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# 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
import torch.nn as nn
from monai.utils import optional_import
Rearrange, _ = optional_import("einops.layers.torch", name="Rearrange")
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 <https://arxiv.org/abs/2010.11929>"
"""
def __init__(
self,
hidden_size: 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.
num_heads (int): number of attention heads.
dropout_rate (float, optional): fraction of the input units to drop. Defaults to 0.0.
qkv_bias (bool, optional): 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.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
self.save_attn = save_attn
self.att_mat = torch.Tensor()
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)
if self.save_attn:
# no gradients and new tensor;
# https://pytorch.org/docs/stable/generated/torch.Tensor.detach.html
self.att_mat = att_mat.detach()
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
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