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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import torch | |
| import torch.nn as nn | |
| from torch import distributions | |
| class RealNVP(nn.Module): | |
| """RealNVP: a flow-based generative model | |
| `Density estimation using Real NVP | |
| arXiv: <https://arxiv.org/abs/1605.08803>`_. | |
| Code is modified from `the official implementation of RLE | |
| <https://github.com/Jeff-sjtu/res-loglikelihood-regression>`_. | |
| See also `real-nvp-pytorch | |
| <https://github.com/senya-ashukha/real-nvp-pytorch>`_. | |
| """ | |
| def get_scale_net(): | |
| """Get the scale model in a single invertable mapping.""" | |
| return nn.Sequential( | |
| nn.Linear(2, 64), nn.LeakyReLU(), nn.Linear(64, 64), | |
| nn.LeakyReLU(), nn.Linear(64, 2), nn.Tanh()) | |
| def get_trans_net(): | |
| """Get the translation model in a single invertable mapping.""" | |
| return nn.Sequential( | |
| nn.Linear(2, 64), nn.LeakyReLU(), nn.Linear(64, 64), | |
| nn.LeakyReLU(), nn.Linear(64, 2)) | |
| def prior(self): | |
| """The prior distribution.""" | |
| return distributions.MultivariateNormal(self.loc, self.cov) | |
| def __init__(self): | |
| super(RealNVP, self).__init__() | |
| self.register_buffer('loc', torch.zeros(2)) | |
| self.register_buffer('cov', torch.eye(2)) | |
| self.register_buffer( | |
| 'mask', torch.tensor([[0, 1], [1, 0]] * 3, dtype=torch.float32)) | |
| self.s = torch.nn.ModuleList( | |
| [self.get_scale_net() for _ in range(len(self.mask))]) | |
| self.t = torch.nn.ModuleList( | |
| [self.get_trans_net() for _ in range(len(self.mask))]) | |
| self.init_weights() | |
| def init_weights(self): | |
| """Initialization model weights.""" | |
| for m in self.modules(): | |
| if isinstance(m, nn.Linear): | |
| nn.init.xavier_uniform_(m.weight, gain=0.01) | |
| def backward_p(self, x): | |
| """Apply mapping form the data space to the latent space and calculate | |
| the log determinant of the Jacobian matrix.""" | |
| log_det_jacob, z = x.new_zeros(x.shape[0]), x | |
| for i in reversed(range(len(self.t))): | |
| z_ = self.mask[i] * z | |
| s = self.s[i](z_) * (1 - self.mask[i]) # torch.exp(s): betas | |
| t = self.t[i](z_) * (1 - self.mask[i]) # gammas | |
| z = (1 - self.mask[i]) * (z - t) * torch.exp(-s) + z_ | |
| log_det_jacob -= s.sum(dim=1) | |
| return z, log_det_jacob | |
| def log_prob(self, x): | |
| """Calculate the log probability of given sample in data space.""" | |
| z, log_det = self.backward_p(x) | |
| return self.prior.log_prob(z) + log_det | |