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Zero
Running
on
Zero
| import torch | |
| cube_corners = torch.tensor([[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0], [0, 0, 1], [ | |
| 1, 0, 1], [0, 1, 1], [1, 1, 1]], dtype=torch.int) | |
| cube_neighbor = torch.tensor([[1, 0, 0], [-1, 0, 0], [0, 1, 0], [0, -1, 0], [0, 0, 1], [0, 0, -1]]) | |
| cube_edges = torch.tensor([0, 1, 1, 5, 4, 5, 0, 4, 2, 3, 3, 7, 6, 7, 2, 6, | |
| 2, 0, 3, 1, 7, 5, 6, 4], dtype=torch.long, requires_grad=False) | |
| def construct_dense_grid(res, device='cuda'): | |
| '''construct a dense grid based on resolution''' | |
| res_v = res + 1 | |
| vertsid = torch.arange(res_v ** 3, device=device) | |
| coordsid = vertsid.reshape(res_v, res_v, res_v)[:res, :res, :res].flatten() | |
| cube_corners_bias = (cube_corners[:, 0] * res_v + cube_corners[:, 1]) * res_v + cube_corners[:, 2] | |
| cube_fx8 = (coordsid.unsqueeze(1) + cube_corners_bias.unsqueeze(0).to(device)) | |
| verts = torch.stack([vertsid // (res_v ** 2), (vertsid // res_v) % res_v, vertsid % res_v], dim=1) | |
| return verts, cube_fx8 | |
| def construct_voxel_grid(coords): | |
| verts = (cube_corners.unsqueeze(0).to(coords) + coords.unsqueeze(1)).reshape(-1, 3) | |
| verts_unique, inverse_indices = torch.unique(verts, dim=0, return_inverse=True) | |
| cubes = inverse_indices.reshape(-1, 8) | |
| return verts_unique, cubes | |
| def cubes_to_verts(num_verts, cubes, value, reduce='mean'): | |
| """ | |
| Args: | |
| cubes [Vx8] verts index for each cube | |
| value [Vx8xM] value to be scattered | |
| Operation: | |
| reduced[cubes[i][j]][k] += value[i][k] | |
| """ | |
| M = value.shape[2] # number of channels | |
| reduced = torch.zeros(num_verts, M, device=cubes.device) | |
| return torch.scatter_reduce(reduced, 0, | |
| cubes.unsqueeze(-1).expand(-1, -1, M).flatten(0, 1), | |
| value.flatten(0, 1), reduce=reduce, include_self=False) | |
| def sparse_cube2verts(coords, feats, training=True): | |
| new_coords, cubes = construct_voxel_grid(coords) | |
| new_feats = cubes_to_verts(new_coords.shape[0], cubes, feats) | |
| if training: | |
| con_loss = torch.mean((feats - new_feats[cubes]) ** 2) | |
| else: | |
| con_loss = 0.0 | |
| return new_coords, new_feats, con_loss | |
| def get_dense_attrs(coords : torch.Tensor, feats : torch.Tensor, res : int, sdf_init=True): | |
| F = feats.shape[-1] | |
| dense_attrs = torch.zeros([res] * 3 + [F], device=feats.device) | |
| if sdf_init: | |
| dense_attrs[..., 0] = 1 # initial outside sdf value | |
| dense_attrs[coords[:, 0], coords[:, 1], coords[:, 2], :] = feats | |
| return dense_attrs.reshape(-1, F) | |
| def get_defomed_verts(v_pos : torch.Tensor, deform : torch.Tensor, res): | |
| return v_pos / res - 0.5 + (1 - 1e-8) / (res * 2) * torch.tanh(deform) | |