn/pointnet.pytorch/master/misc/num_seg_classes.txt --no-check-certificate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nqD82l5afDzu",
        "outputId": "83753c1d-0e8d-49f3-8697-c91a37c049e3"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-01-24 01:00:47--  https://raw.githubusercontent.com/dymnmysn/pointnet.pytorch/master/misc/num_seg_classes.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.108.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 138 [text/plain]\n",
            "Saving to: ‘num_seg_classes.txt’\n",
            "\n",
            "num_seg_classes.txt 100%[===================>]     138  --.-KB/s    in 0s      \n",
            "\n",
            "2022-01-24 01:00:55 (6.96 MB/s) - ‘num_seg_classes.txt’ saved [138/138]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://shapenet.cs.stanford.edu/ericyi/shapenetcore_partanno_segmentation_benchmark_v0.zip --no-check-certificate\n",
        "!unzip shapenetcore_partanno_segmentation_benchmark_v0.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E1UH_5zcZPsx",
        "outputId": "02453485-4979-4699-f83c-b7eaf045cb6a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Model.py\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from pytorch3d.ops import GraphConv\n",
        "from pytorch3d.io import load_objs_as_meshes\n",
        "from pytorch3d.renderer import Textures\n",
        "\n",
        "\n",
        "class STN3d(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(STN3d, self).__init__()\n",
        "        self.conv1 = GraphConv(3, 64)\n",
        "        self.conv2 = GraphConv(64, 128)\n",
        "        self.conv3 = GraphConv(128, 1024)\n",
        "        self.fc1 = nn.Linear(1024, 512)\n",
        "        self.fc2 = nn.Linear(512, 256)\n",
        "        self.fc3 = nn.Linear(256, 9)\n",
        "        self.relu = nn.ReLU()\n",
        "\n",
        "    def forward(self, x):\n",
        "        es = torch.tensor([[0, 1]]).cuda()\n",
        "        x = torch.squeeze(x)\n",
        "        x = F.relu(self.conv1(x, es))\n",
        "        x = F.relu(self.conv2(x, es))\n",
        "        x = F.relu(self.conv3(x, es))\n",
        "        x = torch.max(x, 0, keepdim=True)[0]\n",
        "\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "\n",
        "        ident = torch.eye(3, 3)\n",
        "        if x.is_cuda:\n",
        "            ident = ident.cuda()\n",
        "        x = x.view(3, 3)\n",
        "        x = x + ident\n",
        "        return x\n",
        "\n",
        "\n",
        "class STNkd(nn.Module):\n",
        "    def __init__(self, k=64):\n",
        "        super(STNkd, self).__init__()\n",
        "\n",
        "        self.conv1 = GraphConv(k, 64)\n",
        "        self.conv2 = GraphConv(64, 128)\n",
        "        self.conv3 = GraphConv(128, 1024)\n",
        "        self.fc1 = nn.Linear(1024, 512)\n",
        "        self.fc2 = nn.Linear(512, 256)\n",
        "        self.fc3 = nn.Linear(256, k ** 2)\n",
        "        self.relu = nn.ReLU()\n",
        "        self.k = k\n",
        "\n",
        "    def forward(self, x):\n",
        "        es = torch.tensor([[0, 1]]).cuda()\n",
        "        x = F.relu(self.conv1(x, es))\n",
        "        x = F.relu(self.conv2(x, es))\n",
        "        x = F.relu(self.conv3(x, es))\n",
        "        x = torch.max(x, 0, keepdim=True)[0]\n",
        "\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        ident = torch.eye(self.k, self.k)\n",
        "        if x.is_cuda:\n",
        "            ident = ident.cuda()\n",
        "        x = x.view(self.k, self.k)\n",
        "        x = x + ident\n",
        "        return x\n",
        "\n",
        "\n",
        "class PointNetfeat(nn.Module):\n",
        "    def __init__(self, global_feat=True, feature_transform=False):\n",
        "        super(PointNetfeat, self).__init__()\n",
        "        self.stn = STN3d()\n",
        "        self.conv1 = GraphConv(3, 64)\n",
        "        self.conv2 = GraphConv(64, 128)\n",
        "        self.conv3 = GraphConv(128, 1024)\n",
        "        self.global_feat = global_feat\n",
        "        self.feature_transform = feature_transform\n",
        "        if self.feature_transform:\n",
        "            self.fstn = STNkd(k=64)\n",
        "        if torch.cuda.is_available():\n",
        "            self.device = torch.device(\"cuda:0\")\n",
        "            torch.cuda.set_device(self.device)\n",
        "        else:\n",
        "            self.device = torch.device(\"cpu\")\n",
        "\n",
        "    def forward(self, x):\n",
        "        edges = torch.tensor([[0, 1]]).to(self.device)\n",
        "        n_pts = x.size()[0]\n",
        "        trans = self.stn(x)\n",
        "        x = torch.squeeze(x)\n",
        "        x = F.relu(self.conv1(x, edges))\n",
        "\n",
        "        if self.feature_transform:\n",
        "            trans_feat = self.fstn(x)\n",
        "            x = torch.matmul(x, trans_feat)\n",
        "        else:\n",
        "            trans_feat = None\n",
        "\n",
        "        pointfeat = x\n",
        "        x = F.relu(self.conv2(x, edges))\n",
        "        x = self.conv3(x, edges)\n",
        "        x = torch.max(x, 0, keepdim=True)[0]\n",
        "        if self.global_feat:\n",
        "            return x, trans, trans_feat\n",
        "        else:\n",
        "            x = x.repeat(n_pts,1)\n",
        "            return torch.cat([x, pointfeat], 1), trans, trans_feat\n",
        "\n",
        "\n",
        "class PointNetCls(nn.Module):\n",
        "    def __init__(self, k=2, feature_transform=False):\n",
        "        super(PointNetCls, self).__init__()\n",
        "        self.feature_transform = feature_transform\n",
        "        self.feat = PointNetfeat(global_feat=True, feature_transform=feature_transform)\n",
        "        self.fc1 = nn.Linear(1024, 512)\n",
        "        self.fc2 = nn.Linear(512, 256)\n",
        "        self.fc3 = nn.Linear(256, k)\n",
        "        self.dropout = nn.Dropout(p=0.3)\n",
        "        self.relu = nn.ReLU()\n",
        "\n",
        "    def forward(self, x):\n",
        "        x, trans, trans_feat = self.feat(x)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.dropout(self.fc2(x)))\n",
        "        x = self.fc3(x)\n",
        "        return F.log_softmax(x, dim=1), trans, trans_feat\n",
        "\n",
        "\n",
        "class PointNetDenseCls(nn.Module):\n",
        "    def __init__(self, k=2, feature_transform=False):\n",
        "        super(PointNetDenseCls, self).__init__()\n",
        "        self.k = k\n",
        "        self.feature_transform = feature_transform\n",
        "        self.feat = PointNetfeat(global_feat=False, feature_transform=feature_transform)\n",
        "        self.conv1 = GraphConv(1088, 512)\n",
        "        self.conv2 = GraphConv(512, 256)\n",
        "        self.conv3 = GraphConv(256, 128)\n",
        "        self.conv4 = GraphConv(128, self.k)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x, trans, trans_feat = self.feat(x)\n",
        "        edges = torch.tensor([[0,1]]).cuda()\n",
        "        x = F.relu(self.conv1(x, edges))\n",
        "        x = F.relu(self.conv2(x, edges))\n",
        "        x = F.relu(self.conv3(x, edges))\n",
        "        x = self.conv4(x, edges)\n",
        "        x = F.log_softmax(x, dim=-1)\n",
        "        return x, trans, trans_feat\n",
        "\n",
        "\n",
        "def feature_transform_regularizer(trans):\n",
        "    d = trans.size()[1]\n",
        "    I = torch.eye(d)\n",
        "    if trans.is_cuda:\n",
        "        I = I.cuda()\n",
        "    loss = torch.norm(torch.matmul(trans, trans.transpose(1, 0)) - I)\n",
        "    return loss"
      ],
      "metadata": {
        "id": "Gg-SQ5SnXNxK"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Dataset\n",
        "from __future__ import print_function\n",
        "import torch.utils.data as data\n",
        "import os\n",
        "import os.path\n",
        "import torch\n",
        "import numpy as np\n",
        "import sys\n",
        "from tqdm import tqdm \n",
        "import json\n",
        "\n",
        "def get_segmentation_classes(root):\n",
        "    catfile = os.path.join(root, 'synsetoffset2category.txt')\n",
        "    cat = {}\n",
        "    meta = {}\n",
        "\n",
        "    with open(catfile, 'r') as f:\n",
        "        for line in f:\n",
        "            ls = line.strip().split()\n",
        "            cat[ls[0]] = ls[1]\n",
        "\n",
        "    for item in cat:\n",
        "        dir_seg = os.path.join(root, cat[item], 'points_label')\n",
        "        dir_point = os.path.join(root, cat[item], 'points')\n",
        "        fns = sorted(os.listdir(dir_point))\n",
        "        meta[item] = []\n",
        "        for fn in fns:\n",
        "            token = (os.path.splitext(os.path.basename(fn))[0])\n",
        "            meta[item].append((os.path.join(dir_point, token + '.pts'), os.path.join(dir_seg, token + '.seg')))\n",
        "    \n",
        "    with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), './num_seg_classes.txt'), 'w') as f:\n",
        "        for item in cat:\n",
        "            datapath = []\n",
        "            num_seg_classes = 0\n",
        "            for fn in meta[item]:\n",
        "                datapath.append((item, fn[0], fn[1]))\n",
        "\n",
        "            for i in tqdm(range(len(datapath))):\n",
        "                l = len(np.unique(np.loadtxt(datapath[i][-1]).astype(np.uint8)))\n",
        "                if l > num_seg_classes:\n",
        "                    num_seg_classes = l\n",
        "\n",
        "            print(\"category {} num segmentation classes {}\".format(item, num_seg_classes))\n",
        "            f.write(\"{}\\t{}\\n\".format(item, num_seg_classes))\n",
        "\n",
        "\n",
        "class ShapeNetDataset(data.Dataset):\n",
        "    def __init__(self,\n",
        "                 root,\n",
        "                 npoints=2500,\n",
        "                 classification=False,\n",
        "                 class_choice=None,\n",
        "                 split='train',\n",
        "                 data_augmentation=True):\n",
        "        self.npoints = npoints\n",
        "        self.root = root\n",
        "        self.catfile = os.path.join(self.root, 'synsetoffset2category.txt')\n",
        "        self.cat = {}\n",
        "        self.data_augmentation = data_augmentation\n",
        "        self.classification = classification\n",
        "        self.seg_classes = {}\n",
        "        \n",
        "        with open(self.catfile, 'r') as f:\n",
        "            for line in f:\n",
        "                ls = line.strip().split()\n",
        "                self.cat[ls[0]] = ls[1]\n",
        "        #print(self.cat)\n",
        "        if not class_choice is None:\n",
        "            self.cat = {k: v for k, v in self.cat.items() if k in class_choice}\n",
        "\n",
        "        self.id2cat = {v: k for k, v in self.cat.items()}\n",
        "\n",
        "        self.meta = {}\n",
        "        splitfile = os.path.join(self.root, 'train_test_split', 'shuffled_{}_file_list.json'.format(split))\n",
        "        #from IPython import embed; embed()\n",
        "        filelist = json.load(open(splitfile, 'r'))\n",
        "        for item in self.cat:\n",
        "            self.meta[item] = []\n",
        "\n",
        "        for file in filelist:\n",
        "            _, category, uuid = file.split('/')\n",
        "            if category in self.cat.values():\n",
        "                self.meta[self.id2cat[category]].append((os.path.join(self.root, category, 'points', uuid+'.pts'),\n",
        "                                        os.path.join(self.root, category, 'points_label', uuid+'.seg')))\n",
        "\n",
        "        self.datapath = []\n",
        "        for item in self.cat:\n",
        "            for fn in self.meta[item]:\n",
        "                self.datapath.append((item, fn[0], fn[1]))\n",
        "\n",
        "        self.classes = dict(zip(sorted(self.cat), range(len(self.cat))))\n",
        "        print(self.classes)\n",
        "        with open('./num_seg_classes.txt', 'r') as f:\n",
        "            for line in f:\n",
        "                ls = line.strip().split()\n",
        "                self.seg_classes[ls[0]] = int(ls[1])\n",
        "        self.num_seg_classes = self.seg_classes[list(self.cat.keys())[0]]\n",
        "        print(self.seg_classes, self.num_seg_classes)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        fn = self.datapath[index]\n",
        "        cls = self.classes[self.datapath[index][0]]\n",
        "        point_set = np.loadtxt(fn[1]).astype(np.float32)\n",
        "        seg = np.loadtxt(fn[2]).astype(np.int64)\n",
        "\n",
        "        choice = np.random.choice(len(seg), self.npoints, replace=True)\n",
        "        #resample\n",
        "        point_set = point_set[choice, :]\n",
        "\n",
        "        point_set = point_set - np.expand_dims(np.mean(point_set, axis = 0), 0) # center\n",
        "        dist = np.max(np.sqrt(np.sum(point_set ** 2, axis = 1)),0)\n",
        "        point_set = point_set / dist #scale\n",
        "\n",
        "        if self.data_augmentation:\n",
        "            theta = np.random.uniform(0,np.pi*2)\n",
        "            rotation_matrix = np.array([[np.cos(theta), -np.sin(theta)],[np.sin(theta), np.cos(theta)]])\n",
        "            point_set[:,[0,2]] = point_set[:,[0,2]].dot(rotation_matrix) # random rotation\n",
        "            point_set += np.random.normal(0, 0.02, size=point_set.shape) # random jitter\n",
        "\n",
        "        seg = seg[choice]\n",
        "        point_set = torch.from_numpy(point_set)\n",
        "        point_set.squeeze().transpose(0,1)\n",
        "        seg = torch.from_numpy(seg)\n",
        "        cls = torch.from_numpy(np.array([cls]).astype(np.int64))\n",
        "\n",
        "        if self.classification:\n",
        "            return point_set, cls\n",
        "        else:\n",
        "            return point_set, seg\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.datapath)\n",
        "\n"
      ],
      "metadata": {
        "id": "2L3QZeO-X6Zw"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Train\n",
        "from __future__ import print_function\n",
        "import argparse\n",
        "import os\n",
        "import random\n",
        "import torch\n",
        "import torch.nn.parallel\n",
        "import torch.optim as optim\n",
        "import torch.utils.data\n",
        "import torch.nn.functional as F\n",
        "from tqdm import tqdm\n",
        "\n",
        "batchSize=1\n",
        "num_points=2500\n",
        "workers=2\n",
        "nepoch=50\n",
        "outf='cls'\n",
        "saved_model=''\n",
        "dataset_path='./shapenetcore_partanno_segmentation_benchmark_v0'\n",
        "dataset_type='shapenet'\n",
        "feature_transform=False\n",
        "\n",
        "\n",
        "blue = lambda x: '\\033[94m' + x + '\\033[0m'\n",
        "\n",
        "manualSeed = random.randint(1, 10000)  # fix seed\n",
        "print(\"Random Seed: \", manualSeed)\n",
        "random.seed(manualSeed)\n",
        "torch.manual_seed(manualSeed)\n",
        "\n",
        "dataset = ShapeNetDataset(\n",
        "    root=dataset_path,\n",
        "    classification=True,\n",
        "    npoints=num_points)\n",
        "\n",
        "test_dataset = ShapeNetDataset(\n",
        "    root=dataset_path,\n",
        "    classification=True,\n",
        "    split='test',\n",
        "    npoints=num_points,\n",
        "    data_augmentation=False)\n",
        "\n",
        "\n",
        "dataloader = torch.utils.data.DataLoader(\n",
        "    dataset,\n",
        "    batch_size=batchSize,\n",
        "    shuffle=True,\n",
        "    num_workers=int(workers))\n",
        "\n",
        "testdataloader = torch.utils.data.DataLoader(\n",
        "        test_dataset,\n",
        "        batch_size=batchSize,\n",
        "        shuffle=True,\n",
        "        num_workers=int(workers))\n",
        "\n",
        "print(len(dataset), len(test_dataset))\n",
        "num_classes = len(dataset.classes)\n",
        "print('classes', num_classes)\n",
        "\n",
        "try:\n",
        "    os.makedirs(outf)\n",
        "except OSError:\n",
        "    pass\n",
        "\n",
        "classifier = PointNetCls(k=num_classes, feature_transform=feature_transform)\n",
        "\n",
        "if saved_model != '':\n",
        "    classifier.load_state_dict(torch.load(model))\n",
        "\n",
        "\n",
        "optimizer = optim.Adam(classifier.parameters(), lr=0.001, betas=(0.9, 0.999))\n",
        "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5)\n",
        "classifier.cuda()\n",
        "\n",
        "num_batch = len(dataset) / batchSize\n",
        "\n",
        "for epoch in range(nepoch):\n",
        "    for i, data in enumerate(dataloader, 0):\n",
        "        points, target = data\n",
        "        target = target[:, 0]\n",
        "        points, target = points.cuda(), target.cuda()\n",
        "        optimizer.zero_grad()\n",
        "        classifier = classifier.train()\n",
        "        pred, trans, trans_feat = classifier(points)\n",
        "        loss = F.nll_loss(pred, target)\n",
        "        if feature_transform:\n",
        "            loss += feature_transform_regularizer(trans_feat) * 0.001\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        pred_choice = pred.data.max(1)[1]\n",
        "        correct = pred_choice.eq(target.data).cpu().sum()\n",
        "        if i % 1000 == 0:\n",
        "            print('[%d: %d/%d] train loss: %f accuracy: %f' % (epoch, i, num_batch, loss.item(), correct.item() / float(batchSize)))\n",
        "\n",
        "    scheduler.step()\n",
        "    torch.save(classifier.state_dict(), '%s/cls_model_%d.pth' % (outf, epoch))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qJRW56xLYIaI",
        "outputId": "284c1603-8894-41c2-b9fa-a1370cab8f28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Seed:  730\n",
            "{'Airplane': 0, 'Bag': 1, 'Cap': 2, 'Car': 3, 'Chair': 4, 'Earphone': 5, 'Guitar': 6, 'Knife': 7, 'Lamp': 8, 'Laptop': 9, 'Motorbike': 10, 'Mug': 11, 'Pistol': 12, 'Rocket': 13, 'Skateboard': 14, 'Table': 15}\n",
            "{'Airplane': 4, 'Bag': 2, 'Cap': 2, 'Car': 4, 'Chair': 4, 'Earphone': 3, 'Guitar': 3, 'Knife': 2, 'Lamp': 4, 'Laptop': 2, 'Motorbike': 6, 'Mug': 2, 'Pistol': 3, 'Rocket': 3, 'Skateboard': 3, 'Table': 3} 4\n",
            "{'Airplane': 0, 'Bag': 1, 'Cap': 2, 'Car': 3, 'Chair': 4, 'Earphone': 5, 'Guitar': 6, 'Knife': 7, 'Lamp': 8, 'Laptop': 9, 'Motorbike': 10, 'Mug': 11, 'Pistol': 12, 'Rocket': 13, 'Skateboard': 14, 'Table': 15}\n",
            "{'Airplane': 4, 'Bag': 2, 'Cap': 2, 'Car': 4, 'Chair': 4, 'Earphone': 3, 'Guitar': 3, 'Knife': 2, 'Lamp': 4, 'Laptop': 2, 'Motorbike': 6, 'Mug': 2, 'Pistol': 3, 'Rocket': 3, 'Skateboard': 3, 'Table': 3} 4\n",
            "12137 2874\n",
            "classes 16\n",
            "[0: 0/12137] train loss: 2.743552 accuracy: 0.000000\n",
            "[0: 1000/12137] train loss: 1.812199 accuracy: 0.000000\n",
            "[0: 2000/12137] train loss: 0.896415 accuracy: 0.000000\n",
            "[0: 3000/12137] train loss: 1.099016 accuracy: 0.000000\n",
            "[0: 4000/12137] train loss: 0.272566 accuracy: 1.000000\n",
            "[0: 5000/12137] train loss: 1.655456 accuracy: 0.000000\n",
            "[0: 6000/12137] train loss: 0.674427 accuracy: 1.000000\n",
            "[0: 7000/12137] train loss: 4.513355 accuracy: 0.000000\n",
            "[0: 8000/12137] train loss: 0.396243 accuracy: 1.000000\n",
            "[0: 9000/12137] train loss: 0.422278 accuracy: 1.000000\n",
            "[0: 10000/12137] train loss: 0.193159 accuracy: 1.000000\n",
            "[0: 11000/12137] train loss: 0.709726 accuracy: 1.000000\n",
            "[0: 12000/12137] train loss: 1.667281 accuracy: 0.000000\n",
            "[1: 0/12137] train loss: 0.326488 accuracy: 1.000000\n",
            "[1: 1000/12137] train loss: 0.685222 accuracy: 1.000000\n",
            "[1: 2000/12137] train loss: 0.210490 accuracy: 1.000000\n",
            "[1: 3000/12137] train loss: 2.769515 accuracy: 0.000000\n",
            "[1: 4000/12137] train loss: 0.090367 accuracy: 1.000000\n",
            "[1: 5000/12137] train loss: 2.083482 accuracy: 0.000000\n",
            "[1: 6000/12137] train loss: 2.997533 accuracy: 0.000000\n",
            "[1: 7000/12137] train loss: 0.035658 accuracy: 1.000000\n",
            "[1: 8000/12137] train loss: 2.436874 accuracy: 0.000000\n",
            "[1: 9000/12137] train loss: 0.000217 accuracy: 1.000000\n",
            "[1: 10000/12137] train loss: 0.010355 accuracy: 1.000000\n",
            "[1: 11000/12137] train loss: 0.006621 accuracy: 1.000000\n",
            "[1: 12000/12137] train loss: 0.260901 accuracy: 1.000000\n",
            "[2: 0/12137] train loss: 0.017220 accuracy: 1.000000\n",
            "[2: 1000/12137] train loss: 0.141089 accuracy: 1.000000\n",
            "[2: 2000/12137] train loss: 0.205960 accuracy: 1.000000\n",
            "[2: 3000/12137] train loss: 1.570909 accuracy: 0.000000\n",
            "[2: 4000/12137] train loss: 3.712179 accuracy: 0.000000\n",
            "[2: 5000/12137] train loss: 0.192549 accuracy: 1.000000\n",
            "[2: 6000/12137] train loss: 0.000968 accuracy: 1.000000\n",
            "[2: 7000/12137] train loss: 0.000338 accuracy: 1.000000\n",
            "[2: 8000/12137] train loss: 0.003903 accuracy: 1.000000\n",
            "[2: 9000/12137] train loss: 0.081805 accuracy: 1.000000\n",
            "[2: 10000/12137] train loss: 0.000017 accuracy: 1.000000\n",
            "[2: 11000/12137] train loss: 1.735663 accuracy: 0.000000\n",
            "[2: 12000/12137] train loss: 0.096381 accuracy: 1.000000\n",
            "[3: 0/12137] train loss: 0.025650 accuracy: 1.000000\n",
            "[3: 1000/12137] train loss: 0.056909 accuracy: 1.000000\n",
            "[3: 2000/12137] train loss: 0.000000 accuracy: 1.000000\n",
            "[3: 3000/12137] train loss: 0.339810 accuracy: 1.000000\n",
            "[3: 4000/12137] train loss: 0.022653 accuracy: 1.000000\n",
            "[3: 5000/12137] train loss: 1.132141 accuracy: 0.000000\n",
            "[3: 6000/12137] train loss: 0.558349 accuracy: 1.000000\n",
            "[3: 7000/12137] train loss: 0.880310 accuracy: 0.000000\n",
            "[3: 8000/12137] train loss: 0.241111 accuracy: 1.000000\n",
            "[3: 9000/12137] train loss: 0.110247 accuracy: 1.000000\n",
            "[3: 10000/12137] train loss: 0.003360 accuracy: 1.000000\n",
            "[3: 11000/12137] train loss: 0.005698 accuracy: 1.000000\n",
            "[3: 12000/12137] train loss: 0.003481 accuracy: 1.000000\n",
            "[4: 0/12137] train loss: 0.000481 accuracy: 1.000000\n",
            "[4: 1000/12137] train loss: 0.836931 accuracy: 0.000000\n",
            "[4: 2000/12137] train loss: 0.033210 accuracy: 1.000000\n",
            "[4: 3000/12137] train loss: 0.000000 accuracy: 1.000000\n",
            "[4: 4000/12137] train loss: 0.000720 accuracy: 1.000000\n",
            "[4: 5000/12137] train loss: 0.572503 accuracy: 1.000000\n",
            "[4: 6000/12137] train loss: 0.002864 accuracy: 1.000000\n",
            "[4: 7000/12137] train loss: 0.000000 accuracy: 1.000000\n",
            "[4: 8000/12137] train loss: 0.000011 accuracy: 1.000000\n",
            "[4: 9000/12137] train loss: 0.041717 accuracy: 1.000000\n",
            "[4: 10000/12137] train loss: 0.143793 accuracy: 1.000000\n",
            "[4: 11000/12137] train loss: 0.007004 accuracy: 1.000000\n",
            "[4: 12000/12137] train loss: 0.115644 accuracy: 1.000000\n",
            "[5: 0/12137] train loss: 0.010508 accuracy: 1.000000\n",
            "[5: 1000/12137] train loss: 0.007879 accuracy: 1.000000\n",
            "[5: 2000/12137] train loss: 1.151441 accuracy: 0.000000\n",
            "[5: 3000/12137] train loss: 0.001220 accuracy: 1.000000\n",
            "[5: 4000/12137] train loss: 5.243532 accuracy: 0.000000\n",
            "[5: 5000/12137] train loss: 0.021713 accuracy: 1.000000\n",
            "[5: 6000/12137] train loss: 0.000001 accuracy: 1.000000\n",
            "[5: 7000/12137] train loss: 0.038563 accuracy: 1.000000\n",
            "[5: 8000/12137] train loss: 0.000075 accuracy: 1.000000\n",
            "[5: 9000/12137] train loss: 0.000013 accuracy: 1.000000\n",
            "[5: 10000/12137] train loss: 0.014494 accuracy: 1.000000\n",
            "[5: 11000/12137] train loss: 0.000015 accuracy: 1.000000\n",
            "[5: 12000/12137] train loss: 0.000002 accuracy: 1.000000\n",
            "[6: 0/12137] train loss: 0.030891 accuracy: 1.000000\n",
            "[6: 1000/12137] train loss: 0.003205 accuracy: 1.000000\n",
            "[6: 2000/12137] train loss: 0.003374 accuracy: 1.000000\n",
            "[6: 3000/12137] train loss: 0.000984 accuracy: 1.000000\n",
            "[6: 4000/12137] train loss: 0.002334 accuracy: 1.000000\n",
            "[6: 5000/12137] train loss: 0.037714 accuracy: 1.000000\n",
            "[6: 6000/12137] train loss: 0.442812 accuracy: 1.000000\n",
            "[6: 7000/12137] train loss: 0.000000 accuracy: 1.000000\n",
            "[6: 8000/12137] train loss: 0.000000 accuracy: 1.000000\n",
            "[6: 9000/12137] train loss: 2.819512 accuracy: 0.000000\n",
            "[6: 10000/12137] train loss: 0.000043 accuracy: 1.000000\n",
            "[6: 11000/12137] train loss: 0.000577 accuracy: 1.000000\n",
            "[6: 12000/12137] train loss: 0.098367 accuracy: 1.000000\n",
            "[7: 0/12137] train loss: 0.012700 accuracy: 1.000000\n",
            "[7: 1000/12137] train loss: 0.820374 accuracy: 1.000000\n",
            "[7: 2000/12137] train loss: 0.000092 accuracy: 1.000000\n",
            "[7: 3000/12137] train loss: 0.011228 accuracy: 1.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total_correct = 0\n",
        "total_testset = 0\n",
        "for i,data in tqdm(enumerate(testdataloader, 0)):\n",
        "    points, target = data\n",
        "    target = target[:, 0]\n",
        "    points = points.transpose(2, 1)\n",
        "    points, target = points.cuda(), target.cuda()\n",
        "    classifier = classifier.eval()\n",
        "    pred, _, _ = classifier(points)\n",
        "    pred_choice = pred.data.max(1)[1]\n",
        "    correct = pred_choice.eq(target.data).cpu().sum()\n",
        "    total_correct += correct.item()\n",
        "    total_testset += points.size()[0]\n",
        "\n",
        "print(\"final accuracy {}\".format(total_correct / float(total_testset)))"
      ],
      "metadata": {
        "id": "Y--bWYPhwZYs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = PointNetCls(k=num_classes, feature_transform=feature_transform)\n",
        "classifier.load_state_dict(torch.load(\"./cls/cls_model_10.pth\"))\n",
        "classifier.cuda()\n",
        "total_correct = 0\n",
        "total_testset = 0\n",
        "for i,data in tqdm(enumerate(testdataloader, 0)):\n",
        "    points, target = data\n",
        "    target = target[:, 0]\n",
        "    points, target = points.cuda(), target.cuda()\n",
        "    classifier = classifier.eval()\n",
        "    pred, _, _ = classifier(points)\n",
        "    pred_choice = pred.data.max(1)[1]\n",
        "    correct = pred_choice.eq(target.data).cpu().sum()\n",
        "    total_correct += correct.item()\n",
        "    total_testset += points.size()[0]\n",
        "\n",
        "print(\"final accuracy {}\".format(total_correct / float(total_testset)))"
      ],
      "metadata": {
        "id": "QD7WL9cWeuZB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if i % 10000 == 0:\n",
        "            j, data = next(enumerate(testdataloader, 0))\n",
        "            points, target = data\n",
        "            target = target[:, 0]\n",
        "            points, target = points.cuda(), target.cuda()\n",
        "            classifier = classifier.eval()\n",
        "            pred, _, _ = classifier(points)\n",
        "            loss = F.nll_loss(pred, target)\n",
        "            pred_choice = pred.data.max(1)[1]\n",
        "            correct = pred_choice.eq(target.data).cpu().sum()\n",
        "            print('[%d: %d/%d] %s loss: %f accuracy: %f' % (epoch, i, num_batch, blue('test'), loss.item(), correct.item()/float(batchSize)))"
      ],
      "metadata": {
        "id": "_KfO3biily4H"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}