deeplearning/modulus/modulus-core-v021/_modules/modulus/datapipes/gnn/utils.html

Source code for modulus.datapipes.gnn.utils

# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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import os
import json
import torch

from typing import Dict, Any

try:
    import vtk
except:
    raise ImportError("vtk package is required. Install with pip install vtk.")


[docs]def read_vtp_file(file_path: str) -> Any: """ Read a VTP file and return the polydata. Parameters ---------- file_path : str Path to the VTP file. Returns ------- vtkPolyData The polydata read from the VTP file. """ # Check if file exists if not os.path.exists(file_path): raise FileNotFoundError(f"{file_path} does not exist.") # Check if file has .vtp extension if not file_path.endswith(".vtp"): raise ValueError(f"Expected a .vtp file, got {file_path}") reader = vtk.vtkXMLPolyDataReader() reader.SetFileName(file_path) reader.Update() # Get the polydata polydata = reader.GetOutput() # Check if polydata is valid if polydata is None: raise ValueError(f"Failed to read polydata from {file_path}") return polydata
[docs]def save_json(var: Dict[str, torch.Tensor], file: str) -> None: """ Saves a dictionary of tensors to a JSON file. Parameters ---------- var : Dict[str, torch.Tensor] Dictionary where each value is a PyTorch tensor. file : str Path to the output JSON file. """ var_list = {k: v.numpy().tolist() for k, v in var.items()} with open(file, "w") as f: json.dump(var_list, f)
[docs]def load_json(file: str) -> Dict[str, torch.Tensor]: """ Loads a JSON file into a dictionary of PyTorch tensors. Parameters ---------- file : str Path to the JSON file. Returns ------- Dict[str, torch.Tensor] Dictionary where each value is a PyTorch tensor. """ with open(file, "r") as f: var_list = json.load(f) var = {k: torch.tensor(v, dtype=torch.float) for k, v in var_list.items()} return var
© Copyright 2023, NVIDIA Modulus Team. Last updated on Sep 21, 2023.