`mmagic.models.editors.biggan.biggan_modules`¶

Module Contents¶

Classes¶

`SNConvModule`	Spectral Normalization ConvModule.
`BigGANGenResBlock`	Residual block used in BigGAN's generator.
`BigGANConditionBN`	Conditional Batch Normalization used in BigGAN.
`SelfAttentionBlock`	Self-Attention block used in BigGAN.
`BigGANDiscResBlock`	Residual block used in BigGAN's discriminator.
`BigGANDeepGenResBlock`	Residual block used in BigGAN-Deep's generator.
`BigGANDeepDiscResBlock`	Residual block used in BigGAN-Deep's discriminator.

class mmagic.models.editors.biggan.biggan_modules.SNConvModule(*args, with_spectral_norm=False, spectral_norm_cfg=None, **kwargs)[source]¶

Bases: mmcv.cnn.ConvModule

Spectral Normalization ConvModule.

In this module, we inherit default mmcv.cnn.ConvModule and adopt spectral normalization. The spectral normalization is proposed in: Spectral Normalization for Generative Adversarial Networks.

Parameters

with_spectral_norm (bool, optional) – Whether to use Spectral Normalization. Defaults to False.
spectral_norm_cfg (dict, optional) – Config for Spectral Normalization. Defaults to None.

class mmagic.models.editors.biggan.biggan_modules.BigGANGenResBlock(in_channels, out_channels, dim_after_concat, act_cfg=dict(type='ReLU'), upsample_cfg=dict(type='nearest', scale_factor=2), sn_eps=1e-06, sn_style='ajbrock', with_spectral_norm=True, input_is_label=False, auto_sync_bn=True)[source]¶

Bases: torch.nn.Module

Residual block used in BigGAN’s generator.

Parameters

in_channels (int) – The channel number of the input feature map.
out_channels (int) – The channel number of the output feature map.
dim_after_concat (int) – The channel number of the noise concatenated with the class vector.
act_cfg (dict, optional) – Config for the activation layer. Defaults to dict(type=’ReLU’).
upsample_cfg (dict, optional) – Config for the upsampling operation. Defaults to dict(type=’nearest’, scale_factor=2).
sn_eps (float, optional) – Epsilon value for spectral normalization. Defaults to 1e-6.
sn_style (str, optional) – The style of spectral normalization. If set to ajbrock, implementation by ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py) will be adopted. If set to torch, implementation by PyTorch will be adopted. Defaults to ajbrock.
with_spectral_norm (bool, optional) – Whether to use spectral normalization in this block. Defaults to True.
input_is_label (bool, optional) – Whether the input of BNs’ linear layer is raw label instead of class vector. Defaults to False.
auto_sync_bn (bool, optional) – Whether to use synchronized batch normalization. Defaults to True.

forward(x, y)[source]¶

Forward function.

Parameters

x (torch.Tensor) – Input feature map tensor.
y (torch.Tensor) – Label tensor or class embedding concatenated with noise tensor.

Returns

Output feature map tensor.

Return type

torch.Tensor

class mmagic.models.editors.biggan.biggan_modules.BigGANConditionBN(num_features, linear_input_channels, bn_eps=1e-05, sn_eps=1e-06, sn_style='ajbrock', momentum=0.1, input_is_label=False, with_spectral_norm=True, auto_sync_bn=True)[source]¶

Bases: torch.nn.Module

Conditional Batch Normalization used in BigGAN.

Parameters

num_features (int) – The channel number of the input feature map tensor.
linear_input_channels (int) – The channel number of the linear layers’ input tensor.
bn_eps (float, optional) – Epsilon value for batch normalization. Defaults to 1e-5.
sn_eps (float, optional) – Epsilon value for spectral normalization. Defaults to 1e-6.
sn_style (str, optional) – The style of spectral normalization. If set to ajbrock, implementation by ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py) will be adopted. If set to torch, implementation by PyTorch will be adopted. Defaults to ajbrock.
momentum (float, optional) – The value used for the running_mean and running_var computation. Defaults to 0.1.
input_is_label (bool, optional) – Whether the input of BNs’ linear layer is raw label instead of class vector. Defaults to False.
with_spectral_norm (bool, optional) – Whether to use spectral normalization. Defaults to True.
auto_sync_bn (bool, optional) – Whether to use synchronized batch normalization. Defaults to True.

forward(x, y)[source]¶

Forward function.

Parameters

x (torch.Tensor) – Input feature map tensor.
y (torch.Tensor) – Label tensor or class embedding concatenated with noise tensor.

Returns

Output feature map tensor.

Return type

torch.Tensor

class mmagic.models.editors.biggan.biggan_modules.SelfAttentionBlock(in_channels, with_spectral_norm=True, sn_eps=1e-06, sn_style='ajbrock')[source]¶

Bases: torch.nn.Module

Self-Attention block used in BigGAN.

Parameters

in_channels (int) – The channel number of the input feature map.
with_spectral_norm (bool, optional) – Whether to use spectral normalization. Defaults to True.
sn_eps (float, optional) – Epsilon value for spectral normalization. Defaults to 1e-6.
sn_style (str, optional) – The style of spectral normalization. If set to ajbrock, implementation by ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py) will be adopted. If set to torch, implementation by PyTorch will be adopted. Defaults to ajbrock.

forward(x)[source]¶

Forward function.

Parameters: x (torch.Tensor) – Input feature map tensor.
Returns: Output feature map tensor.
Return type: torch.Tensor

class mmagic.models.editors.biggan.biggan_modules.BigGANDiscResBlock(in_channels, out_channels, act_cfg=dict(type='ReLU', inplace=False), sn_eps=1e-06, sn_style='ajbrock', with_downsample=True, with_spectral_norm=True, is_head_block=False)[source]¶

Bases: torch.nn.Module

Residual block used in BigGAN’s discriminator.

Parameters

in_channels (int) – The channel number of the input tensor.
out_channels (int) – The channel number of the output tensor.
act_cfg (dict, optional) – Config for the activation layer. Defaults to dict(type=’ReLU’, inplace=False).
sn_eps (float, optional) – Epsilon value for spectral normalization. Defaults to 1e-6.
sn_style (str, optional) – The style of spectral normalization. If set to ajbrock, implementation by ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py) will be adopted. If set to torch, implementation by PyTorch will be adopted. Defaults to ajbrock.
with_downsample (bool, optional) – Whether to use downsampling in this block. Defaults to True.
with_spectral_norm (bool, optional) – Whether to use spectral normalization. Defaults to True.
is_head_block (bool, optional) – Whether this block is the first block of BigGAN. Defaults to False.

forward_sc(x)[source]¶

Forward function of shortcut.

Parameters: x (torch.Tensor) – Input feature map tensor.
Returns: Output tensor of shortcut.
Return type: torch.Tensor

forward(x)[source]¶

Forward function.

Parameters: x (torch.Tensor) – Input feature map tensor.
Returns: Output feature map tensor.
Return type: torch.Tensor

class mmagic.models.editors.biggan.biggan_modules.BigGANDeepGenResBlock(in_channels, out_channels, dim_after_concat, act_cfg=dict(type='ReLU'), upsample_cfg=dict(type='nearest', scale_factor=2), sn_eps=1e-06, sn_style='ajbrock', bn_eps=1e-05, with_spectral_norm=True, input_is_label=False, auto_sync_bn=True, channel_ratio=4)[source]¶

Bases: torch.nn.Module

Residual block used in BigGAN-Deep’s generator.

Parameters

in_channels (int) – The channel number of the input feature map.
out_channels (int) – The channel number of the output feature map.
dim_after_concat (int) – The channel number of the noise concatenated with the class vector.
act_cfg (dict, optional) – Config for the activation layer. Defaults to dict(type=’ReLU’).
upsample_cfg (dict, optional) – Config for the upsampling operation. Defaults to dict(type=’nearest’, scale_factor=2).
sn_eps (float, optional) – Epsilon value for spectral normalization. Defaults to 1e-6.
sn_style (str, optional) – The style of spectral normalization. If set to ajbrock, implementation by ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py) will be adopted. If set to torch, implementation by PyTorch will be adopted. Defaults to ajbrock.
bn_eps (float, optional) – Epsilon value for batch normalization. Defaults to 1e-5.
with_spectral_norm (bool, optional) – Whether to use spectral normalization in this block. Defaults to True.
input_is_label (bool, optional) – Whether the input of BNs’ linear layer is raw label instead of class vector. Defaults to False.
auto_sync_bn (bool, optional) – Whether to use synchronized batch normalization. Defaults to True.
channel_ratio (int, optional) – The ratio of the input channels’ number to the hidden channels’ number. Defaults to 4.

forward(x, y)[source]¶

Forward function.

Parameters

x (torch.Tensor) – Input feature map tensor.
y (torch.Tensor) – Label tensor or class embedding concatenated with noise tensor.

Returns

Output feature map tensor.

Return type

torch.Tensor

class mmagic.models.editors.biggan.biggan_modules.BigGANDeepDiscResBlock(in_channels, out_channels, channel_ratio=4, act_cfg=dict(type='ReLU', inplace=False), sn_eps=1e-06, sn_style='ajbrock', with_downsample=True, with_spectral_norm=True)[source]¶

Bases: torch.nn.Module

Residual block used in BigGAN-Deep’s discriminator.

Parameters

in_channels (int) – The channel number of the input tensor.
out_channels (int) – The channel number of the output tensor.
channel_ratio (int, optional) – The ratio of the input channels’ number to the hidden channels’ number. Defaults to 4.
act_cfg (dict, optional) – Config for the activation layer. Defaults to dict(type=’ReLU’, inplace=False).
sn_eps (float, optional) – Epsilon value for spectral normalization. Defaults to 1e-6.
sn_style (str, optional) – The style of spectral normalization. If set to ajbrock, implementation by ajbrock(https://github.com/ajbrock/BigGAN-PyTorch/blob/master/layers.py) will be adopted. If set to torch, implementation by PyTorch will be adopted. Defaults to ajbrock.
with_downsample (bool, optional) – Whether to use downsampling in this block. Defaults to True.
with_spectral_norm (bool, optional) – Whether to use spectral normalization. Defaults to True.

forward_sc(x)[source]¶

Forward function of shortcut.

Parameters: x (torch.Tensor) – Input feature map tensor.
Returns: Output tensor of shortcut.
Return type: torch.Tensor

forward(x)[source]¶

Forward function.

Parameters: x (torch.Tensor) – Input feature map tensor.
Returns: Output feature map tensor.
Return type: torch.Tensor

mmagic.models.editors.biggan.biggan_modules¶

Module Contents¶

Classes¶

`mmagic.models.editors.biggan.biggan_modules`¶