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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©2020 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 typing import Optional
from torch import Tensor
from .base import Datastruct, dataclass, Transform
from ..tools import collate_tensor_with_padding
from .joints2jfeats import Joints2Jfeats
class XYZTransform(Transform):
def __init__(self, joints2jfeats: Joints2Jfeats, **kwargs):
self.joints2jfeats = joints2jfeats
def Datastruct(self, **kwargs):
return XYZDatastruct(_joints2jfeats=self.joints2jfeats,
transforms=self,
**kwargs)
def __repr__(self):
return "XYZTransform()"
@dataclass
class XYZDatastruct(Datastruct):
transforms: XYZTransform
_joints2jfeats: Joints2Jfeats
features: Optional[Tensor] = None
joints_: Optional[Tensor] = None
jfeats_: Optional[Tensor] = None
def __post_init__(self):
self.datakeys = ["features", "joints_", "jfeats_"]
# starting point
if self.features is not None and self.jfeats_ is None:
self.jfeats_ = self.features
@property
def joints(self):
# Cached value
if self.joints_ is not None:
return self.joints_
# self.jfeats_ should be defined
assert self.jfeats_ is not None
self._joints2jfeats.to(self.jfeats.device)
self.joints_ = self._joints2jfeats.inverse(self.jfeats)
return self.joints_
@property
def jfeats(self):
# Cached value
if self.jfeats_ is not None:
return self.jfeats_
# self.joints_ should be defined
assert self.joints_ is not None
self._joints2jfeats.to(self.joints.device)
self.jfeats_ = self._joints2jfeats(self.joints)
return self.jfeats_
def __len__(self):
return len(self.jfeats)
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