mmagic.models.editors.nafnet

Package Contents

Classes

NAFBaseline	The original version of Baseline model in "Simple Baseline for Image
NAFBaselineLocal	The original version of Baseline model in "Simple Baseline for Image
NAFNet	NAFNet.
NAFNetLocal	The original version of NAFNetLocal in "Simple Baseline for Image

class mmagic.models.editors.nafnet.NAFBaseline(img_channel=3, mid_channels=16, middle_blk_num=1, enc_blk_nums=[1, 1, 1, 28], dec_blk_nums=[1, 1, 1, 1], dw_expand=1, ffn_expand=2)

Bases: mmengine.model.BaseModule

The original version of Baseline model in “Simple Baseline for Image Restoration”.

Parameters

• img_channels (int) – Channel number of inputs.

• mid_channels (int) – Channel number of intermediate features.

• middle_blk_num (int) – Number of middle blocks.

• enc_blk_nums (List of int) – Number of blocks for each encoder.

• dec_blk_nums (List of int) – Number of blocks for each decoder.

forward(inp)

Forward function.

Parameters

inp – input tensor image with (B, C, H, W) shape

check_image_size(x)

Check image size and pad images so that it has enough dimension do downsample.

Parameters

x – input tensor image with (B, C, H, W) shape.

class mmagic.models.editors.nafnet.NAFBaselineLocal(*args, train_size=(1, 3, 256, 256), fast_imp=False, **kwargs)

Bases: mmagic.models.editors.nafnet.naf_avgpool2d.Local_Base, NAFBaseline

The original version of Baseline model in “Simple Baseline for Image Restoration”.

Parameters

• img_channels (int) – Channel number of inputs.

• mid_channels (int) – Channel number of intermediate features.

• middle_blk_num (int) – Number of middle blocks.

• enc_blk_nums (List of int) – Number of blocks for each encoder.

• dec_blk_nums (L`ist of int) – Number of blocks for each decoder.

class mmagic.models.editors.nafnet.NAFNet(img_channels=3, mid_channels=16, middle_blk_num=1, enc_blk_nums=[], dec_blk_nums=[])

Bases: mmengine.model.BaseModule

NAFNet.

The original version of NAFNet in “Simple Baseline for Image Restoration”.

Parameters

• img_channels (int) – Channel number of inputs.

• mid_channels (int) – Channel number of intermediate features.

• middle_blk_num (int) – Number of middle blocks.

• enc_blk_nums (List of int) – Number of blocks for each encoder.

• dec_blk_nums (List of int) – Number of blocks for each decoder.

forward(inp)

Forward function.

Parameters

inp – input tensor image with (B, C, H, W) shape

check_image_size(x)

Check image size and pad images so that it has enough dimension do downsample.

Parameters

x – input tensor image with (B, C, H, W) shape.

class mmagic.models.editors.nafnet.NAFNetLocal(*args, train_size=(1, 3, 256, 256), fast_imp=False, **kwargs)

Bases: mmagic.models.editors.nafnet.naf_avgpool2d.Local_Base, NAFNet

The original version of NAFNetLocal in “Simple Baseline for Image Restoration”.

NAFNetLocal uses local average pooling modules than NAFNet.

Parameters

• img_channels (int) – Channel number of inputs.

• mid_channels (int) – Channel number of intermediate features.

• middle_blk_num (int) – Number of middle blocks.

• enc_blk_nums (List of int) – Number of blocks for each encoder.

• dec_blk_nums (List of int) – Number of blocks for each decoder.