PointNet_tutorial_class_1.ipynb

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    "colab": {
      "name": "PointNet_tutorial_class_1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNr3xlapQguh1U+xbktA9Nz",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jacabenga/hello-world/blob/master/PointNet_tutorial_class_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K64x-4sumHp-",
        "colab_type": "text"
      },
      "source": [
        "# PointNet\n",
        "### Tutorial from \n",
        "\n",
        "https://github.com/nikitakaraevv/pointnet/blob/master/nbs/PointNetClass.ipynb\n",
        "\n",
        "Implementation of PointNet: Deep Learning on Point Sets for 3D Classification: https://arxiv.org/abs/1612.00593\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "werbYmqX6tCe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import math\n",
        "import os\n",
        "import torch\n",
        "import scipy.spatial.distance\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision import transforms, utils\n",
        "\n",
        "import plotly.graph_objects as go\n",
        "import plotly.express as px\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib as mpl\n",
        "from pylab import *"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6xVmsEFupKKB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d2bf8c40-a759-434f-dd5e-6718e49b6b72"
      },
      "source": [
        "import random\n",
        "\n",
        "print(random.__file__)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/lib/python3.6/random.py\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YnnbGGNE7vEF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "random.seed=42\n",
        "#function to initialize the pseudo-random number generator in Python. "
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZndPpfjK7L_Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "1df27ca7-a5fc-4984-994f-ddb7cd2f8c9f"
      },
      "source": [
        "!pip install path.py;\n",
        "from path import Path"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: path.py in /usr/local/lib/python3.6/dist-packages (12.5.0)\n",
            "Requirement already satisfied: path in /usr/local/lib/python3.6/dist-packages (from path.py) (15.0.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 38
        },
        "outputId": "8172dd44-834a-48ee-be1e-fee7a7b72166"
      },
      "source": [
        "#upload (from your local machine) a pre-trained model (save.pth).\n",
        "# (as training takes some time).\n",
        " \n",
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-39521b61-8c92-404f-b076-9d151417c190\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-39521b61-8c92-404f-b076-9d151417c190\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "haYfQhJ1gXP7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3645df10-46c4-4dd9-9944-849fd9d3bddc"
      },
      "source": [
        "import os\n",
        "\n",
        "cwd = os.getcwd()  # Get the current working directory (cwd)\n",
        "files = os.listdir(cwd)  # Get all the files in that directory\n",
        "print(\"Files in %r: %s\" % (cwd, files))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files in '/content': ['.config', 'sample_data']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x8ryAuGhhgar",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "outputId": "73eaca1e-ddb5-4ff6-d82b-43c14c9d4f8f"
      },
      "source": [
        "# in order to give Colab access to your GDrive files\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "%cd /content/drive/My Drive/Colab Notebooks"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n",
            "/content/drive/My Drive/Colab Notebooks\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IBpPL_fehsNB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "outputId": "7405d654-8cd7-4e01-9487-5515371b2b3f"
      },
      "source": [
        "!ls  # list of all available files \n",
        "!pwd"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'ANN - regression (Kaggle competition-NY Taxi fare).ipynb'\n",
            " colab_tutorial_GPU_1.ipynb\n",
            " expert_verified\n",
            " __MACOSX\n",
            " MNIST_cnn.py\n",
            " ModelNet10\n",
            " ModelNet10.zip\n",
            " pointnet\n",
            " PointNetSeg\n",
            " PointNet_tutorial_class_1.ipynb\n",
            " PointNet_tutorial_Seg_1\n",
            " PointNet_Tutorial_Seg_2.ipynb\n",
            " points\n",
            " save.pth\n",
            " Style_Transfer.ipynb\n",
            " titanic-passengers.csv\n",
            "/content/drive/My Drive/Colab Notebooks\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wkWxJyzOmBlT",
        "colab_type": "text"
      },
      "source": [
        "## Download the dataset (ModelNet10) directly to the Google Colab Runtime. \n",
        "### It is a smaller version of ModelNet40.\n",
        "## It comprises 10 object categories, 3,991 models for training and 908 for testing."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "plKusU8ym87_",
        "colab_type": "text"
      },
      "source": [
        "### We request the dataset in zip format using !wget protocol"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wsZ8gLti7MCL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "outputId": "ea6d93cc-6f36-44a7-a63c-c326cce93fba"
      },
      "source": [
        "!wget http://3dvision.princeton.edu/projects/2014/3DShapeNets/ModelNet10.zip"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-08-24 10:20:35--  http://3dvision.princeton.edu/projects/2014/3DShapeNets/ModelNet10.zip\n",
            "Resolving 3dvision.princeton.edu (3dvision.princeton.edu)... 128.112.136.61\n",
            "Connecting to 3dvision.princeton.edu (3dvision.princeton.edu)|128.112.136.61|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 473402300 (451M) [application/zip]\n",
            "Saving to: ‘ModelNet10.zip’\n",
            "\n",
            "ModelNet10.zip      100%[===================>] 451.47M  44.1MB/s    in 11s     \n",
            "\n",
            "2020-08-24 10:20:47 (40.7 MB/s) - ‘ModelNet10.zip’ saved [473402300/473402300]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l8oYRpxR-LBY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "79ceafa7-5b0c-4dd9-9950-d87e178e9c2e"
      },
      "source": [
        "!unzip -q ModelNet10.zip"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "replace ModelNet10/.DS_Store? [y]es, [n]o, [A]ll, [N]one, [r]ename: A\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "53gkz54X-eKy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "path = Path('ModelNet10')"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qqJSoZQ2nbDy",
        "colab_type": "text"
      },
      "source": [
        "### These are the 10 object categories the dataset has."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Y8O0UiU7MFK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "fa70458b-f91a-478c-aade-21ca4c2ffbfb"
      },
      "source": [
        "folders = [dir for dir in sorted(os.listdir(path)) if os.path.isdir(path/dir)]\n",
        "classes= {folder: i for i, folder in enumerate(folders)};\n",
        "classes"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bathtub': 0,\n",
              " 'bed': 1,\n",
              " 'chair': 2,\n",
              " 'desk': 3,\n",
              " 'dresser': 4,\n",
              " 'monitor': 5,\n",
              " 'night_stand': 6,\n",
              " 'sofa': 7,\n",
              " 'table': 8,\n",
              " 'toilet': 9}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bYU-xIwPl242",
        "colab_type": "text"
      },
      "source": [
        "### This dataset consists of .off files that contain meshes represented by vertices and triangular faces.\n",
        "### .off  Object File Format: it is a geometry definition file format\n",
        "### We will need a function to read this type of files:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XUm9aaKT7MID",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def read_off(file):\n",
        "    if 'OFF' != file.readline().strip():\n",
        "        raise('Not a valid OFF header')\n",
        "    n_verts, n_faces, __ = tuple([int(s) for s in file.readline().strip().split(' ')])\n",
        "    verts = [[float(s) for s in file.readline().strip().split(' ')] for i_vert in range(n_verts)]\n",
        "    faces = [[int(s) for s in file.readline().strip().split(' ')][1:] for i_face in range(n_faces)]\n",
        "    return verts, faces"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G3M2-y55_9PU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open(path/\"toilet/train/toilet_0004.off\", 'r') as f:\n",
        "  verts, faces = read_off(f)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N4pYdfuGMxPJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "i,j,k=np.array(faces).T\n",
        "x,y,z= np.array(verts).T"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z5I31IMG_9SG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "96041899-c19a-4017-f62a-6b02bbf2700a"
      },
      "source": [
        "len(x)\n",
        "#len(a)\n"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1288"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lt7VBSsUlumQ",
        "colab_type": "text"
      },
      "source": [
        "## This huge function animates and rotates either meshes or pointclouds\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4KOUwrAe_9Us",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def visualize_rotate(data):\n",
        "    x_eye, y_eye, z_eye = 1.25, 1.25, 0.8\n",
        "    frames=[]\n",
        "\n",
        "    def rotate_z(x, y, z, theta):\n",
        "        w = x+1j*y\n",
        "        return np.real(np.exp(1j*theta)*w), np.imag(np.exp(1j*theta)*w), z\n",
        "\n",
        "    for t in np.arange(0, 10.26, 0.1):\n",
        "        xe, ye, ze = rotate_z(x_eye, y_eye, z_eye, -t)\n",
        "        frames.append(dict(layout=dict(scene=dict(camera=dict(eye=dict(x=xe, y=ye, z=ze))))))\n",
        "    fig = go.Figure(data=data,\n",
        "                    layout=go.Layout(\n",
        "                        updatemenus=[dict(type='buttons',\n",
        "                                    showactive=False,\n",
        "                                    y=1,\n",
        "                                    x=0.8,\n",
        "                                    xanchor='left',\n",
        "                                    yanchor='bottom',\n",
        "                                    pad=dict(t=45, r=10),\n",
        "                                    buttons=[dict(label='Play',\n",
        "                                                    method='animate',\n",
        "                                                    args=[None, dict(frame=dict(duration=50, redraw=True),\n",
        "                                                                    transition=dict(duration=0),\n",
        "                                                                    fromcurrent=True,\n",
        "                                                                    mode='immediate'\n",
        "                                                                    )]\n",
        "                                                    )\n",
        "                                            ]\n",
        "                                    )\n",
        "                                ]\n",
        "                    ),\n",
        "                    frames=frames\n",
        "            )\n",
        "    return fig "
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_UBv4xXy7MK1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "9fdc20df-76ad-4f7e-e306-433a88fcef08"
      },
      "source": [
        "visualize_rotate([go.Mesh3d(x=x, y=y, z=z, color='lightpink', opacity=0.50, i=i, j=j, k=k)]).show()\n"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
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              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"d394fc91-bb0c-415e-a5b1-87633309a13c\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
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              "                if (document.getElementById(\"d394fc91-bb0c-415e-a5b1-87633309a13c\")) {\n",
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      "metadata": {
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        "colab_type": "text"
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      "source": [
        "# We can customize our own dataset out of the ModelNet10 to feed it into PointNet for training\n",
        "\n",
        "### Let's visualize vertices\n",
        "### we can visualize the vertices as matplotlib markers\n"
      ]
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    {
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        "visualize_rotate([go.Scatter3d(x=x, y=y, z=z,\n",
        "                                    mode='markers')]).show()\n"
      ],
      "execution_count": 38,
      "outputs": [
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      "metadata": {
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        "colab_type": "text"
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      "source": [
        "### It will be more visible to represent vertices as a point cloud"
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        "id": "ZoRFVRHdZd9G",
        "colab_type": "code",
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        "def pcshow(xs,ys,zs):\n",
        "    data=[go.Scatter3d(x=xs, y=ys, z=zs,\n",
        "                                   mode='markers')]\n",
        "    fig = visualize_rotate(data)\n",
        "    fig.update_traces(marker=dict(size=2,\n",
        "                      line=dict(width=2,\n",
        "                      color='DarkSlateGrey')),\n",
        "                      selector=dict(mode='markers'))\n",
        "    fig.show()"
      ],
      "execution_count": 40,
      "outputs": []
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      "source": [
        "pcshow(x,y,z)\n"
      ],
      "execution_count": 41,
      "outputs": [
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        "id": "QZyKUj-9pPa_",
        "colab_type": "text"
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      "source": [
        "# TRANSFORMS\n",
        "### Let's write a function to sample points on the surface uniformly \n",
        "\n",
        "### (so it will look more like the real object)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zLRFZGcBZeAX",
        "colab_type": "code",
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        "\n",
        "\n",
        "class PointSampler(object):\n",
        "    def __init__(self, output_size):\n",
        "        assert isinstance(output_size, int)\n",
        "        self.output_size = output_size\n",
        "    \n",
        "    def triangle_area(self, pt1, pt2, pt3):\n",
        "        side_a = np.linalg.norm(pt1 - pt2)\n",
        "        side_b = np.linalg.norm(pt2 - pt3)\n",
        "        side_c = np.linalg.norm(pt3 - pt1)\n",
        "        s = 0.5 * ( side_a + side_b + side_c)\n",
        "        return max(s * (s - side_a) * (s - side_b) * (s - side_c), 0)**0.5\n",
        "\n",
        "    def sample_point(self, pt1, pt2, pt3):\n",
        "        # barycentric coordinates on a triangle\n",
        "        # https://mathworld.wolfram.com/BarycentricCoordinates.html\n",
        "        s, t = sorted([random.random(), random.random()])\n",
        "        f = lambda i: s * pt1[i] + (t-s)*pt2[i] + (1-t)*pt3[i]\n",
        "        return (f(0), f(1), f(2))\n",
        "        \n",
        "    \n",
        "    def __call__(self, mesh):\n",
        "        verts, faces = mesh\n",
        "        verts = np.array(verts)\n",
        "        areas = np.zeros((len(faces)))\n",
        "\n",
        "        for i in range(len(areas)):\n",
        "            areas[i] = (self.triangle_area(verts[faces[i][0]],\n",
        "                                           verts[faces[i][1]],\n",
        "                                           verts[faces[i][2]]))\n",
        "            \n",
        "        sampled_faces = (random.choices(faces, \n",
        "                                      weights=areas,\n",
        "                                      cum_weights=None,\n",
        "                                      k=self.output_size))\n",
        "        \n",
        "        sampled_points = np.zeros((self.output_size, 3))\n",
        "\n",
        "        for i in range(len(sampled_faces)):\n",
        "            sampled_points[i] = (self.sample_point(verts[sampled_faces[i][0]],\n",
        "                                                   verts[sampled_faces[i][1]],\n",
        "                                                   verts[sampled_faces[i][2]]))\n",
        "        \n",
        "        return sampled_points\n",
        "    "
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJ9KA1vXZeDW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " pointcloud = PointSampler(500)((verts, faces)) "
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJe6ZE1NcY4b",
        "colab_type": "code",
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          "height": 542
        },
        "outputId": "54f9defb-46f2-46ba-c602-405e2b9015ce"
      },
      "source": [
        "pcshow(*pointcloud.T)"
      ],
      "execution_count": 45,
      "outputs": [
        {
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          "metadata": {
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wug8NvjXk6E8",
        "colab_type": "text"
      },
      "source": [
        "## let's normalize it to the unit sphere\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K86qTh90cY8l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "\n",
        "class Normalize(object):\n",
        "    def __call__(self, pointcloud):\n",
        "        assert len(pointcloud.shape)==2\n",
        "\n",
        "        norm_pointcloud = pointcloud - np.mean(pointcloud, axis=0)\n",
        "        norm_pointcloud /= np.max(np.linalg.norm(norm_pointcloud, axis=1))\n",
        "\n",
        "        return norm_pointcloud"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cVddpANOcY_7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "norm_pointcloud = Normalize()(pointcloud)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uAaswL1EcZDC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "dae8bbec-994e-4fd1-fa18-4a2e4aef58c8"
      },
      "source": [
        "pcshow(*norm_pointcloud.T)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
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          "metadata": {
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        "id": "5_7NTOQ_tcb_",
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        "colab": {}
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      "source": [
        ""
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      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mbPKdjZTk0uw",
        "colab_type": "text"
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      "source": [
        "# DATA AGUMENTATION\n",
        "## We can 'produce' more training data by slight variations of the point cloud data\n",
        "## Let's add random rotation, and random noise\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwOJoL7WtUhg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# DATA AGUMENTATION\n",
        "# Let's add random rotation, and random noise\n",
        "\n",
        "class RandRotation_z(object):\n",
        "    def __call__(self, pointcloud):\n",
        "        assert len(pointcloud.shape)==2\n",
        "\n",
        "        theta = random.random() * 2. * math.pi\n",
        "        rot_matrix = np.array([[ math.cos(theta), -math.sin(theta),   0],\n",
        "                               [math.sin(theta), math.cos(theta),     0],\n",
        "                               [0,                          0,      1]])\n",
        "        \n",
        "        rot_pointcloud = rot_matrix.dot(pointcloud.T).T\n",
        "        return rot_pointcloud\n",
        "\n",
        "class RandomNoise(object):\n",
        "    def __call__(self, pointcloud):\n",
        "        assert len(pointcloud.shape)==2\n",
        "\n",
        "        noise = np.random.normal(0,0.015, (pointcloud.shape))\n",
        "\n",
        "        noisy_pointcloud = pointcloud + noise\n",
        "        return noisy_pointcloud\n",
        "            \n",
        "\n",
        "                                                           "
      ],
      "execution_count": 50,
      "outputs": []
    },
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      "metadata": {
        "id": "02A5HDmStUur",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "rot_pointcloud = RandRotation_z()(norm_pointcloud)\n",
        "noisy_rot_pointcloud = RandomNoise()(rot_pointcloud)"
      ],
      "execution_count": 51,
      "outputs": []
    },
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          "height": 542
        },
        "outputId": "de47a115-d82c-4184-b00c-633364067688"
      },
      "source": [
        "pcshow(*noisy_rot_pointcloud.T)"
      ],
      "execution_count": 52,
      "outputs": [
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              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
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              "\n",
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              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LfJc1pcfksNY",
        "colab_type": "text"
      },
      "source": [
        "## Transform PCD to Tensor\n",
        "\n",
        "### we convert the rotated and noisy PCD to tensors (generalized matrices of N-dimensions), for the learning model to understand the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8s7YW-sJyEfw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class ToTensor(object):\n",
        "    def __call__(self, pointcloud):\n",
        "        assert len(pointcloud.shape)==2\n",
        "\n",
        "        return torch.from_numpy(pointcloud)   "
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ht5PvLg4yEsC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "95a33cbe-1db0-440e-8532-992c7c3fd940"
      },
      "source": [
        "ToTensor()(noisy_rot_pointcloud)"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.2205,  0.1357, -0.7022],\n",
              "        [ 0.2019, -0.4603, -0.3940],\n",
              "        [ 0.0516,  0.4177,  0.4181],\n",
              "        ...,\n",
              "        [-0.2023,  0.1322,  0.7396],\n",
              "        [ 0.3139, -0.2113,  0.1081],\n",
              "        [ 0.3857, -0.1479,  0.1107]], dtype=torch.float64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HfbWpQqwyEvM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def default_transforms():\n",
        "    return transforms.Compose([\n",
        "                               PointSampler(1024),\n",
        "                               Normalize(),\n",
        "                               ToTensor()\n",
        "                               ])"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vim9ZVX9r650",
        "colab_type": "text"
      },
      "source": [
        "### Let's create our custom PyTorch dataset\n",
        "\n",
        "#### like in: https://pytorch.org/tutorials/beginner/data_loading_tutorial.html\n",
        "\n",
        "#### \"[...]In this tutorial, we have seen how to write and use datasets, transforms and dataloader. torchvision package provides some common datasets and transforms. You might not even have to write custom classes.\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ONiiEaX8yEyG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "class PointCloudData(Dataset):\n",
        "    def __init__(self, root_dir, valid=False, folder=\"train\", transform=default_transforms()):\n",
        "        self.root_dir = root_dir\n",
        "        folders = [dir for dir in sorted(os.listdir(root_dir)) if os.path.isdir(root_dir/dir)]\n",
        "        self.classes = {folder: i for i, folder in enumerate(folders)}\n",
        "        self.transforms = transform if not valid else default_transforms()\n",
        "        self.valid = valid\n",
        "        self.files = []\n",
        "        for category in self.classes.keys():\n",
        "            new_dir = root_dir/Path(category)/folder\n",
        "            for file in os.listdir(new_dir):\n",
        "                if file.endswith('.off'):\n",
        "                    sample = {}\n",
        "                    sample['pcd_path'] = new_dir/file\n",
        "                    sample['category'] = category\n",
        "                    self.files.append(sample)\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.files)\n",
        "\n",
        "    def __preproc__(self, file):\n",
        "        verts, faces = read_off(file)\n",
        "        if self.transforms:\n",
        "            pointcloud = self.transforms((verts, faces))\n",
        "        return pointcloud\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        pcd_path = self.files[idx]['pcd_path']\n",
        "        category = self.files[idx]['category']\n",
        "        with open(pcd_path, 'r') as f:\n",
        "            pointcloud = self.__preproc__(f)\n",
        "        return {'pointcloud': pointcloud, \n",
        "                'category': self.classes[category]}\n"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bi8_tafAklWt",
        "colab_type": "text"
      },
      "source": [
        "## Transforms for training. 1024 points per cloud as in the paper\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LJbYDXoxzDDm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_transforms = transforms.Compose([\n",
        "                    PointSampler(1024),\n",
        "                    Normalize(),\n",
        "                    RandRotation_z(),\n",
        "                    RandomNoise(),\n",
        "                    ToTensor()\n",
        "                     ])"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "khZ2oAaLzDGi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_ds = PointCloudData(path, transform=train_transforms)\n",
        "valid_ds= PointCloudData(path, valid=True, folder='test', transform=train_transforms)"
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Gl-xXEt3gNL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "06ab3f22-fbd5-4da4-9dfb-78119131da4c"
      },
      "source": [
        "inv_classes = {i: cat for cat, i in train_ds.classes.items()};\n",
        "inv_classes"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'bathtub',\n",
              " 1: 'bed',\n",
              " 2: 'chair',\n",
              " 3: 'desk',\n",
              " 4: 'dresser',\n",
              " 5: 'monitor',\n",
              " 6: 'night_stand',\n",
              " 7: 'sofa',\n",
              " 8: 'table',\n",
              " 9: 'toilet'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kMGxB_gL3gQI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "24f945c3-6010-42f2-e9d6-12afb23607b1"
      },
      "source": [
        "print('Train dataset size: ', len(train_ds))\n",
        "print('Valid dataset size: ', len(valid_ds))\n",
        "print('Number of classes:', len(train_ds.classes))\n",
        "print('Sample pointcloud shape:', train_ds[0]['pointcloud'].size())\n",
        "print('Class: ', inv_classes[train_ds[3420]['category']])\n"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train dataset size:  3991\n",
            "Valid dataset size:  908\n",
            "Number of classes: 10\n",
            "Sample pointcloud shape: torch.Size([1024, 3])\n",
            "Class:  table\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gmzl5GNJ3gS3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_loader = DataLoader(dataset=train_ds, batch_size=32, shuffle=True)\n",
        "valid_loader = DataLoader(dataset=valid_ds, batch_size=64)"
      ],
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mXnXgdWikf-l",
        "colab_type": "text"
      },
      "source": [
        "# Now the MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rGXoZdl65t5N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import numpy as np\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class Tnet(nn.Module):\n",
        "   def __init__(self, k=3):\n",
        "      super().__init__()\n",
        "      self.k=k\n",
        "      self.conv1 = nn.Conv1d(k,64,1)\n",
        "      self.conv2 = nn.Conv1d(64,128,1)\n",
        "      self.conv3 = nn.Conv1d(128,1024,1)\n",
        "      self.fc1 = nn.Linear(1024,512)\n",
        "      self.fc2 = nn.Linear(512,256)\n",
        "      self.fc3 = nn.Linear(256,k*k)\n",
        "\n",
        "      self.bn1 = nn.BatchNorm1d(64)\n",
        "      self.bn2 = nn.BatchNorm1d(128)\n",
        "      self.bn3 = nn.BatchNorm1d(1024)\n",
        "      self.bn4 = nn.BatchNorm1d(512)\n",
        "      self.bn5 = nn.BatchNorm1d(256)\n",
        "       \n",
        "\n",
        "   def forward(self, input):\n",
        "      # input.shape == (bs,n,3)\n",
        "      bs = input.size(0)\n",
        "      xb = F.relu(self.bn1(self.conv1(input)))\n",
        "      xb = F.relu(self.bn2(self.conv2(xb)))\n",
        "      xb = F.relu(self.bn3(self.conv3(xb)))\n",
        "      pool = nn.MaxPool1d(xb.size(-1))(xb)\n",
        "      flat = nn.Flatten(1)(pool)\n",
        "      xb = F.relu(self.bn4(self.fc1(flat)))\n",
        "      xb = F.relu(self.bn5(self.fc2(xb)))\n",
        "      \n",
        "      #initialize as identity\n",
        "      init = torch.eye(self.k, requires_grad=True).repeat(bs,1,1)\n",
        "      if xb.is_cuda:\n",
        "        init=init.cuda()\n",
        "      matrix = self.fc3(xb).view(-1,self.k,self.k) + init\n",
        "      return matrix\n",
        "\n",
        "\n",
        "class Transform(nn.Module):\n",
        "   def __init__(self):\n",
        "        super().__init__()\n",
        "        self.input_transform = Tnet(k=3)\n",
        "        self.feature_transform = Tnet(k=64)\n",
        "        self.conv1 = nn.Conv1d(3,64,1)\n",
        "\n",
        "        self.conv2 = nn.Conv1d(64,128,1)\n",
        "        self.conv3 = nn.Conv1d(128,1024,1)\n",
        "       \n",
        "\n",
        "        self.bn1 = nn.BatchNorm1d(64)\n",
        "        self.bn2 = nn.BatchNorm1d(128)\n",
        "        self.bn3 = nn.BatchNorm1d(1024)\n",
        "       \n",
        "   def forward(self, input):\n",
        "        matrix3x3 = self.input_transform(input)\n",
        "        # batch matrix multiplication\n",
        "        xb = torch.bmm(torch.transpose(input,1,2), matrix3x3).transpose(1,2)\n",
        "\n",
        "        xb = F.relu(self.bn1(self.conv1(xb)))\n",
        "\n",
        "        matrix64x64 = self.feature_transform(xb)\n",
        "        xb = torch.bmm(torch.transpose(xb,1,2), matrix64x64).transpose(1,2)\n",
        "\n",
        "        xb = F.relu(self.bn2(self.conv2(xb)))\n",
        "        xb = self.bn3(self.conv3(xb))\n",
        "        xb = nn.MaxPool1d(xb.size(-1))(xb)\n",
        "        output = nn.Flatten(1)(xb)\n",
        "        return output, matrix3x3, matrix64x64\n",
        "\n",
        "class PointNet(nn.Module):\n",
        "    def __init__(self, classes = 10):\n",
        "        super().__init__()\n",
        "        self.transform = Transform()\n",
        "        self.fc1 = nn.Linear(1024, 512)\n",
        "        self.fc2 = nn.Linear(512, 256)\n",
        "        self.fc3 = nn.Linear(256, classes)\n",
        "        \n",
        "\n",
        "        self.bn1 = nn.BatchNorm1d(512)\n",
        "        self.bn2 = nn.BatchNorm1d(256)\n",
        "        self.dropout = nn.Dropout(p=0.3)\n",
        "        self.logsoftmax = nn.LogSoftmax(dim=1)\n",
        "\n",
        "    def forward(self, input):\n",
        "        xb, matrix3x3, matrix64x64 = self.transform(input)\n",
        "        xb = F.relu(self.bn1(self.fc1(xb)))\n",
        "        xb = F.relu(self.bn2(self.dropout(self.fc2(xb))))\n",
        "        output = self.fc3(xb)\n",
        "        return self.logsoftmax(output), matrix3x3, matrix64x64"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5TnhUycB5t8L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def pointnetloss(outputs, labels, m3x3, m64x64, alpha=0.0001):\n",
        "    criterion = torch.nn.NLLLoss()\n",
        "    bs=outputs.size(0)\n",
        "    id3x3 = torch.eye(3,requires_grad=True).repeat(bs,1,1)\n",
        "    id64x64 = torch.eye(64, requires_grad=True).repeat(bs,1,1)\n",
        "    if outputs.is_cuda:\n",
        "        id3x3=id3x3.cuda()    \n",
        "        id64x64=id64x64.cuda()\n",
        "    diff3x3 = id3x3-torch.bmm(m3x3,m3x3.transpose(1,2))\n",
        "    diff64x64 = id64x64-torch.bmm(m64x64,m64x64.transpose(1,2))\n",
        "    return criterion(outputs, labels) + alpha * (torch.norm(diff3x3)+torch.norm(diff64x64)) / float(bs)"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kXRcDXGFkSwl",
        "colab_type": "text"
      },
      "source": [
        "## Training Loop"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FUBg_d_C7az_",
        "colab_type": "text"
      },
      "source": [
        "You can find a pretrained model here\n",
        "https://drive.google.com/drive/folders/1nDG0maaqoTkRkVsOLtUAR9X3kn__LMSL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "icGVRJ2lkdmA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0d2fa1d2-6a48-47e1-86ee-06b9591d5d51"
      },
      "source": [
        "device= torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
        "print(device)"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x5wmDeWH5uB5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        },
        "outputId": "c1cdf5d8-2fda-4d30-ef50-0e709eb1fb95"
      },
      "source": [
        "pointnet= PointNet()\n",
        "pointnet.to(device)"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PointNet(\n",
              "  (transform): Transform(\n",
              "    (input_transform): Tnet(\n",
              "      (conv1): Conv1d(3, 64, kernel_size=(1,), stride=(1,))\n",
              "      (conv2): Conv1d(64, 128, kernel_size=(1,), stride=(1,))\n",
              "      (conv3): Conv1d(128, 1024, kernel_size=(1,), stride=(1,))\n",
              "      (fc1): Linear(in_features=1024, out_features=512, bias=True)\n",
              "      (fc2): Linear(in_features=512, out_features=256, bias=True)\n",
              "      (fc3): Linear(in_features=256, out_features=9, bias=True)\n",
              "      (bn1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (bn2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (bn3): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (bn4): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (bn5): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (feature_transform): Tnet(\n",
              "      (conv1): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n",
              "      (conv2): Conv1d(64, 128, kernel_size=(1,), stride=(1,))\n",
              "      (conv3): Conv1d(128, 1024, kernel_size=(1,), stride=(1,))\n",
              "      (fc1): Linear(in_features=1024, out_features=512, bias=True)\n",
              "      (fc2): Linear(in_features=512, out_features=256, bias=True)\n",
              "      (fc3): Linear(in_features=256, out_features=4096, bias=True)\n",
              "      (bn1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (bn2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (bn3): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (bn4): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (bn5): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (conv1): Conv1d(3, 64, kernel_size=(1,), stride=(1,))\n",
              "    (conv2): Conv1d(64, 128, kernel_size=(1,), stride=(1,))\n",
              "    (conv3): Conv1d(128, 1024, kernel_size=(1,), stride=(1,))\n",
              "    (bn1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (bn2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (bn3): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (fc1): Linear(in_features=1024, out_features=512, bias=True)\n",
              "  (fc2): Linear(in_features=512, out_features=256, bias=True)\n",
              "  (fc3): Linear(in_features=256, out_features=10, bias=True)\n",
              "  (bn1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (bn2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (dropout): Dropout(p=0.3, inplace=False)\n",
              "  (logsoftmax): LogSoftmax(dim=1)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A1EF8S9l5mP3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "optimizer = torch.optim.Adam(pointnet.parameters(), lr=0.001)"
      ],
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkE55H675mTl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train(model, train_loader, val_loader=None,  epochs=15, save=True):\n",
        "    for epoch in range(epochs): \n",
        "        pointnet.train()\n",
        "        running_loss = 0.0\n",
        "        for i, data in enumerate(train_loader, 0):\n",
        "            inputs, labels = data['pointcloud'].to(device).float(), data['category'].to(device)\n",
        "            optimizer.zero_grad()\n",
        "            outputs, m3x3, m64x64 = pointnet(inputs.transpose(1,2))\n",
        "\n",
        "            loss = pointnetloss(outputs, labels, m3x3, m64x64)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            # print statistics\n",
        "            running_loss += loss.item()\n",
        "            if i % 10 == 9:    # print every 10 mini-batches\n",
        "                    print('[Epoch: %d, Batch: %4d / %4d], loss: %.3f' %\n",
        "                        (epoch + 1, i + 1, len(train_loader), running_loss / 10))\n",
        "                    running_loss = 0.0\n",
        "\n",
        "        pointnet.eval()\n",
        "        correct = total = 0\n",
        "\n",
        "        # validation\n",
        "        if val_loader:\n",
        "            with torch.no_grad():\n",
        "                for data in val_loader:\n",
        "                    inputs, labels = data['pointcloud'].to(device).float(), data['category'].to(device)\n",
        "                    outputs, __, __ = pointnet(inputs.transpose(1,2))\n",
        "                    _, predicted = torch.max(outputs.data, 1)\n",
        "                    total += labels.size(0)\n",
        "                    correct += (predicted == labels).sum().item()\n",
        "            val_acc = 100. * correct / total\n",
        "            print('Valid accuracy: %d %%' % val_acc)\n",
        "\n",
        "        # save the model\n",
        "        if save:\n",
        "            torch.save(pointnet.state_dict())\n",
        "          "
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8_tnMYu75mVy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "b68a8418-018a-450a-dc65-c3d25970e355"
      },
      "source": [
        "train(pointnet, train_loader, valid_loader, save=False)"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-100-f0f32453eca3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpointnet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave\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[0m",
            "\u001b[0;32m<ipython-input-99-46ae096bac64>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(model, train_loader, val_loader, epochs, save)\u001b[0m\n\u001b[1;32m      3\u001b[0m         \u001b[0mpointnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\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      4\u001b[0m         \u001b[0mrunning_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\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[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m             \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'pointcloud'\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[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'category'\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[1;32m      7\u001b[0m             \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\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/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    361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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--> 363\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    364\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    365\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    401\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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    402\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 403\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    404\u001b[0m         \u001b[0;32mif\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    405\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\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[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\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     43\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\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     45\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     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\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/_utils/fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\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     43\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\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     45\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     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\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-88-5952d75fab90>\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m     30\u001b[0m         \u001b[0mcategory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfiles\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'category'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpcd_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m             \u001b[0mpointcloud\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__preproc__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m         return {'pointcloud': pointcloud, \n\u001b[1;32m     34\u001b[0m                 'category': self.classes[category]}\n",
            "\u001b[0;32m<ipython-input-88-5952d75fab90>\u001b[0m in \u001b[0;36m__preproc__\u001b[0;34m(self, file)\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0mverts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfaces\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_off\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m             \u001b[0mpointcloud\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mverts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfaces\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     26\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mpointcloud\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torchvision/transforms/transforms.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\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     60\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m             \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\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     62\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-43-2d9e6d4d05d4>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, mesh)\u001b[0m\n\u001b[1;32m     29\u001b[0m             areas[i] = (self.triangle_area(verts[faces[i][0]],\n\u001b[1;32m     30\u001b[0m                                            \u001b[0mverts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfaces\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m                                            verts[faces[i][2]]))\n\u001b[0m\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m         sampled_faces = (random.choices(faces, \n",
            "\u001b[0;32m<ipython-input-43-2d9e6d4d05d4>\u001b[0m in \u001b[0;36mtriangle_area\u001b[0;34m(self, pt1, pt2, pt3)\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtriangle_area\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpt1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpt2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpt3\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----> 9\u001b[0;31m         \u001b[0mside_a\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpt1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mpt2\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     10\u001b[0m         \u001b[0mside_b\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpt2\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mpt3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0mside_c\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpt3\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mpt1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mnorm\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/linalg/linalg.py\u001b[0m in \u001b[0;36mnorm\u001b[0;34m(x, ord, axis, keepdims)\u001b[0m\n\u001b[1;32m   2474\u001b[0m                 \u001b[0msqnorm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2475\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[0;32m-> 2476\u001b[0;31m                 \u001b[0msqnorm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\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   2477\u001b[0m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msqnorm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2478\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mkeepdims\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: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GV5uxUjaMuKp",
        "colab_type": "text"
      },
      "source": [
        "## Let's TEST it now"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IoxyPT07Mx4D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "78kELb9dM4CA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pointnet = PointNet()\n",
        "pointnet.load_state_dict(torch.load(\"save.pth\"))\n",
        "pointnet.eval();"
      ],
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nMvDiLDHM7mm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "fa2715e4-c519-40a2-dbb4-64fb63f646ce"
      },
      "source": [
        "all_preds = []\n",
        "all_labels= []\n",
        "with torch.no_grad():\n",
        "  for i, data in enumerate(valid_loader):\n",
        "      print('Batch[%4d / %4d]' % (i+1, len(valid_loader)))\n",
        "\n",
        "      inputs, labels = data['pointcloud'].float(), data['category']\n",
        "      outputs, __, __, = pointnet(inputs.transpose(1,2))\n",
        "      _,preds = torch.max(outputs.data, 1)\n",
        "      all_preds += list(preds.numpy())\n",
        "      all_labels += list(labels.numpy())\n",
        "\n"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Batch[   1 /   15]\n",
            "Batch[   2 /   15]\n",
            "Batch[   3 /   15]\n",
            "Batch[   4 /   15]\n",
            "Batch[   5 /   15]\n",
            "Batch[   6 /   15]\n",
            "Batch[   7 /   15]\n",
            "Batch[   8 /   15]\n",
            "Batch[   9 /   15]\n",
            "Batch[  10 /   15]\n",
            "Batch[  11 /   15]\n",
            "Batch[  12 /   15]\n",
            "Batch[  13 /   15]\n",
            "Batch[  14 /   15]\n",
            "Batch[  15 /   15]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DDrT3CpdM7qq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "7f5df0ec-fa9b-44b5-840f-55ad1658f7dd"
      },
      "source": [
        "cm = confusion_matrix(all_labels, all_preds)\n",
        "cm\n"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 41,   9,   0,   0,   0,   0,   0,   0,   0,   0],\n",
              "       [  1,  96,   2,   0,   0,   0,   0,   1,   0,   0],\n",
              "       [  0,   0, 100,   0,   0,   0,   0,   0,   0,   0],\n",
              "       [  0,   2,   2,  34,   5,   0,  18,   4,  20,   1],\n",
              "       [  0,   0,   0,   0,  38,   0,  47,   0,   1,   0],\n",
              "       [  0,   0,   1,   0,  24,  73,   1,   1,   0,   0],\n",
              "       [  0,   0,   1,   0,   6,   0,  78,   0,   1,   0],\n",
              "       [  1,   0,   1,   0,   1,   0,   1,  96,   0,   0],\n",
              "       [  0,   0,   0,   4,   0,   0,   1,   0,  95,   0],\n",
              "       [  1,   0,  15,   0,   1,   0,   2,   1,   0,  80]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EvqVGOOB_oL9",
        "colab_type": "text"
      },
      "source": [
        "Let's display the confusion matrix it nicely\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "COEMqta-M7tn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import itertools\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# function from https://deeplizard.com/learn/video/0LhiS6yu2qQ\n",
        "def plot_confusion_matrix(cm, classes, normalize=False, title= 'confusion matrix', cmap=plt.cm.Blues):\n",
        "    if normalize:\n",
        "      cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "      print('Normalized confusion matrix')\n",
        "    else:\n",
        "      print('Confusion matrix, without normalization')\n",
        "    \n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt= '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range (cm.shape[1])):\n",
        "      plt.text(j,i, format(cm[i,j], fmt), horizontalalignment='center', color='white' if cm[i,j] > thresh else 'black' )\n",
        "\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2a0fo193_l3r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "outputId": "24638935-9e9e-4fd9-8d34-4e356622bc11"
      },
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plot_confusion_matrix(cm, list(classes.keys()), normalize=True )"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normalized confusion matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X6tFcQK5_fuS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "outputId": "1aa40381-e559-44ed-8434-71355693adaa"
      },
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plot_confusion_matrix(cm,list(classes.keys()), normalize= False)"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HBJyx0wNM7yf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}