# -*- coding: utf-8 -*- # Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is # holder of all proprietary rights on this computer program. # You can only use this computer program if you have closed # a license agreement with MPG or you get the right to use the computer # program from someone who is authorized to grant you that right. # Any use of the computer program without a valid license is prohibited and # liable to prosecution. # # Copyright©2019 Max-Planck-Gesellschaft zur Förderung # der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute # for Intelligent Systems. All rights reserved. # # Contact: ps-license@tuebingen.mpg.de from lib.net.voxelize import Voxelization from lib.dataset.mesh_util import cal_sdf_batch, feat_select, read_smpl_constants from lib.net.NormalNet import NormalNet from lib.net.MLP import MLP from lib.dataset.mesh_util import SMPLX from lib.net.VE import VolumeEncoder from lib.net.HGFilters import * from termcolor import colored from lib.net.BasePIFuNet import BasePIFuNet import torch.nn as nn import torch maskout = False class HGPIFuNet(BasePIFuNet): ''' HG PIFu network uses Hourglass stacks as the image filter. It does the following: 1. Compute image feature stacks and store it in self.im_feat_list self.im_feat_list[-1] is the last stack (output stack) 2. Calculate calibration 3. If training, it index on every intermediate stacks, If testing, it index on the last stack. 4. Classification. 5. During training, error is calculated on all stacks. ''' def __init__(self, cfg, projection_mode='orthogonal', error_term=nn.MSELoss()): super(HGPIFuNet, self).__init__(projection_mode=projection_mode, error_term=error_term) self.l1_loss = nn.SmoothL1Loss() self.opt = cfg.net self.root = cfg.root self.overfit = cfg.overfit channels_IF = self.opt.mlp_dim self.use_filter = self.opt.use_filter self.prior_type = self.opt.prior_type self.smpl_feats = self.opt.smpl_feats self.smpl_dim = self.opt.smpl_dim self.voxel_dim = self.opt.voxel_dim self.hourglass_dim = self.opt.hourglass_dim self.sdf_clip = cfg.sdf_clip / 100.0 self.in_geo = [item[0] for item in self.opt.in_geo] self.in_nml = [item[0] for item in self.opt.in_nml] self.in_geo_dim = sum([item[1] for item in self.opt.in_geo]) self.in_nml_dim = sum([item[1] for item in self.opt.in_nml]) self.in_total = self.in_geo + self.in_nml self.smpl_feat_dict = None self.smplx_data = SMPLX() if self.prior_type == 'icon': if 'image' in self.in_geo: self.channels_filter = [[0, 1, 2, 3, 4, 5], [0, 1, 2, 6, 7, 8]] else: self.channels_filter = [[0, 1, 2], [3, 4, 5]] else: if 'image' in self.in_geo: self.channels_filter = [[0, 1, 2, 3, 4, 5, 6, 7, 8]] else: self.channels_filter = [[0, 1, 2, 3, 4, 5]] channels_IF[0] = self.hourglass_dim if self.use_filter else len( self.channels_filter[0]) if self.prior_type == 'icon' and 'vis' not in self.smpl_feats: if self.use_filter: channels_IF[0] += self.hourglass_dim else: channels_IF[0] += len(self.channels_filter[0]) if self.prior_type == 'icon': channels_IF[0] += self.smpl_dim elif self.prior_type == 'pamir': channels_IF[0] += self.voxel_dim smpl_vertex_code, smpl_face_code, smpl_faces, smpl_tetras = read_smpl_constants( self.smplx_data.tedra_dir) self.voxelization = Voxelization( smpl_vertex_code, smpl_face_code, smpl_faces, smpl_tetras, volume_res=128, sigma=0.05, smooth_kernel_size=7, batch_size=cfg.batch_size, device=torch.device(f"cuda:{cfg.gpus[0]}")) self.ve = VolumeEncoder(3, self.voxel_dim, self.opt.num_stack) else: channels_IF[0] += 1 self.icon_keys = ["smpl_verts", "smpl_faces", "smpl_vis", "smpl_cmap"] self.pamir_keys = [ "voxel_verts", "voxel_faces", "pad_v_num", "pad_f_num" ] self.if_regressor = MLP( filter_channels=channels_IF, name='if', res_layers=self.opt.res_layers, norm=self.opt.norm_mlp, last_op=nn.Sigmoid() if not cfg.test_mode else None) # network if self.use_filter: if self.opt.gtype == "HGPIFuNet": self.F_filter = HGFilter(self.opt, self.opt.num_stack, len(self.channels_filter[0])) else: print( colored(f"Backbone {self.opt.gtype} is unimplemented", 'green')) summary_log = f"{self.prior_type.upper()}:\n" + \ f"w/ Global Image Encoder: {self.use_filter}\n" + \ f"Image Features used by MLP: {self.in_geo}\n" if self.prior_type == "icon": summary_log += f"Geometry Features used by MLP: {self.smpl_feats}\n" summary_log += f"Dim of Image Features (local): 6\n" summary_log += f"Dim of Geometry Features (ICON): {self.smpl_dim}\n" elif self.prior_type == "pamir": summary_log += f"Dim of Image Features (global): {self.hourglass_dim}\n" summary_log += f"Dim of Geometry Features (PaMIR): {self.voxel_dim}\n" else: summary_log += f"Dim of Image Features (global): {self.hourglass_dim}\n" summary_log += f"Dim of Geometry Features (PIFu): 1 (z-value)\n" summary_log += f"Dim of MLP's first layer: {channels_IF[0]}\n" print(colored(summary_log, "yellow")) self.normal_filter = NormalNet(cfg) init_net(self) def get_normal(self, in_tensor_dict): # insert normal features if (not self.training) and (not self.overfit): # print(colored("infer normal","blue")) with torch.no_grad(): feat_lst = [] if "image" in self.in_geo: feat_lst.append( in_tensor_dict['image']) # [1, 3, 512, 512] if 'normal_F' in self.in_geo and 'normal_B' in self.in_geo: if 'normal_F' not in in_tensor_dict.keys( ) or 'normal_B' not in in_tensor_dict.keys(): (nmlF, nmlB) = self.normal_filter(in_tensor_dict) else: nmlF = in_tensor_dict['normal_F'] nmlB = in_tensor_dict['normal_B'] feat_lst.append(nmlF) # [1, 3, 512, 512] feat_lst.append(nmlB) # [1, 3, 512, 512] in_filter = torch.cat(feat_lst, dim=1) else: in_filter = torch.cat([in_tensor_dict[key] for key in self.in_geo], dim=1) return in_filter def get_mask(self, in_filter, size=128): mask = F.interpolate(in_filter[:, self.channels_filter[0]], size=(size, size), mode="bilinear", align_corners=True).abs().sum(dim=1, keepdim=True) != 0.0 return mask def filter(self, in_tensor_dict, return_inter=False): ''' Filter the input images store all intermediate features. :param images: [B, C, H, W] input images ''' in_filter = self.get_normal(in_tensor_dict) features_G = [] if self.prior_type == 'icon': if self.use_filter: features_F = self.F_filter(in_filter[:, self.channels_filter[0]] ) # [(B,hg_dim,128,128) * 4] features_B = self.F_filter(in_filter[:, self.channels_filter[1]] ) # [(B,hg_dim,128,128) * 4] else: features_F = [in_filter[:, self.channels_filter[0]]] features_B = [in_filter[:, self.channels_filter[1]]] for idx in range(len(features_F)): features_G.append( torch.cat([features_F[idx], features_B[idx]], dim=1)) else: if self.use_filter: features_G = self.F_filter(in_filter[:, self.channels_filter[0]]) else: features_G = [in_filter[:, self.channels_filter[0]]] if self.prior_type == 'icon': self.smpl_feat_dict = { k: in_tensor_dict[k] for k in self.icon_keys } elif self.prior_type == "pamir": self.smpl_feat_dict = { k: in_tensor_dict[k] for k in self.pamir_keys } else: pass # print(colored("use z rather than icon or pamir", "green")) # If it is not in training, only produce the last im_feat if not self.training: features_out = [features_G[-1]] else: features_out = features_G if maskout: features_out_mask = [] for feat in features_out: features_out_mask.append( feat * self.get_mask(in_filter, size=feat.shape[2])) features_out = features_out_mask if return_inter: return features_out, in_filter else: return features_out def query(self, features, points, calibs, transforms=None, regressor=None): xyz = self.projection(points, calibs, transforms) (xy, z) = xyz.split([2, 1], dim=1) in_cube = (xyz > -1.0) & (xyz < 1.0) in_cube = in_cube.all(dim=1, keepdim=True).detach().float() preds_list = [] if self.prior_type == 'icon': # smpl_verts [B, N_vert, 3] # smpl_faces [B, N_face, 3] # points [B, 3, N] smpl_sdf, smpl_norm, smpl_cmap, smpl_vis = cal_sdf_batch( self.smpl_feat_dict['smpl_verts'], self.smpl_feat_dict['smpl_faces'], self.smpl_feat_dict['smpl_cmap'], self.smpl_feat_dict['smpl_vis'], xyz.permute(0, 2, 1).contiguous()) # smpl_sdf [B, N, 1] # smpl_norm [B, N, 3] # smpl_cmap [B, N, 3] # smpl_vis [B, N, 1] feat_lst = [smpl_sdf] if 'cmap' in self.smpl_feats: feat_lst.append(smpl_cmap) if 'norm' in self.smpl_feats: feat_lst.append(smpl_norm) if 'vis' in self.smpl_feats: feat_lst.append(smpl_vis) smpl_feat = torch.cat(feat_lst, dim=2).permute(0, 2, 1) vol_feats = features elif self.prior_type == "pamir": voxel_verts = self.smpl_feat_dict[ 'voxel_verts'][:, :-self.smpl_feat_dict['pad_v_num'][0], :] voxel_faces = self.smpl_feat_dict[ 'voxel_faces'][:, :-self.smpl_feat_dict['pad_f_num'][0], :] self.voxelization.update_param( batch_size=voxel_faces.shape[0], smpl_tetra=voxel_faces[0].detach().cpu().numpy()) vol = self.voxelization(voxel_verts) # vol ~ [0,1] vol_feats = self.ve(vol, intermediate_output=self.training) else: vol_feats = features for im_feat, vol_feat in zip(features, vol_feats): # [B, Feat_i + z, N] # normal feature choice by smpl_vis if self.prior_type == 'icon': if 'vis' in self.smpl_feats: point_local_feat = feat_select(self.index(im_feat, xy), smpl_feat[:, [-1], :]) if maskout: normal_mask = torch.tile( point_local_feat.sum(dim=1, keepdims=True) == 0.0, (1, smpl_feat.shape[1], 1)) normal_mask[:, 1:, :] = False smpl_feat[normal_mask] = -1.0 point_feat_list = [point_local_feat, smpl_feat[:, :-1, :]] else: point_local_feat = self.index(im_feat, xy) point_feat_list = [point_local_feat, smpl_feat[:, :, :]] elif self.prior_type == 'pamir': # im_feat [B, hg_dim, 128, 128] # vol_feat [B, vol_dim, 32, 32, 32] point_feat_list = [ self.index(im_feat, xy), self.index(vol_feat, xyz) ] else: point_feat_list = [self.index(im_feat, xy), z] point_feat = torch.cat(point_feat_list, 1) # out of image plane is always set to 0 preds = regressor(point_feat) preds = in_cube * preds preds_list.append(preds) return preds_list def get_error(self, preds_if_list, labels): """calcaulate error Args: preds_list (list): list of torch.tensor(B, 3, N) labels (torch.tensor): (B, N_knn, N) Returns: torch.tensor: error """ error_if = 0 for pred_id in range(len(preds_if_list)): pred_if = preds_if_list[pred_id] error_if += self.error_term(pred_if, labels) error_if /= len(preds_if_list) return error_if def forward(self, in_tensor_dict): """ sample_tensor [B, 3, N] calib_tensor [B, 4, 4] label_tensor [B, 1, N] smpl_feat_tensor [B, 59, N] """ sample_tensor = in_tensor_dict['sample'] calib_tensor = in_tensor_dict['calib'] label_tensor = in_tensor_dict['label'] in_feat = self.filter(in_tensor_dict) preds_if_list = self.query(in_feat, sample_tensor, calib_tensor, regressor=self.if_regressor) error = self.get_error(preds_if_list, label_tensor) return preds_if_list[-1], error