basicsr.archs.rcan_arch

class basicsr.archs.rcan_arch.ChannelAttention(num_feat, squeeze_factor=16)

Bases: Module

Channel attention used in RCAN.

Parameters:

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

• squeeze_factor (int) – Channel squeeze factor. Default: 16.

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class basicsr.archs.rcan_arch.RCAB(num_feat, squeeze_factor=16, res_scale=1)

Bases: Module

Residual Channel Attention Block (RCAB) used in RCAN.

Parameters:

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

• squeeze_factor (int) – Channel squeeze factor. Default: 16.

• res_scale (float) – Scale the residual. Default: 1.

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class basicsr.archs.rcan_arch.RCAN(num_in_ch, num_out_ch, num_feat=64, num_group=10, num_block=16, squeeze_factor=16, upscale=4, res_scale=1, img_range=255.0, rgb_mean=(0.4488, 0.4371, 0.404))

Bases: Module

Residual Channel Attention Networks.

Paper: Image Super-Resolution Using Very Deep Residual Channel Attention Networks

Reference: https://github.com/yulunzhang/RCAN

Parameters:

• num_in_ch (int) – Channel number of inputs.

• num_out_ch (int) – Channel number of outputs.

• num_feat (int) – Channel number of intermediate features. Default: 64.

• num_group (int) – Number of ResidualGroup. Default: 10.

• num_block (int) – Number of RCAB in ResidualGroup. Default: 16.

• squeeze_factor (int) – Channel squeeze factor. Default: 16.

• upscale (int) – Upsampling factor. Support 2^n and 3. Default: 4.

• res_scale (float) – Used to scale the residual in residual block. Default: 1.

• img_range (float) – Image range. Default: 255.

• rgb_mean (tuple[float]) – Image mean in RGB orders. Default: (0.4488, 0.4371, 0.4040), calculated from DIV2K dataset.

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class basicsr.archs.rcan_arch.ResidualGroup(num_feat, num_block, squeeze_factor=16, res_scale=1)

Bases: Module

Residual Group of RCAB.

Parameters:

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

• num_block (int) – Block number in the body network.

• squeeze_factor (int) – Channel squeeze factor. Default: 16.

• res_scale (float) – Scale the residual. Default: 1.

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool