| | 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) |
| |
|
| | |
| |
|
| | dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale |
| | attn = dots.softmax(dim=-1) |
| |
|
| | |
| |
|
| | attn = einsum('b h i j, h g -> b g i j', attn, self.reattn_weights) |
| | attn = self.reattn_norm(attn) |
| |
|
| | |
| |
|
| | 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) |
| |
|
| | 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) |
