Source code for torchgan.layers.minibatchdiscrimination

import torch
import torch.nn as nn

__all__ = ["MinibatchDiscrimination1d"]

# The original paper by Salimans et. al. discusses only 1D minibatch discrimination
[docs]class MinibatchDiscrimination1d(nn.Module): r"""1D Minibatch Discrimination Module as proposed in the paper `"Improved Techniques for Training GANs by Salimans et. al." <>`_ Allows the Discriminator to easily detect mode collapse by augmenting the activations to the succeeding layer with side information that allows it to determine the 'closeness' of the minibatch examples with each other .. math :: M_i = T * f(x_{i}) .. math :: c_b(x_{i}, x_{j}) = \exp(-||M_{i, b} - M_{j, b}||_1) \in \mathbb{R}. .. math :: o(x_{i})_b &= \sum_{j=1}^{n} c_b(x_{i},x_{j}) \in \mathbb{R} \\ .. math :: o(x_{i}) &= \Big[ o(x_{i})_1, o(x_{i})_2, \dots, o(x_{i})_B \Big] \in \mathbb{R}^B \\ .. math :: o(X) \in \mathbb{R}^{n \times B} This is followed by concatenating :math:`o(x_{i})` and :math:`f(x_{i})` where - :math:`f(x_{i}) \in \mathbb{R}^A` : Activations from an intermediate layer - :math:`f(x_{i}) \in \mathbb{R}^A` : Parameter Tensor for generating minibatch discrimination matrix Args: in_features (int): Features input corresponding to dimension :math:`A` out_features (int): Number of output features that are to be concatenated corresponding to dimension :math:`B` intermediate_features (int): Intermediate number of features corresponding to dimension :math:`C` Returns: A Tensor of size :math:`(N, in_features + out_features)` where :math:`N` is the batch size """ def __init__(self, in_features, out_features, intermediate_features=16): super(MinibatchDiscrimination1d, self).__init__() self.in_features = in_features self.out_features = out_features self.intermediate_features = intermediate_features self.T = nn.Parameter( torch.Tensor(in_features, out_features, intermediate_features) ) nn.init.normal_(self.T)
[docs] def forward(self, x): r"""Computes the output of the Minibatch Discrimination Layer Args: x (torch.Tensor): A Torch Tensor of dimensions :math: `(N, infeatures)` Returns: 3D Torch Tensor of size :math: `(N,infeatures + outfeatures)` after applying Minibatch Discrimination """ M =, self.T.view(self.in_features, -1)) M = M.view(-1, self.out_features, self.intermediate_features).unsqueeze(0) M_t = M.permute(1, 0, 2, 3) # Broadcasting reduces the matrix subtraction to the form desired in the paper out = torch.sum(torch.exp(-(torch.abs(M - M_t).sum(3))), dim=0) - 1 return[x, out], 1)