nnsvs.discriminators

Discriminator implementations mostly used for GAN-based post-filters.

All the discriminators must returns list of tensors. The last tensor of the list is regarded as the output of the discrminator. The others are used as intermedieate feature maps.

Conv2dD

class nnsvs.discriminators.Conv2dD(in_dim=None, channels=64, kernel_size=(5, 3), padding=(0, 0), last_sigmoid=False, init_type='kaiming_normal', padding_mode='zeros')[source]

Conv2d-based discriminator

The implementation follows the discrimiantor of the GAN-based post-filters in Kaneko et al. [KTKY17b].

Parameters:

in_dim (int) – Input feature dim
channels (int) – Number of channels
kernel_size (tuple) – Kernel size for 2d-convolution
padding (tuple) – Padding for 2d-convolution
last_sigmoid (bool) – If True, apply sigmoid on the output
init_type (str) – Initialization type
padding_mode (str) – Padding mode