Shortcuts

mmedit.models.editors.pggan.pggan_discriminator

Module Contents

Classes

PGGANDiscriminator

Discriminator for PGGAN.

class mmedit.models.editors.pggan.pggan_discriminator.PGGANDiscriminator(in_scale, label_size=0, base_channels=8192, max_channels=512, in_channels=3, channel_decay=1.0, mbstd_cfg=dict(group_size=4), fused_convdown=True, conv_module_cfg=None, fused_convdown_cfg=None, fromrgb_layer_cfg=None, downsample_cfg=None)[source]

Bases: torch.nn.Module

Discriminator for PGGAN.

Parameters
  • in_scale (int) – The scale of the input image.

  • label_size (int, optional) – Size of the label vector. Defaults to 0.

  • base_channels (int, optional) – The basic channel number of the generator. The other layers contains channels based on this number. Defaults to 8192.

  • max_channels (int, optional) – Maximum channels for the feature maps in the discriminator block. Defaults to 512.

  • in_channels (int, optional) – Number of channels in input images. Defaults to 3.

  • channel_decay (float, optional) – Decay for channels of feature maps. Defaults to 1.0.

  • mbstd_cfg (dict, optional) – Configs for minibatch-stddev layer. Defaults to dict(group_size=4).

  • fused_convdown (bool, optional) – Whether use fused downconv. Defaults to True.

  • conv_module_cfg (dict, optional) – Config for the convolution module used in this generator. Defaults to None.

  • fused_convdown_cfg (dict, optional) – Config for the fused downconv module used in this discriminator. Defaults to None.

  • fromrgb_layer_cfg (dict, optional) – Config for the fromrgb layer. Defaults to None.

  • downsample_cfg (dict, optional) – Config for the downsampling operation. Defaults to None.

_default_fromrgb_cfg[source]
_default_conv_module_cfg[source]
_default_convdown_cfg[source]
_num_out_channels(log_scale: int) int[source]

Calculate the number of output channels of the current network from logarithm of current scale.

Parameters

log_scale (int) – The logarithm of the current scale.

Returns

The number of output channels.

Return type

int

_get_fromrgb_layer(in_channels: int, log2_scale: int) torch.nn.Module[source]

Get the ‘fromrgb’ layer from logarithm of current scale.

Parameters
  • in_channels (int) – The number of input channels.

  • log2_scale (int) – The logarithm of the current scale.

Returns

The built from-rgb layer.

Return type

nn.Module

_get_convdown_block(in_channels: int, log2_scale: int) torch.nn.Module[source]

Get the downsample layer from logarithm of current scale.

Parameters
  • in_channels (int) – The number of input channels.

  • log2_scale (int) – The logarithm of the current scale.

Returns

The built Conv layer.

Return type

nn.Module

forward(x, transition_weight=1.0, curr_scale=- 1)[source]

Forward function.

Parameters
  • x (torch.Tensor) – Input image tensor.

  • transition_weight (float, optional) – The weight used in resolution transition. Defaults to 1.0.

  • curr_scale (int, optional) – The scale for the current inference or training. Defaults to -1.

Returns

Predict score for the input image.

Return type

Tensor

Read the Docs v: latest
Versions
master
latest
stable
zyh-re-docs
zyh-doc-notfound-extend
zyh-api-rendering
Downloads
pdf
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.