summaryrefslogtreecommitdiff
path: root/text_recognizer/networks/convnext
diff options
context:
space:
mode:
Diffstat (limited to 'text_recognizer/networks/convnext')
-rw-r--r--text_recognizer/networks/convnext/__init__.py5
-rw-r--r--text_recognizer/networks/convnext/attention.py79
-rw-r--r--text_recognizer/networks/convnext/convnext.py15
3 files changed, 91 insertions, 8 deletions
diff --git a/text_recognizer/networks/convnext/__init__.py b/text_recognizer/networks/convnext/__init__.py
index 8d8470f..1743f09 100644
--- a/text_recognizer/networks/convnext/__init__.py
+++ b/text_recognizer/networks/convnext/__init__.py
@@ -1,2 +1,7 @@
"""Convnext module."""
from text_recognizer.networks.convnext.convnext import ConvNext
+from text_recognizer.networks.convnext.attention import (
+ Attention,
+ FeedForward,
+ TransformerBlock,
+)
diff --git a/text_recognizer/networks/convnext/attention.py b/text_recognizer/networks/convnext/attention.py
index e69de29..7f03436 100644
--- a/text_recognizer/networks/convnext/attention.py
+++ b/text_recognizer/networks/convnext/attention.py
@@ -0,0 +1,79 @@
+"""Convolution self attention block."""
+
+from einops import reduce, rearrange
+from torch import einsum, nn, Tensor
+import torch.nn.functional as F
+
+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
diff --git a/text_recognizer/networks/convnext/convnext.py b/text_recognizer/networks/convnext/convnext.py
index a4556a0..68de81a 100644
--- a/text_recognizer/networks/convnext/convnext.py
+++ b/text_recognizer/networks/convnext/convnext.py
@@ -1,13 +1,9 @@
-from typing import Sequence
+from typing import Optional, Sequence
-from einops import reduce, rearrange
-from einops.layers.torch import Rearrange
-import torch
-from torch import einsum, nn, Tensor
-import torch.nn.functional as F
+from text_recognizer.networks.convnext.attention import TransformerBlock
+from torch import nn, Tensor
from text_recognizer.networks.convnext.downsample import Downsample
-from text_recognizer.networks.convnext.residual import Residual
from text_recognizer.networks.convnext.norm import LayerNorm
@@ -38,9 +34,11 @@ class ConvNext(nn.Module):
dim_mults: Sequence[int] = (2, 4, 8),
depths: Sequence[int] = (3, 3, 6),
downsampling_factors: Sequence[Sequence[int]] = ((2, 2), (2, 2), (2, 2)),
+ attn: Optional[TransformerBlock] = None,
) -> None:
super().__init__()
dims = (dim, *map(lambda m: m * dim, dim_mults))
+ self.attn = attn
self.out_channels = dims[-1]
self.stem = nn.Conv2d(1, dims[0], kernel_size=(7, 7), padding="same")
self.layers = nn.ModuleList([])
@@ -65,11 +63,12 @@ class ConvNext(nn.Module):
nn.init.trunc_normal_(m.weight, std=0.02)
nn.init.constant_(m.bias, 0)
- def forward(self, x):
+ def forward(self, x: Tensor) -> Tensor:
x = self.stem(x)
for init_block, blocks, down in self.layers:
x = init_block(x)
for fn in blocks:
x = fn(x)
x = down(x)
+ x = self.attn(x)
return self.norm(x)