print_pytorch_geometric_data_info.ipynb aims to print out PyTorch Graph Data flexibly

print_pytorch_geometric_data_info.ipynb

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     "text": [
      "Nodes:\n",
      "  document:\n",
      "    x=torch.Size([10050, 34])\n",
      "    year=torch.Size([10050])\n",
      "    language=torch.Size([10050])\n",
      "  context:\n",
      "    x=torch.Size([903, 1])\n",
      "    year=torch.Size([903])\n",
      "    region=torch.Size([903])\n",
      "  prediction_target:\n",
      "    popularity=torch.Size([301, 3])\n",
      "    sentiment=torch.Size([301, 3])\n",
      "\n",
      "Edges:\n",
      "  ('document', 'semantic', 'document'):\n",
      "    edge_index=torch.Size([2, 30029])\n",
      "    edge_type=torch.Size([30029])\n",
      "  ('document', 'bridging', 'context'):\n",
      "    edge_index=torch.Size([2, 20168])\n",
      "  ('context', 'temporal', 'context'):\n",
      "    edge_index=torch.Size([2, 900])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch_geometric.data import HeteroData\n",
    "\n",
    "def print_data_size_info(file_path):\n",
    "    data = torch.load(file_path)\n",
    "    \n",
    "    if not isinstance(data, HeteroData):\n",
    "        print(\"The loaded data is not a HeteroData object.\")\n",
    "        return\n",
    "    \n",
    "    print(\"Nodes:\")\n",
    "    for node_type in data.node_types:\n",
    "        print(f\"  {node_type}:\")\n",
    "        for attr, tensor in data[node_type].items():\n",
    "            print(f\"    {attr}={tensor.size()}\")\n",
    "    \n",
    "    print(\"\\nEdges:\")\n",
    "    for edge_type in data.edge_types:\n",
    "        print(f\"  {edge_type}:\")\n",
    "        for attr, tensor in data[edge_type].items():\n",
    "            print(f\"    {attr}={tensor.size()}\")\n",
    "\n",
    "# Example usage\n",
    "file_path = 'eurochinaGNN_dataset_revised_bridging_edges.pt'\n",
    "print_data_size_info(file_path)"
   ]
  }
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