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"""
Alle transforms sind grundsätzlich auf batches bezogen!
Vae transforms sind invertierbar
"""
import pickle
from dataclasses import dataclass
from functools import partial, reduce, wraps
import numpy as np
import torch
# Allgemeine Funktionen -------------------------------------------------------------
# Transformations in Pytorch sind am einfachsten.
def load(p):
with open(p, "rb") as stream:
return pickle.load(stream)
def save(obj, p):
with open(p, "wb") as stream:
pickle.dump(obj, stream)
def sequential_function(*functions):
return lambda x: reduce(lambda res, func: func(res), functions, x)
def np_sample(func):
rtn = sequential_function(
lambda x: torch.from_numpy(x).float(),
lambda x: torch.unsqueeze(x, 0),
func,
lambda x: x[0].numpy(),
)
return rtn
# Inverseabvle
class SequentialInversable(torch.nn.Sequential):
def __init__(self, *functions):
super().__init__(*functions)
self.inv_funcs = [f.inv for f in functions]
self.inv_funcs.reverse()
# def forward(self, x):
# return sequential_function(*self.functions)(x)
def inv(self, x):
return sequential_function(*self.inv_funcs)(x)
class LatentSelector(torch.nn.Module):
"""Verarbeitet Tensoren und numpy arrays"""
def __init__(self, ldim: int, selectdim: int):
super().__init__()
self.ldim = ldim
self.selectdim = selectdim
def forward(self, x: torch.Tensor):
return x[:, : self.selectdim]
def inv(self, x: torch.Tensor):
rtn = torch.cat(
[x, torch.zeros((x.shape[0], self.ldim - x.shape[1]), device=x.device)],
dim=1,
)
return rtn
class MinMaxScaler(torch.nn.Module):
#! Bei mehreren Signalen vorsicht mit dem Broadcasting.
def __init__(
self,
_min: torch.Tensor,
_max: torch.Tensor,
min_norm: float = 0.0,
max_norm: float = 1.0,
):
super().__init__()
self._min = _min
self._max = _max
self.min_norm = min_norm
self.max_norm = max_norm
def forward(self, ts):
"""None, no_signals"""
std = (ts - self._min) / (self._max - self._min)
rtn = std * (self.max_norm - self.min_norm) + self.min_norm
return rtn
def inv(self, ts):
std = (ts - self.min_norm) / (self.max_norm - self.min_norm)
rtn = std * (self._max - self._min) + self._min
return rtn
@classmethod
def from_array(cls, arr: torch.Tensor):
_min = torch.min(arr, axis=0).values
_max = torch.max(arr, axis=0).values
return cls(_min, _max)
class LatentSorter(torch.nn.Module):
def __init__(self, kl_dict: dict):
super().__init__()
self.kl_dict = kl_dict
def forward(self, latent):
"""
unsorted -> sorted
latent: (None, latent_dim)
"""
return latent[:, list(self.kl_dict.keys())]
def inv(self, latent):
keys = np.array(list(self.kl_dict.keys()))
return latent[:, torch.from_numpy(keys.argsort())]
@property
def names(self):
rtn = ["{} KL{:.2f}".format(k, v) for k, v in self.kl_dict.items()]
return rtn
def apply_along_axis(function, x, axis: int = 0):
return torch.stack([function(x_i) for x_i in torch.unbind(x, dim=axis)], dim=axis)
# Eingangsshapes bleiben wie sie sind!
class SumField(torch.nn.Module):
"""
time series: [idx, time_step, signal]
image: [idx, signal, time_step, time_step]
"""
def forward(self, ts: torch.Tensor):
"""ts2img"""
samples = ts.shape[0]
time = ts.shape[1]
channels = ts.shape[2]
ts = torch.swapaxes(ts, 1, 2) # Zeitachse ans Ende
ts = torch.reshape(
ts, (samples * channels, time)
) # Zusammenfassen von Channel + idx
#! TODO: Schleife besser lösen
rtn = apply_along_axis(self._mtf_forward, ts, 0)
rtn = torch.reshape(rtn, (samples, channels, time, time))
return rtn
def inv(self, img: torch.Tensor):
"""img2ts"""
rtn = torch.diagonal(img, dim1=2, dim2=3)
rtn = torch.swapaxes(rtn, 1, 2) # Channel und Zeitachse tauschen
return rtn
@staticmethod
def _mtf_forward(ts):
"""For one dimensional time series ts"""
return torch.add(*torch.meshgrid(ts, ts, indexing="ij")) / 2
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