from dataclasses import field
from typing import Dict, Literal, Optional, Tuple, Union
from pydantic.dataclasses import dataclass
from pythae.config import BaseConfig
[docs]
@dataclass
class BaseMultiVAEConfig(BaseConfig):
"""This is the base config for a Multi-Modal VAE model.
Args:
n_modalities (int): The number of modalities. Default: None.
latent_dim (int): The dimension of the latent space. Default: None.
input_dims (dict[str,tuple]) : The modalities'names (str) and input shapes (tuple).
uses_likelihood_rescaling (bool): To mitigate modality collapse, it is possible to use likelihood rescaling.
(see : https://proceedings.mlr.press/v162/javaloy22a.html).
The inputs_dim must be provided to compute the likelihoods rescalings. It is used in a number of models
which is why we include it here. Default to False.
rescale_factors (dict[str, float]): The reconstruction rescaling factors per modality.
If None is provided but uses_likelihood_rescaling is True, a default value proportional to the input modality
size is computed. Default to None.
decoders_dist (Dict[str, Union[function, str]]). The decoder distributions to use per modality.
Per modality, you can provide a string in ['normal','bernoulli','laplace']. For Bernoulli distribution,
the decoder is expected to output **logits**. If None is provided, a normal distribution is used for each modality.
decoder_dist_params (Dict[str,dict]) : Parameters for the output decoder distributions, for
computing the log-probability.
For instance, with normal or laplace distribution, you can pass the scale in this dictionary with
:code:`decoder_dist_params = {'mod1' : {"scale" : 0.75}}`.
"""
n_modalities: int
latent_dim: int = 10
input_dims: Optional[dict] = None
uses_likelihood_rescaling: bool = False
rescale_factors: Optional[dict] = None
decoders_dist: Union[
Dict[str, Literal["normal", "bernoulli", "laplace", "categorical"]], None
] = None
decoder_dist_params: Union[dict, None] = None
custom_architectures: list = field(default_factory=lambda: [])
def __post_init__(self):
super().__post_init__()
if self.input_dims is not None:
self.input_dims = {k: tuple(self.input_dims[k]) for k in self.input_dims}
@dataclass
class EnvironmentConfig(BaseConfig):
"""Base environment config to save python version."""
python_version: str = "3.8"
@dataclass
class BaseAEConfig(BaseConfig):
"""This is the base configuration instance of encoders/decoders models deriving from
:class:`~pythae.config.BaseConfig`.
Args:
input_dim (tuple): The input_data dimension (channels X x_dim X y_dim)
latent_dim (int): The latent space dimension. Default: None.
style_dim (int) : For models with private latent spaces for each modality. Default: 0.
"""
input_dim: Union[Tuple[int, ...], None] = None
latent_dim: int = 10
style_dim: int = 0