# -*- 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.renderer.mesh import compute_normal_batch from lib.dataset.mesh_util import feat_select, read_smpl_constants, surface_field_deformation from lib.net.NormalNet import NormalNet from lib.net.MLP import MLP, DeformationMLP, TransformerEncoderLayer, SDF2Density, SDF2Occ from lib.net.spatial import SpatialEncoder from lib.dataset.PointFeat import PointFeat from lib.dataset.mesh_util import SMPLX from lib.net.VE import VolumeEncoder from lib.net.ResBlkPIFuNet import ResnetFilter from lib.net.UNet import UNet from lib.net.HGFilters import * from lib.net.Transformer import ViTVQ from termcolor import colored from lib.net.BasePIFuNet import BasePIFuNet import torch.nn as nn import torch import numpy as np import matplotlib.pyplot as plt import torch.nn.functional as F from lib.net.nerf_util import raw2outputs def normalize(tensor): min_val = tensor.min() max_val = tensor.max() normalized_tensor = (tensor - min_val) / (max_val - min_val) return normalized_tensor def visualize_feature_map(feature_map, title, filename): feature_map=feature_map.permute(0, 2, 3, 1) # 选择一个样本（如果有多个） sample_index = 0 sample = feature_map[sample_index] # 选择一个通道（如果有多个） channel_index = 0 channel = sample[:, :, channel_index] channel= normalize(channel) plt.imshow(channel.cpu().numpy(), cmap='hot') # plt.title(title) # plt.colorbar() plt.axis('off') plt.savefig(filename, dpi=300,bbox_inches='tight', pad_inches=0) # 保存图片到文件 plt.close() # 关闭图形，释放资源 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.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() image_lst = [0, 1, 2] normal_F_lst = [0, 1, 2] if "image" not in self.in_geo else [3, 4, 5] normal_B_lst = [3, 4, 5] if "image" not in self.in_geo else [6, 7, 8] # only ICON or ICON-Keypoint use visibility if self.prior_type in ["icon", "keypoint"]: if "image" in self.in_geo: self.channels_filter = [ image_lst + normal_F_lst, image_lst + normal_B_lst, ] else: self.channels_filter = [normal_F_lst, normal_B_lst] else: if "image" in self.in_geo: self.channels_filter = [ image_lst + normal_F_lst + normal_B_lst ] else: self.channels_filter = [normal_F_lst + normal_B_lst] use_vis = (self.prior_type in ["icon", "keypoint" ]) and ("vis" in self.smpl_feats) if self.prior_type in ["pamir", "pifu"]: use_vis = 1 if self.use_filter: channels_IF[0] = (self.hourglass_dim) * (2 - use_vis) else: channels_IF[0] = len(self.channels_filter[0]) * (2 - use_vis) if self.prior_type in ["icon", "keypoint"]: channels_IF[0] += self.smpl_dim elif self.prior_type == "pifu": channels_IF[0] += 1 else: print(f"don't support {self.prior_type}!") self.base_keys = ["smpl_verts", "smpl_faces"] self.icon_keys = self.base_keys + [ f"smpl_{feat_name}" for feat_name in self.smpl_feats ] self.keypoint_keys = self.base_keys + [ f"smpl_{feat_name}" for feat_name in self.smpl_feats ] self.pamir_keys = [ "voxel_verts", "voxel_faces", "pad_v_num", "pad_f_num" ] self.pifu_keys = [] # channels_IF[0]+=self.hourglass_dim # 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, # ) self.deform_dim=64 #self.image_filter = ResnetFilter(self.opt, norm_layer=norm_type) #self.image_filter = UNet(3,128) # self.xy_plane_filter=ResnetFilter(self.opt, norm_layer=norm_type) # self.yz_plane_filter=ViTVQ(image_size=512) # ResnetFilter(self.opt, norm_layer=norm_type) # self.xz_plane_filter=ViTVQ(image_size=512) self.image_filter=ViTVQ(image_size=512,channels=9) # self.deformation_mlp=DeformationMLP(input_dim=self.deform_dim,opt=self.opt) self.mlp=TransformerEncoderLayer(skips=4,multires=6,opt=self.opt) # self.sdf2density=SDF2Density() # self.sdf2occ=SDF2Occ() self.color_loss=nn.L1Loss() self.sp_encoder = SpatialEncoder() self.step=0 self.features_costume=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])) # self.refine_filter = FuseHGFilter(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): {3 if (use_vis and not self.use_filter) else 6}\n" summary_log += f"Dim of Geometry Features (ICON): {self.smpl_dim}\n" elif self.prior_type == "keypoint": summary_log += f"Geometry Features used by MLP: {self.smpl_feats}\n" summary_log += f"Dim of Image Features (local): {3 if (use_vis and not self.use_filter) else 6}\n" summary_log += f"Dim of Geometry Features (Keypoint): {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, init_type="normal") 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) image= in_tensor_dict["image"] fuse_image=torch.cat([image,in_filter], dim=1) smpl_normals={ "T_normal_B":in_tensor_dict['normal_B'], "T_normal_R":in_tensor_dict['T_normal_R'], "T_normal_L":in_tensor_dict['T_normal_L'] } features_G = [] # self.smpl_normal=in_tensor_dict['T_normal_L'] if self.prior_type in ["icon", "keypoint"]: if self.use_filter: triplane_features = self.image_filter(fuse_image,smpl_normals) 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: assert 0 F_plane_feat,B_plane_feat,R_plane_feat,L_plane_feat=triplane_features refine_F_plane_feat=F_plane_feat features_G.append(refine_F_plane_feat) features_G.append(B_plane_feat) features_G.append(R_plane_feat) features_G.append(L_plane_feat) features_G.append(torch.cat([features_F[-1],features_B[-1]], dim=1)) else: assert 0 self.smpl_feat_dict = { k: in_tensor_dict[k] if k in in_tensor_dict.keys() else None for k in getattr(self, f"{self.prior_type}_keys") } if 'animated_smpl_verts' not in in_tensor_dict.keys(): self.point_feat_extractor = PointFeat(self.smpl_feat_dict["smpl_verts"], self.smpl_feat_dict["smpl_faces"]) else: assert 0 self.features_G = features_G # If it is not in training, only produce the last im_feat if not self.training: features_out = features_G else: features_out = features_G if return_inter: return features_out, in_filter else: return features_out def query(self, features, points, calibs, transforms=None,type='shape'): xyz = self.projection(points, calibs, transforms) # project to image plane (xy, z) = xyz.split([2, 1], dim=1) zy=torch.cat([xyz[:,2:3],xyz[:,1:2]],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 in ["icon", "keypoint"]: densely_smpl=self.smpl_feat_dict['smpl_verts'].permute(0,2,1) #smpl_origin=self.projection(densely_smpl, torch.inverse(calibs), transforms) smpl_vis=self.smpl_feat_dict['smpl_vis'].permute(0,2,1) #verts_ids=self.smpl_feat_dict['smpl_sample_id'] (smpl_xy,smpl_z)=densely_smpl.split([2,1],dim=1) smpl_zy=torch.cat([densely_smpl[:,2:3],densely_smpl[:,1:2]],dim=1) point_feat_out = self.point_feat_extractor.query( # this extractor changes if has animated smpl xyz.permute(0, 2, 1).contiguous(), self.smpl_feat_dict) vis=point_feat_out['vis'].permute(0,2,1) #sdf_body=-point_feat_out['sdf'] # this sdf needs to be multiplied by -1 feat_lst = [ point_feat_out[key] for key in self.smpl_feats if key in point_feat_out.keys() ] smpl_feat = torch.cat(feat_lst, dim=2).permute(0, 2, 1) if len(features)==5: F_plane_feat1,F_plane_feat2=features[0].chunk(2,dim=1) B_plane_feat1,B_plane_feat2=features[1].chunk(2,dim=1) R_plane_feat1,R_plane_feat2=features[2].chunk(2,dim=1) L_plane_feat1,L_plane_feat2=features[3].chunk(2,dim=1) in_feat=features[4] F_feat=self.index(F_plane_feat1,xy) B_feat=self.index(B_plane_feat1,xy) R_feat=self.index(R_plane_feat1,zy) L_feat=self.index(L_plane_feat1,zy) normal_feat=feat_select(self.index(in_feat, xy),vis) three_plane_feat=(B_feat+R_feat+L_feat)/3 triplane_feat=torch.cat([F_feat,three_plane_feat],dim=1) # 32+32=64 ### smpl query ### smpl_F_feat=self.index(F_plane_feat2,smpl_xy) smpl_B_feat=self.index(B_plane_feat2,smpl_xy) smpl_R_feat=self.index(R_plane_feat2,smpl_zy) smpl_L_feat=self.index(L_plane_feat2,smpl_zy) smpl_three_plane_feat=(smpl_B_feat+smpl_R_feat+smpl_L_feat)/3 smpl_triplane_feat=torch.cat([smpl_F_feat,smpl_three_plane_feat],dim=1) # 32+32=64 bary_centric_feat=self.point_feat_extractor.query_barycentirc_feats(xyz.permute(0,2,1).contiguous() ,smpl_triplane_feat.permute(0,2,1)) final_feat=torch.cat([triplane_feat,bary_centric_feat.permute(0,2,1),normal_feat],dim=1) # 64+64+6=134 if self.features_costume is not None: assert 0 if type=='shape': if 'animated_smpl_verts' in self.smpl_feat_dict.keys(): animated_smpl=self.smpl_feat_dict['animated_smpl_verts'] occ=self.mlp(xyz.permute(0,2,1).contiguous(),animated_smpl, final_feat,smpl_feat,training=self.training,type=type) else: occ=self.mlp(xyz.permute(0,2,1).contiguous(),densely_smpl.permute(0,2,1), final_feat,smpl_feat,training=self.training,type=type) occ=occ*in_cube preds_list.append(occ) elif type=='color': if 'animated_smpl_verts' in self.smpl_feat_dict.keys(): animated_smpl=self.smpl_feat_dict['animated_smpl_verts'] color_preds=self.mlp(xyz.permute(0,2,1).contiguous(),animated_smpl, final_feat,smpl_feat,training=self.training,type=type) else: color_preds=self.mlp(xyz.permute(0,2,1).contiguous(),densely_smpl.permute(0,2,1), final_feat,smpl_feat,training=self.training,type=type) preds_list.append(color_preds) return preds_list def get_error(self, preds_if_list, labels): """calculate 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 += F.binary_cross_entropy(pred_if, labels) error_if /= len(preds_if_list) return error_if def forward(self, in_tensor_dict): sample_tensor = in_tensor_dict["sample"] calib_tensor = in_tensor_dict["calib"] label_tensor = in_tensor_dict["label"] color_sample=in_tensor_dict["sample_color"] color_label=in_tensor_dict["color"] in_feat = self.filter(in_tensor_dict) preds_if_list = self.query(in_feat, sample_tensor, calib_tensor,type='shape') BCEloss = self.get_error(preds_if_list, label_tensor) color_preds=self.query(in_feat, color_sample, calib_tensor,type='color') color_loss=self.color_loss(color_preds[0],color_label) if self.training: self.color3d_loss= color_loss error=BCEloss+color_loss self.grad_loss=torch.tensor(0.).float().to(BCEloss.device) else: error=BCEloss return preds_if_list[-1].detach(), error