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Running
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Zero
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import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
class Residual(nn.Module):
def __init__(self, fn):
super().__init__()
self.fn = fn
def forward(self, x, **kwargs):
return self.fn(x, **kwargs) + x
class SA_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 SA_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 SA_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.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):
b, n, _, h = *x.shape, self.heads
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 = h), qkv)
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 ReAttention(nn.Module):
def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
super().__init__()
inner_dim = dim_head * heads
self.heads = heads
self.scale = dim_head ** -0.5
self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
self.reattn_weights = nn.Parameter(torch.randn(heads, heads))
self.reattn_norm = nn.Sequential(
Rearrange('b h i j -> b i j h'),
nn.LayerNorm(heads),
Rearrange('b i j h -> b h i j')
)
self.to_out = nn.Sequential(
nn.Linear(inner_dim, dim),
nn.Dropout(dropout)
)
def forward(self, x):
b, n, _, h = *x.shape, self.heads
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 = h), qkv)
# attention
dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
attn = dots.softmax(dim=-1)
# re-attention
attn = einsum('b h i j, h g -> b g i j', attn, self.reattn_weights)
attn = self.reattn_norm(attn)
# aggregate and out
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 LeFF(nn.Module):
def __init__(self, dim = 192, scale = 4, depth_kernel = 3):
super().__init__()
scale_dim = dim*scale
self.up_proj = nn.Sequential(nn.Linear(dim, scale_dim),
Rearrange('b n c -> b c n'),
nn.BatchNorm1d(scale_dim),
nn.GELU(),
Rearrange('b c (h w) -> b c h w', h=14, w=14)
)
self.depth_conv = nn.Sequential(nn.Conv2d(scale_dim, scale_dim, kernel_size=depth_kernel, padding=1, groups=scale_dim, bias=False),
nn.BatchNorm2d(scale_dim),
nn.GELU(),
Rearrange('b c h w -> b (h w) c', h=14, w=14)
)
self.down_proj = nn.Sequential(nn.Linear(scale_dim, dim),
Rearrange('b n c -> b c n'),
nn.BatchNorm1d(dim),
nn.GELU(),
Rearrange('b c n -> b n c')
)
def forward(self, x):
x = self.up_proj(x)
x = self.depth_conv(x)
x = self.down_proj(x)
return x
class LCAttention(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_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):
b, n, _, h = *x.shape, self.heads
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 = h), qkv)
q = q[:, :, -1, :].unsqueeze(2) # Only Lth element use as query
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 SA_Transformer(nn.Module):
def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
super().__init__()
self.layers = nn.ModuleList([])
self.norm = nn.LayerNorm(dim)
for _ in range(depth):
self.layers.append(nn.ModuleList([
SA_PreNorm(dim, SA_Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
SA_PreNorm(dim, SA_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 self.norm(x) |