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mmedit.models.editors.nafnet.nafbaseline_net

Module 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

BaselineBlock

Baseline's Block in paper.

class mmedit.models.editors.nafnet.nafbaseline_net.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)[source]

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)[source]

Forward function.

Parameters

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

check_image_size(x)[source]

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 mmedit.models.editors.nafnet.nafbaseline_net.NAFBaselineLocal(*args, train_size=(1, 3, 256, 256), fast_imp=False, **kwargs)[source]

Bases: mmedit.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 mmedit.models.editors.nafnet.nafbaseline_net.BaselineBlock(in_channels, DW_Expand=1, FFN_Expand=2, drop_out_rate=0.0)[source]

Bases: mmengine.model.BaseModule

Baseline’s Block in paper.

Parameters
  • in_channels (int) – number of channels

  • DW_Expand (int) – channel expansion factor for part 1

  • FFN_Expand (int) – channel expansion factor for part 2

  • drop_out_rate (float) – drop out ratio

forward(inp)[source]

Forward Function.

Parameters

inp – input tensor image

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