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authorGustaf Rydholm <gustaf.rydholm@gmail.com>2023-09-03 22:54:09 +0200
committerGustaf Rydholm <gustaf.rydholm@gmail.com>2023-09-03 22:54:09 +0200
commit1732ed564a738a42c1bf6e8127ae810f5658cb06 (patch)
tree8d7f67793fec820850b4f8fd92f762f7f5e4b9f6 /text_recognizer
parent53cfc21cffa4e877ad0959170b47b690d2fdb40f (diff)
Revert "Delete convnext"
This reverts commit 7239bce214607c70a7a91358586f265b2f74de7b.
Diffstat (limited to 'text_recognizer')
-rw-r--r--text_recognizer/network/convnext/__init__.py7
-rw-r--r--text_recognizer/network/convnext/attention.py79
-rw-r--r--text_recognizer/network/convnext/convnext.py77
-rw-r--r--text_recognizer/network/convnext/downsample.py21
-rw-r--r--text_recognizer/network/convnext/norm.py18
-rw-r--r--text_recognizer/network/convnext/residual.py16
6 files changed, 218 insertions, 0 deletions
diff --git a/text_recognizer/network/convnext/__init__.py b/text_recognizer/network/convnext/__init__.py
new file mode 100644
index 0000000..dcff3fc
--- /dev/null
+++ b/text_recognizer/network/convnext/__init__.py
@@ -0,0 +1,7 @@
+"""Convnext module."""
+from text_recognizer.network.convnext.attention import (
+ Attention,
+ FeedForward,
+ TransformerBlock,
+)
+from text_recognizer.network.convnext.convnext import ConvNext
diff --git a/text_recognizer/network/convnext/attention.py b/text_recognizer/network/convnext/attention.py
new file mode 100644
index 0000000..6bc9692
--- /dev/null
+++ b/text_recognizer/network/convnext/attention.py
@@ -0,0 +1,79 @@
+"""Convolution self attention block."""
+
+import torch.nn.functional as F
+from einops import rearrange
+from torch import Tensor, einsum, nn
+
+from text_recognizer.network.convnext.norm import LayerNorm
+from text_recognizer.network.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/network/convnext/convnext.py b/text_recognizer/network/convnext/convnext.py
new file mode 100644
index 0000000..6acf059
--- /dev/null
+++ b/text_recognizer/network/convnext/convnext.py
@@ -0,0 +1,77 @@
+"""ConvNext module."""
+from typing import Optional, Sequence
+
+from torch import Tensor, nn
+
+from text_recognizer.network.convnext.attention import TransformerBlock
+from text_recognizer.network.convnext.downsample import Downsample
+from text_recognizer.network.convnext.norm import LayerNorm
+
+
+class ConvNextBlock(nn.Module):
+ """ConvNext block."""
+
+ def __init__(self, dim: int, dim_out: int, mult: int) -> None:
+ super().__init__()
+ self.ds_conv = nn.Conv2d(
+ dim, dim, kernel_size=(7, 7), padding="same", groups=dim
+ )
+ self.net = nn.Sequential(
+ LayerNorm(dim),
+ nn.Conv2d(dim, dim_out * mult, kernel_size=(3, 3), padding="same"),
+ nn.GELU(),
+ nn.Conv2d(dim_out * mult, dim_out, kernel_size=(3, 3), padding="same"),
+ )
+ self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity()
+
+ def forward(self, x: Tensor) -> Tensor:
+ h = self.ds_conv(x)
+ h = self.net(h)
+ return h + self.res_conv(x)
+
+
+class ConvNext(nn.Module):
+ def __init__(
+ self,
+ dim: int = 16,
+ 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 if attn is not None else nn.Identity()
+ self.out_channels = dims[-1]
+ self.stem = nn.Conv2d(1, dims[0], kernel_size=7, padding="same")
+ self.layers = nn.ModuleList([])
+
+ for i in range(len(dims) - 1):
+ dim_in, dim_out = dims[i], dims[i + 1]
+ self.layers.append(
+ nn.ModuleList(
+ [
+ ConvNextBlock(dim_in, dim_in, 2),
+ nn.ModuleList(
+ [ConvNextBlock(dim_in, dim_in, 2) for _ in range(depths[i])]
+ ),
+ Downsample(dim_in, dim_out, downsampling_factors[i]),
+ ]
+ )
+ )
+ self.norm = LayerNorm(dims[-1])
+
+ def _init_weights(self, m):
+ if isinstance(m, (nn.Conv2d, nn.Linear)):
+ nn.init.trunc_normal_(m.weight, std=0.02)
+ nn.init.constant_(m.bias, 0)
+
+ 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)
diff --git a/text_recognizer/network/convnext/downsample.py b/text_recognizer/network/convnext/downsample.py
new file mode 100644
index 0000000..a8a0466
--- /dev/null
+++ b/text_recognizer/network/convnext/downsample.py
@@ -0,0 +1,21 @@
+"""Convnext downsample module."""
+from typing import Tuple
+
+from einops.layers.torch import Rearrange
+from torch import Tensor, nn
+
+
+class Downsample(nn.Module):
+ """Downsamples feature maps by patches."""
+
+ def __init__(self, dim: int, dim_out: int, factors: Tuple[int, int]) -> None:
+ super().__init__()
+ s1, s2 = factors
+ self.fn = nn.Sequential(
+ Rearrange("b c (h s1) (w s2) -> b (c s1 s2) h w", s1=s1, s2=s2),
+ nn.Conv2d(dim * s1 * s2, dim_out, 1),
+ )
+
+ def forward(self, x: Tensor) -> Tensor:
+ """Applies patch function."""
+ return self.fn(x)
diff --git a/text_recognizer/network/convnext/norm.py b/text_recognizer/network/convnext/norm.py
new file mode 100644
index 0000000..3355de9
--- /dev/null
+++ b/text_recognizer/network/convnext/norm.py
@@ -0,0 +1,18 @@
+"""Layer norm for conv layers."""
+import torch
+from torch import Tensor, nn
+
+
+class LayerNorm(nn.Module):
+ """Layer norm for convolutions."""
+
+ def __init__(self, dim: int) -> None:
+ super().__init__()
+ self.gamma = nn.Parameter(torch.ones(1, dim, 1, 1))
+
+ def forward(self, x: Tensor) -> Tensor:
+ """Applies layer norm."""
+ eps = 1e-5 if x.dtype == torch.float32 else 1e-3
+ var = torch.var(x, dim=1, unbiased=False, keepdim=True)
+ mean = torch.mean(x, dim=1, keepdim=True)
+ return (x - mean) / (var + eps).sqrt() * self.gamma
diff --git a/text_recognizer/network/convnext/residual.py b/text_recognizer/network/convnext/residual.py
new file mode 100644
index 0000000..dfc2847
--- /dev/null
+++ b/text_recognizer/network/convnext/residual.py
@@ -0,0 +1,16 @@
+"""Generic residual layer."""
+from typing import Callable
+
+from torch import Tensor, nn
+
+
+class Residual(nn.Module):
+ """Residual layer."""
+
+ def __init__(self, fn: Callable) -> None:
+ super().__init__()
+ self.fn = fn
+
+ def forward(self, x: Tensor) -> Tensor:
+ """Applies residual fn."""
+ return self.fn(x) + x