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Multimodal conditional mechanism Cross Attention principle:
Self Attention
class SelfAttention(nn.Module):
def __init__(self, emb_dim):
super(SelfAttention, self).__init__()
self.emb_dim = emb_dim
self.Wq = nn.Linear(emb_dim, emb_dim, bias=False)
self.Wk = nn.Linear(emb_dim, emb_dim, bias=False)
self.Wv = nn.Linear(emb_dim, emb_dim, bias=False)
self.fc = nn.Linear(emb_dim, emb_dim)
def forward(self, x, pad_mask=None):
# [batch_szie, seq_len, emb_dim] = [3, 5, 512]
Q = self.Wq(x)
K = self.Wk(x)
V = self.Wv(x)
att_weights = torch.bmm(Q, K.transpose(1, 2)) # [batch_szie, seq_len, seq_len] = [3, 5, 5]
att_weights = att_weights / math.sqrt(self.emb_dim)
if pad_mask is not None:
att_weights = att_weights.masked_fill(pad_mask, -1e9)
att_weights = F.softmax(att_weights, dim=-1)
output = torch.bmm(att_weights, V) # [batch_szie, seq_len, emb_dim] = [3, 5, 512]
output = self.fc(output)
return output, att_weights
Multi-Head Attention
class MultiHeadAttention(nn.Module):
def __init__(self, emb_dim, num_heads, att_dropout=0.0):
super(MultiHeadAttention, self).__init__()
self.emb_dim = emb_dim
self.num_heads = num_heads
self.att_dropout = att_dropout
assert emb_dim % num_heads == 0, "emb_dim must be divisible by num_heads"
self.depth = emb_dim // num_heads
self.Wq = nn.Linear(emb_dim, emb_dim, bias=False)
self.Wk = nn.Linear(emb_dim, emb_dim, bias=False)
self.Wv = nn.Linear(emb_dim, emb_dim, bias=False)
self.fc = nn.Linear(emb_dim, emb_dim)
def forward(self, x, pad_mask=None):
# [batch_szie, seq_len, emb_dim] = [3, 5, 512]
batch_size = x.size(0)
# [batch_szie, seq_len, emb_dim] = [3, 5, 512]
Q = self.Wq(x)
K = self.Wk(x)
V = self.Wv(x)
# 分头 [batch_szie, num_heads, seq_len, depth] = [3, 8, 5, 512/8=64]
Q = Q.view(batch_size, -1, self.num_heads, self.depth).transpose(1, 2)
K = K.view(batch_size, -1, self.num_heads, self.depth).transpose(1, 2)
V = V.view(batch_size, -1, self.num_heads, self.depth).transpose(1, 2)
# [batch_szie, num_heads, seq_len, seq_len] = [3, 8, 5, 5]
att_weights = torch.matmul(Q, K.transpose(-2, -1))
att_weights = att_weights / math.sqrt(self.depth)
if pad_mask is not None:
# 因为是多头，所以mask矩阵维度要扩充到4维 [batch_size, seq_len, seq_len] -> [batch_size, nums_head, seq_len, seq_len]
pad_mask = pad_mask.unsqueeze(1).repeat(1, self.num_heads, 1, 1)
att_weights = att_weights.masked_fill(pad_mask, -1e9)
att_weights = F.softmax(att_weights, dim=-1)
# 自己的多头注意力效果没有torch的好，我猜是因为它的dropout给了att权重，而不是fc
if self.att_dropout > 0.0:
att_weights = F.dropout(att_weights, p=self.att_dropout)
# [batch_szie, num_heads, seq_len, depth] = [3, 8, 5, 64]
output = torch.matmul(att_weights, V)
# 不同头的结果拼接 [batch_szie, seq_len, emb_dim] = [3, 5, 512]
output = output.transpose(1, 2).contiguous().view(batch_size, -1, self.emb_dim)
output = self.fc(output)
return output, att_weights
Cross_MultiAttention
class Cross_MultiAttention(nn.Module):
def __init__(self, in_channels, emb_dim, num_heads, att_dropout=0.0, aropout=0.0):
super(Cross_MultiAttention, self).__init__()
self.emb_dim = emb_dim
self.num_heads = num_heads
self.scale = emb_dim ** -0.5
assert emb_dim % num_heads == 0, "emb_dim must be divisible by num_heads"
self.depth = emb_dim // num_heads
self.proj_in = nn.Conv2d(in_channels, emb_dim, kernel_size=1, stride=1, padding=0)
self.Wq = nn.Linear(emb_dim, emb_dim)
self.Wk = nn.Linear(emb_dim, emb_dim)
self.Wv = nn.Linear(emb_dim, emb_dim)
self.proj_out = nn.Conv2d(emb_dim, in_channels, kernel_size=1, stride=1, padding=0)
def forward(self, x, context, pad_mask=None):
'''
:param x: [batch_size, c, h, w]
:param context: [batch_szie, seq_len, emb_dim]
:param pad_mask: [batch_size, seq_len, seq_len]
:return:
'''
b, c, h, w = x.shape
x = self.proj_in(x) # [batch_size, c, h, w] = [3, 512, 512, 512]
x = rearrange(x, 'b c h w -> b (h w) c') # [batch_size, h*w, c] = [3, 262144, 512]
Q = self.Wq(x) # [batch_size, h*w, emb_dim] = [3, 262144, 512]
K = self.Wk(context) # [batch_szie, seq_len, emb_dim] = [3, 5, 512]
V = self.Wv(context)
Q = Q.view(batch_size, -1, self.num_heads, self.depth).transpose(1, 2) # [batch_size, num_heads, h*w, depth]
K = K.view(batch_size, -1, self.num_heads, self.depth).transpose(1, 2) # [batch_size, num_heads, seq_len, depth]
V = V.view(batch_size, -1, self.num_heads, self.depth).transpose(1, 2)
# [batch_size, num_heads, h*w, seq_len]
att_weights = torch.einsum('bnid,bnjd -> bnij', Q, K)
att_weights = att_weights * self.scale
if pad_mask is not None:
# 因为是多头，所以mask矩阵维度要扩充到4维 [batch_size, h*w, seq_len] -> [batch_size, nums_head, h*w, seq_len]
pad_mask = pad_mask.unsqueeze(1).repeat(1, self.num_heads, 1, 1)
att_weights = att_weights.masked_fill(pad_mask, -1e9)
att_weights = F.softmax(att_weights, dim=-1)
out = torch.einsum('bnij, bnjd -> bnid', att_weights, V)
out = out.transpose(1, 2).contiguous().view(batch_size, -1, self.emb_dim) # [batch_size, h*w, emb_dim]
print(out.shape)
out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w) # [batch_size, c, h, w]
out = self.proj_out(out) # [batch_size, c, h, w]
return out, att_weights
Cross Attention
class CrossAttention(nn.Module):
def __init__(self, in_channels, emb_dim, att_dropout=0.0, aropout=0.0):
super(CrossAttention, self).__init__()
self.emb_dim = emb_dim
self.scale = emb_dim ** -0.5
self.proj_in = nn.Conv2d(in_channels, emb_dim, kernel_size=1, stride=1, padding=0)
self.Wq = nn.Linear(emb_dim, emb_dim)
self.Wk = nn.Linear(emb_dim, emb_dim)
self.Wv = nn.Linear(emb_dim, emb_dim)
self.proj_out = nn.Conv2d(emb_dim, in_channels, kernel_size=1, stride=1, padding=0)
def forward(self, x, context, pad_mask=None):
'''
:param x: [batch_size, c, h, w]
:param context: [batch_szie, seq_len, emb_dim]
:param pad_mask: [batch_size, seq_len, seq_len]
:return:
'''
b, c, h, w = x.shape
x = self.proj_in(x) # [batch_size, c, h, w] = [3, 512, 512, 512]
x = rearrange(x, 'b c h w -> b (h w) c') # [batch_size, h*w, c] = [3, 262144, 512]
Q = self.Wq(x) # [batch_size, h*w, emb_dim] = [3, 262144, 512]
K = self.Wk(context) # [batch_szie, seq_len, emb_dim] = [3, 5, 512]
V = self.Wv(context)
# [batch_size, h*w, seq_len]
att_weights = torch.einsum('bid,bjd -> bij', Q, K)
att_weights = att_weights * self.scale
if pad_mask is not None:
# [batch_size, h*w, seq_len]
att_weights = att_weights.masked_fill(pad_mask, -1e9)
att_weights = F.softmax(att_weights, dim=-1)
out = torch.einsum('bij, bjd -> bid', att_weights, V) # [batch_size, h*w, emb_dim]
out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w) # [batch_size, c, h, w]
out = self.proj_out(out) # [batch_size, c, h, w]
print(out.shape)
return out, att_weights
main
# coding:utf-8
# @Email: [email protected]
# @Time: 2023/3/22 22:58
# @File: att_test.py
'''
Self Attention
Multi-Head Attention
Cross Attention
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from einops import rearrange, repeat
from torch.nn import MultiheadAttention
if __name__ == '__main__':
'''
'''
'''
假设词表映射后输入
batch_size = 3
seq_len = max_len = 5
pad = 0
emb_dim = 512
'''
batch_size = 3
seq_len = 5
emb_dim = 512
# 本例子则词表大小为 301
vocab_size = 301
input_ids = torch.tensor([[100, 200, 300, 300, 0],
[22, 33, 44, 0, 0],
[66, 55, 66, 30, 0]], dtype=torch.long)
pad_mask = input_ids.eq(0) # 逻辑矩阵pad_mask：将填充位置标记为True，其他位置标记为False
# pad_mask = pad_mask.unsqueeze(1).expand(batch_size, seq_len, seq_len) # [batch_size, seq_len, seq_len] = [3, 5, 5]
inputs = nn.Embedding(vocab_size, embedding_dim=emb_dim)(input_ids) # [batch_szie, seq_len, emb_dim] = [3, 5, 512]
# self_att = SelfAttention(emb_dim=emb_dim)
# self_att(inputs, pad_mask=pad_mask)
# multi_att = MultiHeadAttention(emb_dim=emb_dim, num_heads=8)
# multi_att(inputs, pad_mask=pad_mask)
# 定义图片数据 [batch_size, c, h, w]
input_img = torch.randn((3, 3, 512, 512))
pad_mask = pad_mask.unsqueeze(1).expand(batch_size, 512*512, seq_len)
# cross_att = Cross_MultiAttention(in_channels=3, emb_dim=emb_dim, num_heads=8, att_dropout=0.0, aropout=0.0)
# cross_att(x=input_img, context=inputs, pad_mask=pad_mask)
cross_att = CrossAttention(in_channels=3, emb_dim=emb_dim, att_dropout=0.0, aropout=0.0)
cross_att(x=input_img, context=inputs, pad_mask=pad_mask)