shlomo_dl_0009_tensor_rt.ipynb

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  "metadata": {
    "colab": {
      "name": "shlomo_dl_0009_tensor-rt.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "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/deeponcology/PyTorchMedicalAI/blob/master/shlomo_dl_0009_tensor_rt.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "bTPWD8_Bde6e",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Deep Learning in Medical AI 2018/2019 using PyTorch & Google Collab.\n",
        "\n",
        "\n",
        "<table align=\"left\"><td>\n",
        "<a target=\"_blank\" href=\"https://colab.research.google.com/drive/1hGVoskPSerJtUydCoKeUskQG8X4lt3k9?authuser=1#scrollTo=bTPWD8_Bde6e\">\n",
        "    <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>  \n",
        "</td><td>\n",
        "<a target=\"_blank\" href=\"https://github.com/deeponcology/PyTorchMedicalAI/blob/master/shlomo_dl_0009_tensor_rt.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a></td></table>\n",
        "\n",
        "<img src=\"https://github.com/deeponcology/PyTorchMedicalAI/raw/master/assets/tumor_visdom.jpg\" align=\"center\" width=30%>\n",
        "\n",
        "### Author: \n",
        "***Shlomo Kashani***, Head of AI at www.DeepOncology.AI, shlomo@deeponcology.ai \n",
        "\n",
        "\n",
        "### Synopsys:\n",
        "Using TensorRT to extract VGG features using the paper:https://github.com/alexander-rakhlin/ICIAR2018 \n",
        "\n",
        "<img src=\"https://github.com/alexander-rakhlin/ICIAR2018/raw/master/pics/nn_diagram.png\" align=\"center\" width=50%>\n",
        "\n",
        "### DataSets:\n",
        "https://github.com/alexander-rakhlin/ICIAR2018\n"
      ]
    },
    {
      "metadata": {
        "id": "9b43TUE3SWoF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Collab notebook: 009 using TensorRT with Keras and TF"
      ]
    },
    {
      "metadata": {
        "id": "d3aNRnqJafbb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "%reset -f"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WFU8FOhYuVdK",
        "colab_type": "code",
        "outputId": "646ac342-5384-402c-8e4d-0d714c8469c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 6089
        }
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      "cell_type": "code",
      "source": [
        "! wget https://gist.githubusercontent.com/ashokpant/5c4e9481615f54af4025ab2085f85869/raw/e09f10a30ce1b40018f4440df9de788f5d6fdad0/cuda_9.0_cudnn_7.0.sh\n",
        "! chmod +x cuda_9.0_cudnn_7.0.sh\n",
        "! sudo bash cuda_9.0_cudnn_7.0.sh"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
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            "Unpacking cuda-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-license-9-0 (9.0.176-1) ...\n",
            "*** LICENSE AGREEMENT ***\n",
            "By using this software you agree to fully comply with the terms and \n",
            "conditions of the EULA (End User License Agreement). The EULA is located\n",
            "at /usr/local/cuda-9.0/doc/EULA.txt. The EULA can also be found at\n",
            "http://docs.nvidia.com/cuda/eula/index.html. If you do not agree to the\n",
            "terms and conditions of the EULA, do not use the software.\n",
            "\n",
            "Setting up cuda-cusparse-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-cudart-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-nvrtc-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-cusparse-dev-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-cufft-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-cusolver-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-nvml-dev-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-npp-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-cusolver-dev-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-misc-headers-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-cublas-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-nvrtc-dev-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-driver-dev-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-curand-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-nvgraph-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-core-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-libraries-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-runtime-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-cudart-dev-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-cufft-dev-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-npp-dev-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-curand-dev-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-cublas-dev-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-nvgraph-dev-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-command-line-tools-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-demo-suite-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-visual-tools-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-samples-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-libraries-dev-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-documentation-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-toolkit-9-0 (9.0.176-1) ...\n",
            "Setting up cuda-9-0 (9.0.176-1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1) ...\n",
            "--2019-02-02 20:40:38--  https://developer.nvidia.com/compute/cuda/9.0/Prod/patches/1/cuda-repo-ubuntu1604-9-0-local-cublas-performance-update_1.0-1_amd64-deb\n",
            "Resolving developer.nvidia.com (developer.nvidia.com)... 192.229.162.216\n",
            "Connecting to developer.nvidia.com (developer.nvidia.com)|192.229.162.216|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://developer.download.nvidia.com/compute/cuda/9.0/secure/Prod/patches/1/cuda-repo-ubuntu1604-9-0-local-cublas-performance-update_1.0-1_amd64.deb?h4f-zf_bYCDMTfIVSjssaEV5nR3XTItgEREgYwPzWJdUcyWxWt7S8e13B5WjGSwE2BWPvqOzDqIFmrGPEoMbZ2oynRAdtjETpZTiEGbcscsucHxV76ZzSbMLet2r_oe-mIsJlkUGPpNnDt4n8ciwJtMIJ_q7Z-5V1GVfzajJ0UwqjJGkqyIL8rhwJd2D_gnOmPZYrnsfFamsDyj3LwtRXcAlII0ENRhcR9OJYxF0 [following]\n",
            "--2019-02-02 20:40:39--  https://developer.download.nvidia.com/compute/cuda/9.0/secure/Prod/patches/1/cuda-repo-ubuntu1604-9-0-local-cublas-performance-update_1.0-1_amd64.deb?h4f-zf_bYCDMTfIVSjssaEV5nR3XTItgEREgYwPzWJdUcyWxWt7S8e13B5WjGSwE2BWPvqOzDqIFmrGPEoMbZ2oynRAdtjETpZTiEGbcscsucHxV76ZzSbMLet2r_oe-mIsJlkUGPpNnDt4n8ciwJtMIJ_q7Z-5V1GVfzajJ0UwqjJGkqyIL8rhwJd2D_gnOmPZYrnsfFamsDyj3LwtRXcAlII0ENRhcR9OJYxF0\n",
            "Resolving developer.download.nvidia.com (developer.download.nvidia.com)... 192.229.211.70, 2606:2800:21f:3aa:dcf:37b:1ed6:1fb\n",
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            "Length: 100157250 (96M) [application/x-deb]\n",
            "Saving to: ‘cuda-repo-ubuntu1604-9-0-local-cublas-performance-update_1.0-1_amd64-deb’\n",
            "\n",
            "cuda-repo-ubuntu160 100%[===================>]  95.52M  97.8MB/s    in 1.0s    \n",
            "\n",
            "2019-02-02 20:40:40 (97.8 MB/s) - ‘cuda-repo-ubuntu1604-9-0-local-cublas-performance-update_1.0-1_amd64-deb’ saved [100157250/100157250]\n",
            "\n",
            "Selecting previously unselected package cuda-repo-ubuntu1604-9-0-local-cublas-performance-update.\n",
            "(Reading database ... 120168 files and directories currently installed.)\n",
            "Preparing to unpack cuda-repo-ubuntu1604-9-0-local-cublas-performance-update_1.0-1_amd64-deb ...\n",
            "Unpacking cuda-repo-ubuntu1604-9-0-local-cublas-performance-update (1.0-1) ...\n",
            "Setting up cuda-repo-ubuntu1604-9-0-local-cublas-performance-update (1.0-1) ...\n",
            "Get:1 file:/var/cuda-repo-9-0-local-cublas-performance-update  InRelease\n",
            "Ign:1 file:/var/cuda-repo-9-0-local-cublas-performance-update  InRelease\n",
            "Get:2 file:/var/cuda-repo-9-0-local  InRelease\n",
            "Ign:2 file:/var/cuda-repo-9-0-local  InRelease\n",
            "Get:3 file:/var/cuda-repo-9-0-local-cublas-performance-update  Release [574 B]\n",
            "Get:4 file:/var/cuda-repo-9-0-local  Release [574 B]\n",
            "Get:3 file:/var/cuda-repo-9-0-local-cublas-performance-update  Release [574 B]\n",
            "Get:4 file:/var/cuda-repo-9-0-local  Release [574 B]\n",
            "Get:5 file:/var/cuda-repo-9-0-local-cublas-performance-update  Release.gpg [801 B]\n",
            "Get:5 file:/var/cuda-repo-9-0-local-cublas-performance-update  Release.gpg [801 B]\n",
            "Ign:7 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1710/x86_64  InRelease\n",
            "Get:8 file:/var/cuda-repo-9-0-local-cublas-performance-update  Packages [693 B]\n",
            "Hit:9 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease\n",
            "Hit:10 http://security.ubuntu.com/ubuntu bionic-security InRelease\n",
            "Hit:11 http://archive.ubuntu.com/ubuntu bionic InRelease\n",
            "Ign:12 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64  InRelease\n",
            "Hit:13 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1710/x86_64  Release\n",
            "Hit:14 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64  Release\n",
            "Hit:15 http://archive.ubuntu.com/ubuntu bionic-updates InRelease\n",
            "Hit:16 http://archive.ubuntu.com/ubuntu bionic-backports InRelease\n",
            "Reading package lists... Done\n",
            "--2019-02-02 20:40:44--  https://github.com/ashokpant/cudnn_archive/raw/master/v7.0/libcudnn7_7.0.5.15-1+cuda9.0_amd64.deb\n",
            "Resolving github.com (github.com)... 192.30.253.113, 192.30.253.112\n",
            "Connecting to github.com (github.com)|192.30.253.113|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/ashokpant/cudnn_archive/master/v7.0/libcudnn7_7.0.5.15-1%2Bcuda9.0_amd64.deb [following]\n",
            "--2019-02-02 20:40:44--  https://raw.githubusercontent.com/ashokpant/cudnn_archive/master/v7.0/libcudnn7_7.0.5.15-1%2Bcuda9.0_amd64.deb\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
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            "Length: 102521974 (98M) [application/octet-stream]\n",
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            "\n",
            "2019-02-02 20:40:45 (221 MB/s) - ‘libcudnn7_7.0.5.15-1+cuda9.0_amd64.deb’ saved [102521974/102521974]\n",
            "\n",
            "\u001b[1mdpkg:\u001b[0m \u001b[1;33mwarning:\u001b[0m downgrading libcudnn7 from 7.4.1.5-1+cuda9.2 to 7.0.5.15-1+cuda9.0\n",
            "(Reading database ... 120178 files and directories currently installed.)\n",
            "Preparing to unpack libcudnn7_7.0.5.15-1+cuda9.0_amd64.deb ...\n",
            "Unpacking libcudnn7 (7.0.5.15-1+cuda9.0) over (7.4.1.5-1+cuda9.2) ...\n",
            "Setting up libcudnn7 (7.0.5.15-1+cuda9.0) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1) ...\n",
            "Get:1 file:/var/cuda-repo-9-0-local-cublas-performance-update  InRelease\n",
            "Ign:1 file:/var/cuda-repo-9-0-local-cublas-performance-update  InRelease\n",
            "Get:2 file:/var/cuda-repo-9-0-local  InRelease\n",
            "Ign:2 file:/var/cuda-repo-9-0-local  InRelease\n",
            "Get:3 file:/var/cuda-repo-9-0-local-cublas-performance-update  Release [574 B]\n",
            "Get:4 file:/var/cuda-repo-9-0-local  Release [574 B]\n",
            "Get:3 file:/var/cuda-repo-9-0-local-cublas-performance-update  Release [574 B]\n",
            "Get:4 file:/var/cuda-repo-9-0-local  Release [574 B]\n",
            "Ign:6 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1710/x86_64  InRelease\n",
            "Hit:8 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease\n",
            "Hit:9 http://archive.ubuntu.com/ubuntu bionic InRelease\n",
            "Hit:10 http://security.ubuntu.com/ubuntu bionic-security InRelease\n",
            "Ign:11 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64  InRelease\n",
            "Hit:12 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1710/x86_64  Release\n",
            "Hit:13 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64  Release\n",
            "Hit:14 http://archive.ubuntu.com/ubuntu bionic-updates InRelease\n",
            "Hit:15 http://archive.ubuntu.com/ubuntu bionic-backports InRelease\n",
            "Reading package lists... Done\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "cuda-command-line-tools-9-0 is already the newest version (9.0.176-1).\n",
            "cuda-command-line-tools-9-0 set to manually installed.\n",
            "You might want to run 'apt --fix-broken install' to correct these.\n",
            "The following packages have unmet dependencies:\n",
            " libcudnn7-dev : Depends: libcudnn7 (= 7.4.1.5-1+cuda9.2) but 7.0.5.15-1+cuda9.0 is to be installed\n",
            "E: Unmet dependencies. Try 'apt --fix-broken install' with no packages (or specify a solution).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "LnZGOjCgRaTu",
        "colab_type": "code",
        "outputId": "3e567d3c-950e-45b4-9cf0-086034cbb20f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        }
      },
      "cell_type": "code",
      "source": [
        "! sudo apt-get --fix-broken install\n",
        "! nvcc --version"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "Correcting dependencies... Done\n",
            "The following additional packages will be installed:\n",
            "  libcudnn7 libcudnn7-dev\n",
            "The following packages will be upgraded:\n",
            "  libcudnn7 libcudnn7-dev\n",
            "2 upgraded, 0 newly installed, 0 to remove and 31 not upgraded.\n",
            "Need to get 285 MB of archives.\n",
            "After this operation, 96.3 MB of additional disk space will be used.\n",
            "Get:1 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64  libcudnn7-dev 7.4.2.24-1+cuda10.0 [136 MB]\n",
            "Get:2 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64  libcudnn7 7.4.2.24-1+cuda10.0 [149 MB]\n",
            "Fetched 285 MB in 6s (45.7 MB/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 2.)\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",
            "(Reading database ... 120179 files and directories currently installed.)\n",
            "Preparing to unpack .../libcudnn7-dev_7.4.2.24-1+cuda10.0_amd64.deb ...\n",
            "update-alternatives: removing manually selected alternative - switching libcudnn to auto mode\n",
            "Unpacking libcudnn7-dev (7.4.2.24-1+cuda10.0) over (7.4.1.5-1+cuda9.2) ...\n",
            "Preparing to unpack .../libcudnn7_7.4.2.24-1+cuda10.0_amd64.deb ...\n",
            "Unpacking libcudnn7 (7.4.2.24-1+cuda10.0) over (7.0.5.15-1+cuda9.0) ...\n",
            "Setting up libcudnn7 (7.4.2.24-1+cuda10.0) ...\n",
            "Setting up libcudnn7-dev (7.4.2.24-1+cuda10.0) ...\n",
            "update-alternatives: using /usr/include/x86_64-linux-gnu/cudnn_v7.h to provide /usr/include/cudnn.h (libcudnn) in auto mode\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1) ...\n",
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2017 NVIDIA Corporation\n",
            "Built on Fri_Sep__1_21:08:03_CDT_2017\n",
            "Cuda compilation tools, release 9.0, V9.0.176\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "JXcBzT_wJtRm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# TRT version\n",
        "We need to download this file and access it from Drive\n",
        "- https://developer.nvidia.com/nvidia-tensorrt-4x-download\n",
        "- https://developer.nvidia.com/compute/machine-learning/tensorrt/4.0/ga/TensorRT-4.0.1.6.Ubuntu-16.04.4.x86_64-gnu.cuda-9.2.cudnn7.1"
      ]
    },
    {
      "metadata": {
        "id": "sdg1ZP2oJ_3l",
        "colab_type": "code",
        "outputId": "9473299f-f19b-4853-bb99-2902b4f78603",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HzKfosD9KUhz",
        "colab_type": "code",
        "outputId": "20d06912-54ea-4420-d34a-3299ec29a1cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "cell_type": "code",
      "source": [
        "! pwd\n",
        "! ls -la '/content/drive/My Drive/TRT/'\n",
        "\n",
        "# trt_path='/content/drive/My Drive/TRT/TensorRT-4.0.1.6.Ubuntu-16.04.4.x86_64-gnu.cuda-9.2.cudnn7.1.tar.gz'\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n",
            "total 798520\n",
            "-rw------- 1 root root     17854 Feb  2 18:11 image227.png\n",
            "-rw------- 1 root root 410872039 Feb  2 14:04 TensorRT-4.0.1.6.Ubuntu-16.04.4.x86_64-gnu.cuda-9.0.cudnn7.1.tar.gz\n",
            "-rw------- 1 root root 406793742 Feb  2 12:37 TensorRT-4.0.1.6.Ubuntu-16.04.4.x86_64-gnu.cuda-9.2.cudnn7.1.tar.gz\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "chOZ0qI0PecM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# References:\n",
        "- https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html\n",
        "- https://tsmatz.wordpress.com/2018/07/07/tensorrt-tensorflow-python-on-azure-tutorial/\n",
        "- https://jkjung-avt.github.io/tf-trt-models/\n",
        "- https://kezunlin.me/post/dacc4196/\n",
        "- https://blog.csdn.net/MacwinWin/article/details/80177326\n"
      ]
    },
    {
      "metadata": {
        "id": "Vng-sEgiRd7_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Install TRT form the TAR file\n",
        "- https://kezunlin.me/post/dacc4196/"
      ]
    },
    {
      "metadata": {
        "id": "aM1D_j7UQ1wB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33915
        },
        "outputId": "9eace658-cc36-4f47-e867-657a0d2741c7"
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.chdir('/content/drive/My Drive/TRT/')\n",
        "! tar zxvf 'TensorRT-4.0.1.6.Ubuntu-16.04.4.x86_64-gnu.cuda-9.0.cudnn7.1.tar.gz'"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorRT-4.0.1.6/\n",
            "TensorRT-4.0.1.6/data/\n",
            "TensorRT-4.0.1.6/data/mnist/\n",
            "TensorRT-4.0.1.6/data/mnist/5.pgm\n",
            "TensorRT-4.0.1.6/data/mnist/mnist.caffemodel\n",
            "TensorRT-4.0.1.6/data/mnist/deploy.prototxt\n",
            "TensorRT-4.0.1.6/data/mnist/mnist_lenet.caffemodel\n",
            "TensorRT-4.0.1.6/data/mnist/lenet5_custom_pool.uff\n",
            "TensorRT-4.0.1.6/data/mnist/4.pgm\n",
            "TensorRT-4.0.1.6/data/mnist/batches/\n",
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            "TensorRT-4.0.1.6/python/doc/python2.7/docs/_static/img/TensorRT2_perf.PNG\n",
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            "TensorRT-4.0.1.6/python/doc/python2.7/docs/_static/fonts/Lato-BoldItalic.ttf\n",
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            "TensorRT-4.0.1.6/python/doc/python2.7/docs/_static/css/\n",
            "TensorRT-4.0.1.6/python/doc/python2.7/docs/_static/css/badge_only.css\n",
            "TensorRT-4.0.1.6/python/doc/python2.7/docs/_static/css/tensorrt_theme.css\n",
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            "TensorRT-4.0.1.6/python/doc/python2.7/docs/_static/jquery.js\n",
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            "TensorRT-4.0.1.6/python/doc/python2.7/docs/_static/file.png\n",
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            "TensorRT-4.0.1.6/python/doc/python2.7/docs/_static/js/\n",
            "TensorRT-4.0.1.6/python/doc/python2.7/docs/_static/js/theme.js\n",
            "TensorRT-4.0.1.6/python/doc/python2.7/docs/_static/js/modernizr.min.js\n",
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            "TensorRT-4.0.1.6/python/doc/python2.7/docs/_static/websupport.js\n",
            "TensorRT-4.0.1.6/python/doc/python2.7/docs/_static/ajax-loader.gif\n",
            "TensorRT-4.0.1.6/python/doc/python2.7/docs/_static/comment-bright.png\n",
            "TensorRT-4.0.1.6/python/doc/python2.7/docs/workflows/\n",
            "TensorRT-4.0.1.6/python/doc/python2.7/docs/workflows/manually_construct_tensorrt_engine.html\n",
            "TensorRT-4.0.1.6/python/doc/python2.7/docs/workflows/tf_to_tensorrt.html\n",
            "TensorRT-4.0.1.6/python/doc/python2.7/docs/workflows/caffe_to_tensorrt.html\n",
            "TensorRT-4.0.1.6/python/tensorrt-4.0.1.6-cp35-cp35m-linux_x86_64.whl\n",
            "TensorRT-4.0.1.6/python/tensorrt-4.0.1.6-cp27-cp27mu-linux_x86_64.whl\n",
            "TensorRT-4.0.1.6/samples\n",
            "tar: TensorRT-4.0.1.6/samples: Cannot create symlink to ‘targets/x86_64-linux-gnu/samples’: Function not implemented\n",
            "tar: TensorRT-4.0.1.6/python/data/googlenet: Cannot create symlink to ‘../../data/googlenet’: Function not implemented\n",
            "tar: TensorRT-4.0.1.6/python/data/mnist: Cannot create symlink to ‘../../data/mnist’: Function not implemented\n",
            "tar: TensorRT-4.0.1.6/targets/x86_64-linux-gnu/include: Cannot create symlink to ‘../../include’: Function not implemented\n",
            "tar: Exiting with failure status due to previous errors\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "SZgMdSH2Q13R",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "! sudo rm -rf /opt/TensorRT-4.0.1.6/\n",
        "! sudo mv TensorRT-4.0.1.6 /opt/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tYQdBt9dQ18T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "009ceaf9-2d5a-49a3-bdf8-7dee8488116c"
      },
      "cell_type": "code",
      "source": [
        "! ls -la '/opt/TensorRT-4.0.1.6/'"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 1564\n",
            "drwx------ 9 root root    4096 Jun 12  2018 .\n",
            "drwxr-xr-x 1 root root    4096 Feb  2 20:46 ..\n",
            "drwx------ 8 root root    4096 Feb  2 20:46 data\n",
            "drwx------ 5 root root    4096 Jun 12  2018 doc\n",
            "drwx------ 2 root root    4096 Jun 12  2018 graphsurgeon\n",
            "drwx------ 2 root root    4096 Jun 12  2018 include\n",
            "drwx------ 4 root root    4096 Jun 12  2018 python\n",
            "drwx------ 3 root root    4096 Jun 12  2018 targets\n",
            "-r-------- 1 root root 1563257 Jun 12  2018 TensorRT-Release-Notes.pdf\n",
            "drwx------ 2 root root    4096 Jun 12  2018 uff\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "L25_hzBbQ1-_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import os \n",
        "os.chdir('/opt/TensorRT-4.0.1.6/')\n",
        "! sudo ln -s TensorRT-4.0.1.6/ tensorrt\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PBbbWn0HTPtr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# TRT Python "
      ]
    },
    {
      "metadata": {
        "id": "IOvRckHeTJZg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "fa9e2f87-bf2c-4023-da52-89260dcefff8"
      },
      "cell_type": "code",
      "source": [
        "os.chdir('/opt/TensorRT-4.0.1.6/python/')\n",
        "! ls -la \n",
        "!sudo pip3 install tensorrt-4.0.1.6-cp35-cp35m-linux_x86_64.whl "
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 3096\n",
            "drwx------ 4 root root    4096 Jun 12  2018 .\n",
            "drwx------ 9 root root    4096 Feb  2 20:47 ..\n",
            "drwx------ 3 root root    4096 Jun 12  2018 data\n",
            "drwx------ 4 root root    4096 Jun 12  2018 doc\n",
            "-rw------- 1 root root 1571581 Jun 12  2018 tensorrt-4.0.1.6-cp27-cp27mu-linux_x86_64.whl\n",
            "-rw------- 1 root root 1579848 Jun 12  2018 tensorrt-4.0.1.6-cp35-cp35m-linux_x86_64.whl\n",
            "\u001b[31mtensorrt-4.0.1.6-cp35-cp35m-linux_x86_64.whl is not a supported wheel on this platform.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "aLRCu6oTTJf2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "! mv tensorrt-4.0.1.6-cp35-cp35m-linux_x86_64.whl tensorrt-4.0.1.6-cp36-cp36m-linux_x86_64.whl"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "h9pvOUEnTJiq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "e68ac691-2caf-419c-aea5-5e4901680a42"
      },
      "cell_type": "code",
      "source": [
        "!sudo pip3 install tensorrt-4.0.1.6-cp36-cp36m-linux_x86_64.whl "
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[33mRequirement 'tensorrt-4.0.1.6-cp36-cp36m-linux_x86_64.whl' looks like a filename, but the file does not exist\u001b[0m\n",
            "Requirement already satisfied: tensorrt==4.0.1.6 from file:///content/tensorrt-4.0.1.6-cp36-cp36m-linux_x86_64.whl in /usr/local/lib/python3.6/dist-packages (4.0.1.6)\n",
            "Requirement already satisfied: numpy>=1.13.0 in /usr/local/lib/python3.6/dist-packages (from tensorrt==4.0.1.6) (1.14.6)\n",
            "Requirement already satisfied: protobuf>=3.3.0 in /usr/local/lib/python3.6/dist-packages (from tensorrt==4.0.1.6) (3.6.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.3.0->tensorrt==4.0.1.6) (40.7.1)\n",
            "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.3.0->tensorrt==4.0.1.6) (1.11.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "SFqXXsdSTJlZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "80ea5ed2-d213-408e-9936-a22b913ed41f"
      },
      "cell_type": "code",
      "source": [
        "os.chdir('/opt/TensorRT-4.0.1.6/uff')\n",
        "! sudo pip3 install uff-0.4.0-py2.py3-none-any.whl \n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: uff==0.4.0 from file:///opt/TensorRT-4.0.1.6/uff/uff-0.4.0-py2.py3-none-any.whl in /usr/local/lib/python3.6/dist-packages (0.4.0)\n",
            "Requirement already satisfied: numpy>=1.11.0 in /usr/local/lib/python3.6/dist-packages (from uff==0.4.0) (1.14.6)\n",
            "Requirement already satisfied: protobuf>=3.3.0 in /usr/local/lib/python3.6/dist-packages (from uff==0.4.0) (3.6.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.3.0->uff==0.4.0) (40.7.1)\n",
            "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.3.0->uff==0.4.0) (1.11.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "R7kLJwwXTJoE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fff704be-e50b-4aad-c187-ec4dbb0c8d82"
      },
      "cell_type": "code",
      "source": [
        "!which convert-to-uff"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/bin/convert-to-uff\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Fs6AKBhCcMqi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "f751e652-b0c6-4172-8952-3882977b99ae"
      },
      "cell_type": "code",
      "source": [
        "! ls -la /opt/TensorRT-4.0.1.6/graphsurgeon/\n",
        "os.chdir('/opt/TensorRT-4.0.1.6/graphsurgeon')\n",
        "! sudo pip3 install graphsurgeon-0.2.0-py2.py3-none-any.whl"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 24\n",
            "drwx------ 2 root root  4096 Jun 12  2018 .\n",
            "drwx------ 9 root root  4096 Feb  2 20:47 ..\n",
            "-rw------- 1 root root 13338 Jun 12  2018 graphsurgeon-0.2.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied: graphsurgeon==0.2.0 from file:///opt/TensorRT-4.0.1.6/graphsurgeon/graphsurgeon-0.2.0-py2.py3-none-any.whl in /usr/local/lib/python3.6/dist-packages (0.2.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "aPM27JPXcMtZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "4d4b1e0e-ec4b-4fd1-9034-572d43ff0ae1"
      },
      "cell_type": "code",
      "source": [
        "! ls -la /opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/\n",
        "! ls -la /usr/local/cuda\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 20\n",
            "drwx------  5 root root 4096 Jun 12  2018 .\n",
            "drwx------  3 root root 4096 Jun 12  2018 ..\n",
            "drwx------  2 root root 4096 Jun 12  2018 bin\n",
            "drwx------  2 root root 4096 Feb  2 20:49 lib\n",
            "drwx------ 17 root root 4096 Jun 12  2018 samples\n",
            "lrwxrwxrwx 1 root root 8 Feb  2 20:40 /usr/local/cuda -> cuda-9.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "4_RwoTBuLQJP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "52225f25-c7f7-468e-c101-edee8667181f"
      },
      "cell_type": "code",
      "source": [
        "ls -la /opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 20\n",
            "drwx------  5 root root 4096 Jun 12  2018 \u001b[0m\u001b[01;34m.\u001b[0m/\n",
            "drwx------  3 root root 4096 Jun 12  2018 \u001b[01;34m..\u001b[0m/\n",
            "drwx------  2 root root 4096 Jun 12  2018 \u001b[01;34mbin\u001b[0m/\n",
            "drwx------  2 root root 4096 Feb  2 20:49 \u001b[01;34mlib\u001b[0m/\n",
            "drwx------ 17 root root 4096 Jun 12  2018 \u001b[01;34msamples\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "7zEXGwvAcMyd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "os.environ['CUDA_INSTALL_DIR']= \"/usr/local/cuda/\" # NOTE NO + SIGN \n",
        "os.environ['CUDNN_INSTALL_DIR']= \"/usr/local/cuda/\"\n",
        "os.environ['LD_LIBRARY_PATH']+= \":/usr/lib64-nvidia/:/opt/TensorRT-4.0.1.6/include/:/opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/lib/:/opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/bin/\"\n",
        "os.environ['PATH']+= \":/opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/lib:/opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/bin/\"\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "96b0Yxt0ejKF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "e08886e3-10c4-4b36-f5a4-5f70d8e919c4"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# !env | grep cuda\n",
        "!env | grep LD_LIBRARY_PATH\n",
        "# /opt/TensorRT-4.0.1.6/include/"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LD_LIBRARY_PATH=/usr/lib64-nvidia/opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/lib//opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/lib/:/usr/lib64-nvidia/:/opt/TensorRT-4.0.1.6/include/:/opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/lib:/opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/bin//opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/lib/:/usr/lib64-nvidia/:/opt/TensorRT-4.0.1.6/include/:/opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/lib/:/opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/bin/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "KhWAkQWOhEUs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d697e171-e3bf-4810-bcef-d8ab95c43461"
      },
      "cell_type": "code",
      "source": [
        "# ! ls -la  /opt/TensorRT-4.0.1.6/include\n",
        "%%file /etc/ld.so.conf.d/tensorrt\n",
        "\n",
        "#/opt/TensorRT-4.0.1.6/lib\n",
        "/opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/lib\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Overwriting /etc/ld.so.conf.d/tensorrt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "sE-LKT05h3fS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "2ff6d5aa-e0b0-49d4-9dd9-9af24aa05b36"
      },
      "cell_type": "code",
      "source": [
        "! sudo ldconfig\n",
        "\n",
        "! sudo cp /opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/lib* /usr/lib\n",
        "! sudo cp /opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/bin* /usr/bin\n",
        "! sudo cp /opt/TensorRT-4.0.1.6/include/* /usr/local/include/"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cp: -r not specified; omitting directory '/opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/lib'\n",
            "cp: -r not specified; omitting directory '/opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/bin'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5S_yZ9IRcM1F",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "os.chdir('/opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/samples/sampleMNIST')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZJB4jfxZcM3s",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#! make -j 8\n",
        "# Makefile  sampleMNIST.cpp\n",
        "\n",
        "#os.environ['CUDA_INSTALL_DIR']= \"/usr/local/cuda/\" # NOTE NO + SIGN \n",
        "#os.environ['CUDNN_INSTALL_DIR']= \"/usr/local/cuda/\"\n",
        "#os.environ['LD_LIBRARY_PATH']= \"/opt/TensorRT-4.0.1.6/include/\"\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dPcx7iSPnew6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "0cfeb56b-e0ca-4f90-d751-354975767480"
      },
      "cell_type": "code",
      "source": [
        "# see https://stackoverflow.com/questions/50621536/tensorrt-python-package-incompatibility-with-python-3-6/50793199 \n",
        "! sudo find / -name *nv_infer_bindings.cpython-35m-x86_64-linux-gnu.so"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "find: ‘/proc/2718/task/2718/net’: Invalid argument\n",
            "find: ‘/proc/2718/net’: Invalid argument\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "S-h688hvn0e8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fa1281f2-ba4e-4814-eaab-db1a40383614"
      },
      "cell_type": "code",
      "source": [
        "! sudo mv /usr/local/lib/python3.6/dist-packages/tensorrt/infer/_nv_infer_bindings.cpython-35m-x86_64-linux-gnu.so /usr/local/lib/python3.6/dist-packages/tensorrt/infer/_nv_infer_bindings.cpython-36m-x86_64-linux-gnu.so"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mv: cannot stat '/usr/local/lib/python3.6/dist-packages/tensorrt/infer/_nv_infer_bindings.cpython-35m-x86_64-linux-gnu.so': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "TIED2Vpxdlcj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "21f3ba82-7e5a-49a8-a684-b9b35bf80b2d"
      },
      "cell_type": "code",
      "source": [
        "! wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libcudnn7_7.2.1.38-1+cuda9.0_amd64.deb\n",
        "! wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libcudnn7_7.3.0.29-1+cuda9.0_amd64.deb  "
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-02-02 20:47:56--  https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libcudnn7_7.2.1.38-1+cuda9.0_amd64.deb\n",
            "Resolving developer.download.nvidia.com (developer.download.nvidia.com)... 192.229.211.70, 2606:2800:21f:3aa:dcf:37b:1ed6:1fb\n",
            "Connecting to developer.download.nvidia.com (developer.download.nvidia.com)|192.229.211.70|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 122730426 (117M) [application/x-deb]\n",
            "Saving to: ‘libcudnn7_7.2.1.38-1+cuda9.0_amd64.deb’\n",
            "\n",
            "libcudnn7_7.2.1.38- 100%[===================>] 117.04M  3.33MB/s    in 35s     \n",
            "\n",
            "2019-02-02 20:48:31 (3.30 MB/s) - ‘libcudnn7_7.2.1.38-1+cuda9.0_amd64.deb’ saved [122730426/122730426]\n",
            "\n",
            "--2019-02-02 20:48:32--  https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libcudnn7_7.3.0.29-1+cuda9.0_amd64.deb\n",
            "Resolving developer.download.nvidia.com (developer.download.nvidia.com)... 192.229.211.70, 2606:2800:21f:3aa:dcf:37b:1ed6:1fb\n",
            "Connecting to developer.download.nvidia.com (developer.download.nvidia.com)|192.229.211.70|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 125687148 (120M) [application/x-deb]\n",
            "Saving to: ‘libcudnn7_7.3.0.29-1+cuda9.0_amd64.deb’\n",
            "\n",
            "libcudnn7_7.3.0.29- 100%[===================>] 119.86M  3.33MB/s    in 36s     \n",
            "\n",
            "2019-02-02 20:49:09 (3.30 MB/s) - ‘libcudnn7_7.3.0.29-1+cuda9.0_amd64.deb’ saved [125687148/125687148]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "-Ai66ooVfoYH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "GOhWbmyng0uP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "a5a28f49-bafe-489a-8d3f-6d53f07d5606"
      },
      "cell_type": "code",
      "source": [
        " ! sudo find / -name tensorflow*\n",
        "\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/uff/converters/tensorflow\n",
            "/usr/local/lib/python3.6/dist-packages/vis/backend/tensorflow_backend.py\n",
            "/usr/local/lib/python3.6/dist-packages/vis/backend/__pycache__/tensorflow_backend.cpython-36.pyc\n",
            "/usr/local/lib/python3.6/dist-packages/imblearn/tensorflow\n",
            "/usr/local/lib/python2.7/dist-packages/pyarrow/include/arrow/adapters/tensorflow\n",
            "/usr/local/lib/python2.7/dist-packages/pyarrow/tensorflow\n",
            "/usr/local/lib/python2.7/dist-packages/vis/backend/tensorflow_backend.pyc\n",
            "/usr/local/lib/python2.7/dist-packages/vis/backend/tensorflow_backend.py\n",
            "/usr/local/lib/python2.7/dist-packages/imblearn/tensorflow\n",
            "/usr/local/lib/python2.7/dist-packages/tensorboard/_vendor/tensorflow_serving\n",
            "/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.pyc\n",
            "/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py\n",
            "find: ‘/proc/2718/task/2718/net’: Invalid argument\n",
            "find: ‘/proc/2718/net’: Invalid argument\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "i7uidRHocNAa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 918
        },
        "outputId": "c6ebee8b-7d68-4552-c6da-287a6c3a4c79"
      },
      "cell_type": "code",
      "source": [
        "#! sudo dpkg -i libcudnn7_7.3.0.29-1+cuda9.0_amd64.deb # 7.4.2.24-1+cuda10.0 to 7.3.0.29-1+cuda9.0\n",
        "! sudo rm -rf /usr/local/lib/python3.6/dist-packages/tensorflow*\n",
        "! sudo rm -rf /usr/local/lib/python3.6/dist-packages/keras*\n",
        "! sudo rm -rf  /usr/local/lib/python3.6/dist-packages/keras/\n",
        "! sudo rm -rf  /usr/local/lib/python2.7/dist-packages/tensorflow*\n",
        "! sudo pip3 uninstall tensorflow tensorflow-gpu protobuf keras tensorboard\n",
        "! sudo pip3 install tensorflow-gpu==1.8 keras\n",
        "#! sudo pip3 install --upgrade tensorflow-gpu\n",
        "\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[33mSkipping tensorflow as it is not installed.\u001b[0m\n",
            "\u001b[33mSkipping tensorflow-gpu as it is not installed.\u001b[0m\n",
            "\u001b[33mSkipping protobuf as it is not installed.\u001b[0m\n",
            "\u001b[33mSkipping keras as it is not installed.\u001b[0m\n",
            "\u001b[33mSkipping tensorboard as it is not installed.\u001b[0m\n",
            "Collecting tensorflow-gpu==1.8\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/59/41/ba6ac9b63c5bfb90377784e29c4f4c478c74f53e020fa56237c939674f2d/tensorflow_gpu-1.8.0-cp36-cp36m-manylinux1_x86_64.whl (216.2MB)\n",
            "\u001b[K    100% |████████████████████████████████| 216.3MB 107kB/s \n",
            "\u001b[?25hCollecting keras\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5e/10/aa32dad071ce52b5502266b5c659451cfd6ffcbf14e6c8c4f16c0ff5aaab/Keras-2.2.4-py2.py3-none-any.whl (312kB)\n",
            "\u001b[K    100% |████████████████████████████████| 317kB 28.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: absl-py>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.8) (0.7.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.8) (1.11.0)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.8) (0.2.2)\n",
            "Collecting tensorboard<1.9.0,>=1.8.0 (from tensorflow-gpu==1.8)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/59/a6/0ae6092b7542cfedba6b2a1c9b8dceaf278238c39484f3ba03b03f07803c/tensorboard-1.8.0-py3-none-any.whl (3.1MB)\n",
            "\u001b[K    100% |████████████████████████████████| 3.1MB 11.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.8) (0.32.3)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.8) (1.1.0)\n",
            "Collecting protobuf>=3.4.0 (from tensorflow-gpu==1.8)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c2/f9/28787754923612ca9bfdffc588daa05580ed70698add063a5629d1a4209d/protobuf-3.6.1-cp36-cp36m-manylinux1_x86_64.whl (1.1MB)\n",
            "\u001b[K    100% |████████████████████████████████| 1.1MB 17.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.8) (1.14.6)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.8) (1.15.0)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.8) (0.7.1)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from keras) (1.0.8)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from keras) (1.0.7)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras) (2.8.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras) (3.13)\n",
            "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from keras) (1.1.0)\n",
            "Collecting html5lib==0.9999999 (from tensorboard<1.9.0,>=1.8.0->tensorflow-gpu==1.8)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ae/ae/bcb60402c60932b32dfaf19bb53870b29eda2cd17551ba5639219fb5ebf9/html5lib-0.9999999.tar.gz (889kB)\n",
            "\u001b[K    100% |████████████████████████████████| 890kB 24.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.9.0,>=1.8.0->tensorflow-gpu==1.8) (3.0.1)\n",
            "Collecting bleach==1.5.0 (from tensorboard<1.9.0,>=1.8.0->tensorflow-gpu==1.8)\n",
            "  Downloading https://files.pythonhosted.org/packages/33/70/86c5fec937ea4964184d4d6c4f0b9551564f821e1c3575907639036d9b90/bleach-1.5.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied: werkzeug>=0.11.10 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.9.0,>=1.8.0->tensorflow-gpu==1.8) (0.14.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.4.0->tensorflow-gpu==1.8) (40.7.1)\n",
            "Building wheels for collected packages: html5lib\n",
            "  Building wheel for html5lib (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/50/ae/f9/d2b189788efcf61d1ee0e36045476735c838898eef1cad6e29\n",
            "Successfully built html5lib\n",
            "\u001b[31mtfds-nightly 0.0.2.dev201901300014 requires tensorflow-metadata, which is not installed.\u001b[0m\n",
            "\u001b[31mtensor2tensor 1.11.0 requires tensorflow-probability, which is not installed.\u001b[0m\n",
            "\u001b[31mfancyimpute 0.4.2 requires tensorflow, which is not installed.\u001b[0m\n",
            "Installing collected packages: html5lib, bleach, protobuf, tensorboard, tensorflow-gpu, keras\n",
            "  Found existing installation: html5lib 1.0.1\n",
            "    Uninstalling html5lib-1.0.1:\n",
            "      Successfully uninstalled html5lib-1.0.1\n",
            "  Found existing installation: bleach 3.1.0\n",
            "    Uninstalling bleach-3.1.0:\n",
            "      Successfully uninstalled bleach-3.1.0\n",
            "Successfully installed bleach-1.5.0 html5lib-0.9999999 keras-2.2.4 protobuf-3.6.1 tensorboard-1.8.0 tensorflow-gpu-1.8.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "uOH383N9oAv9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "0020755e-b299-4452-aaef-7b753cad01a4"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "#! sudo find / -name libnvinfer.so.* \n",
        "! sudo ln -s /opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/lib/libnvinfer.so.4.1.2 /opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/lib/libnvinfer.so.4\n",
        "\n",
        "! sudo cp -r /opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/lib/*.* /usr/lib/ # SUPER CRITICAL \n",
        "! sudo cp -r /opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/bin/*.* /usr/bin/ # SUPER CRITICAL \n",
        "\n",
        "#/opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/lib/\n",
        "\n",
        "import os\n",
        "os.environ['LD_LIBRARY_PATH']+= \"/opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/lib/\"\n",
        "\n",
        "! python -c \"import tensorflow.contrib.tensorrt as trt; print(trt.__path__)\"\n",
        "\n",
        "\n",
        "from tensorflow.contrib import tensorrt as trt\n",
        "import tensorflow \n",
        "print (tensorflow.__version__)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ln: failed to create symbolic link '/opt/TensorRT-4.0.1.6/targets/x86_64-linux-gnu/lib/libnvinfer.so.4': File exists\n",
            "['/usr/local/lib/python3.6/dist-packages/tensorflow/contrib/tensorrt']\n",
            "1.8.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "fE3mh10OoA3g",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jVNojxIzZXWe",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# VGG in Keras"
      ]
    },
    {
      "metadata": {
        "id": "rM9znnt3oA6C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "18e17955-066e-48a3-e24f-4e611a9f504d"
      },
      "cell_type": "code",
      "source": [
        "! sudo  pip3 install keras_applications==1.0.4 --no-deps\n",
        "! sudo pip3 install keras_preprocessing==1.0.2 --no-deps\n",
        "\n",
        "import os\n",
        "import zipfile\n",
        "import hashlib\n",
        "from six.moves.urllib.error import URLError\n",
        "from six.moves.urllib.request import urlretrieve\n",
        "import argparse\n",
        "from tqdm import tqdm\n",
        "from keras.applications.vgg16 import VGG16, preprocess_input as preprocess_vgg\n",
        "from keras.preprocessing import image\n",
        "from keras import backend as K\n",
        "from keras.models import Model\n",
        "from keras.layers import GlobalAveragePooling2D, Concatenate\n",
        "import numpy as np\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import time, os, glob\n",
        "import cv2\n",
        "\n",
        "from keras.applications.vgg16 import VGG16\n",
        "from keras.optimizers import SGD, Adam\n",
        "from keras.layers import GlobalAveragePooling2D\n",
        "from keras.models import Model\n",
        "from keras.applications.imagenet_utils import preprocess_input\n",
        "\n",
        "from os.path import isfile, join, abspath, exists, isdir, expanduser\n",
        "from os import listdir, makedirs, getcwd, remove"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting keras_applications==1.0.4\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/54/90/8f327deaa37a71caddb59b7b4aaa9d4b3e90c0e76f8c2d1572005278ddc5/Keras_Applications-1.0.4-py2.py3-none-any.whl (43kB)\n",
            "\u001b[K    100% |████████████████████████████████| 51kB 2.5MB/s \n",
            "\u001b[?25hInstalling collected packages: keras-applications\n",
            "  Found existing installation: Keras-Applications 1.0.7\n",
            "    Uninstalling Keras-Applications-1.0.7:\n",
            "      Successfully uninstalled Keras-Applications-1.0.7\n",
            "Successfully installed keras-applications-1.0.4\n",
            "Collecting keras_preprocessing==1.0.2\n",
            "  Downloading https://files.pythonhosted.org/packages/71/26/1e778ebd737032749824d5cba7dbd3b0cf9234b87ab5ec79f5f0403ca7e9/Keras_Preprocessing-1.0.2-py2.py3-none-any.whl\n",
            "Installing collected packages: keras-preprocessing\n",
            "  Found existing installation: Keras-Preprocessing 1.0.8\n",
            "    Uninstalling Keras-Preprocessing-1.0.8:\n",
            "      Successfully uninstalled Keras-Preprocessing-1.0.8\n",
            "Successfully installed keras-preprocessing-1.0.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "l5-sVAN1Zj2y",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Define the VGG feature extractor"
      ]
    },
    {
      "metadata": {
        "id": "aM-OXe4jZLIe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1037
        },
        "outputId": "704ddb30-fa6f-45a4-b55a-a677f1be3fad"
      },
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm # Enable progress bar\n",
        "from keras.models import Model\n",
        "from keras.preprocessing import image\n",
        "from keras.layers import Flatten, Input\n",
        "\n",
        "from keras.layers import Input, Convolution2D, MaxPooling2D, Activation, concatenate, Dropout, warnings\n",
        "    \n",
        "class VGGFV:\n",
        "    __name__ = \"VGGFV\"\n",
        "\n",
        "    def __init__(self, feature_layer=\"block5_conv3\", input_size=224,num_class=1,batch_size=32):\n",
        "        base_model = VGG16(weights='imagenet', include_top=False,\n",
        "                           input_shape=[input_size,input_size,3], classes=num_class)\n",
        "                                        \n",
        "        x2 = GlobalAveragePooling2D()(base_model.get_layer(\"block2_conv2\").output)  # 128\n",
        "        x3 = GlobalAveragePooling2D()(base_model.get_layer(\"block3_conv3\").output)  # 256\n",
        "        x4 = GlobalAveragePooling2D()(base_model.get_layer(\"block4_conv3\").output)  # 512\n",
        "        x5 = GlobalAveragePooling2D()(base_model.get_layer(\"block5_conv3\").output)  # 512        \n",
        "        x = Concatenate(name=\"concat_1408\")([x2,x3,x4,x5])  \n",
        "#         x= concatenate([x2,x3,x4,x5], name=\"concat_1408\")\n",
        "\n",
        "        model = Model(inputs=base_model.input, outputs=x)        \n",
        "#         optimizer = Adam(lr=0.0001)\n",
        "#         model.compile(loss='categorical_crossentropy',\n",
        "#                       optimizer=optimizer,\n",
        "#                       metrics=['accuracy'])\n",
        "        \n",
        "        self.model=model    \n",
        "        self.batch_size = batch_size\n",
        "        self.data_format = K.image_data_format()\n",
        "    \n",
        "    def predict(self, x):\n",
        "        if self.data_format == \"channels_first\":\n",
        "            x = x.transpose(0, 3, 1, 2)\n",
        "        x = preprocess_vgg(x.astype(K.floatx()))\n",
        "        return self.model.predict(x, batch_size=self.batch_size).squeeze()\n",
        "    \n",
        "\n",
        "img_path = \"/content/drive/My Drive/TRT/image227.png\"\n",
        "feature_layer = \"block5_conv3\" # 512\n",
        "input_size=224\n",
        "\n",
        "img = image.load_img(img_path, target_size=(input_size, input_size))\n",
        "x = image.img_to_array(img)\n",
        "x = np.expand_dims(x, axis=0)\n",
        "x = preprocess_vgg(x)\n",
        "print (x.shape)\n",
        "\n",
        "model = VGGFV (feature_layer, input_size=input_size,num_class=1)\n",
        "model.model.summary()\n",
        "\n",
        "\n",
        "vgg_feature_vector = model.predict(x)\n",
        "print (vgg_feature_vector.shape)\n",
        "vgg_feature_vector    "
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 224, 224, 3)\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "block1_conv1 (Conv2D)           (None, 224, 224, 64) 1792        input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "block1_conv2 (Conv2D)           (None, 224, 224, 64) 36928       block1_conv1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block1_pool (MaxPooling2D)      (None, 112, 112, 64) 0           block1_conv2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block2_conv1 (Conv2D)           (None, 112, 112, 128 73856       block1_pool[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block2_conv2 (Conv2D)           (None, 112, 112, 128 147584      block2_conv1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block2_pool (MaxPooling2D)      (None, 56, 56, 128)  0           block2_conv2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block3_conv1 (Conv2D)           (None, 56, 56, 256)  295168      block2_pool[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block3_conv2 (Conv2D)           (None, 56, 56, 256)  590080      block3_conv1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block3_conv3 (Conv2D)           (None, 56, 56, 256)  590080      block3_conv2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block3_pool (MaxPooling2D)      (None, 28, 28, 256)  0           block3_conv3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block4_conv1 (Conv2D)           (None, 28, 28, 512)  1180160     block3_pool[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block4_conv2 (Conv2D)           (None, 28, 28, 512)  2359808     block4_conv1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block4_conv3 (Conv2D)           (None, 28, 28, 512)  2359808     block4_conv2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block4_pool (MaxPooling2D)      (None, 14, 14, 512)  0           block4_conv3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block5_conv1 (Conv2D)           (None, 14, 14, 512)  2359808     block4_pool[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block5_conv2 (Conv2D)           (None, 14, 14, 512)  2359808     block5_conv1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block5_conv3 (Conv2D)           (None, 14, 14, 512)  2359808     block5_conv2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_1 (Glo (None, 128)          0           block2_conv2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_2 (Glo (None, 256)          0           block3_conv3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_3 (Glo (None, 512)          0           block4_conv3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_4 (Glo (None, 512)          0           block5_conv3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "concat_1408 (Concatenate)       (None, 1408)         0           global_average_pooling2d_1[0][0] \n",
            "                                                                 global_average_pooling2d_2[0][0] \n",
            "                                                                 global_average_pooling2d_3[0][0] \n",
            "                                                                 global_average_pooling2d_4[0][0] \n",
            "==================================================================================================\n",
            "Total params: 14,714,688\n",
            "Trainable params: 14,714,688\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n",
            "(1408,)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.2082626e+02, 1.5750623e+02, 1.3263094e+02, ..., 0.0000000e+00,\n",
              "       6.1613008e-02, 1.8149542e-02], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "metadata": {
        "id": "WIWjuu4XZLP5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "172f788a-7056-45ca-b19a-b727b18d39f2"
      },
      "cell_type": "code",
      "source": [
        "%timeit model.predict(x)\n",
        "%timeit model.predict(x)\n",
        "%timeit model.predict(x)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10 loops, best of 3: 23.8 ms per loop\n",
            "10 loops, best of 3: 23.6 ms per loop\n",
            "10 loops, best of 3: 23.6 ms per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "LWS4711uCaNc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Tf frozen VGG graph"
      ]
    },
    {
      "metadata": {
        "id": "UXfLvbwNZLS1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "dc24a669-9144-4e6f-d9f6-d3a6dafcda05"
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf \n",
        "sess = K.get_session()\n",
        "output_names = ['concat_1408/concat'] # concat_1408, TF changes teh names depending on the SESSION\n",
        "\n",
        "frozen_graph = tf.graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), output_names)\n",
        "frozen_graph = tf.graph_util.remove_training_nodes(frozen_graph)\n",
        "# Save the model\n",
        "with open('vgg19_1408.pb', \"wb\") as ofile:\n",
        "    ofile.write(frozen_graph.SerializeToString())"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Froze 26 variables.\n",
            "Converted 26 variables to const ops.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "px7GdnlZZLVq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "a0d82379-b73b-4710-f5f2-0e00878e6a5c"
      },
      "cell_type": "code",
      "source": [
        "batch_size = 1 # change to 128 when you use batch\n",
        "workspace_size_bytes = 1 << 30\n",
        "precision_mode = 'FP16' # use 'FP32' for K80\n",
        "trt_gpu_ops = tf.GPUOptions(per_process_gpu_memory_fraction = 0.50)\n",
        "print (trt_gpu_ops)\n",
        "\n",
        "tf.reset_default_graph()\n",
        "g1 = tf.Graph()\n",
        "with g1.as_default():\n",
        "    #\n",
        "    # Create graph\n",
        "    #\n",
        "    in_images = tf.placeholder(tf.string, name='in_images')\n",
        "    decoded_input = tf.image.decode_png(in_images, channels=3)\n",
        "    float_input = tf.cast(decoded_input, dtype=tf.float32)\n",
        "    # (224, 224, 3) -> (n, 224, 224, 3)\n",
        "    rgb_input = tf.expand_dims(\n",
        "        float_input,\n",
        "        axis=0)\n",
        "    # For VGG preprocess, reduce means and convert to BGR\n",
        "    slice_red = tf.slice(\n",
        "        rgb_input,\n",
        "        [0, 0, 0, 0],\n",
        "        [1, 224, 224, 1])\n",
        "    slice_green = tf.slice(\n",
        "        rgb_input,\n",
        "        [0, 0, 0, 1],\n",
        "        [1, 224, 224, 1])\n",
        "    slice_blue = tf.slice(\n",
        "        rgb_input,\n",
        "        [0, 0, 0, 2],\n",
        "        [1, 224, 224, 1])\n",
        "    sub_red = tf.subtract(slice_red, 123.68)\n",
        "    sub_green = tf.subtract(slice_green, 116.779)\n",
        "    sub_blue = tf.subtract(slice_blue, 103.939)\n",
        "    transferred_input = tf.concat(\n",
        "        [sub_blue, sub_green, sub_red],\n",
        "        3)\n",
        "    #\n",
        "    # Transform to vectors\n",
        "    #\n",
        "    with tf.Session(config=tf.ConfigProto(gpu_options=trt_gpu_ops)) as s1:\n",
        "        with open('/content/drive/My Drive/TRT/image227.png', 'rb') as f:\n",
        "            data1 = f.read()\n",
        "            feed_dict = {\n",
        "                in_images: data1\n",
        "            }\n",
        "            imglist1 = s1.run([transferred_input], feed_dict=feed_dict)\n",
        "            image1 = imglist1[0]\n",
        "            "
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "per_process_gpu_memory_fraction: 0.5\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "jkweXRBWC5tV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Print the TF graph"
      ]
    },
    {
      "metadata": {
        "id": "Zh7hd3CiZLYa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "outputId": "4bc21555-50e8-4a84-a4cc-9bf487946699"
      },
      "cell_type": "code",
      "source": [
        "classifier_model_file = './vgg19_1408.pb'\n",
        "classifier_graph_def = tf.GraphDef()\n",
        "with tf.gfile.Open(classifier_model_file, 'rb') as f:\n",
        "    data = f.read()\n",
        "    classifier_graph_def.ParseFromString(data)\n",
        "print('Loaded classifier graph def')\n",
        "nodes = iter(classifier_graph_def.node)\n",
        "print(next(nodes))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loaded classifier graph def\n",
            "name: \"input_1\"\n",
            "op: \"Placeholder\"\n",
            "attr {\n",
            "  key: \"dtype\"\n",
            "  value {\n",
            "    type: DT_FLOAT\n",
            "  }\n",
            "}\n",
            "attr {\n",
            "  key: \"shape\"\n",
            "  value {\n",
            "    shape {\n",
            "      dim {\n",
            "        size: -1\n",
            "      }\n",
            "      dim {\n",
            "        size: 224\n",
            "      }\n",
            "      dim {\n",
            "        size: 224\n",
            "      }\n",
            "      dim {\n",
            "        size: 3\n",
            "      }\n",
            "    }\n",
            "  }\n",
            "}\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "CpzjkXocDBKj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Generate TRT graph"
      ]
    },
    {
      "metadata": {
        "id": "us4iMK7xZLbO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4031bd08-5c8b-42cb-d8ff-9b812ec15a43"
      },
      "cell_type": "code",
      "source": [
        "output_names = ['concat_1408/concat'] # concat_1408, TF changes teh names depending on the SESSION\n",
        "\n",
        "trt_graph_def = trt.create_inference_graph(\n",
        "    input_graph_def=classifier_graph_def,    \n",
        "    outputs=output_names,\n",
        "    max_batch_size=batch_size,\n",
        "    max_workspace_size_bytes=workspace_size_bytes,\n",
        "    precision_mode=precision_mode)\n",
        "#trt_graph_def=trt.calib_graph_to_infer_graph(trt_graph_def) # For only 'INT8'\n",
        "print('Generated TensorRT graph def')"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Generated TensorRT graph def\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ebrreCgLDPZZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Generate Tensor"
      ]
    },
    {
      "metadata": {
        "id": "XAFT8A76ZLeO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ce4f5ef3-3f0e-4652-ebd9-a0843bcbd5e1"
      },
      "cell_type": "code",
      "source": [
        "tf.reset_default_graph()\n",
        "g2 = tf.Graph()\n",
        "with g2.as_default():\n",
        "    trt_x, trt_y = tf.import_graph_def(\n",
        "        trt_graph_def,\n",
        "        return_elements=['input_1:0', 'concat_1408/concat:0'])\n",
        "        #return_elements=['input:0', 'resnet_v1_50/block1/unit_2/bottleneck_v1/conv3'])\n",
        "    \n",
        "    print('Generated tensor by TensorRT graph')"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Generated tensor by TensorRT graph\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "K4N2jQ0jZLhE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "396c3b75-1757-4c9f-fbce-8a424163c6c5"
      },
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm \n",
        "    \n",
        "iterations = range(10)\n",
        "    \n",
        "with tf.Session(graph=g2, config=tf.ConfigProto(gpu_options=trt_gpu_ops)) as s2:\n",
        "    \n",
        "    #\n",
        "    # predict image1 (tiger)\n",
        "    #\n",
        "    feed_dict2 = {\n",
        "        trt_x: image1\n",
        "    }    \n",
        "    \n",
        "#     print (feed_dict)\n",
        "    start_time = time.process_time()    \n",
        "    result = s2.run([trt_y], feed_dict=feed_dict2) # dont count 1st graph loading time\n",
        "    print (\"result shape: {}\".format( len(result)))\n",
        "    nd_result = result[0]\n",
        "    print (\"nd_result shape: {}\".format( nd_result.shape))\n",
        "    # remove row's dimension\n",
        "    onedim_result = nd_result[0,]    \n",
        "    print (\"onedim_result shape: {}\".format( onedim_result))\n",
        "    stop_time = time.process_time()        \n",
        "    print('{:.2f} milliseconds'.format((stop_time-start_time)*1000))\n",
        "    \n",
        "    start_time = time.process_time()          \n",
        "    for i in tqdm(iterations):\n",
        "        result = s2.run([trt_y], feed_dict=feed_dict2)        \n",
        "    stop_time = time.process_time()\n",
        "        \n",
        "    print('For bench= {}, {:.2f} milliseconds per op'.format(iterations, (stop_time-start_time)*1000/ (len(iterations))))\n",
        "    #\n",
        "    # predict image2 (lion)\n",
        "    #\n",
        "    %timeit s2.run([trt_y], feed_dict=feed_dict2)\n",
        "    %timeit s2.run([trt_y], feed_dict=feed_dict2)\n",
        "    %timeit s2.run([trt_y], feed_dict=feed_dict2)\n",
        "    "
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " 40%|████      | 4/10 [00:00<00:00, 31.72it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "result shape: 1\n",
            "nd_result shape: (1, 1408)\n",
            "onedim_result shape: [1.4889337e+02 4.3705666e+01 6.3620472e+01 ... 0.0000000e+00 1.6365470e-02\n",
            " 0.0000000e+00]\n",
            "202.52 milliseconds\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 10/10 [00:00<00:00, 32.89it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For bench= range(0, 10), 34.82 milliseconds per op\n",
            "10 loops, best of 3: 22.5 ms per loop\n",
            "10 loops, best of 3: 22.3 ms per loop\n",
            "10 loops, best of 3: 22.3 ms per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "JJjYfVu2ZLkA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZRqGgEpnZLm_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "R2pag9SAZLqS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9q6mDXrzoA8b",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1mRrhihioA_C",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "98CUoQLEoBBq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CUX5eJMwoBH_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bW70vAQIZrqM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Do we have cuda?!\n",
        "!which nvcc\n",
        "!nvcc --version"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1hVypDRLTIlV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ex5a3cdKJBDc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "iaqEb_BbaQIL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Let's check if a GPU accelerator card is attached in our machine:\n",
        "!ls -l /dev/nv*"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "W7lzi2oBcBRl",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# This works too, GPU count and name\n",
        "!nvidia-smi -L"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nav3_MGVcOJw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Use this command to see GPU activity while doing Deep Learning tasks, for this command 'nvidia-smi' and for above one to work, go to 'Runtime > change runtime type > Hardware Accelerator > GPU'"
      ]
    },
    {
      "metadata": {
        "id": "TgKf0GmncYLm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "! nvidia-smi"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LO7apCNUcluu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!lscpu |grep 'Model name'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "iogQyqtzctNX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#no.of threads each core is having\n",
        "!lscpu | grep 'Thread(s) per core'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gjkLboa0czY6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#memory that we can use\n",
        "!cat /proc/meminfo | grep 'MemAvailable'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YqgET6GnczdN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#hard disk that we can use\n",
        "!df -h / | awk '{print $4}'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VysafJ-mczmf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zOQOlxCcZb91",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ['PATH'] += ':/usr/local/cuda/bin'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bTTxigDpa7qG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# This magic will create a new CU file\n",
        "# To write a CUDA C program, we need to:\n",
        "\n",
        "#Create a source code fi le with the special fi le name extension of .cu.\n",
        "#Compile the program using the CUDA nvcc compiler.\n",
        "#Run the executable file from the command line, which contains the kernel code executable on the GPU.\n",
        "\n",
        "%%file version.cu\n",
        "#include <thrust/version.h>\n",
        "#include <iostream>\n",
        "\n",
        "int main(void)\n",
        "{\n",
        "  int major = THRUST_MAJOR_VERSION;\n",
        "  int minor = THRUST_MINOR_VERSION;\n",
        "\n",
        "  std::cout << \"Thrust v\" << major << \".\" << minor << std::endl;\n",
        "\n",
        "  return 0;\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "e8zG5DLkbIJt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# nvcc is the CUDA compiler \n",
        "!nvcc version.cu -o version\n",
        "!./version"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aTy-KdqmpO7g",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "###  Memory footprint support libraries/code"
      ]
    },
    {
      "metadata": {
        "id": "JhM0J7WprERD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!ln -sf /opt/bin/nvidia-smi /usr/bin/nvidia-smi\n",
        "!pip install gputil\n",
        "!pip install psutil\n",
        "!pip install humanize\n",
        "import psutil\n",
        "import humanize\n",
        "import os\n",
        "import GPUtil as GPU\n",
        "GPUs = GPU.getGPUs()\n",
        "# XXX: only one GPU on Colab and isn’t guaranteed\n",
        "gpu = GPUs[0]\n",
        "def printm():\n",
        " process = psutil.Process(os.getpid())\n",
        " print(\"Gen RAM Free: \" + humanize.naturalsize( psutil.virtual_memory().available ), \" I Proc size: \" + humanize.naturalsize( process.memory_info().rss))\n",
        " print(\"GPU RAM Free: {0:.0f}MB | Used: {1:.0f}MB | Util {2:3.0f}% | Total {3:.0f}MB\".format(gpu.memoryFree, gpu.memoryUsed, gpu.memoryUtil*100, gpu.memoryTotal))\n",
        "printm()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aN2tmUFVoc-j",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import sys\n",
        "sys.version\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Ey-bpdSnoi1z",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# !pip3 install torch==0.4\n",
        "# !pip3 install torchvision\n",
        "\n",
        "!pip3 install 'torch==0.4.0'\n",
        "!pip3 install 'torchvision==0.2.1'\n",
        "!pip3 install --no-cache-dir -I 'pillow==5.1.0'\n",
        "\n",
        "# Restart Kernel\n",
        "# This workaround is needed to properly upgrade PIL on Google Colab.\n",
        "import os\n",
        "#os._exit(0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3vQ5re38qpPf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Let's print the versions"
      ]
    },
    {
      "metadata": {
        "id": "LfL1bWLKqiVh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import sys\n",
        "print('__Python VERSION:', sys.version)\n",
        "print('__pyTorch VERSION:', torch.__version__)\n",
        "print('__CUDA VERSION')\n",
        "from subprocess import call\n",
        "# call([\"nvcc\", \"--version\"]) does not work\n",
        "! nvcc --version\n",
        "print('__CUDNN VERSION:', torch.backends.cudnn.version())\n",
        "print('__Number CUDA Devices:', torch.cuda.device_count())\n",
        "print('__Devices')\n",
        "# call([\"nvidia-smi\", \"--format=csv\", \"--query-gpu=index,name,driver_version,memory.total,memory.used,memory.free\"])\n",
        "print('Active CUDA Device: GPU', torch.cuda.current_device())\n",
        "\n",
        "print ('Available devices ', torch.cuda.device_count())\n",
        "print ('Current cuda device ', torch.cuda.current_device())\n",
        "\n",
        "use_cuda = torch.cuda.is_available()\n",
        "# use_cuda = False\n",
        "\n",
        "print(\"USE CUDA=\" + str (use_cuda))\n",
        "FloatTensor = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor\n",
        "LongTensor = torch.cuda.LongTensor if use_cuda else torch.LongTensor\n",
        "Tensor = FloatTensor"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "t-e5W6W6EdQ3",
        "colab_type": "code",
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}