mmagic.models.editors.pggan.pggan_generator

Module Contents

Classes

PGGANGenerator

Generator for PGGAN.

class mmagic.models.editors.pggan.pggan_generator.PGGANGenerator(noise_size, out_scale, label_size=0, base_channels=8192, channel_decay=1.0, max_channels=512, fused_upconv=True, conv_module_cfg=None, fused_upconv_cfg=None, upsample_cfg=None)

Bases: mmengine.model.BaseModule

Generator for PGGAN.

Parameters

• noise_size (int) – Size of the input noise vector.

• out_scale (int) – Output scale for the generated 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.

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

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

• fused_upconv (bool, optional) – Whether use fused upconv. Defaults to True.

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

• fused_upconv_cfg (dict, optional) – Config for the fused upconv module used in this generator. Defaults to None.

• upsample_cfg (dict, optional) – Config for the upsampling operation. Defaults to None.

_default_fused_upconv_cfg

_default_conv_module_cfg

_default_upsample_cfg

_get_torgb_layer(in_channels: int)

Get the to-rgb layer based on in_channels.

Parameters

in_channels (int) – Number of input channels.

Returns

To-rgb layer.

Return type

nn.Module

_num_out_channels(log_scale: int)

Calculate the number of output channels based on logarithm of current scale.

Parameters

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

Returns

The current number of output channels.

Return type

int

_get_upconv_block(in_channels, log_scale)

Get the conv block for upsampling.

Parameters

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

• log_scale (int) – The logarithmic of the current scale.

Returns

The conv block for upsampling.

Return type

nn.Module

forward(noise, label=None, num_batches=0, return_noise=False, transition_weight=1.0, curr_scale=- 1)

Forward function.

Parameters

• noise (torch.Tensor | callable | None) – You can directly give a batch of noise through a torch.Tensor or offer a callable function to sample a batch of noise data. Otherwise, the None indicates to use the default noise sampler.

• label (Tensor, optional) – Label vector with shape [N, C]. Defaults to None.

• num_batches (int, optional) – The number of batch size. Defaults to 0.

• return_noise (bool, optional) – If True, noise_batch will be returned in a dict with fake_img. Defaults to False.

• 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

If not return_noise, only the output image

will be returned. Otherwise, a dict contains fake_img and noise_batch will be returned.

Return type

torch.Tensor | dict