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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 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 | |
| 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 | |
| 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) | |