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

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

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

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

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

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

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 p

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 p

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,

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,

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

Convert RGB images to YCbCr images (PyTorch version). It implements the ITU-R BT.601 conversion for standard-definition television. See more de

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

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

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

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

Build dataset from options. Args: dataset_opt (dict): Configuration for dataset. It must contain: name (str): Dataset nam

Calculate PSNR (Peak Signal-to-Noise Ratio) (PyTorch version). Reference: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio Args:

Calculate SSIM (structural similarity) (PyTorch version). ``Paper: Image quality assessment: From error visibility to structural similarity``

Generate the mesh grid, centering at zero. Args: kernel_size (int): Returns: xy (ndarray): with the shape (kernel_size

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

Calculate the rotated sigma matrix (two dimensional matrix). Args: sig_x (float): sig_y (float): theta (float): Radi

r"""Fills the input Tensor with values drawn from a truncated normal distribution. From: https://github.com/rwightman/pytorch-image-models

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

Build loss from options. Args: opt (dict): Configuration. It must contain: type (str): Model type.

Build model from options. Args: opt (dict): Configuration. It must contain: model_type (str): Model type.

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

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. Retu

PyTorch version of cv2.filter2D Args: img (Tensor): (b, c, h, w) kernel (Tensor): (b, k, k)

Args: pred (Tensor): of shape (N, C, H, W). Predicted tensor. target (Tensor): of shape (N, C, H, W). Ground truth

Generate Gaussian noise. Args: img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. sigma (float): Noise s

Add Gaussian noise (PyTorch version). Args: img (Tensor): Shape (b, c, h, w), range[0, 1], float32. scale (float | Tensor): N

Generate poisson noise. Reference: https://github.com/scikit-image/scikit-image/blob/main/skimage/util/noise.py#L37-L219 Args:

Generate a batch of poisson noise (PyTorch version) Args: img (Tensor): Input image, shape (b, c, h, w), range [0, 1], float32.

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

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

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

Make dirs for experiments.

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

Calculate factor corresponding to quality Args: quality(float): Quality for jpeg compression. Returns: float: Compres

Randomly generate bivariate isotropic or anisotropic Gaussian kernels. In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and

Randomly generate bivariate generalized Gaussian kernels. In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_ra

Randomly generate bivariate plateau kernels. In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_range` is ignor

Save networks and training state.

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

Args: x: (b, h, w, c) window_size (int): window size Returns: windows: (num_windows*b, window_size, window_size, c)

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

Initialize the best metric results dict for recording the best metric value and iteration.

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

Set learning rate for warm-up. 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: img (ndarray): Imag

Calculate SSIM (structural similarity) (PyTorch version). It is called by func:`calculate_ssim_pt`. Args: img (Tensor): Images

Add JPG compression artifacts. Args: img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. quality (float):

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