Source code for monai.networks.blocks.transformerblock

# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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from __future__ import annotations

import torch.nn as nn

from monai.networks.blocks.mlp import MLPBlock
from monai.networks.blocks.selfattention import SABlock

[docs]class TransformerBlock(nn.Module): """ A transformer 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, mlp_dim: int, num_heads: int, dropout_rate: float = 0.0, qkv_bias: bool = False, save_attn: bool = False, ) -> None: """ Args: hidden_size (int): dimension of hidden layer. mlp_dim (int): dimension of feedforward layer. num_heads (int): number of attention heads. dropout_rate (float, optional): faction of the input units to drop. Defaults to 0.0. qkv_bias (bool, optional): apply bias term for the qkv linear layer. Defaults to False. save_attn (bool, optional): to make accessible the attention matrix. Defaults to False. """ 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.mlp = MLPBlock(hidden_size, mlp_dim, dropout_rate) self.norm1 = nn.LayerNorm(hidden_size) self.attn = SABlock(hidden_size, num_heads, dropout_rate, qkv_bias, save_attn) self.norm2 = nn.LayerNorm(hidden_size)
[docs] def forward(self, x): x = x + self.attn(self.norm1(x)) x = x + self.mlp(self.norm2(x)) return x