mmagic.models.editors.dic.light_cnn

Module Contents

Classes

LightCNN	LightCNN discriminator with input size 128 x 128.
MaxFeature	Conv2d or Linear layer with max feature selector.

class mmagic.models.editors.dic.light_cnn.LightCNN(in_channels)

Bases: mmengine.model.BaseModule

LightCNN discriminator with input size 128 x 128.

It is used to train DICGAN.

Parameters

in_channels (int) – Channel number of inputs.

forward(x)

Forward function.

Parameters

x (Tensor) – Input tensor.

Returns

Forward results.

Return type

Tensor

init_weights(pretrained=None, strict=True)

Init weights for models.

Parameters

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

• strict (boo, optional) – Whether strictly load the pretrained model. Defaults to True.

class mmagic.models.editors.dic.light_cnn.MaxFeature(in_channels, out_channels, kernel_size=3, stride=1, padding=1, filter_type='conv2d')

Bases: torch.nn.Module

Conv2d or Linear layer with max feature selector.

Generate feature maps with double channels, split them and select the max

feature.

Parameters

• in_channels (int) – Channel number of inputs.

• out_channels (int) – Channel number of outputs.

• kernel_size (int or tuple) – Size of the convolving kernel.

• stride (int or tuple, optional) – Stride of the convolution. Default: 1

• padding (int or tuple, optional) – Zero-padding added to both sides of the input. Default: 1

• filter_type (str) – Type of filter. Options are ‘conv2d’ and ‘linear’. Default: ‘conv2d’.

forward(x)

Forward function.

Parameters

x (Tensor) – Input tensor.

Returns

Forward results.

Return type

Tensor