Delete .ipynb_checkpoints

.ipynb_checkpoints/DataInspect-checkpoint.ipynb

@@ -1,396 +0,0 @@
-{
- "cells": [
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-12T16:11:02.453452Z",
-     "start_time": "2024-12-12T16:10:59.783010Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "import os\n",
-    "import time\n",
-    "from rdkit import Chem\n",
-    "from rdkit import RDLogger;\n",
-    "from torch.utils.data import Dataset\n",
-    "import torch.nn.functional as F\n",
-    "RDLogger.DisableLog('rdApp.*')\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "import pickle\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import math\n",
-    "import dgl\n",
-    "import networkx as nx"
-   ],
-   "id": "1517383df6eb646",
-   "outputs": [],
-   "execution_count": 1
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-12T16:11:02.468893Z",
-     "start_time": "2024-12-12T16:11:02.454576Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "atom_number_index_dict ={\n",
-    "    1:0,\n",
-    "    6:1,\n",
-    "    7:2,\n",
-    "    8:3,\n",
-    "    9:4\n",
-    "}\n",
-    "atom_index_number_dict ={\n",
-    "    0:1,\n",
-    "    1:6,\n",
-    "    2:7,\n",
-    "    3:8,\n",
-    "    4:9\n",
-    "}\n",
-    "def atom_number2index(atom_number):\n",
-    "    return atom_number_index_dict[atom_number]\n",
-    "def atom_index2number(atom_index):\n",
-    "    return atom_index_number_dict[atom_index]"
-   ],
-   "id": "697783252f244e50",
-   "outputs": [],
-   "execution_count": 2
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-12T16:11:03.301916Z",
-     "start_time": "2024-12-12T16:11:03.243204Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "from dgl.data import QM9Dataset\n",
-    "from torch.utils.data import SubsetRandomSampler\n",
-    "from dgl.dataloading import GraphDataLoader\n",
-    "\n",
-    "dataset = QM9Dataset(label_keys=['mu', 'gap'], cutoff=5.0)\n",
-    "dataset_length = len(dataset)\n",
-    "train_idx = torch.arange(dataset_length)\n",
-    "train_sampler = SubsetRandomSampler(train_idx)\n",
-    "def collate_fn(batch):\n",
-    "    graphs, labels = map(list, zip(*batch))\n",
-    "    for g in graphs:\n",
-    "        # g.ndata[\"R\"]->the coordinates of each atom[num_nodes,3], g.ndata[\"Z\"]->the atomic number(H:1,C:6) [num_nodes]\n",
-    "        g.ndata[\"Z_index\"] = torch.tensor([atom_number2index(z.item()) for z in g.ndata[\"Z\"]])\n",
-    "    batched_graph = dgl.batch(graphs)\n",
-    "    return batched_graph, torch.stack(labels)\n",
-    "myGLoader = GraphDataLoader(dataset,collate_fn=collate_fn,batch_size=5, pin_memory=True)"
-   ],
-   "id": "7074f5a11a15ebc6",
-   "outputs": [],
-   "execution_count": 3
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-12T15:59:44.314049Z",
-     "start_time": "2024-12-12T15:59:44.299072Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "# atom_numbers = []\n",
-    "# for g,_ in dataset:\n",
-    "#     for atom_z in g.ndata[\"Z\"]:\n",
-    "#         if atom_z not in atom_numbers:\n",
-    "#             atom_numbers.append(atom_z)\n",
-    "# print(atom_numbers)"
-   ],
-   "id": "5758841a1f281514",
-   "outputs": [],
-   "execution_count": 12
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-12T16:14:46.301537Z",
-     "start_time": "2024-12-12T16:11:12.606641Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "for batch in myGLoader:\n",
-    "    batch_g,label = batch\n",
-    "    batch_g.to(device)\n",
-    "    "
-   ],
-   "id": "13bcff8166b0e35b",
-   "outputs": [],
-   "execution_count": 5
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-12T16:07:15.834856Z",
-     "start_time": "2024-12-12T16:07:15.822783Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "from functools import partial\n",
-    "import sys\n",
-    "sys.path.append(\"lib\")\n",
-    "from lib.metrics import sce_loss\n",
-    "\n",
-    "class GMae(nn.Module):\n",
-    "    def __init__(self, encoder,decoder,\n",
-    "                 in_dim,hidden_dim,out_dim,mask_rate=0.3,replace_rate=0.1,alpha_l=2,\n",
-    "                 embedding_layer_classes=4,embedding_layer_dim=4):\n",
-    "        super(GMae, self).__init__()\n",
-    "        self.Z_embedding = nn.Embedding(embedding_layer_classes,embedding_layer_dim)\n",
-    "        self.encoder = encoder\n",
-    "        self.decoder = decoder\n",
-    "        self.mask_rate = mask_rate\n",
-    "        self.replace_rate = replace_rate\n",
-    "        self.alpha_l = alpha_l\n",
-    "        self.in_dim = in_dim\n",
-    "        self.hidden_dim = hidden_dim\n",
-    "        self.out_dim = out_dim\n",
-    "        self.embedding_layer_classes = embedding_layer_classes\n",
-    "        self.embedding_layer_dim = embedding_layer_dim\n",
-    "        self.enc_mask_token = nn.Parameter(torch.zeros(1,in_dim))\n",
-    "        self.criterion = partial(sce_loss, alpha=alpha_l)\n",
-    "        self.encoder_to_decoder = nn.Linear(hidden_dim, hidden_dim, bias=False)\n",
-    "    def encode_atom_index(self,Z_index):\n",
-    "        return self.Z_embedding(Z_index)\n",
-    "    def encoding_mask_noise(self, g, x, mask_rate=0.3):\n",
-    "        num_nodes = g.num_nodes()\n",
-    "        perm = torch.randperm(num_nodes, device=x.device)\n",
-    "        # random masking\n",
-    "        num_mask_nodes = int(mask_rate * num_nodes)\n",
-    "        mask_nodes = perm[: num_mask_nodes]\n",
-    "        keep_nodes = perm[num_mask_nodes: ]\n",
-    "\n",
-    "        if self.replace_rate > 0:\n",
-    "            num_noise_nodes = int(self.replace_rate * num_mask_nodes)\n",
-    "            perm_mask = torch.randperm(num_mask_nodes, device=x.device)\n",
-    "            token_nodes = mask_nodes[perm_mask[: int((1-self.replace_rate) * num_mask_nodes)]]\n",
-    "            noise_nodes = mask_nodes[perm_mask[-int(self.replace_rate * num_mask_nodes):]]\n",
-    "            noise_to_be_chosen = torch.randperm(num_nodes, device=x.device)[:num_noise_nodes]\n",
-    "            out_x = x.clone()\n",
-    "            out_x[token_nodes] = 0.0\n",
-    "            out_x[noise_nodes] = x[noise_to_be_chosen]\n",
-    "        else:\n",
-    "            out_x = x.clone()\n",
-    "            token_nodes = mask_nodes\n",
-    "            out_x[mask_nodes] = 0.0\n",
-    "\n",
-    "        out_x[token_nodes] += self.enc_mask_token\n",
-    "        use_g = g.clone()\n",
-    "\n",
-    "        return use_g, out_x, (mask_nodes, keep_nodes)    \n",
-    "    def mask_attr_prediction(self, g, x):\n",
-    "        use_g, use_x, (mask_nodes, keep_nodes) = self.encoding_mask_noise(g, x, self.mask_rate)\n",
-    "        enc_rep = self.encoder(use_g, use_x)\n",
-    "        # ---- attribute reconstruction ----\n",
-    "        rep = self.encoder_to_decoder(enc_rep)\n",
-    "        recon = self.decoder(use_g, rep)\n",
-    "        x_init = x[mask_nodes]\n",
-    "        x_rec = recon[mask_nodes]\n",
-    "        loss = self.criterion(x_rec, x_init)\n",
-    "        return loss\n",
-    "\n",
-    "    def embed(self, g, x):\n",
-    "        rep = self.encoder(g, x)\n",
-    "        return rep\n",
-    "    "
-   ],
-   "id": "1a5caea191a642bc",
-   "outputs": [],
-   "execution_count": 5
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-12T16:07:18.136174Z",
-     "start_time": "2024-12-12T16:07:18.122456Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "import dgl.nn as dglnn\n",
-    "import torch.nn as nn\n",
-    "import torch.nn.functional as F\n",
-    "class SimpleGNN(nn.Module):\n",
-    "    def __init__(self, in_feats, hid_feats, out_feats):\n",
-    "        super().__init__()\n",
-    "        self.conv1 = dglnn.SAGEConv(\n",
-    "            in_feats=in_feats, out_feats=hid_feats,aggregator_type=\"mean\")\n",
-    "        self.conv2 = dglnn.SAGEConv(\n",
-    "            in_feats=hid_feats, out_feats=out_feats,aggregator_type=\"mean\")\n",
-    "\n",
-    "    def forward(self, graph, inputs):\n",
-    "        # 输入是节点的特征\n",
-    "        h = self.conv1(graph, inputs)\n",
-    "        h = F.relu(h)\n",
-    "        h = self.conv2(graph, h)\n",
-    "        return h"
-   ],
-   "id": "c99cb509ac0f1054",
-   "outputs": [],
-   "execution_count": 6
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-12T16:07:21.516476Z",
-     "start_time": "2024-12-12T16:07:21.118135Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "sage_enc = SimpleGNN(in_feats=7,hid_feats=4,out_feats=4)\n",
-    "sage_dec = SimpleGNN(in_feats=4,hid_feats=4,out_feats=7)\n",
-    "gmae = GMae(sage_enc,sage_dec,7,4,7,replace_rate=0)\n",
-    "epoches = 20\n",
-    "optimizer = optim.Adam(gmae.parameters(), lr=1e-3)\n",
-    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
-   ],
-   "id": "5a8a4e4dd753b642",
-   "outputs": [],
-   "execution_count": 7
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-12T16:10:52.354863Z",
-     "start_time": "2024-12-12T16:10:52.311090Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "print(f\"epoch {0} started!\")\n",
-    "gmae.train()\n",
-    "gmae.encoder.train()\n",
-    "gmae.decoder.train()\n",
-    "gmae.to(device)\n",
-    "loss_epoch = 0\n",
-    "for batch in myGLoader:\n",
-    "    optimizer.zero_grad()\n",
-    "    batch_g, _ = batch\n",
-    "    R = batch_g.ndata[\"R\"].to(device)\n",
-    "    Z_index = batch_g.ndata[\"Z_index\"].to(device)\n",
-    "    Z_emb = gmae.encode_atom_index(Z_index)\n",
-    "    feat = torch.cat([R,Z_emb],dim=1)\n",
-    "    batch_g = batch_g.to(device)\n",
-    "    # loss = gmae.mask_attr_prediction(batch_g, feat)\n",
-    "    # loss.backward()\n",
-    "    # optimizer.step()\n",
-    "    # loss_epoch+=loss.item()"
-   ],
-   "id": "224529a988b81ef5",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0 started!\n"
-     ]
-    },
-    {
-     "ename": "RuntimeError",
-     "evalue": "CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[1;31mRuntimeError\u001B[0m                              Traceback (most recent call last)",
-      "Cell \u001B[1;32mIn[12], line 10\u001B[0m\n\u001B[0;32m      8\u001B[0m optimizer\u001B[38;5;241m.\u001B[39mzero_grad()\n\u001B[0;32m      9\u001B[0m batch_g, _ \u001B[38;5;241m=\u001B[39m batch\n\u001B[1;32m---> 10\u001B[0m R \u001B[38;5;241m=\u001B[39m \u001B[43mbatch_g\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mndata\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mR\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mto\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdevice\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     11\u001B[0m Z_index \u001B[38;5;241m=\u001B[39m batch_g\u001B[38;5;241m.\u001B[39mndata[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mZ_index\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mto(device)\n\u001B[0;32m     12\u001B[0m Z_emb \u001B[38;5;241m=\u001B[39m gmae\u001B[38;5;241m.\u001B[39mencode_atom_index(Z_index)\n",
-      "\u001B[1;31mRuntimeError\u001B[0m: CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n"
-     ]
-    }
-   ],
-   "execution_count": 12
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-12T15:59:46.502050Z",
-     "start_time": "2024-12-12T15:59:44.442506Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "from datetime import datetime\n",
-    "\n",
-    "current_time = datetime.now().strftime(\"%m-%d@%H_%M\")\n",
-    "best_loss = 10000\n",
-    "for epoch in range(epoches):\n",
-    "    print(f\"epoch {epoch} started!\")\n",
-    "    gmae.train()\n",
-    "    gmae.encoder.train()\n",
-    "    gmae.decoder.train()\n",
-    "    gmae.to(device)\n",
-    "    loss_epoch = 0\n",
-    "    for batch in myGLoader:\n",
-    "        optimizer.zero_grad()\n",
-    "        batch_g, _ = batch\n",
-    "        R = batch_g.ndata[\"R\"].to(device)\n",
-    "        Z_index = batch_g.ndata[\"Z_index\"].to(device)\n",
-    "        Z_emb = gmae.encode_atom_index(Z_index)\n",
-    "        feat = torch.cat([R,Z_emb],dim=1)\n",
-    "        batch_g = batch_g.to(device)\n",
-    "        loss = gmae.mask_attr_prediction(batch_g, feat)\n",
-    "        loss.backward()\n",
-    "        optimizer.step()\n",
-    "        loss_epoch+=loss.item()\n",
-    "    if loss_epoch < best_loss:\n",
-    "        formatted_loss_epoch = f\"{loss_epoch:.3f}\"\n",
-    "        save_path = f\"./experiments/consumption/gmae/{current_time}/gmae_epoch-{epoch}-{formatted_loss_epoch}.pt\"\n",
-    "        save_dir = os.path.dirname(save_path)\n",
-    "        if not os.path.exists(save_dir):\n",
-    "            os.makedirs(save_dir,exist_ok=True)\n",
-    "        torch.save(gmae.state_dict(), save_path)\n",
-    "        best_loss = loss_epoch\n",
-    "        print(f\"best model saved-loss:{formatted_loss_epoch}-save_path:{save_path}\")\n",
-    "    print(f\"epoch {epoch}: loss {loss_epoch}\")"
-   ],
-   "id": "a22599c4e591125b",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0 started!\n"
-     ]
-    },
-    {
-     "ename": "RuntimeError",
-     "evalue": "CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[1;31mRuntimeError\u001B[0m                              Traceback (most recent call last)",
-      "Cell \u001B[1;32mIn[18], line 19\u001B[0m\n\u001B[0;32m     17\u001B[0m Z_emb \u001B[38;5;241m=\u001B[39m gmae\u001B[38;5;241m.\u001B[39mencode_atom_index(Z_index)\n\u001B[0;32m     18\u001B[0m feat \u001B[38;5;241m=\u001B[39m torch\u001B[38;5;241m.\u001B[39mcat([R,Z_emb],dim\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m)\n\u001B[1;32m---> 19\u001B[0m batch_g \u001B[38;5;241m=\u001B[39m \u001B[43mbatch_g\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mto\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdevice\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     20\u001B[0m loss \u001B[38;5;241m=\u001B[39m gmae\u001B[38;5;241m.\u001B[39mmask_attr_prediction(batch_g, feat)\n\u001B[0;32m     21\u001B[0m loss\u001B[38;5;241m.\u001B[39mbackward()\n",
-      "File \u001B[1;32mE:\\Anaconda\\envs\\gnn_course\\lib\\site-packages\\dgl\\heterograph.py:5730\u001B[0m, in \u001B[0;36mDGLGraph.to\u001B[1;34m(self, device, **kwargs)\u001B[0m\n\u001B[0;32m   5728\u001B[0m \u001B[38;5;66;03m# 2. Copy misc info\u001B[39;00m\n\u001B[0;32m   5729\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_batch_num_nodes \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m-> 5730\u001B[0m     new_bnn \u001B[38;5;241m=\u001B[39m {\n\u001B[0;32m   5731\u001B[0m         k: F\u001B[38;5;241m.\u001B[39mcopy_to(num, device, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m   5732\u001B[0m         \u001B[38;5;28;01mfor\u001B[39;00m k, num \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_batch_num_nodes\u001B[38;5;241m.\u001B[39mitems()\n\u001B[0;32m   5733\u001B[0m     }\n\u001B[0;32m   5734\u001B[0m     ret\u001B[38;5;241m.\u001B[39m_batch_num_nodes \u001B[38;5;241m=\u001B[39m new_bnn\n\u001B[0;32m   5735\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_batch_num_edges \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
-      "File \u001B[1;32mE:\\Anaconda\\envs\\gnn_course\\lib\\site-packages\\dgl\\heterograph.py:5731\u001B[0m, in \u001B[0;36m<dictcomp>\u001B[1;34m(.0)\u001B[0m\n\u001B[0;32m   5728\u001B[0m \u001B[38;5;66;03m# 2. Copy misc info\u001B[39;00m\n\u001B[0;32m   5729\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_batch_num_nodes \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m   5730\u001B[0m     new_bnn \u001B[38;5;241m=\u001B[39m {\n\u001B[1;32m-> 5731\u001B[0m         k: \u001B[43mF\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcopy_to\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnum\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdevice\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   5732\u001B[0m         \u001B[38;5;28;01mfor\u001B[39;00m k, num \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_batch_num_nodes\u001B[38;5;241m.\u001B[39mitems()\n\u001B[0;32m   5733\u001B[0m     }\n\u001B[0;32m   5734\u001B[0m     ret\u001B[38;5;241m.\u001B[39m_batch_num_nodes \u001B[38;5;241m=\u001B[39m new_bnn\n\u001B[0;32m   5735\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_batch_num_edges \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
-      "File \u001B[1;32mE:\\Anaconda\\envs\\gnn_course\\lib\\site-packages\\dgl\\backend\\pytorch\\tensor.py:143\u001B[0m, in \u001B[0;36mcopy_to\u001B[1;34m(input, ctx, **kwargs)\u001B[0m\n\u001B[0;32m    141\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m ctx\u001B[38;5;241m.\u001B[39mindex \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m    142\u001B[0m         th\u001B[38;5;241m.\u001B[39mcuda\u001B[38;5;241m.\u001B[39mset_device(ctx\u001B[38;5;241m.\u001B[39mindex)\n\u001B[1;32m--> 143\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43minput\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcuda\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    144\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m    145\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mRuntimeError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mInvalid context\u001B[39m\u001B[38;5;124m\"\u001B[39m, ctx)\n",
-      "\u001B[1;31mRuntimeError\u001B[0m: CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n"
-     ]
-    }
-   ],
-   "execution_count": 18
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "name": "gnn_course",
-   "language": "python",
-   "display_name": "gnn_course"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 5
-}