vit-pytorch/vit_pytorch/rvt.py

from math import sqrt, pi, log

import torch
from torch import nn, einsum
import torch.nn.functional as F

from einops import rearrange, repeat
from einops.layers.torch import Rearrange

# rotary embeddings

def rotate_every_two(x):
    x = rearrange(x, '... (d j) -> ... d j', j = 2)
    x1, x2 = x.unbind(dim = -1)
    x = torch.stack((-x2, x1), dim = -1)
    return rearrange(x, '... d j -> ... (d j)')

class AxialRotaryEmbedding(nn.Module):
    def __init__(self, dim, max_freq = 10):
        super().__init__()
        self.dim = dim
        scales = torch.logspace(0., log(max_freq / 2) / log(2), self.dim // 4, base = 2)
        self.register_buffer('scales', scales)

    def forward(self, x):
        device, dtype, n = x.device, x.dtype, int(sqrt(x.shape[-2]))

        seq = torch.linspace(-1., 1., steps = n, device = device)
        seq = seq.unsqueeze(-1)

        scales = self.scales[(*((None,) * (len(seq.shape) - 1)), Ellipsis)]
        scales = scales.to(x)

        seq = seq * scales * pi

        x_sinu = repeat(seq, 'i d -> i j d', j = n)
        y_sinu = repeat(seq, 'j d -> i j d', i = n)

        sin = torch.cat((x_sinu.sin(), y_sinu.sin()), dim = -1)
        cos = torch.cat((x_sinu.cos(), y_sinu.cos()), dim = -1)

        sin, cos = map(lambda t: rearrange(t, 'i j d -> (i j) d'), (sin, cos))
        sin, cos = map(lambda t: repeat(t, 'n d -> () n (d j)', j = 2), (sin, cos))
        return sin, cos

class DepthWiseConv2d(nn.Module):
    def __init__(self, dim_in, dim_out, kernel_size, padding, stride = 1, bias = True):
        super().__init__()
        self.net = nn.Sequential(
            nn.Conv2d(dim_in, dim_in, kernel_size = kernel_size, padding = padding, groups = dim_in, stride = stride, bias = bias),
            nn.Conv2d(dim_in, dim_out, kernel_size = 1, bias = bias)
        )
    def forward(self, x):
        return self.net(x)

# helper classes

class 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 SpatialConv(nn.Module):
    def __init__(self, dim_in, dim_out, kernel, bias = False):
        super().__init__()
        self.conv = DepthWiseConv2d(dim_in, dim_out, kernel, padding = kernel // 2, bias = False)

    def forward(self, x, fmap_dims):
        cls_token, x = x[:, :1], x[:, 1:]
        x = rearrange(x, 'b (h w) d -> b d h w', **fmap_dims)
        x = self.conv(x)
        x = rearrange(x, 'b d h w -> b (h w) d')
        return torch.cat((cls_token, x), dim = 1)

class GEGLU(nn.Module):
    def forward(self, x):
        x, gates = x.chunk(2, dim = -1)
        return F.gelu(gates) * x

class FeedForward(nn.Module):
    def __init__(self, dim, hidden_dim, dropout = 0.):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(dim, hidden_dim * 2),
            GEGLU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, dim),
            nn.Dropout(dropout)
        )
    def forward(self, x):
        return self.net(x)

class Attention(nn.Module):
    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0., conv_query_kernel = 5):
        super().__init__()
        inner_dim = dim_head *  heads

        self.heads = heads
        self.scale = dim_head ** -0.5

        self.attend = nn.Softmax(dim = -1)

        self.to_q = SpatialConv(dim, inner_dim, conv_query_kernel, bias = False)

        self.to_kv = nn.Linear(dim, inner_dim * 2, bias = False)

        self.to_out = nn.Sequential(
            nn.Linear(inner_dim, dim),
            nn.Dropout(dropout)
        )

    def forward(self, x, pos_emb, fmap_dims):
        b, n, _, h = *x.shape, self.heads

        q = self.to_q(x, fmap_dims = fmap_dims)
        qkv = (q, *self.to_kv(x).chunk(2, dim = -1))

        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h = h), qkv)

        # apply 2d rotary embeddings to queries and keys, excluding CLS tokens

        sin, cos = pos_emb
        (q_cls, q), (k_cls, k) = map(lambda t: (t[:, :1], t[:, 1:]), (q, k))
        q, k = map(lambda t: (t * cos) + (rotate_every_two(t) * sin), (q, k))

        # concat back the CLS tokens

        q = torch.cat((q_cls, q), dim = 1)
        k = torch.cat((k_cls, k), dim = 1)

        dots = einsum('b i d, b j d -> b i j', q, k) * self.scale

        attn = self.attend(dots)

        out = einsum('b i j, b j d -> b i d', attn, v)
        out = rearrange(out, '(b h) n d -> b n (h d)', h = h)
        return self.to_out(out)

class Transformer(nn.Module):
    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
        super().__init__()
        self.layers = nn.ModuleList([])
        self.pos_emb = AxialRotaryEmbedding(dim_head)
        for _ in range(depth):
            self.layers.append(nn.ModuleList([
                PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
                PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
            ]))
    def forward(self, x, fmap_dims):
        pos_emb = self.pos_emb(x[:, 1:])

        for attn, ff in self.layers:
            x = attn(x, pos_emb = pos_emb, fmap_dims = fmap_dims) + x
            x = ff(x) + x
        return x

# Rotary Vision Transformer

class RvT(nn.Module):
    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.):
        super().__init__()
        assert image_size % patch_size == 0, 'Image dimensions must be divisible by the patch size.'
        num_patches = (image_size // patch_size) ** 2
        patch_dim = channels * patch_size ** 2

        self.patch_size = patch_size
        self.to_patch_embedding = nn.Sequential(
            Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size),
            nn.Linear(patch_dim, dim),
        )

        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)

        self.mlp_head = nn.Sequential(
            nn.LayerNorm(dim),
            nn.Linear(dim, num_classes)
        )

    def forward(self, img):
        b, _, h, w, p = *img.shape, self.patch_size

        x = self.to_patch_embedding(img)
        n = x.shape[1]

        cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
        x = torch.cat((cls_tokens, x), dim=1)

        fmap_dims = {'h': h // p, 'w': w // p}
        x = self.transformer(x, fmap_dims = fmap_dims)

        return self.mlp_head(x)