Can't use the spnn.Conv3d

[qifeng22]

import random
from typing import Any, Dict

import numpy as np
import torch
import torch.utils.data
from torch import nn
from torch.cuda import amp

import torchsparse
from torchsparse import SparseTensor
from torchsparse import nn as spnn
from torchsparse.nn import functional as F
from torchsparse.utils.collate import sparse_collate_fn
from torchsparse.utils.quantize import sparse_quantize


inputs = np.random.uniform(-10, 10, size=(10, 4))

coords, feats = inputs[:, :3], inputs

coords -= np.min(coords, axis=0, keepdims=True)

coords, indices = sparse_quantize(coords, 0.01, return_index=True)

coords = torch.tensor(coords, dtype=torch.int)
feats = torch.tensor(feats[indices], dtype=torch.float)


input = SparseTensor(coords=coords, feats=feats)

tt = spnn.Conv3d(4, 3, 1,stride=2).cuda()(input)
print(tt.feats)

[Zhijian Liu]

For SparseTensor, the 'coords' need to be a four-channel vector, where the first three dimensions represent the voxelized coordinates, and the last one should indicate the batch index.