mmagic.models.archs.multi_layer_disc

Module Contents

Classes

MultiLayerDiscriminator

Multilayer Discriminator.

class mmagic.models.archs.multi_layer_disc.MultiLayerDiscriminator(in_channels: int, max_channels: int, num_convs: int = 5, fc_in_channels: Optional[int] = None, fc_out_channels: int = 1024, kernel_size: int = 5, conv_cfg: Optional[dict] = None, norm_cfg: Optional[dict] = None, act_cfg: Optional[dict] = dict(type='ReLU'), out_act_cfg: Optional[dict] = dict(type='ReLU'), with_input_norm: bool = True, with_out_convs: bool = False, with_spectral_norm: bool = False, **kwargs)

Bases: torch.nn.Module

Multilayer Discriminator.

This is a commonly used structure with stacked multiply convolution layers.

Parameters

• in_channels (int) – Input channel of the first input convolution.

• max_channels (int) – The maximum channel number in this structure.

• num_conv (int) – Number of stacked intermediate convs (including input conv but excluding output conv). Default to 5.

• fc_in_channels (int | None) – Input dimension of the fully connected layer. If fc_in_channels is None, the fully connected layer will be removed. Default to None.

• fc_out_channels (int) – Output dimension of the fully connected layer. Default to 1024.

• kernel_size (int) – Kernel size of the conv modules. Default to 5.

• conv_cfg (dict) – Config dict to build conv layer.

• norm_cfg (dict) – Config dict to build norm layer.

• act_cfg (dict) – Config dict for activation layer, “relu” by default.

• out_act_cfg (dict) – Config dict for output activation, “relu” by default.

• with_input_norm (bool) – Whether add normalization after the input conv. Default to True.

• with_out_convs (bool) – Whether add output convs to the discriminator. The output convs contain two convs. The first out conv has the same setting as the intermediate convs but a stride of 1 instead of 2. The second out conv is a conv similar to the first out conv but reduces the number of channels to 1 and has no activation layer. Default to False.

• with_spectral_norm (bool) – Whether use spectral norm after the conv layers. Default to False.

• kwargs (keyword arguments) –

forward(x: torch.Tensor) → torch.Tensor

Forward Function.

Parameters

x (torch.Tensor) – Input tensor with shape of (n, c, h, w).

Returns

Output tensor with shape of (n, c, h’, w’) or (n, c).

Return type

torch.Tensor

init_weights(pretrained: Optional[str] = None) → None

Init weights for models.

Parameters

pretrained (str, optional) – Path for pretrained weights. If given None, pretrained weights will not be loaded. Defaults to None.