diffpool_implementation.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip3 install  dgl -f https://data.dgl.ai/wheels/torch-2.3/repo.html\n",
        "!pip3 install numpy\n",
        "!pip3 install torch\n",
        "!pip3 install networkx\n",
        "!pip3 install matplotlib\n",
        "!pip install torch_geometric\n",
        "!pip install fsspec==2024.3.1"
      ],
      "metadata": {
        "id": "O2rvuQzzg6xi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NCRP11UXbsrZ",
        "outputId": "d75d188b-62c3-418b-fd52-8662899c13e0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "DGL backend not selected or invalid.  Assuming PyTorch for now.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting the default backend to \"pytorch\". You can change it in the ~/.dgl/config.json file or export the DGLBACKEND environment variable.  Valid options are: pytorch, mxnet, tensorflow (all lowercase)\n"
          ]
        }
      ],
      "source": [
        "import dgl\n",
        "import os\n",
        "import torch\n",
        "import scipy\n",
        "import argparse\n",
        "import seaborn\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "import matplotlib.pyplot as plt\n",
        "from torch.utils.data import Dataset\n",
        "from torch_geometric.datasets import TUDataset\n",
        "from sklearn.model_selection import train_test_split\n",
        "from torch_geometric.utils import to_dense_adj\n",
        "from torch.utils.data import DataLoader, random_split\n",
        "from dgl.data.utils import download, extract_archive, get_download_dir, _get_dgl_url"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class GraphSAGE(nn.Module):\n",
        "    '''\n",
        "    We consider GraphSAGE as Basic GNN module as proposed by Authors.\n",
        "    You can use other GNN Models like GCN,GAT,GIN etc as well.\n",
        "    '''\n",
        "    def __init__(self, infeat, outfeat, device='cpu', use_bn=True, mean=False, add_self=False):\n",
        "        super().__init__()\n",
        "        self.add_self = add_self\n",
        "        self.use_bn = use_bn\n",
        "        self.device = device\n",
        "        self.mean = mean\n",
        "        self.W = nn.Linear(infeat, outfeat, bias=True)\n",
        "        nn.init.xavier_uniform_(self.W.weight, gain=nn.init.calculate_gain('relu'))\n",
        "\n",
        "    def forward(self, x, adj):\n",
        "        if self.add_self:\n",
        "            adj = adj + torch.eye(adj.size(0)).to(self.device)\n",
        "\n",
        "        if self.mean:\n",
        "            adj = adj / adj.sum(1, keepdim=True)\n",
        "\n",
        "        h_k_N = torch.matmul(adj, x)\n",
        "        h_k = self.W(h_k_N)\n",
        "        h_k = F.normalize(h_k, dim=2, p=2)\n",
        "        h_k = F.relu(h_k)\n",
        "        if self.use_bn:\n",
        "            self.bn = nn.BatchNorm1d(h_k.size(1)).to(self.device)\n",
        "            h_k = self.bn(h_k)\n",
        "        return h_k"
      ],
      "metadata": {
        "id": "6yrK5cuPe-j-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class DiffPool(nn.Module):\n",
        "    '''\n",
        "    Architecture of DiffPool Module.\n",
        "    '''\n",
        "    def __init__(self, nfeat, nnext, nhid, device='cpu'):\n",
        "        super(DiffPool, self).__init__()\n",
        "        self.device = device\n",
        "        # GNN to generate embedding\n",
        "        self.embed = GraphSAGE(nfeat, nhid, device=self.device, use_bn=True)\n",
        "        # GNN to generate cluster assignment matrix S\n",
        "        self.assign_mat = GraphSAGE(nfeat, nnext, device=self.device, use_bn=True)\n",
        "\n",
        "    def forward(self, x, adj, log=False):\n",
        "        # Equation - 5\n",
        "        z_l = self.embed(x, adj)\n",
        "\n",
        "        # Equation - 6\n",
        "        s_l = F.softmax(self.assign_mat(x, adj), dim=-1)\n",
        "\n",
        "        # Equation - 3\n",
        "        xnext = torch.matmul(s_l.transpose(-1, -2), z_l)\n",
        "\n",
        "        # Equation - 4\n",
        "        anext = (s_l.transpose(-1, -2)).matmul(adj).matmul(s_l)\n",
        "\n",
        "        return xnext, anext"
      ],
      "metadata": {
        "id": "SrOiXK_ZfGJG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Model(nn.Module):\n",
        "    '''\n",
        "    Architecture of Overall Graph Classifier. As proposed by authors, we apply a DIFFPOOL layer after every two GRAPHSAGE layers\n",
        "    '''\n",
        "    def __init__(self, pool_size, device, input_shape, n_classes):\n",
        "        super().__init__()\n",
        "        self.input_shape = input_shape\n",
        "        self.device = device\n",
        "        self.layers = nn.ModuleList([\n",
        "            GraphSAGE(input_shape, 30, device=self.device),\n",
        "            GraphSAGE(30, 30, device=self.device),\n",
        "            DiffPool(30, pool_size, 30, device=self.device),\n",
        "            GraphSAGE(30, 30, device=self.device),\n",
        "            GraphSAGE(30, 30, device=self.device),\n",
        "        ])\n",
        "        self.classifier = Classifier(n_classes)\n",
        "\n",
        "    def forward(self, x, adj):\n",
        "        for layer in self.layers:\n",
        "            if isinstance(layer, GraphSAGE):\n",
        "                x = layer(x, adj)\n",
        "            elif isinstance(layer, DiffPool):\n",
        "                x, adj = layer(x, adj)\n",
        "\n",
        "        readout_x = x.sum(dim=1)\n",
        "        return readout_x\n",
        "\n",
        "    def loss(self, output, labels):\n",
        "        criterion = nn.CrossEntropyLoss()\n",
        "        loss = criterion(output, labels)\n",
        "        return loss"
      ],
      "metadata": {
        "id": "nNeVE5HyfQhP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Classifier(nn.Module):\n",
        "    def __init__(self, n_classes):\n",
        "        super().__init__()\n",
        "        self.classifier = nn.Sequential(nn.Linear(30, 50),nn.ReLU(),nn.Linear(50, n_classes))\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.classifier(x)"
      ],
      "metadata": {
        "id": "vtzW89cQfLgm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class CollateFn:\n",
        "    def __init__(self, device='cpu'):\n",
        "        self.device = device\n",
        "\n",
        "    def __call__(self, batch):\n",
        "        adj_tensor_list = []\n",
        "        features_list = []\n",
        "        mask_list = []\n",
        "        # (adj, features), labels = list(zip(*batch))\n",
        "        max_num_nodes = max([item.x.shape[0] for item in batch])\n",
        "        labels = []\n",
        "        # for (edge_index, x, y) in batch:\n",
        "        for d in batch:\n",
        "            # A Adj F Feature L Label\n",
        "            A = to_dense_adj(d.edge_index).squeeze(0)\n",
        "            F = d.x\n",
        "            # print(F.size())\n",
        "            L = d.y\n",
        "            labels.append(L)\n",
        "\n",
        "            length = F.shape[0]\n",
        "            pad_len_F = max_num_nodes - F.shape[0]\n",
        "            pad_len_A = max_num_nodes - A.shape[0]\n",
        "\n",
        "            adj_tensor_list.append(np.pad(A, ((0, pad_len_A), (0, pad_len_A)), mode='constant'))\n",
        "            features_list.append(np.pad(F, ((0, pad_len_F), (0, 0)), mode='constant'))\n",
        "            # mask = np.zeros(max_num_nodes)\n",
        "            # mask[:length] = 1\n",
        "            # mask_list.append(mask)\n",
        "        stacked_adj  = torch.from_numpy(np.stack(adj_tensor_list, 0)).float().to(self.device)\n",
        "        stacked_feat = torch.from_numpy(np.stack(features_list, 0)).float().to(self.device)\n",
        "        stacked_y    = torch.from_numpy(np.stack(labels, 0)).long().to(self.device)\n",
        "\n",
        "        return stacked_adj, stacked_feat, stacked_y"
      ],
      "metadata": {
        "id": "wl8_3-EqexBc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def main():\n",
        "    print(\"Diff Pool Easy Code\")\n",
        "    train_ratio = 0.8\n",
        "    batch_size = 20\n",
        "    link_pred = False\n",
        "    epochs = 1000\n",
        "\n",
        "    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "    dataset = TUDataset(root='/content/ENZYMES',name = 'ENZYMES')\n",
        "    dataset_size = len(dataset)\n",
        "    train_size = int(dataset_size * train_ratio)\n",
        "    test_size = dataset_size - train_size\n",
        "\n",
        "    max_num_nodes = max([item.x.shape[0] for item in dataset])\n",
        "    n_classes = 6\n",
        "    train_data, test_data = random_split(dataset, (train_size, test_size))\n",
        "    input_shape = 3\n",
        "    train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True,collate_fn=CollateFn(device))\n",
        "    test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=True,collate_fn=CollateFn(device))\n",
        "\n",
        "    model = Model(pool_size=6, device=device, input_shape=input_shape, n_classes=n_classes).to(device)\n",
        "\n",
        "    model.train()\n",
        "    optimizer = optim.Adam(model.parameters())\n",
        "\n",
        "    for e in tqdm(range(epochs)):\n",
        "        # utils.e = e\n",
        "        epoch_losses_list = []\n",
        "        true_sample = 0\n",
        "        model.train()\n",
        "\n",
        "        for i, (adj, features, batch_labels) in enumerate(train_loader):\n",
        "        # for data in train_loader:\n",
        "        #     adj=data.edge_index.to_dense_adj\n",
        "        #     features = data.x\n",
        "        #     batch_labels = data.y\n",
        "\n",
        "            # utils.train_iter += 1\n",
        "            graph_feat = model(features, adj)\n",
        "            output = model.classifier(graph_feat)\n",
        "            batch_labels = batch_labels.squeeze(1)\n",
        "\n",
        "            # print(batch_labels.size())\n",
        "            # print(output.size())\n",
        "\n",
        "            loss = model.loss(output, batch_labels)\n",
        "\n",
        "            loss.backward()\n",
        "            torch.nn.utils.clip_grad_norm_(model.parameters(), 2.0)\n",
        "            optimizer.step()\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            epoch_losses_list.append(loss.item())\n",
        "            iter_true_sample = (output.argmax(dim=1).long() == batch_labels.long()). \\\n",
        "                float().sum().item()\n",
        "            iter_acc = float(iter_true_sample) / output.shape[0]\n",
        "            # utils.writer.add_scalar(\"iter train acc\", iter_acc, utils.train_iter)\n",
        "            # print(f\"{utils.train_iter} iter train acc: {iter_acc}\")\n",
        "            true_sample += iter_true_sample\n",
        "\n",
        "        acc = true_sample / train_size\n",
        "        # print(f\"Epoch:{e}  \\t train_acc:{acc:.2f}\")\n",
        "        print(f\"Epoch:{e}, Loss :{np.mean(epoch_losses_list):.4f}, train_acc:{acc:.2f}\")\n",
        "        # print(\"Loss : \",np.mean(epoch_losses_list))\n",
        "\n",
        "        test_loss_list = []\n",
        "        true_sample = 0\n",
        "        model.eval()\n",
        "        with torch.no_grad():\n",
        "            for i, (adj, features, batch_labels) in enumerate(test_loader):\n",
        "                # utils.test_iter += 1\n",
        "                graph_feat = model(features, adj)\n",
        "                batch_labels = batch_labels.squeeze(1)\n",
        "                output = model.classifier(graph_feat)\n",
        "                loss = model.loss(output, batch_labels)\n",
        "                test_loss_list.append(loss.item())\n",
        "                iter_true_sample = (output.argmax(dim=1).long() == batch_labels.long()). \\\n",
        "                    float().sum().item()\n",
        "                iter_acc = float(iter_true_sample) / output.shape[0]\n",
        "                # utils.writer.add_scalar(\"iter test acc\", iter_acc, utils.test_iter)\n",
        "                # print(f\"{utils.test_iter} iter test acc: {iter_acc}\")\n",
        "                true_sample += iter_true_sample\n",
        "        acc = true_sample / test_size\n",
        "        # utils.writer.add_scalar(\"Epoch Acc\", acc, e)\n",
        "        # print(f\"Epoch:{e}  \\t val_acc:{acc:.2f}\")\n",
        "        print(f\"Epoch:{e}, Test_acc:{acc:.2f}\")"
      ],
      "metadata": {
        "id": "CgoMpmCccOAh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "main()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dgSdTg3clBNW",
        "outputId": "fda4ff20-648c-4bc2-8cbd-ac9be41a8734"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Diff Pool Easy Code\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading https://www.chrsmrrs.com/graphkerneldatasets/ENZYMES.zip\n",
            "Processing...\n",
            "Done!\n",
            "/usr/local/lib/python3.10/dist-packages/torch_geometric/io/fs.py:215: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
            "  return torch.load(f, map_location)\n",
            "  0%|          | 1/1000 [00:02<41:56,  2.52s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:0, Loss :2.0091, train_acc:0.14\n",
            "Epoch:0, Test_acc:0.20\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 2/1000 [00:03<23:54,  1.44s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:1, Loss :1.7590, train_acc:0.19\n",
            "Epoch:1, Test_acc:0.25\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 3/1000 [00:03<18:49,  1.13s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:2, Loss :1.6974, train_acc:0.26\n",
            "Epoch:2, Test_acc:0.28\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 4/1000 [00:04<15:38,  1.06it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:3, Loss :1.6859, train_acc:0.30\n",
            "Epoch:3, Test_acc:0.26\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 5/1000 [00:05<13:01,  1.27it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:4, Loss :1.6329, train_acc:0.33\n",
            "Epoch:4, Test_acc:0.30\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  1%|          | 6/1000 [00:05<11:22,  1.46it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:5, Loss :1.6214, train_acc:0.32\n",
            "Epoch:5, Test_acc:0.29\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  1%|          | 7/1000 [00:06<10:21,  1.60it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:6, Loss :1.6332, train_acc:0.30\n",
            "Epoch:6, Test_acc:0.31\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  1%|          | 8/1000 [00:06<09:40,  1.71it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:7, Loss :1.5813, train_acc:0.37\n",
            "Epoch:7, Test_acc:0.30\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  1%|          | 9/1000 [00:07<09:14,  1.79it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:8, Loss :1.5662, train_acc:0.36\n",
            "Epoch:8, Test_acc:0.28\n"
          ]
        },
        {
          "output_type": "stream",
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          "text": [
            "\r  1%|          | 10/1000 [00:07<08:50,  1.87it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:9, Loss :1.5467, train_acc:0.35\n",
            "Epoch:9, Test_acc:0.35\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  1%|          | 11/1000 [00:08<08:41,  1.90it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:10, Loss :1.5681, train_acc:0.34\n",
            "Epoch:10, Test_acc:0.33\n"
          ]
        },
        {
          "output_type": "stream",
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          "text": [
            "\r  1%|          | 12/1000 [00:08<08:28,  1.94it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:11, Loss :1.5440, train_acc:0.34\n",
            "Epoch:11, Test_acc:0.32\n"
          ]
        },
        {
          "output_type": "stream",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:12, Loss :1.5236, train_acc:0.35\n",
            "Epoch:12, Test_acc:0.36\n"
          ]
        },
        {
          "output_type": "stream",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:13, Loss :1.4981, train_acc:0.36\n",
            "Epoch:13, Test_acc:0.34\n"
          ]
        },
        {
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:14, Loss :1.5168, train_acc:0.38\n",
            "Epoch:14, Test_acc:0.34\n"
          ]
        },
        {
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:15, Loss :1.5007, train_acc:0.39\n",
            "Epoch:15, Test_acc:0.33\n"
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          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1000/1000 [09:00<00:00,  1.85it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:999, Loss :0.0191, train_acc:0.99\n",
            "Epoch:999, Test_acc:0.50\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    }
  ]
}