Source code for monai.networks.blocks.selfattention

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# Licensed under the Apache License, Version 2.0 (the "License");
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import torch
import torch.nn as nn

from monai.utils import optional_import

Rearrange, _ = optional_import("einops.layers.torch", name="Rearrange")

[docs]class SABlock(nn.Module): """ A self-attention block, based on: "Dosovitskiy et al., An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <>" """
[docs] def __init__(self, hidden_size: int, num_heads: int, dropout_rate: float = 0.0, qkv_bias: bool = False) -> None: """ Args: hidden_size: dimension of hidden layer. num_heads: number of attention heads. dropout_rate: faction of the input units to drop. qkv_bias: bias term for the qkv linear layer. """ super().__init__() if not (0 <= dropout_rate <= 1): raise ValueError("dropout_rate should be between 0 and 1.") if hidden_size % num_heads != 0: raise ValueError("hidden size should be divisible by num_heads.") self.num_heads = num_heads self.out_proj = nn.Linear(hidden_size, hidden_size) self.qkv = nn.Linear(hidden_size, hidden_size * 3, bias=qkv_bias) self.input_rearrange = Rearrange("b h (qkv l d) -> qkv b l h d", qkv=3, l=num_heads) self.out_rearrange = Rearrange("b h l d -> b l (h d)") self.drop_output = nn.Dropout(dropout_rate) self.drop_weights = nn.Dropout(dropout_rate) self.head_dim = hidden_size // num_heads self.scale = self.head_dim**-0.5
[docs] def forward(self, x): output = self.input_rearrange(self.qkv(x)) q, k, v = output[0], output[1], output[2] att_mat = (torch.einsum("blxd,blyd->blxy", q, k) * self.scale).softmax(dim=-1) att_mat = self.drop_weights(att_mat) x = torch.einsum("bhxy,bhyd->bhxd", att_mat, v) x = self.out_rearrange(x) x = self.out_proj(x) x = self.drop_output(x) return x