Module flowcon.flows.realnvp

Implementations of Real NVP.

Classes

class SimpleRealNVP (features, hidden_features, num_layers, num_blocks_per_layer, use_volume_preserving=False, activation=<function relu>, dropout_probability=0.0, batch_norm_within_layers=False, batch_norm_between_layers=False)

An simplified version of Real NVP for 1-dim inputs.

This implementation uses 1-dim checkerboard masking but doesn't use multi-scaling.

Reference:

L. Dinh et al., Density estimation using Real NVP, ICLR 2017.

Constructor.

Args

transform
A Transform object, it transforms data into noise.
distribution
A AutoregressiveTransform object, the base distribution of the flow that generates the noise.
embedding_net
A nn.Module which has trainable parameters to encode the context (condition). It is trained jointly with the flow.
Expand source code 
class SimpleRealNVP(Flow):
    """An simplified version of Real NVP for 1-dim inputs.

    This implementation uses 1-dim checkerboard masking but doesn't use multi-scaling.

    Reference:
    > L. Dinh et al., Density estimation using Real NVP, ICLR 2017.
    """

    def __init__(
        self,
        features,
        hidden_features,
        num_layers,
        num_blocks_per_layer,
        use_volume_preserving=False,
        activation=F.relu,
        dropout_probability=0.0,
        batch_norm_within_layers=False,
        batch_norm_between_layers=False,
    ):

        if use_volume_preserving:
            coupling_constructor = AdditiveCouplingTransform
        else:
            coupling_constructor = AffineCouplingTransform

        mask = torch.ones(features)
        mask[::2] = -1

        def create_resnet(in_features, out_features):
            return nets.ResidualNet(
                in_features,
                out_features,
                hidden_features=hidden_features,
                num_blocks=num_blocks_per_layer,
                activation=activation,
                dropout_probability=dropout_probability,
                use_batch_norm=batch_norm_within_layers,
            )

        layers = []
        for _ in range(num_layers):
            transform = coupling_constructor(
                mask=mask, transform_net_create_fn=create_resnet
            )
            layers.append(transform)
            mask *= -1
            if batch_norm_between_layers:
                layers.append(BatchNorm(features=features))

        super().__init__(
            transform=CompositeTransform(layers),
            distribution=StandardNormal([features]),
        )

Ancestors

Class variables

var call_super_init : bool
var dump_patches : bool
var training : bool

Inherited members