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        "<a href=\"https://colab.research.google.com/github/nicolas-chaulet/torch-points3d/blob/master/notebooks/PartSegmentationKPConv.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Setup packages\n",
        "!pip install torch==1.3.1 pyvista torchvision==0.4.2 pytorch-lightning\n",
        "!pip install --upgrade jsonschema\n",
        "!pip install torch-points3d\n",
        "!apt-get install -qq xvfb libgl1-mesa-glx"
      ],
      "execution_count": 3,
      "outputs": [
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          "text": [
            "Collecting torch==1.3.1\n",
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            "\u001b[K     |████████████████████████████████| 734.6MB 23kB/s \n",
            "\u001b[?25hCollecting pyvista\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/07/fd/f0863f26a9a5b1a088f8e34a8d05b6c495e98ee42045dd2a8e998bed740a/pyvista-0.24.2.tar.gz (1.2MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2MB 46.3MB/s \n",
            "\u001b[?25hCollecting torchvision==0.4.2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9b/e2/2b1f88a363ae37b2ba52cfb785ddfb3dd5f7e7ec9459e96fd8299b84ae39/torchvision-0.4.2-cp36-cp36m-manylinux1_x86_64.whl (10.2MB)\n",
            "\u001b[K     |████████████████████████████████| 10.2MB 15.1MB/s \n",
            "\u001b[?25hCollecting pytorch-lightning\n",
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            "\u001b[K     |████████████████████████████████| 256kB 55.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch==1.3.1) (1.18.4)\n",
            "Requirement already satisfied: imageio in /usr/local/lib/python3.6/dist-packages (from pyvista) (2.4.1)\n",
            "Collecting appdirs\n",
            "  Downloading https://files.pythonhosted.org/packages/3b/00/2344469e2084fb287c2e0b57b72910309874c3245463acd6cf5e3db69324/appdirs-1.4.4-py2.py3-none-any.whl\n",
            "Collecting scooby>=0.5.1\n",
            "  Downloading https://files.pythonhosted.org/packages/88/8d/0a76d82331e36c6176a0b50e53d03eaae1d67bb2ee7542a42cb54e350fba/scooby-0.5.4.tar.gz\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/2e/3f/f39c0189d2d82d7a0730de6d560551d6c799586c3d409b9c5fe558479f21/meshio-4.0.13-py3-none-any.whl (132kB)\n",
            "\u001b[K     |████████████████████████████████| 133kB 42.4MB/s \n",
            "\u001b[?25hCollecting vtk\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/22/f5/30e11e1ad21701e1cd185b046979107930419a74a5602c6b899dc8523fe4/vtk-8.1.2-cp36-cp36m-manylinux1_x86_64.whl (48.9MB)\n",
            "\u001b[K     |████████████████████████████████| 48.9MB 52kB/s \n",
            "\u001b[?25hRequirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision==0.4.2) (7.0.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision==0.4.2) (1.12.0)\n",
            "Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.6/dist-packages (from pytorch-lightning) (4.41.1)\n",
            "Requirement already satisfied: tensorboard>=1.14 in /usr/local/lib/python3.6/dist-packages (from pytorch-lightning) (2.2.1)\n",
            "Collecting future>=0.17.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/45/0b/38b06fd9b92dc2b68d58b75f900e97884c45bedd2ff83203d933cf5851c9/future-0.18.2.tar.gz (829kB)\n",
            "\u001b[K     |████████████████████████████████| 829kB 45.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyyaml>=3.13 in /usr/local/lib/python3.6/dist-packages (from pytorch-lightning) (3.13)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from meshio<5.0,>=4.0.3->pyvista) (1.6.0)\n",
            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch-lightning) (0.9.0)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch-lightning) (1.6.0.post3)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch-lightning) (3.2.1)\n",
            "Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch-lightning) (0.34.2)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch-lightning) (2.23.0)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch-lightning) (1.7.2)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch-lightning) (46.3.0)\n",
            "Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch-lightning) (1.28.1)\n",
            "Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch-lightning) (3.10.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch-lightning) (0.4.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch-lightning) (1.0.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->meshio<5.0,>=4.0.3->pyvista) (3.1.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard>=1.14->pytorch-lightning) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard>=1.14->pytorch-lightning) (2.9)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard>=1.14->pytorch-lightning) (2020.4.5.1)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard>=1.14->pytorch-lightning) (3.0.4)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard>=1.14->pytorch-lightning) (0.2.8)\n",
            "Requirement already satisfied: cachetools<3.2,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard>=1.14->pytorch-lightning) (3.1.1)\n",
            "Requirement already satisfied: rsa<4.1,>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard>=1.14->pytorch-lightning) (4.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=1.14->pytorch-lightning) (1.3.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.6/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard>=1.14->pytorch-lightning) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=1.14->pytorch-lightning) (3.1.0)\n",
            "Building wheels for collected packages: pyvista, scooby, future\n",
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            "Successfully built pyvista scooby future\n",
            "Installing collected packages: torch, appdirs, scooby, meshio, vtk, pyvista, torchvision, future, pytorch-lightning\n",
            "  Found existing installation: torch 1.5.0+cu101\n",
            "    Uninstalling torch-1.5.0+cu101:\n",
            "      Successfully uninstalled torch-1.5.0+cu101\n",
            "  Found existing installation: torchvision 0.6.0+cu101\n",
            "    Uninstalling torchvision-0.6.0+cu101:\n",
            "      Successfully uninstalled torchvision-0.6.0+cu101\n",
            "  Found existing installation: future 0.16.0\n",
            "    Uninstalling future-0.16.0:\n",
            "      Successfully uninstalled future-0.16.0\n",
            "Successfully installed appdirs-1.4.4 future-0.18.2 meshio-4.0.13 pytorch-lightning-0.7.6 pyvista-0.24.2 scooby-0.5.4 torch-1.3.1 torchvision-0.4.2 vtk-8.1.2\n",
            "Collecting jsonschema\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c5/8f/51e89ce52a085483359217bc72cdbf6e75ee595d5b1d4b5ade40c7e018b8/jsonschema-3.2.0-py2.py3-none-any.whl (56kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 1.9MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from jsonschema) (1.6.0)\n",
            "Requirement already satisfied, skipping upgrade: attrs>=17.4.0 in /usr/local/lib/python3.6/dist-packages (from jsonschema) (19.3.0)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.11.0 in /usr/local/lib/python3.6/dist-packages (from jsonschema) (1.12.0)\n",
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            "Installing collected packages: jsonschema\n",
            "  Found existing installation: jsonschema 2.6.0\n",
            "    Uninstalling jsonschema-2.6.0:\n",
            "      Successfully uninstalled jsonschema-2.6.0\n",
            "Successfully installed jsonschema-3.2.0\n",
            "Collecting torch-points3d\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e6/94/d68c34d0d38810cb37600d6ee1f16ba3bfbe0814a6b2151b7dc2341ccd0a/torch_points3d-0.2.2-py3-none-any.whl (226kB)\n",
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            "  Downloading https://files.pythonhosted.org/packages/08/4e/a268613fa6a92ffbc65b89e66fc8be5590801937185007f0f7bcb75ea21f/torch_sparse-0.4.3.tar.gz\n",
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            "Collecting wandb<0.9.0,>=0.8.18\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d1/c7/8bf2c62c3f133f45e135a8a116e4e0f162043248e3db54de30996eaf1a8a/wandb-0.8.36-py2.py3-none-any.whl (1.4MB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/56/dc/0e3d3732fd62c73fbb3317fc7bba22574832ab7a8e075620557bd4311641/numba-0.49.1-cp36-cp36m-manylinux2014_x86_64.whl (3.6MB)\n",
            "\u001b[K     |████████████████████████████████| 3.6MB 8.4MB/s \n",
            "\u001b[?25hCollecting torch-cluster==1.4.5\n",
            "  Downloading https://files.pythonhosted.org/packages/c3/70/1d827d6fd1e03bb5ae84852dd0070c6574105c37e7b935284f6e990932db/torch_cluster-1.4.5.tar.gz\n",
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            "  Downloading https://files.pythonhosted.org/packages/b7/b2/d7f70a85d3f6b0365517782632f150e3bbc2fb8e998cd69e27deba599aae/torchnet-0.0.4.tar.gz\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/77/d5/5dd5f07a8f69d6538db9a720158bfeee0d9107b63988250b15c84cc59ac1/pytorch_metric_learning-0.9.86-py3-none-any.whl (74kB)\n",
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            "\u001b[?25hCollecting hydra-core<0.12.0,>=0.11.2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/28/2f/e8dbf1266062d4041cbdcd725cb91210d9811aefb9cdf5fc04cb381bf9b9/hydra_core-0.11.3-py3-none-any.whl (72kB)\n",
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            "\u001b[?25hCollecting torch-geometric==1.3.2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f3/50/0a802f0bfa68058bf025d219ec6fbe806a5b891bba6702e28be7b83679fb/torch_geometric-1.3.2.tar.gz (126kB)\n",
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            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
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            "  Downloading https://files.pythonhosted.org/packages/b8/c3/8bad887ffa55c86f120ef5ae252dc0e357b3bd956d9fbf45242bacc46290/torch_scatter-1.4.0.tar.gz\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1b/95/9a20eebcedab2c1c63fad59fe19a0469edfc2a25b8576497e8084629c2ff/sentry_sdk-0.14.4-py2.py3-none-any.whl (104kB)\n",
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            "  Downloading https://files.pythonhosted.org/packages/c4/6f/cf9a3056045518f06184e804bae89390eb706168349daa9dff8ac609962a/gql-0.2.0.tar.gz\n",
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            "Installing collected packages: torch-sparse, sentry-sdk, shortuuid, pathtools, watchdog, graphql-core, gql, docker-pycreds, configparser, subprocess32, smmap, gitdb, GitPython, wandb, numba, torch-cluster, jsonpointer, jsonpatch, torchfile, websocket-client, visdom, torchnet, pytorch-metric-learning, omegaconf, hydra-core, plyfile, isodate, rdflib, torch-geometric, torch-points-kernels, torch-scatter, torch-points3d\n",
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            "    Uninstalling numba-0.48.0:\n",
            "      Successfully uninstalled numba-0.48.0\n",
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    {
      "cell_type": "code",
      "metadata": {
        "id": "7_9hcYlFvOv6",
        "colab_type": "code",
        "outputId": "86e91b94-5550-4f01-b68e-f2e760c66ebf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Needed for remote rendering \n",
        "import os\n",
        "os.environ[\"DISPLAY\"] = \":1.0\"\n",
        "os.environ[\"PYVISTA_OFF_SCREEN\"]=\"true\"\n",
        "os.environ[\"PYVISTA_PLOT_THEME\"]=\"true\"\n",
        "os.environ[\"PYVISTA_USE_PANEL\"]=\"true\"\n",
        "os.environ[\"PYVISTA_AUTO_CLOSE\"]=\"false\"\n",
        "os.system(\"Xvfb :1 -screen 0 1024x768x24 > /dev/null 2>&1 &\")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HGza3XTYvQNC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import sys\n",
        "from omegaconf import OmegaConf\n",
        "import pyvista as pv\n",
        "import torch\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q44YjGjrvg2j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "DIR = \"\" # Replace with your root directory, the data will go in DIR/data."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "izN8KU_tv5JR",
        "colab_type": "text"
      },
      "source": [
        "<p align=\"center\">\n",
        "  <img width=\"40%\" src=\"https://raw-hub.myxuebi.top/nicolas-chaulet/torch-points3d/master/docs/logo.png\" />\n",
        "</p>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mgox3ksbv-s1",
        "colab_type": "text"
      },
      "source": [
        "# Segmenting objects in part with KPConv\n",
        "In this notebook we will solve the task of segmenting an object into its sub parts by using a [KPConv](https://arxiv.org/abs/1904.08889) deep neural network.\n",
        "We will work on [ShapeNet](https://www.shapenet.org/) dataset which contains 48,600 3D models over 55 common categories with part annotations. We will show you how you can use Torch Points3D to setup a KPConv backbone with a multi head classifier and train it on ShapeNet. We will in particular cover the CPU pre processing of the data that allows a complex KPConv to perform well."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wyFtUQhGybFc",
        "colab_type": "text"
      },
      "source": [
        "## The dataset\n",
        "We use Torch Points3D version of ShapeNet that provides automatic download (be patient, it takes some time...) of the data, a tested metric tracker as well as methods for pre computing the spatial operations such as neighbour search and grid sampling on CPU.\n",
        "\n",
        "Let's start with the data config (if you want more details about that part of Torch Points3D please refer to this notebook)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JnLlPJ_dmzOV",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Configure the dataset {run: \"auto\"}\n",
        "CATEGORY = \"All\" #@param [\"Airplane\", \"Bag\", \"All\", \"Motorbike\"] {allow-input: true}\n",
        "USE_NORMALS = True #@param {type:\"boolean\"}|"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PBRS6vxSvjRo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "shapenet_yaml = \"\"\"\n",
        "class: shapenet.ShapeNetDataset\n",
        "task: segmentation\n",
        "dataroot: %s\n",
        "normal: %r                                    # Use normal vectors as features\n",
        "first_subsampling: 0.02                       # Grid size of the input data\n",
        "pre_transforms:                               # Offline transforms, done only once\n",
        "    - transform: NormalizeScale           \n",
        "    - transform: GridSampling\n",
        "      params:\n",
        "        size: ${first_subsampling}\n",
        "train_transforms:                             # Data augmentation pipeline\n",
        "    - transform: RandomNoise\n",
        "      params:\n",
        "        sigma: 0.01\n",
        "        clip: 0.05\n",
        "    - transform: RandomScaleAnisotropic\n",
        "      params:\n",
        "        scales: [0.9,1.1]\n",
        "    - transform: AddOnes\n",
        "    - transform: AddFeatsByKeys\n",
        "      params:\n",
        "        list_add_to_x: [True]\n",
        "        feat_names: [\"ones\"]\n",
        "        delete_feats: [True]\n",
        "test_transforms:\n",
        "    - transform: AddOnes\n",
        "    - transform: AddFeatsByKeys\n",
        "      params:\n",
        "        list_add_to_x: [True]\n",
        "        feat_names: [\"ones\"]\n",
        "        delete_feats: [True]\n",
        "\"\"\" % (os.path.join(DIR,\"data\"), USE_NORMALS) \n",
        "\n",
        "from omegaconf import OmegaConf\n",
        "params = OmegaConf.create(shapenet_yaml)\n",
        "if CATEGORY != \"All\":\n",
        "    params.category = CATEGORY"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eQFRmfUYzXX1",
        "colab_type": "code",
        "outputId": "57010774-624e-40aa-a498-78d6d4b5b5d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        }
      },
      "source": [
        "from torch_points3d.datasets.segmentation import ShapeNetDataset\n",
        "dataset = ShapeNetDataset(params)\n",
        "dataset"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip\n",
            "Extracting data/shapenet/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "  0%|          | 9/12137 [00:00<02:28, 81.77it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Processing...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 12137/12137 [02:16<00:00, 88.68it/s]\n",
            "100%|██████████| 1870/1870 [00:21<00:00, 88.32it/s]\n",
            "100%|██████████| 2874/2874 [00:32<00:00, 87.91it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset: ShapeNetDataset \n",
              "\u001b[0;95mpre_transform \u001b[0m= Compose([\n",
              "    NormalizeScale(),\n",
              "    GridSampling(grid_size=0.02, quantize_coords=False, mode=mean),\n",
              "])\n",
              "\u001b[0;95mtest_transform \u001b[0m= Compose([\n",
              "    AddOnes(),\n",
              "    AddFeatsByKeys(ones=True),\n",
              "])\n",
              "\u001b[0;95mtrain_transform \u001b[0m= Compose([\n",
              "    RandomNoise(sigma=0.01, clip=0.05),\n",
              "    RandomScaleAnisotropic([0.9, 1.1]),\n",
              "    AddOnes(),\n",
              "    AddFeatsByKeys(ones=True),\n",
              "])\n",
              "\u001b[0;95mval_transform \u001b[0m= None\n",
              "\u001b[0;95minference_transform \u001b[0m= Compose([\n",
              "    NormalizeScale(),\n",
              "    GridSampling(grid_size=0.02, quantize_coords=False, mode=mean),\n",
              "    AddOnes(),\n",
              "    AddFeatsByKeys(ones=True),\n",
              "])\n",
              "Size of \u001b[0;95mtrain_dataset \u001b[0m= 14007\n",
              "Size of \u001b[0;95mtest_dataset \u001b[0m= 2874\n",
              "Size of \u001b[0;95mval_dataset \u001b[0m= 0\n",
              "\u001b[0;95mBatch size =\u001b[0m None"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wAN1JCiU1pFW",
        "colab_type": "code",
        "cellView": "form",
        "outputId": "a5fc125a-156f-4887-f04f-bb8784a79a44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        }
      },
      "source": [
        "#@title Plot samples with part annotations { run: \"auto\" }\n",
        "objectid_1 = 9 #@param {type:\"slider\", min:0, max:100, step:1}\n",
        "objectid_2 = 82 #@param {type:\"slider\", min:0, max:100, step:1}\n",
        "objectid_3 = 95 #@param {type:\"slider\", min:0, max:100, step:1}\n",
        "\n",
        "samples = [objectid_1,objectid_2,objectid_3]\n",
        "p = pv.Plotter(notebook=True,shape=(1, len(samples)),window_size=[1024,412])\n",
        "for i in range(len(samples)):\n",
        "    p.subplot(0, i)\n",
        "    sample = dataset.train_dataset[samples[i]]\n",
        "    point_cloud = pv.PolyData(sample.pos.numpy())\n",
        "    point_cloud['y'] = sample.y.numpy()\n",
        "    p.add_points(point_cloud,  show_scalar_bar=False, point_size=3)\n",
        "    p.camera_position = [-1,5, -10]\n",
        "p.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=1024x412 at 0x7F36A1E97860>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sLSGJVf30G1C",
        "colab_type": "text"
      },
      "source": [
        "## Model for part segmentation\n",
        "Let's start by creating a multihead segmentation module with one segmentation head per category. We provide that as part of Torch Points3D but let's reproduce it here for sake of completeness."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V4k79sQhzryp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torch_points3d.core.common_modules import MLP, UnaryConv\n",
        "\n",
        "class MultiHeadClassifier(torch.nn.Module):\n",
        "    \"\"\" Allows segregated segmentation in case the category of an object is known. \n",
        "    This is the case in ShapeNet for example.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    in_features -\n",
        "        size of the input channel\n",
        "    cat_to_seg\n",
        "        category to segment maps for example:\n",
        "        {\n",
        "            'Airplane': [0,1,2],\n",
        "            'Table': [3,4]\n",
        "        }\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, in_features, cat_to_seg, dropout_proba=0.5, bn_momentum=0.1):\n",
        "        super().__init__()\n",
        "        self._cat_to_seg = {}\n",
        "        self._num_categories = len(cat_to_seg)\n",
        "        self._max_seg_count = 0\n",
        "        self._max_seg = 0\n",
        "        self._shifts = torch.zeros((self._num_categories,), dtype=torch.long)\n",
        "        for i, seg in enumerate(cat_to_seg.values()):\n",
        "            self._max_seg_count = max(self._max_seg_count, len(seg))\n",
        "            self._max_seg = max(self._max_seg, max(seg))\n",
        "            self._shifts[i] = min(seg)\n",
        "            self._cat_to_seg[i] = seg\n",
        "\n",
        "        self.channel_rasing = MLP(\n",
        "            [in_features, self._num_categories * in_features], bn_momentum=bn_momentum, bias=False\n",
        "        )\n",
        "        if dropout_proba:\n",
        "            self.channel_rasing.add_module(\"Dropout\", torch.nn.Dropout(p=dropout_proba))\n",
        "\n",
        "        self.classifier = UnaryConv((self._num_categories, in_features, self._max_seg_count))\n",
        "        self._bias = torch.nn.Parameter(torch.zeros(self._max_seg_count,))\n",
        "\n",
        "    def forward(self, features, category_labels, **kwargs):\n",
        "        assert features.dim() == 2\n",
        "        self._shifts = self._shifts.to(features.device)\n",
        "        in_dim = features.shape[-1]\n",
        "        features = self.channel_rasing(features)\n",
        "        features = features.reshape((-1, self._num_categories, in_dim))\n",
        "        features = features.transpose(0, 1)  # [num_categories, num_points, in_dim]\n",
        "        features = self.classifier(features) + self._bias  # [num_categories, num_points, max_seg]\n",
        "        ind = category_labels.unsqueeze(-1).repeat(1, 1, features.shape[-1]).long()\n",
        "\n",
        "        logits = features.gather(0, ind).squeeze(0)\n",
        "        softmax = torch.nn.functional.log_softmax(logits, dim=-1)\n",
        "\n",
        "        output = torch.zeros(logits.shape[0], self._max_seg + 1).to(features.device)\n",
        "        cats_in_batch = torch.unique(category_labels)\n",
        "        for cat in cats_in_batch:\n",
        "            cat_mask = category_labels == cat\n",
        "            seg_indices = self._cat_to_seg[cat.item()]\n",
        "            probs = softmax[cat_mask, : len(seg_indices)]\n",
        "            output[cat_mask, seg_indices[0] : seg_indices[-1] + 1] = probs\n",
        "        \n",
        "        return output"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vk2LyH-IT2KG",
        "colab_type": "text"
      },
      "source": [
        "The model we implement here follows the main architecture proposed in the [original paper](https://arxiv.org/abs/1904.08889):\n",
        "\n",
        "<p align=\"center\">\n",
        "  <img width=\"70%\" src=\"https://drive.google.com/uc?export=view&id=1CJppQ88T69whjYsJc016L3_E_rtcJ8n1\" />\n",
        "</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZnyHC6Gx1nIw",
        "colab_type": "code",
        "outputId": "ca2f91c0-c759-4d5e-ad6f-08ac489edf37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "from torch_points3d.applications.kpconv import KPConv\n",
        "\n",
        "\n",
        "class PartSegKPConv(torch.nn.Module):\n",
        "    def __init__(self, cat_to_seg):\n",
        "        super().__init__()\n",
        "        self.unet = KPConv(\n",
        "            architecture=\"unet\", \n",
        "            input_nc=USE_NORMALS * 3, \n",
        "            num_layers=4, \n",
        "            in_grid_size=0.02\n",
        "            )\n",
        "        self.classifier = MultiHeadClassifier(self.unet.output_nc, cat_to_seg)\n",
        "    \n",
        "    @property\n",
        "    def conv_type(self):\n",
        "        \"\"\" This is needed by the dataset to infer which batch collate should be used\"\"\"\n",
        "        return self.unet.conv_type\n",
        "    \n",
        "    def get_batch(self):\n",
        "        return self.batch\n",
        "    \n",
        "    def get_output(self):\n",
        "        \"\"\" This is needed by the tracker to get access to the ouputs of the network\"\"\"\n",
        "        return self.output\n",
        "    \n",
        "    def get_labels(self):\n",
        "        \"\"\" Needed by the tracker in order to access ground truth labels\"\"\"\n",
        "        return self.labels\n",
        "    \n",
        "    def get_current_losses(self):\n",
        "        \"\"\" Entry point for the tracker to grab the loss \"\"\"\n",
        "        return {\"loss_seg\": float(self.loss_seg)}\n",
        "\n",
        "    def forward(self, data):\n",
        "        self.labels = data.y\n",
        "        self.batch = data.batch\n",
        "        \n",
        "        # Forward through unet and classifier\n",
        "        data_features = self.unet(data)\n",
        "        self.output = self.classifier(data_features.x, data.category)\n",
        "\n",
        "         # Set loss for the backward pass\n",
        "        self.loss_seg = torch.nn.functional.nll_loss(self.output, self.labels)\n",
        "        return self.output\n",
        "\n",
        "    def get_spatial_ops(self):\n",
        "        return self.unet.get_spatial_ops()\n",
        "        \n",
        "    def backward(self):\n",
        "         self.loss_seg.backward() \n",
        "\n",
        "    "
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ERROR:root:The testing module requires faiss. You can install the GPU version with the command 'conda install faiss-gpu -c pytorch' \n",
            "                        or the CPU version with 'conda install faiss-cpu -c pytorch'. Learn more at https://github.com/facebookresearch/faiss/blob/master/INSTALL.md\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QQ0GUiv22KKb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = PartSegKPConv(dataset.class_to_segments)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k9Bx8GjE3Kt7",
        "colab_type": "text"
      },
      "source": [
        "## The data loaders and CPU pre computing features\n",
        "KPConv is quite demanding on spatial operations such as grid sampling and radius search. On the network loaded here we have 10 KPConv layers on the encoder which means 10 radius search operations with varying number of neighbours. We observed a significant performance gain by moving those operations to the CPU where they can easily be optimised with suitable data structures such as kd-tree. We use [nonaflann](https://github.com/jlblancoc/nanoflann) in the back-end, a 3D optimised kd-tree implementation. Note that this is beneficiary only if you have access to multiple CPU threads.\n",
        "\n",
        "You can decide to precompute those spatial operations by setting the `precompute_multi_scale` parameter to `True` when creating the data loaders. The dataset will mine the model to figure out which spatial operations are required and in which order."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1k4CNG12RXN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "NUM_WORKERS = 4\n",
        "BATCH_SIZE = 16\n",
        "dataset.create_dataloaders(\n",
        "    model,\n",
        "    batch_size=BATCH_SIZE, \n",
        "    num_workers=NUM_WORKERS, \n",
        "    shuffle=True, \n",
        "    precompute_multi_scale=True \n",
        "    )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4wdS3y_aVIbm",
        "colab_type": "code",
        "outputId": "e2dba960-dedf-4a45-dd64-d926e6f895e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "sample = next(iter(dataset.train_dataloader))\n",
        "sample.keys"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['x', 'y', 'pos', 'multiscale', 'upsample', 'batch', 'category']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A5pl2g9ap1Fq",
        "colab_type": "text"
      },
      "source": [
        "Our `sample` contains the pre computed spatial information in the `multiscale` (encoder side) and `upsample` (decoder) attrivutes. The decoder pre computing is quite simple and just involves some basic caching for the nearest neighbour interpolation operation. Let's take a look at the encoder side of things first. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z0o8yQF4FWvV",
        "colab_type": "code",
        "outputId": "69e1f669-55e6-4d83-81d2-e6983717f144",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        }
      },
      "source": [
        "sample.multiscale"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Batch(batch=[35485], idx_neighboors=[35485, 25], pos=[35485, 3]),\n",
              " Batch(batch=[35485], idx_neighboors=[35485, 25], pos=[35485, 3]),\n",
              " Batch(batch=[23248], idx_neighboors=[23248, 25], pos=[23248, 3]),\n",
              " Batch(batch=[23248], idx_neighboors=[23248, 30], pos=[23248, 3]),\n",
              " Batch(batch=[8544], idx_neighboors=[8544, 30], pos=[8544, 3]),\n",
              " Batch(batch=[8544], idx_neighboors=[8544, 38], pos=[8544, 3]),\n",
              " Batch(batch=[1916], idx_neighboors=[1916, 38], pos=[1916, 3]),\n",
              " Batch(batch=[1916], idx_neighboors=[1916, 38], pos=[1916, 3]),\n",
              " Batch(batch=[376], idx_neighboors=[376, 38], pos=[376, 3]),\n",
              " Batch(batch=[376], idx_neighboors=[376, 38], pos=[376, 3])]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B-2bjfQyqUyH",
        "colab_type": "text"
      },
      "source": [
        "`sample.multiscale` contains 10 different versions of the input batch, each one of these versions contains the location of the points in `pos` as well as the neighbours of these points in the previous point cloud. We will first look at the points coming out of each downsampling layer (strided convolution), we have 5 of them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qmR0S-F5qTvV",
        "colab_type": "code",
        "cellView": "form",
        "outputId": "81156010-3a13-401b-9315-66833b1f6fdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "source": [
        "#@title Successive downsampling {run:\"auto\"}\n",
        "sample_in_batch = 0 #@param {type:\"slider\", min:0, max:5, step:1}\n",
        "ms_data = sample.multiscale \n",
        "num_downsize = int(len(ms_data) / 2)\n",
        "p = pv.Plotter(notebook=True,shape=(1, num_downsize),window_size=[1024,256])\n",
        "for i in range(0,num_downsize):\n",
        "    p.subplot(0, i)\n",
        "    pos = ms_data[2*i].pos[ms_data[2*i].batch == sample_in_batch].numpy()\n",
        "    point_cloud = pv.PolyData(pos)\n",
        "    point_cloud['y'] = pos[:,1]\n",
        "    p.add_points(point_cloud,  show_scalar_bar=False, point_size=3)\n",
        "    p.add_text(\"Layer {}\".format(i+1),font_size=10)\n",
        "    p.camera_position = [-1,5, -10]\n",
        "p.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=1024x256 at 0x7F36A20F25F8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONPofTEptKyL",
        "colab_type": "text"
      },
      "source": [
        "Let's now take one point in a layer (query point) and show its neighbours in the previous layer (support point)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-uVmbA_OqDrg",
        "colab_type": "code",
        "cellView": "form",
        "outputId": "110c84a1-9aec-4b27-f9ef-79f682047987",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        }
      },
      "source": [
        "#@title Explore Neighborhood {run: \"auto\"}\n",
        "selected_layer = 7 #@param {type:\"slider\", min:1, max:9, step:1}\n",
        "sample_in_batch = 0 #@param {type:\"slider\", min:0, max:5, step:1}\n",
        "point1_id = 46 #@param {type:\"slider\", min:0, max:600, step:1}\n",
        "point2_id =  0#@param {type:\"slider\", min:0, max:600, step:1}\n",
        "\n",
        "p = pv.Plotter(notebook=True,shape=(1, 2),window_size=[1024,412])\n",
        "\n",
        "# Selected layer\n",
        "p.subplot(0, 1)\n",
        "ms_data = sample.multiscale[selected_layer]\n",
        "pos = ms_data.pos[ms_data.batch == sample_in_batch].numpy()\n",
        "nei = ms_data.idx_neighboors[ms_data.batch == sample_in_batch]\n",
        "point_cloud = pv.PolyData(pos)\n",
        "p.add_points(point_cloud,  show_scalar_bar=False, point_size=3,opacity=0.3)\n",
        "p.add_points(pos[point1_id,:],  show_scalar_bar=False, point_size=7.0,color='red')\n",
        "p.add_points(pos[point2_id,:],  show_scalar_bar=False, point_size=7.0,color='green')\n",
        "p.camera_position = [-1,5, -10]\n",
        "\n",
        "# Previous layer\n",
        "p.subplot(0, 0)\n",
        "ms_data = sample.multiscale[selected_layer-1]\n",
        "pos = ms_data.pos[ms_data.batch == sample_in_batch].numpy()\n",
        "point_cloud = pv.PolyData(pos)\n",
        "p.add_points(point_cloud,  show_scalar_bar=False,point_size=3, opacity=0.3)\n",
        "nei_pos = ms_data.pos[nei[point1_id]].numpy()\n",
        "nei_pos = nei_pos[nei[point1_id] >= 0]\n",
        "p.add_points(nei_pos,  show_scalar_bar=False, point_size=3.0,color='red')\n",
        "nei_pos = ms_data.pos[nei[point2_id]].numpy()\n",
        "nei_pos = nei_pos[nei[point2_id] >= 0]\n",
        "p.add_points(nei_pos,  show_scalar_bar=False, point_size=3.0,color='green')\n",
        "p.camera_position = [-1,5, -10]\n",
        "\n",
        "p.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=1024x412 at 0x7F36A1F93D30>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OsOeG_VXW5lC",
        "colab_type": "text"
      },
      "source": [
        "## Training loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hg3O2swzW7if",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tqdm.auto import tqdm\n",
        "import time\n",
        "\n",
        "class Trainer:\n",
        "    def __init__(self,model, dataset, num_epoch = 50, device=torch.device('cuda')):\n",
        "        self.num_epoch = num_epoch\n",
        "        self._model = model\n",
        "        self._dataset=dataset\n",
        "        self.device = device\n",
        "\n",
        "    def fit(self):\n",
        "        self.optimizer = torch.optim.Adam(self._model.parameters(), lr=0.001)\n",
        "        self.tracker = self._dataset.get_tracker(False, True)\n",
        "\n",
        "        for i in range(self.num_epoch):\n",
        "            print(\"=========== EPOCH %i ===========\" % i)\n",
        "            time.sleep(0.5)\n",
        "            self.train_epoch()\n",
        "            self.tracker.publish(i)\n",
        "            self.test_epoch()\n",
        "            self.tracker.publish(i)\n",
        "\n",
        "    def train_epoch(self):\n",
        "        self._model.to(self.device)\n",
        "        self._model.train()\n",
        "        self.tracker.reset(\"train\")\n",
        "        train_loader = self._dataset.train_dataloader\n",
        "        iter_data_time = time.time()\n",
        "        with tqdm(train_loader) as tq_train_loader:\n",
        "            for i, data in enumerate(tq_train_loader):\n",
        "                t_data = time.time() - iter_data_time\n",
        "                iter_start_time = time.time()\n",
        "                self.optimizer.zero_grad()\n",
        "                data.to(self.device)\n",
        "                self._model.forward(data)\n",
        "                self._model.backward()\n",
        "                self.optimizer.step()\n",
        "                if i % 10 == 0:\n",
        "                    self.tracker.track(self._model)\n",
        "\n",
        "                tq_train_loader.set_postfix(\n",
        "                    **self.tracker.get_metrics(),\n",
        "                    data_loading=float(t_data),\n",
        "                    iteration=float(time.time() - iter_start_time),\n",
        "                )\n",
        "                iter_data_time = time.time()\n",
        "\n",
        "    def test_epoch(self):\n",
        "        self._model.to(self.device)\n",
        "        self._model.eval()\n",
        "        self.tracker.reset(\"test\")\n",
        "        test_loader = self._dataset.test_dataloaders[0]\n",
        "        iter_data_time = time.time()\n",
        "        with tqdm(test_loader) as tq_test_loader:\n",
        "            for i, data in enumerate(tq_test_loader):\n",
        "                t_data = time.time() - iter_data_time\n",
        "                iter_start_time = time.time()\n",
        "                data.to(self.device)\n",
        "                self._model.forward(data)           \n",
        "                self.tracker.track(self._model)\n",
        "\n",
        "                tq_test_loader.set_postfix(\n",
        "                    **self.tracker.get_metrics(),\n",
        "                    data_loading=float(t_data),\n",
        "                    iteration=float(time.time() - iter_start_time),\n",
        "                )\n",
        "                iter_data_time = time.time()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xFaIG1SBchHg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "trainer = Trainer(model, dataset)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydhhP0CdcqO1",
        "colab_type": "code",
        "outputId": "13f5dbaf-5f8a-489c-a8ba-b602658cdaae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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      "source": [
        "trainer.fit()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "=========== EPOCH 0 ===========\n"
          ],
          "name": "stdout"
        },
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          "output_type": "stream",
          "text": [
            "\n",
            "=========== EPOCH 1 ===========\n"
          ],
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          "output_type": "stream",
          "text": [
            "\n",
            "=========== EPOCH 2 ===========\n"
          ],
          "name": "stdout"
        },
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          },
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "=========== EPOCH 3 ===========\n"
          ],
          "name": "stdout"
        },
        {
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            ]
          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "=========== EPOCH 4 ===========\n"
          ],
          "name": "stdout"
        },
        {
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "=========== EPOCH 5 ===========\n"
          ],
          "name": "stdout"
        },
        {
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          "data": {
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            "\n"
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              "HBox(children=(FloatProgress(value=0.0, max=180.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "=========== EPOCH 6 ===========\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          },
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          "output_type": "stream",
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            "\n"
          ],
          "name": "stdout"
        },
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              "HBox(children=(FloatProgress(value=0.0, max=180.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "=========== EPOCH 7 ===========\n"
          ],
          "name": "stdout"
        },
        {
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          ],
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            ]
          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "=========== EPOCH 8 ===========\n"
          ],
          "name": "stdout"
        },
        {
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          ],
          "name": "stdout"
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "=========== EPOCH 9 ===========\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "\n"
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          "name": "stdout"
        },
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "=========== EPOCH 10 ===========\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "\n"
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          "name": "stdout"
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        {
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "=========== EPOCH 11 ===========\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y69S3OsL2UKL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%load_ext tensorboard\n",
        "%tensorboard --logdir lightning_logs/version_4/ # Change for your log location"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tiXF_nwx7VCl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}