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Zero
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import math
import torch
import torch.nn as nn
# FFN
def FeedForward(dim, mult=4):
inner_dim = int(dim * mult)
return nn.Sequential(
nn.LayerNorm(dim),
nn.Linear(dim, inner_dim, bias=False),
nn.GELU(),
nn.Linear(inner_dim, dim, bias=False),
)
def reshape_tensor(x, heads):
bs, length, width = x.shape
# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
x = x.view(bs, length, heads, -1)
# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
x = x.transpose(1, 2)
# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
x = x.reshape(bs, heads, length, -1)
return x
class PerceiverAttentionCA(nn.Module):
def __init__(self, *, dim=3072, dim_head=128, heads=16, kv_dim=2048):
super().__init__()
self.scale = dim_head ** -0.5
self.dim_head = dim_head
self.heads = heads
inner_dim = dim_head * heads
self.norm1 = nn.LayerNorm(dim if kv_dim is None else kv_dim)
self.norm2 = nn.LayerNorm(dim)
self.to_q = nn.Linear(dim, inner_dim, bias=False)
self.to_kv = nn.Linear(dim if kv_dim is None else kv_dim, inner_dim * 2, bias=False)
self.to_out = nn.Linear(inner_dim, dim, bias=False)
def forward(self, x, latents):
"""
Args:
x (torch.Tensor): image features
shape (b, n1, D)
latent (torch.Tensor): latent features
shape (b, n2, D)
"""
x = self.norm1(x)
latents = self.norm2(latents)
b, seq_len, _ = latents.shape
q = self.to_q(latents)
k, v = self.to_kv(x).chunk(2, dim=-1)
q = reshape_tensor(q, self.heads)
k = reshape_tensor(k, self.heads)
v = reshape_tensor(v, self.heads)
# attention
scale = 1 / math.sqrt(math.sqrt(self.dim_head))
weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards
weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
out = weight @ v
out = out.permute(0, 2, 1, 3).reshape(b, seq_len, -1)
return self.to_out(out)
class PerceiverAttention(nn.Module):
def __init__(self, *, dim, dim_head=64, heads=8, kv_dim=None):
super().__init__()
self.scale = dim_head ** -0.5
self.dim_head = dim_head
self.heads = heads
inner_dim = dim_head * heads
self.norm1 = nn.LayerNorm(dim if kv_dim is None else kv_dim)
self.norm2 = nn.LayerNorm(dim)
self.to_q = nn.Linear(dim, inner_dim, bias=False)
self.to_kv = nn.Linear(dim if kv_dim is None else kv_dim, inner_dim * 2, bias=False)
self.to_out = nn.Linear(inner_dim, dim, bias=False)
def forward(self, x, latents):
"""
Args:
x (torch.Tensor): image features
shape (b, n1, D)
latent (torch.Tensor): latent features
shape (b, n2, D)
"""
x = self.norm1(x)
latents = self.norm2(latents)
b, seq_len, _ = latents.shape
q = self.to_q(latents)
kv_input = torch.cat((x, latents), dim=-2)
k, v = self.to_kv(kv_input).chunk(2, dim=-1)
q = reshape_tensor(q, self.heads)
k = reshape_tensor(k, self.heads)
v = reshape_tensor(v, self.heads)
# attention
scale = 1 / math.sqrt(math.sqrt(self.dim_head))
weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards
weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
out = weight @ v
out = out.permute(0, 2, 1, 3).reshape(b, seq_len, -1)
return self.to_out(out)
class IDFormer(nn.Module):
"""
- perceiver resampler like arch (compared with previous MLP-like arch)
- we concat id embedding (generated by arcface) and query tokens as latents
- latents will attend each other and interact with vit features through cross-attention
- vit features are multi-scaled and inserted into IDFormer in order, currently, each scale corresponds to two
IDFormer layers
"""
def __init__(
self,
dim=1024,
depth=10,
dim_head=64,
heads=16,
num_id_token=5,
num_queries=32,
output_dim=2048,
ff_mult=4,
):
super().__init__()
self.num_id_token = num_id_token
self.dim = dim
self.num_queries = num_queries
assert depth % 5 == 0
self.depth = depth // 5
scale = dim ** -0.5
self.latents = nn.Parameter(torch.randn(1, num_queries, dim) * scale)
self.proj_out = nn.Parameter(scale * torch.randn(dim, output_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),
]
)
)
for i in range(5):
setattr(
self,
f'mapping_{i}',
nn.Sequential(
nn.Linear(1024, 1024),
nn.LayerNorm(1024),
nn.LeakyReLU(),
nn.Linear(1024, 1024),
nn.LayerNorm(1024),
nn.LeakyReLU(),
nn.Linear(1024, dim),
),
)
self.id_embedding_mapping = nn.Sequential(
nn.Linear(1280, 1024),
nn.LayerNorm(1024),
nn.LeakyReLU(),
nn.Linear(1024, 1024),
nn.LayerNorm(1024),
nn.LeakyReLU(),
nn.Linear(1024, dim * num_id_token),
)
def forward(self, x, y):
latents = self.latents.repeat(x.size(0), 1, 1)
x = self.id_embedding_mapping(x)
x = x.reshape(-1, self.num_id_token, self.dim)
latents = torch.cat((latents, x), dim=1)
for i in range(5):
vit_feature = getattr(self, f'mapping_{i}')(y[i])
ctx_feature = torch.cat((x, vit_feature), dim=1)
for attn, ff in self.layers[i * self.depth: (i + 1) * self.depth]:
latents = attn(ctx_feature, latents) + latents
latents = ff(latents) + latents
latents = latents[:, :self.num_queries]
latents = latents @ self.proj_out
return latents |