Active_Learning_GCN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/NitinPrasad5/Active-Learning_Graph-Convolution-Network/blob/main/Active_Learning_GCN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wD_uOY6ePN3z",
        "outputId": "978dec0e-4f4e-4d89-a5af-fb146bb9eefa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data"
      ],
      "metadata": {
        "id": "yanTGH6X48Dc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "class SubsetSequentialSampler(torch.utils.data.Sampler):\n",
        "    r\"\"\"Samples elements sequentially from a given list of indices, without replacement.\n",
        "\n",
        "    Arguments:\n",
        "        indices (sequence): a sequence of indices\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, indices):\n",
        "        self.indices = indices\n",
        "\n",
        "    def __iter__(self):\n",
        "        return (self.indices[i] for i in range(len(self.indices)))\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.indices)"
      ],
      "metadata": {
        "id": "dFWOLHV45WMb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load DataSet"
      ],
      "metadata": {
        "id": "JaajeiND5JLy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "import torchvision.transforms as T\n",
        "from torchvision.datasets import CIFAR100, CIFAR10, FashionMNIST, SVHN, GTSRB\n",
        "\n",
        "class MyDataset(Dataset):\n",
        "    def __init__(self, dataset_name, train_flag, transf):\n",
        "        self.dataset_name = dataset_name\n",
        "        if self.dataset_name == \"cifar10\":\n",
        "            self.cifar10 = CIFAR10('../cifar10', train=train_flag,\n",
        "                                    download=True, transform=transf)\n",
        "        if self.dataset_name == \"svhn\":\n",
        "            self.svhn = SVHN('../svhn', split=\"train\",\n",
        "                                    download=True, transform=transf)\n",
        "        if self.dataset_name == \"gtsrb\":\n",
        "            self.gtsrb = GTSRB('../gtsrb', split=\"train\",\n",
        "                                    download=True, transform=transf)\n",
        "\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        if self.dataset_name == \"cifar10\":\n",
        "            data, target = self.cifar10[index]\n",
        "        if self.dataset_name == \"svhn\":\n",
        "            data, target = self.svhn[index]\n",
        "        if self.dataset_name == \"gtsrb\":\n",
        "            data, target = self.gtsrb[index]\n",
        "        return data, target, index\n",
        "\n",
        "    def __len__(self):\n",
        "        if self.dataset_name == \"cifar10\":\n",
        "            return len(self.cifar10)\n",
        "        elif self.dataset_name == \"svhn\":\n",
        "            return len(self.svhn)\n",
        "        elif self.dataset_name == \"gtsrb\":\n",
        "            return len(self.gtsrb)\n",
        "\n",
        "# Data\n",
        "def load_dataset(dataset):\n",
        "    target_size = (32, 32)\n",
        "    train_transform = T.Compose([\n",
        "        T.Resize(target_size),\n",
        "        T.RandomHorizontalFlip(),\n",
        "        T.RandomCrop(size=32, padding=4),\n",
        "        T.ToTensor(),\n",
        "        T.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]) # T.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761)) # CIFAR-100\n",
        "    ])\n",
        "\n",
        "    test_transform = T.Compose([\n",
        "        T.Resize(target_size),\n",
        "        T.ToTensor(),\n",
        "        T.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]) # T.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761)) # CIFAR-100\n",
        "    ])\n",
        "\n",
        "\n",
        "    if dataset == 'cifar10':\n",
        "        data_train = CIFAR10('../cifar10', train=True, download=True, transform=train_transform)\n",
        "        data_unlabeled = MyDataset(dataset, True, test_transform)\n",
        "        data_test  = CIFAR10('../cifar10', train=False, download=True, transform=test_transform)\n",
        "        NO_CLASSES = 10\n",
        "        adden = ADDENDUM\n",
        "        no_train = NUM_TRAIN\n",
        "\n",
        "    elif dataset == 'gtsrb':\n",
        "        data_train = GTSRB('../gtsrb', split='train', download=True, transform=train_transform)\n",
        "        data_unlabeled = MyDataset(dataset, True, test_transform)\n",
        "        data_test  = GTSRB('../gtsrb', split='test', download=True, transform=test_transform)\n",
        "        NO_CLASSES = 43\n",
        "        adden = ADDENDUM\n",
        "        no_train = len(data_train)#NUM_TRAIN\n",
        "\n",
        "    elif dataset == 'svhn':\n",
        "        data_train = SVHN('../svhn', split='train', download=True,\n",
        "                                    transform=T.Compose([T.ToTensor()]))\n",
        "        data_unlabeled = MyDataset(dataset, True, T.Compose([T.ToTensor()]))\n",
        "        data_test  = SVHN('../svhn', split='test', download=True,\n",
        "                                    transform=T.Compose([T.ToTensor()]))\n",
        "        NO_CLASSES = 10\n",
        "        adden = ADDENDUM\n",
        "        no_train = NUM_TRAIN\n",
        "    return data_train, data_unlabeled, data_test, adden, NO_CLASSES, no_train"
      ],
      "metadata": {
        "id": "n-Li-NCp6cfT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Models"
      ],
      "metadata": {
        "id": "rNaoIWil4-Pz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Query Model\n",
        "\n"
      ],
      "metadata": {
        "id": "w7OzJRn15q6r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.init as init\n",
        "import torch.nn.functional as F\n",
        "from torch.nn.parameter import Parameter\n",
        "from torch.nn.modules.module import Module\n",
        "\n",
        "class GraphConvolution(Module):\n",
        "    \"\"\"\n",
        "    Simple GCN layer, similar to https://arxiv.org/abs/1609.02907\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, in_features, out_features, bias=True):\n",
        "        super(GraphConvolution, self).__init__()\n",
        "        self.in_features = in_features\n",
        "        self.out_features = out_features\n",
        "        self.weight = Parameter(torch.FloatTensor(in_features, out_features))\n",
        "        if bias:\n",
        "            self.bias = Parameter(torch.FloatTensor(out_features))\n",
        "        else:\n",
        "            self.register_parameter('bias', None)\n",
        "        self.reset_parameters()\n",
        "\n",
        "    def reset_parameters(self):\n",
        "        stdv = 1. / math.sqrt(self.weight.size(1))\n",
        "        self.weight.data.uniform_(-stdv, stdv)\n",
        "        if self.bias is not None:\n",
        "            self.bias.data.uniform_(-stdv, stdv)\n",
        "\n",
        "    def forward(self, input, adj):\n",
        "        support = torch.mm(input, self.weight)\n",
        "        output = torch.spmm(adj, support)\n",
        "        if self.bias is not None:\n",
        "            return output + self.bias\n",
        "        else:\n",
        "            return output\n",
        "\n",
        "    def __repr__(self):\n",
        "        return self.__class__.__name__ + ' (' \\\n",
        "               + str(self.in_features) + ' -> ' \\\n",
        "               + str(self.out_features) + ')'\n",
        "\n",
        "class GCN(nn.Module):\n",
        "    def __init__(self, nfeat, nhid, nclass, dropout):\n",
        "        super(GCN, self).__init__()\n",
        "\n",
        "        self.gc1 = GraphConvolution(nfeat, nhid)\n",
        "        self.gc2 = GraphConvolution(nhid, nhid)\n",
        "        self.gc3 = GraphConvolution(nhid, nclass)\n",
        "        self.dropout = dropout\n",
        "        self.linear = nn.Linear(nclass, 1)\n",
        "\n",
        "    def forward(self, x, adj):\n",
        "        x = F.relu(self.gc1(x, adj))\n",
        "        feat = F.dropout(x, self.dropout, training=self.training)\n",
        "        x = self.gc3(feat, adj)\n",
        "        #x = self.linear(x)\n",
        "        # x = F.softmax(x, dim=1)\n",
        "        return torch.sigmoid(x), feat, torch.cat((feat,x),1)\n"
      ],
      "metadata": {
        "id": "7ETM9QJz5bOL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ResNet"
      ],
      "metadata": {
        "id": "aaNPD1Mi6CX-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''ResNet & VGG in PyTorch.\n",
        "\n",
        "'''\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import numpy as np\n",
        "from torch.distributions import Normal\n",
        "import math\n",
        "\n",
        "class BasicBlock(nn.Module):\n",
        "    expansion = 1\n",
        "\n",
        "    def __init__(self, in_planes, planes, stride=1):\n",
        "        super(BasicBlock, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(planes)\n",
        "        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn2 = nn.BatchNorm2d(planes)\n",
        "\n",
        "        self.shortcut = nn.Sequential()\n",
        "        if stride != 1 or in_planes != self.expansion*planes:\n",
        "            self.shortcut = nn.Sequential(\n",
        "                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),\n",
        "                nn.BatchNorm2d(self.expansion*planes)\n",
        "            )\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = F.relu(self.bn1(self.conv1(x)))\n",
        "        out = self.bn2(self.conv2(out))\n",
        "        out += self.shortcut(x)\n",
        "        out = F.relu(out)\n",
        "        return out\n",
        "\n",
        "\n",
        "class Bottleneck(nn.Module):\n",
        "    expansion = 4\n",
        "\n",
        "    def __init__(self, in_planes, planes, stride=1):\n",
        "        super(Bottleneck, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(planes)\n",
        "        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n",
        "        self.bn2 = nn.BatchNorm2d(planes)\n",
        "        self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)\n",
        "        self.bn3 = nn.BatchNorm2d(self.expansion*planes)\n",
        "\n",
        "        self.shortcut = nn.Sequential()\n",
        "        if stride != 1 or in_planes != self.expansion*planes:\n",
        "            self.shortcut = nn.Sequential(\n",
        "                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),\n",
        "                nn.BatchNorm2d(self.expansion*planes)\n",
        "            )\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = F.relu(self.bn1(self.conv1(x)))\n",
        "        out = F.relu(self.bn2(self.conv2(out)))\n",
        "        out = self.bn3(self.conv3(out))\n",
        "        out += self.shortcut(x)\n",
        "        out = F.relu(out)\n",
        "        return out\n",
        "\n",
        "\n",
        "class ResNet(nn.Module):\n",
        "    def __init__(self, block, num_blocks, num_classes=10):\n",
        "        super(ResNet, self).__init__()\n",
        "        self.in_planes = 64\n",
        "\n",
        "        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(64)\n",
        "        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)\n",
        "        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)\n",
        "        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)\n",
        "        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)\n",
        "        self.linear = nn.Linear(512*block.expansion, num_classes)\n",
        "        # self.linear2 = nn.Linear(1000, num_classes)\n",
        "\n",
        "    def _make_layer(self, block, planes, num_blocks, stride):\n",
        "        strides = [stride] + [1]*(num_blocks-1)\n",
        "        layers = []\n",
        "        for stride in strides:\n",
        "            layers.append(block(self.in_planes, planes, stride))\n",
        "            self.in_planes = planes * block.expansion\n",
        "        return nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = F.relu(self.bn1(self.conv1(x)))\n",
        "        out1 = self.layer1(out)\n",
        "        out2 = self.layer2(out1)\n",
        "        out3 = self.layer3(out2)\n",
        "        out4 = self.layer4(out3)\n",
        "        out = F.avg_pool2d(out4, 4)\n",
        "        outf = out.view(out.size(0), -1)\n",
        "        # outl = self.linear(outf)\n",
        "        out = self.linear(outf)\n",
        "        return out, outf, [out1, out2, out3, out4]\n",
        "\n",
        "\n",
        "def ResNet18(num_classes = 10):\n",
        "    return ResNet(BasicBlock, [2,2,2,2], num_classes)\n"
      ],
      "metadata": {
        "id": "iD76o0Cn6HST"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Selection Method"
      ],
      "metadata": {
        "id": "HRJTtkAd5QPD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import DataLoader\n",
        "import torch.optim as optim\n",
        "# Custom\n",
        "# from config import *\n",
        "# from models.query_models import VAE, Discriminator, GCN\n",
        "# from data.sampler import SubsetSequentialSampler\n",
        "# from kcenterGreedy import kCenterGreedy\n",
        "\n",
        "def BCEAdjLoss(scores, lbl, nlbl, l_adj):\n",
        "    lnl = torch.log(scores[lbl])\n",
        "    lnu = torch.log(1 - scores[nlbl])\n",
        "    labeled_score = torch.mean(lnl)\n",
        "    unlabeled_score = torch.mean(lnu)\n",
        "    bce_adj_loss = -labeled_score - l_adj*unlabeled_score\n",
        "    return bce_adj_loss\n",
        "\n",
        "\n",
        "def aff_to_adj(x, y=None):\n",
        "    x = x.detach().cpu().numpy()\n",
        "    adj = np.matmul(x, x.transpose())\n",
        "    adj +=  -1.0*np.eye(adj.shape[0])\n",
        "    adj_diag = np.sum(adj, axis=0) #rowise sum\n",
        "    adj = np.matmul(adj, np.diag(1/adj_diag))\n",
        "    adj = adj + np.eye(adj.shape[0])\n",
        "    adj = torch.Tensor(adj).cuda()\n",
        "\n",
        "    return adj\n",
        "\n",
        "\n",
        "def get_features(models, unlabeled_loader):\n",
        "    models['backbone'].eval()\n",
        "    with torch.cuda.device(CUDA_VISIBLE_DEVICES):\n",
        "        features = torch.tensor([]).cuda()\n",
        "    with torch.no_grad():\n",
        "            for inputs, _, _ in unlabeled_loader:\n",
        "                with torch.cuda.device(CUDA_VISIBLE_DEVICES):\n",
        "                    inputs = inputs.cuda()\n",
        "                    _, features_batch, _ = models['backbone'](inputs)\n",
        "                features = torch.cat((features, features_batch), 0)\n",
        "            feat = features #.detach().cpu().numpy()\n",
        "    return feat\n",
        "\n",
        "\n",
        "\n",
        "# Select the indices of the unlablled data according to the methods\n",
        "def query_samples(model, method, data_unlabeled, subset, labeled_set, cycle, args):\n",
        "\n",
        "    if method == 'Random':\n",
        "        arg = np.random.randint(SUBSET, size=SUBSET)\n",
        "\n",
        "    if method == 'UncertainGCN':\n",
        "        # Create unlabeled dataloader for the unlabeled subset\n",
        "        unlabeled_loader = DataLoader(data_unlabeled, batch_size=BATCH,\n",
        "                sampler=SubsetSequentialSampler(subset+labeled_set), # more convenient if we maintain the order of subset\n",
        "                                    pin_memory=True)\n",
        "        binary_labels = torch.cat((torch.zeros([SUBSET, 1]),(torch.ones([len(labeled_set),1]))),0)\n",
        "\n",
        "\n",
        "        features = get_features(model, unlabeled_loader)\n",
        "        features = nn.functional.normalize(features)\n",
        "        adj = aff_to_adj(features)\n",
        "\n",
        "        gcn_module = GCN(nfeat=features.shape[1],\n",
        "                         nhid=args.hidden_units,\n",
        "                         nclass=1,\n",
        "                         dropout=args.dropout_rate).cuda()\n",
        "\n",
        "        models      = {'gcn_module': gcn_module}\n",
        "\n",
        "        optim_backbone = optim.Adam(models['gcn_module'].parameters(), lr=LR_GCN, weight_decay=WDECAY)\n",
        "        optimizers = {'gcn_module': optim_backbone}\n",
        "        # if cycle > 0:\n",
        "        #     lbl = np.arange(SUBSET, SUBSET + ADDENDUM + 200, 1)\n",
        "        # else:\n",
        "        #     lbl = np.arange(SUBSET, SUBSET + ADDENDUM, 1)\n",
        "\n",
        "        lbl = np.arange(SUBSET, SUBSET+(cycle+1)*ADDENDUM, 1)\n",
        "        nlbl = np.arange(0, SUBSET, 1)\n",
        "\n",
        "        ############\n",
        "        for _ in range(200):\n",
        "\n",
        "            optimizers['gcn_module'].zero_grad()\n",
        "            outputs, _, _ = models['gcn_module'](features, adj)\n",
        "            lamda = args.lambda_loss\n",
        "            loss = BCEAdjLoss(outputs, lbl, nlbl, lamda)\n",
        "            loss.backward()\n",
        "            optimizers['gcn_module'].step()\n",
        "\n",
        "\n",
        "        models['gcn_module'].eval()\n",
        "        with torch.no_grad():\n",
        "            with torch.cuda.device(CUDA_VISIBLE_DEVICES):\n",
        "                inputs = features.cuda()\n",
        "                labels = binary_labels.cuda()\n",
        "\n",
        "            scores, _, feat = models['gcn_module'](inputs, adj)\n",
        "\n",
        "            s_margin = args.s_margin\n",
        "            scores_median = np.squeeze(torch.abs(scores[:SUBSET] - s_margin).detach().cpu().numpy())\n",
        "            arg = np.argsort(-(scores_median))\n",
        "\n",
        "            print(\"Max confidence value: \",torch.max(scores.data))\n",
        "            print(\"Mean confidence value: \",torch.mean(scores.data))\n",
        "            preds = torch.round(scores)\n",
        "            correct_labeled = (preds[SUBSET:,0] == labels[SUBSET:,0]).sum().item() / ((cycle+1)*ADDENDUM)\n",
        "            correct_unlabeled = (preds[:SUBSET,0] == labels[:SUBSET,0]).sum().item() / SUBSET\n",
        "            correct = (preds[:,0] == labels[:,0]).sum().item() / (SUBSET + (cycle+1)*ADDENDUM)\n",
        "            print(\"Labeled classified: \", correct_labeled)\n",
        "            print(\"Unlabeled classified: \", correct_unlabeled)\n",
        "            print(\"Total classified: \", correct)\n",
        "\n",
        "    return arg"
      ],
      "metadata": {
        "id": "8Wn9Ss586szr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train Test"
      ],
      "metadata": {
        "id": "Jno9bnrJ5Stj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import torch\n",
        "from tqdm import tqdm\n",
        "import numpy as np\n",
        "import torchvision.transforms as T\n",
        "# import models.resnet as resnet\n",
        "\n",
        "\n",
        "def test(models, epoch, method, dataloaders, mode='val'):\n",
        "    assert mode == 'val' or mode == 'test'\n",
        "    models['backbone'].eval()\n",
        "    total = 0\n",
        "    correct = 0\n",
        "    with torch.no_grad():\n",
        "        for (inputs, labels) in dataloaders[mode]:\n",
        "            with torch.cuda.device(CUDA_VISIBLE_DEVICES):\n",
        "                inputs = inputs.cuda()\n",
        "                labels = labels.cuda()\n",
        "\n",
        "            scores, _, _ = models['backbone'](inputs)\n",
        "            _, preds = torch.max(scores.data, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (preds == labels).sum().item()\n",
        "\n",
        "    return 100 * correct / total\n",
        "\n",
        "def get_sub_labeled_data(models, epoch, method, dataloaders, mode='val'):\n",
        "    assert mode == 'val' or mode == 'test'\n",
        "    models['backbone'].eval()\n",
        "    data_indices = []\n",
        "    confidence_scores = []\n",
        "    with torch.no_grad():\n",
        "        for batch_idx, (inputs, labels) in enumerate(dataloaders['labeled']):\n",
        "            sampled_indices = list(dataloaders['labeled'].batch_sampler.sampler.indices)\n",
        "            indices_in_batch = sampled_indices[batch_idx * BATCH:(batch_idx + 1) * BATCH]\n",
        "            data_indices += indices_in_batch\n",
        "            with torch.cuda.device(CUDA_VISIBLE_DEVICES):\n",
        "                inputs = inputs.cuda()\n",
        "                labels = labels.cuda()\n",
        "\n",
        "            scores, _, _ = models['backbone'](inputs)\n",
        "            _, preds = torch.max(scores.data, 1)\n",
        "            #predicted_scores = scores[range(len(preds)), preds]\n",
        "            predicted_scores = [max(scores[i]) for i in range(len(scores))]\n",
        "            confidence_scores.extend(predicted_scores)\n",
        "\n",
        "    combined_data = list(zip(data_indices, confidence_scores))\n",
        "    sorted_data = sorted(combined_data, key=lambda x: x[1])\n",
        "    sorted_indices = [index for index, _ in sorted_data]\n",
        "    confidence_scores = [score for index, score in sorted_data]\n",
        "    print(\"first 5 comf score: \",confidence_scores[0],confidence_scores[1],confidence_scores[2],confidence_scores[3],confidence_scores[4])\n",
        "    return sorted_indices\n",
        "\n",
        "\n",
        "iters = 0\n",
        "def train_epoch(models, method, criterion, optimizers, dataloaders, epoch, epoch_loss):\n",
        "    models['backbone'].train()\n",
        "    if method == 'lloss':\n",
        "        models['module'].train()\n",
        "    global iters\n",
        "    for data in tqdm(dataloaders['train'], leave=False, total=len(dataloaders['train'])):\n",
        "        with torch.cuda.device(CUDA_VISIBLE_DEVICES):\n",
        "            inputs = data[0].cuda()\n",
        "            labels = data[1].cuda()\n",
        "\n",
        "        iters += 1\n",
        "\n",
        "        optimizers['backbone'].zero_grad()\n",
        "\n",
        "        scores, _, features = models['backbone'](inputs)\n",
        "        target_loss = criterion(scores, labels)\n",
        "\n",
        "        m_backbone_loss = torch.sum(target_loss) / target_loss.size(0)\n",
        "        loss = m_backbone_loss\n",
        "\n",
        "        loss.backward()\n",
        "        optimizers['backbone'].step()\n",
        "        if method == 'lloss':\n",
        "            optimizers['module'].step()\n",
        "    return loss\n",
        "\n",
        "def train(models, method, criterion, optimizers, schedulers, dataloaders, num_epochs, epoch_loss):\n",
        "    print('>> Train a Model.')\n",
        "    best_acc = 0.\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "\n",
        "        best_loss = torch.tensor([0.5]).cuda()\n",
        "        loss = train_epoch(models, method, criterion, optimizers, dataloaders, epoch, epoch_loss)\n",
        "\n",
        "        schedulers['backbone'].step()\n",
        "        if method == 'lloss':\n",
        "            schedulers['module'].step()\n",
        "\n",
        "        if epoch % 20  == 7:\n",
        "            acc = test(models, epoch, method, dataloaders, mode='test')\n",
        "            # acc = test(models, dataloaders, mc, 'test')\n",
        "            if best_acc < acc:\n",
        "                best_acc = acc\n",
        "                print('Val Acc: {:.3f} \\t Best Acc: {:.3f}'.format(acc, best_acc))\n",
        "    print('>> Finished.')"
      ],
      "metadata": {
        "id": "WHVT61am61T0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Config"
      ],
      "metadata": {
        "id": "-f3rIeTc5AcD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "''' Configuration File.\n",
        "'''\n",
        "\n",
        "CUDA_VISIBLE_DEVICES = 0\n",
        "NUM_TRAIN = 39208 # N \\Fashion MNIST 60000, cifar 10/100 50000\n",
        "# NUM_VAL   = 50000 - NUM_TRAIN\n",
        "BATCH     = 128 # B\n",
        "SUBSET    = 10000 # M\n",
        "ADDENDUM  = 1000 # K\n",
        "\n",
        "MARGIN = 1.0 # xi\n",
        "WEIGHT = 1.0 # lambda\n",
        "\n",
        "TRIALS = 1\n",
        "CYCLES = 3\n",
        "\n",
        "EPOCH = 200\n",
        "EPOCH_GCN = 200\n",
        "LR = 3e-1\n",
        "LR_GCN = 1e-3\n",
        "MILESTONES = [160, 360,560,760,960]\n",
        "EPOCHL = 120#20 #120 # After 120 epochs, stop\n",
        "EPOCHV = 100 # VAAL number of epochs\n",
        "\n",
        "MOMENTUM = 0.9\n",
        "WDECAY = 5e-4#2e-3# 5e-4"
      ],
      "metadata": {
        "id": "Y3oAy8Dk6Pf7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Main"
      ],
      "metadata": {
        "id": "I9ah0XbD5Lw7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "GCN Active Learning\n",
        "'''\n",
        "\n",
        "# Python\n",
        "import os\n",
        "import random\n",
        "import torch\n",
        "import numpy as np\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import DataLoader\n",
        "import torch.optim.lr_scheduler as lr_scheduler\n",
        "from torch.utils.data.sampler import SubsetRandomSampler\n",
        "import torchvision.transforms as T\n",
        "import torchvision.models as models\n",
        "import argparse\n",
        "# Custom\n",
        "# import models.resnet as resnet\n",
        "# from models.resnet import vgg11\n",
        "# from models.query_models import LossNet\n",
        "# from train_test import train, test\n",
        "# from load_dataset import load_dataset\n",
        "# from selection_methods import query_samples\n",
        "# from config import *\n",
        "\n",
        "\n",
        "parser = argparse.ArgumentParser()\n",
        "parser.add_argument('-f')\n",
        "parser.add_argument(\"-l\",\"--lambda_loss\",type=float, default=1.2,\n",
        "                    help=\"Adjustment graph loss parameter between the labeled and unlabeled\")\n",
        "parser.add_argument(\"-s\",\"--s_margin\", type=float, default=0.1,\n",
        "                    help=\"Confidence margin of graph\")\n",
        "parser.add_argument(\"-n\",\"--hidden_units\", type=int, default=128,\n",
        "                    help=\"Number of hidden units of the graph\")\n",
        "parser.add_argument(\"-r\",\"--dropout_rate\", type=float, default=0.3,\n",
        "                    help=\"Dropout rate of the graph neural network\")\n",
        "parser.add_argument(\"-d\",\"--dataset\", type=str, default=\"gtsrb\",\n",
        "                    help=\"\")\n",
        "parser.add_argument(\"-e\",\"--no_of_epochs\", type=int, default=20,\n",
        "                    help=\"Number of epochs for the active learner\")\n",
        "parser.add_argument(\"-m\",\"--method_type\", type=str, default=\"UncertainGCN\",\n",
        "                    help=\"\")\n",
        "parser.add_argument(\"-c\",\"--cycles\", type=int, default=4,\n",
        "                    help=\"Number of active learning cycles\")\n",
        "parser.add_argument(\"-t\",\"--total\", type=bool, default=False,\n",
        "                    help=\"Training on the entire dataset\")\n",
        "\n",
        "args = parser.parse_args()\n",
        "\n",
        "##\n",
        "# Main\n",
        "if __name__ == '__main__':\n",
        "\n",
        "    method = args.method_type\n",
        "    methods = ['Random', 'UncertainGCN', 'CoreGCN', 'CoreSet', 'lloss','VAAL']\n",
        "    datasets = ['cifar10', 'cifar100', 'fashionmnist','svhn','gtsrb']\n",
        "    assert method in methods, 'No method %s! Try options %s'%(method, methods)\n",
        "    assert args.dataset in datasets, 'No dataset %s! Try options %s'%(args.dataset, datasets)\n",
        "    '''\n",
        "    method_type: 'Random', 'UncertainGCN', 'CoreGCN', 'CoreSet', 'lloss','VAAL'\n",
        "    '''\n",
        "    results = open('results_'+str(args.method_type)+\"_\"+args.dataset +'_main'+str(args.cycles)+str(args.total)+'.txt','w')\n",
        "    print(\"Dataset: %s\"%args.dataset)\n",
        "    print(\"Method type:%s\"%method)\n",
        "    if args.total:\n",
        "        TRIALS = 1\n",
        "        CYCLES = 1\n",
        "    else:\n",
        "        CYCLES = args.cycles\n",
        "    for trial in range(TRIALS):\n",
        "\n",
        "        # Load training and testing dataset\n",
        "        data_train, data_unlabeled, data_test, adden, NO_CLASSES, no_train = load_dataset(args.dataset)\n",
        "        # Don't predefine budget size. Configure it in the config.py: ADDENDUM = adden\n",
        "        NUM_TRAIN = no_train\n",
        "        indices = list(range(NUM_TRAIN))\n",
        "        random.shuffle(indices)\n",
        "\n",
        "        if args.total:\n",
        "            labeled_set= indices\n",
        "        else:\n",
        "            labeled_set = indices[:ADDENDUM]\n",
        "            unlabeled_set = [x for x in indices if x not in labeled_set]\n",
        "\n",
        "        train_loader = DataLoader(data_train, batch_size=BATCH,\n",
        "                                    sampler=SubsetRandomSampler(labeled_set),\n",
        "                                    pin_memory=True, drop_last=True)\n",
        "\n",
        "        test_loader  = DataLoader(data_test, batch_size=BATCH)\n",
        "\n",
        "        dataloaders  = {'train': train_loader, 'test': test_loader}\n",
        "\n",
        "        for cycle in range(CYCLES):\n",
        "\n",
        "            # Randomly sample 10000 unlabeled data points\n",
        "            if not args.total:\n",
        "                random.shuffle(unlabeled_set)\n",
        "                subset = unlabeled_set[:SUBSET]\n",
        "\n",
        "            # Model - create new instance for every cycle so that it resets\n",
        "            with torch.cuda.device(CUDA_VISIBLE_DEVICES):\n",
        "                resnet18 = ResNet18(num_classes=NO_CLASSES).cuda()\n",
        "\n",
        "            models = {'backbone': resnet18}\n",
        "\n",
        "            # Loss, criterion and scheduler (re)initialization\n",
        "            criterion      = nn.CrossEntropyLoss(reduction='none')\n",
        "            optim_backbone = optim.SGD(models['backbone'].parameters(), lr=LR,\n",
        "                momentum=MOMENTUM, weight_decay=WDECAY)\n",
        "\n",
        "            sched_backbone = lr_scheduler.MultiStepLR(optim_backbone, milestones=MILESTONES)\n",
        "            optimizers = {'backbone': optim_backbone}\n",
        "            schedulers = {'backbone': sched_backbone}\n",
        "\n",
        "            # Training and testing\n",
        "            train(models, method, criterion, optimizers, schedulers, dataloaders, args.no_of_epochs, EPOCHL)\n",
        "            acc = test(models, EPOCH, method, dataloaders, mode='test')\n",
        "            print('Trial {}/{} || Cycle {}/{} || Label set size {}: Test acc {}'.format(trial+1, TRIALS, cycle+1, CYCLES, len(labeled_set), acc))\n",
        "            np.array([method, trial+1, TRIALS, cycle+1, CYCLES, len(labeled_set), acc]).tofile(results, sep=\" \")\n",
        "            results.write(\"\\n\")\n",
        "\n",
        "\n",
        "            if cycle == (CYCLES-1):\n",
        "                # Reached final training cycle\n",
        "                print(\"Finished.\")\n",
        "                break\n",
        "            # Get the indices of the unlabeled samples to train on next cycle\n",
        "            arg = query_samples(models, method, data_unlabeled, subset, labeled_set, cycle, args)\n",
        "\n",
        "            # Update the labeled dataset and the unlabeled dataset, respectively\n",
        "            sampled_from_labeled = get_sub_labeled_data(models, EPOCH, method, dataloaders, mode='test')\n",
        "            labeled_set = list(torch.tensor(subset)[arg][-ADDENDUM:].numpy()) + sampled_from_labeled[:200]\n",
        "            listd = list(torch.tensor(subset)[arg][:-ADDENDUM].numpy())\n",
        "            unlabeled_set = listd + unlabeled_set[SUBSET:]\n",
        "            print(len(labeled_set), min(labeled_set), max(labeled_set))\n",
        "            # Create a new dataloader for the updated labeled dataset\n",
        "            dataloaders['train'] = DataLoader(data_train, batch_size=BATCH,\n",
        "                                            sampler=SubsetRandomSampler(labeled_set),\n",
        "                                            pin_memory=True)\n",
        "\n",
        "    results.close()\n",
        "\n",
        "import shutil\n",
        "\n",
        "# Define the destination directory in Google Drive\n",
        "drive_directory_cifar = \"/content/drive/MyDrive/B.Tech./4th year/sem1/AML/Assignment1/Programming_Assignment/Results_CIFAR10\"\n",
        "drive_directory_cifar = \"/content/drive/MyDrive/B.Tech./4th year/sem1/AML/Assignment1/Programming_Assignment/Results_GTSRB\"\n",
        "\n",
        "result_file_path = 'content/results_'+str(args.method_type)+\"_\"+args.dataset +'_main'+str(args.cycles)+str(args.total)+'.txt'\n",
        "\n",
        "# Copy the file to the destination directory in Google Drive\n",
        "if(args.dataset == 'cifar10'):\n",
        "    shutil.copy(file_path, drive_directory_cifar)\n",
        "elif(args.dataset == 'gtsrb'):\n",
        "    shutil.copy(file_path, drive_directory_gtsrb)\n"
      ],
      "metadata": {
        "id": "o6NjVaoc6jpb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Transfer learning"
      ],
      "metadata": {
        "id": "-_AOO7RHcmHz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "GCN Active Learning\n",
        "'''\n",
        "\n",
        "# Python\n",
        "import os\n",
        "import random\n",
        "import torch\n",
        "import numpy as np\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import DataLoader\n",
        "import torch.optim.lr_scheduler as lr_scheduler\n",
        "from torch.utils.data.sampler import SubsetRandomSampler\n",
        "import torchvision.transforms as T\n",
        "import torchvision.models as models\n",
        "import argparse\n",
        "# Custom\n",
        "# import models.resnet as resnet\n",
        "# from models.resnet import vgg11\n",
        "# from models.query_models import LossNet\n",
        "# from train_test import train, test\n",
        "# from load_dataset import load_dataset\n",
        "# from selection_methods import query_samples\n",
        "# from config import *\n",
        "\n",
        "\n",
        "parser = argparse.ArgumentParser()\n",
        "parser.add_argument('-f')\n",
        "parser.add_argument(\"-l\",\"--lambda_loss\",type=float, default=1.2,\n",
        "                    help=\"Adjustment graph loss parameter between the labeled and unlabeled\")\n",
        "parser.add_argument(\"-s\",\"--s_margin\", type=float, default=0.1,\n",
        "                    help=\"Confidence margin of graph\")\n",
        "parser.add_argument(\"-n\",\"--hidden_units\", type=int, default=128,\n",
        "                    help=\"Number of hidden units of the graph\")\n",
        "parser.add_argument(\"-r\",\"--dropout_rate\", type=float, default=0.3,\n",
        "                    help=\"Dropout rate of the graph neural network\")\n",
        "parser.add_argument(\"-d\",\"--dataset\", type=str, default=\"gtsrb\",\n",
        "                    help=\"\")\n",
        "parser.add_argument(\"-e\",\"--no_of_epochs\", type=int, default=200,\n",
        "                    help=\"Number of epochs for the active learner\")\n",
        "parser.add_argument(\"-m\",\"--method_type\", type=str, default=\"UncertainGCN\",\n",
        "                    help=\"\")\n",
        "parser.add_argument(\"-c\",\"--cycles\", type=int, default=10,\n",
        "                    help=\"Number of active learning cycles\")\n",
        "parser.add_argument(\"-t\",\"--total\", type=bool, default=False,\n",
        "                    help=\"Training on the entire dataset\")\n",
        "\n",
        "args = parser.parse_args()\n",
        "\n",
        "##\n",
        "# Main\n",
        "if __name__ == '__main__':\n",
        "\n",
        "    method = args.method_type\n",
        "    methods = ['Random', 'UncertainGCN', 'CoreGCN', 'CoreSet', 'lloss','VAAL']\n",
        "    datasets = ['cifar10', 'cifar100', 'fashionmnist','svhn','gtsrb']\n",
        "    assert method in methods, 'No method %s! Try options %s'%(method, methods)\n",
        "    assert args.dataset in datasets, 'No dataset %s! Try options %s'%(args.dataset, datasets)\n",
        "    '''\n",
        "    method_type: 'Random', 'UncertainGCN', 'CoreGCN', 'CoreSet', 'lloss','VAAL'\n",
        "    '''\n",
        "    results = open('results_'+str(args.method_type)+\"_\"+args.dataset +'_main'+str(args.cycles)+str(args.total)+'.txt','w')\n",
        "    print(\"Dataset: %s\"%args.dataset)\n",
        "    print(\"Method type:%s\"%method)\n",
        "    if args.total:\n",
        "        TRIALS = 1\n",
        "        CYCLES = 1\n",
        "    else:\n",
        "        CYCLES = args.cycles\n",
        "    for trial in range(TRIALS):\n",
        "\n",
        "        # Load training and testing dataset\n",
        "        data_train, data_unlabeled, data_test, adden, NO_CLASSES, no_train = load_dataset(args.dataset)\n",
        "        # Don't predefine budget size. Configure it in the config.py: ADDENDUM = adden\n",
        "        NUM_TRAIN = no_train\n",
        "        indices = list(range(NUM_TRAIN))\n",
        "        random.shuffle(indices)\n",
        "\n",
        "        if args.total:\n",
        "            labeled_set= indices\n",
        "        else:\n",
        "            labeled_set = indices[:ADDENDUM]\n",
        "            labeled_set_till_now = labeled_set\n",
        "            unlabeled_set = [x for x in indices if x not in labeled_set]\n",
        "\n",
        "        train_loader = DataLoader(data_train, batch_size=BATCH,\n",
        "                                    sampler=SubsetRandomSampler(labeled_set),\n",
        "                                    pin_memory=True, drop_last=True)\n",
        "\n",
        "        test_loader  = DataLoader(data_test, batch_size=BATCH)\n",
        "\n",
        "        labeled_loader = DataLoader(data_train, batch_size=BATCH,\n",
        "                                    sampler=SubsetRandomSampler(labeled_set_till_now),\n",
        "                                    pin_memory=True, drop_last=True)\n",
        "\n",
        "        dataloaders  = {'train': train_loader, 'test': test_loader, 'labeled': labeled_loader}\n",
        "\n",
        "        # Model - create new instance for every cycle so that it resets\n",
        "        with torch.cuda.device(CUDA_VISIBLE_DEVICES):\n",
        "            resnet18 = ResNet18(num_classes=NO_CLASSES).cuda()\n",
        "\n",
        "        models = {'backbone': resnet18}\n",
        "\n",
        "        # Loss, criterion and scheduler (re)initialization\n",
        "        criterion      = nn.CrossEntropyLoss(reduction='none')\n",
        "        optim_backbone = optim.SGD(models['backbone'].parameters(), lr=LR,\n",
        "            momentum=MOMENTUM, weight_decay=WDECAY)\n",
        "\n",
        "        sched_backbone = lr_scheduler.MultiStepLR(optim_backbone, milestones=MILESTONES, gamma = 0.5)\n",
        "        optimizers = {'backbone': optim_backbone}\n",
        "        schedulers = {'backbone': sched_backbone}\n",
        "\n",
        "        for cycle in range(CYCLES):\n",
        "\n",
        "            # Randomly sample 10000 unlabeled data points\n",
        "            if not args.total:\n",
        "                random.shuffle(unlabeled_set)\n",
        "                subset = unlabeled_set[:SUBSET]\n",
        "\n",
        "            # Training and testing\n",
        "            train(models, method, criterion, optimizers, schedulers, dataloaders, args.no_of_epochs, EPOCHL)\n",
        "            acc = test(models, EPOCH, method, dataloaders, mode='test')\n",
        "            print('Trial {}/{} || Cycle {}/{} || Label set size {}: Test acc {}'.format(trial+1, TRIALS, cycle+1, CYCLES, len(labeled_set), acc))\n",
        "            np.array([method, trial+1, TRIALS, cycle+1, CYCLES, len(labeled_set), acc]).tofile(results, sep=\" \")\n",
        "            results.write(\"\\n\")\n",
        "\n",
        "\n",
        "            if cycle == (CYCLES-1):\n",
        "                # Reached final training cycle\n",
        "                print(\"Finished.\")\n",
        "                break\n",
        "            # Get the indices of the unlabeled samples to train on next cycle\n",
        "            arg = query_samples(models, method, data_unlabeled, subset, labeled_set_till_now, cycle, args)\n",
        "\n",
        "            # Update the labeled dataset and the unlabeled dataset, respectively\n",
        "            sampled_from_labeled = get_sub_labeled_data(models, EPOCH, method, dataloaders, mode='test')\n",
        "            labeled_set = list(torch.tensor(subset)[arg][-ADDENDUM:].numpy()) + sampled_from_labeled[:int(0.25*len(labeled_set_till_now))]\n",
        "            labeled_set_till_now += list(torch.tensor(subset)[arg][-ADDENDUM:].numpy())\n",
        "            listd = list(torch.tensor(subset)[arg][:-ADDENDUM].numpy())\n",
        "            unlabeled_set = listd + unlabeled_set[SUBSET:]\n",
        "            print(len(labeled_set), min(labeled_set), max(labeled_set))\n",
        "            # Create a new dataloader for the updated labeled dataset\n",
        "            dataloaders['train'] = DataLoader(data_train, batch_size=BATCH,\n",
        "                                            sampler=SubsetRandomSampler(labeled_set),\n",
        "                                            pin_memory=True)\n",
        "\n",
        "    results.close()"
      ],
      "metadata": {
        "id": "94XKl_Kickwr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08d92ef4-003c-4bef-8192-3b26549ea2d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset: gtsrb\n",
            "Method type:UncertainGCN\n",
            ">> Train a Model.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val Acc: 7.688 \t Best Acc: 7.688\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val Acc: 15.503 \t Best Acc: 15.503\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val Acc: 18.068 \t Best Acc: 18.068\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val Acc: 40.340 \t Best Acc: 40.340\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val Acc: 41.188 \t Best Acc: 41.188\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val Acc: 48.741 \t Best Acc: 48.741\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val Acc: 71.607 \t Best Acc: 71.607\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val Acc: 75.859 \t Best Acc: 75.859\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">> Finished.\n",
            "Trial 1/1 || Cycle 1/10 || Label set size 1000: Test acc 76.41330166270784\n",
            "first 5 comf score:  tensor(8.7798, device='cuda:0') tensor(9.1963, device='cuda:0') tensor(9.2943, device='cuda:0') tensor(9.3665, device='cuda:0') tensor(9.3794, device='cuda:0')\n",
            "1250 84 26615\n",
            ">> Train a Model.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val Acc: 69.913 \t Best Acc: 69.913\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val Acc: 71.433 \t Best Acc: 71.433\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val Acc: 74.576 \t Best Acc: 74.576\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val Acc: 79.477 \t Best Acc: 79.477\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val Acc: 85.867 \t Best Acc: 85.867\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val Acc: 87.379 \t Best Acc: 87.379\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">> Finished.\n",
            "Trial 1/1 || Cycle 2/10 || Label set size 1250: Test acc 87.34758511480602\n",
            "Max confidence value:  tensor(0.8237, device='cuda:0')\n",
            "Mean confidence value:  tensor(0.4273, device='cuda:0')\n",
            "Labeled classified:  0.4305\n",
            "Unlabeled classified:  0.8279\n",
            "Total classified:  0.7616666666666667\n",
            "first 5 comf score:  tensor(2.6027, device='cuda:0') tensor(3.3345, device='cuda:0') tensor(3.8747, device='cuda:0') tensor(3.8804, device='cuda:0') tensor(3.9254, device='cuda:0')\n",
            "1500 0 26575\n",
            ">> Train a Model.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val Acc: 84.964 \t Best Acc: 84.964\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val Acc: 90.245 \t Best Acc: 90.245\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Plots\n",
        "run this cell only when you have run the above scripts for the dataset and have the results.txt files in your folders"
      ],
      "metadata": {
        "id": "4Lq2ujAbOtkZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "uZLeweKlPDp2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "methods = ['Random', 'UncertainGCN', 'CoreGCN', 'CoreSet', 'lloss']\n",
        "columns = ['method','trial','tota_trial','cycle','total_cycle','label_set_size','accuracy']"
      ],
      "metadata": {
        "id": "LXm90zahP3Su"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## CIFAR10\n",
        "dataset used in the paper"
      ],
      "metadata": {
        "id": "aY_IXHBtPXGd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "folder_path = '/content/drive/MyDrive/B.Tech./4th year/sem1/AML/Assignment1/Programming_Assignment/Results_CIFAR10'\n",
        "dataset = 'cifar10'\n",
        "\n",
        "plt.figure(figsize=(8,5), facecolor='w',edgecolor='k')\n",
        "for method in methods:\n",
        "    path = folder_path + '/results_{}_{}_main10False.txt'.format(method,dataset)\n",
        "\n",
        "    results = pd.read_csv(path, sep = ' ', names = columns)\n",
        "    results['accuracy'] = results['accuracy'].str.replace(\"'\", '').astype(np.float64)\n",
        "    results['cycle'] = results['cycle'].str.replace(\"'\", '').astype(np.int64)\n",
        "    plt.plot(results['cycle']*1000, results['accuracy'])\n",
        "\n",
        "\n",
        "plt.grid(True, alpha=0.5, color='k', linestyle='--',)\n",
        "plt.xlabel('Number of Labeled Samples')\n",
        "plt.ylabel('Testing Accuracy')\n",
        "plt.legend(['Random', 'UncertainGCN', 'CoreGCN', 'CoreSet', 'lloss'],loc = 'lower right')\n",
        "plt.title('Results {}'.format(dataset))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "iXGvp9ZfPJ-U",
        "outputId": "ff4516ee-32f7-41e1-96bc-3be16ff83be3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Results cifar10')"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## GTSRB\n",
        "dataset which I have used"
      ],
      "metadata": {
        "id": "dY6KEYp7PqCz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "folder_path = '/content/drive/MyDrive/B.Tech./4th year/sem1/AML/Assignment1/Programming_Assignment/Results_GTSRB'\n",
        "dataset = 'gtsrb'\n",
        "\n",
        "plt.figure(figsize=(8,5))\n",
        "for method in methods:\n",
        "    path = folder_path + '/results_{}_{}_main10False.txt'.format(method,dataset)\n",
        "    results = pd.read_csv(path, sep = ' ', names = columns)\n",
        "    results['accuracy'] = results['accuracy'].str.replace(\"'\", '').astype(np.float64)\n",
        "    results['cycle'] = results['cycle'].str.replace(\"'\", '').astype(np.int64)\n",
        "    plt.plot(results['cycle']*1000, results['accuracy'])\n",
        "\n",
        "plt.grid(True, alpha=0.5, color='k', linestyle='--',)\n",
        "plt.xlabel('Number of Labeled Samples')\n",
        "plt.ylabel('Testing Accuracy')\n",
        "plt.legend(['UncertainGCN', 'Random', 'CoreGCN', 'CoreSet', 'lloss'],loc = 'lower right')\n",
        "plt.title('Results {}'.format(dataset))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "STvJ4M-nPpK1",
        "outputId": "8758ac41-727d-437f-9090-bfb64eab5f7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Results gtsrb')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# On Modified algorithm"
      ],
      "metadata": {
        "id": "OmsOQLrX6NlY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "folder_path = '/content/drive/MyDrive/B.Tech./4th year/sem1/AML/Assignment1/Programming_Assignment/Results_Modified_Algorithm/Results_GTSRB'\n",
        "dataset = 'gtsrb'\n",
        "\n",
        "methods = ['UncertainGCN']\n",
        "\n",
        "plt.figure(figsize=(8,5))\n",
        "for method in methods:\n",
        "    path = folder_path + '/results_{}_{}_main10False.txt'.format(method,dataset)\n",
        "    results = pd.read_csv(path, sep = ' ', names = columns)\n",
        "    results['accuracy'] = results['accuracy'].str.replace(\"'\", '').astype(np.float64)\n",
        "    results['label_set_size'] = results['label_set_size'].str.replace(\"'\", '').astype(np.int64)\n",
        "    plt.plot(results['label_set_size'], results['accuracy'])\n",
        "\n",
        "plt.grid(True, alpha=0.5, color='k', linestyle='--',)\n",
        "plt.xlabel('Number of Labeled Samples used in training')\n",
        "plt.ylabel('Testing Accuracy')\n",
        "plt.legend([ 'Random','UncertainGCN'],loc = 'lower right')\n",
        "plt.title('Results {}'.format(dataset))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "oWHUOpR56R5U",
        "outputId": "bcd55f4f-33a6-4a3c-8a04-3b05fcfb8c09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Results gtsrb')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}