mmagic.models.editors.edsr.edsr_net

Module Contents

Classes

EDSRNet	EDSR network structure.
UpsampleModule	Upsample module used in EDSR.

class mmagic.models.editors.edsr.edsr_net.EDSRNet(in_channels, out_channels, mid_channels=64, num_blocks=16, upscale_factor=4, res_scale=1, rgb_mean=[0.4488, 0.4371, 0.404], rgb_std=[1.0, 1.0, 1.0])

Bases: mmengine.model.BaseModule

EDSR network structure.

Paper: Enhanced Deep Residual Networks for Single Image Super-Resolution. Ref repo: https://github.com/thstkdgus35/EDSR-PyTorch

Parameters

• in_channels (int) – Channel number of inputs.

• out_channels (int) – Channel number of outputs.

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

• num_blocks (int) – Block number in the trunk network. Default: 16.

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

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

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

• rgb_std (list[float]) – Image std in RGB orders. In EDSR, it uses [1.0, 1.0, 1.0]. Default: [1.0, 1.0, 1.0].

forward(x)

Forward function.

Parameters

x (Tensor) – Input tensor with shape (n, c, h, w).

Returns

Forward results.

Return type

Tensor

class mmagic.models.editors.edsr.edsr_net.UpsampleModule(scale, mid_channels)

Bases: torch.nn.Sequential

Upsample module used in EDSR.

Parameters

• scale (int) – Scale factor. Supported scales: 2^n and 3.

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