Cross Attention

class vformer.attention.cross.CrossAttention(query_dim, context_dim, num_heads=8, head_dim=64)

Bases: Module

This variant of Cross Attention is iteratively used in Perciever IO.

In Cross-Attention, cls token from one branch and patch token from another branch are fused together.

Parameters

• query_dim (int) – Dimension of query array

• context_dim (int) – Dimension of context array

• num_heads (int) – Number of cross-attention heads

• head_dim (int) – Dimension of each head

forward(x, context, mask=None)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class vformer.attention.cross.CrossAttentionWithClsToken(cls_dim, patch_dim, num_heads=8, head_dim=64)

Bases: Module

Cross-Attention with Cls Token introduced in Paper: CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification

In Cross-Attention, cls token from one branch and patch token from another branch are fused together.

Parameters

• cls_dim (int) – Dimension of cls token embedding

• patch_dim (int) – Dimension of patch token embeddings cls token to be fused with

• num_heads (int) – Number of cross-attention heads

• head_dim (int) – Dimension of each head

forward(cls, patches)

Parameters

• x (torch.Tensor) – Input tensor

• cls (torch.Tensor) – CLS token from one branch

• patch (torch.Tensor) – patch tokens from another branch

Returns

Returns output tensor by applying cross attention on input tensor

Return type

torch.Tensor

training: bool