Computes and stores the average and current value

A basic CSwin Transformer layer for one stage. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input r

r""" CSwin Transformer Block. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resulotion.

Sets the learning rate of each parameter group to follow a linear warmup schedule between warmup_start_lr and base_lr followed by a cosine anneali

r""" Image to Patch Embedding Args: img_size (int): Image size. Default: 224. patch_size (int): Patch token size. Default: 4.

r""" Image to Patch Unembedding Args: img_size (int): Image size. Default: 224. patch_size (int): Patch token size. Default: 4.

Residual CSwin Transformer Block (RSTB). Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resoluti

r""" SwinIR A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer. Args: img_size (in

Upsample module. Args: scale (int): Scale factor. Supported scales: 2^n and 3. num_feat (int): Channel number of intermediate fea

r""" Window based multi-head self attention (W-MSA) module with relative position bias. Args: dim (int): Number of input channels.

Cosine annealing with restarts learning rate scheme. An example of config: periods = [10, 10, 10, 10] restart_weights = [1, 0.5, 0.5, 0.5

Cosine annealing with restarts learning rate scheme. An example of config: periods = [10, 10, 10, 10] restart_weights = [1, 0.5, 0.5, 0.

Args: optimizer (torch.nn.optimizer): Torch optimizer. milestones (list): Iterations that will decrease learning rate. g

MultiStep with restarts learning rate scheme. Args: optimizer (torch.nn.optimizer): Torch optimizer. milestones (list): Iteratio

A progress bar which can print the progress modified from https://github.com/hellock/cvbase/blob/master/cvbase/progress.py

UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) Used in lightweight SR to save parameter

Args: optimizer (torch.nn.optimizer): Torch optimizer. milestones (list): Iterations that will decrease learning rate. g