MultiGarmentNetwork.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MultiGarmentNetwork.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "_g8MypeX2xH3",
        "dNgiuLKxTsOc",
        "OM9ej-8zPjFz",
        "BEzfWxKEO73S",
        "L4ZBRRUiTMcb"
      ],
      "authorship_tag": "ABX9TyM6I+210czib3e8tXwna5v/",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/khushgrover/smart-match/blob/main/MultiGarmentNetwork.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_g8MypeX2xH3"
      },
      "source": [
        "### **DIRT**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LNvMQzf716Wi"
      },
      "source": [
        "!cp /usr/local/lib/python3.6/dist-packages/tensorflow/libtensorflow_framework.so.2 /usr/lib/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wcWywAPt19Uk"
      },
      "source": [
        "!export LD_LIBRARY_PATH=\"/usr/lib/libtensorflow_framework.so.2\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xkt3vk_k2Awn",
        "outputId": "ad33ebb1-9311-462f-8ae6-1d7cfe0fb17a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!pip install tensorflow-gpu==2.1.0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow-gpu==2.1.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0a/93/c7bca39b23aae45cd2e85ad3871c81eccc63b9c5276e926511e2e5b0879d/tensorflow_gpu-2.1.0-cp36-cp36m-manylinux2010_x86_64.whl (421.8MB)\n",
            "\u001b[K     |████████████████████████████████| 421.8MB 31kB/s \n",
            "\u001b[?25hRequirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.1.0) (3.3.0)\n",
            "Requirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.1.0) (3.12.4)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.1.0) (0.2.0)\n",
            "Collecting tensorboard<2.2.0,>=2.1.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d9/41/bbf49b61370e4f4d245d4c6051dfb6db80cec672605c91b1652ac8cc3d38/tensorboard-2.1.1-py3-none-any.whl (3.8MB)\n",
            "\u001b[K     |████████████████████████████████| 3.9MB 46.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.1.0) (0.10.0)\n",
            "Collecting keras-applications>=1.0.8\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/71/e3/19762fdfc62877ae9102edf6342d71b28fbfd9dea3d2f96a882ce099b03f/Keras_Applications-1.0.8-py3-none-any.whl (50kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 8.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.1.0) (1.18.5)\n",
            "Collecting tensorflow-estimator<2.2.0,>=2.1.0rc0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/18/90/b77c328a1304437ab1310b463e533fa7689f4bfc41549593056d812fab8e/tensorflow_estimator-2.1.0-py2.py3-none-any.whl (448kB)\n",
            "\u001b[K     |████████████████████████████████| 450kB 53.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.1.0) (1.12.1)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.1.0) (0.8.1)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.1.0) (1.15.0)\n",
            "Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.1.0) (0.35.1)\n",
            "Collecting gast==0.2.2\n",
            "  Downloading https://files.pythonhosted.org/packages/4e/35/11749bf99b2d4e3cceb4d55ca22590b0d7c2c62b9de38ac4a4a7f4687421/gast-0.2.2.tar.gz\n",
            "Requirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.1.0) (1.1.2)\n",
            "Requirement already satisfied: scipy==1.4.1; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.1.0) (1.4.1)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.1.0) (1.1.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.1.0) (1.32.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.8.0->tensorflow-gpu==2.1.0) (50.3.0)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu==2.1.0) (2.23.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu==2.1.0) (1.0.1)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu==2.1.0) (1.17.2)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu==2.1.0) (0.4.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu==2.1.0) (3.2.2)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow-gpu==2.1.0) (2.10.0)\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<2.2.0,>=2.1.0->tensorflow-gpu==2.1.0) (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<2.2.0,>=2.1.0->tensorflow-gpu==2.1.0) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu==2.1.0) (2020.6.20)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu==2.1.0) (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<2.2.0,>=2.1.0->tensorflow-gpu==2.1.0) (0.2.8)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu==2.1.0) (4.6)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu==2.1.0) (4.1.1)\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<2.2.0,>=2.1.0->tensorflow-gpu==2.1.0) (1.3.0)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu==2.1.0) (2.0.0)\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<2.2.0,>=2.1.0->tensorflow-gpu==2.1.0) (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<2.2.0,>=2.1.0->tensorflow-gpu==2.1.0) (3.1.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu==2.1.0) (3.2.0)\n",
            "Building wheels for collected packages: gast\n",
            "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gast: filename=gast-0.2.2-cp36-none-any.whl size=7542 sha256=f4bc4c5977730c2b0bee59f1e7dee19e58aac4d65b3c0e3718f6a5d94e3c3b40\n",
            "  Stored in directory: /root/.cache/pip/wheels/5c/2e/7e/a1d4d4fcebe6c381f378ce7743a3ced3699feb89bcfbdadadd\n",
            "Successfully built gast\n",
            "\u001b[31mERROR: tensorflow 2.3.0 has requirement gast==0.3.3, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 2.3.0 has requirement tensorboard<3,>=2.3.0, but you'll have tensorboard 2.1.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 2.3.0 has requirement tensorflow-estimator<2.4.0,>=2.3.0, but you'll have tensorflow-estimator 2.1.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow-probability 0.11.0 has requirement gast>=0.3.2, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n",
            "Installing collected packages: tensorboard, keras-applications, tensorflow-estimator, gast, tensorflow-gpu\n",
            "  Found existing installation: tensorboard 2.3.0\n",
            "    Uninstalling tensorboard-2.3.0:\n",
            "      Successfully uninstalled tensorboard-2.3.0\n",
            "  Found existing installation: tensorflow-estimator 2.3.0\n",
            "    Uninstalling tensorflow-estimator-2.3.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.3.0\n",
            "  Found existing installation: gast 0.3.3\n",
            "    Uninstalling gast-0.3.3:\n",
            "      Successfully uninstalled gast-0.3.3\n",
            "Successfully installed gast-0.2.2 keras-applications-1.0.8 tensorboard-2.1.1 tensorflow-estimator-2.1.0 tensorflow-gpu-2.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F9NgXnBZ2GfJ",
        "outputId": "c9f99ec6-ee77-49e7-f0f1-688b6e1325ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "!git clone https://github.com/khushgrover/dirt.git dirt-repo"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'dirt-repo'...\n",
            "remote: Enumerating objects: 68, done.\u001b[K\n",
            "remote: Counting objects: 100% (68/68), done.\u001b[K\n",
            "remote: Compressing objects: 100% (42/42), done.\u001b[K\n",
            "remote: Total 229 (delta 38), reused 47 (delta 26), pack-reused 161\u001b[K\n",
            "Receiving objects: 100% (229/229), 383.54 KiB | 21.31 MiB/s, done.\n",
            "Resolving deltas: 100% (117/117), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "36ThfhbP2KBY",
        "outputId": "9492a5ea-888f-4fef-8a7c-3dd5124f92fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "!cd dirt-repo; mkdir build; cd build ; cmake -DOpenGL_GL_PREFERENCE=GLVND -DCMAKE_CUDA_FLAGS='-DNDEBUG' ../csrc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-- The CXX compiler identification is GNU 7.5.0\n",
            "-- The CUDA compiler identification is NVIDIA 10.1.243\n",
            "-- Check for working CXX compiler: /usr/bin/c++\n",
            "-- Check for working CXX compiler: /usr/bin/c++ -- works\n",
            "-- Detecting CXX compiler ABI info\n",
            "-- Detecting CXX compiler ABI info - done\n",
            "-- Detecting CXX compile features\n",
            "-- Detecting CXX compile features - done\n",
            "-- Check for working CUDA compiler: /usr/local/cuda/bin/nvcc\n",
            "-- Check for working CUDA compiler: /usr/local/cuda/bin/nvcc -- works\n",
            "-- Detecting CUDA compiler ABI info\n",
            "-- Detecting CUDA compiler ABI info - done\n",
            "-- Found OpenGL: /usr/lib/x86_64-linux-gnu/libOpenGL.so  found components:  OpenGL EGL \n",
            "-- Configuring done\n",
            "-- Generating done\n",
            "-- Build files have been written to: /content/dirt-repo/build\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HHxV627A2NzH",
        "outputId": "d5570545-cede-4895-856a-61cfb7446595",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!cd dirt-repo/build ; make -j5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[35m\u001b[1mScanning dependencies of target rasterise\u001b[0m\n",
            "[ 14%] \u001b[32mBuilding CXX object CMakeFiles/rasterise.dir/rasterise_egl.cpp.o\u001b[0m\n",
            "[ 28%] \u001b[32mBuilding CUDA object CMakeFiles/rasterise.dir/rasterise_egl.cu.o\u001b[0m\n",
            "[ 42%] \u001b[32mBuilding CUDA object CMakeFiles/rasterise.dir/rasterise_grad_egl.cu.o\u001b[0m\n",
            "[ 57%] \u001b[32mBuilding CXX object CMakeFiles/rasterise.dir/shaders.cpp.o\u001b[0m\n",
            "[ 71%] \u001b[32mBuilding CXX object CMakeFiles/rasterise.dir/rasterise_grad_egl.cpp.o\u001b[0m\n",
            "In file included from \u001b[01m\u001b[K/content/dirt-repo/csrc/rasterise_egl.cpp:24:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h:3:2:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K#warning \"Deprecated header file, please either include the main Eigen/CXX11/Tensor header or the respective TensorDeviceGpu.h file\" [\u001b[01;35m\u001b[K-Wcpp\u001b[m\u001b[K]\n",
            " #\u001b[01;35m\u001b[Kwarning\u001b[m\u001b[K \"Deprecated header file, please either include the main Eigen/CXX11/Tensor header or the respective TensorDeviceGpu.h file\"\n",
            "  \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/content/dirt-repo/csrc/rasterise_grad_egl.cpp:22:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h:3:2:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K#warning \"Deprecated header file, please either include the main Eigen/CXX11/Tensor header or the respective TensorDeviceGpu.h file\" [\u001b[01;35m\u001b[K-Wcpp\u001b[m\u001b[K]\n",
            " #\u001b[01;35m\u001b[Kwarning\u001b[m\u001b[K \"Deprecated header file, please either include the main Eigen/CXX11/Tensor header or the respective TensorDeviceGpu.h file\"\n",
            "  \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/absl/strings/string_view.h(495): warning: expression has no effect\n",
            "\n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/absl/strings/string_view.h(495): warning: expression has no effect\n",
            "\n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/tensorflow/core/platform/env.h(356): warning: overloaded virtual function \"tensorflow::Env::RegisterFileSystem\" is only partially overridden in class \"tensorflow::EnvWrapper\"\n",
            "\n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/tensorflow/core/platform/env.h(356): warning: overloaded virtual function \"tensorflow::Env::RegisterFileSystem\" is only partially overridden in class \"tensorflow::EnvWrapper\"\n",
            "\n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/absl/types/optional.h(425): warning: expression has no effect\n",
            "          detected during instantiation of \"const T &absl::optional<T>::operator*() const & [with T=stream_executor::dnn::AlgorithmDesc]\" \n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/tensorflow/stream_executor/dnn.h(804): here\n",
            "\n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/absl/types/optional.h(425): warning: expression has no effect\n",
            "          detected during instantiation of \"const T &absl::optional<T>::operator*() const & [with T=size_t]\" \n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/tensorflow/stream_executor/dnn.h(858): here\n",
            "\n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/absl/types/optional.h(425): warning: expression has no effect\n",
            "          detected during instantiation of \"const T &absl::optional<T>::operator*() const & [with T=stream_executor::dnn::AlgorithmDesc]\" \n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/tensorflow/stream_executor/dnn.h(804): here\n",
            "\n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/absl/types/optional.h(425): warning: expression has no effect\n",
            "          detected during instantiation of \"const T &absl::optional<T>::operator*() const & [with T=size_t]\" \n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/tensorflow/stream_executor/dnn.h(858): here\n",
            "\n",
            "\u001b[01m\u001b[K/content/dirt-repo/csrc/rasterise_grad_egl.cpp:\u001b[m\u001b[K In lambda function:\n",
            "\u001b[01m\u001b[K/content/dirt-repo/csrc/rasterise_grad_egl.cpp:49:90:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Ktensorflow::Status tensorflow::shape_inference::InferenceContext::ReplaceDim(tensorflow::shape_inference::ShapeHandle, tensorflow::int64, tensorflow::shape_inference::DimensionHandle, tensorflow::shape_inference::ShapeHandle*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
            "         c->ReplaceDim(c->input(0), 2, c->Dim(c->input(2), 3), &grad_vertex_colours_shape)\u001b[01;35m\u001b[K;\u001b[m\u001b[K  // i.e. vertex-colors has same shape as vertices, but with final dimension replaced by channel count\n",
            "                                                                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/content/dirt-repo/csrc/rasterise_grad_egl.cpp:13:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/tensorflow/core/framework/shape_inference.h:418:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kdeclared here\n",
            "   Status \u001b[01;36m\u001b[KReplaceDim\u001b[m\u001b[K(ShapeHandle s, int64 dim_index, DimensionHandle new_dim,\n",
            "          \u001b[01;36m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/absl/strings/string_view.h(495): warning: expression has no effect\n",
            "\n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/absl/strings/string_view.h(495): warning: expression has no effect\n",
            "\n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/tensorflow/core/platform/env.h(356): warning: overloaded virtual function \"tensorflow::Env::RegisterFileSystem\" is only partially overridden in class \"tensorflow::EnvWrapper\"\n",
            "\n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/absl/types/optional.h(425): warning: expression has no effect\n",
            "          detected during instantiation of \"const T &absl::optional<T>::operator*() const & [with T=stream_executor::dnn::AlgorithmDesc]\" \n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/tensorflow/stream_executor/dnn.h(804): here\n",
            "\n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/absl/types/optional.h(425): warning: expression has no effect\n",
            "          detected during instantiation of \"const T &absl::optional<T>::operator*() const & [with T=size_t]\" \n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/tensorflow/stream_executor/dnn.h(858): here\n",
            "\n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/tensorflow/core/platform/env.h(356): warning: overloaded virtual function \"tensorflow::Env::RegisterFileSystem\" is only partially overridden in class \"tensorflow::EnvWrapper\"\n",
            "\n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/absl/types/optional.h(425): warning: expression has no effect\n",
            "          detected during instantiation of \"const T &absl::optional<T>::operator*() const & [with T=stream_executor::dnn::AlgorithmDesc]\" \n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/tensorflow/stream_executor/dnn.h(804): here\n",
            "\n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/absl/types/optional.h(425): warning: expression has no effect\n",
            "          detected during instantiation of \"const T &absl::optional<T>::operator*() const & [with T=size_t]\" \n",
            "/usr/local/lib/python2.7/dist-packages/tensorflow_core/include/tensorflow/stream_executor/dnn.h(858): here\n",
            "\n",
            "[ 85%] \u001b[32m\u001b[1mLinking CUDA device code CMakeFiles/rasterise.dir/cmake_device_link.o\u001b[0m\n",
            "[100%] \u001b[32m\u001b[1mLinking CXX shared library /content/dirt-repo/dirt/librasterise.so\u001b[0m\n",
            "[100%] Built target rasterise\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M68q5Zw22QUe",
        "outputId": "c3c85535-a21a-4c94-e32d-48a1ebbb0b3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "!cd dirt-repo ; ldd dirt/librasterise.so"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\tlinux-vdso.so.1 (0x00007ffd9abb3000)\n",
            "\t/usr/lib/x86_64-linux-gnu/libtcmalloc.so.4 (0x00007f6f68b23000)\n",
            "\tlibEGL.so.1 => /usr/lib64-nvidia/libEGL.so.1 (0x00007f6f68911000)\n",
            "\tlibOpenGL.so.0 => /usr/lib64-nvidia/libOpenGL.so.0 (0x00007f6f686dc000)\n",
            "\tlibtensorflow_framework.so.2 => /usr/lib/libtensorflow_framework.so.2 (0x00007f6f668cd000)\n",
            "\tlibrt.so.1 => /lib/x86_64-linux-gnu/librt.so.1 (0x00007f6f666c5000)\n",
            "\tlibpthread.so.0 => /lib/x86_64-linux-gnu/libpthread.so.0 (0x00007f6f664a6000)\n",
            "\tlibdl.so.2 => /lib/x86_64-linux-gnu/libdl.so.2 (0x00007f6f662a2000)\n",
            "\tlibstdc++.so.6 => /usr/lib/x86_64-linux-gnu/libstdc++.so.6 (0x00007f6f65f19000)\n",
            "\tlibm.so.6 => /lib/x86_64-linux-gnu/libm.so.6 (0x00007f6f65b7b000)\n",
            "\tlibgcc_s.so.1 => /lib/x86_64-linux-gnu/libgcc_s.so.1 (0x00007f6f65963000)\n",
            "\tlibc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007f6f65572000)\n",
            "\t/lib64/ld-linux-x86-64.so.2 (0x00007f6f69084000)\n",
            "\tlibunwind.so.8 => /usr/lib/x86_64-linux-gnu/libunwind.so.8 (0x00007f6f65357000)\n",
            "\tlibGLdispatch.so.0 => /usr/lib64-nvidia/libGLdispatch.so.0 (0x00007f6f65084000)\n",
            "\tliblzma.so.5 => /lib/x86_64-linux-gnu/liblzma.so.5 (0x00007f6f64e5e000)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-tzejM-2STM",
        "outputId": "78788502-e231-4a00-e76f-cda7b5b6b401",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "_rasterise_module = tf.load_op_library('/content/dirt-repo/dirt/librasterise.so')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mZtGRHOa2UGF",
        "outputId": "bd8b9ca3-0329-49dd-b870-2847dab9a1ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        }
      },
      "source": [
        "!cd dirt-repo ; pip install -e ."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Obtaining file:///content/dirt-repo\n",
            "Requirement already satisfied: tensorflow-gpu>=1.6 in /usr/local/lib/python3.6/dist-packages (from dirt==0.3.0) (2.1.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from dirt==0.3.0) (1.18.5)\n",
            "Requirement already satisfied: tensorboard<2.2.0,>=2.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (2.1.1)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (1.1.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (1.32.0)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (0.10.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (3.3.0)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (1.12.1)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (0.8.1)\n",
            "Requirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (1.1.2)\n",
            "Requirement already satisfied: tensorflow-estimator<2.2.0,>=2.1.0rc0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (2.1.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (1.0.8)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (1.15.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (0.2.0)\n",
            "Requirement already satisfied: scipy==1.4.1; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (1.4.1)\n",
            "Requirement already satisfied: gast==0.2.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (0.2.2)\n",
            "Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (0.35.1)\n",
            "Requirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (3.12.4)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.6->dirt==0.3.0) (1.17.2)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.6->dirt==0.3.0) (3.2.2)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.6->dirt==0.3.0) (0.4.1)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.6->dirt==0.3.0) (2.23.0)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.6->dirt==0.3.0) (50.3.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.6->dirt==0.3.0) (1.0.1)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow-gpu>=1.6->dirt==0.3.0) (2.10.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.6->dirt==0.3.0) (4.6)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.6->dirt==0.3.0) (4.1.1)\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<2.2.0,>=2.1.0->tensorflow-gpu>=1.6->dirt==0.3.0) (0.2.8)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.6->dirt==0.3.0) (2.0.0)\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<2.2.0,>=2.1.0->tensorflow-gpu>=1.6->dirt==0.3.0) (1.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.6->dirt==0.3.0) (2020.6.20)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.6->dirt==0.3.0) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.6->dirt==0.3.0) (3.0.4)\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<2.2.0,>=2.1.0->tensorflow-gpu>=1.6->dirt==0.3.0) (1.24.3)\n",
            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3\"->google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.6->dirt==0.3.0) (0.4.8)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.6->dirt==0.3.0) (3.2.0)\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<2.2.0,>=2.1.0->tensorflow-gpu>=1.6->dirt==0.3.0) (3.1.0)\n",
            "Installing collected packages: dirt\n",
            "  Running setup.py develop for dirt\n",
            "Successfully installed dirt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NTH6XzGZ2a4g",
        "outputId": "3d1049a9-c043-4863-acd2-f85a0bf7e115",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 921
        }
      },
      "source": [
        "!cd dirt-repo ; python tests/square_test.py"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-10-26 12:33:13.403569: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2020-10-26 12:33:13.403676: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2020-10-26 12:33:13.403696: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n",
            "2020-10-26 12:33:14.245673: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2020-10-26 12:33:14.299967: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-10-26 12:33:14.300594: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.73GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2020-10-26 12:33:14.300878: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-10-26 12:33:14.507121: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-10-26 12:33:14.658016: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-10-26 12:33:14.672564: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-10-26 12:33:14.956137: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-10-26 12:33:14.968704: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-10-26 12:33:15.491563: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-10-26 12:33:15.491757: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-10-26 12:33:15.492536: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-10-26 12:33:15.493128: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0\n",
            "2020-10-26 12:33:15.493553: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
            "2020-10-26 12:33:15.498872: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200000000 Hz\n",
            "2020-10-26 12:33:15.499112: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2220840 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-10-26 12:33:15.499145: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-10-26 12:33:15.640781: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-10-26 12:33:15.641582: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2220a00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-10-26 12:33:15.641616: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n",
            "2020-10-26 12:33:15.642766: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-10-26 12:33:15.643429: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.73GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2020-10-26 12:33:15.643501: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-10-26 12:33:15.643526: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-10-26 12:33:15.643552: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-10-26 12:33:15.643576: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-10-26 12:33:15.643598: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-10-26 12:33:15.643621: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-10-26 12:33:15.643644: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-10-26 12:33:15.643728: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-10-26 12:33:15.644394: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-10-26 12:33:15.644929: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0\n",
            "2020-10-26 12:33:15.645038: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-10-26 12:33:15.646343: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-10-26 12:33:15.646380: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102]      0 \n",
            "2020-10-26 12:33:15.646393: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0:   N \n",
            "2020-10-26 12:33:15.646557: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-10-26 12:33:15.647279: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-10-26 12:33:15.647865: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2020-10-26 12:33:15.648007: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14221 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "2020-10-26 12:33:16.288266: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2020-10-26 12:33:16.322758: I /content/dirt-repo/csrc/gl_common.h:66] selected egl device #0 to match cuda device #0 for thread 0x7f8c0ffff700\n",
            "2020-10-26 12:33:16.452575: I /content/dirt-repo/csrc/gl_common.h:84] successfully created new GL context on thread 0x7f8c0ffff700 (EGL = 1.5, GL = 4.6.0 NVIDIA 418.67, renderer = Tesla T4/PCIe/SSE2)\n",
            "2020-10-26 12:33:16.909799: I /content/dirt-repo/csrc/rasterise_egl.cpp:266] reinitialised framebuffer with size 128 x 128\n",
            "successful: all pixels agree\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tVKVcpYA2W3Q"
      },
      "source": [
        "refresh"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0GBdEh4o2eW9",
        "outputId": "ad5e1732-1b07-42bc-8bea-8b5ad354031f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "\n",
        "import dirt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dNgiuLKxTsOc"
      },
      "source": [
        "### **MESH**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v3_KFuRfvHJ8",
        "outputId": "96968a49-a78b-48c0-d716-e7f3f4790e0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        }
      },
      "source": [
        "!sudo apt-get install libboost-dev\n",
        "!sudo apt-get install python3-opengl"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "libboost-dev is already the newest version (1.65.1.0ubuntu1).\n",
            "libboost-dev set to manually installed.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 21 not upgraded.\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "Suggested packages:\n",
            "  libgle3\n",
            "The following NEW packages will be installed:\n",
            "  python3-opengl\n",
            "0 upgraded, 1 newly installed, 0 to remove and 21 not upgraded.\n",
            "Need to get 497 kB of archives.\n",
            "After this operation, 5,416 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 python3-opengl all 3.1.0+dfsg-1 [497 kB]\n",
            "Fetched 497 kB in 1s (856 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package python3-opengl.\n",
            "(Reading database ... 144611 files and directories currently installed.)\n",
            "Preparing to unpack .../python3-opengl_3.1.0+dfsg-1_all.deb ...\n",
            "Unpacking python3-opengl (3.1.0+dfsg-1) ...\n",
            "Setting up python3-opengl (3.1.0+dfsg-1) ...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YFj3RaB-vqXt",
        "outputId": "003c3882-a6bd-4168-d8d4-817bf7af6381",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "!git clone https://github.com/MPI-IS/mesh.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'mesh'...\n",
            "remote: Enumerating objects: 304, done.\u001b[K\n",
            "remote: Total 304 (delta 0), reused 0 (delta 0), pack-reused 304\u001b[K\n",
            "Receiving objects: 100% (304/304), 19.69 MiB | 44.32 MiB/s, done.\n",
            "Resolving deltas: 100% (151/151), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N5EIXAgEwE2V",
        "outputId": "e699780e-1d0e-4594-de13-7c7600c9a38f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!cd mesh ; BOOST_INCLUDE_DIRS=/usr/include/boost make all"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[0;36m----- [ mesh_package ] Installing with the interpreter /usr/local/bin/python (version 3.6.9)\u001b[0m\n",
            "Collecting setuptools\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/6d/38/c21ef5034684ffc0412deefbb07d66678332290c14bb5269c85145fbd55e/setuptools-50.3.2-py3-none-any.whl (785kB)\n",
            "\u001b[K     |████████████████████████████████| 788kB 12.3MB/s \n",
            "\u001b[?25hCollecting numpy\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/63/97/af8a92864a04bfa48f1b5c9b1f8bf2ccb2847f24530026f26dd223de4ca0/numpy-1.19.2-cp36-cp36m-manylinux2010_x86_64.whl (14.5MB)\n",
            "\u001b[K     |████████████████████████████████| 14.5MB 237kB/s \n",
            "\u001b[?25hCollecting matplotlib\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/cd/d6/8c4dfb23151d5a494c66ebbfdb5c8c433b44ec07fae52da5939fcda0943f/matplotlib-3.3.2-cp36-cp36m-manylinux1_x86_64.whl (11.6MB)\n",
            "\u001b[K     |████████████████████████████████| 11.6MB 164kB/s \n",
            "\u001b[?25hCollecting scipy\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8a/14/d0475ceeaa047f3eae2dc7597c3d40228e1d5beee42c1730f3c96ffb5526/scipy-1.5.3-cp36-cp36m-manylinux1_x86_64.whl (25.9MB)\n",
            "\u001b[K     |████████████████████████████████| 25.9MB 106kB/s \n",
            "\u001b[?25hRequirement already up-to-date: pyopengl in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 5)) (3.1.5)\n",
            "Collecting pillow\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5f/19/d4c25111d36163698396f93c363114cf1cddbacb24744f6612f25b6aa3d0/Pillow-8.0.1-cp36-cp36m-manylinux1_x86_64.whl (2.2MB)\n",
            "\u001b[K     |████████████████████████████████| 2.2MB 44.7MB/s \n",
            "\u001b[?25hRequirement already up-to-date: pyzmq in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 7)) (19.0.2)\n",
            "Collecting pyyaml\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/64/c2/b80047c7ac2478f9501676c988a5411ed5572f35d1beff9cae07d321512c/PyYAML-5.3.1.tar.gz (269kB)\n",
            "\u001b[K     |████████████████████████████████| 276kB 53.3MB/s \n",
            "\u001b[?25hCollecting opencv-python\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a2/72/134fdc8d766a6004ee96ce84446f38457a6345fc0ce85721326cbf2adc87/opencv_python-4.4.0.44-cp36-cp36m-manylinux2014_x86_64.whl (49.5MB)\n",
            "\u001b[K     |████████████████████████████████| 49.5MB 60kB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /usr/local/lib/python3.6/dist-packages (from matplotlib->-r requirements.txt (line 3)) (2.4.7)\n",
            "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->-r requirements.txt (line 3)) (0.10.0)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->-r requirements.txt (line 3)) (2.8.1)\n",
            "Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->-r requirements.txt (line 3)) (1.2.0)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2020.06.20 in /usr/local/lib/python3.6/dist-packages (from matplotlib->-r requirements.txt (line 3)) (2020.6.20)\n",
            "Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib->-r requirements.txt (line 3)) (1.15.0)\n",
            "Building wheels for collected packages: pyyaml\n",
            "  Building wheel for pyyaml (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyyaml: filename=PyYAML-5.3.1-cp36-cp36m-linux_x86_64.whl size=44619 sha256=03a5b6f726141285e5be45654fbac931f0b8ab7d7f90ee4c1c6761f96e684e7d\n",
            "  Stored in directory: /root/.cache/pip/wheels/a7/c1/ea/cf5bd31012e735dc1dfea3131a2d5eae7978b251083d6247bd\n",
            "Successfully built pyyaml\n",
            "\u001b[31mERROR: tensorflow 2.3.0 has requirement gast==0.3.3, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 2.3.0 has requirement numpy<1.19.0,>=1.16.0, but you'll have numpy 1.19.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 2.3.0 has requirement scipy==1.4.1, but you'll have scipy 1.5.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 2.3.0 has requirement tensorboard<3,>=2.3.0, but you'll have tensorboard 2.1.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 2.3.0 has requirement tensorflow-estimator<2.4.0,>=2.3.0, but you'll have tensorflow-estimator 2.1.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow-probability 0.11.0 has requirement gast>=0.3.2, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow-gpu 2.1.0 has requirement scipy==1.4.1; python_version >= \"3\", but you'll have scipy 1.5.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Installing collected packages: setuptools, numpy, pillow, matplotlib, scipy, pyyaml, opencv-python\n",
            "  Found existing installation: setuptools 50.3.0\n",
            "    Uninstalling setuptools-50.3.0:\n",
            "      Successfully uninstalled setuptools-50.3.0\n",
            "  Found existing installation: numpy 1.18.5\n",
            "    Uninstalling numpy-1.18.5:\n",
            "      Successfully uninstalled numpy-1.18.5\n",
            "  Found existing installation: Pillow 7.0.0\n",
            "    Uninstalling Pillow-7.0.0:\n",
            "      Successfully uninstalled Pillow-7.0.0\n",
            "  Found existing installation: matplotlib 3.2.2\n",
            "    Uninstalling matplotlib-3.2.2:\n",
            "      Successfully uninstalled matplotlib-3.2.2\n",
            "  Found existing installation: scipy 1.4.1\n",
            "    Uninstalling scipy-1.4.1:\n",
            "      Successfully uninstalled scipy-1.4.1\n",
            "  Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "  Found existing installation: opencv-python 4.1.2.30\n",
            "    Uninstalling opencv-python-4.1.2.30:\n",
            "      Successfully uninstalled opencv-python-4.1.2.30\n",
            "Successfully installed matplotlib-3.3.2 numpy-1.19.2 opencv-python-4.4.0.44 pillow-8.0.1 pyyaml-5.3.1 scipy-1.5.3 setuptools-50.3.2\n",
            "Package                       Version         Location          \n",
            "----------------------------- --------------- ------------------\n",
            "absl-py                       0.10.0          \n",
            "alabaster                     0.7.12          \n",
            "albumentations                0.1.12          \n",
            "altair                        4.1.0           \n",
            "argon2-cffi                   20.1.0          \n",
            "asgiref                       3.2.10          \n",
            "astor                         0.8.1           \n",
            "astropy                       4.0.1.post1     \n",
            "astunparse                    1.6.3           \n",
            "async-generator               1.10            \n",
            "atari-py                      0.2.6           \n",
            "atomicwrites                  1.4.0           \n",
            "attrs                         20.2.0          \n",
            "audioread                     2.1.8           \n",
            "autograd                      1.3             \n",
            "Babel                         2.8.0           \n",
            "backcall                      0.2.0           \n",
            "beautifulsoup4                4.6.3           \n",
            "bleach                        3.2.1           \n",
            "blis                          0.4.1           \n",
            "bokeh                         2.1.1           \n",
            "Bottleneck                    1.3.2           \n",
            "branca                        0.4.1           \n",
            "bs4                           0.0.1           \n",
            "CacheControl                  0.12.6          \n",
            "cachetools                    4.1.1           \n",
            "catalogue                     1.0.0           \n",
            "certifi                       2020.6.20       \n",
            "cffi                          1.14.3          \n",
            "chainer                       7.4.0           \n",
            "chardet                       3.0.4           \n",
            "click                         7.1.2           \n",
            "cloudpickle                   1.3.0           \n",
            "cmake                         3.12.0          \n",
            "cmdstanpy                     0.9.5           \n",
            "colorlover                    0.3.0           \n",
            "community                     1.0.0b1         \n",
            "contextlib2                   0.5.5           \n",
            "convertdate                   2.2.2           \n",
            "coverage                      3.7.1           \n",
            "coveralls                     0.5             \n",
            "crcmod                        1.7             \n",
            "cufflinks                     0.17.3          \n",
            "cupy-cuda101                  7.4.0           \n",
            "cvxopt                        1.2.5           \n",
            "cvxpy                         1.0.31          \n",
            "cycler                        0.10.0          \n",
            "cymem                         2.0.3           \n",
            "Cython                        0.29.21         \n",
            "daft                          0.0.4           \n",
            "dask                          2.12.0          \n",
            "dataclasses                   0.7             \n",
            "datascience                   0.10.6          \n",
            "debugpy                       1.0.0rc2        \n",
            "decorator                     4.4.2           \n",
            "defusedxml                    0.6.0           \n",
            "descartes                     1.1.0           \n",
            "dill                          0.3.2           \n",
            "dirt                          0.3.0           /content/dirt-repo\n",
            "distributed                   1.25.3          \n",
            "Django                        3.1.1           \n",
            "dlib                          19.18.0         \n",
            "dm-tree                       0.1.5           \n",
            "docopt                        0.6.2           \n",
            "docutils                      0.16            \n",
            "dopamine-rl                   1.0.5           \n",
            "earthengine-api               0.1.236         \n",
            "easydict                      1.9             \n",
            "ecos                          2.0.7.post1     \n",
            "editdistance                  0.5.3           \n",
            "en-core-web-sm                2.2.5           \n",
            "entrypoints                   0.3             \n",
            "ephem                         3.7.7.1         \n",
            "et-xmlfile                    1.0.1           \n",
            "fa2                           0.3.5           \n",
            "fancyimpute                   0.4.3           \n",
            "fastai                        1.0.61          \n",
            "fastdtw                       0.3.4           \n",
            "fastprogress                  1.0.0           \n",
            "fastrlock                     0.5             \n",
            "fbprophet                     0.7.1           \n",
            "feather-format                0.4.1           \n",
            "filelock                      3.0.12          \n",
            "firebase-admin                4.4.0           \n",
            "fix-yahoo-finance             0.0.22          \n",
            "Flask                         1.1.2           \n",
            "folium                        0.8.3           \n",
            "future                        0.16.0          \n",
            "gast                          0.2.2           \n",
            "GDAL                          2.2.2           \n",
            "gdown                         3.6.4           \n",
            "gensim                        3.6.0           \n",
            "geographiclib                 1.50            \n",
            "geopy                         1.17.0          \n",
            "gin-config                    0.3.0           \n",
            "glob2                         0.7             \n",
            "google                        2.0.3           \n",
            "google-api-core               1.16.0          \n",
            "google-api-python-client      1.7.12          \n",
            "google-auth                   1.17.2          \n",
            "google-auth-httplib2          0.0.4           \n",
            "google-auth-oauthlib          0.4.1           \n",
            "google-cloud-bigquery         1.21.0          \n",
            "google-cloud-core             1.0.3           \n",
            "google-cloud-datastore        1.8.0           \n",
            "google-cloud-firestore        1.7.0           \n",
            "google-cloud-language         1.2.0           \n",
            "google-cloud-storage          1.18.1          \n",
            "google-cloud-translate        1.5.0           \n",
            "google-colab                  1.0.0           \n",
            "google-pasta                  0.2.0           \n",
            "google-resumable-media        0.4.1           \n",
            "googleapis-common-protos      1.52.0          \n",
            "googledrivedownloader         0.4             \n",
            "graphviz                      0.10.1          \n",
            "grpcio                        1.32.0          \n",
            "gspread                       3.0.1           \n",
            "gspread-dataframe             3.0.8           \n",
            "gym                           0.17.2          \n",
            "h5py                          2.10.0          \n",
            "HeapDict                      1.0.1           \n",
            "holidays                      0.10.3          \n",
            "holoviews                     1.13.4          \n",
            "html5lib                      1.0.1           \n",
            "httpimport                    0.5.18          \n",
            "httplib2                      0.17.4          \n",
            "httplib2shim                  0.0.3           \n",
            "humanize                      0.5.1           \n",
            "hyperopt                      0.1.2           \n",
            "ideep4py                      2.0.0.post3     \n",
            "idna                          2.10            \n",
            "image                         1.5.32          \n",
            "imageio                       2.4.1           \n",
            "imagesize                     1.2.0           \n",
            "imbalanced-learn              0.4.3           \n",
            "imblearn                      0.0             \n",
            "imgaug                        0.2.9           \n",
            "importlib-metadata            2.0.0           \n",
            "imutils                       0.5.3           \n",
            "inflect                       2.1.0           \n",
            "iniconfig                     1.1.1           \n",
            "intel-openmp                  2020.0.133      \n",
            "intervaltree                  2.1.0           \n",
            "ipykernel                     4.10.1          \n",
            "ipython                       5.5.0           \n",
            "ipython-genutils              0.2.0           \n",
            "ipython-sql                   0.3.9           \n",
            "ipywidgets                    7.5.1           \n",
            "itsdangerous                  1.1.0           \n",
            "jax                           0.2.0           \n",
            "jaxlib                        0.1.55          \n",
            "jdcal                         1.4.1           \n",
            "jedi                          0.17.2          \n",
            "jieba                         0.42.1          \n",
            "Jinja2                        2.11.2          \n",
            "joblib                        0.16.0          \n",
            "jpeg4py                       0.1.4           \n",
            "jsonschema                    2.6.0           \n",
            "jupyter                       1.0.0           \n",
            "jupyter-client                5.3.5           \n",
            "jupyter-console               5.2.0           \n",
            "jupyter-core                  4.6.3           \n",
            "jupyterlab-pygments           0.1.2           \n",
            "kaggle                        1.5.8           \n",
            "kapre                         0.1.3.1         \n",
            "Keras                         2.4.3           \n",
            "Keras-Applications            1.0.8           \n",
            "Keras-Preprocessing           1.1.2           \n",
            "keras-vis                     0.4.1           \n",
            "kiwisolver                    1.2.0           \n",
            "knnimpute                     0.1.0           \n",
            "korean-lunar-calendar         0.2.1           \n",
            "librosa                       0.6.3           \n",
            "lightgbm                      2.2.3           \n",
            "llvmlite                      0.31.0          \n",
            "lmdb                          0.99            \n",
            "lucid                         0.3.8           \n",
            "LunarCalendar                 0.0.9           \n",
            "lxml                          4.2.6           \n",
            "Markdown                      3.2.2           \n",
            "MarkupSafe                    1.1.1           \n",
            "matplotlib                    3.3.2           \n",
            "matplotlib-venn               0.11.5          \n",
            "missingno                     0.4.2           \n",
            "mistune                       0.8.4           \n",
            "mizani                        0.6.0           \n",
            "mkl                           2019.0          \n",
            "mlxtend                       0.14.0          \n",
            "more-itertools                8.5.0           \n",
            "moviepy                       0.2.3.5         \n",
            "mpmath                        1.1.0           \n",
            "msgpack                       1.0.0           \n",
            "multiprocess                  0.70.10         \n",
            "multitasking                  0.0.9           \n",
            "murmurhash                    1.0.2           \n",
            "music21                       5.5.0           \n",
            "natsort                       5.5.0           \n",
            "nbclient                      0.5.0           \n",
            "nbconvert                     5.6.1           \n",
            "nbformat                      5.0.7           \n",
            "nest-asyncio                  1.4.1           \n",
            "networkx                      2.5             \n",
            "nibabel                       3.0.2           \n",
            "nltk                          3.2.5           \n",
            "notebook                      5.3.1           \n",
            "np-utils                      0.5.12.1        \n",
            "numba                         0.48.0          \n",
            "numexpr                       2.7.1           \n",
            "numpy                         1.19.2          \n",
            "nvidia-ml-py3                 7.352.0         \n",
            "oauth2client                  4.1.3           \n",
            "oauthlib                      3.1.0           \n",
            "okgrade                       0.4.3           \n",
            "opencv-contrib-python         4.1.2.30        \n",
            "opencv-python                 4.4.0.44        \n",
            "openpyxl                      2.5.9           \n",
            "opt-einsum                    3.3.0           \n",
            "osqp                          0.6.1           \n",
            "packaging                     20.4            \n",
            "palettable                    3.3.0           \n",
            "pandas                        1.1.2           \n",
            "pandas-datareader             0.9.0           \n",
            "pandas-gbq                    0.13.2          \n",
            "pandas-profiling              1.4.1           \n",
            "pandocfilters                 1.4.2           \n",
            "panel                         0.9.7           \n",
            "param                         1.9.3           \n",
            "parso                         0.7.1           \n",
            "pathlib                       1.0.1           \n",
            "patsy                         0.5.1           \n",
            "pexpect                       4.8.0           \n",
            "pickleshare                   0.7.5           \n",
            "Pillow                        8.0.1           \n",
            "pip                           19.3.1          \n",
            "pip-tools                     4.5.1           \n",
            "plac                          1.1.3           \n",
            "plotly                        4.4.1           \n",
            "plotnine                      0.6.0           \n",
            "pluggy                        0.7.1           \n",
            "portpicker                    1.3.1           \n",
            "prefetch-generator            1.0.1           \n",
            "preshed                       3.0.2           \n",
            "prettytable                   0.7.2           \n",
            "progressbar2                  3.38.0          \n",
            "prometheus-client             0.8.0           \n",
            "promise                       2.3             \n",
            "prompt-toolkit                1.0.18          \n",
            "protobuf                      3.12.4          \n",
            "psutil                        5.4.8           \n",
            "psycopg2                      2.7.6.1         \n",
            "ptyprocess                    0.6.0           \n",
            "py                            1.9.0           \n",
            "pyarrow                       0.14.1          \n",
            "pyasn1                        0.4.8           \n",
            "pyasn1-modules                0.2.8           \n",
            "pycocotools                   2.0.2           \n",
            "pycparser                     2.20            \n",
            "pyct                          0.4.8           \n",
            "pydata-google-auth            1.1.0           \n",
            "pydot                         1.3.0           \n",
            "pydot-ng                      2.0.0           \n",
            "pydotplus                     2.0.2           \n",
            "PyDrive                       1.3.1           \n",
            "pyemd                         0.5.1           \n",
            "pyglet                        1.5.0           \n",
            "Pygments                      2.6.1           \n",
            "pygobject                     3.26.1          \n",
            "pymc3                         3.7             \n",
            "PyMeeus                       0.3.7           \n",
            "pymongo                       3.11.0          \n",
            "pymystem3                     0.2.0           \n",
            "PyOpenGL                      3.1.5           \n",
            "pyparsing                     2.4.7           \n",
            "pyrsistent                    0.17.3          \n",
            "pysndfile                     1.3.8           \n",
            "PySocks                       1.7.1           \n",
            "pystan                        2.19.1.1        \n",
            "pytest                        3.6.4           \n",
            "python-apt                    1.6.5+ubuntu0.3 \n",
            "python-chess                  0.23.11         \n",
            "python-dateutil               2.8.1           \n",
            "python-louvain                0.14            \n",
            "python-slugify                4.0.1           \n",
            "python-utils                  2.4.0           \n",
            "pytz                          2018.9          \n",
            "pyviz-comms                   0.7.6           \n",
            "PyWavelets                    1.1.1           \n",
            "PyYAML                        5.3.1           \n",
            "pyzmq                         19.0.2          \n",
            "qtconsole                     4.7.7           \n",
            "QtPy                          1.9.0           \n",
            "regex                         2019.12.20      \n",
            "requests                      2.23.0          \n",
            "requests-oauthlib             1.3.0           \n",
            "resampy                       0.2.2           \n",
            "retrying                      1.3.3           \n",
            "rpy2                          3.2.7           \n",
            "rsa                           4.6             \n",
            "scikit-image                  0.16.2          \n",
            "scikit-learn                  0.22.2.post1    \n",
            "scipy                         1.5.3           \n",
            "screen-resolution-extra       0.0.0           \n",
            "scs                           2.1.2           \n",
            "seaborn                       0.11.0          \n",
            "Send2Trash                    1.5.0           \n",
            "setuptools                    50.3.2          \n",
            "setuptools-git                1.2             \n",
            "Shapely                       1.7.1           \n",
            "simplegeneric                 0.8.1           \n",
            "six                           1.15.0          \n",
            "sklearn                       0.0             \n",
            "sklearn-pandas                1.8.0           \n",
            "slugify                       0.0.1           \n",
            "smart-open                    2.2.0           \n",
            "snowballstemmer               2.0.0           \n",
            "sortedcontainers              2.2.2           \n",
            "spacy                         2.2.4           \n",
            "Sphinx                        1.8.5           \n",
            "sphinxcontrib-serializinghtml 1.1.4           \n",
            "sphinxcontrib-websupport      1.2.4           \n",
            "SQLAlchemy                    1.3.19          \n",
            "sqlparse                      0.3.1           \n",
            "srsly                         1.0.2           \n",
            "statsmodels                   0.10.2          \n",
            "sympy                         1.1.1           \n",
            "tables                        3.4.4           \n",
            "tabulate                      0.8.7           \n",
            "tblib                         1.7.0           \n",
            "tensorboard                   2.1.1           \n",
            "tensorboard-plugin-wit        1.7.0           \n",
            "tensorboardcolab              0.0.22          \n",
            "tensorflow                    2.3.0           \n",
            "tensorflow-addons             0.8.3           \n",
            "tensorflow-datasets           2.1.0           \n",
            "tensorflow-estimator          2.1.0           \n",
            "tensorflow-gcs-config         2.3.0           \n",
            "tensorflow-gpu                2.1.0           \n",
            "tensorflow-hub                0.9.0           \n",
            "tensorflow-metadata           0.24.0          \n",
            "tensorflow-privacy            0.2.2           \n",
            "tensorflow-probability        0.11.0          \n",
            "termcolor                     1.1.0           \n",
            "terminado                     0.9.1           \n",
            "testpath                      0.4.4           \n",
            "text-unidecode                1.3             \n",
            "textblob                      0.15.3          \n",
            "textgenrnn                    1.4.1           \n",
            "Theano                        1.0.5           \n",
            "thinc                         7.4.0           \n",
            "tifffile                      2020.9.3        \n",
            "toml                          0.10.1          \n",
            "toolz                         0.11.1          \n",
            "torch                         1.6.0+cu101     \n",
            "torchsummary                  1.5.1           \n",
            "torchtext                     0.3.1           \n",
            "torchvision                   0.7.0+cu101     \n",
            "tornado                       5.1.1           \n",
            "tqdm                          4.41.1          \n",
            "traitlets                     4.3.3           \n",
            "tweepy                        3.6.0           \n",
            "typeguard                     2.7.1           \n",
            "typing-extensions             3.7.4.3         \n",
            "tzlocal                       1.5.1           \n",
            "umap-learn                    0.4.6           \n",
            "uritemplate                   3.0.1           \n",
            "urllib3                       1.24.3          \n",
            "vega-datasets                 0.8.0           \n",
            "wasabi                        0.8.0           \n",
            "wcwidth                       0.2.5           \n",
            "webencodings                  0.5.1           \n",
            "Werkzeug                      1.0.1           \n",
            "wheel                         0.35.1          \n",
            "widgetsnbextension            3.5.1           \n",
            "wordcloud                     1.5.0           \n",
            "wrapt                         1.12.1          \n",
            "xarray                        0.15.1          \n",
            "xgboost                       0.90            \n",
            "xkit                          0.0.0           \n",
            "xlrd                          1.1.0           \n",
            "xlwt                          1.3.0           \n",
            "yellowbrick                   0.9.1           \n",
            "zict                          2.0.0           \n",
            "zipp                          3.2.0           \n",
            "/usr/local/lib/python3.6/dist-packages/pip/_internal/commands/install.py:283: UserWarning: Disabling all use of wheels due to the use of --build-options / --global-options / --install-options.\n",
            "  cmdoptions.check_install_build_global(options)\n",
            "Created temporary directory: /tmp/pip-ephem-wheel-cache-nrbs7266\n",
            "Created temporary directory: /tmp/pip-req-tracker-lxoyp0pk\n",
            "Created requirements tracker '/tmp/pip-req-tracker-lxoyp0pk'\n",
            "Created temporary directory: /tmp/pip-install-va_tek54\n",
            "Processing /content/mesh\n",
            "  Created temporary directory: /tmp/pip-req-build-y1552oxd\n",
            "  Added file:///content/mesh to build tracker '/tmp/pip-req-tracker-lxoyp0pk'\n",
            "    Running setup.py (path:/tmp/pip-req-build-y1552oxd/setup.py) egg_info for package from file:///content/mesh\n",
            "    Running command python setup.py egg_info\n",
            "    [VERSION] read version is 0.4\n",
            "    running egg_info\n",
            "    creating /tmp/pip-req-build-y1552oxd/pip-egg-info/psbody_mesh.egg-info\n",
            "    writing /tmp/pip-req-build-y1552oxd/pip-egg-info/psbody_mesh.egg-info/PKG-INFO\n",
            "    writing dependency_links to /tmp/pip-req-build-y1552oxd/pip-egg-info/psbody_mesh.egg-info/dependency_links.txt\n",
            "    writing namespace_packages to /tmp/pip-req-build-y1552oxd/pip-egg-info/psbody_mesh.egg-info/namespace_packages.txt\n",
            "    writing requirements to /tmp/pip-req-build-y1552oxd/pip-egg-info/psbody_mesh.egg-info/requires.txt\n",
            "    writing top-level names to /tmp/pip-req-build-y1552oxd/pip-egg-info/psbody_mesh.egg-info/top_level.txt\n",
            "    writing manifest file '/tmp/pip-req-build-y1552oxd/pip-egg-info/psbody_mesh.egg-info/SOURCES.txt'\n",
            "    reading manifest template 'MANIFEST.in'\n",
            "    writing manifest file '/tmp/pip-req-build-y1552oxd/pip-egg-info/psbody_mesh.egg-info/SOURCES.txt'\n",
            "  Source in /tmp/pip-req-build-y1552oxd has version 0.4, which satisfies requirement psbody-mesh==0.4 from file:///content/mesh\n",
            "  Removed psbody-mesh==0.4 from file:///content/mesh from build tracker '/tmp/pip-req-tracker-lxoyp0pk'\n",
            "Skipping wheel build for psbody-mesh, due to binaries being disabled for it.\n",
            "Installing collected packages: psbody-mesh\n",
            "  Created temporary directory: /tmp/pip-record-vcuiii9s\n",
            "    Running command /usr/bin/python3 -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/tmp/pip-req-build-y1552oxd/setup.py'\"'\"'; __file__='\"'\"'/tmp/pip-req-build-y1552oxd/setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record /tmp/pip-record-vcuiii9s/install-record.txt --single-version-externally-managed --compile --boost-location=/usr/include/boost\n",
            "    [VERSION] read version is 0.4\n",
            "    running install\n",
            "    running build\n",
            "    running build_py\n",
            "    creating build\n",
            "    creating build/lib.linux-x86_64-3.6\n",
            "    creating build/lib.linux-x86_64-3.6/psbody\n",
            "    copying psbody-mesh-namespace/__init__.py -> build/lib.linux-x86_64-3.6/psbody\n",
            "    creating build/lib.linux-x86_64-3.6/psbody/mesh\n",
            "    copying mesh/sphere.py -> build/lib.linux-x86_64-3.6/psbody/mesh\n",
            "    copying mesh/texture.py -> build/lib.linux-x86_64-3.6/psbody/mesh\n",
            "    copying mesh/meshviewer.py -> build/lib.linux-x86_64-3.6/psbody/mesh\n",
            "    copying mesh/colors.py -> build/lib.linux-x86_64-3.6/psbody/mesh\n",
            "    copying mesh/landmarks.py -> build/lib.linux-x86_64-3.6/psbody/mesh\n",
            "    copying mesh/utils.py -> build/lib.linux-x86_64-3.6/psbody/mesh\n",
            "    copying mesh/version.py -> build/lib.linux-x86_64-3.6/psbody/mesh\n",
            "    copying mesh/fonts.py -> build/lib.linux-x86_64-3.6/psbody/mesh\n",
            "    copying mesh/mesh.py -> build/lib.linux-x86_64-3.6/psbody/mesh\n",
            "    copying mesh/lines.py -> build/lib.linux-x86_64-3.6/psbody/mesh\n",
            "    copying mesh/search.py -> build/lib.linux-x86_64-3.6/psbody/mesh\n",
            "    copying mesh/__init__.py -> build/lib.linux-x86_64-3.6/psbody/mesh\n",
            "    copying mesh/processing.py -> build/lib.linux-x86_64-3.6/psbody/mesh\n",
            "    copying mesh/errors.py -> build/lib.linux-x86_64-3.6/psbody/mesh\n",
            "    copying mesh/arcball.py -> build/lib.linux-x86_64-3.6/psbody/mesh\n",
            "    creating build/lib.linux-x86_64-3.6/psbody/mesh/topology\n",
            "    copying mesh/topology/decimation.py -> build/lib.linux-x86_64-3.6/psbody/mesh/topology\n",
            "    copying mesh/topology/subdivision.py -> build/lib.linux-x86_64-3.6/psbody/mesh/topology\n",
            "    copying mesh/topology/__init__.py -> build/lib.linux-x86_64-3.6/psbody/mesh/topology\n",
            "    copying mesh/topology/linear_mesh_transform.py -> build/lib.linux-x86_64-3.6/psbody/mesh/topology\n",
            "    copying mesh/topology/connectivity.py -> build/lib.linux-x86_64-3.6/psbody/mesh/topology\n",
            "    creating build/lib.linux-x86_64-3.6/psbody/mesh/geometry\n",
            "    copying mesh/geometry/vert_normals.py -> build/lib.linux-x86_64-3.6/psbody/mesh/geometry\n",
            "    copying mesh/geometry/rodrigues.py -> build/lib.linux-x86_64-3.6/psbody/mesh/geometry\n",
            "    copying mesh/geometry/cross_product.py -> build/lib.linux-x86_64-3.6/psbody/mesh/geometry\n",
            "    copying mesh/geometry/triangle_area.py -> build/lib.linux-x86_64-3.6/psbody/mesh/geometry\n",
            "    copying mesh/geometry/__init__.py -> build/lib.linux-x86_64-3.6/psbody/mesh/geometry\n",
            "    copying mesh/geometry/tri_normals.py -> build/lib.linux-x86_64-3.6/psbody/mesh/geometry\n",
            "    copying mesh/geometry/barycentric_coordinates_of_projection.py -> build/lib.linux-x86_64-3.6/psbody/mesh/geometry\n",
            "    creating build/lib.linux-x86_64-3.6/psbody/mesh/serialization\n",
            "    copying mesh/serialization/serialization.py -> build/lib.linux-x86_64-3.6/psbody/mesh/serialization\n",
            "    copying mesh/serialization/__init__.py -> build/lib.linux-x86_64-3.6/psbody/mesh/serialization\n",
            "    running build_ext\n",
            "    [CGAL] deflating cgal from \"mesh/thirdparty/CGAL-4.7.tar.gz\" to \"/tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6\"\n",
            "    building 'psbody.mesh.aabb_normals' extension\n",
            "    creating build/temp.linux-x86_64-3.6/mesh\n",
            "    creating build/temp.linux-x86_64-3.6/mesh/src\n",
            "    x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DNDEBUG=1 -DCGAL_NDEBUG=1 -DMESH_CGAL_AVOID_COMPILED_VERSION=1 -DCGAL_HAS_NO_THREADS=1 -DCGAL_NO_AUTOLINK_CGAL=1 -Imesh/src -I/usr/local/lib/python3.6/dist-packages/numpy/core/include -I/tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include -I/usr/include/boost -I/usr/include/python3.6m -c mesh/src/aabb_normals.cpp -o build/temp.linux-x86_64-3.6/mesh/src/aabb_normals.o -O3 -fopenmp\n",
            "    In file included from /usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/ndarraytypes.h:1822:0,\n",
            "                     from /usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n",
            "                     from /usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/arrayobject.h:4,\n",
            "                     from mesh/src/aabb_normals.cpp:4:\n",
            "    /usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n",
            "     #warning \"Using deprecated NumPy API, disable it with \" \\\n",
            "      ^~~~~~~\n",
            "    mesh/src/aabb_normals.cpp: In function ‘PyObject* aabbtree_normals_nearest(PyObject*, PyObject*)’:\n",
            "    mesh/src/aabb_normals.cpp:150:36: warning: narrowing conversion of ‘S’ from ‘size_t {aka long unsigned int}’ to ‘npy_intp {aka long int}’ inside { } [-Wnarrowing]\n",
            "         npy_intp result1_dims[] = {1, S};\n",
            "                                        ^\n",
            "    mesh/src/aabb_normals.cpp:157:40: warning: narrowing conversion of ‘S’ from ‘size_t {aka long unsigned int}’ to ‘npy_intp {aka long int}’ inside { } [-Wnarrowing]\n",
            "             npy_intp result2_dims[] = {S, 3};\n",
            "                                            ^\n",
            "    x86_64-linux-gnu-g++ -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.6/mesh/src/aabb_normals.o -o build/lib.linux-x86_64-3.6/psbody/mesh/aabb_normals.cpython-36m-x86_64-linux-gnu.so -O3 -fopenmp\n",
            "    building 'psbody.mesh.spatialsearch' extension\n",
            "    x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DNDEBUG=1 -DCGAL_NDEBUG=1 -DMESH_CGAL_AVOID_COMPILED_VERSION=1 -DCGAL_HAS_NO_THREADS=1 -DCGAL_NO_AUTOLINK_CGAL=1 -Imesh/src -I/usr/local/lib/python3.6/dist-packages/numpy/core/include -I/tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include -I/usr/include/boost -I/usr/include/python3.6m -c mesh/src/spatialsearchmodule.cpp -o build/temp.linux-x86_64-3.6/mesh/src/spatialsearchmodule.o -O3 -fopenmp\n",
            "    In file included from /usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/ndarraytypes.h:1822:0,\n",
            "                     from /usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n",
            "                     from /usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/arrayobject.h:4,\n",
            "                     from mesh/src/spatialsearchmodule.cpp:4:\n",
            "    /usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n",
            "     #warning \"Using deprecated NumPy API, disable it with \" \\\n",
            "      ^~~~~~~\n",
            "    mesh/src/spatialsearchmodule.cpp: In function ‘PyObject* spatialsearch_aabbtree_nearest(PyObject*, PyObject*)’:\n",
            "    mesh/src/spatialsearchmodule.cpp:190:36: warning: narrowing conversion of ‘S’ from ‘size_t {aka long unsigned int}’ to ‘npy_intp {aka long int}’ inside { } [-Wnarrowing]\n",
            "         npy_intp result1_dims[] = {1, S};\n",
            "                                        ^\n",
            "    mesh/src/spatialsearchmodule.cpp:198:36: warning: narrowing conversion of ‘S’ from ‘size_t {aka long unsigned int}’ to ‘npy_intp {aka long int}’ inside { } [-Wnarrowing]\n",
            "         npy_intp result3_dims[] = {S, 3};\n",
            "                                        ^\n",
            "    mesh/src/spatialsearchmodule.cpp: In function ‘PyObject* spatialsearch_aabbtree_nearest_alongnormal(PyObject*, PyObject*)’:\n",
            "    mesh/src/spatialsearchmodule.cpp:249:33: warning: narrowing conversion of ‘S’ from ‘size_t {aka long unsigned int}’ to ‘npy_intp {aka long int}’ inside { } [-Wnarrowing]\n",
            "         npy_intp result1_dims[] = {S};\n",
            "                                     ^\n",
            "    mesh/src/spatialsearchmodule.cpp:258:36: warning: narrowing conversion of ‘S’ from ‘size_t {aka long unsigned int}’ to ‘npy_intp {aka long int}’ inside { } [-Wnarrowing]\n",
            "         npy_intp result3_dims[] = {S, 3};\n",
            "                                        ^\n",
            "    mesh/src/spatialsearchmodule.cpp: In function ‘PyObject* spatialsearch_aabbtree_intersections_indices(PyObject*, PyObject*, PyObject*)’:\n",
            "    mesh/src/spatialsearchmodule.cpp:396:58: warning: narrowing conversion of ‘mesh_intersections.std::vector<unsigned int>::size()’ from ‘std::vector<unsigned int>::size_type {aka long unsigned int}’ to ‘npy_intp {aka long int}’ inside { } [-Wnarrowing]\n",
            "             npy_intp result_dims[] = {mesh_intersections.size()};\n",
            "                                       ~~~~~~~~~~~~~~~~~~~~~~~^~\n",
            "    mesh/src/spatialsearchmodule.cpp: At global scope:\n",
            "    mesh/src/spatialsearchmodule.cpp:316:19: warning: ‘PyObject* spatialsearch_aabbtree_intersections_indices(PyObject*, PyObject*, PyObject*)’ defined but not used [-Wunused-function]\n",
            "     static PyObject * spatialsearch_aabbtree_intersections_indices(PyObject *self, PyObject *args, PyObject *keywds) {\n",
            "                       ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "    x86_64-linux-gnu-g++ -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.6/mesh/src/spatialsearchmodule.o -o build/lib.linux-x86_64-3.6/psbody/mesh/spatialsearch.cpython-36m-x86_64-linux-gnu.so -O3 -fopenmp\n",
            "    building 'psbody.mesh.visibility' extension\n",
            "    x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DNDEBUG=1 -DCGAL_NDEBUG=1 -DMESH_CGAL_AVOID_COMPILED_VERSION=1 -DCGAL_HAS_NO_THREADS=1 -DCGAL_NO_AUTOLINK_CGAL=1 -Imesh/src -I/usr/local/lib/python3.6/dist-packages/numpy/core/include -I/tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include -I/usr/include/boost -I/usr/include/python3.6m -c mesh/src/py_visibility.cpp -o build/temp.linux-x86_64-3.6/mesh/src/py_visibility.o -O3 -fopenmp\n",
            "    In file included from /usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/ndarraytypes.h:1822:0,\n",
            "                     from /usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n",
            "                     from /usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/arrayobject.h:4,\n",
            "                     from mesh/src/py_visibility.cpp:6:\n",
            "    /usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n",
            "     #warning \"Using deprecated NumPy API, disable it with \" \\\n",
            "      ^~~~~~~\n",
            "    mesh/src/py_visibility.cpp: In function ‘PyObject* visibility_compute(PyObject*, PyObject*, PyObject*)’:\n",
            "    mesh/src/py_visibility.cpp:89:72: warning: ISO C++ forbids converting a string constant to ‘char*’ [-Wwrite-strings]\n",
            "                                      \"extra_v\", \"extra_f\", \"min_dist\", NULL};\n",
            "                                                                            ^\n",
            "    mesh/src/py_visibility.cpp:89:72: warning: ISO C++ forbids converting a string constant to ‘char*’ [-Wwrite-strings]\n",
            "    mesh/src/py_visibility.cpp:89:72: warning: ISO C++ forbids converting a string constant to ‘char*’ [-Wwrite-strings]\n",
            "    mesh/src/py_visibility.cpp:89:72: warning: ISO C++ forbids converting a string constant to ‘char*’ [-Wwrite-strings]\n",
            "    mesh/src/py_visibility.cpp:89:72: warning: ISO C++ forbids converting a string constant to ‘char*’ [-Wwrite-strings]\n",
            "    mesh/src/py_visibility.cpp:89:72: warning: ISO C++ forbids converting a string constant to ‘char*’ [-Wwrite-strings]\n",
            "    mesh/src/py_visibility.cpp:89:72: warning: ISO C++ forbids converting a string constant to ‘char*’ [-Wwrite-strings]\n",
            "    mesh/src/py_visibility.cpp:89:72: warning: ISO C++ forbids converting a string constant to ‘char*’ [-Wwrite-strings]\n",
            "    mesh/src/py_visibility.cpp:89:72: warning: ISO C++ forbids converting a string constant to ‘char*’ [-Wwrite-strings]\n",
            "    mesh/src/py_visibility.cpp:120:32: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]\n",
            "                 for(size_t pp=0; pp<nv; ++pp){\n",
            "                                  ~~^~~\n",
            "    mesh/src/py_visibility.cpp:127:32: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]\n",
            "                 for(size_t tt=0; tt<nf; ++tt) {\n",
            "                                  ~~^~~\n",
            "    mesh/src/py_visibility.cpp:141:36: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]\n",
            "                     for(size_t pp=0; pp<nv_extra; ++pp){\n",
            "                                      ~~^~~~~~~~~\n",
            "    mesh/src/py_visibility.cpp:148:36: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]\n",
            "                     for(size_t tt=0; tt<nf_extra; ++tt) {\n",
            "                                      ~~^~~~~~~~~\n",
            "    mesh/src/py_visibility.cpp:174:45: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]\n",
            "                 if (n_dims[1] != 3 || n_dims[0] != search->points.size()) {\n",
            "                                       ~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~\n",
            "    mesh/src/py_visibility.cpp:195:58: warning: narrowing conversion of ‘C’ from ‘size_t {aka long unsigned int}’ to ‘npy_intp {aka long int}’ inside { } [-Wnarrowing]\n",
            "             npy_intp result_dims[] = {C,search->points.size()};\n",
            "                                                              ^\n",
            "    mesh/src/py_visibility.cpp:195:56: warning: narrowing conversion of ‘search->TreeAndTri::points.std::vector<CGAL::Point_3<CGAL::Simple_cartesian<double> > >::size()’ from ‘std::vector<CGAL::Point_3<CGAL::Simple_cartesian<double> > >::size_type {aka long unsigned int}’ to ‘npy_intp {aka long int}’ inside { } [-Wnarrowing]\n",
            "             npy_intp result_dims[] = {C,search->points.size()};\n",
            "                                         ~~~~~~~~~~~~~~~~~~~^~\n",
            "    mesh/src/py_visibility.cpp: In instantiation of ‘npy_intp parse_pyarray(const PyArrayObject*, const boost::array<CTYPE, 3>*&) [with CTYPE = double; int PYTYPE = 12; npy_intp = long int; PyArrayObject = tagPyArrayObject_fields]’:\n",
            "    mesh/src/py_visibility.cpp:115:76:   required from here\n",
            "    mesh/src/py_visibility.cpp:66:16: warning: converting to non-pointer type ‘long int’ from NULL [-Wconversion-null]\n",
            "             return NULL;\n",
            "                    ^~~~\n",
            "    mesh/src/py_visibility.cpp:71:16: warning: converting to non-pointer type ‘long int’ from NULL [-Wconversion-null]\n",
            "             return NULL;\n",
            "                    ^~~~\n",
            "    mesh/src/py_visibility.cpp: In instantiation of ‘npy_intp parse_pyarray(const PyArrayObject*, const boost::array<CTYPE, 3>*&) [with CTYPE = unsigned int; int PYTYPE = 6; npy_intp = long int; PyArrayObject = tagPyArrayObject_fields]’:\n",
            "    mesh/src/py_visibility.cpp:116:78:   required from here\n",
            "    mesh/src/py_visibility.cpp:66:16: warning: converting to non-pointer type ‘long int’ from NULL [-Wconversion-null]\n",
            "             return NULL;\n",
            "                    ^~~~\n",
            "    mesh/src/py_visibility.cpp:71:16: warning: converting to non-pointer type ‘long int’ from NULL [-Wconversion-null]\n",
            "             return NULL;\n",
            "                    ^~~~\n",
            "    mesh/src/py_visibility.cpp:135:42: warning: ‘faces_extra_arr’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n",
            "                     const array<uint32_t,3>* faces_extra_arr;\n",
            "                                              ^~~~~~~~~~~~~~~\n",
            "    mesh/src/py_visibility.cpp:134:40: warning: ‘verts_extra_arr’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n",
            "                     const array<double,3>* verts_extra_arr;\n",
            "                                            ^~~~~~~~~~~~~~~\n",
            "    mesh/src/py_visibility.cpp:113:38: warning: ‘faces_arr’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n",
            "                 const array<uint32_t,3>* faces_arr;\n",
            "                                          ^~~~~~~~~\n",
            "    mesh/src/py_visibility.cpp:112:36: warning: ‘verts_arr’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n",
            "                 const array<double,3>* verts_arr;\n",
            "                                        ^~~~~~~~~\n",
            "    x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DNDEBUG=1 -DCGAL_NDEBUG=1 -DMESH_CGAL_AVOID_COMPILED_VERSION=1 -DCGAL_HAS_NO_THREADS=1 -DCGAL_NO_AUTOLINK_CGAL=1 -Imesh/src -I/usr/local/lib/python3.6/dist-packages/numpy/core/include -I/tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include -I/usr/include/boost -I/usr/include/python3.6m -c mesh/src/visibility.cpp -o build/temp.linux-x86_64-3.6/mesh/src/visibility.o -O3 -fopenmp\n",
            "    In file included from /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Cartesian/Cartesian_base.h:68:0,\n",
            "                     from /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Simple_cartesian.h:28,\n",
            "                     from mesh/src/visibility.cpp:6:\n",
            "    /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Cartesian/function_objects.h: In function ‘void _internal_compute(const TreeAndTri*, const double*, const double*, size_t, bool, const double*, const double&, uint32_t*, double*)’:\n",
            "    /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Cartesian/function_objects.h:940:47: warning: ‘*((void*)& zoff +16)’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n",
            "      return v.x() * w.x() + v.y() * w.y() + v.z() * w.z();\n",
            "    mesh/src/visibility.cpp:77:31: note: ‘*((void*)& zoff +16)’ was declared here\n",
            "             K::Vector_3 xoff,yoff,zoff;\n",
            "                                   ^~~~\n",
            "    In file included from /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Cartesian/Cartesian_base.h:68:0,\n",
            "                     from /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Simple_cartesian.h:28,\n",
            "                     from mesh/src/visibility.cpp:6:\n",
            "    /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Cartesian/function_objects.h:940:31: warning: ‘*((void*)& zoff +8)’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n",
            "      return v.x() * w.x() + v.y() * w.y() + v.z() * w.z();\n",
            "    mesh/src/visibility.cpp:77:31: note: ‘*((void*)& zoff +8)’ was declared here\n",
            "             K::Vector_3 xoff,yoff,zoff;\n",
            "                                   ^~~~\n",
            "    In file included from /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Cartesian/Cartesian_base.h:68:0,\n",
            "                     from /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Simple_cartesian.h:28,\n",
            "                     from mesh/src/visibility.cpp:6:\n",
            "    /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Cartesian/function_objects.h:940:15: warning: ‘zoff’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n",
            "      return v.x() * w.x() + v.y() * w.y() + v.z() * w.z();\n",
            "    mesh/src/visibility.cpp:77:31: note: ‘zoff’ was declared here\n",
            "             K::Vector_3 xoff,yoff,zoff;\n",
            "                                   ^~~~\n",
            "    In file included from /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Cartesian/Cartesian_base.h:68:0,\n",
            "                     from /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Simple_cartesian.h:28,\n",
            "                     from mesh/src/visibility.cpp:6:\n",
            "    /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Cartesian/function_objects.h:940:47: warning: ‘*((void*)& yoff +16)’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n",
            "      return v.x() * w.x() + v.y() * w.y() + v.z() * w.z();\n",
            "    mesh/src/visibility.cpp:77:26: note: ‘*((void*)& yoff +16)’ was declared here\n",
            "             K::Vector_3 xoff,yoff,zoff;\n",
            "                              ^~~~\n",
            "    In file included from /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Cartesian/Cartesian_base.h:68:0,\n",
            "                     from /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Simple_cartesian.h:28,\n",
            "                     from mesh/src/visibility.cpp:6:\n",
            "    /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Cartesian/function_objects.h:940:31: warning: ‘*((void*)& yoff +8)’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n",
            "      return v.x() * w.x() + v.y() * w.y() + v.z() * w.z();\n",
            "    mesh/src/visibility.cpp:77:26: note: ‘*((void*)& yoff +8)’ was declared here\n",
            "             K::Vector_3 xoff,yoff,zoff;\n",
            "                              ^~~~\n",
            "    In file included from /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Cartesian/Cartesian_base.h:68:0,\n",
            "                     from /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Simple_cartesian.h:28,\n",
            "                     from mesh/src/visibility.cpp:6:\n",
            "    /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Cartesian/function_objects.h:940:15: warning: ‘yoff’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n",
            "      return v.x() * w.x() + v.y() * w.y() + v.z() * w.z();\n",
            "    mesh/src/visibility.cpp:77:26: note: ‘yoff’ was declared here\n",
            "             K::Vector_3 xoff,yoff,zoff;\n",
            "                              ^~~~\n",
            "    In file included from /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Cartesian/Cartesian_base.h:68:0,\n",
            "                     from /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Simple_cartesian.h:28,\n",
            "                     from mesh/src/visibility.cpp:6:\n",
            "    /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Cartesian/function_objects.h:940:47: warning: ‘*((void*)& xoff +16)’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n",
            "      return v.x() * w.x() + v.y() * w.y() + v.z() * w.z();\n",
            "    mesh/src/visibility.cpp:77:21: note: ‘*((void*)& xoff +16)’ was declared here\n",
            "             K::Vector_3 xoff,yoff,zoff;\n",
            "                         ^~~~\n",
            "    In file included from /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Cartesian/Cartesian_base.h:68:0,\n",
            "                     from /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Simple_cartesian.h:28,\n",
            "                     from mesh/src/visibility.cpp:6:\n",
            "    /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Cartesian/function_objects.h:940:31: warning: ‘*((void*)& xoff +8)’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n",
            "      return v.x() * w.x() + v.y() * w.y() + v.z() * w.z();\n",
            "    mesh/src/visibility.cpp:77:21: note: ‘*((void*)& xoff +8)’ was declared here\n",
            "             K::Vector_3 xoff,yoff,zoff;\n",
            "                         ^~~~\n",
            "    In file included from /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Cartesian/Cartesian_base.h:68:0,\n",
            "                     from /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Simple_cartesian.h:28,\n",
            "                     from mesh/src/visibility.cpp:6:\n",
            "    /tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include/CGAL/Cartesian/function_objects.h:940:15: warning: ‘xoff’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n",
            "      return v.x() * w.x() + v.y() * w.y() + v.z() * w.z();\n",
            "    mesh/src/visibility.cpp:77:21: note: ‘xoff’ was declared here\n",
            "             K::Vector_3 xoff,yoff,zoff;\n",
            "                         ^~~~\n",
            "    mesh/src/visibility.cpp:100:69: warning: ‘planeoff’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n",
            "                         double t = -(zoff*(verts_v[ivert]-CGAL::ORIGIN) - planeoff)/(zoff*dir);\n",
            "                                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~\n",
            "    mesh/src/visibility.cpp:78:16: note: ‘planeoff’ was declared here\n",
            "             double planeoff;\n",
            "                    ^~~~~~~~\n",
            "    x86_64-linux-gnu-g++ -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.6/mesh/src/py_visibility.o build/temp.linux-x86_64-3.6/mesh/src/visibility.o -o build/lib.linux-x86_64-3.6/psbody/mesh/visibility.cpython-36m-x86_64-linux-gnu.so -O3 -fopenmp\n",
            "    building 'psbody.mesh.serialization.plyutils' extension\n",
            "    x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DNDEBUG=1 -Imesh/src -I/usr/local/lib/python3.6/dist-packages/numpy/core/include -I/tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include -I/usr/include/boost -I/usr/include/python3.6m -c mesh/src/plyutils.c -o build/temp.linux-x86_64-3.6/mesh/src/plyutils.o -O3 -fopenmp\n",
            "    x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DNDEBUG=1 -Imesh/src -I/usr/local/lib/python3.6/dist-packages/numpy/core/include -I/tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include -I/usr/include/boost -I/usr/include/python3.6m -c mesh/src/rply.c -o build/temp.linux-x86_64-3.6/mesh/src/rply.o -O3 -fopenmp\n",
            "    x86_64-linux-gnu-g++ -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.6/mesh/src/plyutils.o build/temp.linux-x86_64-3.6/mesh/src/rply.o -o build/lib.linux-x86_64-3.6/psbody/mesh/serialization/plyutils.cpython-36m-x86_64-linux-gnu.so -O3 -fopenmp\n",
            "    building 'psbody.mesh.serialization.loadobj' extension\n",
            "    x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DNDEBUG=1 -Imesh/src -I/usr/local/lib/python3.6/dist-packages/numpy/core/include -I/tmp/pip-req-build-y1552oxd/build/temp.linux-x86_64-3.6/CGAL-4.7/include -I/usr/include/boost -I/usr/include/python3.6m -c mesh/src/py_loadobj.cpp -o build/temp.linux-x86_64-3.6/mesh/src/py_loadobj.o -O3 -fopenmp\n",
            "    In file included from /usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/ndarraytypes.h:1822:0,\n",
            "                     from /usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n",
            "                     from /usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/arrayobject.h:4,\n",
            "                     from mesh/src/py_loadobj.cpp:5:\n",
            "    /usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n",
            "     #warning \"Using deprecated NumPy API, disable it with \" \\\n",
            "      ^~~~~~~\n",
            "    mesh/src/py_loadobj.cpp: In function ‘PyObject* loadobj(PyObject*, PyObject*, PyObject*)’:\n",
            "    mesh/src/py_loadobj.cpp:150:36: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]\n",
            "                         for (int i=1; i<(localf.size()-1); ++i) {\n",
            "                                       ~^~~~~~~~~~~~~~~~~~\n",
            "    mesh/src/py_loadobj.cpp:159:36: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]\n",
            "                         for (int i=1; i<(localft.size()-1); ++i){\n",
            "                                       ~^~~~~~~~~~~~~~~~~~~\n",
            "    mesh/src/py_loadobj.cpp:166:36: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]\n",
            "                         for (int i=1; i<(localfn.size()-1); ++i){\n",
            "                                       ~^~~~~~~~~~~~~~~~~~~\n",
            "    x86_64-linux-gnu-g++ -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.6/mesh/src/py_loadobj.o -o build/lib.linux-x86_64-3.6/psbody/mesh/serialization/loadobj.cpython-36m-x86_64-linux-gnu.so -O3 -fopenmp\n",
            "    running build_scripts\n",
            "    creating build/scripts-3.6\n",
            "    copying and adjusting bin/meshviewer -> build/scripts-3.6\n",
            "    changing mode of build/scripts-3.6/meshviewer from 644 to 755\n",
            "    running install_lib\n",
            "    Skipping installation of /usr/local/lib/python3.6/dist-packages/psbody/__init__.py (namespace package)\n",
            "    copying psbody/mesh/sphere.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh\n",
            "    copying psbody/mesh/texture.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh\n",
            "    copying psbody/mesh/meshviewer.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh\n",
            "    copying psbody/mesh/colors.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh\n",
            "    copying psbody/mesh/landmarks.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh\n",
            "    copying psbody/mesh/utils.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh\n",
            "    copying psbody/mesh/version.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh\n",
            "    copying psbody/mesh/fonts.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh\n",
            "    copying psbody/mesh/mesh.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh\n",
            "    copying psbody/mesh/lines.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh\n",
            "    copying psbody/mesh/search.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh\n",
            "    copying psbody/mesh/__init__.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh\n",
            "    copying psbody/mesh/processing.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh\n",
            "    copying psbody/mesh/visibility.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/dist-packages/psbody/mesh\n",
            "    copying psbody/mesh/aabb_normals.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/dist-packages/psbody/mesh\n",
            "    copying psbody/mesh/errors.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh\n",
            "    copying psbody/mesh/arcball.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh\n",
            "    copying psbody/mesh/spatialsearch.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/dist-packages/psbody/mesh\n",
            "    copying psbody/mesh/serialization/loadobj.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/dist-packages/psbody/mesh/serialization\n",
            "    copying psbody/mesh/serialization/serialization.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh/serialization\n",
            "    copying psbody/mesh/serialization/__init__.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh/serialization\n",
            "    copying psbody/mesh/serialization/plyutils.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/dist-packages/psbody/mesh/serialization\n",
            "    copying psbody/mesh/topology/decimation.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh/topology\n",
            "    copying psbody/mesh/topology/subdivision.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh/topology\n",
            "    copying psbody/mesh/topology/__init__.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh/topology\n",
            "    copying psbody/mesh/topology/linear_mesh_transform.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh/topology\n",
            "    copying psbody/mesh/topology/connectivity.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh/topology\n",
            "    copying psbody/mesh/geometry/vert_normals.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh/geometry\n",
            "    copying psbody/mesh/geometry/rodrigues.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh/geometry\n",
            "    copying psbody/mesh/geometry/cross_product.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh/geometry\n",
            "    copying psbody/mesh/geometry/triangle_area.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh/geometry\n",
            "    copying psbody/mesh/geometry/__init__.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh/geometry\n",
            "    copying psbody/mesh/geometry/tri_normals.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh/geometry\n",
            "    copying psbody/mesh/geometry/barycentric_coordinates_of_projection.py -> /usr/local/lib/python3.6/dist-packages/psbody/mesh/geometry\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/sphere.py to sphere.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/texture.py to texture.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/meshviewer.py to meshviewer.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/colors.py to colors.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/landmarks.py to landmarks.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/utils.py to utils.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/version.py to version.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/fonts.py to fonts.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/mesh.py to mesh.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/lines.py to lines.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/search.py to search.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/__init__.py to __init__.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/processing.py to processing.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/errors.py to errors.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/arcball.py to arcball.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/serialization/serialization.py to serialization.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/serialization/__init__.py to __init__.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/topology/decimation.py to decimation.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/topology/subdivision.py to subdivision.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/topology/__init__.py to __init__.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/topology/linear_mesh_transform.py to linear_mesh_transform.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/topology/connectivity.py to connectivity.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/geometry/vert_normals.py to vert_normals.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/geometry/rodrigues.py to rodrigues.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/geometry/cross_product.py to cross_product.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/geometry/triangle_area.py to triangle_area.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/geometry/__init__.py to __init__.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/geometry/tri_normals.py to tri_normals.cpython-36.pyc\n",
            "    byte-compiling /usr/local/lib/python3.6/dist-packages/psbody/mesh/geometry/barycentric_coordinates_of_projection.py to barycentric_coordinates_of_projection.cpython-36.pyc\n",
            "    running install_egg_info\n",
            "    running egg_info\n",
            "    creating psbody_mesh.egg-info\n",
            "    writing psbody_mesh.egg-info/PKG-INFO\n",
            "    writing dependency_links to psbody_mesh.egg-info/dependency_links.txt\n",
            "    writing namespace_packages to psbody_mesh.egg-info/namespace_packages.txt\n",
            "    writing requirements to psbody_mesh.egg-info/requires.txt\n",
            "    writing top-level names to psbody_mesh.egg-info/top_level.txt\n",
            "    writing manifest file 'psbody_mesh.egg-info/SOURCES.txt'\n",
            "    reading manifest template 'MANIFEST.in'\n",
            "    writing manifest file 'psbody_mesh.egg-info/SOURCES.txt'\n",
            "    Copying psbody_mesh.egg-info to /usr/local/lib/python3.6/dist-packages/psbody_mesh-0.4-py3.6.egg-info\n",
            "    Installing /usr/local/lib/python3.6/dist-packages/psbody_mesh-0.4-py3.6-nspkg.pth\n",
            "    running install_scripts\n",
            "    copying build/scripts-3.6/meshviewer -> /usr/local/bin\n",
            "    changing mode of /usr/local/bin/meshviewer to 755\n",
            "    writing list of installed files to '/tmp/pip-record-vcuiii9s/install-record.txt'\n",
            "    Running setup.py install for psbody-mesh ... \u001b[?25l\u001b[?25hdone\n",
            "  Removing source in /tmp/pip-req-build-y1552oxd\n",
            "Successfully installed psbody-mesh-0.4\n",
            "Cleaning up...\n",
            "Removed build tracker '/tmp/pip-req-tracker-lxoyp0pk'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HpgXi5OEWqDo",
        "outputId": "0b020ba9-5db7-4567-a114-406637141309",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "import random, string, urllib.request, json, getpass\n",
        "\n",
        "#Generate root password\n",
        "password = ''.join(random.choice(string.ascii_letters + string.digits) for i in range(20))\n",
        "\n",
        "#Download ngrok\n",
        "! wget -q -c -nc https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
        "! unzip -qq -n ngrok-stable-linux-amd64.zip\n",
        "\n",
        "#Setup sshd\n",
        "! apt-get install -qq -o=Dpkg::Use-Pty=0 openssh-server pwgen > /dev/null\n",
        "\n",
        "#Set root password\n",
        "! echo root:$password | chpasswd\n",
        "! mkdir -p /var/run/sshd\n",
        "! echo \"PermitRootLogin yes\" >> /etc/ssh/sshd_config\n",
        "! echo \"PasswordAuthentication yes\" >> /etc/ssh/sshd_config\n",
        "! echo \"LD_LIBRARY_PATH=/usr/lib64-nvidia\" >> /root/.bashrc\n",
        "! echo \"export LD_LIBRARY_PATH\" >> /root/.bashrc\n",
        "\n",
        "#Run sshd\n",
        "get_ipython().system_raw('/usr/sbin/sshd -D &')\n",
        "\n",
        "#Ask token\n",
        "print(\"Copy authtoken from https://dashboard.ngrok.com/auth\")\n",
        "authtoken = getpass.getpass()\n",
        "\n",
        "#Create tunnel\n",
        "get_ipython().system_raw('./ngrok authtoken $authtoken && ./ngrok tcp 22 &')\n",
        "\n",
        "#Get public address and print connect command\n",
        "with urllib.request.urlopen('http://localhost:4040/api/tunnels') as response:\n",
        "  data = json.loads(response.read().decode())\n",
        "  (host, port) = data['tunnels'][0]['public_url'][6:].split(':')\n",
        "  print(f'SSH command: ssh -p{port} root@{host}')\n",
        "\n",
        "#Print root password\n",
        "print(f'Root password: {password}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Copy authtoken from https://dashboard.ngrok.com/auth\n",
            "··········\n",
            "SSH command: ssh -p10312 root@0.tcp.ngrok.io\n",
            "Root password: cagExvfFokYwlY3CJyen\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KManuj2GX-9g"
      },
      "source": [
        "in one terminal: meshviewer open --port 3000\n",
        "\n",
        "in other : ssh -R 3000:127.0.0.1:3000 (user@host)\n",
        "\n",
        "user@host = -p----- ngrok port\n",
        "\n",
        "run on remote: meshviewer view -p 3000 --timeout 1 ./data/unittest/sphere.obj"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OM9ej-8zPjFz"
      },
      "source": [
        "### **MGN-Py3**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "80hdg10LP2QV",
        "outputId": "66390658-e35a-49b1-df77-8963b18b9cd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "!git clone https://github.com/minar09/MGN-Py3.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'MGN-Py3'...\n",
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            "remote: Compressing objects:   1% (1/52)\u001b[K\rremote: Compressing objects:   3% (2/52)\u001b[K\rremote: Compressing objects:   5% (3/52)\u001b[K\rremote: Compressing objects:   7% (4/52)\u001b[K\rremote: Compressing objects:   9% (5/52)\u001b[K\rremote: Compressing objects:  11% (6/52)\u001b[K\rremote: Compressing objects:  13% (7/52)\u001b[K\rremote: Compressing objects:  15% (8/52)\u001b[K\rremote: Compressing objects:  17% (9/52)\u001b[K\rremote: Compressing objects:  19% (10/52)\u001b[K\rremote: Compressing objects:  21% (11/52)\u001b[K\rremote: Compressing objects:  23% (12/52)\u001b[K\rremote: Compressing objects:  25% (13/52)\u001b[K\rremote: Compressing objects:  26% (14/52)\u001b[K\rremote: Compressing objects:  28% (15/52)\u001b[K\rremote: Compressing objects:  30% (16/52)\u001b[K\rremote: Compressing objects:  32% (17/52)\u001b[K\rremote: Compressing objects:  34% (18/52)\u001b[K\rremote: Compressing objects:  36% (19/52)\u001b[K\rremote: Compressing objects:  38% (20/52)\u001b[K\rremote: Compressing objects:  40% (21/52)\u001b[K\rremote: Compressing objects:  42% (22/52)\u001b[K\rremote: Compressing objects:  44% (23/52)\u001b[K\rremote: Compressing objects:  46% (24/52)\u001b[K\rremote: Compressing objects:  48% (25/52)\u001b[K\rremote: Compressing objects:  50% (26/52)\u001b[K\rremote: Compressing objects:  51% (27/52)\u001b[K\rremote: Compressing objects:  53% (28/52)\u001b[K\rremote: Compressing objects:  55% (29/52)\u001b[K\rremote: Compressing objects:  57% (30/52)\u001b[K\rremote: Compressing objects:  59% (31/52)\u001b[K\rremote: Compressing objects:  61% (32/52)\u001b[K\rremote: Compressing objects:  63% (33/52)\u001b[K\rremote: Compressing objects:  65% (34/52)\u001b[K\rremote: Compressing objects:  67% (35/52)\u001b[K\rremote: Compressing objects:  69% (36/52)\u001b[K\rremote: Compressing objects:  71% (37/52)\u001b[K\rremote: Compressing objects:  73% (38/52)\u001b[K\rremote: Compressing objects:  75% (39/52)\u001b[K\rremote: Compressing objects:  76% (40/52)\u001b[K\rremote: Compressing objects:  78% (41/52)\u001b[K\rremote: Compressing objects:  80% (42/52)\u001b[K\rremote: Compressing objects:  82% (43/52)\u001b[K\rremote: Compressing objects:  84% (44/52)\u001b[K\rremote: Compressing objects:  86% (45/52)\u001b[K\rremote: Compressing objects:  88% (46/52)\u001b[K\rremote: Compressing objects:  90% (47/52)\u001b[K\rremote: Compressing objects:  92% (48/52)\u001b[K\rremote: Compressing objects:  94% (49/52)\u001b[K\rremote: Compressing objects:  96% (50/52)\u001b[K\rremote: Compressing objects:  98% (51/52)\u001b[K\rremote: Compressing objects: 100% (52/52)\u001b[K\rremote: Compressing objects: 100% (52/52), done.\u001b[K\n",
            "remote: Total 70 (delta 25), reused 55 (delta 17), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (70/70), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rv0og2ulP_bO",
        "outputId": "4edabfd7-5ba7-4e7c-9801-cdf214f72d44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%cd MGN-Py3/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/MGN-Py3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PLT16IBkPvKt",
        "outputId": "026b97f9-b87e-4378-c12b-f94eaee9aa1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!pip install -r requirements.txt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow-gpu>=1.13.2 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 1)) (2.1.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 2)) (3.3.2)\n",
            "Collecting chumpy\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/01/f7/865755c8bdb837841938de622e6c8b5cb6b1c933bde3bd3332f0cd4574f1/chumpy-0.70.tar.gz (50kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 4.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: dirt in /content/dirt-repo (from -r requirements.txt (line 4)) (0.3.0)\n",
            "Requirement already satisfied: psbody-mesh in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 5)) (0.4)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (0.10.0)\n",
            "Requirement already satisfied: tensorboard<2.2.0,>=2.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (2.1.1)\n",
            "Requirement already satisfied: gast==0.2.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (0.2.2)\n",
            "Requirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (1.1.2)\n",
            "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (1.19.2)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (1.15.0)\n",
            "Requirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (3.12.4)\n",
            "Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (0.35.1)\n",
            "Collecting scipy==1.4.1; python_version >= \"3\"\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/dc/29/162476fd44203116e7980cfbd9352eef9db37c49445d1fec35509022f6aa/scipy-1.4.1-cp36-cp36m-manylinux1_x86_64.whl (26.1MB)\n",
            "\u001b[K     |████████████████████████████████| 26.1MB 107kB/s \n",
            "\u001b[?25hRequirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (0.2.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (1.0.8)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (0.8.1)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (3.3.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (1.1.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (1.32.0)\n",
            "Requirement already satisfied: tensorflow-estimator<2.2.0,>=2.1.0rc0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (2.1.0)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (1.12.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->-r requirements.txt (line 2)) (1.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->-r requirements.txt (line 2)) (2.8.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.6/dist-packages (from matplotlib->-r requirements.txt (line 2)) (8.0.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /usr/local/lib/python3.6/dist-packages (from matplotlib->-r requirements.txt (line 2)) (2.4.7)\n",
            "Requirement already satisfied: certifi>=2020.06.20 in /usr/local/lib/python3.6/dist-packages (from matplotlib->-r requirements.txt (line 2)) (2020.6.20)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->-r requirements.txt (line 2)) (0.10.0)\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.6/dist-packages (from psbody-mesh->-r requirements.txt (line 5)) (4.4.0.44)\n",
            "Requirement already satisfied: pyopengl in /usr/local/lib/python3.6/dist-packages (from psbody-mesh->-r requirements.txt (line 5)) (3.1.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from psbody-mesh->-r requirements.txt (line 5)) (5.3.1)\n",
            "Requirement already satisfied: pyzmq in /usr/local/lib/python3.6/dist-packages (from psbody-mesh->-r requirements.txt (line 5)) (19.0.2)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (2.23.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (1.0.1)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (0.4.1)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (1.17.2)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (50.3.2)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (3.2.2)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (2.10.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (3.0.4)\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<2.2.0,>=2.1.0->tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (1.24.3)\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<2.2.0,>=2.1.0->tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (1.3.0)\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<2.2.0,>=2.1.0->tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (0.2.8)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (4.1.1)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (4.6)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (2.0.0)\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<2.2.0,>=2.1.0->tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (3.1.0)\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<2.2.0,>=2.1.0->tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (0.4.8)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu>=1.13.2->-r requirements.txt (line 1)) (3.2.0)\n",
            "Building wheels for collected packages: chumpy\n",
            "  Building wheel for chumpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for chumpy: filename=chumpy-0.70-cp36-none-any.whl size=58293 sha256=f6db2c28eed5cfcc5c7487ff50d2b4d63df577fc241ab4c24200977c5bb479c3\n",
            "  Stored in directory: /root/.cache/pip/wheels/0a/73/91/b8dbef9746a907c8c008fc194a9691224ca3371b55f16f1dbd\n",
            "Successfully built chumpy\n",
            "\u001b[31mERROR: tensorflow 2.3.0 has requirement gast==0.3.3, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 2.3.0 has requirement numpy<1.19.0,>=1.16.0, but you'll have numpy 1.19.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 2.3.0 has requirement tensorboard<3,>=2.3.0, but you'll have tensorboard 2.1.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 2.3.0 has requirement tensorflow-estimator<2.4.0,>=2.3.0, but you'll have tensorflow-estimator 2.1.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Installing collected packages: scipy, chumpy\n",
            "  Found existing installation: scipy 1.5.3\n",
            "    Uninstalling scipy-1.5.3:\n",
            "      Successfully uninstalled scipy-1.5.3\n",
            "Successfully installed chumpy-0.70 scipy-1.4.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "scipy"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BEzfWxKEO73S"
      },
      "source": [
        "### **SMPLify**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7-FWfIRdO1D9",
        "outputId": "a0a12bf5-32e8-4bfd-830a-5e5806160f66",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!unzip '/content/drive/My Drive/MGN/mpips-smplify_public_v2.zip'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/drive/My Drive/MGN/mpips-smplify_public_v2.zip\n",
            "   creating: smplify_public/\n",
            "   creating: smplify_public/code/\n",
            "  inflating: smplify_public/code/fit_3d.py  \n",
            "   creating: smplify_public/code/lib/\n",
            "   creating: smplify_public/code/models/\n",
            "  inflating: smplify_public/code/render_model.py  \n",
            "  inflating: smplify_public/code/show_humaneva.py  \n",
            "  inflating: smplify_public/code/visualize_mesh_sequence.m  \n",
            "  inflating: smplify_public/code/visualize_mesh_sequence.py  \n",
            "  inflating: smplify_public/code/models/basicModel_neutral_lbs_10_207_0_v1.0.0.pkl  \n",
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            "  inflating: smplify_public/code/models/regressors_locked_normalized_male.npz  \n",
            "  inflating: smplify_public/code/lib/capsule_body.py  \n",
            "  inflating: smplify_public/code/lib/capsule_ch.py  \n",
            " extracting: smplify_public/code/lib/__init__.py  \n",
            "  inflating: smplify_public/code/lib/max_mixture_prior.py  \n",
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            "  inflating: smplify_public/README.md  \n",
            "  inflating: smplify_public/requirements.txt  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kS958VLnPNC-",
        "outputId": "244175a9-6fbf-454c-bfcd-eaa40f2e5b76",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!mkdir assets"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘assets’: File exists\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RpAGPeLfPOpi"
      },
      "source": [
        "!cp smplify_public/code/models/basicModel_neutral_lbs_10_207_0_v1.0.0.pkl assets/neutral_smpl.pkl"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wpQhXmg1PQU7"
      },
      "source": [
        "!rm -r smplify_public/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gepMjFfCQNDz"
      },
      "source": [
        "### **Dataset Part 1**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N2gMTyQcQevx",
        "outputId": "d5f724ba-b0e7-462c-ac1c-de298401db5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!unzip '/content/drive/My Drive/MGN/Multi-Garmentdataset.zip'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/drive/My Drive/MGN/Multi-Garmentdataset.zip\n",
            "   creating: Multi-Garment_dataset/\n",
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          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WWXHjylrTI1z"
      },
      "source": [
        "### **weights file**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hp55rSF-smU-",
        "outputId": "e0ba5b1a-d216-4081-cf31-4612608d247c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SAN6zrrkYel-",
        "outputId": "bc750931-3826-4a6d-dd60-23d7c26252c2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!unzip '/content/drive/My Drive/MGN/weights.zip'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/drive/My Drive/MGN/weights.zip\n",
            "   creating: Multi-Garment Network/\n",
            "   creating: Multi-Garment Network/saved_model/\n",
            "  inflating: Multi-Garment Network/test_data.pkl  \n",
            "   creating: Multi-Garment Network/assets/\n",
            "  inflating: Multi-Garment Network/saved_model/checkpoint  \n",
            "  inflating: Multi-Garment Network/saved_model/ckpt-26.index  \n",
            "  inflating: Multi-Garment Network/saved_model/ckpt-26.data-00000-of-00001  "
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PS-8-62WZBn0"
      },
      "source": [
        "!cp -r '/content/MGN-Py3/Multi-Garment Network/assests/garment_basis_35_temp20' '/content/MGN-Py3/assets/garment_basis_35_temp20'\n",
        "!cp -r '/content/MGN-Py3/Multi-Garment Network/saved_model' '/content/MGN-Py3/assets/'\n",
        "!cp -r '/content/MGN-Py3/Multi-Garment Network/test_data.pkl' '/content/MGN-Py3/assets/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UAD3e71_ayvY"
      },
      "source": [
        "!rm -rf 'Multi-Garment Network'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L4ZBRRUiTMcb"
      },
      "source": [
        "### **visualize scans**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ulw9nPcXQngL"
      },
      "source": [
        "import os\n",
        "from os.path import split, join, exists\n",
        "from glob import glob\n",
        "import pickle as pkl  # Python 3 change\n",
        "from shutil import copyfile\n",
        "from psbody.mesh import Mesh, MeshViewerLocal\n",
        "import numpy as np\n",
        "import cv2\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    path = 'Multi-Garment_dataset/125611508622317'\n",
        "    # path = 'Multi-Garment_dataset/125611511816959'\n",
        "\n",
        "    scan = Mesh(filename=join(path, 'scan.obj'))\n",
        "    seg = np.load(join(path, 'scan_labels.npy'))\n",
        "    tex_file = join(path, 'scan_tex.jpg')\n",
        "\n",
        "    scan.set_texture_image(tex_file)\n",
        "\n",
        "    mv = MeshViewerLocal(shape=[2, 2], port=3000)\n",
        "    mv[0][0].set_static_meshes([scan], blocking=True)\n",
        "    # scan.show()\n",
        "    # input('next?')\n",
        "\n",
        "    # scan2 = Mesh(filename=join(path, 'scan.obj'))\n",
        "    # scan2.set_vertex_colors_from_weights(seg.reshape(-1,))\n",
        "    # scan2.show()\n",
        "    # input('finish?')\n",
        "\n",
        "    print('Done')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cc5uB7lpWK9j"
      },
      "source": [
        "### **dress SMPL**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gEsW14uTZBYg",
        "outputId": "5e5b39c4-0387-41cd-cded-c1c91f32bb51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%cd MGN-Py3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/MGN-Py3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mqOM96ckTdXF",
        "outputId": "519f1b64-6ec8-4041-e25b-edb0f60793cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "from psbody.mesh import Mesh, MeshViewers\n",
        "import numpy as np\n",
        "import pickle as pkl  # Python 3 change\n",
        "from utils.smpl_paths import SmplPaths\n",
        "from lib.ch_smpl import Smpl\n",
        "from utils.interpenetration_ind import remove_interpenetration_fast\n",
        "from os.path import join, split\n",
        "from glob import glob\n",
        "\n",
        "\n",
        "def load_smpl_from_file(file):\n",
        "    dat = pkl.load(open(file))\n",
        "    dp = SmplPaths(gender=dat['gender'])\n",
        "    smpl_h = Smpl(dp.get_hres_smpl_model_data())\n",
        "\n",
        "    smpl_h.pose[:] = dat['pose']\n",
        "    smpl_h.betas[:] = dat['betas']\n",
        "    smpl_h.trans[:] = dat['trans']\n",
        "\n",
        "    return smpl_h\n",
        "\n",
        "\n",
        "def pose_garment(garment, vert_indices, smpl_params):\n",
        "    '''\n",
        "    :param smpl_params: dict with pose, betas, v_template, trans, gender\n",
        "    '''\n",
        "    dp = SmplPaths(gender=smpl_params['gender'])\n",
        "    smpl = Smpl(dp.get_hres_smpl_model_data())\n",
        "    smpl.pose[:] = 0\n",
        "    smpl.betas[:] = smpl_params['betas']\n",
        "    # smpl.v_template[:] = smpl_params['v_template']\n",
        "\n",
        "    offsets = np.zeros_like(smpl.r)\n",
        "    offsets[vert_indices] = garment.v - smpl.r[vert_indices]\n",
        "    smpl.v_personal[:] = offsets\n",
        "    smpl.pose[:] = smpl_params['pose']\n",
        "    smpl.trans[:] = smpl_params['trans']\n",
        "\n",
        "    mesh = Mesh(smpl.r, smpl.f).keep_vertices(vert_indices)\n",
        "    return mesh\n",
        "\n",
        "\n",
        "def retarget(garment_mesh, src, tgt):\n",
        "    '''\n",
        "    For each vertex finds the closest point and\n",
        "    :return:\n",
        "    '''\n",
        "    from psbody.mesh import Mesh\n",
        "    verts, _ = src.closest_vertices(garment_mesh.v)\n",
        "    verts = np.array(verts)\n",
        "    tgt_garment = garment_mesh.v - src.v[verts] + tgt.v[verts]\n",
        "    return Mesh(tgt_garment, garment_mesh.f)\n",
        "\n",
        "\n",
        "def dress(smpl_tgt, body_src, garment, vert_inds, garment_tex=None):\n",
        "    '''\n",
        "    :param smpl: SMPL in the output pose\n",
        "    :param garment: garment mesh in t-pose\n",
        "    :param body_src: garment body in t-pose\n",
        "    :param garment_tex: texture file\n",
        "    :param vert_inds: vertex association b/w smpl and garment\n",
        "    :return:\n",
        "    To use texture files, garments must have vt, ft\n",
        "    '''\n",
        "    tgt_params = {'pose': np.array(smpl_tgt.pose.r), 'trans': np.array(\n",
        "        smpl_tgt.trans.r), 'betas': np.array(smpl_tgt.betas.r), 'gender': 'neutral'}\n",
        "    smpl_tgt.pose[:] = 0\n",
        "    body_tgt = Mesh(smpl_tgt.r, smpl_tgt.f)\n",
        "\n",
        "    # Re-target\n",
        "    ret = retarget(garment, body_src, body_tgt)\n",
        "\n",
        "    # Re-pose\n",
        "    ret_posed = pose_garment(ret, vert_inds, tgt_params)\n",
        "    body_tgt_posed = pose_garment(body_tgt, range(len(body_tgt.v)), tgt_params)\n",
        "\n",
        "    # Remove intersections\n",
        "    ret_posed_interp = remove_interpenetration_fast(ret_posed, body_tgt_posed)\n",
        "    ret_posed_interp.vt = garment.vt\n",
        "    ret_posed_interp.ft = garment.ft\n",
        "    ret_posed_interp.set_texture_image(garment_tex)\n",
        "\n",
        "    return ret_posed_interp\n",
        "\n",
        "\n",
        "path = 'Multi-Garment_dataset/'\n",
        "all_scans = glob(path + '*')\n",
        "garment_classes = ['Pants', 'ShortPants',\n",
        "                   'ShirtNoCoat', 'TShirtNoCoat', 'LongCoat']\n",
        "gar_dict = {}\n",
        "for gar in garment_classes:\n",
        "    gar_dict[gar] = glob(join(path, '*', gar + '.obj'))\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    dp = SmplPaths()\n",
        "    vt, ft = dp.get_vt_ft_hres()\n",
        "    smpl = Smpl(dp.get_hres_smpl_model_data())\n",
        "\n",
        "    # This file contains correspondances between garment vertices and smpl body\n",
        "    # fts_file = 'assets/garment_fts.pkl'\n",
        "    fts_file = 'assets-win/garment_fts_unix.pkl'\n",
        "    vert_indices, fts = pkl.load(open(fts_file))\n",
        "    fts['naked'] = ft\n",
        "\n",
        "    # Choose any garment type as source\n",
        "    # garment_type = 'TShirtNoCoat'\n",
        "    garment_type = 'Pants'\n",
        "    # index = np.random.randint(0, len(gar_dict[garment_type]))   ## Randomly pick from the digital wardrobe\n",
        "    # path = split(gar_dict[garment_type][index])[0]\n",
        "    path = 'Multi-Garment_dataset/125611508622317'\n",
        "\n",
        "    garment_org_body_unposed = load_smpl_from_file(\n",
        "        join(path, 'registration.pkl'))\n",
        "    garment_org_body_unposed.pose[:] = 0\n",
        "    garment_org_body_unposed.trans[:] = 0\n",
        "    garment_org_body_unposed = Mesh(\n",
        "        garment_org_body_unposed.v, garment_org_body_unposed.f)\n",
        "\n",
        "    garment_unposed = Mesh(filename=join(path, garment_type + '.obj'))\n",
        "    garment_tex = join(path, 'multi_tex.jpg')\n",
        "\n",
        "    # Generate random SMPL body (Feel free to set up ur own smpl) as target subject\n",
        "    smpl.pose[:] = np.random.randn(72) * 0.05\n",
        "    smpl.betas[:] = np.random.randn(10) * 0.01\n",
        "    smpl.trans[:] = 0\n",
        "    tgt_body = Mesh(smpl.r, smpl.f)\n",
        "\n",
        "    vert_inds = vert_indices[garment_type]\n",
        "    garment_unposed.set_texture_image(garment_tex)\n",
        "\n",
        "    new_garment = dress(smpl, garment_org_body_unposed,\n",
        "                        garment_unposed, vert_inds, garment_tex)\n",
        "\n",
        "    mvs = MeshViewers((1, 2))\n",
        "    mvs[0][0].set_static_meshes([garment_unposed])\n",
        "    mvs[0][1].set_static_meshes([new_garment, tgt_body])\n",
        "    raw_input(\"Done?\")\n",
        "\n",
        "    print('Done')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "EOFError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mEOFError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-2b1678c94986>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'__main__'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0mdp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSmplPaths\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---> 96\u001b[0;31m     \u001b[0mvt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_vt_ft_hres\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     97\u001b[0m     \u001b[0msmpl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSmpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_hres_smpl_model_data\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[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/MGN-Py3/utils/smpl_paths.py\u001b[0m in \u001b[0;36mget_vt_ft_hres\u001b[0;34m()\u001b[0m\n\u001b[1;32m     82\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_vt_ft_hres\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---> 84\u001b[0;31m         \u001b[0mvt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSmplPaths\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_vt_ft\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     85\u001b[0m         \u001b[0mvt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_hres\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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[0mft\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mvt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mft\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/MGN-Py3/utils/smpl_paths.py\u001b[0m in \u001b[0;36mget_vt_ft\u001b[0;34m()\u001b[0m\n\u001b[1;32m     77\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_vt_ft\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---> 79\u001b[0;31m         \u001b[0mvt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpkl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msmpl_vt_ft_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\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     80\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mvt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mft\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mEOFError\u001b[0m: Ran out of input"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9tZxQQf3WO8p",
        "outputId": "57411c27-62e5-4d71-aefe-0a205df5dd8e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "!git clone https://github.com/bharat-b7/MultiGarmentNetwork.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'MultiGarmentNetwork'...\n",
            "remote: Enumerating objects: 3, done.\u001b[K\n",
            "remote: Counting objects: 100% (3/3), done.\u001b[K\n",
            "remote: Compressing objects: 100% (3/3), done.\u001b[K\n",
            "remote: Total 101 (delta 0), reused 1 (delta 0), pack-reused 98\u001b[K\n",
            "Receiving objects: 100% (101/101), 7.87 MiB | 21.49 MiB/s, done.\n",
            "Resolving deltas: 100% (40/40), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6tUHS0l-XL9H"
      },
      "source": [
        "!cp '/content/MultiGarmentNetwork/assets/smpl_vt_ft.pkl' '/content/MGN-Py3/assets/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lE8rQ0LVs1FS"
      },
      "source": [
        "### **test_data** ###"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g1ml3jwXXbaU"
      },
      "source": [
        "import pickle as pkl\n",
        "\n",
        "test_data = pkl.load( open('/content/Multi-Garment Network/test_data.pkl', 'rb')   ,   encoding='latin1')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OQRdBx6Is8dP",
        "outputId": "9ebadd04-9dfb-4a9d-abeb-87159d5a958c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(test_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "        [ 3.31858280e-01,  1.11472357e-01,  1.39933381e+00],\n",
            "        [-1.50225754e-01,  4.47777173e-01,  1.30229167e+00],\n",
            "        [-2.58250978e-01,  2.29978202e-01,  1.26035974e+00],\n",
            "        [-2.35780520e-01,  7.65197587e-02,  1.18669963e+00],\n",
            "        [-6.82955109e-03, -2.47974089e-02,  7.90543514e-01],\n",
            "        [ 8.60138015e-02, -2.78250900e-02,  1.18562166e+00],\n",
            "        [ 7.28356217e-02, -4.62942487e-01,  8.09138326e-01],\n",
            "        [ 8.30312913e-02, -8.29956099e-01,  1.19193311e+00],\n",
            "        [-9.93141868e-02, -2.64662087e-02,  6.59663156e-01],\n",
            "        [-6.07080180e-02, -4.55387345e-01,  1.13045092e+00],\n",
            "        [-2.35029789e-02, -8.33104674e-01,  7.89894141e-01],\n",
            "        [-1.00000000e+00, -1.00000000e+00,  0.00000000e+00],\n",
            "        [-1.00000000e+00, -1.00000000e+00,  0.00000000e+00],\n",
            "        [ 6.41622229e-02,  5.94834139e-01,  1.02807927e+00],\n",
            "        [-7.50823083e-02,  6.09429543e-01,  9.87535068e-01],\n",
            "        [-9.96432063e-05, -8.31051496e-01,  1.07880458e+00],\n",
            "        [-2.41912988e-02, -8.38008308e-01,  1.28473732e+00],\n",
            "        [-2.05786052e-02, -8.83539931e-01,  1.31046095e+00],\n",
            "        [-4.62587839e-04, -8.28893541e-01,  8.83725955e-01],\n",
            "        [ 3.06393162e-02, -8.32370259e-01,  9.10957223e-01],\n",
            "        [ 9.95586656e-02, -8.73654627e-01,  6.32174091e-01]]]), 'J_2d_5': array([[[-1.00000000e+00, -1.00000000e+00,  0.00000000e+00],\n",
            "        [ 9.18348395e-04,  4.56182693e-01,  1.12380102e+00],\n",
            "        [ 9.18394438e-02,  4.41350459e-01,  1.26461027e+00],\n",
            "        [ 2.44046113e-01,  2.50133545e-01,  9.42153686e-01],\n",
            "        [ 2.67804954e-01,  7.77399896e-02,  1.14476563e+00],\n",
            "        [-7.04898083e-02,  4.79220979e-01,  1.18269507e+00],\n",
            "        [-1.61696199e-01,  2.93250098e-01,  1.06431207e+00],\n",
            "        [-3.45008146e-01,  7.83034001e-02,  9.79892011e-01],\n",
            "        [ 1.66944351e-02, -3.58188548e-02,  9.77350161e-01],\n",
            "        [ 8.87060631e-02, -3.01757458e-02,  6.41277387e-01],\n",
            "        [ 5.29334619e-02, -4.19516580e-01,  8.96496774e-01],\n",
            "        [ 1.24469165e-01, -7.99062856e-01,  1.10460847e+00],\n",
            "        [-6.74971924e-02, -4.63551261e-02,  9.32206492e-01],\n",
            "        [-6.35434040e-03, -4.58294641e-01,  1.17881331e+00],\n",
            "        [ 6.55352811e-02, -8.54300046e-01,  1.01351806e+00],\n",
            "        [-1.00000000e+00, -1.00000000e+00,  0.00000000e+00],\n",
            "        [-1.00000000e+00, -1.00000000e+00,  0.00000000e+00],\n",
            "        [ 1.93606640e-02,  5.78070471e-01,  1.40031059e+00],\n",
            "        [-6.97376483e-02,  6.14175333e-01,  9.29730740e-01],\n",
            "        [-5.89978409e-02, -8.56127662e-01,  1.08461522e+00],\n",
            "        [-5.43161429e-02, -8.68146284e-01,  9.31306016e-01],\n",
            "        [ 9.16061129e-02, -8.87859311e-01,  1.06966440e+00],\n",
            "        [ 1.83438002e-02, -8.19929352e-01,  9.55881423e-01],\n",
            "        [ 5.01977773e-02, -8.16069648e-01,  1.14724052e+00],\n",
            "        [ 1.49363284e-01, -8.37773169e-01,  9.41359613e-01]],\n",
            "\n",
            "       [[-1.00000000e+00, -1.00000000e+00,  0.00000000e+00],\n",
            "        [-1.44025326e-02,  4.44936796e-01,  1.11902162e+00],\n",
            "        [ 9.92458307e-02,  4.37742760e-01,  9.50809544e-01],\n",
            "        [ 1.56383206e-01,  2.51959849e-01,  8.77708183e-01],\n",
            "        [ 9.53881334e-02,  1.39745295e-01,  8.47085405e-01],\n",
            "        [-1.25767596e-01,  4.48634883e-01,  1.28784233e+00],\n",
            "        [-2.88815126e-01,  2.13047064e-01,  9.29314911e-01],\n",
            "        [-3.68329986e-01,  4.65799604e-02,  8.44133279e-01],\n",
            "        [-3.29152737e-03, -2.47310427e-02,  7.88503316e-01],\n",
            "        [ 6.65103088e-02, -2.32914564e-02,  1.01372873e+00],\n",
            "        [ 7.66099527e-02, -4.45576037e-01,  8.48210827e-01],\n",
            "        [ 9.57295572e-02, -7.93604226e-01,  9.04377528e-01],\n",
            "        [-7.99398172e-02, -3.10617556e-02,  1.11805242e+00],\n",
            "        [-3.39615003e-02, -4.57325170e-01,  1.02593490e+00],\n",
            "        [ 2.15118684e-02, -8.10985246e-01,  9.61763931e-01],\n",
            "        [-1.00000000e+00, -1.00000000e+00,  0.00000000e+00],\n",
            "        [-1.00000000e+00, -1.00000000e+00,  0.00000000e+00],\n",
            "        [-1.80973579e-02,  5.93339715e-01,  1.02378894e+00],\n",
            "        [-1.07594971e-01,  6.17770592e-01,  1.10976868e+00],\n",
            "        [-9.14939717e-02, -8.27064722e-01,  8.20178566e-01],\n",
            "        [-8.85646380e-02, -8.38682049e-01,  1.30398008e+00],\n",
            "        [ 4.78794828e-02, -8.52847955e-01,  1.23146790e+00],\n",
            "        [-2.79796474e-02, -8.08365114e-01,  1.01661706e+00],\n",
            "        [ 4.12493791e-03, -8.04978268e-01,  9.83061432e-01],\n",
            "        [ 1.19525149e-01, -8.31760984e-01,  9.10110418e-01]]]), 'J_2d_6': array([[[-1.00000000e+00, -1.00000000e+00,  0.00000000e+00],\n",
            "        [ 1.65562534e-02,  4.50844940e-01,  1.02873492e+00],\n",
            "        [ 3.64696422e-02,  4.17705944e-01,  1.00740649e+00],\n",
            "        [-1.00000000e+00, -1.00000000e+00,  0.00000000e+00],\n",
            "        [-1.00000000e+00, -1.00000000e+00,  0.00000000e+00],\n",
            "        [ 3.40543829e-02,  4.87390593e-01,  1.07672733e+00],\n",
            "        [ 1.26622142e-01,  3.16180504e-01,  1.40327597e+00],\n",
            "        [-6.29738935e-02,  1.20726491e-01,  7.72354810e-01],\n",
            "        [ 9.99132064e-03, -3.62409170e-02,  1.11611285e+00],\n",
            "        [ 1.45028750e-03, -3.49348605e-02,  9.84857080e-01],\n",
            "        [-2.76012940e-02, -4.31512370e-01,  9.55027502e-01],\n",
            "        [ 3.95908636e-02, -8.02333715e-01,  8.48842462e-01],\n",
            "        [ 9.56337726e-03, -4.20066374e-02,  1.06357703e+00],\n",
            "        [ 8.04839206e-03, -4.66735808e-01,  9.79831899e-01],\n",
            "        [ 5.70670002e-02, -8.65633520e-01,  1.07847917e+00],\n",
            "        [-1.00000000e+00, -1.00000000e+00,  0.00000000e+00],\n",
            "        [-1.00000000e+00, -1.00000000e+00,  0.00000000e+00],\n",
            "        [-1.72765810e-02,  5.66870368e-01,  7.41557997e-01],\n",
            "        [-4.53282969e-03,  6.22621000e-01,  1.15198244e+00],\n",
            "        [-1.13914479e-01, -9.10282300e-01,  1.09762748e+00],\n",
            "        [-8.68995134e-02, -9.17412591e-01,  1.19520195e+00],\n",
            "        [ 8.73102609e-02, -8.92058876e-01,  9.91334353e-01],\n",
            "        [-1.26368498e-01, -8.61248780e-01,  8.73921077e-01],\n",
            "        [-1.02961626e-01, -8.49936206e-01,  1.31445547e+00],\n",
            "        [ 6.82875847e-02, -8.33263763e-01,  8.77310306e-01]],\n",
            "\n",
            "       [[-1.00000000e+00, -1.00000000e+00,  0.00000000e+00],\n",
            "        [ 2.60178006e-02,  4.41148218e-01,  1.26237389e+00],\n",
            "        [ 3.08961643e-02,  4.13465679e-01,  9.77349480e-01],\n",
            "        [-1.00000000e+00, -1.00000000e+00,  0.00000000e+00],\n",
            "        [-1.00000000e+00, -1.00000000e+00,  0.00000000e+00],\n",
            "        [ 4.00385679e-02,  4.66146032e-01,  8.65799582e-01],\n",
            "        [-1.03723554e-01,  2.56790794e-01,  9.43852041e-01],\n",
            "        [-3.12955838e-01,  8.39699658e-02,  8.03462575e-01],\n",
            "        [-3.09712845e-03, -2.42941344e-02,  1.17088030e+00],\n",
            "        [-4.47219956e-03, -2.35260500e-02,  1.08890093e+00],\n",
            "        [ 3.17069418e-02, -4.35116664e-01,  1.41913002e+00],\n",
            "        [ 7.18558574e-02, -7.64852635e-01,  6.82630566e-01],\n",
            "        [-4.66508819e-03, -3.02735319e-02,  1.03127675e+00],\n",
            "        [ 2.29605572e-02, -4.57310980e-01,  1.24778677e+00],\n",
            "        [ 6.91436240e-02, -7.84354481e-01,  6.04546982e-01],\n",
            "        [-1.00000000e+00, -1.00000000e+00,  0.00000000e+00],\n",
            "        [-1.00000000e+00, -1.00000000e+00,  0.00000000e+00],\n",
            "        [-3.25208993e-02,  5.88538214e-01,  1.11591695e+00],\n",
            "        [-9.39701524e-04,  6.33241491e-01,  8.30575646e-01],\n",
            "        [-8.38183176e-02, -8.63517208e-01,  8.66354890e-01],\n",
            "        [-5.36487927e-02, -8.67100985e-01,  6.56917513e-01],\n",
            "        [ 9.52324259e-02, -8.12979432e-01,  8.49720206e-01],\n",
            "        [-8.04267728e-02, -8.28840663e-01,  1.08724034e+00],\n",
            "        [-5.83351608e-02, -8.15507658e-01,  7.48666680e-01],\n",
            "        [ 9.95971181e-02, -7.92755564e-01,  7.85091936e-01]]]), 'J_2d_7': array([[[-0.04744605,  0.62717309,  0.68097601],\n",
            "        [ 0.0387153 ,  0.45534029,  0.79099395],\n",
            "        [-0.01799282,  0.43349834,  1.17234862],\n",
            "        [-0.13118681,  0.24159914,  1.00271973],\n",
            "        [-0.27721831,  0.08119225,  0.95321334],\n",
            "        [ 0.13056766,  0.46940191,  0.93539127],\n",
            "        [ 0.3252923 ,  0.25136057,  1.10967149],\n",
            "        [ 0.29335432,  0.05027301,  0.69642053],\n",
            "        [ 0.00696582, -0.03571927,  0.70615275],\n",
            "        [-0.06973545, -0.03140308,  0.87217553],\n",
            "        [-0.05085177, -0.42932966,  0.9837887 ],\n",
            "        [-0.0275485 , -0.78348769,  0.71368656],\n",
            "        [ 0.08422765, -0.04463349,  1.23057331],\n",
            "        [ 0.0460681 , -0.45986855,  1.40498057],\n",
            "        [ 0.04831351, -0.8335371 ,  1.09588988],\n",
            "        [-0.05225004,  0.64289404,  1.08369435],\n",
            "        [-0.01432315,  0.65668998,  1.2990844 ],\n",
            "        [-0.02228824,  0.59068557,  0.67097994],\n",
            "        [ 0.07798179,  0.62482743,  1.20502046],\n",
            "        [-0.08258589, -0.91949674,  1.35798222],\n",
            "        [-0.04548321, -0.91850124,  0.75424388],\n",
            "        [ 0.06748562, -0.8511838 ,  1.04921725],\n",
            "        [-0.16418675, -0.87427531,  1.14226357],\n",
            "        [-0.15585026, -0.85743274,  1.01075555],\n",
            "        [-0.00676509, -0.80731031,  0.77768253]],\n",
            "\n",
            "       [[-0.03959608,  0.6152172 ,  1.02753123],\n",
            "        [ 0.04042347,  0.4301699 ,  1.0895722 ],\n",
            "        [-0.03971848,  0.40767455,  1.15208505],\n",
            "        [-0.21605817,  0.24143352,  0.75514545],\n",
            "        [-0.40982478,  0.15257644,  0.79437634],\n",
            "        [ 0.14441369,  0.44514578,  1.2508866 ],\n",
            "        [ 0.11757936,  0.26190013,  1.1507714 ],\n",
            "        [-0.06247903,  0.1072493 ,  0.76902892],\n",
            "        [-0.00654465, -0.02436899,  0.62029024],\n",
            "        [-0.06621512, -0.02475058,  0.83142488],\n",
            "        [-0.04558864, -0.44022699,  0.75728061],\n",
            "        [-0.02702686, -0.76794711,  0.77236184],\n",
            "        [ 0.05488395, -0.02883238,  1.01115131],\n",
            "        [ 0.02812022, -0.45783175,  0.8499426 ],\n",
            "        [ 0.02205761, -0.79107556,  0.58409743],\n",
            "        [-0.04985385,  0.63458012,  0.83857465],\n",
            "        [-0.00838877,  0.64491292,  1.35466491],\n",
            "        [-0.03316778,  0.59065274,  1.20372014],\n",
            "        [ 0.07945612,  0.61723355,  1.02338991],\n",
            "        [-0.09493133, -0.8943821 ,  1.21312303],\n",
            "        [-0.05949193, -0.89168518,  0.80330891],\n",
            "        [ 0.03478142, -0.81782964,  0.94825208],\n",
            "        [-0.12492117, -0.87206619,  0.61810502],\n",
            "        [-0.12687716, -0.85515711,  0.90149477],\n",
            "        [-0.01333039, -0.79020926,  0.98401829]]]), 'J_0': array([[[ 2.43338440e-03,  5.79947777e-01, -1.84121530e+00],\n",
            "        [ 1.16548585e-02,  4.50820834e-01, -1.97739197e+00],\n",
            "        [-9.88676938e-02,  4.46406655e-01, -2.00464881e+00],\n",
            "        [-3.11200051e-01,  2.60840924e-01, -2.04513503e+00],\n",
            "        [-4.73575871e-01,  7.71391147e-02, -1.97019897e+00],\n",
            "        [ 1.25697124e-01,  4.51540607e-01, -1.98770888e+00],\n",
            "        [ 3.07987445e-01,  2.39463085e-01, -2.06757097e+00],\n",
            "        [ 4.01709456e-01,  4.60933815e-02, -1.93895299e+00],\n",
            "        [ 6.13947320e-03, -3.54371541e-02, -1.97264104e+00],\n",
            "        [-1.04134610e-01, -3.62107618e-02, -1.97154772e+00],\n",
            "        [-6.80099522e-02, -4.40035530e-01, -2.00263435e+00],\n",
            "        [-6.47355132e-02, -8.12714900e-01, -2.05219892e+00],\n",
            "        [ 1.16586126e-01, -3.87886106e-02, -1.96586858e+00],\n",
            "        [ 7.37100449e-02, -4.43431711e-01, -1.98902707e+00],\n",
            "        [ 6.15563130e-02, -8.22677883e-01, -2.03477486e+00],\n",
            "        [-2.52772964e-02,  6.04518740e-01, -1.85959518e+00],\n",
            "        [ 2.63667906e-02,  6.09658985e-01, -1.85733432e+00],\n",
            "        [-6.37718775e-02,  5.84268835e-01, -1.94626204e+00],\n",
            "        [ 7.41104269e-02,  5.97834268e-01, -1.93972232e+00],\n",
            "        [ 7.12000171e-02, -8.66961117e-01, -1.87368962e+00],\n",
            "        [ 9.23012406e-02, -8.64249402e-01, -1.90068861e+00],\n",
            "        [ 5.87002108e-02, -8.47258851e-01, -2.06438126e+00],\n",
            "        [-6.12567307e-02, -8.71171983e-01, -1.88582750e+00],\n",
            "        [-8.44845747e-02, -8.69022413e-01, -1.91178334e+00],\n",
            "        [-6.45879553e-02, -8.43228120e-01, -2.08090561e+00]],\n",
            "\n",
            "       [[ 3.74554908e-02,  5.73811131e-01, -1.85200733e+00],\n",
            "        [-2.91942462e-03,  4.32332596e-01, -1.99146384e+00],\n",
            "        [-1.44847369e-01,  4.34846327e-01, -2.00115307e+00],\n",
            "        [-3.07689961e-01,  2.50950386e-01, -1.90401363e+00],\n",
            "        [-3.78537968e-01,  1.23316814e-01, -1.72317314e+00],\n",
            "        [ 1.38877984e-01,  4.22945449e-01, -2.00233724e+00],\n",
            "        [ 2.49960649e-01,  2.13521039e-01, -1.88631110e+00],\n",
            "        [ 2.50426810e-01,  5.31126660e-02, -1.71562644e+00],\n",
            "        [-1.09533582e-02, -2.44824482e-02, -1.97814225e+00],\n",
            "        [-1.03371731e-01, -2.87473877e-02, -1.98076139e+00],\n",
            "        [-6.73381777e-02, -4.53093525e-01, -2.01351862e+00],\n",
            "        [-3.87668124e-02, -7.95053669e-01, -2.04989076e+00],\n",
            "        [ 8.16653966e-02, -2.52082533e-02, -1.97241112e+00],\n",
            "        [ 4.04496854e-02, -4.43541894e-01, -1.99119035e+00],\n",
            "        [-9.39609367e-04, -7.86307923e-01, -2.03796441e+00],\n",
            "        [ 1.06248622e-02,  6.02768328e-01, -1.85932794e+00],\n",
            "        [ 5.92870740e-02,  6.01423660e-01, -1.87173904e+00],\n",
            "        [-5.55922853e-02,  5.96214870e-01, -1.93078122e+00],\n",
            "        [ 8.14975150e-02,  5.92088238e-01, -1.96249408e+00],\n",
            "        [ 2.85954889e-02, -8.49860908e-01, -1.88484504e+00],\n",
            "        [ 4.44793708e-02, -8.46817281e-01, -1.91365965e+00],\n",
            "        [-1.20524167e-02, -8.20811288e-01, -2.06569095e+00],\n",
            "        [-3.40522941e-02, -8.54437035e-01, -1.89160425e+00],\n",
            "        [-5.72866687e-02, -8.52571578e-01, -1.91580485e+00],\n",
            "        [-3.88868071e-02, -8.26054336e-01, -2.07889081e+00]]]), 'J_1': array([[[ 0.14161865,  0.56040781, -1.87184237],\n",
            "        [ 0.02213193,  0.44772595, -1.95740495],\n",
            "        [-0.06923846,  0.44173568, -1.89089945],\n",
            "        [-0.23706767,  0.25111783, -1.75866427],\n",
            "        [-0.28776082,  0.06168675, -1.59278547],\n",
            "        [ 0.09237222,  0.45273226, -2.05167853],\n",
            "        [ 0.12213323,  0.24069658, -2.245223  ],\n",
            "        [ 0.2871024 ,  0.05514641, -2.2431467 ],\n",
            "        [ 0.00834097, -0.03572653, -1.96776299],\n",
            "        [-0.06043345, -0.03496901, -1.88336514],\n",
            "        [-0.03029206, -0.43970725, -1.91473502],\n",
            "        [-0.05710315, -0.8148477 , -1.92378416],\n",
            "        [ 0.08423094, -0.04107265, -2.04602064],\n",
            "        [ 0.04724968, -0.44228795, -1.97974731],\n",
            "        [-0.02359398, -0.81792853, -1.99618925],\n",
            "        [ 0.11489958,  0.58805971, -1.85688404],\n",
            "        [ 0.14430195,  0.59100452, -1.89956585],\n",
            "        [ 0.01841774,  0.57994084, -1.86976643],\n",
            "        [ 0.09879603,  0.58823459, -1.98282126],\n",
            "        [ 0.09748714, -0.85744536, -1.88970927],\n",
            "        [ 0.09206481, -0.85743593, -1.92341316],\n",
            "        [-0.04808488, -0.84314741, -2.01332488],\n",
            "        [ 0.09364166, -0.86301175, -1.84517082],\n",
            "        [ 0.0596876 , -0.8621162 , -1.83807948],\n",
            "        [-0.08111548, -0.84687029, -1.93510986]],\n",
            "\n",
            "       [[ 0.06338801,  0.58346517, -1.95224028],\n",
            "        [-0.05162402,  0.43161458, -2.01743335],\n",
            "        [-0.15789982,  0.42982358, -1.92342441],\n",
            "        [-0.19042539,  0.25402837, -1.72978555],\n",
            "        [-0.10134081,  0.14283004, -1.54645565],\n",
            "        [ 0.04197827,  0.42523222, -2.12487066],\n",
            "        [ 0.21518839,  0.22566535, -2.1149009 ],\n",
            "        [ 0.34626588,  0.07644433, -1.99115742],\n",
            "        [-0.01410633, -0.02443921, -1.97426014],\n",
            "        [-0.08064235, -0.02654642, -1.91003452],\n",
            "        [-0.06725116, -0.45379759, -1.93227955],\n",
            "        [-0.07310154, -0.79772249, -1.96113266],\n",
            "        [ 0.05486167, -0.02718465, -2.03627391],\n",
            "        [ 0.01660768, -0.44504671, -1.99960632],\n",
            "        [-0.04271154, -0.78710801, -1.9843504 ],\n",
            "        [ 0.0371967 ,  0.61088712, -1.93883571],\n",
            "        [ 0.06288741,  0.61066108, -1.98200797],\n",
            "        [-0.05925017,  0.59738339, -1.94247688],\n",
            "        [ 0.0154753 ,  0.59641645, -2.0617624 ],\n",
            "        [ 0.0854641 , -0.83672855, -1.88783401],\n",
            "        [ 0.07724023, -0.83626998, -1.92006063],\n",
            "        [-0.06835662, -0.8237818 , -1.99495802],\n",
            "        [ 0.03903162, -0.85091486, -1.84692239],\n",
            "        [ 0.00548318, -0.84998013, -1.84880199],\n",
            "        [-0.09292493, -0.82988516, -1.98129208]]]), 'J_2': array([[[ 1.66667557e-01,  5.67417147e-01, -1.99505277e+00],\n",
            "        [ 2.55138333e-02,  4.49664586e-01, -1.96778157e+00],\n",
            "        [ 1.43500155e-02,  4.43056276e-01, -1.85579434e+00],\n",
            "        [ 4.63772918e-03,  2.52634563e-01, -1.64354104e+00],\n",
            "        [ 9.88396970e-02,  6.41429242e-02, -1.49691165e+00],\n",
            "        [ 2.66738057e-04,  4.55226339e-01, -2.08083988e+00],\n",
            "        [-1.29620004e-01,  2.46709931e-01, -2.23232522e+00],\n",
            "        [-1.62492292e-02,  6.15633374e-02, -2.35220364e+00],\n",
            "        [ 1.17080248e-02, -3.56033436e-02, -1.96581619e+00],\n",
            "        [ 2.61741175e-02, -3.26677785e-02, -1.85727420e+00],\n",
            "        [ 2.23176058e-02, -4.38328808e-01, -1.89515256e+00],\n",
            "        [-1.14072645e-03, -8.13224300e-01, -1.88368171e+00],\n",
            "        [ 5.99880343e-03, -4.31994159e-02, -2.07526555e+00],\n",
            "        [ 3.21298876e-02, -4.43629750e-01, -1.99974847e+00],\n",
            "        [-1.64424862e-02, -8.19757203e-01, -1.95328557e+00],\n",
            "        [ 1.59627893e-01,  5.94520594e-01, -1.96470025e+00],\n",
            "        [ 1.47730069e-01,  5.98236324e-01, -2.01513429e+00],\n",
            "        [ 8.54270478e-02,  5.84431390e-01, -1.90229654e+00],\n",
            "        [ 5.56895186e-02,  5.94761762e-01, -2.03752811e+00],\n",
            "        [ 1.43329676e-01, -8.59898369e-01, -1.97635891e+00],\n",
            "        [ 1.14325352e-01, -8.59717662e-01, -1.99424434e+00],\n",
            "        [-4.54054952e-02, -8.44935786e-01, -1.94599660e+00],\n",
            "        [ 1.63265527e-01, -8.55339389e-01, -1.93144742e+00],\n",
            "        [ 1.43545572e-01, -8.55480209e-01, -1.90266738e+00],\n",
            "        [-2.57782448e-02, -8.46264965e-01, -1.87531613e+00]],\n",
            "\n",
            "       [[ 7.31753125e-02,  5.82354681e-01, -1.97570019e+00],\n",
            "        [-5.88115761e-02,  4.32652399e-01, -1.95417257e+00],\n",
            "        [-7.17968867e-02,  4.21966315e-01, -1.81365283e+00],\n",
            "        [ 3.30854608e-02,  2.31239397e-01, -1.66598306e+00],\n",
            "        [ 2.20902419e-01,  1.09510752e-01, -1.60465585e+00],\n",
            "        [-6.44566346e-02,  4.37245088e-01, -2.09620058e+00],\n",
            "        [ 6.38400455e-02,  2.44501092e-01, -2.22380569e+00],\n",
            "        [ 2.41295768e-01,  9.26425262e-02, -2.23625936e+00],\n",
            "        [-1.34741906e-02, -2.42693909e-02, -1.97106926e+00],\n",
            "        [-1.38933109e-02, -2.96195536e-02, -1.87899013e+00],\n",
            "        [-3.71414886e-03, -4.55399325e-01, -1.91787831e+00],\n",
            "        [-4.77311294e-02, -7.96505839e-01, -1.94591265e+00],\n",
            "        [-9.07682145e-03, -2.38304449e-02, -2.06341564e+00],\n",
            "        [-1.96591912e-02, -4.43248936e-01, -2.03038392e+00],\n",
            "        [-6.55715461e-02, -7.86312891e-01, -1.99700779e+00],\n",
            "        [ 6.46412124e-02,  6.08252329e-01, -1.94615427e+00],\n",
            "        [ 5.33057168e-02,  6.11627985e-01, -1.99496523e+00],\n",
            "        [-7.86025888e-03,  5.93189023e-01, -1.88299740e+00],\n",
            "        [-3.63511410e-02,  6.02413962e-01, -2.02043525e+00],\n",
            "        [ 9.28749108e-02, -8.39262431e-01, -2.01491003e+00],\n",
            "        [ 6.48173984e-02, -8.36825162e-01, -2.03295589e+00],\n",
            "        [-9.25085535e-02, -8.22817880e-01, -1.98971490e+00],\n",
            "        [ 1.10589339e-01, -8.48945094e-01, -1.96095875e+00],\n",
            "        [ 8.80135163e-02, -8.48685350e-01, -1.93621828e+00],\n",
            "        [-7.69518129e-02, -8.28626807e-01, -1.94461038e+00]]]), 'J_3': array([[[ 1.23287627e-01,  5.85496260e-01, -2.05847889e+00],\n",
            "        [ 2.96342039e-02,  4.51160158e-01, -1.96565317e+00],\n",
            "        [ 9.23582905e-02,  4.42359280e-01, -1.87478461e+00],\n",
            "        [ 2.11905673e-01,  2.48228368e-01, -1.70716822e+00],\n",
            "        [ 3.69657001e-01,  5.25011069e-02, -1.65449461e+00],\n",
            "        [-6.14724836e-02,  4.59494355e-01, -2.02916090e+00],\n",
            "        [-2.60145018e-01,  2.61036851e-01, -2.08831205e+00],\n",
            "        [-2.37571974e-01,  7.69601087e-02, -2.25440563e+00],\n",
            "        [ 1.43243648e-02, -3.56227950e-02, -1.96880384e+00],\n",
            "        [ 9.79584268e-02, -3.51892241e-02, -1.89986558e+00],\n",
            "        [ 5.92193357e-02, -4.39736105e-01, -1.94768946e+00],\n",
            "        [ 2.32481261e-02, -8.13515622e-01, -1.92822084e+00],\n",
            "        [-6.29359649e-02, -4.06753003e-02, -2.04523586e+00],\n",
            "        [-3.49249036e-02, -4.45749318e-01, -2.01006967e+00],\n",
            "        [-7.61530954e-02, -8.24258486e-01, -1.97235401e+00],\n",
            "        [ 1.34697249e-01,  6.09539277e-01, -2.02693702e+00],\n",
            "        [ 9.49401034e-02,  6.15571865e-01, -2.05976675e+00],\n",
            "        [ 1.12992198e-01,  5.87858569e-01, -1.93530883e+00],\n",
            "        [ 7.82492026e-03,  6.04162989e-01, -2.02357363e+00],\n",
            "        [ 4.61544366e-02, -8.63242753e-01, -2.07977558e+00],\n",
            "        [ 1.16721197e-02, -8.61234796e-01, -2.07838799e+00],\n",
            "        [-9.72008832e-02, -8.49999297e-01, -1.95306033e+00],\n",
            "        [ 8.82438479e-02, -8.54852894e-01, -2.08558744e+00],\n",
            "        [ 9.81698740e-02, -8.55990687e-01, -2.05239833e+00],\n",
            "        [ 1.10408872e-02, -8.46768310e-01, -1.90508609e+00]],\n",
            "\n",
            "       [[ 4.57828998e-02,  5.82281860e-01, -2.02495872e+00],\n",
            "        [-3.44340587e-02,  4.31585604e-01, -1.92098440e+00],\n",
            "        [ 5.21437452e-02,  4.19159745e-01, -1.81027493e+00],\n",
            "        [ 2.33864292e-01,  2.33373444e-01, -1.77048768e+00],\n",
            "        [ 4.16837250e-01,  1.18717310e-01, -1.85482336e+00],\n",
            "        [-1.35394724e-01,  4.38410678e-01, -2.02086094e+00],\n",
            "        [-1.27308105e-01,  2.49746867e-01, -2.20613037e+00],\n",
            "        [ 9.39296433e-04,  1.03111285e-01, -2.33502026e+00],\n",
            "        [-1.09941994e-02, -2.44832061e-02, -1.96924565e+00],\n",
            "        [ 5.19561921e-02, -2.89362084e-02, -1.90191404e+00],\n",
            "        [ 3.53150260e-02, -4.55376999e-01, -1.92483968e+00],\n",
            "        [ 1.26736204e-03, -7.98705822e-01, -1.91101627e+00],\n",
            "        [-7.11321232e-02, -2.48605091e-02, -2.03934509e+00],\n",
            "        [-5.42091776e-02, -4.43259095e-01, -1.99811308e+00],\n",
            "        [-5.74085564e-02, -7.85141118e-01, -1.93495109e+00],\n",
            "        [ 6.05759200e-02,  6.07557829e-01, -1.99738756e+00],\n",
            "        [ 1.84357686e-02,  6.11842353e-01, -2.02437319e+00],\n",
            "        [ 5.25734648e-02,  5.90930472e-01, -1.90173359e+00],\n",
            "        [-6.34247074e-02,  6.02738620e-01, -1.98026843e+00],\n",
            "        [ 5.68402493e-02, -8.38602181e-01, -2.04644288e+00],\n",
            "        [ 2.34333598e-02, -8.36673749e-01, -2.04298442e+00],\n",
            "        [-7.26603664e-02, -8.21440099e-01, -1.91177969e+00],\n",
            "        [ 9.92242857e-02, -8.51875293e-01, -2.03713219e+00],\n",
            "        [ 1.01803193e-01, -8.51005363e-01, -2.00365196e+00],\n",
            "        [-1.63481785e-02, -8.30671368e-01, -1.88844768e+00]]]), 'J_4': array([[[ 1.41186063e-02,  5.88840862e-01, -2.09238004e+00],\n",
            "        [ 1.65329999e-02,  4.51717835e-01, -1.96401641e+00],\n",
            "        [ 1.25164933e-01,  4.45440173e-01, -1.94086140e+00],\n",
            "        [ 3.32939064e-01,  2.55440269e-01, -1.90353059e+00],\n",
            "        [ 4.88655700e-01,  6.46017235e-02, -1.97553938e+00],\n",
            "        [-9.48293584e-02,  4.56349616e-01, -1.94811073e+00],\n",
            "        [-2.72072041e-01,  2.49608682e-01, -1.85727034e+00],\n",
            "        [-3.67762263e-01,  6.44517186e-02, -1.99333742e+00],\n",
            "        [ 1.48409145e-02, -3.56265163e-02, -1.97222078e+00],\n",
            "        [ 1.24827777e-01, -3.53605517e-02, -1.97713097e+00],\n",
            "        [ 8.68289881e-02, -4.39370698e-01, -1.99026870e+00],\n",
            "        [ 7.43005523e-02, -8.05880196e-01, -1.90815403e+00],\n",
            "        [-9.52775548e-02, -4.01713784e-02, -1.97561968e+00],\n",
            "        [-3.23983690e-02, -4.44250253e-01, -1.97299140e+00],\n",
            "        [-9.18136595e-03, -8.20858965e-01, -1.91271037e+00],\n",
            "        [ 4.29969490e-02,  6.13620320e-01, -2.07624668e+00],\n",
            "        [-8.66050272e-03,  6.17671739e-01, -2.07356942e+00],\n",
            "        [ 8.97868671e-02,  5.91771419e-01, -1.99449493e+00],\n",
            "        [-4.78260822e-02,  6.02963380e-01, -1.98831748e+00],\n",
            "        [-1.34702669e-02, -8.63236945e-01, -2.07440634e+00],\n",
            "        [-3.47394994e-02, -8.60969537e-01, -2.04751751e+00],\n",
            "        [-7.05621085e-03, -8.45878035e-01, -1.88305447e+00],\n",
            "        [ 7.16481762e-02, -8.67737335e-01, -2.07026645e+00],\n",
            "        [ 9.51221549e-02, -8.64740835e-01, -2.04556465e+00],\n",
            "        [ 7.39775730e-02, -8.35616644e-01, -1.87682648e+00]],\n",
            "\n",
            "       [[-3.01813075e-02,  5.77769059e-01, -2.07288915e+00],\n",
            "        [-4.54267776e-03,  4.32709322e-01, -1.93677724e+00],\n",
            "        [ 1.35867954e-01,  4.26676698e-01, -1.92159712e+00],\n",
            "        [ 2.96223646e-01,  2.42406347e-01, -2.01830917e+00],\n",
            "        [ 3.64352007e-01,  1.23072173e-01, -2.20505751e+00],\n",
            "        [-1.46598558e-01,  4.33564046e-01, -1.93383113e+00],\n",
            "        [-2.67874346e-01,  2.37601501e-01, -2.06343000e+00],\n",
            "        [-2.65759075e-01,  8.58629458e-02, -2.24109068e+00],\n",
            "        [-8.09346820e-03, -2.44542494e-02, -1.96958746e+00],\n",
            "        [ 8.33257266e-02, -2.74359632e-02, -1.96929543e+00],\n",
            "        [ 6.99506418e-02, -4.53877996e-01, -1.95812010e+00],\n",
            "        [ 7.82787758e-02, -7.96867527e-01, -1.91760099e+00],\n",
            "        [-9.94372393e-02, -2.61698248e-02, -1.97486002e+00],\n",
            "        [-6.03271277e-02, -4.43023125e-01, -1.94299944e+00],\n",
            "        [-2.33663987e-02, -7.83134985e-01, -1.87743233e+00],\n",
            "        [-1.50807643e-03,  6.04771480e-01, -2.06507847e+00],\n",
            "        [-5.03452702e-02,  6.06901612e-01, -2.05360227e+00],\n",
            "        [ 6.25355202e-02,  5.93403654e-01, -1.99242310e+00],\n",
            "        [-7.50842348e-02,  5.99207349e-01, -1.96372589e+00],\n",
            "        [-1.51258245e-03, -8.45651724e-01, -2.03231415e+00],\n",
            "        [-2.56945146e-02, -8.42922747e-01, -2.00893871e+00],\n",
            "        [-2.03044957e-02, -8.17787802e-01, -1.84859462e+00],\n",
            "        [-1.90246507e-03, -8.52903585e-01, -2.05507853e+00],\n",
            "        [ 2.98861737e-02, -8.51642672e-01, -2.04346920e+00],\n",
            "        [ 9.29382097e-02, -8.28245413e-01, -1.89341797e+00]]]), 'J_5': array([[[-1.04866872e-01,  5.81839161e-01, -2.05684619e+00],\n",
            "        [-4.63995800e-04,  4.50462433e-01, -1.97218209e+00],\n",
            "        [ 9.19742976e-02,  4.49408814e-01, -2.03369228e+00],\n",
            "        [ 2.63681480e-01,  2.72183064e-01, -2.17328358e+00],\n",
            "        [ 3.12798849e-01,  9.14012161e-02, -2.34819810e+00],\n",
            "        [-6.76523014e-02,  4.51669702e-01, -1.88240199e+00],\n",
            "        [-1.38107210e-01,  2.48681240e-01, -1.69368287e+00],\n",
            "        [-3.01807684e-01,  6.83186860e-02, -1.74255350e+00],\n",
            "        [ 1.51195483e-02, -3.54327157e-02, -1.97569536e+00],\n",
            "        [ 8.98131791e-02, -3.10814427e-02, -2.05717100e+00],\n",
            "        [ 5.31686371e-02, -4.33335700e-01, -2.06301591e+00],\n",
            "        [ 1.21975128e-01, -7.92987827e-01, -1.98204182e+00],\n",
            "        [-6.54456560e-02, -4.41011456e-02, -1.90011261e+00],\n",
            "        [-7.52195680e-03, -4.45816616e-01, -1.94284748e+00],\n",
            "        [ 6.15452074e-02, -8.20795927e-01, -1.91889849e+00],\n",
            "        [-7.54479256e-02,  6.08823119e-01, -2.06747211e+00],\n",
            "        [-1.07832772e-01,  6.10126381e-01, -2.02697612e+00],\n",
            "        [ 1.84057694e-02,  5.92852352e-01, -2.04829733e+00],\n",
            "        [-6.90468667e-02,  5.97045512e-01, -1.94152195e+00],\n",
            "        [-6.10680887e-02, -8.66990033e-01, -2.02256651e+00],\n",
            "        [-5.53938546e-02, -8.64495685e-01, -1.98882768e+00],\n",
            "        [ 8.57404753e-02, -8.44973987e-01, -1.90075623e+00],\n",
            "        [ 1.78107946e-02, -8.62515614e-01, -2.10095993e+00],\n",
            "        [ 5.12085422e-02, -8.57380261e-01, -2.09832856e+00],\n",
            "        [ 1.44822214e-01, -8.21065521e-01, -1.95739559e+00]],\n",
            "\n",
            "       [[-1.40691242e-01,  5.69211993e-01, -2.00468750e+00],\n",
            "        [-1.52394709e-02,  4.29981476e-01, -1.93009489e+00],\n",
            "        [ 9.85712704e-02,  4.41550566e-01, -2.01459945e+00],\n",
            "        [ 1.71150166e-01,  2.78609324e-01, -2.20847020e+00],\n",
            "        [ 1.13004593e-01,  1.68233192e-01, -2.40437236e+00],\n",
            "        [-1.16452067e-01,  4.11438082e-01, -1.83163395e+00],\n",
            "        [-2.70384665e-01,  1.98772799e-01, -1.86341092e+00],\n",
            "        [-3.71552485e-01,  4.68759865e-02, -2.00991894e+00],\n",
            "        [-4.61390985e-03, -2.44134867e-02, -1.97158098e+00],\n",
            "        [ 6.61205499e-02, -2.36816523e-02, -2.03068514e+00],\n",
            "        [ 7.58221947e-02, -4.49760660e-01, -2.01598305e+00],\n",
            "        [ 9.42837370e-02, -7.94226859e-01, -1.99879307e+00],\n",
            "        [-7.78971741e-02, -2.97924759e-02, -1.91561326e+00],\n",
            "        [-3.44584591e-02, -4.46405478e-01, -1.94953775e+00],\n",
            "        [ 1.96118309e-02, -7.91482954e-01, -1.94919752e+00],\n",
            "        [-1.16409479e-01,  5.99238514e-01, -2.01561350e+00],\n",
            "        [-1.45326898e-01,  5.95803829e-01, -1.97471250e+00],\n",
            "        [-1.95286217e-02,  5.95455857e-01, -2.00434921e+00],\n",
            "        [-1.03074284e-01,  5.85083876e-01, -1.89155135e+00],\n",
            "        [-9.55282933e-02, -8.51802072e-01, -2.05696277e+00],\n",
            "        [-9.10389041e-02, -8.49980440e-01, -2.02413195e+00],\n",
            "        [ 4.49852870e-02, -8.26379953e-01, -1.93524252e+00],\n",
            "        [-3.08216164e-02, -8.49539213e-01, -2.09895489e+00],\n",
            "        [ 2.87433145e-03, -8.46837131e-01, -2.10108186e+00],\n",
            "        [ 1.17143486e-01, -8.25916660e-01, -1.98319272e+00]]]), 'J_6': array([[[-1.12898779e-01,  5.86813428e-01, -1.99097052e+00],\n",
            "        [ 1.51212254e-02,  4.50097728e-01, -1.99391600e+00],\n",
            "        [ 3.68713851e-02,  4.39636717e-01, -2.10208628e+00],\n",
            "        [ 5.71503642e-02,  2.45041075e-01, -2.30707611e+00],\n",
            "        [-3.51079285e-02,  5.52375624e-02, -2.45260400e+00],\n",
            "        [ 3.07600920e-02,  4.59598180e-01, -1.88333855e+00],\n",
            "        [ 1.05568263e-01,  2.66901833e-01, -1.68594618e+00],\n",
            "        [-5.13694440e-02,  9.64884639e-02, -1.59624684e+00],\n",
            "        [ 8.49538087e-03, -3.58396332e-02, -1.97511149e+00],\n",
            "        [ 6.39853714e-05, -3.64572738e-02, -2.08426249e+00],\n",
            "        [-2.93708722e-02, -4.37786028e-01, -2.02626332e+00],\n",
            "        [ 3.83285359e-02, -8.06109834e-01, -2.00662590e+00],\n",
            "        [ 7.62443808e-03, -3.92344835e-02, -1.86542271e+00],\n",
            "        [ 6.33564635e-03, -4.44654794e-01, -1.90273845e+00],\n",
            "        [ 5.29404343e-02, -8.24213699e-01, -1.90166056e+00],\n",
            "        [-9.87279529e-02,  6.10907986e-01, -2.02135975e+00],\n",
            "        [-9.30592710e-02,  6.16407620e-01, -1.97008063e+00],\n",
            "        [-1.93401711e-02,  5.88176811e-01, -2.07229405e+00],\n",
            "        [-5.73101562e-03,  6.03407008e-01, -1.93559201e+00],\n",
            "        [-1.09418070e-01, -8.65019527e-01, -1.89791628e+00],\n",
            "        [-8.28268760e-02, -8.61856732e-01, -1.87627987e+00],\n",
            "        [ 8.17122593e-02, -8.49536989e-01, -1.90202408e+00],\n",
            "        [-1.26352449e-01, -8.52960703e-01, -1.97800616e+00],\n",
            "        [-1.04596153e-01, -8.53120157e-01, -2.00470794e+00],\n",
            "        [ 6.72089951e-02, -8.38291390e-01, -2.00927669e+00]],\n",
            "\n",
            "       [[-1.12479615e-01,  5.76270455e-01, -1.92497149e+00],\n",
            "        [ 2.41501720e-02,  4.33173913e-01, -1.96112376e+00],\n",
            "        [ 3.10292488e-02,  4.35395941e-01, -2.10315920e+00],\n",
            "        [-7.15704743e-02,  2.61566289e-01, -2.27214365e+00],\n",
            "        [-2.59064772e-01,  1.45785824e-01, -2.34513559e+00],\n",
            "        [ 3.51643987e-02,  4.24699799e-01, -1.81964765e+00],\n",
            "        [-8.98108899e-02,  2.19713670e-01, -1.70885364e+00],\n",
            "        [-2.70523905e-01,  7.23646063e-02, -1.72119257e+00],\n",
            "        [-4.43133695e-03, -2.40313413e-02, -1.97562184e+00],\n",
            "        [-6.06139144e-03, -2.43638450e-02, -2.06835009e+00],\n",
            "        [ 3.12949718e-02, -4.49760938e-01, -2.06444470e+00],\n",
            "        [ 7.29265305e-02, -7.92648222e-01, -2.06980752e+00],\n",
            "        [-5.69953219e-03, -2.85406780e-02, -1.88290510e+00],\n",
            "        [ 2.09629570e-02, -4.45023759e-01, -1.94356382e+00],\n",
            "        [ 6.77493086e-02, -7.85380939e-01, -1.99983982e+00],\n",
            "        [-1.05380439e-01,  6.03987411e-01, -1.95321083e+00],\n",
            "        [-9.29257096e-02,  6.04989520e-01, -1.90457904e+00],\n",
            "        [-3.42327291e-02,  5.94968362e-01, -2.01904706e+00],\n",
            "        [-2.19136163e-03,  5.96750872e-01, -1.88213572e+00],\n",
            "        [-8.35622859e-02, -8.48023211e-01, -1.96139010e+00],\n",
            "        [-5.35941198e-02, -8.45530834e-01, -1.94754278e+00],\n",
            "        [ 9.45103377e-02, -8.19893161e-01, -2.01421090e+00],\n",
            "        [-8.40208977e-02, -8.52363174e-01, -2.05390743e+00],\n",
            "        [-6.20960005e-02, -8.49073296e-01, -2.07943034e+00],\n",
            "        [ 1.01875492e-01, -8.23500553e-01, -2.07468341e+00]]]), 'J_7': array([[[-0.04622867,  0.59452609, -1.89326207],\n",
            "        [ 0.03697106,  0.45163188, -1.98096018],\n",
            "        [-0.02011823,  0.45059674, -2.07600236],\n",
            "        [-0.14946314,  0.27275223, -2.25475921],\n",
            "        [-0.32259358,  0.09414141, -2.3157591 ],\n",
            "        [ 0.12340287,  0.44903661, -1.91057518],\n",
            "        [ 0.29845758,  0.23193538, -1.84288011],\n",
            "        [ 0.24673868,  0.0425445 , -1.69019103],\n",
            "        [ 0.00550872, -0.03533139, -1.97553827],\n",
            "        [-0.0729963 , -0.03227436, -2.0526391 ],\n",
            "        [-0.05328491, -0.43851982, -2.03997842],\n",
            "        [-0.02996173, -0.81234852, -2.07079663],\n",
            "        [ 0.07839481, -0.04229776, -1.89270856],\n",
            "        [ 0.04327894, -0.44607471, -1.937319  ],\n",
            "        [ 0.04641163, -0.82556968, -1.97813549],\n",
            "        [-0.05164177,  0.61981656, -1.92553321],\n",
            "        [-0.01483962,  0.6210885 , -1.88894943],\n",
            "        [-0.02385421,  0.59592982, -2.01495799],\n",
            "        [ 0.07341038,  0.59969682, -1.91689754],\n",
            "        [-0.07861242, -0.86197608, -1.87228629],\n",
            "        [-0.04388487, -0.86115814, -1.87253698],\n",
            "        [ 0.06603894, -0.85162071, -1.99825128],\n",
            "        [-0.16293936, -0.86155009, -1.96815617],\n",
            "        [-0.15740264, -0.85951635, -2.00207951],\n",
            "        [-0.00851562, -0.844287  , -2.08870373]],\n",
            "\n",
            "       [[-0.03878416,  0.58298986, -1.89260405],\n",
            "        [ 0.03902992,  0.4307159 , -1.99976116],\n",
            "        [-0.04347893,  0.43179299, -2.115384  ],\n",
            "        [-0.23616323,  0.26257845, -2.17214463],\n",
            "        [-0.43191557,  0.16048024, -2.10068683],\n",
            "        [ 0.13557197,  0.42253588, -1.89578276],\n",
            "        [ 0.10007077,  0.22587867, -1.7225298 ],\n",
            "        [-0.0514766 ,  0.08656142, -1.61197071],\n",
            "        [-0.00784426, -0.02412823, -1.97749359],\n",
            "        [-0.06918799, -0.02536567, -2.04686062],\n",
            "        [-0.04816421, -0.45197418, -2.0505209 ],\n",
            "        [-0.02942362, -0.79628986, -2.07093807],\n",
            "        [ 0.050978  , -0.02751391, -1.90589535],\n",
            "        [ 0.02599518, -0.44584196, -1.94492235],\n",
            "        [ 0.02062596, -0.79053548, -1.99586257],\n",
            "        [-0.04920145,  0.61014745, -1.92032873],\n",
            "        [-0.00923956,  0.61026769, -1.88993356],\n",
            "        [-0.03482712,  0.59610212, -2.01565257],\n",
            "        [ 0.07526032,  0.59586728, -1.92808979],\n",
            "        [-0.09053396, -0.84182131, -1.87985371],\n",
            "        [-0.05735723, -0.84124403, -1.88424634],\n",
            "        [ 0.03372459, -0.82710939, -2.01988826],\n",
            "        [-0.12248922, -0.84686102, -1.93950066],\n",
            "        [-0.12654339, -0.84484377, -1.97313923],\n",
            "        [-0.01541439, -0.82867568, -2.09444864]]])}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dbxjApT6KMkd",
        "outputId": "b5ddf14f-6541-4617-eb80-c142e1669988",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "fig = plt.figure(figsize=(5,20))\n",
        "for i in range(1,17,2):\n",
        "  num = int((i-1)/2)\n",
        "  ax = fig.add_subplot(8, 2, i)\n",
        "  imgplot = plt.imshow(test_data['rendered'][0,:,:,:,num])\n",
        "\n",
        "  ax = fig.add_subplot(8, 2, i+1)\n",
        "  imgplot = plt.imshow(test_data['rendered'][1,:,:,:,num])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1440 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Chf4uLSQtsS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}