mirror of
https://github.com/lucidrains/vit-pytorch.git
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234 lines
7.0 KiB
Python
234 lines
7.0 KiB
Python
"""
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ViT + Hyper-Connections + Register Tokens
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https://arxiv.org/abs/2409.19606
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"""
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import torch
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from torch import nn, tensor
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from torch.nn import Module, ModuleList
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from einops import rearrange, repeat, reduce, einsum, pack, unpack
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from einops.layers.torch import Rearrange
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# b - batch, h - heads, n - sequence, e - expansion rate / residual streams, d - feature dimension
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# helpers
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def pair(t):
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return t if isinstance(t, tuple) else (t, t)
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def posemb_sincos_2d(h, w, dim, temperature: int = 10000, dtype = torch.float32):
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y, x = torch.meshgrid(torch.arange(h), torch.arange(w), indexing="ij")
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assert (dim % 4) == 0, "feature dimension must be multiple of 4 for sincos emb"
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omega = torch.arange(dim // 4) / (dim // 4 - 1)
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omega = 1.0 / (temperature ** omega)
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y = y.flatten()[:, None] * omega[None, :]
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x = x.flatten()[:, None] * omega[None, :]
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pe = torch.cat((x.sin(), x.cos(), y.sin(), y.cos()), dim=1)
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return pe.type(dtype)
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# hyper connections
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class HyperConnection(Module):
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def __init__(
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self,
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dim,
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num_residual_streams,
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layer_index
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):
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""" Appendix J - Algorithm 2, Dynamic only """
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super().__init__()
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self.norm = nn.LayerNorm(dim, bias = False)
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self.num_residual_streams = num_residual_streams
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self.layer_index = layer_index
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self.static_beta = nn.Parameter(torch.ones(num_residual_streams))
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init_alpha0 = torch.zeros((num_residual_streams, 1))
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init_alpha0[layer_index % num_residual_streams, 0] = 1.
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self.static_alpha = nn.Parameter(torch.cat([init_alpha0, torch.eye(num_residual_streams)], dim = 1))
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self.dynamic_alpha_fn = nn.Parameter(torch.zeros(dim, num_residual_streams + 1))
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self.dynamic_alpha_scale = nn.Parameter(tensor(1e-2))
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self.dynamic_beta_fn = nn.Parameter(torch.zeros(dim))
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self.dynamic_beta_scale = nn.Parameter(tensor(1e-2))
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def width_connection(self, residuals):
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normed = self.norm(residuals)
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wc_weight = (normed @ self.dynamic_alpha_fn).tanh()
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dynamic_alpha = wc_weight * self.dynamic_alpha_scale
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alpha = dynamic_alpha + self.static_alpha
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dc_weight = (normed @ self.dynamic_beta_fn).tanh()
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dynamic_beta = dc_weight * self.dynamic_beta_scale
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beta = dynamic_beta + self.static_beta
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# width connection
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mix_h = einsum(alpha, residuals, '... e1 e2, ... e1 d -> ... e2 d')
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branch_input, residuals = mix_h[..., 0, :], mix_h[..., 1:, :]
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return branch_input, residuals, beta
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def depth_connection(
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self,
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residuals,
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branch_output,
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beta
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):
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return einsum(branch_output, beta, "b n d, b n e -> b n e d") + residuals
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# classes
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class FeedForward(Module):
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def __init__(self, dim, hidden_dim):
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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.Linear(hidden_dim, dim),
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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):
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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.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
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self.to_out = nn.Linear(inner_dim, dim, bias = False)
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def forward(self, x):
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x = self.norm(x)
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qkv = 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 n d', h = self.heads), qkv)
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dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
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attn = self.attend(dots)
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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(Module):
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def __init__(self, dim, depth, heads, dim_head, mlp_dim, num_residual_streams):
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super().__init__()
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self.num_residual_streams = num_residual_streams
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self.norm = nn.LayerNorm(dim)
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self.layers = ModuleList([])
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for layer_index in range(depth):
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self.layers.append(nn.ModuleList([
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HyperConnection(dim, num_residual_streams, layer_index),
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Attention(dim, heads = heads, dim_head = dim_head),
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HyperConnection(dim, num_residual_streams, layer_index),
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FeedForward(dim, mlp_dim)
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]))
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def forward(self, x):
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x = repeat(x, 'b n d -> b n e d', e = self.num_residual_streams)
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for attn_hyper_conn, attn, ff_hyper_conn, ff in self.layers:
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x, attn_res, beta = attn_hyper_conn.width_connection(x)
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x = attn(x)
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x = attn_hyper_conn.depth_connection(attn_res, x, beta)
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x, ff_res, beta = ff_hyper_conn.width_connection(x)
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x = ff(x)
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x = ff_hyper_conn.depth_connection(ff_res, x, beta)
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x = reduce(x, 'b n e d -> b n d', 'sum')
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return self.norm(x)
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class SimpleViT(nn.Module):
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def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, num_residual_streams, num_register_tokens = 4, channels = 3, dim_head = 64):
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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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patch_dim = channels * patch_height * patch_width
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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.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.register_tokens = nn.Parameter(torch.randn(num_register_tokens, dim))
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self.pos_embedding = posemb_sincos_2d(
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h = image_height // patch_height,
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w = image_width // patch_width,
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dim = dim,
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)
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self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, num_residual_streams)
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self.pool = "mean"
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self.to_latent = nn.Identity()
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self.linear_head = nn.Linear(dim, num_classes)
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def forward(self, img):
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batch, device = img.shape[0], img.device
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x = self.to_patch_embedding(img)
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x += self.pos_embedding.to(x)
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r = repeat(self.register_tokens, 'n d -> b n d', b = batch)
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x, ps = pack([x, r], 'b * d')
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x = self.transformer(x)
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x, _ = unpack(x, ps, 'b * d')
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x = x.mean(dim = 1)
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x = self.to_latent(x)
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return self.linear_head(x)
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# main
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if __name__ == '__main__':
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vit = SimpleViT(
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num_classes = 1000,
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image_size = 256,
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patch_size = 8,
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dim = 1024,
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depth = 12,
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heads = 8,
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mlp_dim = 2048,
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num_residual_streams = 8
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)
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images = torch.randn(3, 3, 256, 256)
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logits = vit(images)
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