This is a more standard version of the position embedding, very similar to the one used by the Attention is all you need paper, generalized t

Definition of the DXtrem network.

Residual convolution module.

Residual convolution module.

A predictor that runs the model asynchronously, possibly on >1 GPUs. Because rendering the visualization takes considerably amount of time,

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

Feature fusion block.

This class computes an assignment between the targets and the predictions of the network For efficiency reasons, the targets don't include the no

Interpolation module.

This module is used in RetinaNet to generate extra layers, P6 and P7 from C5 feature.

This module is used in RetinaNet to generate extra layers, P6 and P7 from C5 feature.

Very simple multi-layer perceptron (also called FFN)

Very simple multi-layer perceptron (also called FFN)

Multilayer perceptron.

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

Roberta Head for masked language modeling.

This class computes the loss for DETR. The process happens in two steps: 1) we compute hungarian assignment between ground truth boxes and

Split-Attention Conv2d

Split-Attention Conv2d with dcn

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