def __init__(
self,
x_size: int,
y_size: int,
arch: str = 'A', # ['PRQ', 'UMNN']
num_transforms: int = 5,
lu_linear: bool = False,
moments: Tuple[torch.Tensor, torch.Tensor] = None,
**kwargs,
):
kwargs.setdefault('hidden_features', 64)
kwargs.setdefault('num_blocks', 2)
kwargs.setdefault('use_residual_blocks', False)
kwargs.setdefault('use_batch_norm', False)
kwargs['activation'] = ACTIVATIONS[kwargs.get('activation', 'ReLU')]()
if arch == 'PRQ':
kwargs['tails'] = 'linear'
kwargs.setdefault('num_bins', 8)
kwargs.setdefault('tail_bound', 1.)
tfrm = transforms.MaskedPiecewiseRationalQuadraticAutoregressiveTransform
elif arch == 'UMNN':
kwargs.setdefault('integrand_net_layers', [64, 64, 64])
kwargs.setdefault('cond_size', 32)
kwargs.setdefault('nb_steps', 32)
tfrm = transforms.MaskedUMNNAutoregressiveTransform
else: # arch == 'A'
tfrm = transforms.MaskedAffineAutoregressiveTransform
compose = []
if moments is not None:
shift, scale = moments
compose.append(
transforms.PointwiseAffineTransform(-shift / scale, 1 / scale))
for _ in range(num_transforms if x_size > 1 else 1):
compose.extend([
tfrm(
features=x_size,
context_features=y_size,
**kwargs,
),
transforms.RandomPermutation(features=x_size),
])
if lu_linear:
compose.append(transforms.LULinear(x_size,
identity_init=True), )
transform = transforms.CompositeTransform(compose)
distribution = distributions.StandardNormal((x_size, ))
super().__init__(transform, distribution)
def create_flow(flow_type):
distribution = distributions.StandardNormal((3,))
if flow_type == 'lu_flow':
transform = transforms.CompositeTransform([
transforms.RandomPermutation(3),
transforms.LULinear(3, identity_init=False)
])
elif flow_type == 'qr_flow':
transform = transforms.QRLinear(3, num_householder=3)
else:
raise RuntimeError('Unknown type')
return flows.Flow(transform, distribution)
def create_linear_transform(param_dim):
"""Create the composite linear transform PLU.
Arguments:
input_dim {int} -- dimension of the space
Returns:
Transform -- nde.Transform object
"""
return transforms.CompositeTransform([
transforms.RandomPermutation(features=param_dim),
transforms.LULinear(param_dim, identity_init=True)
])
def _make_scalar_linear_transform(transform, features):
if transform == "permutation":
return transforms.RandomPermutation(features=features)
elif transform == "lu":
return transforms.CompositeTransform(
[transforms.RandomPermutation(features=features), transforms.LULinear(features, identity_init=True)]
)
elif transform == "svd":
return transforms.CompositeTransform(
[
transforms.RandomPermutation(features=features),
transforms.SVDLinear(features, num_householder=10, identity_init=True),
]
)
else:
raise ValueError
def _create_linear_transform(self):
return transforms.CompositeTransform([
transforms.RandomPermutation(features=self.dimensions),
transforms.LULinear(self.dimensions, identity_init=True)
])
def neural_net_nsf(
self,
hidden_features,
num_blocks,
num_bins,
xDim,
thetaDim,
batch_x=None,
batch_theta=None,
tail=3.,
bounded=False,
embedding_net=torch.nn.Identity()) -> torch.nn.Module:
"""Builds NSF p(x|y).
Args:
batch_x: Batch of xs, used to infer dimensionality and (optional) z-scoring.
batch_y: Batch of ys, used to infer dimensionality and (optional) z-scoring.
z_score_x: Whether to z-score xs passing into the network.
z_score_y: Whether to z-score ys passing into the network.
hidden_features: Number of hidden features.
num_transforms: Number of transforms.
embedding_net: Optional embedding network for y.
kwargs: Additional arguments that are passed by the build function but are not
relevant for maf and are therefore ignored.
Returns:
Neural network.
"""
basic_transform = [
transforms.CompositeTransform([
transforms.PiecewiseRationalQuadraticCouplingTransform(
mask=create_alternating_binary_mask(features=xDim,
even=(i % 2 == 0)).to(
self.args.device),
transform_net_create_fn=lambda in_features, out_features: nets.
ResidualNet(
in_features=in_features,
out_features=out_features,
hidden_features=hidden_features,
context_features=thetaDim,
num_blocks=2,
activation=torch.relu,
dropout_probability=0.,
use_batch_norm=False,
),
num_bins=num_bins,
tails='linear',
tail_bound=tail,
apply_unconditional_transform=False,
),
transforms.RandomPermutation(features=xDim,
device=self.args.device),
transforms.LULinear(xDim, identity_init=True),
]) for i in range(num_blocks)
]
transform = transforms.CompositeTransform(basic_transform).to(
self.args.device)
if batch_theta != None:
if bounded:
transform_bounded = transforms.Logit(self.args.device)
if self.sim.min[0].item() != 0 or self.sim.max[0].item() != 1:
transfomr_affine = transforms.PointwiseAffineTransform(
shift=-self.sim.min / (self.sim.max - self.sim.min),
scale=1. / (self.sim.max - self.sim.min))
transform = transforms.CompositeTransform(
[transfomr_affine, transform_bounded, transform])
else:
transform = transforms.CompositeTransform(
[transform_bounded, transform])
else:
transform_zx = standardizing_transform(batch_x)
transform = transforms.CompositeTransform(
[transform_zx, transform])
embedding_net = torch.nn.Sequential(standardizing_net(batch_theta),
embedding_net)
distribution = distributions_.StandardNormal((xDim, ),
self.args.device)
neural_net = flows.Flow(self,
transform,
distribution,
embedding_net=embedding_net).to(
self.args.device)
else:
distribution = distributions_.StandardNormal((xDim, ),
self.args.device)
neural_net = flows.Flow(self, transform,
distribution).to(self.args.device)
return neural_net
def get_flow(
model: str,
dim_distribution: int,
dim_context: Optional[int] = None,
embedding: Optional[torch.nn.Module] = None,
hidden_features: int = 50,
made_num_mixture_components: int = 10,
made_num_blocks: int = 4,
flow_num_transforms: int = 5,
mean=0.0,
std=1.0,
) -> torch.nn.Module:
"""Density estimator
Args:
model: Model, one of maf / made / nsf
dim_distribution: Dim of distribution
dim_context: Dim of context
embedding: Embedding network
hidden_features: For all, number of hidden features
made_num_mixture_components: For MADEs only, number of mixture components
made_num_blocks: For MADEs only, number of blocks
flow_num_transforms: For flows only, number of transforms
mean: For normalization
std: For normalization
Returns:
Neural network
"""
standardizing_transform = transforms.AffineTransform(shift=-mean / std,
scale=1 / std)
features = dim_distribution
context_features = dim_context
if model == "made":
transform = standardizing_transform
distribution = distributions_.MADEMoG(
features=features,
hidden_features=hidden_features,
context_features=context_features,
num_blocks=made_num_blocks,
num_mixture_components=made_num_mixture_components,
use_residual_blocks=True,
random_mask=False,
activation=torch.relu,
dropout_probability=0.0,
use_batch_norm=False,
custom_initialization=True,
)
neural_net = flows.Flow(transform, distribution, embedding)
elif model == "maf":
transform = transforms.CompositeTransform([
transforms.CompositeTransform([
transforms.MaskedAffineAutoregressiveTransform(
features=features,
hidden_features=hidden_features,
context_features=context_features,
num_blocks=2,
use_residual_blocks=False,
random_mask=False,
activation=torch.tanh,
dropout_probability=0.0,
use_batch_norm=True,
),
transforms.RandomPermutation(features=features),
]) for _ in range(flow_num_transforms)
])
transform = transforms.CompositeTransform(
[standardizing_transform, transform])
distribution = distributions_.StandardNormal((features, ))
neural_net = flows.Flow(transform, distribution, embedding)
elif model == "nsf":
transform = transforms.CompositeTransform([
transforms.CompositeTransform([
transforms.PiecewiseRationalQuadraticCouplingTransform(
mask=create_alternating_binary_mask(features=features,
even=(i % 2 == 0)),
transform_net_create_fn=lambda in_features, out_features:
nets.ResidualNet(
in_features=in_features,
out_features=out_features,
hidden_features=hidden_features,
context_features=context_features,
num_blocks=2,
activation=torch.relu,
dropout_probability=0.0,
use_batch_norm=False,
),
num_bins=10,
tails="linear",
tail_bound=3.0,
apply_unconditional_transform=False,
),
transforms.LULinear(features, identity_init=True),
]) for i in range(flow_num_transforms)
])
transform = transforms.CompositeTransform(
[standardizing_transform, transform])
distribution = distributions_.StandardNormal((features, ))
neural_net = flows.Flow(transform, distribution, embedding)
elif model == "nsf_bounded":
transform = transforms.CompositeTransform([
transforms.CompositeTransform([
transforms.PiecewiseRationalQuadraticCouplingTransform(
mask=create_alternating_binary_mask(
features=dim_distribution, even=(i % 2 == 0)),
transform_net_create_fn=lambda in_features, out_features:
nets.ResidualNet(
in_features=in_features,
out_features=out_features,
hidden_features=hidden_features,
context_features=context_features,
num_blocks=2,
activation=F.relu,
dropout_probability=0.0,
use_batch_norm=False,
),
num_bins=10,
tails="linear",
tail_bound=np.sqrt(
3), # uniform with sqrt(3) bounds has unit-variance
apply_unconditional_transform=False,
),
transforms.RandomPermutation(features=dim_distribution),
]) for i in range(flow_num_transforms)
])
transform = transforms.CompositeTransform(
[standardizing_transform, transform])
distribution = StandardUniform(shape=(dim_distribution, ))
neural_net = flows.Flow(transform, distribution, embedding)
else:
raise ValueError
return neural_net
