# 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.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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 <https://arxiv.org/abs/2010.11929>"
"""
[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
```