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

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

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

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

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

Read a sequence of images from a given folder path. Args: path (list[str] | str): List of image paths or image folder path. requi

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

Paired random crop. It crops lists of lq and gt images with corresponding locations. Args: img_gts (list[ndarray] | ndarray): GT ima

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.

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

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

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

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

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

Recover from quantized flow. Args: dx (ndarray): Quantized dx. dy (ndarray): Quantized dy. max_val (float): Maximum value

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

Generate paired paths from folders. Args: folders (list[str]): A list of folder path. The order of list should be [input_fold

Generate paired paths from lmdb files. Contents of lmdb. Taking the `lq.lmdb` for example, the file structure is: lq.lmdb ├── data.mdb

Generate paired paths from an meta information file. Each line in the meta information file contains the image names and image shape (usually

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

read image from lmdb with key (w/ and w/o fixed size) size: (C, H, W) tuple

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:

Downsamping with Gaussian kernel used in the DUF official code. Args: x (Tensor): Frames to be downsampled, with shape (b, t, c, h, w).

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

Generate an index list for reading `num_frames` frames from a sequence of images. Args: crt_idx (int): Current center index.

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

Get the position from a period list. It will return the index of the right-closest number in the period list. For example, the cumulative_per

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

Make dirs for experiments.

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

Parse option file. Args: opt_path (str): Option file path. is_train (str): Indicate whether in training or not. Default: True.

Generate paths from lmdb. Args: folder (str): Folder path. Returns: list[str]: Returned path list.

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

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

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

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

get image path list from image folder

get image path list from lmdb meta info

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.

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

bgr version of rgb2ycbcr only_y: only return Y channel Input: uint8, [0, 255] float, [0, 1]

Check resume states and pretrain_network paths. Args: opt (dict): Options. resume_iter (int): Resume iteration.

Compute features. Args: block (ndarray): 2D Image block. Returns: list: Features with length of 18.