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2f5e0ac | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | import os
import sys
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', '..')))
import utils3d
import numpy as np
import torch
def run():
for i in range(100):
if i == 0:
spatial = []
L = 1
N = 5
faces = np.array([[0, 1, 2, 3, 4]])
expected = np.array([[0, 1, 2], [0, 2, 3], [0, 3, 4]])
else:
dim = np.random.randint(4)
spatial = [np.random.randint(1, 10) for _ in range(dim)]
L = np.random.randint(1, 1000)
N = np.random.randint(3, 10)
faces = np.random.randint(0, 10000, size=(*spatial, L, N))
expected = utils3d.numpy.triangulate(faces)
device = [torch.device('cpu'), torch.device('cuda')][np.random.randint(2)]
faces = torch.tensor(faces, device=device)
actual = utils3d.torch.triangulate(faces).cpu().numpy()
assert np.allclose(expected, actual), '\n' + \
'Input:\n' + \
f'{faces}\n' + \
'Actual:\n' + \
f'{actual}\n' + \
'Expected:\n' + \
f'{expected}'
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