notebooks/object_recognition_REST_API.ipynb

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  "metadata": {
    "colab": {
      "name": "object_recognition_REST_API.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hz9mBbHKQdXZ"
      },
      "source": [
        "# Summary\n",
        "\n",
        "This notebook contains instructions to serve the final models.   "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BaPgMt6TTWae"
      },
      "source": [
        "## Serving the model with an API\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XlrK8TrRT_tx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f62569fd-1338-4736-a8dd-1867ec6acd8a"
      },
      "source": [
        "!git clone https://github.com/Cyril9227/computer-vision-REST-API.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'computer-vision-REST-API'...\n",
            "remote: Enumerating objects: 170, done.\u001b[K\n",
            "remote: Counting objects: 100% (170/170), done.\u001b[K\n",
            "remote: Compressing objects: 100% (127/127), done.\u001b[K\n",
            "remote: Total 170 (delta 48), reused 145 (delta 26), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (170/170), 19.89 MiB | 35.61 MiB/s, done.\n",
            "Resolving deltas: 100% (48/48), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dVvAQ5elUBG2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "82110c8e-35fc-404a-e2f9-ae8243d2b7b1"
      },
      "source": [
        "# install dependencies: \n",
        "\n",
        "# This is needed to access the REST API in Google Colab\n",
        "!pip install flask-ngrok\n",
        "\n",
        "!pip install pyyaml==5.1\n",
        "import torch, torchvision\n",
        "print(torch.__version__, torch.cuda.is_available())\n",
        "!gcc --version\n",
        "# opencv is pre-installed on colab\n",
        "\n",
        "# install detectron2: (Colab has CUDA 10.1 + torch 1.7)\n",
        "# See https://detectron2.readthedocs.io/tutorials/install.html for instructions\n",
        "import torch\n",
        "assert torch.__version__.startswith(\"1.7\")\n",
        "!pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.7/index.html\n",
        "# After installation, you need to \"restart runtime\" in Colab. This line can also restart runtime"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Successfully installed flask-ngrok-0.0.25\n",
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            "  Found existing installation: PyYAML 3.13\n",
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            "1.7.0+cu101 True\n",
            "gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n",
            "Copyright (C) 2017 Free Software Foundation, Inc.\n",
            "This is free software; see the source for copying conditions.  There is NO\n",
            "warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n",
            "\n",
            "Looking in links: https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.7/index.html\n",
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            "Building wheels for collected packages: fvcore, iopath\n",
            "  Building wheel for fvcore (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Successfully built fvcore iopath\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: yacs, Pillow, portalocker, iopath, fvcore, detectron2\n",
            "  Found existing installation: Pillow 7.0.0\n",
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            "      Successfully uninstalled Pillow-7.0.0\n",
            "Successfully installed Pillow-8.0.1 detectron2-0.3+cu101 fvcore-0.1.2.post20201218 iopath-0.1.2 portalocker-2.0.0 yacs-0.1.8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "PIL"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nol9coV7ZU2Z"
      },
      "source": [
        "# # Uncomment this cell to download and split the dataset in task_2/data/\n",
        "# # However, the model also works with images from another distribution\n",
        "# # For instance : https://nspt4kids.com/wp-content/uploads/2014/08/boy-kicking-ball.jpg\n",
        "# !python /content/computer-vision-REST-API/MaskRCNN_finetune/src/data/make_dataset.py --output-filepath-raw-dataset \"/content/computer-vision-REST-API/REST_API_flask/data/raw/\" --output-base-path-split-dataset \"/content/computer-vision-REST-API/REST_API_flask/data/processed/\" --clean-folder"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ms_itfJORsBe"
      },
      "source": [
        "Ngrok will generate a random URL that looks like http://.....ngrok.io, the API is accesible throught this URL.\n",
        "\n",
        "The script takes as input 4 parameters : the upload folder target where the local files will be stored for prediction, the output folder where the predictions will be stored, whether to use cpu for inference and whether to use ngrok (don't put this flag if you are testing on your local machine).\n",
        "\n",
        "The API is pretty simple, it offers a button to upload a file and a button to run the inference.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "haERY2AEesXM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99b1427c-012b-4e45-f0ed-c9019e64e88e"
      },
      "source": [
        "%cd /content/computer-vision-REST-API/REST_API_flask/\n",
        "!python flask_app.py --upload-folder \"/content/computer-vision-REST-API/REST_API_flask/images\" --use-ngrok --infer-with-cpu"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/computer-vision-REST-API/REST_API_flask\n",
            "** fvcore version of PathManager will be deprecated soon. **\n",
            "** Please migrate to the version in iopath repo. **\n",
            "https://github.com/facebookresearch/iopath \n",
            "\n",
            "** fvcore version of PathManager will be deprecated soon. **\n",
            "** Please migrate to the version in iopath repo. **\n",
            "https://github.com/facebookresearch/iopath \n",
            "\n",
            "ResNet101_FPN_model_final.pth?dl=1: 503MB [00:13, 36.0MB/s]               \n",
            " * Serving Flask app \"flask_app\" (lazy loading)\n",
            " * Environment: production\n",
            "\u001b[31m   WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
            "\u001b[2m   Use a production WSGI server instead.\u001b[0m\n",
            " * Debug mode: off\n",
            " * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n",
            " * Running on http://9d001af5f2f7.ngrok.io\n",
            " * Traffic stats available on http://127.0.0.1:4040\n",
            "127.0.0.1 - - [30/Dec/2020 04:33:48] \"\u001b[37mGET / HTTP/1.1\u001b[0m\" 200 -\n",
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            "/usr/local/lib/python3.6/dist-packages/detectron2/modeling/roi_heads/fast_rcnn.py:124: UserWarning: This overload of nonzero is deprecated:\n",
            "\tnonzero()\n",
            "Consider using one of the following signatures instead:\n",
            "\tnonzero(*, bool as_tuple) (Triggered internally at  /pytorch/torch/csrc/utils/python_arg_parser.cpp:882.)\n",
            "  filter_inds = filter_mask.nonzero()\n",
            "127.0.0.1 - - [30/Dec/2020 04:34:31] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [30/Dec/2020 04:34:31] \"\u001b[37mGET /static/predictions/téléchargement_prediction.PNG HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [30/Dec/2020 04:34:44] \"\u001b[37mGET /download_prediction HTTP/1.1\u001b[0m\" 200 -\n",
            "^C\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}