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import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange
# 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 FeedForward(nn.Module):
def __init__(self, dim, hidden_dim, dropout = 0.):
super().__init__()
self.net = nn.Sequential(
nn.Linear(dim, hidden_dim),
nn.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_qkv = nn.Linear(dim, inner_dim * 3, 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):
qkv = self.to_qkv(x).chunk(3, dim = -1)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv)
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 CrossAttention(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.to_k = nn.Linear(dim, inner_dim , bias=False)
self.to_v = nn.Linear(dim, inner_dim , bias = False)
self.to_q = 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_qkv, query_length=1):
h = self.heads
k = self.to_k(x_qkv)[:, query_length:]
k = rearrange(k, 'b n (h d) -> b h n d', h = h)
v = self.to_v(x_qkv)[:, query_length:]
v = rearrange(v, 'b n (h d) -> b h n d', h = h)
q = self.to_q(x_qkv)[:, :query_length]
q = rearrange(q, 'b n (h d) -> b h n d', h = h)
dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
attn = dots.softmax(dim=-1)
out = einsum('b h i j, b h j d -> b h i d', attn, v)
out = rearrange(out, 'b h n d -> b n (h d)')
out = self.to_out(out)
return out
class TransformerEncoder(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([
PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
]))
def forward(self, x):
for attn, ff in self.layers:
x = attn(x) + x
x = ff(x) + x
return x
class TransformerDecoder(nn.Module):
def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
super().__init__()
self.pos_embedding = nn.Parameter(torch.randn(1, 6, dim))
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(nn.ModuleList([
PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
PreNorm(dim, CrossAttention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
]))
def forward(self, x, y):
x = x + self.pos_embedding[:, :x.shape[1]]
for sattn, cattn, ff in self.layers:
x = sattn(x) + x
xy = torch.cat((x,y), dim=1)
x = cattn(xy, query_length=x.shape[1]) + x
x = ff(x) + x
return x
class Network(nn.Module):
def __init__(self, opts):
super(Network, self).__init__()
self.transformer_encoder = TransformerEncoder(dim=512, depth=6, heads=8, dim_head=64, mlp_dim=512, dropout=0)
self.transformer_decoder = TransformerDecoder(dim=512, depth=6, heads=8, dim_head=64, mlp_dim=512, dropout=0)
self.layer1 = nn.Linear(3, 256)
self.layer2 = nn.Linear(512, 256)
self.layer3 = nn.Linear(512, 512)
self.layer4 = nn.Linear(512, 512)
self.mlp_head = nn.Sequential(
nn.Linear(512, 512)
)
def forward(self, w, x, y, alpha=1.):
#w: latent vectors
#x: flow vectors
#y: StyleGAN features
xh = F.relu(self.layer1(x))
yh = F.relu(self.layer2(y))
xyh = torch.cat([xh,yh], dim=2)
xyh = F.relu(self.layer3(xyh))
xyh = self.transformer_encoder(xyh)
wh = F.relu(self.layer4(w))
h = self.transformer_decoder(wh, xyh)
h = self.mlp_head(h)
w_hat = w+alpha*h
return w_hat
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