mmagic.models.editors.pggan.pggan_discriminator

Module Contents

Classes

PGGANDiscriminator

Discriminator for PGGAN.

class mmagic.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)

Bases: mmengine.model.BaseModule

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

_default_conv_module_cfg

_default_convdown_cfg

_num_out_channels(log_scale: int) → int

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

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

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)

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