import torch import torch.nn as nn import nvdiffrast.torch as dr from util.flexicubes_geometry import FlexiCubesGeometry class Renderer(nn.Module): def __init__(self, tet_grid_size, camera_angle_num, scale, geo_type): super().__init__() self.tet_grid_size = tet_grid_size self.camera_angle_num = camera_angle_num self.scale = scale self.geo_type = geo_type # self.glctx = dr.RasterizeCudaContext() if self.geo_type == "flex": self.flexicubes = FlexiCubesGeometry(grid_res = self.tet_grid_size) def forward(self, data, sdf, deform, verts, tets, training=False, weight = None): results = {} deform = torch.tanh(deform) / self.tet_grid_size * self.scale / 0.95 if self.geo_type == "flex": deform = deform *0.5 v_deformed = verts + deform verts_list = [] faces_list = [] reg_list = [] n_shape = verts.shape[0] for i in range(n_shape): verts_i, faces_i, reg_i = self.flexicubes.get_mesh(v_deformed[i], sdf[i].squeeze(dim=-1), with_uv=False, indices=tets, weight_n=weight[i], is_training=training) verts_list.append(verts_i) faces_list.append(faces_i) reg_list.append(reg_i) verts = verts_list faces = faces_list flexicubes_surface_reg = torch.cat(reg_list).mean() flexicubes_weight_reg = (weight ** 2).mean() results["flex_surf_loss"] = flexicubes_surface_reg results["flex_weight_loss"] = flexicubes_weight_reg return results, verts, faces