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import os
import torch
from torch import nn
from einops import rearrange, repeat
from torch import einsum
class PerceiverAttention(nn.Module):
def __init__(
self,
*,
dim,
dim_head=64,
heads=8
):
super().__init__()
self.scale = dim_head ** -0.5
self.heads = heads
inner_dim = dim_head * heads # 512
self.norm_media = nn.LayerNorm(dim)
self.norm_learns = nn.LayerNorm(dim)
self.to_q = nn.Linear(dim, inner_dim, bias=False) # 4096×512
self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) # 4096×1024
self.to_out = nn.Linear(inner_dim, dim, bias=False) # 512×4096
def forward(self, x, learns): # x(b, 256, 4096), learns(b, 3, 4096)
x = self.norm_media(x)
learns = self.norm_learns(learns)
b, n, h = *x.shape[:2], self.heads
q = self.to_q(learns) # q(b, 3, 512)
# 注意：在PerceiverResampler中，将输入和learns拼接后进行attention计算
kv_input = torch.cat((x, learns), dim=-2) # kv_input(b, 259, 4096)
k, v = self.to_kv(kv_input).chunk(2, dim=-1) # (b, 259, 1024)->k, v(b, 259, 512)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v)) # q(b, 8, 3, 64) k, v(b, 8, 259, 64)
q = q * self.scale
# attention计算
sim = einsum('b h i d, b h j d -> b h i j', q, k)
sim = sim - sim.amax(dim=-1, keepdim=True).detach()
attn = sim.softmax(dim=-1) # sim, attn(b, 8, 3, 259)
out = einsum('b h i j, b h j d -> b h i d', attn, v) # out(b, 8, 3, 64)
out = rearrange(out, 'b h n d -> b n (h d)') # out(b, 3, 512)
return self.to_out(out) # return(b, 3, 4096)
class PerceiverResampler(nn.Module):
def __init__(
self,
*,
dim,
depth=6,
dim_head=64,
heads=8,
num_learns=3,
ff_mult=4,
):
super().__init__()
self.learns = nn.Parameter(torch.randn(num_learns, dim))
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(
nn.ModuleList(
[
PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
FeedForward(dim=dim, mult=ff_mult),
]
)
)
self.norm = nn.LayerNorm(dim)
def forward(self, x):
"""
Args:
x (torch.Tensor): image features
shape (b, 256, 4096)
Returns:
shape (b, 3, 4096) where 3 is self.num_learns
"""
b, n, d = x.shape
learns = repeat(self.learns, "n d -> b n d", b=b)
for attn, ff in self.layers:
learns = attn(x, learns) + learns
learns = ff(learns) + learns
return self.norm(learns)
class MLP(nn.Module):
def __init__(self, input_dim, hidden_mult=4): # input_dim = 256
super().__init__()
self.ff1 = FeedForward_2(input_dim, input_dim, hidden_mult)
self.ff2 = FeedForward_2(input_dim, 3, hidden_mult)
def forward(self, x):
x = x.permute(0, 2, 1)
x = self.ff1(x)
x = self.ff2(x)
x = x.permute(0, 2, 1)
return x
class MLP_6763(nn.Module):
def __init__(self, input_dim, output_dim, hidden_mult=2):
super().__init__()
self.ff1 = FeedForward_2(input_dim, output_dim, hidden_mult)
self.ff2 = FeedForward_2(output_dim, output_dim, hidden_mult)
def forward(self, x):
b, n, d = x.shape
x = x.view(b, -1)
x = self.ff1(x)
x = self.ff2(x)
return x
class FeedForward(nn.Module):
def __init__(self, dim, mult=4):
super().__init__()
self.net = nn.Sequential(
nn.LayerNorm(dim),
nn.Linear(dim, dim * mult),
nn.GELU(),
nn.Linear(dim * mult, dim),
)
def forward(self, x):
return self.net(x)
class FeedForward_2(nn.Module):
def __init__(self, input_dim, output_dim, mult=4):
super().__init__()
self.net = nn.Sequential(
nn.LayerNorm(input_dim),
nn.Linear(input_dim, input_dim * mult),
nn.GELU(),
nn.Linear(input_dim * mult, output_dim),
)
def forward(self, x):
return self.net(x) |