mirror of
https://github.com/lucidrains/vit-pytorch.git
synced 2025-12-30 08:02:29 +00:00
1616288e30
* add xcit * use Rearrange layers * give cross correlation transformer a final norm at end * document
284 lines
8.3 KiB
Python
284 lines
8.3 KiB
Python
from random import randrange
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import torch
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from torch import nn, einsum
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from torch.nn import Module, ModuleList
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import torch.nn.functional as F
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from einops import rearrange, repeat, pack, unpack
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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 pack_one(t, pattern):
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return pack([t], pattern)
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def unpack_one(t, ps, pattern):
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return unpack(t, ps, pattern)[0]
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def l2norm(t):
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return F.normalize(t, dim = -1, p = 2)
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def dropout_layers(layers, dropout):
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if dropout == 0:
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return layers
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num_layers = len(layers)
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to_drop = torch.zeros(num_layers).uniform_(0., 1.) < dropout
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# make sure at least one layer makes it
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if all(to_drop):
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rand_index = randrange(num_layers)
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to_drop[rand_index] = False
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layers = [layer for (layer, drop) in zip(layers, to_drop) if not drop]
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return layers
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# classes
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class LayerScale(Module):
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def __init__(self, dim, fn, depth):
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super().__init__()
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if depth <= 18:
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init_eps = 0.1
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elif 18 > depth <= 24:
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init_eps = 1e-5
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else:
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init_eps = 1e-6
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self.fn = fn
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self.scale = nn.Parameter(torch.full((dim,), init_eps))
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def forward(self, x, **kwargs):
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return self.fn(x, **kwargs) * self.scale
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class FeedForward(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(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.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.attend = nn.Softmax(dim = -1)
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self.dropout = nn.Dropout(dropout)
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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):
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h = self.heads
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x = self.norm(x)
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context = x if not exists(context) else torch.cat((x, context), dim = 1)
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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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sim = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
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attn = self.attend(sim)
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attn = self.dropout(attn)
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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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class XCAttention(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.norm = nn.LayerNorm(dim)
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self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
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self.temperature = nn.Parameter(torch.ones(heads, 1, 1))
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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_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):
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h = self.heads
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x, ps = pack_one(x, 'b * d')
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x = self.norm(x)
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q, k, v = self.to_qkv(x).chunk(3, dim = -1)
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h d n', h = h), (q, k, v))
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q, k = map(l2norm, (q, k))
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sim = einsum('b h i n, b h j n -> b h i j', q, k) * self.temperature.exp()
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attn = self.attend(sim)
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attn = self.dropout(attn)
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out = einsum('b h i j, b h j n -> b h i n', attn, v)
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out = rearrange(out, 'b h d n -> b n (h d)')
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out = unpack_one(out, ps, 'b * d')
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return self.to_out(out)
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class LocalPatchInteraction(Module):
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def __init__(self, dim, kernel_size = 3):
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super().__init__()
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assert (kernel_size % 2) == 1
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padding = kernel_size // 2
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self.net = nn.Sequential(
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nn.LayerNorm(dim),
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Rearrange('b h w c -> b c h w'),
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nn.Conv2d(dim, dim, kernel_size, padding = padding, groups = dim),
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nn.BatchNorm2d(dim),
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nn.GELU(),
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nn.Conv2d(dim, dim, kernel_size, padding = padding, groups = dim),
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Rearrange('b c h w -> b h w c'),
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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 Transformer(Module):
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def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0., layer_dropout = 0.):
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super().__init__()
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self.layers = ModuleList([])
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self.layer_dropout = layer_dropout
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for ind in range(depth):
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layer = ind + 1
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self.layers.append(ModuleList([
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LayerScale(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout), depth = layer),
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LayerScale(dim, FeedForward(dim, mlp_dim, dropout = dropout), depth = layer)
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]))
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def forward(self, x, context = None):
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layers = dropout_layers(self.layers, dropout = self.layer_dropout)
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for attn, ff in layers:
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x = attn(x, context = context) + x
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x = ff(x) + x
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return x
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class XCATransformer(Module):
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def __init__(self, dim, depth, heads, dim_head, mlp_dim, local_patch_kernel_size = 3, dropout = 0., layer_dropout = 0.):
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super().__init__()
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self.layers = ModuleList([])
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self.layer_dropout = layer_dropout
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for ind in range(depth):
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layer = ind + 1
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self.layers.append(ModuleList([
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LayerScale(dim, XCAttention(dim, heads = heads, dim_head = dim_head, dropout = dropout), depth = layer),
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LayerScale(dim, LocalPatchInteraction(dim, local_patch_kernel_size), depth = layer),
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LayerScale(dim, FeedForward(dim, mlp_dim, dropout = dropout), depth = layer)
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]))
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def forward(self, x):
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layers = dropout_layers(self.layers, dropout = self.layer_dropout)
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for cross_covariance_attn, local_patch_interaction, ff in layers:
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x = cross_covariance_attn(x) + x
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x = local_patch_interaction(x) + x
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x = ff(x) + x
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return x
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class XCiT(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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patch_size,
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num_classes,
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dim,
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depth,
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cls_depth,
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heads,
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mlp_dim,
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dim_head = 64,
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dropout = 0.,
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emb_dropout = 0.,
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local_patch_kernel_size = 3,
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layer_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.LayerNorm(patch_dim),
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nn.Linear(patch_dim, dim),
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nn.LayerNorm(dim)
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)
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self.pos_embedding = nn.Parameter(torch.randn(1, num_patches, dim))
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self.cls_token = nn.Parameter(torch.randn(dim))
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self.dropout = nn.Dropout(emb_dropout)
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self.xcit_transformer = XCATransformer(dim, depth, heads, dim_head, mlp_dim, local_patch_kernel_size, dropout, layer_dropout)
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self.final_norm = nn.LayerNorm(dim)
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self.cls_transformer = Transformer(dim, cls_depth, heads, dim_head, mlp_dim, dropout, layer_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 forward(self, img):
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x = self.to_patch_embedding(img)
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x, ps = pack_one(x, 'b * d')
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b, n, _ = x.shape
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x += self.pos_embedding[:, :n]
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x = unpack_one(x, ps, 'b * d')
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x = self.dropout(x)
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x = self.xcit_transformer(x)
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x = self.final_norm(x)
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cls_tokens = repeat(self.cls_token, 'd -> b 1 d', b = b)
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x = rearrange(x, 'b ... d -> b (...) d')
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cls_tokens = self.cls_transformer(cls_tokens, context = x)
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return self.mlp_head(cls_tokens[:, 0])
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