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| from __future__ import division, print_function | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import numpy as np | |
| from torch.autograd import Function | |
| import voxelize_cuda | |
| class VoxelizationFunction(Function): | |
| """ | |
| Definition of differentiable voxelization function | |
| Currently implemented only for cuda Tensors | |
| """ | |
| 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, device): | |
| 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 = device | |
| 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, batch_size, smpl_tetra): | |
| self.batch_size = batch_size | |
| self.smpl_tetraderon_indices = smpl_tetra | |
| smpl_vertex_code_batch = np.tile(self.smpl_vertex_code, | |
| (self.batch_size, 1, 1)) | |
| smpl_face_code_batch = np.tile(self.smpl_face_code, | |
| (self.batch_size, 1, 1)) | |
| smpl_face_indices_batch = np.tile(self.smpl_face_indices, | |
| (self.batch_size, 1, 1)) | |
| smpl_tetraderon_indices_batch = np.tile(self.smpl_tetraderon_indices, | |
| (self.batch_size, 1, 1)) | |
| smpl_vertex_code_batch = torch.from_numpy( | |
| smpl_vertex_code_batch).contiguous().to(self.device) | |
| smpl_face_code_batch = torch.from_numpy( | |
| smpl_face_code_batch).contiguous().to(self.device) | |
| smpl_face_indices_batch = torch.from_numpy( | |
| smpl_face_indices_batch).contiguous().to(self.device) | |
| smpl_tetraderon_indices_batch = torch.from_numpy( | |
| smpl_tetraderon_indices_batch).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 | |
| """ | |
| assert (smpl_vertices.size()[0] == self.batch_size) | |
| 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] | |
| device = vertices.device | |
| face = self.smpl_face_indices_batch + ( | |
| torch.arange(bs, dtype=torch.int32).to(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] | |
| device = vertices.device | |
| tets = self.smpl_tetraderon_indices_batch + ( | |
| torch.arange(bs, dtype=torch.int32).to(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') | |