Depth network based on neural window FC-CRFs architecture.

Depth network based on neural window FC-CRFs architecture.

Sampler that restricts data loading to a subset of the dataset. It is especially useful in conjunction with :class:`torch.nn.parallel.Distrib

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

A basic NeWCRFs layer for one stage. Args: dim (int): Number of feature channels depth (int): Depths of this stage. num_

A basic NeWCRFs layer for one stage. Args: dim (int): Number of feature channels depth (int): Depths of this stage. num_

A basic Swin Transformer layer for one stage. Args: dim (int): Number of feature channels depth (int): Depths of this stage.

A basic Swin Transformer layer for one stage. Args: dim (int): Number of feature channels depth (int): Depths of this stage.

CRF Block. Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. window_size (int): Win

CRF Block. Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. window_size (int): Win

Layer to transform depth into point cloud

Sampler that restricts data loading to a subset of the dataset. It is especially useful in conjunction with :class:`torch.nn.parallel.Distrib

Multilayer perceptron.

Multilayer perceptron.

Multilayer perceptron.

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

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

Unified Perceptual Parsing for Scene Understanding. This head is the implementation of `UPerNet <https://arxiv.org/abs/1807.10221>`_. Ar

Unified Perceptual Parsing for Scene Understanding. This head is the implementation of `UPerNet <https://arxiv.org/abs/1807.10221>`_. Ar

Image to Patch Embedding Args: patch_size (int): Patch token size. Default: 4. in_chans (int): Number of input image channels. De

Image to Patch Embedding Args: patch_size (int): Patch token size. Default: 4. in_chans (int): Number of input image channels. D

Swin Transformer backbone. A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - https:/

Swin Transformer Block. Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. window_si

Swin Transformer Block. Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. window_siz

Swin Transformer Block. Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. window_siz

Swin Transformer backbone. A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - https:/

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

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

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

Base class for BaseDecodeHead. Args: in_channels (int|Sequence[int]): Input channels. channels (int): Channels after modules, bef

Base class for BaseDecodeHead. Args: in_channels (int|Sequence[int]): Input channels. channels (int): Channels after modules, bef

r""" Patch Merging Layer. Args: input_resolution (tuple[int]): Resolution of input feature. dim (int): Number of input channels.

Layer to transform depth into point cloud

Patch Merging Layer Args: dim (int): Number of input channels. norm_layer (nn.Module, optional): Normalization layer. Default: n

Patch Merging Layer Args: dim (int): Number of input channels. norm_layer (nn.Module, optional): Normalization layer. Default:

r""" Patch Reduction Layer. Args: input_resolution (tuple[int]): Resolution of input feature. dim (int): Number of input channels.