# -*- 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