Deep Leakage from Gradients.ipynb

deep-leakage-from-gradients.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/gist/Lyken17/91b81526a8245a028d4f85ccc9191884/deep-leakage-from-gradients.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "1.3.1 0.4.2\n"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "from pprint import pprint\n",
    "\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import grad\n",
    "import torchvision\n",
    "from torchvision import models, datasets, transforms\n",
    "torch.manual_seed(50)\n",
    "\n",
    "print(torch.__version__, torchvision.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Files already downloaded and verified\nDataset size: 50000\nRunning on cpu\n"
    }
   ],
   "source": [
    "dst = datasets.CIFAR100(\"~/.torch\", download=True)\n",
    "print(\"Dataset size: %d\" % len(dst))\n",
    "tp = transforms.Compose([\n",
    "    transforms.Resize(32),\n",
    "    transforms.CenterCrop(32),\n",
    "    transforms.ToTensor()\n",
    "])\n",
    "tt = transforms.ToPILImage()\n",
    "\n",
    "device = \"cpu\"\n",
    "if torch.cuda.is_available():\n",
    "    device = \"cuda\"\n",
    "print(\"Running on %s\" % device)\n",
    "\n",
    "def label_to_onehot(target, num_classes=100):\n",
    "    target = torch.unsqueeze(target, 1)\n",
    "    onehot_target = torch.zeros(target.size(0), num_classes, device=target.device)\n",
    "    onehot_target.scatter_(1, target, 1)\n",
    "    return onehot_target\n",
    "\n",
    "def cross_entropy_for_onehot(pred, target):\n",
    "    return torch.mean(torch.sum(- target * F.log_softmax(pred, dim=-1), 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LeNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(LeNet, self).__init__()\n",
    "        act = nn.Sigmoid\n",
    "        # act = nn.ReLU\n",
    "        self.body = nn.Sequential(\n",
    "            nn.Conv2d(3, 12, kernel_size=5, padding=5//2, stride=2),\n",
    "            act(),\n",
    "            nn.Conv2d(12, 12, kernel_size=5, padding=5//2, stride=2),\n",
    "            act(),\n",
    "            nn.Conv2d(12, 12, kernel_size=5, padding=5//2, stride=1),\n",
    "            act(),\n",
    "            nn.Conv2d(12, 12, kernel_size=5, padding=5//2, stride=1),\n",
    "            act(),\n",
    "        )\n",
    "        self.fc = nn.Sequential(\n",
    "            nn.Linear(768, 100)\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        out = self.body(x)\n",
    "        out = out.view(out.size(0), -1)\n",
    "        # print(out.size())\n",
    "        out = self.fc(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weights_init(m):\n",
    "    if hasattr(m, \"weight\") and m.weight is not None:\n",
    "        m.weight.data.uniform_(-0.5, 0.5)\n",
    "    if hasattr(m, \"bias\") and m.bias is not None:\n",
    "        m.bias.data.uniform_(-0.5, 0.5)\n",
    "  \n",
    "net = LeNet().to(device)\n",
    "# net = ResNet18().to(device)\n",
    "    \n",
    "net.apply(weights_init)\n",
    "criterion = cross_entropy_for_onehot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "torch.Size([1, 3, 32, 32])\ntorch.Size([1, 100])\nGT label is 84. \nOnehot label is 84.\n"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "######### honest partipant #########\n",
    "img_index = [25]\n",
    "batch_size = len(img_index)\n",
    "gt_data = []\n",
    "gt_label = []\n",
    "gt_onehot_label = []\n",
    "for idx in img_index:\n",
    "    gt_data.append(tp(dst[idx][0]).to(device))\n",
    "    label = torch.Tensor([dst[idx][1]]).long().to(device)\n",
    "    label = label.view(1, )\n",
    "    gt_label.append(label)\n",
    "    gt_onehot_label.append(torch.squeeze(label_to_onehot(label, num_classes=100),dim=0))\n",
    "\n",
    "gt_data = torch.stack(gt_data, dim = 0)\n",
    "gt_onehot_label = torch.stack(gt_onehot_label, dim = 0)\n",
    "print(gt_data.shape)\n",
    "print(gt_onehot_label.shape)\n",
    "\n",
    "for i in range(batch_size):\n",
    "    plt.subplot(1, batch_size, i + 1)\n",
    "    plt.imshow(tt(gt_data[i].cpu()))\n",
    "    plt.title(\"gt %d\" % i)\n",
    "    plt.axis('off')\n",
    "    print(\"GT label is %d.\" % gt_label[i].item(), \"\\nOnehot label is %d.\" % torch.argmax(gt_onehot_label[i], dim=-1).item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "torch.Size([12, 3, 5, 5])\ntensor(0.1503)\ntensor(8.2829e-05)\n"
    }
   ],
   "source": [
    "# compute original gradient \n",
    "out = net(gt_data)\n",
    "y = criterion(out, gt_onehot_label)\n",
    "dy_dx = torch.autograd.grad(y, net.parameters())\n",
    "\n",
    "# share the gradients with other clients\n",
    "original_dy_dx = list((_.detach().clone() for _ in dy_dx))\n",
    "print(original_dy_dx[0].shape)\n",
    "print(abs(original_dy_dx[0]).max())\n",
    "print(abs(original_dy_dx[0]).min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Dummy label is 41.\n"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plt_dummy():\n",
    "    for i in range(batch_size):\n",
    "        plt.subplot(1, batch_size, i + 1)\n",
    "        plt.imshow(tt(dummy_data[i].cpu()))\n",
    "        plt.title(\"Dummy %d\" % i)\n",
    "        plt.axis('off')\n",
    "        print(\"Dummy label is %d.\" % torch.argmax(dummy_label[i], dim=-1).item())\n",
    "\n",
    "# generate dummy data and label\n",
    "dummy_data = torch.randn(gt_data.size()).to(device).requires_grad_(True)\n",
    "dummy_label = torch.randn(gt_onehot_label.size()).to(device).requires_grad_(True)\n",
    "plt_dummy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "0 38.0978\n10 0.5274\n20 0.0933\n30 0.0187\n40 0.0052\n50 0.0016\n60 0.0005\n70 0.0002\n80 0.0001\n90 0.0000\n100 0.0000\n110 0.0000\n120 0.0000\n130 0.0000\n140 0.0000\n150 0.0000\n160 0.0000\n170 0.0000\n180 0.0000\n190 0.0000\n200 0.0000\n210 0.0000\n220 0.0000\n230 0.0000\n240 0.0000\n250 0.0000\n260 0.0000\n270 0.0000\n280 0.0000\n290 0.0000\n"
    }
   ],
   "source": [
    "def predict():\n",
    "    pred = net(dummy_data) \n",
    "    dummy_onehot_label = F.softmax(dummy_label, dim=-1)\n",
    "    dummy_loss = criterion(pred, dummy_onehot_label) # TODO: fix the gt_label to dummy_label in both code and slides.\n",
    "    dummy_dy_dx = torch.autograd.grad(dummy_loss, net.parameters(), create_graph=True)\n",
    "    \n",
    "    grad_diff = 0\n",
    "    grad_count = 0\n",
    "    for gx, gy in zip(dummy_dy_dx, original_dy_dx): # TODO: fix the variablas here\n",
    "        grad_diff += ((gx - gy) ** 2).sum()\n",
    "        grad_count += gx.nelement()\n",
    "    # grad_diff = grad_diff / grad_count * 1000\n",
    "    \n",
    "    return grad_diff\n",
    "\n",
    "def lbfgs(num_iters):\n",
    "    optimizer = torch.optim.LBFGS([dummy_data, dummy_label] )\n",
    "    # optimizer = myLBFGS([dummy_data, dummy_label] )\n",
    "\n",
    "    history = []\n",
    "    for iters in range(num_iters):\n",
    "        def closure():\n",
    "            optimizer.zero_grad()\n",
    "            grad_diff = predict()\n",
    "            grad_diff.backward()\n",
    "            return grad_diff\n",
    "        \n",
    "        optimizer.step(closure)\n",
    "        if iters % 10 == 0: \n",
    "            current_loss = closure()\n",
    "            print(iters, \"%.4f\" % current_loss.item())\n",
    "        history.append(tt(dummy_data[0].cpu()))\n",
    "    \n",
    "    return history\n",
    "\n",
    "def gradient_descent(num_iters, learning_rate):\n",
    "    global dummy_data, dummy_label\n",
    "    history = []\n",
    "    for iters in range(num_iters):\n",
    "        grad_diff = predict()\n",
    "        Grad = torch.autograd.grad(grad_diff, (dummy_data, dummy_label))\n",
    "        dummy_data = dummy_data - learning_rate * Grad[0]\n",
    "        dummy_label = dummy_label - learning_rate * Grad[1]\n",
    "        if iters % 10 == 0: \n",
    "            current_loss = predict()\n",
    "            print(iters, \"%.4f\" % current_loss.item())\n",
    "        history.append(tt(dummy_data[0].cpu()))\n",
    "\n",
    "    return history\n",
    "\n",
    "def gradient_descent_batched(num_iters, learning_rate):\n",
    "    global dummy_data, dummy_label\n",
    "    history = []\n",
    "    batch_size = dummy_data.shape[0]\n",
    "    for iters in range(num_iters):\n",
    "        target = iters % batch_size\n",
    "\n",
    "        grad_diff = predict()\n",
    "        Grad = torch.autograd.grad(grad_diff, (dummy_data, dummy_label))\n",
    "        dummy_data[target] = dummy_data[target] - learning_rate * Grad[0][target]\n",
    "        dummy_label[target] = dummy_label[target] - learning_rate * Grad[1][target]\n",
    "        if iters % 10 == 0: \n",
    "            current_loss = predict()\n",
    "            print(iters, \"%.4f\" % current_loss.item())\n",
    "        history.append(tt(dummy_data[0].cpu()))\n",
    "\n",
    "    return history\n",
    "\n",
    "history = lbfgs(300)\n",
    "# history = gradient_descent_batched(300, 1e-3)\n",
    "# history = gradient_descent(300, 1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Dummy label is 84.\n"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 864x576 with 30 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "for i in range(30):\n",
    "  plt.subplot(3, 10, i + 1)\n",
    "  iter = len(history) - (30-i)*10\n",
    "  plt.imshow(history[iter])\n",
    "  plt.title(\"iter=%d\" % iter)\n",
    "  plt.axis('off')\n",
    "\n",
    "print(\"Dummy label is %d.\" % torch.argmax(dummy_label[0], dim=-1).item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Dummy label is 84.\n"
    },
    {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_dummy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "body.0.weight\ntorch.Size([12, 3, 5, 5])\nbody.0.bias\ntorch.Size([12])\nbody.2.weight\ntorch.Size([12, 12, 5, 5])\nbody.2.bias\ntorch.Size([12])\nbody.4.weight\ntorch.Size([12, 12, 5, 5])\nbody.4.bias\ntorch.Size([12])\nbody.6.weight\ntorch.Size([12, 12, 5, 5])\nbody.6.bias\ntorch.Size([12])\nfc.0.weight\ntorch.Size([100, 768])\nfc.0.bias\ntorch.Size([100])\n"
    }
   ],
   "source": [
    "# state = net.state_dict()\n",
    "# for key in state.keys():\n",
    "#     print(key, state[key].size())\n",
    "\n",
    "for name,para in net.named_parameters():\n",
    "    print(name)\n",
    "    print(para.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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