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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: ps-license@tuebingen.mpg.de | |
| from typing import Optional | |
| import torch | |
| from torch import Tensor, nn | |
| from pathlib import Path | |
| import os | |
| class Rots2Rfeats(nn.Module): | |
| def __init__(self, path: Optional[str] = None, | |
| normalization: bool = True, | |
| eps: float = 1e-12, | |
| **kwargs) -> None: | |
| if normalization and path is None: | |
| raise TypeError("You should provide a path if normalization is on.") | |
| super().__init__() | |
| self.normalization = normalization | |
| self.eps = eps | |
| if normalization: | |
| # workaround for cluster local/sync | |
| rel_p = path.split('/') | |
| # superhacky it is for the datatype ugly stuff change it, copy the main stuff to seperate_pairs dict | |
| if rel_p[-1] == 'separate_pairs': | |
| rel_p.remove('separate_pairs') | |
| ######################################################## | |
| # rel_p = rel_p[rel_p.index('deps'):] | |
| rel_p = '/'.join(rel_p) | |
| # path = hydra.utils.get_original_cwd() + '/' + rel_p | |
| path = rel_p | |
| mean_path = Path(path) / "rfeats_mean.pt" | |
| std_path = Path(path) / "rfeats_std.pt" | |
| self.register_buffer('mean', torch.load(mean_path)) | |
| self.register_buffer('std', torch.load(std_path)) | |
| def normalize(self, features: Tensor) -> Tensor: | |
| if self.normalization: | |
| features = (features - self.mean)/(self.std + self.eps) | |
| return features | |
| def unnormalize(self, features: Tensor) -> Tensor: | |
| if self.normalization: | |
| features = features * self.std + self.mean | |
| return features | |