API for multiscale architectures
Multiscale architectures are suitable for image modeling.
Classic multiscale architectures
- class torchflows.architectures.MultiscaleNICE(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
Multiscale version of NICE.
- References:
Dinh et al. “NICE: Non-linear Independent Components Estimation” (2015); https://arxiv.org/abs/1410.8516.
Dinh et al. “Density estimation using Real NVP” (2017); https://arxiv.org/abs/1605.08803.
- __init__(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
Bijection constructor.
- Parameters:
event_shape – shape of the event tensor.
context_shape – shape of the context tensor.
kwargs – unused.
- class torchflows.architectures.MultiscaleRealNVP(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
Multiscale version of Real NVP.
Reference: Dinh et al. “Density estimation using Real NVP” (2017); https://arxiv.org/abs/1605.08803.
- __init__(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
Bijection constructor.
- Parameters:
event_shape – shape of the event tensor.
context_shape – shape of the context tensor.
kwargs – unused.
- class torchflows.architectures.MultiscaleRQNSF(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
Multiscale version of C-RQNSF.
- References:
Durkan et al. “Neural Spline Flows” (2019); https://arxiv.org/abs/1906.04032.
Dinh et al. “Density estimation using Real NVP” (2017); https://arxiv.org/abs/1605.08803.
- __init__(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
Bijection constructor.
- Parameters:
event_shape – shape of the event tensor.
context_shape – shape of the context tensor.
kwargs – unused.
- class torchflows.architectures.MultiscaleLRSNSF(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
Multiscale version of C-LRS.
- References:
Dolatabadi et al. “Invertible Generative Modeling using Linear Rational Splines” (2020); https://arxiv.org/abs/2001.05168.
Dinh et al. “Density estimation using Real NVP” (2017); https://arxiv.org/abs/1605.08803.
- __init__(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
Bijection constructor.
- Parameters:
event_shape – shape of the event tensor.
context_shape – shape of the context tensor.
kwargs – unused.
- class torchflows.bijections.finite.multiscale.architectures.MultiscaleDeepSigmoid(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
- __init__(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
Bijection constructor.
- Parameters:
event_shape – shape of the event tensor.
context_shape – shape of the context tensor.
kwargs – unused.
- class torchflows.bijections.finite.multiscale.architectures.MultiscaleDenseSigmoid(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
- __init__(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
Bijection constructor.
- Parameters:
event_shape – shape of the event tensor.
context_shape – shape of the context tensor.
kwargs – unused.
- class torchflows.bijections.finite.multiscale.architectures.MultiscaleDeepDenseSigmoid(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
- __init__(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
Bijection constructor.
- Parameters:
event_shape – shape of the event tensor.
context_shape – shape of the context tensor.
kwargs – unused.
Glow-style multiscale architectures
- class torchflows.architectures.AffineGlow(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
- __init__(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
Bijection constructor.
- Parameters:
event_shape – shape of the event tensor.
context_shape – shape of the context tensor.
kwargs – unused.
- class torchflows.architectures.ShiftGlow(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
- __init__(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
Bijection constructor.
- Parameters:
event_shape – shape of the event tensor.
context_shape – shape of the context tensor.
kwargs – unused.
- class torchflows.bijections.finite.multiscale.architectures.RQSGlow(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
- __init__(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
Bijection constructor.
- Parameters:
event_shape – shape of the event tensor.
context_shape – shape of the context tensor.
kwargs – unused.
- class torchflows.bijections.finite.multiscale.architectures.LRSGlow(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
- __init__(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
Bijection constructor.
- Parameters:
event_shape – shape of the event tensor.
context_shape – shape of the context tensor.
kwargs – unused.
- class torchflows.bijections.finite.multiscale.architectures.DeepSigmoidGlow(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
- __init__(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
Bijection constructor.
- Parameters:
event_shape – shape of the event tensor.
context_shape – shape of the context tensor.
kwargs – unused.
- class torchflows.bijections.finite.multiscale.architectures.DenseSigmoidGlow(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
- __init__(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
Bijection constructor.
- Parameters:
event_shape – shape of the event tensor.
context_shape – shape of the context tensor.
kwargs – unused.
- class torchflows.bijections.finite.multiscale.architectures.DeepDenseSigmoidGlow(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
- __init__(event_shape: int | Size | Tuple[int, ...], n_layers: int = None, **kwargs)
Bijection constructor.
- Parameters:
event_shape – shape of the event tensor.
context_shape – shape of the context tensor.
kwargs – unused.