Stores assignments between predicted and truth boxes. Attributes: num_gts (int): the number of truth boxes considered when computing this

This class represents masks in the form of polygons. Polygons is a list of three levels. The first level of the list corresponds to objects,

This class represents masks in the form of bitmaps. Args: masks (ndarray): ndarray of masks in shape (N, H, W), where N is th

ResLayer to build ResNet style backbone for RPF in detectoRS. The difference between this module and base class is that we pass ``rfp_inplane

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

Gather-and-Expansion Layer. Args: in_channels (int): Number of input channels. out_channels (int): Number of output channels.

Standard points generator for multi-level (Mlvl) feature maps in 2D points-based detectors. Args: strides (list[int] | list[tuple[int

Image to Patch Embedding

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

This class is almost the same as official pycocotools package. It implements some snake case function aliases. So that the COCO class has the

Vision Transformer with support for patch or hybrid CNN input stage

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

A simple pipeline to load image.

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

Attention Refinement Module (ARM) to refine the features of each stage. Args: in_channels (int): The number of input channels. ou

Cross Stage Partial Layer. Args: in_channels (int): The input channels of the CSP layer. out_channels (int): The output channels

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

ModulatedDeformConv2d with normalization layer used in DyHead. This module cannot be configured with `conv_cfg=dict(type='DCNv2')` because Dy

Feature Adaption Module. Feature Adaption Module is implemented based on DCN v1. It uses anchor shape prediction rather than feature map to

Data structure for instance-level annnotations or predictions. Subclass of :class:`GeneralData`. All value in `data_fields` should have the s

Non-bottleneck block of ERFNet. Args: channels (int): Number of channels in Non-bottleneck block. drop_rate (float): Probability

Wrapper for detector's inference with ONNXRuntime.

r""" Image to Patch Embedding Args: img_size (int): Image size. Default: 224. patch_size (int): Patch token size. Default: 4.

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

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

Bbox sampling result. Example: >>> # xdoctest: +IGNORE_WANT >>> from mmdet.core.bbox.samplers.sampling_result import * # NOQA

Applies padding to input (if needed) so that input can get fully covered by filter you specified. It support two modes "same" and "corner". The

Applies padding to input (if needed) so that input can get fully covered by filter you specified. It support two modes "same" and "corner". The

Bidirectional Corner Pooling Module (TopLeft, BottomRight, etc.) Args: in_channels (int): Input channels of module. out_channels

Cascade Feature Fusion Unit in ICNet. Args: low_channels (int): The number of input channels for low resolution feature map.

A wrapper of concatenated dataset. Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but support evaluation and formatting results

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

Detection block in YOLO neck. Let out_channels = n, the DetectionBlock contains: Six ConvLayers, 1 Conv2D Layer and 1 YoloLayer. The firs

Downsampler block of ERFNet. This module is a little different from basical ConvModule. The features from Conv and MaxPool layers are con

Hourglass Module for HourglassNet backbone. Generate module recursively and use BasicBlock as the base unit. Args: depth (int): Dept

Downsampling module for CGNet.

Load annotations for semantic segmentation. Args: reduce_zero_label (bool): Whether reduce all label value by 1. Usually used

ResidualConvUnit, pre-activate residual unit. Args: in_channels (int): number of channels in the input feature map. act_cfg (dict

Shuffle the list and do it again after the list have traversed. The implementation logic is referred to https://github.com/wutong16/Distribut

Squeeze-and-Excitation Module. Args: channels (int): The input (and output) channels of the SE layer. ratio (int): Squeeze ratio

Self-Attention Module. Args: in_channels (int): Input channels of key/query feature. channels (int): Output channels of key/query

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

r"""Samples elements randomly from a given list of indices, without replacement. Arguments: indices (sequence): a sequence of indices

Task decomposition module in task-aligned predictor of TOOD. Args: feat_channels (int): Number of feature channels in TOOD head.

Wrapper for detector's inference with TensorRT.

Implements one encoder layer in Vision Transformer. Args: embed_dims (int): The feature dimension. num_heads (int): Parallel atte

Upsampler block of ERFNet. Args: in_channels (int): Number of input channels. out_channels (int): Number of output channels.

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

ASPP (Atrous Spatial Pyramid Pooling) This is an implementation of the ASPP module used in DetectoRS (https://arxiv.org/pdf/2006.02334.pdf)

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

An implementation of the absolute position embedding in PVT. Args: pos_shape (int): The shape of the absolute position embedding.

AdaptiveConv used to adapt the sampling location with the anchors. Args: in_channels (int): Number of channels in the input image

r""" Window based multi-head self attention (W-MSA) module with relative position bias. It supports both of shifted and non-shifted window. A

r""" Window based multi-head self attention (W-MSA) module with relative position bias. It supports both of shifted and non-shifted window. A

r""" Window based multi-head self attention (W-MSA) module with relative position bias. It supports both of shifted and non-shifted window. A

r""" Swin Transformer A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - https://arxiv

Try to convert inputs to FP16 and CPU if got a PyTorch's CUDA Out of Memory error. It will do the following steps: 1. First retry after c

Window based multi-head self-attention (W-MSA) module with relative position bias. Args: embed_dims (int): Number of input channels.

Implements one encoder layer in Vision Transformer. Args: embed_dims (int): The feature dimension. num_heads (int): Parallel atte

Bilateral Guided Aggregation Layer to fuse the complementary information from both Detail Branch and Semantic Branch. Args: out_chann

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

A basic Swin Transformer layer for one stage. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input re

A basic Swin Transformer layer for one stage. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input re

A basic Swin Transformer layer for one stage. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input re

A basic Swin Transformer layer for one stage. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input re

Basic residual block. This block is a little different from the block in the ResNet backbone. The kernel size of conv1 is 1 in this block whi

Bottleneck block for DilatedEncoder used in `YOLOF. <https://arxiv.org/abs/2103.09460>`. The Bottleneck contains three ConvLayers and one re

Channel Attention Module (CAM)

Context Embedding Block for large receptive filed in Semantic Branch. Args: in_channels (int): Number of input channels. Defa

This wrapper is for loading the panoptic style annotation file. The format is shown in the CocoPanopticDataset class. Args: annotati

A generic data loader where the images are arranged in this way: :: root/dog/xxx.png root/dog/xxy.png root/dog/xxz.png

Center Weighting module to adjust the category-specific prior distributions. Args: force_topk (bool): When no point falls into gt_bbo

r"""Sampler that restricts data loading to the label of the dataset. A class-aware sampling strategy to effectively tackle the non-uniform cl

A wrapper of repeated dataset with repeat factor. Suitable for training on class imbalanced datasets like LVIS. Following the sampling strate

The Conditional Position Encoding (CPE) module. The CPE is the implementation of 'Conditional Positional Encodings for Vision Transformers <h

Context Path to provide sufficient receptive field. Args: backbone_cfg:(dict): Config of backbone of Context Path. co