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# -*- coding: utf-8 -*-
#
# Copyright (C) 2019 Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG),
# acting on behalf of its Max Planck Institute for Intelligent Systems and the
# Max Planck Institute for Biological Cybernetics. All rights reserved.
#
# 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.
# Contact: [email protected]
#
#
# If you use this code in a research publication please consider citing the following:
#
# Expressive Body Capture: 3D Hands, Face, and Body from a Single Image <https://arxiv.org/abs/1904.05866>
#
#
# Code Developed by:
# Nima Ghorbani <https://nghorbani.github.io/>
#
# 2021.02.12
from typing import List, Dict
from psbody.mesh import Mesh
from body_visualizer.tools.psbody_mesh_tools import rotateXYZ, points_to_cubes, points_to_spheres
from torch import nn
import torch
from human_body_prior.tools.model_loader import load_model
import numpy as np
from body_visualizer.tools.vis_tools import colors
from human_body_prior.tools.omni_tools import copy2cpu as c2c
from psbody.mesh import MeshViewers
from human_body_prior.tools.omni_tools import log2file
from human_body_prior.models.vposer_model import VPoser
from human_body_prior.tools.omni_tools import flatten_list
def visualize(points, bm_f, mvs, kpts_colors, verbosity=2, logger=None):
from human_body_prior.tools.omni_tools import log2file
if logger is None: logger = log2file()
def view(opt_objs, body_v, virtual_markers, opt_it):
if verbosity <= 0: return
opt_objs_cpu = {k: c2c(v) for k, v in opt_objs.items()}
total_loss = np.sum([np.sum(v) for k, v in opt_objs_cpu.items()])
message = 'it {} -- [total loss = {:.2e}] - {}'.format(opt_it, total_loss, ' | '.join(['%s = %2.2e' % (k, np.sum(v)) for k, v in opt_objs_cpu.items()]))
logger(message)
if verbosity>1:
bs = body_v.shape[0]
np.random.seed(100)
frame_ids = list(range(bs)) if bs <= len(mvs) else np.random.choice(bs , size=len(mvs), replace=False).tolist()
if bs > len(mvs): message += ' -- [frame_ids: {}]'.format(frame_ids)
for dispId, fId in enumerate(frame_ids): # check for the number of frames in mvs and show a randomly picked number of frames in body if there is more to show than row*cols available
new_body_v = rotateXYZ(body_v[fId], [-90,0,0])
orig_mrk_mesh = points_to_spheres(rotateXYZ(c2c(points[fId]), [-90,0,0]), radius=0.01, color=kpts_colors)
virtual_markers_mesh = points_to_cubes(rotateXYZ(virtual_markers[fId], [-90,0,0]), radius=0.01, color=kpts_colors)
new_body_mesh = Mesh(new_body_v, bm_f, vc=colors['grey'])
# linev = rotateXYZ(np.hstack((c2c(points[fId]), virtual_markers[fId])).reshape((-1, 3)), [-90,0,0])
# linee = np.arange(len(linev)).reshape((-1, 2))
# ll = Lines(v=linev, e=linee)
# ll.vc = (ll.v * 0. + 1) * np.array([0.00, 0.00, 1.00])
# mvs[dispId].set_dynamic_lines([ll])
# orig_mrk_mesh = points_to_spheres(data_pc, radius=0.01, vc=colors['blue'])
mvs[dispId].set_dynamic_meshes([orig_mrk_mesh, virtual_markers_mesh])
mvs[dispId].set_static_meshes([new_body_mesh])
mvs[0].set_titlebar(message)
# if out_dir is not None: mv.save_snapshot(os.path.join(out_dir, '%05d_it_%.5d.png' %(frame_id, opt_it)))
return view
class AdamInClosure():
def __init__(self, var_list, lr, max_iter=100, tolerance_change=1e-5):
self.optimizer = torch.optim.Adam(var_list, lr)
self.max_iter = max_iter
self.tolerance_change = tolerance_change
def step(self, closure):
prev_loss = None
for it in range(self.max_iter):
loss = closure()
self.optimizer.step()
if prev_loss is None:
prev_loss = loss
continue
if torch.isnan(loss):
# breakpoint()
break
if abs(loss - prev_loss) < self.tolerance_change:
print('abs(loss - prev_loss) < self.tolerance_change')
break
def zero_grad(self):
self.optimizer.zero_grad()
def ik_fit(optimizer, source_kpts_model, static_vars, vp_model, extra_params={}, on_step=None, gstep=0):
data_loss = extra_params.get('data_loss', torch.nn.SmoothL1Loss(reduction='mean'))
# data_loss =
# data_loss = torch.nn.L1Loss(reduction='mean')#change with SmoothL1
def fit(weights, free_vars):
fit.gstep += 1
optimizer.zero_grad()
free_vars['pose_body'] = vp_model.decode(free_vars['poZ_body'])['pose_body'].contiguous().view(-1, 63)
nonan_mask = torch.isnan(free_vars['poZ_body']).sum(-1) == 0
opt_objs = {}
res = source_kpts_model(free_vars)
opt_objs['data'] = data_loss(res['source_kpts'], static_vars['target_kpts'])
opt_objs['betas'] = torch.pow(free_vars['betas'][nonan_mask],2).sum()
opt_objs['poZ_body'] = torch.pow(free_vars['poZ_body'][nonan_mask],2).sum()
opt_objs = {k: opt_objs[k]*v for k, v in weights.items() if k in opt_objs.keys()}
loss_total = torch.sum(torch.stack(list(opt_objs.values())))
# breakpoint()
loss_total.backward()
if on_step is not None:
on_step(opt_objs, c2c(res['body'].v), c2c(res['source_kpts']), fit.gstep)
fit.free_vars = {k:v for k,v in free_vars.items()}# if k in IK_Engine.fields_to_optimize}
# fit.nonan_mask = nonan_mask
fit.final_loss = loss_total
return loss_total
fit.gstep = gstep
fit.final_loss = None
fit.free_vars = {}
# fit.nonan_mask = None
return fit
class IK_Engine(nn.Module):
def __init__(self,
vposer_expr_dir: str,
data_loss,
optimizer_args: dict={'type':'ADAM'},
stepwise_weights: List[Dict]=[{'data': 10., 'poZ_body': .01, 'betas': .5}],
display_rc: tuple = (2,1),
verbosity: int = 1,
logger=None,
):
'''
:param vposer_expr_dir: The vposer directory that holds the settings and model snapshot
:param data_loss: should be a pytorch callable (source, target) that returns the accumulated loss
:param optimizer_args: arguments for optimizers
:param stepwise_weights: list of dictionaries. each list element defines weights for one full step of optimization
if a weight value is left out, its respective object item will be removed as well. imagine optimizing without data term!
:param display_rc: number of row and columns in case verbosity > 1
:param verbosity: 0: silent, 1: text, 2: text/visual. running 2 over ssh would need extra work
:param logger: an instance of human_body_prior.tools.omni_tools.log2file
'''
super(IK_Engine, self).__init__()
assert isinstance(stepwise_weights, list), ValueError('stepwise_weights should be a list of dictionaries.')
assert np.all(['data' in l for l in stepwise_weights]), ValueError('The term data should be available in every weight of anealed optimization step: {}'.format(stepwise_weights))
self.data_loss = torch.nn.SmoothL1Loss(reduction='mean') if data_loss is None else data_loss
self.stepwise_weights = stepwise_weights
self.verbosity = verbosity
self.optimizer_args = optimizer_args
self.logger = log2file() if logger is None else logger
if verbosity>1:
mvs = MeshViewers(display_rc, keepalive=True)
self.mvs = flatten_list(mvs)
self.mvs[0].set_background_color(colors['white'])
else:
self.mvs=None
self.vp_model, _ = load_model(vposer_expr_dir,
model_code=VPoser,
remove_words_in_model_weights='vp_model.',
disable_grad=True)
def forward(self, source_kpts, target_kpts, initial_body_params={}):
'''
source_kpts is a function that given body parameters computes source key points that should match target key points
Try to reconstruct the bps signature by optimizing the body_poZ
'''
# if self.rt_ps.verbosity > 0: self.logger('Processing {} frames'.format(points.shape[0]))
bs = target_kpts.shape[0]
on_step = visualize(target_kpts,
kpts_colors=source_kpts.kpts_colors,
bm_f=source_kpts.bm_f,
mvs=self.mvs,
verbosity=self.verbosity,
logger=self.logger)
comp_device = target_kpts.device
# comp_device = self.vp_model.named_parameters().__next__()[1].device
if 'pose_body' not in initial_body_params:
initial_body_params['pose_body'] = torch.zeros([bs, 63], device=comp_device, dtype=torch.float, requires_grad=False)
if 'trans' not in initial_body_params:
initial_body_params['trans'] = torch.zeros([bs, 3], device=comp_device, dtype=torch.float, requires_grad=False)
if 'betas' not in initial_body_params:
initial_body_params['betas'] = torch.zeros([bs, 10], device=comp_device, dtype=torch.float, requires_grad=False)
if 'root_orient' not in initial_body_params:
initial_body_params['root_orient'] = torch.zeros([bs, 3], device=comp_device, dtype=torch.float, requires_grad=False)
initial_body_params['poZ_body'] = self.vp_model.encode(initial_body_params['pose_body']).mean
free_vars = {k: torch.nn.Parameter(v.detach(), requires_grad=True) for k,v in initial_body_params.items() if k in ['betas', 'trans', 'poZ_body', 'root_orient']}
static_vars = {
'target_kpts': target_kpts,
# 'trans': initial_body_params['trans'].detach(),
# 'betas': initial_body_params['betas'].detach(),
# 'poZ_body': initial_body_params['poZ_body'].detach()
}
if self.optimizer_args['type'].upper() == 'LBFGS':
optimizer = torch.optim.LBFGS(list(free_vars.values()),
lr=self.optimizer_args.get('lr', 1),
max_iter=self.optimizer_args.get('max_iter', 100),
tolerance_change=self.optimizer_args.get('tolerance_change', 1e-5),
max_eval=self.optimizer_args.get('max_eval', None),
history_size=self.optimizer_args.get('history_size', 100),
line_search_fn='strong_wolfe')
elif self.optimizer_args['type'].upper() == 'ADAM':
optimizer = AdamInClosure(list(free_vars.values()),
lr=self.optimizer_args.get('lr', 1e-3),
max_iter=self.optimizer_args.get('max_iter', 100),
tolerance_change=self.optimizer_args.get('tolerance_change', 1e-5),
)
else:
raise ValueError('optimizer_type not recognized.')
gstep = 0
closure = ik_fit(optimizer,
source_kpts_model=source_kpts,
static_vars=static_vars,
vp_model=self.vp_model,
extra_params={'data_loss': self.data_loss},
on_step=on_step,
gstep=gstep)
# try:
for wts in self.stepwise_weights:
optimizer.step(lambda: closure(wts, free_vars))
free_vars = closure.free_vars
# except:
#
# pass
# if closure.final_loss is None or torch.isnan(closure.final_loss) or torch.any(torch.isnan(free_vars['trans'])):
# if self.verbosity > 0:
# self.logger('NaN observed in the optimization results. you might want to restart the refinment procedure.')
# breakpoint()
# return None
return closure.free_vars#, closure.nonan_mask
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