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on
Zero
Running
on
Zero
| 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) |