Generates a gaussian mask of weights for tile contributions

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

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): Images wi

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

Adaptive instance normalization. Adjust the reference features to have the similar color and illuminations as those in the degradate features

USM sharpening. borrowed from real-ESRGAN Input image: I; Blurry image: B. 1. K = I + weight * (I - B) 2. Mask = 1 if abs(I - B) > thresho

generate an anisotropic Gaussian kernel Args: ksize : e.g., 15, kernel size theta : [0, pi], rotation angle range l1

generate an anisotropic Gaussian kernel Args: ksize : e.g., 15, kernel size theta : [0, pi], rotation angle range l1

Kai Zhang (github: https://github.com/cszn)

Create a 1D, 2D, or 3D average pooling module.

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

Generate a bivariate isotropic or anisotropic Gaussian kernel. In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored.

Generate a bivariate generalized Gaussian kernel. ``Paper: Parameter Estimation For Multivariate Generalized Gaussian Distributions`` In the

Generate a plateau-like anisotropic kernel. 1 / (1+x^(beta)) Reference: https://stats.stackexchange.com/questions/203629/is-there-a-plateau-

x: image, NxcxHxW k: kernel, Nx1xhxw

PSNR metric. im1, im2: h x w x , [0, 255], uint8

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

SSIM the same outputs as MATLAB's im1, im2: h x w x , [0, 255], uint8

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

Check whether the input is a mirror-extended sequence. If mirror-extended, the i-th (i=0, ..., t-1) frame is equal to the (t-1-i)-th frame.

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

jitter color: randomly jitter the RGB values, in numpy formats

jitter color: randomly jitter the brightness, contrast, saturation, and hue, in torch Tensor formats

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

Compute optical flow using SPyNet for feature alignment. Note that if the input is an mirror-extended sequence, 'flows_forward' is no

Conv block used in MSDilationBlock.

Performs data augmentation of the input image Input: image: a cv2 (OpenCV) image mode: int. Choice of transformation to apply

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

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). From: https://github.com/rwightman/pytorch-image-models/

Build pretrained Inception model for FID computation. The Inception model for FID computation uses a different set of weights and has a sligh

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

Get facial component (left_eye, right_eye, mouth) coordinates from a pre-loaded pth file

Get local weights for generating the artifact map of LDL. It is only called by the `get_refined_artifact_map` function. Args: residu

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

Calculate the artifact map of LDL (Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution. In CVPR 2

Get target label. Args: input (Tensor): Input tensor. target_is_real (bool): Whether the target is real or fake.

This matches the implementation in Denoising Diffusion Probabilistic Models: From Fairseq. Build sinusoidal embeddings. This matches

Calculate image gradient. Input: im: h x w x c numpy array

Calculate image gradient. Input: im: h x w x c numpy array