main_superpixels_graph_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Main Driver Notebook for Training Graph NNs on Image Superpixels Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MODELS\n",
    "- GatedGCN \n",
    "- GCN \n",
    "- GAT \n",
    "- GraphSage  \n",
    "- GIN  \n",
    "- MoNet  \n",
    "- DiffPool\n",
    "- MLP\n",
    "\n",
    "### DATASET\n",
    "- MNIST  \n",
    "- CIFAR10\n",
    "\n",
    "### TASK\n",
    "- Graph Classification, i.e. Image Classification in classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "    IMPORTING LIBS\n",
    "\"\"\"\n",
    "import dgl\n",
    "\n",
    "import numpy as np\n",
    "import os\n",
    "import socket\n",
    "import time\n",
    "import random\n",
    "import glob\n",
    "import argparse, json\n",
    "import pickle\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "from tensorboardX import SummaryWriter\n",
    "from tqdm import tqdm\n",
    "\n",
    "class DotDict(dict):\n",
    "    def __init__(self, **kwds):\n",
    "        self.update(kwds)\n",
    "        self.__dict__ = self"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# \"\"\"\n",
    "#     AUTORELOAD IPYTHON EXTENSION FOR RELOADING IMPORTED MODULES\n",
    "# \"\"\"\n",
    "\n",
    "def in_ipynb():\n",
    "    try:\n",
    "        cfg = get_ipython().config \n",
    "        return True\n",
    "    except NameError:\n",
    "        return False\n",
    "    \n",
    "notebook_mode = in_ipynb()\n",
    "print(notebook_mode)\n",
    "\n",
    "if notebook_mode == True:\n",
    "    %load_ext autoreload\n",
    "    %autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "    IMPORTING CUSTOM MODULES/METHODS\n",
    "\"\"\"\n",
    "from nets.superpixels_graph_classification.load_net import gnn_model # import all GNNS\n",
    "from data.data import LoadData # import dataset\n",
    "from train.train_superpixels_graph_classification import train_epoch, evaluate_network # import train functions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "    GPU Setup\n",
    "\"\"\"\n",
    "def gpu_setup(use_gpu, gpu_id):\n",
    "    os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n",
    "    os.environ[\"CUDA_VISIBLE_DEVICES\"] = str(gpu_id)  \n",
    "\n",
    "    if torch.cuda.is_available() and use_gpu:\n",
    "        print('cuda available with GPU:',torch.cuda.get_device_name(0))\n",
    "        device = torch.device(\"cuda\")\n",
    "    else:\n",
    "        print('cuda not available')\n",
    "        device = torch.device(\"cpu\")\n",
    "    return device\n",
    "\n",
    "\n",
    "use_gpu = True\n",
    "gpu_id = -1\n",
    "device = None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[I] Loading data (notebook) ...\n",
      "[I] Loading dataset MNIST...\n",
      "train, test, val sizes : 55000 10000 5000\n",
      "[I] Finished loading.\n",
      "[I] Data load time: 35.4297s\n",
      "[I] Finished loading.\n"
     ]
    }
   ],
   "source": [
    "# \"\"\"\n",
    "#     USER CONTROLS\n",
    "# \"\"\"\n",
    "if notebook_mode == True:\n",
    "    \n",
    "    MODEL_NAME = 'GatedGCN'\n",
    "    # MODEL_NAME = 'MoNet'\n",
    "    # MODEL_NAME = 'GCN'\n",
    "    # MODEL_NAME = 'GAT'\n",
    "    # MODEL_NAME = 'GraphSage'\n",
    "    # MODEL_NAME = 'DiffPool'\n",
    "    # MODEL_NAME = 'MLP'\n",
    "    # MODEL_NAME = 'GIN'\n",
    "\n",
    "    DATASET_NAME = 'MNIST'\n",
    "    #DATASET_NAME = 'CIFAR10'\n",
    "\n",
    "    out_dir = 'out/superpixels_graph_classification/'\n",
    "    root_log_dir = out_dir + 'logs/' + MODEL_NAME + \"_\" + DATASET_NAME + \"_\" + time.strftime('%Hh%Mm%Ss_on_%b_%d_%Y')\n",
    "    root_ckpt_dir = out_dir + 'checkpoints/' + MODEL_NAME + \"_\" + DATASET_NAME + \"_\" + time.strftime('%Hh%Mm%Ss_on_%b_%d_%Y')\n",
    "\n",
    "    print(\"[I] Loading data (notebook) ...\")\n",
    "    dataset = LoadData(DATASET_NAME)\n",
    "    trainset, valset, testset = dataset.train, dataset.val, dataset.test\n",
    "    print(\"[I] Finished loading.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "MODEL_NAME = 'GatedGCN'\n",
    "MODEL_NAME = 'GCN'\n",
    "MODEL_NAME = 'GAT'\n",
    "#MODEL_NAME = 'GraphSage'\n",
    "#MODEL_NAME = 'MLP'\n",
    "#MODEL_NAME = 'DiffPool'\n",
    "#MODEL_NAME = 'GIN'\n",
    "#MODEL_NAME = 'MoNet'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True hidden dim: 152\n"
     ]
    }
   ],
   "source": [
    "# \"\"\"\n",
    "#     PARAMETERS\n",
    "# \"\"\"\n",
    "if notebook_mode == True:\n",
    "\n",
    "    n_heads = -1\n",
    "    edge_feat = False\n",
    "    pseudo_dim_MoNet = -1\n",
    "    kernel = -1\n",
    "    gnn_per_block = -1\n",
    "    embedding_dim = -1\n",
    "    pool_ratio = -1\n",
    "    n_mlp_GIN = -1\n",
    "    gated = False\n",
    "    self_loop = False\n",
    "    #self_loop = True\n",
    "    max_time = 48\n",
    "    \n",
    "    \n",
    "\n",
    "    if MODEL_NAME == 'GatedGCN':\n",
    "        seed=41; epochs=1000; batch_size=5; init_lr=5e-5; lr_reduce_factor=0.5; lr_schedule_patience=25; min_lr = 1e-6; weight_decay=0\n",
    "        L=4; hidden_dim=70; out_dim=hidden_dim; dropout=0.0; readout='mean'\n",
    "\n",
    "    if MODEL_NAME == 'GCN':\n",
    "        seed=41; epochs=1000; batch_size=5; init_lr=5e-5; lr_reduce_factor=0.5; lr_schedule_patience=25; min_lr = 1e-6; weight_decay=0\n",
    "        L=4; hidden_dim=146; out_dim=hidden_dim; dropout=0.0; readout='mean'\n",
    "\n",
    "    if MODEL_NAME == 'GAT':\n",
    "        seed=41; epochs=1000; batch_size=50; init_lr=5e-5; lr_reduce_factor=0.5; lr_schedule_patience=25; min_lr = 1e-6; weight_decay=0\n",
    "        L=4; n_heads=8; hidden_dim=19; out_dim=n_heads*hidden_dim; dropout=0.0; readout='mean'\n",
    "        print('True hidden dim:',out_dim)\n",
    "\n",
    "    if MODEL_NAME == 'GraphSage':\n",
    "        seed=41; epochs=1000; batch_size=50; init_lr=5e-5; lr_reduce_factor=0.5; lr_schedule_patience=25; min_lr = 1e-6; weight_decay=0\n",
    "        L=4; hidden_dim=108; out_dim=hidden_dim; dropout=0.0; readout='mean'\n",
    "\n",
    "    if MODEL_NAME == 'MLP':\n",
    "        seed=41; epochs=1000; batch_size=50; init_lr=5e-4; lr_reduce_factor=0.5; lr_schedule_patience=25; min_lr = 1e-6; weight_decay=0\n",
    "        gated=False; # MEAN\n",
    "        L=4; hidden_dim=168; out_dim=hidden_dim; dropout=0.0; readout='mean'\n",
    "        gated=True; # GATED\n",
    "        L=4; hidden_dim=150; out_dim=hidden_dim; dropout=0.0; readout='mean'\n",
    "        \n",
    "    if MODEL_NAME == 'DiffPool':\n",
    "        seed=41; epochs=1000; batch_size=50; init_lr=5e-4; lr_reduce_factor=0.5; lr_schedule_patience=25; min_lr = 1e-6; weight_decay=0\n",
    "        L=4; hidden_dim=32; out_dim=hidden_dim; dropout=0.0; readout='mean'\n",
    "        n_heads=8; gnn_per_block=3; batch_size=128; pool_ratio=0.15\n",
    "        embedding_dim=32; # MNIST\n",
    "        #embedding_dim=16; # CIFAR10\n",
    "\n",
    "    if MODEL_NAME == 'GIN':\n",
    "        seed=41; epochs=1000; batch_size=50; init_lr=5e-4; lr_reduce_factor=0.5; lr_schedule_patience=25; min_lr = 1e-6; weight_decay=0\n",
    "        L=4; hidden_dim=110; out_dim=hidden_dim; dropout=0.0; readout='mean'\n",
    "        n_mlp_GIN = 2; learn_eps_GIN=True; neighbor_aggr_GIN='sum'\n",
    "\n",
    "    if MODEL_NAME == 'MoNet':\n",
    "        seed=41; epochs=1000; batch_size=50; init_lr=5e-4; lr_reduce_factor=0.5; lr_schedule_patience=25; min_lr = 1e-6; weight_decay=0\n",
    "        L=4; hidden_dim=90; out_dim=hidden_dim; dropout=0.0; readout='mean'\n",
    "        pseudo_dim_MoNet=2; kernel=3;\n",
    "      \n",
    "    \n",
    "    \n",
    "    # generic new_params\n",
    "    net_params = {}\n",
    "    net_params['device'] = device\n",
    "    net_params['gated'] = gated  # for mlpnet baseline\n",
    "    net_params['in_dim'] = trainset[0][0].ndata['feat'][0].size(0)\n",
    "    net_params['in_dim_edge'] = trainset[0][0].edata['feat'][0].size(0)\n",
    "    net_params['residual'] = True\n",
    "    net_params['hidden_dim'] = hidden_dim\n",
    "    net_params['out_dim'] = out_dim\n",
    "    num_classes = len(np.unique(np.array(trainset[:][1])))\n",
    "    net_params['n_classes'] = num_classes\n",
    "    net_params['n_heads'] = n_heads\n",
    "    net_params['L'] = L  # min L should be 2\n",
    "    net_params['readout'] = \"sum\"\n",
    "    net_params['graph_norm'] = True\n",
    "    net_params['batch_norm'] = True\n",
    "    net_params['in_feat_dropout'] = 0.0\n",
    "    net_params['dropout'] = 0.0\n",
    "    net_params['edge_feat'] = edge_feat\n",
    "    net_params['self_loop'] = self_loop\n",
    "\n",
    "    # for MLPNet \n",
    "    net_params['gated'] = gated  \n",
    "    \n",
    "    # specific for MoNet\n",
    "    net_params['pseudo_dim_MoNet'] = pseudo_dim_MoNet\n",
    "    net_params['kernel'] = kernel\n",
    "    \n",
    "    # specific for GIN\n",
    "    net_params['n_mlp_GIN'] = n_mlp_GIN\n",
    "    net_params['learn_eps_GIN'] = True\n",
    "    net_params['neighbor_aggr_GIN'] = 'sum'\n",
    "    \n",
    "    # specific for graphsage\n",
    "    net_params['sage_aggregator'] = 'meanpool'    \n",
    "\n",
    "    # specific for diffpoolnet\n",
    "    net_params['data_mode'] = 'default'\n",
    "    net_params['gnn_per_block'] = gnn_per_block\n",
    "    net_params['embedding_dim'] = embedding_dim     \n",
    "    net_params['pool_ratio'] = pool_ratio\n",
    "    net_params['linkpred'] = True\n",
    "    net_params['num_pool'] = 1\n",
    "    net_params['cat'] = False\n",
    "    net_params['batch_size'] = batch_size   \n",
    "\n",
    "    # calculate assignment dimension: pool_ratio * largest graph's maximum\n",
    "    # number of nodes  in the dataset\n",
    "    max_num_nodes_train = max([trainset[i][0].number_of_nodes() for i in range(len(trainset))])\n",
    "    max_num_nodes_test = max([testset[i][0].number_of_nodes() for i in range(len(testset))])\n",
    "    max_num_node = max(max_num_nodes_train, max_num_nodes_test)\n",
    "    net_params['assign_dim'] = int(max_num_node * net_params['pool_ratio']) * net_params['batch_size']\n",
    "\n",
    "    # setting seeds\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    if device == 'cuda':\n",
    "        torch.cuda.manual_seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL DETAILS:\n",
      "\n",
      "MODEL/Total parameters: GAT 110400\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "    VIEWING MODEL CONFIG AND PARAMS\n",
    "\"\"\"\n",
    "def view_model_param(MODEL_NAME, net_params):\n",
    "    model = gnn_model(MODEL_NAME, net_params)\n",
    "    total_param = 0\n",
    "    print(\"MODEL DETAILS:\\n\")\n",
    "    #print(model)\n",
    "    for param in model.parameters():\n",
    "        # print(param.data.size())\n",
    "        total_param += np.prod(list(param.data.size()))\n",
    "    print('MODEL/Total parameters:', MODEL_NAME, total_param)\n",
    "    return total_param\n",
    "\n",
    "\n",
    "if notebook_mode == True:\n",
    "    view_model_param(MODEL_NAME, net_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "    TRAINING CODE\n",
    "\"\"\"\n",
    "\n",
    "def train_val_pipeline(MODEL_NAME, dataset, params, net_params, dirs):\n",
    "    t0 = time.time()\n",
    "    per_epoch_time = []\n",
    "        \n",
    "    DATASET_NAME = dataset.name\n",
    "    \n",
    "    if MODEL_NAME in ['GCN', 'GAT']:\n",
    "        if net_params['self_loop']:\n",
    "            print(\"[!] Adding graph self-loops for GCN/GAT models (central node trick).\")\n",
    "            dataset._add_self_loops()\n",
    "    \n",
    "    trainset, valset, testset = dataset.train, dataset.val, dataset.test\n",
    "        \n",
    "    root_log_dir, root_ckpt_dir, write_file_name, write_config_file = dirs\n",
    "    device = net_params['device']\n",
    "    \n",
    "    # Write the network and optimization hyper-parameters in folder config/\n",
    "    with open(write_config_file + '.txt', 'w') as f:\n",
    "        f.write(\"\"\"Dataset: {},\\nModel: {}\\n\\nparams={}\\n\\nnet_params={}\\n\\n\\nTotal Parameters: {}\\n\\n\"\"\"\\\n",
    "                .format(DATASET_NAME, MODEL_NAME, params, net_params, net_params['total_param']))\n",
    "        \n",
    "    log_dir = os.path.join(root_log_dir, \"RUN_\" + str(0))\n",
    "    writer = SummaryWriter(log_dir=log_dir)\n",
    "\n",
    "    # setting seeds\n",
    "    random.seed(params['seed'])\n",
    "    np.random.seed(params['seed'])\n",
    "    torch.manual_seed(params['seed'])\n",
    "    if device == 'cuda':\n",
    "        torch.cuda.manual_seed(params['seed'])\n",
    "    \n",
    "    print(\"Training Graphs: \", len(trainset))\n",
    "    print(\"Validation Graphs: \", len(valset))\n",
    "    print(\"Test Graphs: \", len(testset))\n",
    "    print(\"Number of Classes: \", net_params['n_classes'])\n",
    "\n",
    "    model = gnn_model(MODEL_NAME, net_params)\n",
    "    model = model.to(device)\n",
    "\n",
    "    optimizer = optim.Adam(model.parameters(), lr=params['init_lr'], weight_decay=params['weight_decay'])\n",
    "    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min',\n",
    "                                                     factor=params['lr_reduce_factor'],\n",
    "                                                     patience=params['lr_schedule_patience'],\n",
    "                                                     verbose=True)\n",
    "    \n",
    "    epoch_train_losses, epoch_val_losses = [], []\n",
    "    epoch_train_accs, epoch_val_accs = [], [] \n",
    "    \n",
    "    # batching exception for Diffpool\n",
    "    drop_last = True if MODEL_NAME == 'DiffPool' else False\n",
    "    \n",
    "    train_loader = DataLoader(trainset, batch_size=params['batch_size'], shuffle=True, drop_last=drop_last, collate_fn=dataset.collate)\n",
    "    val_loader = DataLoader(valset, batch_size=params['batch_size'], shuffle=False, drop_last=drop_last, collate_fn=dataset.collate)\n",
    "    test_loader = DataLoader(testset, batch_size=params['batch_size'], shuffle=False, drop_last=drop_last, collate_fn=dataset.collate)\n",
    "\n",
    "    # At any point you can hit Ctrl + C to break out of training early.\n",
    "    try:\n",
    "        with tqdm(range(params['epochs'])) as t:\n",
    "            for epoch in t:\n",
    "\n",
    "                t.set_description('Epoch %d' % epoch)\n",
    "\n",
    "                start = time.time()\n",
    "\n",
    "                epoch_train_loss, epoch_train_acc, optimizer = train_epoch(model, optimizer, device, train_loader, epoch)\n",
    "                epoch_val_loss, epoch_val_acc = evaluate_network(model, device, val_loader, epoch)\n",
    "\n",
    "                epoch_train_losses.append(epoch_train_loss)\n",
    "                epoch_val_losses.append(epoch_val_loss)\n",
    "                epoch_train_accs.append(epoch_train_acc)\n",
    "                epoch_val_accs.append(epoch_val_acc)\n",
    "\n",
    "                writer.add_scalar('train/_loss', epoch_train_loss, epoch)\n",
    "                writer.add_scalar('val/_loss', epoch_val_loss, epoch)\n",
    "                writer.add_scalar('train/_acc', epoch_train_acc, epoch)\n",
    "                writer.add_scalar('val/_acc', epoch_val_acc, epoch)\n",
    "                writer.add_scalar('learning_rate', optimizer.param_groups[0]['lr'], epoch)\n",
    "\n",
    "                _, epoch_test_acc = evaluate_network(model, device, test_loader, epoch)                \n",
    "                t.set_postfix(time=time.time()-start, lr=optimizer.param_groups[0]['lr'],\n",
    "                              train_loss=epoch_train_loss, val_loss=epoch_val_loss,\n",
    "                              train_acc=epoch_train_acc, val_acc=epoch_val_acc,\n",
    "                              test_acc=epoch_test_acc)    \n",
    "\n",
    "                per_epoch_time.append(time.time()-start)\n",
    "\n",
    "                # Saving checkpoint\n",
    "                ckpt_dir = os.path.join(root_ckpt_dir, \"RUN_\")\n",
    "                if not os.path.exists(ckpt_dir):\n",
    "                    os.makedirs(ckpt_dir)\n",
    "                torch.save(model.state_dict(), '{}.pkl'.format(ckpt_dir + \"/epoch_\" + str(epoch)))\n",
    "\n",
    "                files = glob.glob(ckpt_dir + '/*.pkl')\n",
    "                for file in files:\n",
    "                    epoch_nb = file.split('_')[-1]\n",
    "                    epoch_nb = int(epoch_nb.split('.')[0])\n",
    "                    if epoch_nb < epoch-1:\n",
    "                        os.remove(file)\n",
    "\n",
    "                scheduler.step(epoch_val_loss)\n",
    "\n",
    "                if optimizer.param_groups[0]['lr'] < params['min_lr']:\n",
    "                    print(\"\\n!! LR EQUAL TO MIN LR SET.\")\n",
    "                    break\n",
    "                    \n",
    "                # Stop training after params['max_time'] hours\n",
    "                if time.time()-t0 > params['max_time']*3600:\n",
    "                    print('-' * 89)\n",
    "                    print(\"Max_time for training elapsed {:.2f} hours, so stopping\".format(params['max_time']))\n",
    "                    break\n",
    "    \n",
    "    except KeyboardInterrupt:\n",
    "        print('-' * 89)\n",
    "        print('Exiting from training early because of KeyboardInterrupt')\n",
    "    \n",
    "    _, test_acc = evaluate_network(model, device, test_loader, epoch)\n",
    "    _, train_acc = evaluate_network(model, device, train_loader, epoch)\n",
    "    print(\"Test Accuracy: {:.4f}\".format(test_acc))\n",
    "    print(\"Train Accuracy: {:.4f}\".format(train_acc))\n",
    "    print(\"TOTAL TIME TAKEN: {:.4f}s\".format(time.time()-t0))\n",
    "    print(\"AVG TIME PER EPOCH: {:.4f}s\".format(np.mean(per_epoch_time)))\n",
    "\n",
    "    writer.close()\n",
    "\n",
    "    \"\"\"\n",
    "        Write the results in out_dir/results folder\n",
    "    \"\"\"\n",
    "    with open(write_file_name + '.txt', 'w') as f:\n",
    "        f.write(\"\"\"Dataset: {},\\nModel: {}\\n\\nparams={}\\n\\nnet_params={}\\n\\n{}\\n\\nTotal Parameters: {}\\n\\n\n",
    "    FINAL RESULTS\\nTEST ACCURACY: {:.4f}\\nTRAIN ACCURACY: {:.4f}\\n\\n\n",
    "    Total Time Taken: {:.4f} hrs\\nAverage Time Per Epoch: {:.4f} s\\n\\n\\n\"\"\"\\\n",
    "          .format(DATASET_NAME, MODEL_NAME, params, net_params, model, net_params['total_param'],\n",
    "                  np.mean(np.array(test_acc))*100, np.mean(np.array(train_acc))*100, (time.time()-t0)/3600, np.mean(per_epoch_time)))\n",
    "        \n",
    "    \n",
    "    # send results to gmail\n",
    "    try:\n",
    "        from gmail import send\n",
    "        subject = 'Result for Dataset: {}, Model: {}'.format(DATASET_NAME, MODEL_NAME)\n",
    "        body = \"\"\"Dataset: {},\\nModel: {}\\n\\nparams={}\\n\\nnet_params={}\\n\\n{}\\n\\nTotal Parameters: {}\\n\\n\n",
    "    FINAL RESULTS\\nTEST ACCURACY: {:.4f}\\nTRAIN ACCURACY: {:.4f}\\n\\n\n",
    "    Total Time Taken: {:.4f} hrs\\nAverage Time Per Epoch: {:.4f} s\\n\\n\\n\"\"\"\\\n",
    "          .format(DATASET_NAME, MODEL_NAME, params, net_params, model, net_params['total_param'],\n",
    "                  np.mean(np.array(test_acc))*100, np.mean(np.array(train_acc))*100, (time.time()-t0)/3600, np.mean(per_epoch_time))\n",
    "        send(subject, body)\n",
    "    except:\n",
    "        pass\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convert main_superpixels_graph_classification.ipynb to main_superpixels_graph_classification.py\n",
      "Clean main_superpixels_graph_classification.py\n",
      "Done. \n",
      "[I] Loading dataset MNIST...\n",
      "train, test, val sizes : 55000 10000 5000\n",
      "[I] Finished loading.\n",
      "[I] Data load time: 40.6646s\n",
      "cuda not available\n",
      "MODEL DETAILS:\n",
      "\n",
      "MODEL/Total parameters: GAT 110400\n",
      "Training Graphs:  55000\n",
      "Validation Graphs:  5000\n",
      "Test Graphs:  10000\n",
      "Number of Classes:  10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 0:   0%|          | 0/1000 [01:39<?, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------------------------------------------\n",
      "Exiting from training early because of KeyboardInterrupt\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-13711a826678>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    233\u001b[0m     \u001b[0mcleaner_main\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'main_superpixels_graph_classification'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 235\u001b[0;31m     \u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-10-13711a826678>\u001b[0m in \u001b[0;36mmain\u001b[0;34m(notebook_mode, config)\u001b[0m\n\u001b[1;32m    197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m     \u001b[0mnet_params\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'total_param'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mview_model_param\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMODEL_NAME\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnet_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m     \u001b[0mtrain_val_pipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMODEL_NAME\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnet_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-9-5850591b5cae>\u001b[0m in \u001b[0;36mtrain_val_pipeline\u001b[0;34m(MODEL_NAME, dataset, params, net_params, dirs)\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluate_network\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m     \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluate_network\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Test Accuracy: {:.4f}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_acc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Train Accuracy: {:.4f}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_acc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/benchmarking-gnn/train/train_superpixels_graph_classification.py\u001b[0m in \u001b[0;36mevaluate_network\u001b[0;34m(model, device, data_loader, epoch)\u001b[0m\n\u001b[1;32m     48\u001b[0m             \u001b[0mbatch_snorm_n\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch_snorm_n\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0mbatch_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_graphs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_e\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_snorm_n\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_snorm_e\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m             \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_scores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0mepoch_test_loss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/benchmarking-gnn/nets/superpixels_graph_classification/gat_net.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, g, h, e, snorm_n, snorm_e)\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0min_feat_dropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mconv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m             \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msnorm_n\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m         \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'h'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/benchmark_gnn/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/benchmarking-gnn/layers/gat_layer.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, g, h, snorm_n)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msnorm_n\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m         \u001b[0mh_in\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m \u001b[0;31m# for residual connection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m         \u001b[0mhead_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mattn_head\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msnorm_n\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mattn_head\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheads\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'cat'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/benchmarking-gnn/layers/gat_layer.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msnorm_n\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m         \u001b[0mh_in\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m \u001b[0;31m# for residual connection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m         \u001b[0mhead_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mattn_head\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msnorm_n\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mattn_head\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheads\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'cat'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/benchmark_gnn/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/benchmarking-gnn/layers/gat_layer.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, g, h, snorm_n)\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'z'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_edges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medge_attention\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m         \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m         \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'h'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_norm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/benchmark_gnn/lib/python3.7/site-packages/dgl/graph.py\u001b[0m in \u001b[0;36mupdate_all\u001b[0;34m(self, message_func, reduce_func, apply_node_func)\u001b[0m\n\u001b[1;32m   2745\u001b[0m                                           \u001b[0mreduce_func\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreduce_func\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2746\u001b[0m                                           apply_func=apply_node_func)\n\u001b[0;32m-> 2747\u001b[0;31m             \u001b[0mRuntime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2749\u001b[0m     def prop_nodes(self,\n",
      "\u001b[0;32m~/miniconda3/envs/benchmark_gnn/lib/python3.7/site-packages/dgl/runtime/runtime.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(prog)\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mexe\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m             \u001b[0;31m# prog.pprint_exe(exe)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m             \u001b[0mexe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/benchmark_gnn/lib/python3.7/site-packages/dgl/runtime/ir/executor.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m             \u001b[0mmail_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfdmail\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m             \u001b[0mudf_ret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmail_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFrameRef\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mudf_ret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/benchmark_gnn/lib/python3.7/site-packages/dgl/runtime/degree_bucketing.py\u001b[0m in \u001b[0;36m_rfunc_wrapper\u001b[0;34m(node_data, mail_data)\u001b[0m\n\u001b[1;32m    151\u001b[0m         \u001b[0mreshaped_mail_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLazyDict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_reshaped_getter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmail_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0mnbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNodeBatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvbkt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreshaped_mail_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mreduce_udf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_rfunc_wrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/benchmarking-gnn/layers/gat_layer.py\u001b[0m in \u001b[0;36mreduce_func\u001b[0;34m(self, nodes)\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreduce_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnodes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m         \u001b[0malpha\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msoftmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnodes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmailbox\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'e'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m         \u001b[0malpha\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnodes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmailbox\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'z'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/benchmark_gnn/lib/python3.7/site-packages/dgl/utils.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    283\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_keys\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__contains__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/benchmark_gnn/lib/python3.7/site-packages/dgl/runtime/degree_bucketing.py\u001b[0m in \u001b[0;36m_reshaped_getter\u001b[0;34m(key)\u001b[0m\n\u001b[1;32m    146\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_rfunc_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmail_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0m_reshaped_getter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 148\u001b[0;31m             \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmail_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    149\u001b[0m             \u001b[0mnew_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvbkt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdeg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/benchmark_gnn/lib/python3.7/site-packages/dgl/utils.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    283\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_keys\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__contains__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/benchmark_gnn/lib/python3.7/site-packages/dgl/frame.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(key)\u001b[0m\n\u001b[1;32m    653\u001b[0m         \"\"\"\n\u001b[1;32m    654\u001b[0m         \u001b[0mrows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 655\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLazyDict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_frame\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    656\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    657\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/benchmark_gnn/lib/python3.7/site-packages/dgl/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m     95\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m             \u001b[0muser_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtousertensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgather_row\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muser_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeats\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/benchmark_gnn/lib/python3.7/site-packages/dgl/backend/pytorch/tensor.py\u001b[0m in \u001b[0;36mgather_row\u001b[0;34m(data, row_index)\u001b[0m\n\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgather_row\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 152\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mth\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex_select\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mslice_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbegin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "def main(notebook_mode=False,config=None):\n",
    "    \n",
    "    \"\"\"\n",
    "        USER CONTROLS\n",
    "    \"\"\"\n",
    "    \n",
    "    # terminal mode\n",
    "    if notebook_mode==False:\n",
    "        \n",
    "        parser = argparse.ArgumentParser()\n",
    "        parser.add_argument('--config', help=\"Please give a config.json file with training/model/data/param details\")\n",
    "        parser.add_argument('--gpu_id', help=\"Please give a value for gpu id\")\n",
    "        parser.add_argument('--model', help=\"Please give a value for model name\")\n",
    "        parser.add_argument('--dataset', help=\"Please give a value for dataset name\")\n",
    "        parser.add_argument('--out_dir', help=\"Please give a value for out_dir\")\n",
    "        parser.add_argument('--seed', help=\"Please give a value for seed\")\n",
    "        parser.add_argument('--epochs', help=\"Please give a value for epochs\")\n",
    "        parser.add_argument('--batch_size', help=\"Please give a value for batch_size\")\n",
    "        parser.add_argument('--init_lr', help=\"Please give a value for init_lr\")\n",
    "        parser.add_argument('--lr_reduce_factor', help=\"Please give a value for lr_reduce_factor\")\n",
    "        parser.add_argument('--lr_schedule_patience', help=\"Please give a value for lr_schedule_patience\")\n",
    "        parser.add_argument('--min_lr', help=\"Please give a value for min_lr\")\n",
    "        parser.add_argument('--weight_decay', help=\"Please give a value for weight_decay\")\n",
    "        parser.add_argument('--print_epoch_interval', help=\"Please give a value for print_epoch_interval\")    \n",
    "        parser.add_argument('--L', help=\"Please give a value for L\")\n",
    "        parser.add_argument('--hidden_dim', help=\"Please give a value for hidden_dim\")\n",
    "        parser.add_argument('--out_dim', help=\"Please give a value for out_dim\")\n",
    "        parser.add_argument('--residual', help=\"Please give a value for residual\")\n",
    "        parser.add_argument('--edge_feat', help=\"Please give a value for edge_feat\")\n",
    "        parser.add_argument('--readout', help=\"Please give a value for readout\")\n",
    "        parser.add_argument('--kernel', help=\"Please give a value for kernel\")\n",
    "        parser.add_argument('--n_heads', help=\"Please give a value for n_heads\")\n",
    "        parser.add_argument('--gated', help=\"Please give a value for gated\")\n",
    "        parser.add_argument('--in_feat_dropout', help=\"Please give a value for in_feat_dropout\")\n",
    "        parser.add_argument('--dropout', help=\"Please give a value for dropout\")\n",
    "        parser.add_argument('--graph_norm', help=\"Please give a value for graph_norm\")\n",
    "        parser.add_argument('--batch_norm', help=\"Please give a value for batch_norm\")\n",
    "        parser.add_argument('--sage_aggregator', help=\"Please give a value for sage_aggregator\")\n",
    "        parser.add_argument('--data_mode', help=\"Please give a value for data_mode\")\n",
    "        parser.add_argument('--num_pool', help=\"Please give a value for num_pool\")\n",
    "        parser.add_argument('--gnn_per_block', help=\"Please give a value for gnn_per_block\")\n",
    "        parser.add_argument('--embedding_dim', help=\"Please give a value for embedding_dim\")\n",
    "        parser.add_argument('--pool_ratio', help=\"Please give a value for pool_ratio\")\n",
    "        parser.add_argument('--linkpred', help=\"Please give a value for linkpred\")\n",
    "        parser.add_argument('--cat', help=\"Please give a value for cat\")\n",
    "        parser.add_argument('--self_loop', help=\"Please give a value for self_loop\")\n",
    "        parser.add_argument('--max_time', help=\"Please give a value for max_time\")\n",
    "        args = parser.parse_args()\n",
    "        with open(args.config) as f:\n",
    "            config = json.load(f)\n",
    "            \n",
    "\n",
    "        # device\n",
    "        if args.gpu_id is not None:\n",
    "            config['gpu']['id'] = int(args.gpu_id)\n",
    "            config['gpu']['use'] = True\n",
    "        device = gpu_setup(config['gpu']['use'], config['gpu']['id'])\n",
    "\n",
    "        # model, dataset, out_dir\n",
    "        if args.model is not None:\n",
    "            MODEL_NAME = args.model\n",
    "        else:\n",
    "            MODEL_NAME = config['model']\n",
    "        if args.dataset is not None:\n",
    "            DATASET_NAME = args.dataset\n",
    "        else:\n",
    "            DATASET_NAME = config['dataset']\n",
    "        dataset = LoadData(DATASET_NAME)\n",
    "        if args.out_dir is not None:\n",
    "            out_dir = args.out_dir\n",
    "        else:\n",
    "            out_dir = config['out_dir']\n",
    "\n",
    "        # parameters\n",
    "        params = config['params']\n",
    "        if args.seed is not None:\n",
    "            params['seed'] = int(args.seed)\n",
    "        if args.epochs is not None:\n",
    "            params['epochs'] = int(args.epochs)\n",
    "        if args.batch_size is not None:\n",
    "            params['batch_size'] = int(args.batch_size)\n",
    "        if args.init_lr is not None:\n",
    "            params['init_lr'] = float(args.init_lr)\n",
    "        if args.lr_reduce_factor is not None:\n",
    "            params['lr_reduce_factor'] = float(args.lr_reduce_factor)\n",
    "        if args.lr_schedule_patience is not None:\n",
    "            params['lr_schedule_patience'] = int(args.lr_schedule_patience)\n",
    "        if args.min_lr is not None:\n",
    "            params['min_lr'] = float(args.min_lr)\n",
    "        if args.weight_decay is not None:\n",
    "            params['weight_decay'] = float(args.weight_decay)\n",
    "        if args.print_epoch_interval is not None:\n",
    "            params['print_epoch_interval'] = int(args.print_epoch_interval)\n",
    "        if args.max_time is not None:\n",
    "            params['max_time'] = float(args.max_time)\n",
    "\n",
    "        # network parameters\n",
    "        net_params = config['net_params']\n",
    "        net_params['device'] = device\n",
    "        net_params['gpu_id'] = config['gpu']['id']\n",
    "        net_params['batch_size'] = params['batch_size']\n",
    "        if args.L is not None:\n",
    "            net_params['L'] = int(args.L)\n",
    "        if args.hidden_dim is not None:\n",
    "            net_params['hidden_dim'] = int(args.hidden_dim)\n",
    "        if args.out_dim is not None:\n",
    "            net_params['out_dim'] = int(args.out_dim)   \n",
    "        if args.residual is not None:\n",
    "            net_params['residual'] = True if args.residual=='True' else False\n",
    "        if args.edge_feat is not None:\n",
    "            net_params['edge_feat'] = True if args.edge_feat=='True' else False\n",
    "        if args.readout is not None:\n",
    "            net_params['readout'] = args.readout\n",
    "        if args.kernel is not None:\n",
    "            net_params['kernel'] = int(args.kernel)\n",
    "        if args.n_heads is not None:\n",
    "            net_params['n_heads'] = int(args.n_heads)\n",
    "        if args.gated is not None:\n",
    "            net_params['gated'] = True if args.gated=='True' else False\n",
    "        if args.in_feat_dropout is not None:\n",
    "            net_params['in_feat_dropout'] = float(args.in_feat_dropout)\n",
    "        if args.dropout is not None:\n",
    "            net_params['dropout'] = float(args.dropout)\n",
    "        if args.graph_norm is not None:\n",
    "            net_params['graph_norm'] = True if args.graph_norm=='True' else False\n",
    "        if args.batch_norm is not None:\n",
    "            net_params['batch_norm'] = True if args.batch_norm=='True' else False\n",
    "        if args.sage_aggregator is not None:\n",
    "            net_params['sage_aggregator'] = args.sage_aggregator\n",
    "        if args.data_mode is not None:\n",
    "            net_params['data_mode'] = args.data_mode\n",
    "        if args.num_pool is not None:\n",
    "            net_params['num_pool'] = int(args.num_pool)\n",
    "        if args.gnn_per_block is not None:\n",
    "            net_params['gnn_per_block'] = int(args.gnn_per_block)\n",
    "        if args.embedding_dim is not None:\n",
    "            net_params['embedding_dim'] = int(args.embedding_dim)\n",
    "        if args.pool_ratio is not None:\n",
    "            net_params['pool_ratio'] = float(args.pool_ratio)\n",
    "        if args.linkpred is not None:\n",
    "            net_params['linkpred'] = True if args.linkpred=='True' else False\n",
    "        if args.cat is not None:\n",
    "            net_params['cat'] = True if args.cat=='True' else False\n",
    "        if args.self_loop is not None:\n",
    "            net_params['self_loop'] = True if args.self_loop=='True' else False\n",
    "\n",
    "            \n",
    "    # notebook mode\n",
    "    if notebook_mode:\n",
    "        \n",
    "        # parameters\n",
    "        params = config['params']\n",
    "        \n",
    "        # dataset\n",
    "        DATASET_NAME = config['dataset']\n",
    "        dataset = LoadData(DATASET_NAME)\n",
    "        \n",
    "        # device\n",
    "        device = gpu_setup(config['gpu']['use'], config['gpu']['id'])\n",
    "        out_dir = config['out_dir']\n",
    "        \n",
    "        # GNN model\n",
    "        MODEL_NAME = config['model']\n",
    "        \n",
    "        # network parameters\n",
    "        net_params = config['net_params']\n",
    "        net_params['device'] = device\n",
    "        net_params['gpu_id'] = config['gpu']['id']\n",
    "        net_params['batch_size'] = params['batch_size']\n",
    "              \n",
    "            \n",
    "    # Superpixels\n",
    "    net_params['in_dim'] = dataset.train[0][0].ndata['feat'][0].size(0)\n",
    "    net_params['in_dim_edge'] = dataset.train[0][0].edata['feat'][0].size(0)\n",
    "    num_classes = len(np.unique(np.array(dataset.train[:][1])))\n",
    "    net_params['n_classes'] = num_classes\n",
    "\n",
    "    if MODEL_NAME == 'DiffPool':\n",
    "        # calculate assignment dimension: pool_ratio * largest graph's maximum\n",
    "        # number of nodes  in the dataset\n",
    "        max_num_nodes_train = max([dataset.train[i][0].number_of_nodes() for i in range(len(dataset.train))])\n",
    "        max_num_nodes_test = max([dataset.test[i][0].number_of_nodes() for i in range(len(dataset.test))])\n",
    "        max_num_node = max(max_num_nodes_train, max_num_nodes_test)\n",
    "        net_params['assign_dim'] = int(max_num_node * net_params['pool_ratio']) * net_params['batch_size']\n",
    "    \n",
    "    root_log_dir = out_dir + 'logs/' + MODEL_NAME + \"_\" + DATASET_NAME + \"_GPU\" + str(config['gpu']['id']) + \"_\" + time.strftime('%Hh%Mm%Ss_on_%b_%d_%Y')\n",
    "    root_ckpt_dir = out_dir + 'checkpoints/' + MODEL_NAME + \"_\" + DATASET_NAME + \"_GPU\" + str(config['gpu']['id']) + \"_\" + time.strftime('%Hh%Mm%Ss_on_%b_%d_%Y')\n",
    "    write_file_name = out_dir + 'results/result_' + MODEL_NAME + \"_\" + DATASET_NAME + \"_GPU\" + str(config['gpu']['id']) + \"_\" + time.strftime('%Hh%Mm%Ss_on_%b_%d_%Y')\n",
    "    write_config_file = out_dir + 'configs/config_' + MODEL_NAME + \"_\" + DATASET_NAME + \"_GPU\" + str(config['gpu']['id']) + \"_\" + time.strftime('%Hh%Mm%Ss_on_%b_%d_%Y')\n",
    "    dirs = root_log_dir, root_ckpt_dir, write_file_name, write_config_file\n",
    "\n",
    "    if not os.path.exists(out_dir + 'results'):\n",
    "        os.makedirs(out_dir + 'results')\n",
    "        \n",
    "    if not os.path.exists(out_dir + 'configs'):\n",
    "        os.makedirs(out_dir + 'configs')\n",
    "\n",
    "    net_params['total_param'] = view_model_param(MODEL_NAME, net_params)\n",
    "    train_val_pipeline(MODEL_NAME, dataset, params, net_params, dirs)\n",
    "\n",
    "    \n",
    "    \n",
    "if notebook_mode==True:\n",
    "    \n",
    "    config = {}\n",
    "    # gpu config\n",
    "    gpu = {}\n",
    "    gpu['use'] = use_gpu\n",
    "    gpu['id'] = gpu_id\n",
    "    config['gpu'] = gpu\n",
    "    # GNN model, dataset, out_dir\n",
    "    config['model'] = MODEL_NAME\n",
    "    config['dataset'] = DATASET_NAME\n",
    "    config['out_dir'] = out_dir\n",
    "    # parameters\n",
    "    params = {}\n",
    "    params['seed'] = seed\n",
    "    params['epochs'] = epochs\n",
    "    params['batch_size'] = batch_size\n",
    "    params['init_lr'] = init_lr\n",
    "    params['lr_reduce_factor'] = lr_reduce_factor \n",
    "    params['lr_schedule_patience'] = lr_schedule_patience\n",
    "    params['min_lr'] = min_lr\n",
    "    params['weight_decay'] = weight_decay\n",
    "    params['print_epoch_interval'] = 5\n",
    "    params['max_time'] = max_time\n",
    "    config['params'] = params\n",
    "    # network parameters\n",
    "    config['net_params'] = net_params\n",
    "    \n",
    "    # convert to .py format\n",
    "    from utils.cleaner_main import *\n",
    "    cleaner_main('main_superpixels_graph_classification')\n",
    "    \n",
    "    main(True,config)\n",
    "    \n",
    "else:\n",
    "    \n",
    "    main()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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