Quickstart_MedicalZoo.ipynb

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  "metadata": {
    "colab": {
      "name": "Quickstart MedicalZoo.ipynb",
      "provenance": [],
      "mount_file_id": "1wJpLzfKOTHYXpQIOxVjB6d6U-VWnAqWA",
      "authorship_tag": "ABX9TyOTkj84KVvcdwCTCKq3zu8h",
      "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/black0017/MedicalZooPytorch/blob/master/Quickstart_MedicalZoo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0EJ77EgqRPgf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "42NSNnzBRHeJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "bb620582-20a4-4787-ac9d-8f341ef35050"
      },
      "source": [
        "!git clone https://github.com/black0017/MedicalZooPytorch\n",
        "!pip install -r ./MedicalZooPytorch/installation/requirements.txt"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'MedicalZooPytorch'...\n",
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            "remote: Total 755 (delta 278), reused 317 (delta 110), pack-reused 225\u001b[K\n",
            "Receiving objects: 100% (755/755), 60.43 MiB | 11.64 MiB/s, done.\n",
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            "Collecting visdom\n",
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            "  Downloading https://files.pythonhosted.org/packages/82/53/73ca86f2a680c705dcd1708be4887c559dfe9ed250486dd3ccd8821b8ccb/jsonpatch-1.25-py2.py3-none-any.whl\n",
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            "Collecting jsonpointer>=1.9\n",
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            "Building wheels for collected packages: torchnet, visdom, torchfile\n",
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            "Installing collected packages: jsonpointer, jsonpatch, torchfile, websocket-client, visdom, torchnet, torchsummaryX\n",
            "Successfully installed jsonpatch-1.25 jsonpointer-2.0 torchfile-0.1.0 torchnet-0.0.4 torchsummaryX-1.3.0 visdom-0.1.8.9 websocket-client-0.57.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZmsDH1FIXfcB",
        "colab_type": "text"
      },
      "source": [
        "## Extract datasets\n",
        "To train in any dataset extract the required datasets in `datasets`  folder, e.g., for iseg2017 extract your data here `MedicalZooPytorch/datasets/iseg_2017/iSeg-2017-Training/` \n",
        "\n",
        "in the following line I copy my data from google drive to datasets folder\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0YmYHcJ4Yr-J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%cp -r '/content/drive/My Drive/MEDICAL/iseg_2017/iSeg-2017-Training/' '/content/MedicalZooPytorch/datasets/iseg_2017/'\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ir_bFiOgX_CI",
        "colab_type": "text"
      },
      "source": [
        "## Copy train file outside of test\n",
        "because of  relative imports of lib problem"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MSZs1P07RVpn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5bc7a08f-4a4f-4c1d-d9a4-7b0b337d080b"
      },
      "source": [
        "%cd /content/MedicalZooPytorch\n",
        "%cp /content/MedicalZooPytorch/tests/train_with_trainer_class.py /content/MedicalZooPytorch/train_with_trainer_class.py \n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/MedicalZooPytorch\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GlgRjMq_iCxY",
        "colab_type": "text"
      },
      "source": [
        "# Start training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4HpkFJaDiDEN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "105ab359-9932-43ad-8d13-1551bd63976d"
      },
      "source": [
        "! python train_with_trainer_class.py"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SELECT subject with ID 1 for Validation\n",
            "Mode: train Subvolume samples to generate:  1000  Volumes:  9\n",
            "SELECT subject with ID 1 for Validation\n",
            "Mode: val Subvolume samples to generate:  100  Volumes:  1\n",
            "Full validation volume has been generated\n",
            "DATA SAMPLES HAVE BEEN GENERATED SUCCESSFULLY\n",
            "Building Model . . . . . . . .DENSENET2\n",
            "out: [1, 25, 25, 50, 50, 50, 75, 75, 75]\n",
            "in: [1, 26, 51, 101, 151, 201, 276, 351, 426]\n",
            "out: [1, 25, 25, 50, 50, 50, 75, 75, 75]\n",
            "in: [1, 26, 51, 101, 151, 201, 276, 351, 426]\n",
            "DENSENET2 Number of params: 4775782\n",
            "Model transferred in GPU.....\n",
            "2020-04-24 09:35:15.252311: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "START TRAINING...\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}