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1ac2018 |
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import torch
import torch.nn as nn
from . import functional as F
__all__ = ['Voxelization']
def my_voxelization(features, coords, resolution):
b, c, _ = features.shape
result = torch.zeros(b, c + 1, resolution * resolution * resolution, device=features.device, dtype=torch.float)
r = resolution
r2 = resolution * resolution
indices = coords[:, 0] * r2 + coords[:, 1] * r + coords[:, 2]
indices = indices.unsqueeze(dim=1).expand(-1, result.shape[1], -1)
features = torch.cat([features, torch.ones(features.shape[0], 1, features.shape[2], device=features.device, dtype=features.dtype)], dim=1)
out_feature = result.scatter_(index=indices.long(), src=features, dim=2, reduce='add')
cnt = out_feature[:, -1:, :]
zero_mask = (cnt == 0).float()
cnt = cnt * (1 - zero_mask) + zero_mask * 1e-5
vox_feature = out_feature[:, :-1, :] / cnt
return vox_feature.view(b, c, resolution, resolution, resolution)
class Voxelization(nn.Module):
def __init__(self, resolution, normalize=True, eps=0, scale_pvcnn=False):
super().__init__()
self.r = int(resolution)
self.normalize = normalize
self.eps = eps
self.scale_pvcnn = scale_pvcnn
assert not normalize
def forward(self, features, coords):
with torch.no_grad():
coords = coords.detach()
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
else:
if self.scale_pvcnn:
norm_coords = (coords + 1) / 2.0 # [0, 1]
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)
new_vox_feat = my_voxelization(features, vox_coords, self.r)
return new_vox_feat, norm_coords
def extra_repr(self):
return 'resolution={}{}'.format(self.r, ', normalized eps = {}'.format(self.eps) if self.normalize else '')
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