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import torch
from torch import nn
from einops import rearrange
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class GELU(nn.Module):
def __init__(self):
super(GELU, self).__init__()
def forward(self, x):
return 0.5*x*(1+F.tanh(np.sqrt(2/np.pi)*(x+0.044715*torch.pow(x,3))))
# helpers
def pair(t):
return t if isinstance(t, tuple) else (t, t)
# classes
class PreNorm(nn.Module):
def __init__(self, dim, fn):
super().__init__()
self.norm = nn.LayerNorm(dim)
self.fn = fn
def forward(self, x, **kwargs):
return self.fn(self.norm(x), **kwargs)
class DualPreNorm(nn.Module):
def __init__(self, dim, fn):
super().__init__()
self.normx = nn.LayerNorm(dim)
self.normy = nn.LayerNorm(dim)
self.fn = fn
def forward(self, x, y, **kwargs):
return self.fn(self.normx(x), self.normy(y), **kwargs)
class FeedForward(nn.Module):
def __init__(self, dim, hidden_dim, dropout = 0.):
super().__init__()
self.net = nn.Sequential(
nn.Linear(dim, hidden_dim),
GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, dim),
nn.Dropout(dropout)
)
def forward(self, x):
return self.net(x)
class Attention(nn.Module):
def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
super().__init__()
inner_dim = dim_head * heads
project_out = not (heads == 1 and dim_head == dim)
self.heads = heads
self.scale = dim_head ** -0.5
self.attend = nn.Softmax(dim = -1)
self.to_q = nn.Linear(dim, inner_dim, bias = False)
self.to_k = nn.Linear(dim, inner_dim, bias = False)
self.to_v = nn.Linear(dim, inner_dim, bias = False)
self.to_out = nn.Sequential(
nn.Linear(inner_dim, dim),
nn.Dropout(dropout)
) if project_out else nn.Identity()
def forward(self, x, y):
# qk = self.to_qk(x).chunk(2, dim = -1) #
q = rearrange(self.to_q(x), 'b n (h d) -> b h n d', h = self.heads) # q,k from the zero feature
k = rearrange(self.to_k(x), 'b n (h d) -> b h n d', h = self.heads) # v from the reference features
v = rearrange(self.to_v(y), 'b n (h d) -> b h n d', h = self.heads)
dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
attn = self.attend(dots)
out = torch.matmul(attn, v)
out = rearrange(out, 'b h n d -> b n (h d)')
return self.to_out(out)
class Transformer(nn.Module):
def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
super().__init__()
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(nn.ModuleList([
DualPreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
]))
def forward(self, x, y): # x is the cropped, y is the foreign reference
bs,c,h,w = x.size()
# img to embedding
x = x.view(bs,c,-1).permute(0,2,1)
y = y.view(bs,c,-1).permute(0,2,1)
for attn, ff in self.layers:
x = attn(x, y) + x
x = ff(x) + x
x = x.view(bs,h,w,c).permute(0,3,1,2)
return x
class RETURNX(nn.Module):
def __init__(self,):
super().__init__()
def forward(self, x, y): # x is the cropped, y is the foreign reference
return x |