A conv block that bundles conv/norm/activation layers. This block simplifies the usage of convolution layers, which are commonly used with a

A registry to map strings to classes. Registered object could be built from registry. Example: >>> MODELS = Registry('models')

A residual block that can optionally change the number of channels. :param channels: the number of input channels. :param emb_channels: t

Depthwise separable convolution module. See https://arxiv.org/pdf/1704.04861.pdf for details. This module can replace a ConvModule with the

Normlize image by given mean and std.

An attention block that allows spatial positions to attend to each other. Originally ported from here, but adapted to the N-d case. https

Compose multiple transforms sequentially. Args: transforms (Sequence[dict | callable]): Sequence of transform object or confi

Feature fusion block.

Transformer block for image-like data. First, project the input (aka embedding) and reshape to b, t, d. Then apply standard transform

A sequential module that passes timestep embeddings to the children that support it as an extra input.

A downsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determini

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). Args: drop_prob (float): Drop rate for path

Feature fusion block.

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

InvertedResidual block for MobileNetV2. Args: in_channels (int): The input channels of the InvertedResidual block. out_channels (

ModuleList in openmmlab. Args: modules (iterable, optional): an iterable of modules to add. init_cfg (dict, optional): Initializa

Image to Patch Embedding

Interpolation module.

Class that manages loss scaling in mixed precision training which supports both dynamic or static mode. The implementation refers to http

Pooling Pyramid Module used in PSPNet. Args: pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid Module.

A progress bar which can print the progress.

ResLayer to build ResNet style backbone. Args: block (nn.Module): block used to build ResLayer. inplanes (int): inplanes of block

A learnable scale parameter. This layer scales the input by a learnable factor. It multiplies a learnable scale parameter of shape (1,) with

Sequential module in openmmlab. Args: init_cfg (dict, optional): Initialization config dict.

An upsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determinin

A wrapper of concatenated dataset. Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but concat the group flag for image aspect ratio.

Context Guided Block for CGNet. This class consists of four components: local feature extractor, surrounding feature extractor, joint feature

A container for any type of objects. Typically tensors will be stacked in the collate function and sliced along some dimension in the scatter

Downsampling module for CGNet.

Prepare sample for usage as network input.

A module which performs QKV attention and splits in a different order.

Residual convolution module.

Residual convolution module.

Resize sample to given size (width, height).

Make a ANN used SelfAttentionBlock. Args: low_in_channels (int): Input channels of lower level feature, which is the key feat

A flexible Timer class. :Example: >>> import time >>> import annotator.uniformer.mmcv as mmcv >>> with mmcv.Timer(): >>> # s

Adaptive Context Module used in APCNet. Args: pool_scale (int): Pooling scale used in Adaptive Context Module to extract regi

Asymmetric Fusion Non-local Block(AFNB) Args: low_in_channels (int): Input channels of lower level feature, which is the key

Asymmetric Pyramid Non-local Block (APNB) Args: in_channels (int): Input channels of key/query feature, which is the key feat

Atrous Spatial Pyramid Pooling (ASPP) Module. Args: dilations (tuple[int]): Dilation rate of each layer. in_channels (int): Input

Attention layer for Encoder block. Args: dim (int): Dimension for the input vector. num_heads (int): Number of parallel attention

Basic convolutional block for UNet. This module consists of several plain convolutional layers. Args: in_channels (int): Number of i

Implements encoder block with residual connection. Args: dim (int): The feature dimension. num_heads (int): Number of parallel at

Channel Attention Module (CAM)

A facility for config and config files. It supports common file formats as configs: python/json/yaml. The interface is the same as a dict obj

ContextBlock module in GCNet. See 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond' (https://arxiv.org/abs/1904.11492)

Criss-Cross Attention Module. .. note:: Before v1.3.13, we use a CUDA op. Since v1.3.13, we switch to a pure PyTorch and equivale

Dynamic Convolutional Module used in DMNet. Args: filter_size (int): The filter size of generated convolution kernel used in

Using Euclidean distances of points for FPS.

Atrous Spatial Pyramid Pooling (ASPP) Module with depthwise separable conv.

Disentangled Non-Local Blocks. Args: temperature (float): Temperature to adjust attention. Default: 0.05

Expectation Maximization Attention Module used in EMANet. Args: channels (int): Channels of the whole module. num_bases (int): Nu

Encoding Module used in EncNet. Args: in_channels (int): Input channels. num_codes (int): Number of code words. conv_cfg