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wrapper.py
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wrapper.py
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import torch
from torch import nn
import numpy as np
from ..base import Flow
from .coupling import PiecewiseRationalQuadraticCoupling
from .autoregressive import MaskedPiecewiseRationalQuadraticAutoregressive
from ...nets.resnet import ResidualNet
from ...utils.masks import create_alternating_binary_mask
from ...utils.nn import PeriodicFeaturesElementwise
from ...utils.splines import DEFAULT_MIN_DERIVATIVE
class CoupledRationalQuadraticSpline(Flow):
"""
Neural spline flow coupling layer, wrapper for the implementation
of Durkan et al., see [source](https://github.com/bayesiains/nsf)
"""
def __init__(
self,
num_input_channels,
num_blocks,
num_hidden_channels,
num_bins=8,
tails="linear",
tail_bound=3.0,
activation=nn.ReLU,
dropout_probability=0.0,
reverse_mask=False,
):
"""Constructor
Args:
num_input_channels (int): Flow dimension
num_blocks (int): Number of residual blocks of the parameter NN
num_hidden_channels (int): Number of hidden units of the NN
num_bins (int): Number of bins
tails (str): Behaviour of the tails of the distribution, can be linear, circular for periodic distribution, or None for distribution on the compact interval
tail_bound (float): Bound of the spline tails
activation (torch module): Activation function
dropout_probability (float): Dropout probability of the NN
reverse_mask (bool): Flag whether the reverse mask should be used
"""
super().__init__()
def transform_net_create_fn(in_features, out_features):
return ResidualNet(
in_features=in_features,
out_features=out_features,
context_features=None,
hidden_features=num_hidden_channels,
num_blocks=num_blocks,
activation=activation(),
dropout_probability=dropout_probability,
use_batch_norm=False,
)
self.prqct = PiecewiseRationalQuadraticCoupling(
mask=create_alternating_binary_mask(num_input_channels, even=reverse_mask),
transform_net_create_fn=transform_net_create_fn,
num_bins=num_bins,
tails=tails,
tail_bound=tail_bound,
# Setting True corresponds to equations (4), (5), (6) in the NSF paper:
apply_unconditional_transform=True,
)
def forward(self, z):
z, log_det = self.prqct.inverse(z)
return z, log_det.view(-1)
def inverse(self, z):
z, log_det = self.prqct(z)
return z, log_det.view(-1)
class CircularCoupledRationalQuadraticSpline(Flow):
"""
Neural spline flow coupling layer with circular coordinates
"""
def __init__(
self,
num_input_channels,
num_blocks,
num_hidden_channels,
ind_circ,
num_bins=8,
tail_bound=3.0,
activation=nn.ReLU,
dropout_probability=0.0,
reverse_mask=False,
mask=None,
init_identity=True,
):
"""Constructor
Args:
num_input_channels (int): Flow dimension
num_blocks (int): Number of residual blocks of the parameter NN
num_hidden_channels (int): Number of hidden units of the NN
ind_circ (Iterable): Indices of the circular coordinates
num_bins (int): Number of bins
tail_bound (float or Iterable): Bound of the spline tails
activation (torch module): Activation function
dropout_probability (float): Dropout probability of the NN
reverse_mask (bool): Flag whether the reverse mask should be used
mask (torch tensor): Mask to be used, alternating masked generated is None
init_identity (bool): Flag, initialize transform as identity
"""
super().__init__()
if mask is None:
mask = create_alternating_binary_mask(num_input_channels, even=reverse_mask)
features_vector = torch.arange(num_input_channels)
identity_features = features_vector.masked_select(mask <= 0)
ind_circ = torch.tensor(ind_circ)
ind_circ_id = []
for i, id in enumerate(identity_features):
if id in ind_circ:
ind_circ_id += [i]
if torch.is_tensor(tail_bound):
scale_pf = np.pi / tail_bound[ind_circ_id]
else:
scale_pf = np.pi / tail_bound
def transform_net_create_fn(in_features, out_features):
if len(ind_circ_id) > 0:
pf = PeriodicFeaturesElementwise(in_features, ind_circ_id, scale_pf)
else:
pf = None
net = ResidualNet(
in_features=in_features,
out_features=out_features,
context_features=None,
hidden_features=num_hidden_channels,
num_blocks=num_blocks,
activation=activation(),
dropout_probability=dropout_probability,
use_batch_norm=False,
preprocessing=pf,
)
if init_identity:
torch.nn.init.constant_(net.final_layer.weight, 0.0)
torch.nn.init.constant_(
net.final_layer.bias, np.log(np.exp(1 - DEFAULT_MIN_DERIVATIVE) - 1)
)
return net
tails = [
"circular" if i in ind_circ else "linear" for i in range(num_input_channels)
]
self.prqct = PiecewiseRationalQuadraticCoupling(
mask=mask,
transform_net_create_fn=transform_net_create_fn,
num_bins=num_bins,
tails=tails,
tail_bound=tail_bound,
apply_unconditional_transform=True,
)
def forward(self, z):
z, log_det = self.prqct.inverse(z)
return z, log_det.view(-1)
def inverse(self, z):
z, log_det = self.prqct(z)
return z, log_det.view(-1)
class AutoregressiveRationalQuadraticSpline(Flow):
"""
Neural spline flow coupling layer, wrapper for the implementation
of Durkan et al., see [sources](https://github.com/bayesiains/nsf)
"""
def __init__(
self,
num_input_channels,
num_blocks,
num_hidden_channels,
num_context_channels=None,
num_bins=8,
tail_bound=3,
activation=nn.ReLU,
dropout_probability=0.0,
permute_mask=False,
init_identity=True,
):
"""Constructor
Args:
num_input_channels (int): Flow dimension
num_blocks (int): Number of residual blocks of the parameter NN
num_hidden_channels (int): Number of hidden units of the NN
num_context_channels (int): Number of context/conditional channels
num_bins (int): Number of bins
tail_bound (int): Bound of the spline tails
activation (torch.nn.Module): Activation function
dropout_probability (float): Dropout probability of the NN
permute_mask (bool): Flag, permutes the mask of the NN
init_identity (bool): Flag, initialize transform as identity
"""
super().__init__()
self.mprqat = MaskedPiecewiseRationalQuadraticAutoregressive(
features=num_input_channels,
hidden_features=num_hidden_channels,
context_features=num_context_channels,
num_bins=num_bins,
tails="linear",
tail_bound=tail_bound,
num_blocks=num_blocks,
use_residual_blocks=True,
random_mask=False,
permute_mask=permute_mask,
activation=activation(),
dropout_probability=dropout_probability,
use_batch_norm=False,
init_identity=init_identity,
)
def forward(self, z, context=None):
z, log_det = self.mprqat.inverse(z, context=context)
return z, log_det.view(-1)
def inverse(self, z, context=None):
z, log_det = self.mprqat(z, context=context)
return z, log_det.view(-1)
class CircularAutoregressiveRationalQuadraticSpline(Flow):
"""
Neural spline flow coupling layer, wrapper for the implementation
of Durkan et al., see [sources](https://github.com/bayesiains/nsf)
"""
def __init__(
self,
num_input_channels,
num_blocks,
num_hidden_channels,
ind_circ,
num_context_channels=None,
num_bins=8,
tail_bound=3,
activation=nn.ReLU,
dropout_probability=0.0,
permute_mask=True,
init_identity=True,
):
"""Constructor
Args:
num_input_channels (int): Flow dimension
num_blocks (int): Number of residual blocks of the parameter NN
num_hidden_channels (int): Number of hidden units of the NN
ind_circ (Iterable): Indices of the circular coordinates
num_context_channels (int): Number of context/conditional channels
num_bins (int): Number of bins
tail_bound (int): Bound of the spline tails
activation (torch module): Activation function
dropout_probability (float): Dropout probability of the NN
permute_mask (bool): Flag, permutes the mask of the NN
init_identity (bool): Flag, initialize transform as identity
"""
super().__init__()
tails = [
"circular" if i in ind_circ else "linear" for i in range(num_input_channels)
]
self.mprqat = MaskedPiecewiseRationalQuadraticAutoregressive(
features=num_input_channels,
hidden_features=num_hidden_channels,
context_features=num_context_channels,
num_bins=num_bins,
tails=tails,
tail_bound=tail_bound,
num_blocks=num_blocks,
use_residual_blocks=True,
random_mask=False,
permute_mask=permute_mask,
activation=activation(),
dropout_probability=dropout_probability,
use_batch_norm=False,
init_identity=init_identity,
)
def forward(self, z, context=None):
z, log_det = self.mprqat.inverse(z, context=context)
return z, log_det.view(-1)
def inverse(self, z, context=None):
z, log_det = self.mprqat(z, context=context)
return z, log_det.view(-1)