ICON / lib /net /HGPIFuNet.py
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# -*- 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]
# set ourlier point features as uniform values
smpl_outlier = torch.abs(smpl_sdf).ge(self.sdf_clip)
smpl_sdf[smpl_outlier] = torch.sign(smpl_sdf[smpl_outlier])
feat_lst = [smpl_sdf]
if 'cmap' in self.smpl_feats:
smpl_cmap[smpl_outlier.repeat(
1, 1, 3)] = smpl_sdf[smpl_outlier].repeat(1, 1, 3)
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