`mmagic.models.editors.gca.resgca_dec`¶

Module Contents¶

Classes¶

`BasicBlockDec`	Basic residual block for decoder.
`ResNetDec`	ResNet decoder for image matting.
`ResShortcutDec`	ResNet decoder for image matting with shortcut connection.
`ResGCADecoder`	ResNet decoder with shortcut connection and gca module.

class mmagic.models.editors.gca.resgca_dec.BasicBlockDec(in_channels, out_channels, kernel_size=3, stride=1, interpolation=None, conv_cfg=None, norm_cfg=dict(type='BN'), act_cfg=dict(type='ReLU'), with_spectral_norm=False)[source]¶

Bases: mmagic.models.editors.gca.resgca_enc.BasicBlock

Basic residual block for decoder.

For decoder, we use ConvTranspose2d with kernel_size 4 and padding 1 for conv1. And the output channel of conv1 is modified from out_channels to in_channels.

build_conv1(in_channels, out_channels, kernel_size, stride, conv_cfg, norm_cfg, act_cfg, with_spectral_norm)[source]¶

Build conv1 of the block.

Parameters

in_channels (int) – The input channels of the ConvModule.
out_channels (int) – The output channels of the ConvModule.
kernel_size (int) – The kernel size of the ConvModule.
stride (int) – The stride of the ConvModule. If stride is set to 2, then conv_cfg will be overwritten as dict(type='Deconv') and kernel_size will be overwritten as 4.
conv_cfg (dict) – The conv config of the ConvModule.
norm_cfg (dict) – The norm config of the ConvModule.
act_cfg (dict) – The activation config of the ConvModule.
with_spectral_norm (bool) – Whether use spectral norm.

Returns

The built ConvModule.

Return type

nn.Module

build_conv2(in_channels, out_channels, kernel_size, conv_cfg, norm_cfg, with_spectral_norm)[source]¶

Build conv2 of the block.

Parameters

in_channels (int) – The input channels of the ConvModule.
out_channels (int) – The output channels of the ConvModule.
kernel_size (int) – The kernel size of the ConvModule.
conv_cfg (dict) – The conv config of the ConvModule.
norm_cfg (dict) – The norm config of the ConvModule.
with_spectral_norm (bool) – Whether use spectral norm.

Returns

The built ConvModule.

Return type

nn.Module

class mmagic.models.editors.gca.resgca_dec.ResNetDec(block, layers, in_channels, kernel_size=3, conv_cfg=None, norm_cfg=dict(type='BN'), act_cfg=dict(type='LeakyReLU', negative_slope=0.2, inplace=True), with_spectral_norm=False, late_downsample=False, init_cfg: Optional[dict] = None)[source]¶

Bases: mmengine.model.BaseModule

ResNet decoder for image matting.

This class is adopted from https://github.com/Yaoyi-Li/GCA-Matting.

Parameters

block (str) – Type of residual block. Currently only BasicBlockDec is implemented.
layers (list[int]) – Number of layers in each block.
in_channels (int) – Channel num of input features.
kernel_size (int) – Kernel size of the conv layers in the decoder.
conv_cfg (dict) – dictionary to construct convolution layer. If it is None, 2d convolution will be applied. Default: None.
norm_cfg (dict) – Config dict for normalization layer. “BN” by default.
act_cfg (dict) – Config dict for activation layer, “ReLU” by default.
with_spectral_norm (bool) – Whether use spectral norm after conv. Default: False.
late_downsample (bool) – Whether to adopt late downsample strategy, Default: False.
init_cfg (dict, optional) – Initialization config dict. Default: None.

init_weights()[source]¶: Init weights for the module.

_make_layer(block, planes, num_blocks, conv_cfg, norm_cfg, act_cfg, with_spectral_norm)[source]¶

forward(x)[source]¶

Forward function.

Parameters: x (Tensor) – Input tensor with shape (N, C, H, W).
Returns: Output tensor.
Return type: Tensor

class mmagic.models.editors.gca.resgca_dec.ResShortcutDec(block, layers, in_channels, kernel_size=3, conv_cfg=None, norm_cfg=dict(type='BN'), act_cfg=dict(type='LeakyReLU', negative_slope=0.2, inplace=True), with_spectral_norm=False, late_downsample=False, init_cfg: Optional[dict] = None)[source]¶

Bases: ResNetDec

ResNet decoder for image matting with shortcut connection.

feat1 --------------------------- conv2 --- out
                               |
feat2 ---------------------- conv1
                          |
feat3 ----------------- layer4
                     |
feat4 ------------ layer3
                |
feat5 ------- layer2
           |
out ---  layer1

Parameters

block (str) – Type of residual block. Currently only BasicBlockDec is implemented.
layers (list[int]) – Number of layers in each block.
in_channels (int) – Channel number of input features.
kernel_size (int) – Kernel size of the conv layers in the decoder.
conv_cfg (dict) – Dictionary to construct convolution layer. If it is None, 2d convolution will be applied. Default: None.
norm_cfg (dict) – Config dict for normalization layer. “BN” by default.
act_cfg (dict) – Config dict for activation layer, “ReLU” by default.
late_downsample (bool) – Whether to adopt late downsample strategy, Default: False.

forward(inputs)[source]¶

Forward function of resnet shortcut decoder.

Parameters

inputs (dict) –

Output dictionary of the ResNetEnc containing:

out (Tensor): Output of the ResNetEnc.
feat1 (Tensor): Shortcut connection from input image.
feat2 (Tensor): Shortcut connection from conv2 of ResNetEnc.
feat3 (Tensor): Shortcut connection from layer1 of ResNetEnc.
feat4 (Tensor): Shortcut connection from layer2 of ResNetEnc.
feat5 (Tensor): Shortcut connection from layer3 of ResNetEnc.

Returns

Output tensor.

Return type

Tensor

class mmagic.models.editors.gca.resgca_dec.ResGCADecoder(block, layers, in_channels, kernel_size=3, conv_cfg=None, norm_cfg=dict(type='BN'), act_cfg=dict(type='LeakyReLU', negative_slope=0.2, inplace=True), with_spectral_norm=False, late_downsample=False)[source]¶

Bases: ResShortcutDec

ResNet decoder with shortcut connection and gca module.

feat1 ---------------------------------------- conv2 --- out
                                            |
feat2 ----------------------------------- conv1
                                       |
feat3 ------------------------------ layer4
                                  |
feat4, img_feat -- gca_module - layer3
                |
feat5 ------- layer2
           |
out ---  layer1

gca module also requires unknown tensor generated by trimap which is ignored in the above graph.

Parameters

block (str) – Type of residual block. Currently only BasicBlockDec is implemented.
layers (list[int]) – Number of layers in each block.
in_channels (int) – Channel number of input features.
kernel_size (int) – Kernel size of the conv layers in the decoder.
conv_cfg (dict) – Dictionary to construct convolution layer. If it is None, 2d convolution will be applied. Default: None.
norm_cfg (dict) – Config dict for normalization layer. “BN” by default.
act_cfg (dict) – Config dict for activation layer, “ReLU” by default.
with_spectral_norm (bool) – Whether use spectral norm. Default: False.
late_downsample (bool) – Whether to adopt late downsample strategy, Default: False.

forward(inputs)[source]¶

Forward function of resnet shortcut decoder.

Parameters

inputs (dict) –

Output dictionary of the ResGCAEncoder containing:

out (Tensor): Output of the ResGCAEncoder.
feat1 (Tensor): Shortcut connection from input image.
feat2 (Tensor): Shortcut connection from conv2 of ResGCAEncoder.
feat3 (Tensor): Shortcut connection from layer1 of ResGCAEncoder.
feat4 (Tensor): Shortcut connection from layer2 of ResGCAEncoder.
feat5 (Tensor): Shortcut connection from layer3 of ResGCAEncoder.
img_feat (Tensor): Image feature extracted by guidance head.
unknown (Tensor): Unknown tensor generated by trimap.

Returns

Output tensor.

Return type

Tensor

mmagic.models.editors.gca.resgca_dec¶

Module Contents¶

Classes¶

`mmagic.models.editors.gca.resgca_dec`¶