lib/net/voxelize.py

from __future__ import division, print_function

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import voxelize_cuda
from torch.autograd import Function


class VoxelizationFunction(Function):
    """
    Definition of differentiable voxelization function
    Currently implemented only for cuda Tensors
    """
    @staticmethod
    def forward(
        ctx,
        smpl_vertices,
        smpl_face_center,
        smpl_face_normal,
        smpl_vertex_code,
        smpl_face_code,
        smpl_tetrahedrons,
        volume_res,
        sigma,
        smooth_kernel_size,
    ):
        """
        forward pass
        Output format: (batch_size, z_dims, y_dims, x_dims, channel_num)
        """
        assert smpl_vertices.size()[1] == smpl_vertex_code.size()[1]
        assert smpl_face_center.size()[1] == smpl_face_normal.size()[1]
        assert smpl_face_center.size()[1] == smpl_face_code.size()[1]
        ctx.batch_size = smpl_vertices.size()[0]
        ctx.volume_res = volume_res
        ctx.sigma = sigma
        ctx.smooth_kernel_size = smooth_kernel_size
        ctx.smpl_vertex_num = smpl_vertices.size()[1]
        ctx.device = smpl_vertices.device

        smpl_vertices = smpl_vertices.contiguous()
        smpl_face_center = smpl_face_center.contiguous()
        smpl_face_normal = smpl_face_normal.contiguous()
        smpl_vertex_code = smpl_vertex_code.contiguous()
        smpl_face_code = smpl_face_code.contiguous()
        smpl_tetrahedrons = smpl_tetrahedrons.contiguous()

        occ_volume = torch.cuda.FloatTensor(
            ctx.batch_size, ctx.volume_res, ctx.volume_res, ctx.volume_res
        ).fill_(0.0)
        semantic_volume = torch.cuda.FloatTensor(
            ctx.batch_size, ctx.volume_res, ctx.volume_res, ctx.volume_res, 3
        ).fill_(0.0)
        weight_sum_volume = torch.cuda.FloatTensor(
            ctx.batch_size, ctx.volume_res, ctx.volume_res, ctx.volume_res
        ).fill_(1e-3)

        # occ_volume [B, volume_res, volume_res, volume_res]
        # semantic_volume [B, volume_res, volume_res, volume_res, 3]
        # weight_sum_volume [B, volume_res, volume_res, volume_res]

        (
            occ_volume,
            semantic_volume,
            weight_sum_volume,
        ) = voxelize_cuda.forward_semantic_voxelization(
            smpl_vertices,
            smpl_vertex_code,
            smpl_tetrahedrons,
            occ_volume,
            semantic_volume,
            weight_sum_volume,
            sigma,
        )

        return semantic_volume


class Voxelization(nn.Module):
    """
    Wrapper around the autograd function VoxelizationFunction
    """
    def __init__(
        self,
        smpl_vertex_code,
        smpl_face_code,
        smpl_face_indices,
        smpl_tetraderon_indices,
        volume_res,
        sigma,
        smooth_kernel_size,
        batch_size,
    ):
        super(Voxelization, self).__init__()
        assert len(smpl_face_indices.shape) == 2
        assert len(smpl_tetraderon_indices.shape) == 2
        assert smpl_face_indices.shape[1] == 3
        assert smpl_tetraderon_indices.shape[1] == 4

        self.volume_res = volume_res
        self.sigma = sigma
        self.smooth_kernel_size = smooth_kernel_size
        self.batch_size = batch_size
        self.device = None

        self.smpl_vertex_code = smpl_vertex_code
        self.smpl_face_code = smpl_face_code
        self.smpl_face_indices = smpl_face_indices
        self.smpl_tetraderon_indices = smpl_tetraderon_indices

    def update_param(self, voxel_faces):

        self.device = voxel_faces.device

        self.smpl_tetraderon_indices = voxel_faces

        smpl_vertex_code_batch = torch.tile(self.smpl_vertex_code, (self.batch_size, 1, 1))
        smpl_face_code_batch = torch.tile(self.smpl_face_code, (self.batch_size, 1, 1))
        smpl_face_indices_batch = torch.tile(self.smpl_face_indices, (self.batch_size, 1, 1))

        smpl_vertex_code_batch = (smpl_vertex_code_batch.contiguous().to(self.device))
        smpl_face_code_batch = (smpl_face_code_batch.contiguous().to(self.device))
        smpl_face_indices_batch = (smpl_face_indices_batch.contiguous().to(self.device))
        smpl_tetraderon_indices_batch = (self.smpl_tetraderon_indices.contiguous().to(self.device))

        self.register_buffer("smpl_vertex_code_batch", smpl_vertex_code_batch)
        self.register_buffer("smpl_face_code_batch", smpl_face_code_batch)
        self.register_buffer("smpl_face_indices_batch", smpl_face_indices_batch)
        self.register_buffer("smpl_tetraderon_indices_batch", smpl_tetraderon_indices_batch)

    def forward(self, smpl_vertices):
        """
        Generate semantic volumes from SMPL vertices
        """
        self.check_input(smpl_vertices)
        smpl_faces = self.vertices_to_faces(smpl_vertices)
        smpl_tetrahedrons = self.vertices_to_tetrahedrons(smpl_vertices)
        smpl_face_center = self.calc_face_centers(smpl_faces)
        smpl_face_normal = self.calc_face_normals(smpl_faces)
        smpl_surface_vertex_num = self.smpl_vertex_code_batch.size()[1]
        smpl_vertices_surface = smpl_vertices[:, :smpl_surface_vertex_num, :]
        vol = VoxelizationFunction.apply(
            smpl_vertices_surface,
            smpl_face_center,
            smpl_face_normal,
            self.smpl_vertex_code_batch,
            self.smpl_face_code_batch,
            smpl_tetrahedrons,
            self.volume_res,
            self.sigma,
            self.smooth_kernel_size,
        )
        return vol.permute((0, 4, 1, 2, 3))    # (bzyxc --> bcdhw)

    def vertices_to_faces(self, vertices):
        assert vertices.ndimension() == 3
        bs, nv = vertices.shape[:2]
        face = (
            self.smpl_face_indices_batch +
            (torch.arange(bs, dtype=torch.int32).to(self.device) * nv)[:, None, None]
        )
        vertices_ = vertices.reshape((bs * nv, 3))
        return vertices_[face.long()]

    def vertices_to_tetrahedrons(self, vertices):
        assert vertices.ndimension() == 3
        bs, nv = vertices.shape[:2]
        tets = (
            self.smpl_tetraderon_indices_batch +
            (torch.arange(bs, dtype=torch.int32).to(self.device) * nv)[:, None, None]
        )
        vertices_ = vertices.reshape((bs * nv, 3))
        return vertices_[tets.long()]

    def calc_face_centers(self, face_verts):
        assert len(face_verts.shape) == 4
        assert face_verts.shape[2] == 3
        assert face_verts.shape[3] == 3
        bs, nf = face_verts.shape[:2]
        face_centers = (
            face_verts[:, :, 0, :] + face_verts[:, :, 1, :] + face_verts[:, :, 2, :]
        ) / 3.0
        face_centers = face_centers.reshape((bs, nf, 3))
        return face_centers

    def calc_face_normals(self, face_verts):
        assert len(face_verts.shape) == 4
        assert face_verts.shape[2] == 3
        assert face_verts.shape[3] == 3
        bs, nf = face_verts.shape[:2]
        face_verts = face_verts.reshape((bs * nf, 3, 3))
        v10 = face_verts[:, 0] - face_verts[:, 1]
        v12 = face_verts[:, 2] - face_verts[:, 1]
        normals = F.normalize(torch.cross(v10, v12), eps=1e-5)
        normals = normals.reshape((bs, nf, 3))
        return normals

    def check_input(self, x):
        if x.device == "cpu":
            raise TypeError("Voxelization module supports only cuda tensors")
        if x.type() != "torch.cuda.FloatTensor":
            raise TypeError("Voxelization module supports only float32 tensors")