KT-pFL.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyO72KBjCClM10YMlgd2BRyR",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TuringSu/Awesome-Federated-Learning-on-Graph-and-Tabular-Data/blob/main/KT-pFL.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_U1zqcF-Fmd8",
        "outputId": "87a4491e-9c6b-442a-caca-ed449479a363"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'KT-pFL'...\n",
            "remote: Enumerating objects: 132, done.\u001b[K\n",
            "remote: Counting objects: 100% (10/10), done.\u001b[K\n",
            "remote: Compressing objects: 100% (10/10), done.\u001b[K\n",
            "remote: Total 132 (delta 4), reused 0 (delta 0), pack-reused 122\u001b[K\n",
            "Receiving objects: 100% (132/132), 1.26 MiB | 8.19 MiB/s, done.\n",
            "Resolving deltas: 100% (48/48), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/TuringSu/KT-pFL.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SXG3OXglTKwX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cd /content/KT-pFL/src"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gcADx2VxGOG7",
        "outputId": "f26baa81-8d3c-4541-d29c-edab77be8481"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/KT-pFL/src\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python KT-pFL.py --conf ../conf/CIFAR_balance_conf.json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "esxjgCLIGRfA",
        "outputId": "a8160f38-1803-4460-cf0e-40859bee010a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Namespace(conf='../conf/CIFAR_balance_conf.json', use_pretrained_model=False, gpu=None, dataset='cifar', iid=True)\n",
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "X shape : (3000, 32, 32, 3)\n",
            "y shape : (3000,)\n",
            "X shape : (600, 32, 32, 3)\n",
            "y shape : (600,)\n",
            "10    500\n",
            "15    500\n",
            "14    500\n",
            "12    500\n",
            "13    500\n",
            "11    500\n",
            "dtype: int64\n",
            "============================================================\n",
            "============================================================\n",
            "X shape : (600, 32, 32, 3)\n",
            "y shape : (600,)\n",
            "model 0 : CNN_128_256\n",
            "model 1 : CNN_128_384\n",
            "model 2 : CNN_128_512\n",
            "model 3 : CNN_256_256\n",
            "model 4 : CNN_256_512\n",
            "model 5 : CNN_64_128_256\n",
            "model 6 : CNN_64_128_192\n",
            "model 7 : CNN_128_192_256\n",
            "model 8 : CNN_128_128_128\n",
            "model 9 : CNN_128_128_192\n",
            "calculate the theoretical upper bounds for participants: \n",
            "start model initialization: \n",
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n",
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            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
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            "\n",
            "Numb samples of each label:\n",
            " [6000, 6000, 6000, 6000, 6000, 6000, 6000, 6000, 6000, 6000]\n",
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            "IDX1: [60 60 60 60 60 60 60 60 60 60]\n",
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            "IDX2: [5396 5396 5002 5384 5488 5860 5790 5072 5798 5386]\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/KT-pFL/src/KT-pFL.py\", line 91, in <module>\n",
            "    main()\n",
            "  File \"/content/KT-pFL/src/KT-pFL.py\", line 67, in main\n",
            "    kt_pfl = KT_pFL(parties,\n",
            "  File \"/content/KT-pFL/src/engine_kt_pfl.py\", line 346, in __init__\n",
            "    X_train, y_train, X_test, y_test = read_user_data(i, newdata, \"Mnist\")\n",
            "  File \"/content/KT-pFL/src/model_utils.py\", line 299, in read_user_data\n",
            "    X_train = torch.Tensor(X_train).view(-1, NUM_CHANNELS, IMAGE_SIZE, IMAGE_SIZE).type(torch.float32)\n",
            "RuntimeError: shape '[-1, 1, 28, 28]' is invalid for input of size 10450944\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0fo6VzJNPRWU",
        "outputId": "1137dd3d-4dd7-4fd0-cf5d-ebbbd7764559"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/root\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -r /content/KT-pFL.zip /content/KT-pFL"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vRMdGwUrPV56",
        "outputId": "ee2ea09e-55e9-42e1-c1a8-a8e85ec9d3ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: content/KT-pFL/ (stored 0%)\n",
            "  adding: content/KT-pFL/README.md (deflated 9%)\n",
            "  adding: content/KT-pFL/save/ (stored 0%)\n",
            "  adding: content/KT-pFL/save/mnist/ (stored 0%)\n",
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            "  adding: content/KT-pFL/pretained_model/ (stored 0%)\n",
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      "cell_type": "code",
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        "id": "PRRsfH1oYHZG",
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      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: re: command not found\n"
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}