def forward(self, pred, targ):
"""
:param pred: NxTx6
:param targ: NxTx6
:return: N
"""
s = pred.size()
num_poses = s[0] * s[1]
abs_loss =\
torch.exp(-self.beta) * self.loss_fn(
pred.view(num_poses, -1)[:, :3],
targ.view(num_poses, -1)[:, :3]
) + self.beta + \
torch.exp(-self.gamma) * self.loss_fn(
pred.view(num_poses, -1)[:, 3:],
targ.view(num_poses, -1)[:, 3:]
) + self.gamma
pred_vos = pose_utils.calc_vos_simple(pred)
targ_vos = pose_utils.calc_vos_simple(targ)
s = pred_vos.size()
num_poses = s[0] * s[1]
rel_loss =\
torch.exp(-self.rel_beta) * self.loss_fn(
pred_vos.view(num_poses, -1)[:, :3],
targ_vos.view(num_poses, -1)[:, :3]
) + self.rel_beta +\
torch.exp(-self.rel_gamma) * self.loss_fn(
pred_vos.view(num_poses, -1)[:, 3:],
targ_vos.view(num_poses, -1)[:, 3:]
) + self.rel_gamma
loss = abs_loss + rel_loss
return loss
def forward(self, pred, targ):
"""
:param pred: N x T x 6
:param targ: N x T x 6
:return:
"""
# absolute pose loss
s = pred.size()
abs_loss =\
torch.exp(-self.sax) * self.t_loss_fn(pred.view(-1, *s[2:])[:, :3],
targ.view(-1, *s[2:])[:, :3]) + \
self.sax + \
torch.exp(-self.saq) * self.q_loss_fn(pred.view(-1, *s[2:])[:, 3:],
targ.view(-1, *s[2:])[:, 3:]) + \
self.saq
# get the VOs
pred_vos = pose_utils.calc_vos_simple(pred)
targ_vos = pose_utils.calc_vos_simple(targ)
# VO loss
s = pred_vos.size()
vo_loss = \
torch.exp(-self.srx) * self.t_loss_fn(pred_vos.view(-1, *s[2:])[:, :3],
targ_vos.view(-1, *s[2:])[:, :3]) + \
self.srx + \
torch.exp(-self.srq) * self.q_loss_fn(pred_vos.view(-1, *s[2:])[:, 3:],
targ_vos.view(-1, *s[2:])[:, 3:]) + \
self.srq
# total loss
loss = abs_loss + vo_loss
return loss
def forward(self, pred, targ):
"""
:param pred: N x T x 6
:param targ: N x T x 6
:return:
"""
if self.dual_target:
pred, sem_pred = pred[0], pred[1]
targ, sem_targ = targ[0], targ[1]
s = pred.size()
# absolute pose loss
# get the VOs
pred_vos = pose_utils.calc_vos_simple(pred)
targ_vos = pose_utils.calc_vos_simple(targ)
t_loss = self.t_loss_fn(pred.view(-1, *s[2:])[:, :3],
targ.view(-1, *s[2:])[:, :3])
q_loss = self.q_loss_fn(pred.view(-1, *s[2:])[:, 3:],
targ.view(-1, *s[2:])[:, 3:])
vo_t_loss = self.t_loss_fn(pred_vos.view(-1, *s[2:])[:, :3],
targ_vos.view(-1, *s[2:])[:, :3])
vo_q_loss = self.q_loss_fn(pred_vos.view(-1, *s[2:])[:, 3:],
targ_vos.view(-1, *s[2:])[:, 3:])
abs_loss =(
torch.exp(-self.sax) * t_loss +
self.sax +
torch.exp(-self.saq) * q_loss +
self.saq
)
vo_loss = (
torch.exp(-self.srx) * vo_t_loss +
self.srx +
torch.exp(-self.srq) * vo_q_loss +
self.srq
)
loss_pose = (abs_loss + vo_loss)
if self.dual_target:
s = sem_targ.size()
sem_targ = sem_targ.view(-1,*s[2:])
s_loss = self.sem_loss(sem_pred, sem_targ)
loss_semantic = torch.exp(-self.sas) * s_loss + self.sas
else:
loss_semantic = 0
loss = loss_pose + loss_semantic
return loss
def forward(self, pred, targ):
"""
:param pred: N x T x 6
:param targ: N x T x 6
:return:
"""
if self.dual_target:
pred, sem_pred = pred[0], pred[1]
targ, sem_targ = targ[0], targ[1]
s = pred.size()
# absolute pose loss
# get the VOs
pred_vos = pose_utils.calc_vos_simple(pred)
targ_vos = pose_utils.calc_vos_simple(targ)
t_loss = self.t_loss_fn(pred.view(-1, *s[2:])[:, :3],
targ.view(-1, *s[2:])[:, :3])
q_loss = self.q_loss_fn(pred.view(-1, *s[2:])[:, 3:],
targ.view(-1, *s[2:])[:, 3:])
vo_t_loss = self.t_loss_fn(pred_vos.view(-1, *s[2:])[:, :3],
targ_vos.view(-1, *s[2:])[:, :3])
vo_q_loss = self.q_loss_fn(pred_vos.view(-1, *s[2:])[:, 3:],
targ_vos.view(-1, *s[2:])[:, 3:])
if self.learn_log:
loss_pose = (
t_loss / (2 * torch.exp(2 * self.sax)) +
q_loss / (2 * torch.exp(2 * self.saq)) +
vo_t_loss / (2 * torch.exp(2 * self.srx)) +
vo_q_loss / (2 * torch.exp(2 * self.srq)) +
self.sax + self.saq + self.srx + self.srq
)
else:
loss_pose = (
t_loss / (2 * self.sax * self.sax) +
q_loss / (2 * self.saq * self.saq) +
vo_t_loss / (2 * self.srx * self.srx) +
vo_q_loss / (2 * self.srq * self.srq) +
torch.log(self.sax * self.saq * self.srx * self.srq)
)
if self.dual_target:
s = sem_targ.size()
sem_targ = sem_targ.view(-1,*s[2:])
s_loss = self.sem_loss(sem_pred, sem_targ)
if self.learn_log:
loss_semantic = (s_loss / torch.exp(2 * self.sas) +
self.sas)
else:
loss_semantic = (s_loss / (self.sas * self.sas) +
torch.log(self.sas))
else:
loss_semantic = 0
loss = loss_pose + loss_semantic
return loss
def forward(self, pred, targ):
"""
:param pred: N x T x 6
:param targ: N x T x 6
:return:
"""
if self.dual_target:
pred, sem_pred = pred[0], pred[1]
targ, sem_targ = targ[0], targ[1]
if len(pred.size()) < 3:
pred = pred.unsqueeze(0)
s = pred.size()
# absolute pose loss
# get the VOs
pred_vos = pose_utils.calc_vos_simple(pred)
targ_vos = pose_utils.calc_vos_simple(targ)
t_loss = self.t_loss_fn(pred.view(-1, *s[2:])[:, :3],
targ.view(-1, *s[2:])[:, :3])
q_loss = self.q_loss_fn(pred.view(-1, *s[2:])[:, 3:],
targ.view(-1, *s[2:])[:, 3:])
vo_t_loss = self.t_loss_fn(pred_vos.view(-1, *s[2:])[:, :3],
targ_vos.view(-1, *s[2:])[:, :3]) if not ((type(pred_vos) == list and len(pred_vos) == 0) or (type(targ_vos) == list and len(targ_vos) == 0)) else 0.0
vo_q_loss = self.q_loss_fn(pred_vos.view(-1, *s[2:])[:, 3:],
targ_vos.view(-1, *s[2:])[:, 3:]) if not ((type(pred_vos) == list and len(pred_vos) == 0) or (type(targ_vos) == list and len(targ_vos) == 0)) else 0.0
abs_loss =(
torch.exp(-self.sax) * t_loss +
self.sax +
torch.exp(-self.saq) * q_loss +
self.saq
)
vo_loss = (
torch.exp(-self.srx) * vo_t_loss +
self.srx +
torch.exp(-self.srq) * vo_q_loss +
self.srq
)
loss_pose = (abs_loss + vo_loss)
loss_list = [t_loss, q_loss]
if self.dual_target:
s = sem_targ.size()
sem_targ = sem_targ.view(-1,*s[2:])
s_loss = self.sem_loss(sem_pred, sem_targ)
loss_semantic = torch.exp(-self.sas) * s_loss + self.sas
loss_list.append(s_loss)
else:
loss_semantic = 0
loss = loss_pose + loss_semantic
return loss, loss_list