Scan a directory to find the interested files. Args: dir_path (str): Path of the directory. suffix (str | tuple(str), optional):

Make lmdb from images. Contents of lmdb. The file structure is: example.lmdb ├── data.mdb ├── lock.mdb ├── meta_info.txt The

Get the root logger. The logger will be initialized if it has not been initialized. By default a StreamHandler will be added. If `log_file` i

Prepare image path list and keys for DIV2K dataset. Args: folder_path (str): Folder path. Returns: list[str]: Image path lis

Write image to file. Args: img (ndarray): Image array to be written. file_path (str): Image file path. params (None or li

Read an image from bytes. Args: content (bytes): Image bytes got from files or other streams. flag (str): Flags specifying the co

Reorder images to 'HWC' order. If the input_order is (h, w), return (h, w, 1); If the input_order is (c, h, w), return (h, w, c); If the

Save networks and training state.

Convert torch Tensors into image numpy arrays. After clamping to [min, max], values will be normalized to [0, 1]. Args: tensor (Tens

Change to Y channel of YCbCr. Args: img (ndarray): Images with range [0, 255]. Returns: (ndarray): Images with range [0, 255

Convert the type and range of the input image. It converts the input image to np.float32 type and range of [0, 1]. It is mainly used for pre-

Convert the type and range of the image according to dst_type. It converts the image to desired type and range. If `dst_type` is np.uint8, im

Create model. Args: opt (dict): Configuration. It constains: model_type (str): Model type.

Get bare model, especially under wrapping with DistributedDataParallel or DataParallel.

Create dataloader. Args: dataset (torch.utils.data.Dataset): Dataset. dataset_opt (dict): Dataset options. It contains the follow

Create dataset. Args: dataset_opt (dict): Configuration for dataset. It constains: name (str): Dataset name. type

Numpy array to tensor. Args: imgs (list[ndarray] | ndarray): Input images. bgr2rgb (bool): Whether to change bgr to rgb.

Load network. Args: load_path (str): The path of networks to be loaded. net (nn.Module): Network. strict

mkdirs. If path exists, rename it with timestamp and create a new one. Args: path (str): Folder path.

Save networks. Args: net (nn.Module | list[nn.Module]): Network(s) to be saved. net_label (str): Network label.

Validation function. Args: dataloader (torch.utils.data.DataLoader): Validation dataloader. current_iter (int): Curre

Calculate weights and indices, used for imresize function. Args: in_length (int): Input length. out_length (int): Output length.

cubic function used for calculate_weights_indices.

dict to string for printing options. Args: opt (dict): Option dict. indent_level (int): Indent level. Default: 1. Return:

Estimate AGGD (Asymmetric Generalized Gaussian Distribution) paramters. Args: block (ndarray): 2D Image block. Returns: tupl

Crop images to subimages. Args: opt (dict): Configuration dict. It contains: input_folder (str): Path to the input folder.

Get environment information. Currently, only log the software version.

Make dirs for experiments.

Model to device. It also warps models with DistributedDataParallel or DataParallel. Args: net (nn.Module)

reduce loss dict. In distributed training, it averages the losses among different GPUs . Args: loss_dict (OrderedDict):

Save training states during training, which will be used for resuming. Args: epoch (int): Current epoch. curr

Set up schedulers.

Get human readable file size. Args: size (int): File size. suffix (str): Suffix. Default: 'B'. Return: str: Formated

Get the initial lr, which is set by the scheduler.

Initialize slurm distributed training environment. If argument ``port`` is not specified, then the master port will be system environment var

Print keys with differnet name or different size when loading models. 1. Print keys with differnet names. 2. If strict=False, print t

Set learning rate for warmup. Args: lr_groups_l (list): List for lr_groups, each for an optimizer.

Calculate SSIM (structural similarity) for one channel images. It is called by func:`calculate_ssim`. Args: img1 (ndarray): Images w

Augment: horizontal flips OR rotate (0, 90, 180, 270 degrees). We use vertical flip and transpose for rotation implementation. All the images

Convert a BGR image to YCbCr image. The bgr version of rgb2ycbcr. It implements the ITU-R BT.601 conversion for standard-definition telev

Calculate PSNR (Peak Signal-to-Noise Ratio). Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio Args: img1 (ndarray/tenso

Calculate SSIM (structural similarity). Ref: Image quality assessment: From error visibility to structural similarity The results are th