Spaces:
Runtime error
Runtime error
File size: 2,466 Bytes
4409449 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
# -*- 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: ps-license@tuebingen.mpg.de
from dataclasses import dataclass, fields
class Transform:
def collate(self, lst_datastruct):
from ..tools import collate_tensor_with_padding
example = lst_datastruct[0]
def collate_or_none(key):
if example[key] is None:
return None
key_lst = [x[key] for x in lst_datastruct]
return collate_tensor_with_padding(key_lst)
kwargs = {key: collate_or_none(key) for key in example.datakeys}
return self.Datastruct(**kwargs)
# Inspired from SMPLX library
# need to define "datakeys" and transforms
@dataclass
class Datastruct:
def __getitem__(self, key):
return getattr(self, key)
def __setitem__(self, key, value):
self.__dict__[key] = value
def get(self, key, default=None):
return getattr(self, key, default)
def __iter__(self):
return self.keys()
def keys(self):
keys = [t.name for t in fields(self)]
return iter(keys)
def values(self):
values = [getattr(self, t.name) for t in fields(self)]
return iter(values)
def items(self):
data = [(t.name, getattr(self, t.name)) for t in fields(self)]
return iter(data)
def to(self, *args, **kwargs):
for key in self.datakeys:
if self[key] is not None:
self[key] = self[key].to(*args, **kwargs)
return self
@property
def device(self):
return self[self.datakeys[0]].device
def detach(self):
def detach_or_none(tensor):
if tensor is not None:
return tensor.detach()
return None
kwargs = {key: detach_or_none(self[key]) for key in self.datakeys}
return self.transforms.Datastruct(**kwargs)
|