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mesh_net.py
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mesh_net.py
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# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import app
from absl import flags
import os
import os.path as osp
import sys
sys.path.insert(0,'third_party')
import numpy as np
import configparser
import time
import torch
import torchvision
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import trimesh
import pytorch3d
import pytorch3d.loss
import pdb
from ext_utils import mesh
from ext_utils.quatlib import q_rnd_m, q_scale_m
from ext_utils.util_rot import compute_geodesic_distance_from_two_matrices
from ext_utils import geometry as geom_utils
from ext_nnutils import net_blocks as nb
from ext_nnutils.mesh_net import MeshNet
import kornia
import configparser
import soft_renderer as sr
from nnutils.geom_utils import pinhole_cam, obj_to_cam, render_flow_soft_3
from nnutils.geom_utils import label_colormap
citylabs = label_colormap()
#-------------- flags -------------#
#----------------------------------#
flags.DEFINE_boolean('noise', True, 'Add random noise to pose')
flags.DEFINE_boolean('symmetric', True, 'Use symmetric mesh or not')
flags.DEFINE_boolean('symmetric_loss', True, 'Use symmetric loss or not')
flags.DEFINE_integer('nz_feat', 200, 'Encoded feature size')
flags.DEFINE_boolean('texture', True, 'if true uses texture!')
flags.DEFINE_boolean('symmetric_texture', True, 'if true texture is symmetric!')
flags.DEFINE_integer('subdivide', 3, '# to subdivide icosahedron, 3=642verts, 4=2562 verts')
flags.DEFINE_integer('symidx', 0, 'symmetry index: 0-x 1-y 2-z')
flags.DEFINE_integer('n_bones', 1, 'num of meshes')
flags.DEFINE_string('n_faces', '1280','number of faces for remeshing')
flags.DEFINE_integer('n_hypo', 1, 'num of hypothesis cameras')
flags.DEFINE_boolean('only_mean_sym', False, 'If true, only the meanshape is symmetric')
flags.DEFINE_string('dataname', 'fashion', 'name of the test data')
flags.DEFINE_string('opt_tex', 'no', 'optimize texture')
flags.DEFINE_float('rscale', 1.0, 'scale random variance')
flags.DEFINE_float('l1tex_wt', 1.0, 'weight of l1 texture')
flags.DEFINE_float('sigval', 1e-4, 'blur radius of soft renderer')
def render_flow_soft_2(renderer_soft, verts, faces, verts_pos0, verts_pos1, pp0, pp1, proj_cam0,proj_cam1):
# flow (no splat): 1) get mask; 2) render 3D coords for 1st/2nd frame
n_hypo = verts.shape[0] // faces.shape[0]
faces = faces[:,None].repeat(1,n_hypo,1,1).view(-1,faces.shape[1],3)
verts_pos0 = verts_pos0.clone()
verts_pos1 = verts_pos1.clone()
offset = torch.Tensor( renderer_soft.transform.transformer._eye ).cuda()[np.newaxis,np.newaxis]
verts_pre = verts[:,:,:3]+offset; verts_pre[:,:,1] = -1*verts_pre[:,:,1]
nb = verts.shape[0]
verts_pos_px = renderer_soft.render_mesh(sr.Mesh(torch.cat([verts_pre ,verts_pre],0),
torch.cat([faces ,faces],0),
textures=torch.cat([verts_pos0[:,:,:3],verts_pos1[:,:,:3]],0),texture_type='vertex')).clone()
fgmask = verts_pos_px[:nb,-1]
verts_pos_px = verts_pos_px[:,:3]
verts_pos0_px = verts_pos_px[:nb].permute(0,2,3,1)
verts_pos1_px = verts_pos_px[nb:].permute(0,2,3,1)
bgmask = (verts_pos0_px[:,:,:,2]<1e-9) | (verts_pos1_px[:,:,:,2]<1e-9)
verts_pos0_px[bgmask]=10
verts_pos1_px[bgmask]=10
# projet 3D verts with different intrinsics
verts_pos0_px[:,:,:,1] = pp0[:,1:2,np.newaxis]+verts_pos0_px[:,:,:,1].clone()*proj_cam0[:,:1,np.newaxis] / verts_pos0_px[:,:,:,2].clone()
verts_pos0_px[:,:,:,0] = pp0[:,0:1,np.newaxis]+verts_pos0_px[:,:,:,0].clone()*proj_cam0[:,:1,np.newaxis] / verts_pos0_px[:,:,:,2].clone()
verts_pos1_px[:,:,:,1] = pp1[:,1:2,np.newaxis]+verts_pos1_px[:,:,:,1].clone()*proj_cam1[:,:1,np.newaxis] / verts_pos1_px[:,:,:,2].clone()
verts_pos1_px[:,:,:,0] = pp1[:,0:1,np.newaxis]+verts_pos1_px[:,:,:,0].clone()*proj_cam1[:,:1,np.newaxis] / verts_pos1_px[:,:,:,2].clone()
flow_fw = (verts_pos1_px - verts_pos0_px.detach())[:,:,:,:2]
flow_fw[bgmask] = flow_fw[bgmask].detach()
return flow_fw, bgmask, fgmask
def reg_decay(curr_steps, max_steps, min_wt,max_wt):
"""
max weight to min weight
"""
if curr_steps>max_steps:current = min_wt
else:
current = np.exp(curr_steps/float(max_steps)*(np.log(min_wt)-np.log(max_wt))) * max_wt
return current
class LASR(MeshNet):
def __init__(self, input_shape, opts, nz_feat=100):
super(LASR, self).__init__(input_shape, opts, nz_feat)
self.rest_rs = torch.Tensor([[0,0,0,1]]).repeat(opts.n_bones-1,1).cuda()
self.transg = torch.Tensor([[0,0,0]]).cuda().repeat(opts.n_bones-1,1) # not including the body-to-world transform
self.ctl_rs = torch.Tensor([[0,0,0,1]]).repeat(opts.n_hypo*(opts.n_bones-1),1).cuda()
self.rest_ts = torch.zeros(opts.n_hypo*(opts.n_bones-1),3).cuda()
self.ctl_ts = torch.zeros(opts.n_hypo*(opts.n_bones-1),3).cuda()
self.log_ctl = torch.Tensor([[0,0,0]]).cuda().repeat(opts.n_hypo*(opts.n_bones-1),1) # control point varuance
if self.opts.n_bones>1:
self.ctl_rs = nn.Parameter(self.ctl_rs)
self.rest_ts = nn.Parameter(self.rest_ts)
self.ctl_ts = nn.Parameter(self.ctl_ts)
self.log_ctl = nn.Parameter(self.log_ctl)
# For renderering.
self.renderer_soft = sr.SoftRenderer(image_size=opts.img_size, sigma_val=1e-4,
camera_mode='look_at',perspective=False, aggr_func_rgb='hard',
light_mode='vertex', light_intensity_ambient=1.,light_intensity_directionals=0.)
self.renderer_softflf = sr.SoftRenderer(image_size=opts.img_size, sigma_val=1e-4, gamma_val=1e-2,
camera_mode='look_at',perspective=False, aggr_func_rgb='softmax',
light_mode='vertex', light_intensity_ambient=1.,light_intensity_directionals=0.)
self.renderer_softflb = sr.SoftRenderer(image_size=opts.img_size, sigma_val=1e-4, gamma_val=1e-2,
camera_mode='look_at',perspective=False, aggr_func_rgb='softmax',
light_mode='vertex', light_intensity_ambient=1.,light_intensity_directionals=0.)
self.renderer_softtex = sr.SoftRenderer(image_size=opts.img_size, sigma_val=1e-4,gamma_val=1e-2,
camera_mode='look_at',perspective=False, aggr_func_rgb='softmax',
light_mode='vertex', light_intensity_ambient=1.,light_intensity_directionals=0.)
self.renderer_softpart = sr.SoftRenderer(image_size=opts.img_size, sigma_val=1e-4,gamma_val=1e-4,
camera_mode='look_at',perspective=False, aggr_func_rgb='softmax',
light_mode='vertex', light_intensity_ambient=1.,light_intensity_directionals=0.)
def forward(self, batch_input):
if self.training:
local_batch_size = batch_input['input_imgs '].shape[0]//2
for k,v in batch_input.items():
batch_input[k] = v.view(local_batch_size,2,-1).permute(1,0,2).reshape(v.shape)
self.input_imgs = batch_input['input_imgs ']
self.imgs = batch_input['imgs ']
self.masks = batch_input['masks ']
self.cams = batch_input['cams ']
self.depth_gt = batch_input['depth_gt ']
self.flow = batch_input['flow ']
self.dts_barrier = batch_input['dts_barrier ']
self.ddts_barrier = batch_input['ddts_barrier']
self.mask_contour = batch_input['mask_contour']
self.pp = batch_input['pp ']
self.occ = batch_input['occ ']
self.oriimg_shape = batch_input['oriimg_shape']
self.frameid = batch_input['frameid']
self.dataid = batch_input['dataid']
self.is_canonical = batch_input['is_canonical']
else:
local_batch_size = 1
self.input_imgs = batch_input
img = self.input_imgs
# torch.cuda.synchronize()
# start_time = time.time()
opts = self.opts
pred_v, tex,faces= self.get_mean_shape(local_batch_size)
if self.training:
tex = tex.view(2*local_batch_size,-1,opts.n_hypo,tex.shape[-2],3)
texnew = torch.zeros_like(tex[:,0])
for i in range(local_batch_size*2):
texnew[i] = tex[i,self.dataid[i]]
tex = texnew.reshape(-1,tex.shape[-2],3)
faces = faces.cuda()
def set_bn_eval(m):
classname = m.__class__.__name__
if classname.find('BatchNorm') != -1:
m.eval()
#if opts.n_hypo==1:
self.apply(set_bn_eval)
img_feat = self.encoder.forward(img)
scale, trans, quat, depth, ppoint = self.code_predictor.forward(img_feat)
if not self.training:
return scale,trans,quat, depth, ppoint
# transform the CNN-predicted focal length
# (wrt original image without cropping) to the cropped image
scale = self.cams[:,:1]*scale # here assumes intrinsic may change
# transform depth as well, to ensure the rendered silhouette
# occupies enough pixels of the cropped image at initialization
depth[:,:1] = self.cams[:,:1]* depth[:,:1]; depth = depth.view(-1,1)
# transform the CNN-predicted principal points
# (wrt original image, before cropping) to the cropped image
ppb1 = self.cams[:local_batch_size,:1]*self.pp[:local_batch_size]/(opts.img_size/2.)
ppb2 = self.cams[local_batch_size:,:1]*self.pp[local_batch_size:]/(opts.img_size/2.)
# represent pp of croped frame 2 as transformed pp of cropped frame 1
# to reduce ambiguity caused by inconsistent pp over time
ppa1 = ppoint[:local_batch_size] + ppb1 + 1
ppa2 = ppa1 * (self.cams[local_batch_size:,:1] / self.cams[:local_batch_size,:1])
ppoint[local_batch_size:]= ppa2 - ppb2 -1
quat = quat.view(-1,9)
if opts.noise and self.epoch>0 and self.iters<100 and self.iters>1:
# add noise
decay_factor = 0.2*(1e-4)**(self.iters/100)
decay_factor_r = decay_factor * np.ones(quat.shape[0])
### smaller noise for bones
decay_factor_r = decay_factor_r.reshape((-1,opts.n_bones))
decay_factor_r[:,1:] *= 1
decay_factor_r[:,0] *= 1
decay_factor_r = decay_factor_r.flatten()
noise = q_scale_m(q_rnd_m(b=quat.shape[0]), decay_factor_r) # wxyz
noise = torch.Tensor(noise).cuda()
noise = torch.cat([noise[:,1:], noise[:,:1]],-1)
quat = quat.view(-1,3,3).matmul(kornia.quaternion_to_rotation_matrix(noise)).view(-1,9)
decay_factor_s = decay_factor
scale = scale * (decay_factor_s*torch.normal(torch.zeros(scale.shape).cuda(),opts.rscale)).exp()
depth = depth.view(local_batch_size*2,1,opts.n_bones,1).repeat(1,opts.n_hypo,1,1).view(-1,1)
trans = trans.view(local_batch_size*2,1,opts.n_bones,2).repeat(1,opts.n_hypo,1,1).view(-1,2)
if opts.use_gtpose:
# w/ gt cam
quat_pred = quat.clone()
scale_pred = scale.clone()
trans_pred = trans.clone()
ppoint_pred = ppoint.clone()
depth_pred = depth.clone()
scale = 10*self.cams[:,:1]
trans = self.cams[:,1:3]
quat = kornia.quaternion_to_rotation_matrix( torch.cat( (self.cams[:,4:],self.cams[:,3:4] ) ,-1) ).view(-1,9)
depth = self.depth_gt[:]
halforisize = 0.5*opts.img_size / self.cams[:,:1] # half size before scaling
ppoint = (0.5*self.oriimg_shape - self.pp[:]) / halforisize - 1
## rendering
# obj-cam rigid transform; proj_cam: [focal, tx,ty,qw,qx,qy,qz];
# 1st/2nd frame stored as 1st:[0:local_batch_size], 2nd: [local_batch_size:-1]
# transforms [body-to-cam, part1-to-body, ...]
Rmat = quat.view(-1,3,3).permute(0,2,1)
Tmat = torch.cat([trans, depth],1)
if opts.n_bones>1:
# skin computation
# GMM
dis_norm = (self.ctl_ts.view(opts.n_hypo,-1,1,3) - pred_v.view(2*local_batch_size,opts.n_hypo,-1,3)[0,:,None].detach()) # p-v, H,J,1,3 - H,1,N,3
dis_norm = dis_norm.matmul(kornia.quaternion_to_rotation_matrix(self.ctl_rs).view(opts.n_hypo,-1,3,3)) # h,j,n,3
dis_norm = self.log_ctl.exp().view(opts.n_hypo,-1,1,3) * dis_norm.pow(2) # (p-v)^TS(p-v)
skin = (-10 * dis_norm.sum(3)).softmax(1)[:,:,:,None] # h,j,n,1
# color palette
colormap = torch.Tensor(citylabs[:skin.shape[1]]).cuda() # 5x3
skin_colors = (skin[self.optim_idx] * colormap[:,None]).sum(0)/256.
skin = skin.repeat(local_batch_size*2,1,1,1)
# rest_ts: joint center
# ctl_ts: control points
rest_ts = self.rest_ts[:,None,:,None].repeat(local_batch_size*2,1,1,1).view(-1,opts.n_bones-1,3,1) # bh,m,3,1
ctl_ts = self.ctl_ts[:,None,:,None].repeat(local_batch_size*2,1,1,1).view(-1,opts.n_bones-1,3,1) # bh,m,3,1
Rmat = Rmat.view(-1,opts.n_bones,3,3)
Tmat = Tmat.view(-1,opts.n_bones,3,1)
Tmat[:,1:] = -Rmat[:,1:].matmul(rest_ts)+Tmat[:,1:]+rest_ts
Rmat[:,1:] = Rmat[:,1:].permute(0,1,3,2)
Rmat = Rmat.view(-1,3,3)
Tmat = Tmat.view(-1,3)
self.joints_proj = obj_to_cam(rest_ts[:,:,:,0], Rmat.detach(), Tmat[:,np.newaxis].detach(), opts.n_bones, opts.n_hypo,torch.eye(opts.n_bones-1)[None,:,:,None].cuda())
self.joints_proj = pinhole_cam(self.joints_proj, ppoint.detach(), scale.detach())
self.ctl_proj = obj_to_cam(ctl_ts[:,:,:,0], Rmat.detach(), Tmat[:,np.newaxis].detach(), opts.n_bones, opts.n_hypo, torch.eye(opts.n_bones-1)[None,:,:,None].cuda())
self.ctl_proj = pinhole_cam(self.ctl_proj, ppoint.detach(), scale.detach())
else:skin=None
self.deform_v = obj_to_cam(pred_v, Rmat.view(-1,3,3), Tmat[:,np.newaxis,:],opts.n_bones, opts.n_hypo,skin,tocam=False)
# torch.cuda.synchronize()
# print('before rend time:%.2f'%(time.time()-start_time))
# 1) flow rendering
verts_fl = obj_to_cam(pred_v, Rmat, Tmat[:,np.newaxis,:],opts.n_bones, opts.n_hypo,skin)
verts_fl = torch.cat([verts_fl,torch.ones_like(verts_fl[:, :, 0:1])], dim=-1)
verts_pos0 = verts_fl.view(2*local_batch_size,opts.n_hypo,-1,4)[:local_batch_size].clone().view(local_batch_size*opts.n_hypo,-1,4)
verts_pos1 = verts_fl.view(2*local_batch_size,opts.n_hypo,-1,4)[local_batch_size:].clone().view(local_batch_size*opts.n_hypo,-1,4)
verts_fl = pinhole_cam(verts_fl, ppoint, scale)
dmax=verts_fl[:,:,-2].max()
dmin=verts_fl[:,:,-2].min()
self.renderer_softflf.rasterizer.near=dmin-(dmax-dmin)/2
self.renderer_softflf.rasterizer.far= dmax+(dmax-dmin)/2
self.renderer_softflb.rasterizer.near=dmin-(dmax-dmin)/2
self.renderer_softflb.rasterizer.far= dmax+(dmax-dmin)/2
self.renderer_softtex.rasterizer.near=dmin-(dmax-dmin)/2
self.renderer_softtex.rasterizer.far= dmax+(dmax-dmin)/2
if opts.sigval!=1e-4:
self.renderer_soft.rasterizer.sigma_val= opts.sigval
self.renderer_softflf.rasterizer.sigma_val=opts.sigval
self.renderer_softflb.rasterizer.sigma_val=opts.sigval
self.renderer_softtex.rasterizer.sigma_val=opts.sigval
self.flow_fw, self.bgmask_fw, self.fgmask_flowf = render_flow_soft_2(self.renderer_softflf, verts_fl.view(2*local_batch_size,opts.n_hypo,-1,4)[:local_batch_size].view(-1,verts_fl.shape[1],4),
faces[:local_batch_size],
verts_pos0, verts_pos1,
ppoint[:,None][:local_batch_size].repeat(1,opts.n_hypo,1).view(-1,2),
ppoint[:,None][local_batch_size:].repeat(1,opts.n_hypo,1).view(-1,2),
scale[:,None][:local_batch_size].view(-1,1),
scale[:,None][local_batch_size:].view(-1,1))
self.flow_bw, self.bgmask_bw, self.fgmask_flowb = render_flow_soft_2(self.renderer_softflb, verts_fl.view(2*local_batch_size,opts.n_hypo,-1,4)[local_batch_size:].view(-1,verts_fl.shape[1],4),
faces[local_batch_size:],
verts_pos1, verts_pos0,
ppoint[:,None][local_batch_size:].repeat(1,opts.n_hypo,1).view(-1,2),
ppoint[:,None][:local_batch_size].repeat(1,opts.n_hypo,1).view(-1,2),
scale[:,None][local_batch_size:].view(-1,1),
scale[:,None][:local_batch_size].view(-1,1))
self.bgmask = torch.cat([self.bgmask_fw, self.bgmask_bw],0)
self.fgmask_flow = torch.cat([self.fgmask_flowf, self.fgmask_flowb],0)
self.flow_rd = torch.cat([self.flow_fw, self.flow_bw ],0)
# torch.cuda.synchronize()
# print('before rend + flow time:%.2f'%(time.time()-start_time))
# 2) silhouette
Rmat_mask = Rmat.clone().view(-1,opts.n_bones,3,3)
Rmat_mask = torch.cat([Rmat_mask[:,:1].detach(), Rmat_mask[:,1:]],1).view(-1,3,3)
verts_mask = obj_to_cam(pred_v, Rmat_mask, Tmat[:,np.newaxis,:],opts.n_bones, opts.n_hypo,skin)
verts_mask = torch.cat([verts_mask,torch.ones_like(verts_mask[:, :, 0:1])], dim=-1)
verts_mask = pinhole_cam(verts_mask, ppoint, scale)
if opts.opt_tex=='yes':
# 3) texture rendering
Rmat_tex = Rmat.clone().view(2*local_batch_size,opts.n_hypo,opts.n_bones,3,3).view(-1,3,3)
verts_tex = obj_to_cam(pred_v, Rmat_tex, Tmat[:,np.newaxis,:],opts.n_bones, opts.n_hypo,skin)
verts_tex = torch.cat([verts_tex,torch.ones_like(verts_tex[:, :, 0:1])], dim=-1)
verts_tex = pinhole_cam(verts_tex, ppoint, scale)
offset = torch.Tensor( self.renderer_softtex.transform.transformer._eye ).cuda()[np.newaxis,np.newaxis]
verts_pre = verts_tex[:,:,:3]+offset; verts_pre[:,:,1] = -1*verts_pre[:,:,1]
self.renderer_softtex.rasterizer.background_color = [1,1,1]
self.texture_render = self.renderer_softtex.render_mesh(sr.Mesh(verts_pre, faces[:,None].repeat(1,opts.n_hypo,1,1).view(-1,faces.shape[1],3), textures=tex, texture_type=self.texture_type)).clone()
self.mask_pred = self.texture_render[:,-1]
self.fgmask_tex = self.texture_render[:,-1]
self.texture_render = self.texture_render[:,:3]
img_obs = self.imgs[:]*(self.masks[:]>0).float()[:,None]
img_rnd = self.texture_render*(self.fgmask_tex)[:,None]
img_white = 1-(self.masks[:]>0).float()[:,None] + img_obs
# torch.cuda.synchronize()
# print('before rend + flow + sil + tex time:%.2f'%(time.time()-start_time))
if opts.n_bones>1 and self.iters==0:
# part rendering
self.part_render = self.renderer_softpart.render_mesh(sr.Mesh(verts_pre.view(2*local_batch_size,opts.n_hypo,-1,3)[:1,self.optim_idx].detach(), faces[:1], textures=skin_colors[None], texture_type='vertex'))[:,:3].detach()
# losses
# 1) mask loss
mask_pred = self.mask_pred.view(2*local_batch_size,-1,opts.img_size, opts.img_size)
self.mask_loss_sub = (mask_pred - self.masks[:,None]).pow(2)
#self.mask_loss_sub = 0
#for i in range (5): # 256,128,64,32,16
# diff_img = (F.interpolate(mask_pred , scale_factor=(0.5)**i,mode='area')
# - F.interpolate(self.masks[:,None], scale_factor=(0.5)**i,mode='area')).pow(2)
# self.mask_loss_sub += F.interpolate(diff_img, mask_pred.shape[2:4])
#self.mask_loss_sub *= 0.2
tmplist = torch.zeros(2*local_batch_size, opts.n_hypo).cuda()
for i in range(2*local_batch_size):
for j in range(opts.n_hypo):
tmplist[i,j] = self.mask_loss_sub[i,j][self.occ[i]!=0].mean()
self.mask_loss_sub = 0.5 * tmplist
self.mask_loss = self.mask_loss_sub.mean() # get rid of invalid pixels (out of border)
self.total_loss = self.mask_loss.clone()
# 2) flow loss
flow_rd = self.flow_rd.view(2*local_batch_size,-1,opts.img_size, opts.img_size,2)
mask = (~self.bgmask).view(2*local_batch_size,-1,opts.img_size, opts.img_size) & ((self.occ!=0)[:,None] & (self.masks[:]>0) [:,None]).repeat(1,opts.n_hypo,1,1)
self.flow_rd_map = torch.norm((flow_rd-self.flow[:,None,:2].permute(0,1,3,4,2)),2,-1)
#self.flow_rd_map = 0
#for i in range (5): # 256,128,64,32,16
# diff_img = torch.norm((F.interpolate(flow_rd, scale_factor=((0.5)**i,(0.5)**i,1),mode='area')-
# F.interpolate(self.flow[:,None,:2].permute(0,1,3,4,2), scale_factor=((0.5)**i,(0.5)**i,1),mode='area')),2,-1)
# self.flow_rd_map += F.interpolate(diff_img, flow_rd.shape[2:4])
#self.flow_rd_map *= 0.2
self.vis_mask = mask.clone()
weights_flow = (-self.occ).sigmoid()[:,None].repeat(1,opts.n_hypo,1,1)
for i in range(weights_flow.shape[0]):
weights_flow[i] = weights_flow[i] / weights_flow[i][mask[i]].mean()
self.flow_rd_map = self.flow_rd_map * weights_flow
tmplist = torch.zeros(2*local_batch_size, opts.n_hypo).cuda()
for i in range(2*local_batch_size):
for j in range(opts.n_hypo):
tmplist[i,j] = self.flow_rd_map[i,j][mask[i,j]].mean()
if mask[i,j].sum()==0: tmplist[i,j]=0
self.flow_rd_loss_sub = 0.5*tmplist
self.flow_rd_loss = self.flow_rd_loss_sub.mean()
self.total_loss += self.flow_rd_loss
# 3) texture loss
if opts.opt_tex=='yes':
imgobs_rep = img_obs[:,None].repeat(1,opts.n_hypo,1,1,1).view(-1,3,opts.img_size,opts.img_size)
imgwhite_rep = img_white[:,None].repeat(1,opts.n_hypo,1,1,1).view(-1,3,opts.img_size,opts.img_size)
obspair = torch.cat([imgobs_rep, imgwhite_rep],0)
rndpair = torch.cat([img_rnd, self.texture_render],0)
tmplist = torch.zeros(2*local_batch_size, opts.n_hypo).cuda()
for i in range(2*local_batch_size):
for j in range(opts.n_hypo):
tmplist[i,j] += (img_obs[i] - img_rnd.view(2*local_batch_size,-1,3,opts.img_size, opts.img_size)[i,j]).abs().mean(0)[self.occ[i]!=0].mean()
tmplist[i,j] += (img_white[i] - self.texture_render.view(2*local_batch_size,-1,3,opts.img_size, opts.img_size)[i,j]).abs().mean(0)[self.occ[i]!=0].mean()
#tmplist[i,j] = 0
#for k in range(5):
# diff_img = (F.interpolate(img_obs[i] ,scale_factor=(0.5)**i,mode='area')-
# F.interpolate(img_rnd.view(2*local_batch_size,-1,3,opts.img_size, opts.img_size)[i,j],scale_factor=(0.5)**i,mode='area')
# ).abs()
# tmplist[i,j] += F.interpolate(diff_img[None], img_obs.shape[2:4])[0].mean(0)[self.occ[i]!=0].mean()
# diff_img = (F.interpolate(img_white[i] ,scale_factor=(0.5)**i,mode='area')-
# F.interpolate(self.texture_render.view(2*local_batch_size,-1,3,opts.img_size, opts.img_size)[i,j],scale_factor=(0.5)**i,mode='area')
# ).abs()
# tmplist[i,j] += F.interpolate(diff_img[None], img_obs.shape[2:4])[0].mean(0)[self.occ[i]!=0].mean()
#tmplist[i,j] *= 0.2
tmplist[i,j] *= 2*opts.l1tex_wt
percept_loss = self.ptex_loss.forward_pair(2*obspair-1, 2*rndpair-1)
tmplist += 0.005*percept_loss.view(2,-1).sum(0).view(local_batch_size*2,opts.n_hypo)
self.texture_loss_sub = 0.25*tmplist
self.texture_loss = self.texture_loss_sub.mean()
self.total_loss += self.texture_loss
# 4) shape smoothness/symmetry
factor=int(opts.n_faces)/1280
if opts.n_hypo>1:
factor = 1 # possibly related to symmetry loss?
else:
factor = reg_decay(self.epoch, opts.num_epochs, 0.05, 0.5)
self.triangle_loss_sub = factor*0.005*self.triangle_loss_fn_sr(pred_v)*(4**opts.subdivide)/64.
self.triangle_loss_sub +=factor*5e-4*self.flatten_loss(pred_v)*(2**opts.subdivide/8.0)
self.triangle_loss_sub = self.triangle_loss_sub.view(2*local_batch_size,opts.n_hypo)
self.triangle_loss = self.triangle_loss_sub.mean()
self.total_loss += self.triangle_loss
if (not opts.symmetric) and opts.symmetric_loss:
# symmetry
pointa = pred_v.view(2*local_batch_size, opts.n_hypo,-1,3)[0]
pointb = torch.Tensor([[[-1,1,1]]]).cuda()*pointa
mesha = pytorch3d.structures.meshes.Meshes(verts=pointa, faces=faces[:1].repeat(opts.n_hypo,1,1).detach())
meshb = pytorch3d.structures.meshes.Meshes(verts=pointb, faces=faces[:1].repeat(opts.n_hypo,1,1).detach())
pointa = pytorch3d.structures.Pointclouds(pointa)
pointb = pytorch3d.structures.Pointclouds(pointb)
fac=1
self.total_loss += fac*pytorch3d.loss.point_mesh_face_distance(mesha, pointb)
self.total_loss += fac*pytorch3d.loss.point_mesh_face_distance(meshb, pointa)
if opts.opt_tex=='yes':
# color
pointa = pred_v[:1].detach()
pointb = torch.Tensor([[[-1,1,1]]]).cuda()*pointa
_,_,idx1,_ = self.chamLoss(pointa,pointb)
self.total_loss += (self.tex[0][idx1[0].long()].detach() - self.tex[0]).abs().mean()*1e-3
# 5) shape deformation loss
if opts.n_bones>1:
# bones
self.bone_rot_l1 = compute_geodesic_distance_from_two_matrices(
quat.view(-1,opts.n_hypo,opts.n_bones,9)[:,:,1:].reshape(-1,3,3),
torch.eye(3).cuda().repeat(2*local_batch_size*opts.n_hypo*(opts.n_bones-1),1,1)).mean() # small rotation
self.bone_trans_l1 = torch.cat([trans,depth],1).view(-1,opts.n_hypo,opts.n_bones,3)[:,:,1:].abs().mean()
if opts.n_hypo>1:
factor=1
else:
factor = reg_decay(self.epoch, opts.num_epochs, 0.05, 0.5)
self.lmotion_loss_sub = factor*(self.deform_v - pred_v).norm(2,-1).mean(-1).view(2*local_batch_size,opts.n_hypo)
self.lmotion_loss = self.lmotion_loss_sub.mean()
self.total_loss += self.lmotion_loss
# skins
self.arap_loss = self.arap_loss_fn(self.deform_v[:local_batch_size*opts.n_hypo], self.deform_v[local_batch_size*opts.n_hypo:]).mean()*(4**opts.subdivide)/64.
self.total_loss += self.arap_loss
# 6) bone symmetry
if opts.n_bones>1 and opts.symmetric_loss:
pointa = self.ctl_ts.view(opts.n_hypo, -1,3)
pointb = torch.Tensor([[[-1,1,1]]]).cuda()*pointa
self.total_loss += 0.1*pytorch3d.loss.chamfer_distance(pointa, pointb)[0]
# 7) camera loss
if opts.use_gtpose:
self.cam_loss = compute_geodesic_distance_from_two_matrices(quat.view(-1,3,3), quat_pred.view(-1,3,3)).mean()
self.cam_loss += (scale_pred - scale).abs().mean()
self.cam_loss += (trans_pred - trans).abs().mean()
self.cam_loss += (depth_pred - depth).abs().mean()
self.cam_loss += (ppoint_pred - ppoint).abs().mean()
self.cam_loss = 0.2 * self.cam_loss
else:
self.rotg_sm_sub = compute_geodesic_distance_from_two_matrices(quat.view(-1,opts.n_hypo,opts.n_bones,9)[:local_batch_size,:].view(-1,3,3),
quat.view(-1,opts.n_hypo,opts.n_bones,9)[local_batch_size:,:].view(-1,3,3)).view(-1,opts.n_hypo,opts.n_bones)
self.cam_loss = 0.001*self.rotg_sm_sub.mean()
if opts.n_bones>1:
self.cam_loss += 0.01*(trans.view(-1,opts.n_hypo,opts.n_bones,2)[:local_batch_size,:,1:] -
trans.view(-1,opts.n_hypo,opts.n_bones,2)[local_batch_size:,:,1:]).abs().mean()
self.cam_loss += 0.01*(depth.view(-1,opts.n_hypo,opts.n_bones,1)[:local_batch_size,:,1:] -
depth.view(-1,opts.n_hypo,opts.n_bones,1)[local_batch_size:,:,1:]).abs().mean()
self.total_loss += self.cam_loss
# 8) aux losses
# pull far away from the camera center
self.total_loss += 0.02*F.relu(2-Tmat.view(-1, 1, opts.n_bones, 3)[:,:,:1,-1]).mean()
if opts.n_bones>1:
bone_loc_loss = 0.1* F.grid_sample(self.ddts_barrier.repeat(1,opts.n_hypo,1,1).view(-1,1,opts.img_size,opts.img_size), self.joints_proj[:,:,:2].view(-1,opts.n_bones-1,1,2),padding_mode='border').mean()
ctl_loc_loss = 0.1* F.grid_sample(self.ddts_barrier.repeat(1,opts.n_hypo,1,1).view(-1,1,opts.img_size,opts.img_size), self.ctl_proj[:,:,:2].view(-1,opts.n_bones-1,1,2) ,padding_mode='border').mean()
self.total_loss += 100*(bone_loc_loss+ctl_loc_loss)
aux_output={}
aux_output['flow_rd_map'] = self.flow_rd_map
aux_output['flow_rd'] = self.flow_rd
aux_output['vis_mask'] = self.vis_mask
aux_output['mask_pred'] = self.mask_pred
aux_output['total_loss'] = self.total_loss
aux_output['mask_loss'] = self.mask_loss
aux_output['texture_loss'] = self.texture_loss
aux_output['flow_rd_loss'] = self.flow_rd_loss
aux_output['triangle_loss'] = self.triangle_loss
if opts.n_bones>1:
aux_output['lmotion_loss'] = self.lmotion_loss
aux_output['current_nscore'] = self.texture_loss_sub.mean(0) + self.flow_rd_loss_sub.mean(0) + self.mask_loss_sub.mean(0)
if opts.n_hypo > 1:
for ihp in range(opts.n_hypo):
aux_output['mask_hypo_%d'%(ihp)] = self.mask_loss_sub[:,ihp].mean()
aux_output['flow_hypo_%d'%(ihp)] = self.flow_rd_loss_sub[:,ihp].mean()
aux_output['tex_hypo_%d'%(ihp)] = self.texture_loss_sub[:,ihp].mean()
try:
aux_output['part_render'] = self.part_render
aux_output['texture_render'] = self.texture_render
aux_output['ctl_proj'] = self.ctl_proj
except:pass
return self.total_loss, aux_output