Source code for

import re

import torch
from torch._tensor_str import PRINT_OPTS, _tensor_str
from import parse_txt_array
from import Data

def parse_off(src):
    # Some files may contain a bug and do not have a carriage return after OFF.
    if src[0] == 'OFF':
        src = src[1:]
        src[0] = src[0][3:]

    num_nodes, num_faces = [int(item) for item in src[0].split()[:2]]

    pos = parse_txt_array(src[1:1 + num_nodes])

    face = src[1 + num_nodes:1 + num_nodes + num_faces]
    face = face_to_tri(face)

    data = Data(pos=pos)
    data.face = face

    return data

def face_to_tri(face):
    face = [[int(x) for x in line.strip().split()] for line in face]

    triangle = torch.tensor([line[1:] for line in face if line[0] == 3])
    triangle =

    rect = torch.tensor([line[1:] for line in face if line[0] == 4])
    rect =

    if rect.numel() > 0:
        first, second = rect[:, [0, 1, 2]], rect[:, [1, 2, 3]]
        return[triangle, first, second], dim=0).t().contiguous()
        return triangle.t().contiguous()

[docs]def read_off(path): r"""Reads an OFF (Object File Format) file, returning both the position of nodes and their connectivity in a :class:`` object. Args: path (str): The path to the file. """ with open(path, 'r') as f: src ='\n')[:-1] return parse_off(src)
[docs]def write_off(data, path): r"""Writes a :class:`` object to an OFF (Object File Format) file. Args: data (:class:``): The data object. path (str): The path to the file. """ num_nodes, num_faces = data.pos.size(0), data.face.size(1) pos = face = data.face.t() num_vertices = torch.full((num_faces, 1), face.size(1), dtype=torch.long) face =[num_vertices, face], dim=-1) threshold = PRINT_OPTS.threshold torch.set_printoptions(threshold=float('inf')) pos_repr = re.sub(',', '', _tensor_str(pos, indent=0)) pos_repr = '\n'.join([x[2:-1] for x in pos_repr.split('\n')])[:-1] face_repr = re.sub(',', '', _tensor_str(face, indent=0)) face_repr = '\n'.join([x[2:-1] for x in face_repr.split('\n')])[:-1] with open(path, 'w') as f: f.write('OFF\n{} {} 0\n'.format(num_nodes, num_faces)) f.write(pos_repr) f.write('\n') f.write(face_repr) f.write('\n') torch.set_printoptions(threshold=threshold)