My PyTorch implementations of deep generative models.
Install the Python package deepgenmodels from this repository with:
pip install git+https://github.com/ameya98/DeepGenerativeModels.git --upgradeThe project is structured as such:
deepgenmodels/
- utils/
- mixture_distribution
- MixtureDistribution
- autoregressive/
- nade
- NADE
- fvsbn
- FVSBN
- normalizing_flows/
- realnvp
- RealNVP
- RealNVPStacked
Thus, to import RealNVP (only one layer) and RealNVPStacked (multiple layers), one would do:
from deepgenmodels.normalizing_flows import RealNVP, RealNVPStackedNote that to use the class-conditioned RealNVP, pass class_condition=True in the RealNVP constructor, and a MixtureDistribution as the base_dist parameter. An example:
from torch.distributions import MultivariateNormal
from deepgenmodels.utils import MixtureDistribution
# Define distribution.
inp_dimensions = 4
dist = MixtureDistribution(dists=[
MultivariateNormal(loc=torch.ones(inp_dimensions) * -1.0, covariance_matrix=torch.eye(inp_dimensions)),
MultivariateNormal(loc=torch.ones(inp_dimensions) * +1.0, covariance_matrix=torch.eye(inp_dimensions)),
], dims=inp_dimensions)
# Define characteristics of each RealNVP layer.
num_layers = 3
layer_wise_dict = {
layer_num: {
'mu_net': deepcopy(mu_net),
'sig_net': deepcopy(sig_net),
'base_dist': dist,
'dims': inp_dimensions,
'class_condition': True,
} for layer_num in range(num_layers)
}
# Create stacked RealNVP model.
model = RealNVPStacked(layer_wise_dict)
Latent Distribution: Zero-Mean Unit-Variance Gaussian
Latent Distribution: Mixture of Unit-Variance Gaussians Centered at [-1, -1] (Class 0) and [1, 1] (Class 1).
