File size: 2,138 Bytes
6fc43ab |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
# 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 monai.networks.blocks.mlp import MLPBlock
from typing import Sequence, Union
import torch
import torch.nn as nn
from ..nn.selfattention import SABlock
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>"
"""
def __init__(
self, hidden_size: int, mlp_dim: int, num_heads: int, dropout_rate: float = 0.0, qkv_bias: bool = False
) -> None:
"""
Args:
hidden_size: dimension of hidden layer.
mlp_dim: dimension of feedforward layer.
num_heads: number of attention heads.
dropout_rate: faction of the input units to drop.
qkv_bias: apply 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.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)
self.norm2 = nn.LayerNorm(hidden_size)
def forward(self, x, return_attention=False):
y, attn = self.attn(self.norm1(x))
if return_attention:
return attn
x = x + y
x = x + self.mlp(self.norm2(x))
return x
|