mirror of
https://github.com/lucidrains/vit-pytorch.git
synced 2025-12-30 16:12:29 +00:00
217 lines
7.1 KiB
Python
217 lines
7.1 KiB
Python
import torch
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from torch import nn
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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 pair(t):
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return t if isinstance(t, tuple) else (t, t)
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# controlling freezing of layers
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def set_module_requires_grad_(module, requires_grad):
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for param in module.parameters():
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param.requires_grad = requires_grad
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def freeze_all_layers_(module):
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set_module_requires_grad_(module, False)
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def unfreeze_all_layers_(module):
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set_module_requires_grad_(module, True)
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# classes
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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.LayerNorm(dim),
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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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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.norm = nn.LayerNorm(dim)
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self.attend = nn.Softmax(dim = -1)
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self.dropout = nn.Dropout(dropout)
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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, attn_mask = None, memories = None):
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x = self.norm(x)
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x_kv = x # input for key / values projection
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if exists(memories):
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# add memories to key / values if it is passed in
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memories = repeat(memories, 'n d -> b n d', b = x.shape[0]) if memories.ndim == 2 else memories
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x_kv = torch.cat((x_kv, memories), dim = 1)
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qkv = (self.to_q(x), *self.to_kv(x_kv).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 = self.heads), qkv)
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dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
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if exists(attn_mask):
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dots = dots.masked_fill(~attn_mask, -torch.finfo(dots.dtype).max)
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attn = self.attend(dots)
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attn = self.dropout(attn)
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out = torch.matmul(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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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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for _ in range(depth):
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self.layers.append(nn.ModuleList([
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Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout),
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FeedForward(dim, mlp_dim, dropout = dropout)
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]))
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def forward(self, x, attn_mask = None, memories = None):
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for ind, (attn, ff) in enumerate(self.layers):
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layer_memories = memories[ind] if exists(memories) else None
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x = attn(x, attn_mask = attn_mask, memories = layer_memories) + x
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x = ff(x) + x
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return x
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class ViT(nn.Module):
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def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.):
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super().__init__()
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image_height, image_width = pair(image_size)
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patch_height, patch_width = pair(patch_size)
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assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'
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num_patches = (image_height // patch_height) * (image_width // patch_width)
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patch_dim = channels * patch_height * patch_width
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assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'
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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_height, p2 = patch_width),
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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(emb_dropout)
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self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
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self.mlp_head = nn.Sequential(
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nn.LayerNorm(dim),
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nn.Linear(dim, num_classes)
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)
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def img_to_tokens(self, img):
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x = self.to_patch_embedding(img)
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cls_tokens = repeat(self.cls_token, '1 n d -> b n d', b = x.shape[0])
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x = torch.cat((cls_tokens, x), dim = 1)
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x += self.pos_embedding
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x = self.dropout(x)
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return x
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def forward(self, img):
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x = self.img_to_tokens(img)
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x = self.transformer(x)
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cls_tokens = x[:, 0]
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return self.mlp_head(cls_tokens)
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# adapter with learnable memories per layer, memory CLS token, and learnable adapter head
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class Adapter(nn.Module):
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def __init__(
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self,
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*,
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vit,
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num_memories_per_layer = 10,
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num_classes = 2,
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):
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super().__init__()
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assert isinstance(vit, ViT)
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# extract some model variables needed
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dim = vit.cls_token.shape[-1]
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layers = len(vit.transformer.layers)
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num_patches = vit.pos_embedding.shape[-2]
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self.vit = vit
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# freeze ViT backbone - only memories will be finetuned
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freeze_all_layers_(vit)
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# learnable parameters
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self.memory_cls_token = nn.Parameter(torch.randn(dim))
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self.memories_per_layer = nn.Parameter(torch.randn(layers, num_memories_per_layer, dim))
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self.mlp_head = nn.Sequential(
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nn.LayerNorm(dim),
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nn.Linear(dim, num_classes)
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)
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# specialized attention mask to preserve the output of the original ViT
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# it allows the memory CLS token to attend to all other tokens (and the learnable memory layer tokens), but not vice versa
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attn_mask = torch.ones((num_patches, num_patches), dtype = torch.bool)
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attn_mask = F.pad(attn_mask, (1, num_memories_per_layer), value = False) # main tokens cannot attend to learnable memories per layer
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attn_mask = F.pad(attn_mask, (0, 0, 1, 0), value = True) # memory CLS token can attend to everything
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self.register_buffer('attn_mask', attn_mask)
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def forward(self, img):
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b = img.shape[0]
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tokens = self.vit.img_to_tokens(img)
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# add task specific memory tokens
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memory_cls_tokens = repeat(self.memory_cls_token, 'd -> b 1 d', b = b)
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tokens = torch.cat((memory_cls_tokens, tokens), dim = 1)
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# pass memories along with image tokens through transformer for attending
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out = self.vit.transformer(tokens, memories = self.memories_per_layer, attn_mask = self.attn_mask)
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# extract memory CLS tokens
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memory_cls_tokens = out[:, 0]
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# pass through task specific adapter head
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return self.mlp_head(memory_cls_tokens)
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