Image to Patch Embedding

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

r""" Stem layer of InternImage Args: in_channels (int): number of input channels out_channels (int): number of output channels

Computes and stores the average and current value

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

Auxiliary classification loss for each refined class output.

Multilayer perceptron.

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

Pixel loss for each refined map output.

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

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

Image to Patch Embedding

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