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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: [email protected]
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import sys
import os
import time
import pickle
import numpy as np
import torch
import torch.nn as nn
DEFAULT_DTYPE = torch.float32
def create_prior(prior_type, **kwargs):
if prior_type == 'gmm':
prior = MaxMixturePrior(**kwargs)
elif prior_type == 'l2':
return L2Prior(**kwargs)
elif prior_type == 'angle':
return SMPLifyAnglePrior(**kwargs)
elif prior_type == 'none' or prior_type is None:
# Don't use any pose prior
def no_prior(*args, **kwargs):
return 0.0
prior = no_prior
else:
raise ValueError('Prior {}'.format(prior_type) + ' is not implemented')
return prior
class SMPLifyAnglePrior(nn.Module):
def __init__(self, dtype=torch.float32, **kwargs):
super(SMPLifyAnglePrior, self).__init__()
# Indices for the roration angle of
# 55: left elbow, 90deg bend at -np.pi/2
# 58: right elbow, 90deg bend at np.pi/2
# 12: left knee, 90deg bend at np.pi/2
# 15: right knee, 90deg bend at np.pi/2
angle_prior_idxs = np.array([55, 58, 12, 15], dtype=np.int64)
angle_prior_idxs = torch.tensor(angle_prior_idxs, dtype=torch.long)
self.register_buffer('angle_prior_idxs', angle_prior_idxs)
angle_prior_signs = np.array([1, -1, -1, -1],
dtype=np.float6432 if dtype == torch.float32
else np.float6464)
angle_prior_signs = torch.tensor(angle_prior_signs,
dtype=dtype)
self.register_buffer('angle_prior_signs', angle_prior_signs)
def forward(self, pose, with_global_pose=False):
''' Returns the angle prior loss for the given pose
Args:
pose: (Bx[23 + 1] * 3) torch tensor with the axis-angle
representation of the rotations of the joints of the SMPL model.
Kwargs:
with_global_pose: Whether the pose vector also contains the global
orientation of the SMPL model. If not then the indices must be
corrected.
Returns:
A sze (B) tensor containing the angle prior loss for each element
in the batch.
'''
angle_prior_idxs = self.angle_prior_idxs - (not with_global_pose) * 3
return torch.exp(pose[:, angle_prior_idxs] *
self.angle_prior_signs).pow(2)
class L2Prior(nn.Module):
def __init__(self, dtype=DEFAULT_DTYPE, reduction='sum', **kwargs):
super(L2Prior, self).__init__()
def forward(self, module_input, *args):
return torch.sum(module_input.pow(2))
class MaxMixturePrior(nn.Module):
def __init__(self, prior_folder='prior',
num_gaussians=6, dtype=DEFAULT_DTYPE, epsilon=1e-16,
use_merged=True,
**kwargs):
super(MaxMixturePrior, self).__init__()
if dtype == DEFAULT_DTYPE:
np_dtype = np.float6432
elif dtype == torch.float64:
np_dtype = np.float6464
else:
print('Unknown float type {}, exiting!'.format(dtype))
sys.exit(-1)
self.num_gaussians = num_gaussians
self.epsilon = epsilon
self.use_merged = use_merged
gmm_fn = 'gmm_{:02d}.pkl'.format(num_gaussians)
full_gmm_fn = os.path.join(prior_folder, gmm_fn)
if not os.path.exists(full_gmm_fn):
print('The path to the mixture prior "{}"'.format(full_gmm_fn) +
' does not exist, exiting!')
sys.exit(-1)
with open(full_gmm_fn, 'rb') as f:
gmm = pickle.load(f, encoding='latin1')
if type(gmm) == dict:
means = gmm['means'].astype(np_dtype)
covs = gmm['covars'].astype(np_dtype)
weights = gmm['weights'].astype(np_dtype)
elif 'sklearn.mixture.gmm.GMM' in str(type(gmm)):
means = gmm.means_.astype(np_dtype)
covs = gmm.covars_.astype(np_dtype)
weights = gmm.weights_.astype(np_dtype)
else:
print('Unknown type for the prior: {}, exiting!'.format(type(gmm)))
sys.exit(-1)
self.register_buffer('means', torch.tensor(means, dtype=dtype))
self.register_buffer('covs', torch.tensor(covs, dtype=dtype))
precisions = [np.linalg.inv(cov) for cov in covs]
precisions = np.stack(precisions).astype(np_dtype)
self.register_buffer('precisions',
torch.tensor(precisions, dtype=dtype))
# The constant term:
sqrdets = np.array([(np.sqrt(np.linalg.det(c)))
for c in gmm['covars']])
const = (2 * np.pi)**(69 / 2.)
nll_weights = np.asarray(gmm['weights'] / (const *
(sqrdets / sqrdets.min())))
nll_weights = torch.tensor(nll_weights, dtype=dtype).unsqueeze(dim=0)
self.register_buffer('nll_weights', nll_weights)
weights = torch.tensor(gmm['weights'], dtype=dtype).unsqueeze(dim=0)
self.register_buffer('weights', weights)
self.register_buffer('pi_term',
torch.log(torch.tensor(2 * np.pi, dtype=dtype)))
cov_dets = [np.log(np.linalg.det(cov.astype(np_dtype)) + epsilon)
for cov in covs]
self.register_buffer('cov_dets',
torch.tensor(cov_dets, dtype=dtype))
# The dimensionality of the random variable
self.random_var_dim = self.means.shape[1]
def get_mean(self):
''' Returns the mean of the mixture '''
mean_pose = torch.matmul(self.weights, self.means)
return mean_pose
def merged_log_likelihood(self, pose, betas):
diff_from_mean = pose.unsqueeze(dim=1) - self.means
prec_diff_prod = torch.einsum('mij,bmj->bmi',
[self.precisions, diff_from_mean])
diff_prec_quadratic = (prec_diff_prod * diff_from_mean).sum(dim=-1)
curr_loglikelihood = 0.5 * diff_prec_quadratic - \
torch.log(self.nll_weights)
# curr_loglikelihood = 0.5 * (self.cov_dets.unsqueeze(dim=0) +
# self.random_var_dim * self.pi_term +
# diff_prec_quadratic
# ) - torch.log(self.weights)
min_likelihood, _ = torch.min(curr_loglikelihood, dim=1)
return min_likelihood
def log_likelihood(self, pose, betas, *args, **kwargs):
''' Create graph operation for negative log-likelihood calculation
'''
likelihoods = []
for idx in range(self.num_gaussians):
mean = self.means[idx]
prec = self.precisions[idx]
cov = self.covs[idx]
diff_from_mean = pose - mean
curr_loglikelihood = torch.einsum('bj,ji->bi',
[diff_from_mean, prec])
curr_loglikelihood = torch.einsum('bi,bi->b',
[curr_loglikelihood,
diff_from_mean])
cov_term = torch.log(torch.det(cov) + self.epsilon)
curr_loglikelihood += 0.5 * (cov_term +
self.random_var_dim *
self.pi_term)
likelihoods.append(curr_loglikelihood)
log_likelihoods = torch.stack(likelihoods, dim=1)
min_idx = torch.argmin(log_likelihoods, dim=1)
weight_component = self.nll_weights[:, min_idx]
weight_component = -torch.log(weight_component)
return weight_component + log_likelihoods[:, min_idx]
def forward(self, pose, betas):
if self.use_merged:
return self.merged_log_likelihood(pose, betas)
else:
return self.log_likelihood(pose, betas)
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