mirror of
https://github.com/lucidrains/vit-pytorch.git
synced 2025-12-30 08:02:29 +00:00
271 lines
8.8 KiB
Python
271 lines
8.8 KiB
Python
import torch
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from torch import nn, einsum
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import torch.nn.functional as F
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from einops import rearrange, repeat
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from einops.layers.torch import Rearrange
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# helpers
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def exists(val):
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return val is not None
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def default(val, d):
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return val if exists(val) else d
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# pre-layernorm
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class PreNorm(nn.Module):
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def __init__(self, dim, fn):
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super().__init__()
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self.norm = nn.LayerNorm(dim)
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self.fn = fn
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def forward(self, x, **kwargs):
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return self.fn(self.norm(x), **kwargs)
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# feedforward
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class FeedForward(nn.Module):
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def __init__(self, dim, hidden_dim, dropout = 0.):
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super().__init__()
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self.net = nn.Sequential(
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nn.Linear(dim, hidden_dim),
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nn.GELU(),
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nn.Dropout(dropout),
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nn.Linear(hidden_dim, dim),
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nn.Dropout(dropout)
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)
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def forward(self, x):
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return self.net(x)
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# attention
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class Attention(nn.Module):
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def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
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super().__init__()
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inner_dim = dim_head * heads
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self.heads = heads
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self.scale = dim_head ** -0.5
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self.attend = nn.Softmax(dim = -1)
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self.to_q = nn.Linear(dim, inner_dim, bias = False)
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self.to_kv = nn.Linear(dim, inner_dim * 2, bias = False)
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self.to_out = nn.Sequential(
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nn.Linear(inner_dim, dim),
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nn.Dropout(dropout)
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)
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def forward(self, x, context = None, kv_include_self = False):
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b, n, _, h = *x.shape, self.heads
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context = default(context, x)
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if kv_include_self:
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context = torch.cat((x, context), dim = 1) # cross attention requires CLS token includes itself as key / value
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qkv = (self.to_q(x), *self.to_kv(context).chunk(2, dim = -1))
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)
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dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
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attn = self.attend(dots)
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out = einsum('b h i j, b h j d -> b h i d', attn, v)
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out = rearrange(out, 'b h n d -> b n (h d)')
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return self.to_out(out)
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# transformer encoder, for small and large patches
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class Transformer(nn.Module):
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def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
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super().__init__()
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self.layers = nn.ModuleList([])
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self.norm = nn.LayerNorm(dim)
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for _ in range(depth):
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self.layers.append(nn.ModuleList([
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PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
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PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
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]))
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def forward(self, x):
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for attn, ff in self.layers:
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x = attn(x) + x
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x = ff(x) + x
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return self.norm(x)
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# projecting CLS tokens, in the case that small and large patch tokens have different dimensions
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class ProjectInOut(nn.Module):
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def __init__(self, dim_in, dim_out, fn):
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super().__init__()
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self.fn = fn
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need_projection = dim_in != dim_out
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self.project_in = nn.Linear(dim_in, dim_out) if need_projection else nn.Identity()
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self.project_out = nn.Linear(dim_out, dim_in) if need_projection else nn.Identity()
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def forward(self, x, *args, **kwargs):
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x = self.project_in(x)
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x = self.fn(x, *args, **kwargs)
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x = self.project_out(x)
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return x
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# cross attention transformer
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class CrossTransformer(nn.Module):
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def __init__(self, sm_dim, lg_dim, depth, heads, dim_head, dropout):
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super().__init__()
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self.layers = nn.ModuleList([])
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for _ in range(depth):
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self.layers.append(nn.ModuleList([
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ProjectInOut(sm_dim, lg_dim, PreNorm(lg_dim, Attention(lg_dim, heads = heads, dim_head = dim_head, dropout = dropout))),
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ProjectInOut(lg_dim, sm_dim, PreNorm(sm_dim, Attention(sm_dim, heads = heads, dim_head = dim_head, dropout = dropout)))
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]))
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def forward(self, sm_tokens, lg_tokens):
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(sm_cls, sm_patch_tokens), (lg_cls, lg_patch_tokens) = map(lambda t: (t[:, :1], t[:, 1:]), (sm_tokens, lg_tokens))
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for sm_attend_lg, lg_attend_sm in self.layers:
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sm_cls = sm_attend_lg(sm_cls, context = lg_patch_tokens, kv_include_self = True) + sm_cls
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lg_cls = lg_attend_sm(lg_cls, context = sm_patch_tokens, kv_include_self = True) + lg_cls
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sm_tokens = torch.cat((sm_cls, sm_patch_tokens), dim = 1)
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lg_tokens = torch.cat((lg_cls, lg_patch_tokens), dim = 1)
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return sm_tokens, lg_tokens
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# multi-scale encoder
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class MultiScaleEncoder(nn.Module):
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def __init__(
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self,
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*,
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depth,
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sm_dim,
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lg_dim,
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sm_enc_params,
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lg_enc_params,
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cross_attn_heads,
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cross_attn_depth,
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cross_attn_dim_head = 64,
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dropout = 0.
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):
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super().__init__()
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self.layers = nn.ModuleList([])
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for _ in range(depth):
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self.layers.append(nn.ModuleList([
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Transformer(dim = sm_dim, dropout = dropout, **sm_enc_params),
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Transformer(dim = lg_dim, dropout = dropout, **lg_enc_params),
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CrossTransformer(sm_dim = sm_dim, lg_dim = lg_dim, depth = cross_attn_depth, heads = cross_attn_heads, dim_head = cross_attn_dim_head, dropout = dropout)
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]))
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def forward(self, sm_tokens, lg_tokens):
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for sm_enc, lg_enc, cross_attend in self.layers:
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sm_tokens, lg_tokens = sm_enc(sm_tokens), lg_enc(lg_tokens)
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sm_tokens, lg_tokens = cross_attend(sm_tokens, lg_tokens)
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return sm_tokens, lg_tokens
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# patch-based image to token embedder
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class ImageEmbedder(nn.Module):
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def __init__(
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self,
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*,
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dim,
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image_size,
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patch_size,
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dropout = 0.
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):
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super().__init__()
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assert image_size % patch_size == 0, 'Image dimensions must be divisible by the patch size.'
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num_patches = (image_size // patch_size) ** 2
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patch_dim = 3 * patch_size ** 2
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self.to_patch_embedding = nn.Sequential(
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Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size),
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nn.Linear(patch_dim, dim),
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)
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self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
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self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
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self.dropout = nn.Dropout(dropout)
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def forward(self, img):
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x = self.to_patch_embedding(img)
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b, n, _ = x.shape
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cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
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x = torch.cat((cls_tokens, x), dim=1)
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x += self.pos_embedding[:, :(n + 1)]
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return self.dropout(x)
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# cross ViT class
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class CrossViT(nn.Module):
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def __init__(
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self,
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*,
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image_size,
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num_classes,
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sm_dim,
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lg_dim,
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sm_patch_size = 12,
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sm_enc_depth = 1,
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sm_enc_heads = 8,
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sm_enc_mlp_dim = 2048,
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sm_enc_dim_head = 64,
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lg_patch_size = 16,
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lg_enc_depth = 4,
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lg_enc_heads = 8,
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lg_enc_mlp_dim = 2048,
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lg_enc_dim_head = 64,
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cross_attn_depth = 2,
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cross_attn_heads = 8,
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cross_attn_dim_head = 64,
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depth = 3,
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dropout = 0.1,
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emb_dropout = 0.1
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):
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super().__init__()
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self.sm_image_embedder = ImageEmbedder(dim = sm_dim, image_size = image_size, patch_size = sm_patch_size, dropout = emb_dropout)
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self.lg_image_embedder = ImageEmbedder(dim = lg_dim, image_size = image_size, patch_size = lg_patch_size, dropout = emb_dropout)
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self.multi_scale_encoder = MultiScaleEncoder(
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depth = depth,
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sm_dim = sm_dim,
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lg_dim = lg_dim,
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cross_attn_heads = cross_attn_heads,
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cross_attn_dim_head = cross_attn_dim_head,
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cross_attn_depth = cross_attn_depth,
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sm_enc_params = dict(
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depth = sm_enc_depth,
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heads = sm_enc_heads,
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mlp_dim = sm_enc_mlp_dim,
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dim_head = sm_enc_dim_head
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),
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lg_enc_params = dict(
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depth = lg_enc_depth,
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heads = lg_enc_heads,
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mlp_dim = lg_enc_mlp_dim,
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dim_head = lg_enc_dim_head
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),
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dropout = dropout
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)
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self.sm_mlp_head = nn.Sequential(nn.LayerNorm(sm_dim), nn.Linear(sm_dim, num_classes))
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self.lg_mlp_head = nn.Sequential(nn.LayerNorm(lg_dim), nn.Linear(lg_dim, num_classes))
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def forward(self, img):
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sm_tokens = self.sm_image_embedder(img)
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lg_tokens = self.lg_image_embedder(img)
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sm_tokens, lg_tokens = self.multi_scale_encoder(sm_tokens, lg_tokens)
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sm_cls, lg_cls = map(lambda t: t[:, 0], (sm_tokens, lg_tokens))
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sm_logits = self.sm_mlp_head(sm_cls)
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lg_logits = self.lg_mlp_head(lg_cls)
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return sm_logits + lg_logits
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