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.

Initialize network weights. Args: module_list (list[nn.Module] | nn.Module): Modules to be initialized. scale (float): Scale init

Dequantize an array. Args: arr (ndarray): Input array. min_val (scalar): Minimum value to be clipped. max_val (scalar): M

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

Dynamically instantiate class. Args: modules (list[importlib modules]): List of modules from importlib files. cls_typ

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

Generate Gaussian kernel used in `duf_downsample`. Args: kernel_size (int): Kernel size. Default: 13. sigma (float): Sigma of the

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

We now only use wandb to sync tensorboard log.

Compute the average value for each mask area in the image and assign it to the entire mask area. Parameters: - image

Mod crop images, used during testing. Args: img (ndarray): Input image. scale (int): Scale factor. Returns: ndarray:

Calculate NIQE (Natural Image Quality Evaluator) metric. Ref: Making a "Completely Blind" Image Quality Analyzer. This implementation could p

Support OrderedDict for yaml. Returns: yaml Loader and Dumper.

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

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

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

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

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

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

Print the str and parameter number of a network. Args: net (nn.Module)

Process a list of source and target directories.

Process a directory and save generated masks to the target directory. Only process the images in the first level of the given directory (non-

Quantize an array of (-inf, inf) to [0, levels-1]. Args: arr (ndarray): Input array. min_val (scalar): Minimum value to be clippe

Quantize flow to [0, 255]. After this step, the size of flow will be much smaller, and can be dumped as jpeg images. Args: flow

Read image worker. Args: path (str): Image path. key (str): Image key. compress_level (int): Compress level when encoding

Read npz file and return the array.

Reduce loss as specified. Args: loss (Tensor): Elementwise loss tensor. reduction (str): Options are 'none', 'mean' and 'sum'.

Reload the optimizers and schedulers for resumed training. Args: resume_state (dict): Resume state.

Set random seeds.