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import torch
import torch.nn as nn
from . import functional as F
__all__ = ['Voxelization']
class Voxelization(nn.Module):
def __init__(self, resolution, normalize=True, eps=0):
super().__init__()
self.r = int(resolution)
self.normalize = normalize
self.eps = eps
def forward(self, features, coords):
coords = coords.detach()
norm_coords = coords - coords.mean(2, keepdim=True)
if self.normalize:
norm_coords = norm_coords / (norm_coords.norm(dim=1, keepdim=True).max(dim=2, keepdim=True).values * 2.0 + self.eps) + 0.5 # within a unit cube of size 1x1x1
else:
norm_coords = (norm_coords + 1) / 2.0
norm_coords = torch.clamp(norm_coords * self.r, 0, self.r - 1)
vox_coords = torch.round(norm_coords).to(torch.int32)
return F.avg_voxelize(features, vox_coords, self.r), norm_coords
def extra_repr(self):
return 'resolution={}{}'.format(self.r, ', normalized eps = {}'.format(self.eps) if self.normalize else '')
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