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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 '') | |