Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

A general file client to access files in different backend. The client loads a file or text in a specified backend from its path and return i

Image to Patch Embedding

A single linear layer which does a 1x1 conv

A single linear layer which does a 1x1 conv

Use preactivation version of residual block, the same as taming

r"""This function calculate exact padding values for 4D tensor inputs, and support the same padding mode as tensorflow. Args: kernel

Subclass torch's LayerNorm to handle fp16.

Image to Patch Embedding

The registry that provides name -> object mapping, to support third-party users' custom modules. To create a registry (e.g. a backbone r

The registry that provides name -> object mapping, to support third-party users' custom modules. To create a registry (e.g. a backbone r

The registry that provides name -> object mapping, to support third-party users' custom modules. To create a registry (e.g. a backbone r

Reference: https://github.com/joe-siyuan-qiao/WeightStandardization

Convolutional Position Encoding. Note: This module is similar to the conditional position encoding in CPVT.

Convolutional relative position encoding.

MSE (L2) loss. Args: loss_weight (float): Loss weight for MSE loss. Default: 1.0. reduction (str): Specifies the reduction to app

InceptionC block patched for FID computation

Factorized attention with convolutional relative position encoding class.

r""" LayerNorm that supports two data formats: channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_

Serial block class. Note: In this implementation, each serial block only contains a conv-attention and a FFN (MLP) module.

This JPEG algorithm result is slightly different from cv2. DiffJPEG supports batch processing. Args: differentiable(bool): If True, u

InceptionA block patched for FID computation

Reduce feature map size by factorized pointwise(stride=2).

A general file client to access files in different backend. The client loads a file or text in a specified backend from its path and return i

Pretrained InceptionV3 network returning feature maps

NAFNet Block. This block is the main building component of NAFNet. It consists of a main branch with LayerNorm, Depth-wise convolution, S

Pair version of center crop

activation layer. ------------ # Arguments - relu type: type of relu layer, candidates are - ReLU - LeakyReLU:

Reduce feature map size by factorized pointwise(stride=2).

Layer Normalization for 2D data (e.g., images). Applies Layer Normalization over a mini-batch of 2D inputs. The mean and standard-deviat