basicsr.archs.rcan_arch
- class basicsr.archs.rcan_arch.ChannelAttention(num_feat, squeeze_factor=16)[source]
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)[source]
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)[source]
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)[source]
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))[source]
Bases:
Module
Residual Channel Attention Networks.
- Paper: Image Super-Resolution Using Very Deep Residual Channel Attention
Networks
Ref git repo: 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)[source]
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)[source]
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)[source]
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