mmagic.models.editors.dcgan.dcgan_discriminator

Module Contents

Classes

DCGANDiscriminator

Discriminator for DCGAN.

class mmagic.models.editors.dcgan.dcgan_discriminator.DCGANDiscriminator(input_scale, output_scale, out_channels, in_channels=3, base_channels=128, default_norm_cfg=dict(type='BN'), default_act_cfg=dict(type='LeakyReLU'), out_act_cfg=None, init_cfg=None)

Bases: mmengine.model.BaseModule

Discriminator for DCGAN.

Implementation Details for DCGAN architecture:

1. Adopt convolution in the discriminator;

2. Use batchnorm in the discriminator except for the input and final output layer;

3. Use LeakyReLU in the discriminator in addition to the output layer.

Parameters

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

• output_scale (int) – The final scale of the convolutional feature.

• out_channels (int) – The channel number of the final output layer.

• in_channels (int, optional) – The channel number of the input image. Defaults to 3.

• base_channels (int, optional) – The basic channel number of the generator. The other layers contains channels based on this number. Defaults to 128.

• default_norm_cfg (dict, optional) – Norm config for all of layers except for the final output layer. Defaults to dict(type='BN').

• default_act_cfg (dict, optional) – Activation config for all of layers except for the final output layer. Defaults to dict(type='ReLU').

• out_act_cfg (dict, optional) – Activation config for the final output layer. Defaults to dict(type='Tanh').

• pretrained (str, optional) – Path for the pretrained model. Default to None.

• init_cfg (dict, optional) – Initialization config dict. Default: None.

forward(x)

Forward function.

Parameters

x (torch.Tensor) – Fake or real image tensor.

Returns

Prediction for the reality of the input image.

Return type

torch.Tensor

init_weights()

Init weights for models.

We just use the initialization method proposed in the original paper.