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"""Convolution self attention block."""
import torch.nn.functional as F
from einops import rearrange
from torch import Tensor, einsum, nn
from text_recognizer.networks.convnext.norm import LayerNorm
from text_recognizer.networks.convnext.residual import Residual
def l2norm(t: Tensor) -> Tensor:
return F.normalize(t, dim=-1)
class FeedForward(nn.Module):
def __init__(self, dim: int, mult: int = 4) -> None:
super().__init__()
inner_dim = int(dim * mult)
self.fn = Residual(
nn.Sequential(
LayerNorm(dim),
nn.Conv2d(dim, inner_dim, 1, bias=False),
nn.GELU(),
LayerNorm(inner_dim),
nn.Conv2d(inner_dim, dim, 1, bias=False),
)
)
def forward(self, x: Tensor) -> Tensor:
return self.fn(x)
class Attention(nn.Module):
def __init__(
self, dim: int, heads: int = 4, dim_head: int = 64, scale: int = 8
) -> None:
super().__init__()
self.scale = scale
self.heads = heads
inner_dim = heads * dim_head
self.norm = LayerNorm(dim)
self.to_qkv = nn.Conv2d(dim, inner_dim * 3, 1, bias=False)
self.to_out = nn.Conv2d(inner_dim, dim, 1, bias=False)
def forward(self, x: Tensor) -> Tensor:
h, w = x.shape[-2:]
residual = x.clone()
x = self.norm(x)
q, k, v = self.to_qkv(x).chunk(3, dim=1)
q, k, v = map(
lambda t: rearrange(t, "b (h c) ... -> b h (...) c", h=self.heads),
(q, k, v),
)
q, k = map(l2norm, (q, k))
sim = einsum("b h i d, b h j d -> b h i j", q, k) * self.scale
attn = sim.softmax(dim=-1)
out = einsum("b h i j, b h j d -> b h i d", attn, v)
out = rearrange(out, "b h (x y) d -> b (h d) x y", x=h, y=w)
return self.to_out(out) + residual
class TransformerBlock(nn.Module):
def __init__(self, attn: Attention, ff: FeedForward) -> None:
super().__init__()
self.attn = attn
self.ff = ff
def forward(self, x: Tensor) -> Tensor:
x = self.attn(x)
x = self.ff(x)
return x
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