Rename and add flash atten

6 files changed, 0 insertions, 218 deletions

diff --git a/text_recognizer/networks/convnext/__init__.py b/text_recognizer/networks/convnext/__init__.py
deleted file mode 100644
index faebe6f..0000000
--- a/text_recognizer/networks/convnext/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-"""Convnext module."""
-from text_recognizer.networks.convnext.attention import (
-    Attention,
-    FeedForward,
-    TransformerBlock,
-)
-from text_recognizer.networks.convnext.convnext import ConvNext
diff --git a/text_recognizer/networks/convnext/attention.py b/text_recognizer/networks/convnext/attention.py
deleted file mode 100644
index 1334feb..0000000
--- a/text_recognizer/networks/convnext/attention.py
+++ /dev/null
@@ -1,79 +0,0 @@
-"""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
diff --git a/text_recognizer/networks/convnext/convnext.py b/text_recognizer/networks/convnext/convnext.py
deleted file mode 100644
index 9419a15..0000000
--- a/text_recognizer/networks/convnext/convnext.py
+++ /dev/null
@@ -1,77 +0,0 @@
-"""ConvNext module."""
-from typing import Optional, Sequence
-
-from torch import Tensor, nn
-
-from text_recognizer.networks.convnext.attention import TransformerBlock
-from text_recognizer.networks.convnext.downsample import Downsample
-from text_recognizer.networks.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/networks/convnext/downsample.py b/text_recognizer/networks/convnext/downsample.py
deleted file mode 100644
index a8a0466..0000000
--- a/text_recognizer/networks/convnext/downsample.py
+++ /dev/null
@@ -1,21 +0,0 @@
-"""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/networks/convnext/norm.py b/text_recognizer/networks/convnext/norm.py
deleted file mode 100644
index 3355de9..0000000
--- a/text_recognizer/networks/convnext/norm.py
+++ /dev/null
@@ -1,18 +0,0 @@
-"""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/networks/convnext/residual.py b/text_recognizer/networks/convnext/residual.py
deleted file mode 100644
index dfc2847..0000000
--- a/text_recognizer/networks/convnext/residual.py
+++ /dev/null
@@ -1,16 +0,0 @@
-"""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