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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 Joints2Jfeats(nn.Module):
def __init__(self,
path: Optional[str] = None,
normalization: bool = False,
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
# workaround for cluster local/sync
if path is not None:
# rel_p = path.split('/')
# rel_p = rel_p[rel_p.index('deps'):]
# rel_p = '/'.join(rel_p)
pass
if normalization:
mean_path = Path(path) / "jfeats_mean.pt"
std_path = Path(path) / "jfeats_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
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