Rename and add flash atten

19 files changed, 0 insertions, 837 deletions

diff --git a/text_recognizer/networks/__init__.py b/text_recognizer/networks/__init__.py
deleted file mode 100644
index f921882..0000000
--- a/text_recognizer/networks/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-"""Network modules"""
-from text_recognizer.networks.conv_transformer import ConvTransformer
diff --git a/text_recognizer/networks/conv_transformer.py b/text_recognizer/networks/conv_transformer.py
deleted file mode 100644
index d36162a..0000000
--- a/text_recognizer/networks/conv_transformer.py
+++ /dev/null
@@ -1,49 +0,0 @@
-"""Base network module."""
-from typing import Type
-
-from torch import Tensor, nn
-
-from text_recognizer.networks.transformer.decoder import Decoder
-
-
-class ConvTransformer(nn.Module):
-    """Base transformer network."""
-
-    def __init__(
-        self,
-        encoder: Type[nn.Module],
-        decoder: Decoder,
-    ) -> None:
-        super().__init__()
-        self.encoder = encoder
-        self.decoder = decoder
-
-    def encode(self, img: Tensor) -> Tensor:
-        """Encodes images to latent representation."""
-        return self.encoder(img)
-
-    def decode(self, tokens: Tensor, img_features: Tensor) -> Tensor:
-        """Decodes latent images embedding into characters."""
-        return self.decoder(tokens, img_features)
-
-    def forward(self, img: Tensor, tokens: Tensor) -> Tensor:
-        """Encodes images into token logtis.
-
-        Args:
-            img (Tensor): Input image(s).
-            tokens (Tensor): token embeddings.
-
-        Shapes:
-            - img: :math: `(B, 1, H, W)`
-            - tokens: :math: `(B, Sy)`
-            - logits: :math: `(B, Sy, C)`
-
-            where B is the batch size, H is the image height, W is the image
-            width, Sy the output length, and C is the number of classes.
-
-        Returns:
-            Tensor: Sequence of logits.
-        """
-        img_features = self.encode(img)
-        logits = self.decode(tokens, img_features)
-        return logits
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
diff --git a/text_recognizer/networks/image_encoder.py b/text_recognizer/networks/image_encoder.py
deleted file mode 100644
index ab60560..0000000
--- a/text_recognizer/networks/image_encoder.py
+++ /dev/null
@@ -1,45 +0,0 @@
-"""Encodes images to latent embeddings."""
-from typing import Tuple, Type
-
-from torch import Tensor, nn
-
-from text_recognizer.networks.transformer.embeddings.axial import (
-    AxialPositionalEmbeddingImage,
-)
-
-
-class ImageEncoder(nn.Module):
-    """Encodes images to latent embeddings."""
-
-    def __init__(
-        self,
-        encoder: Type[nn.Module],
-        pixel_embedding: AxialPositionalEmbeddingImage,
-    ) -> None:
-        super().__init__()
-        self.encoder = encoder
-        self.pixel_embedding = pixel_embedding
-
-    def forward(self, img: Tensor) -> Tensor:
-        """Encodes an image into a latent feature vector.
-
-        Args:
-            img (Tensor): Image tensor.
-
-        Shape:
-            - x: :math: `(B, C, H, W)`
-            - z: :math: `(B, Sx, D)`
-
-            where Sx is the length of the flattened feature maps projected from
-            the encoder. D latent dimension for each pixel in the projected
-            feature maps.
-
-        Returns:
-            Tensor: A Latent embedding of the image.
-        """
-        z = self.encoder(img)
-        z = z + self.pixel_embedding(z)
-        z = z.flatten(start_dim=2)
-        # Permute tensor from [B, E, Ho * Wo] to [B, Sx, E]
-        z = z.permute(0, 2, 1)
-        return z
diff --git a/text_recognizer/networks/text_decoder.py b/text_recognizer/networks/text_decoder.py
deleted file mode 100644
index 500bcf9..0000000
--- a/text_recognizer/networks/text_decoder.py
+++ /dev/null
@@ -1,55 +0,0 @@
-"""Text decoder."""
-import torch
-from torch import Tensor, nn
-
-from text_recognizer.networks.transformer.decoder import Decoder
-
-
-class TextDecoder(nn.Module):
-    """Decodes images to token logits."""
-
-    def __init__(
-        self,
-        dim: int,
-        num_classes: int,
-        pad_index: Tensor,
-        decoder: Decoder,
-    ) -> None:
-        super().__init__()
-        self.dim = dim
-        self.num_classes = num_classes
-        self.pad_index = pad_index
-        self.decoder = decoder
-        self.token_embedding = nn.Embedding(
-            num_embeddings=self.num_classes, embedding_dim=self.dim
-        )
-        self.to_logits = nn.Linear(in_features=self.dim, out_features=self.num_classes)
-
-    def forward(self, tokens: Tensor, img_features: Tensor) -> Tensor:
-        """Decodes latent images embedding into logit tokens.
-
-        Args:
-            tokens (Tensor): Token indecies.
-            img_features (Tensor): Latent images embedding.
-
-        Shapes:
-            - tokens: :math: `(B, Sy)`
-            - img_features: :math: `(B, Sx, D)`
-            - logits: :math: `(B, Sy, C)`
-
-            where Sy is the length of the output, C is the number of classes
-            and D is the hidden dimension.
-
-        Returns:
-            Tensor: Sequence of logits.
-        """
-        tokens = tokens.long()
-        mask = tokens != self.pad_index
-        tokens = self.token_embedding(tokens)
-        tokens = self.decoder(x=tokens, context=img_features, mask=mask)
-        logits = (
-            tokens @ torch.transpose(self.token_embedding.weight.to(tokens.dtype), 0, 1)
-        ).float()
-        logits = self.to_logits(tokens)  # [B, Sy, C]
-        logits = logits.permute(0, 2, 1)  # [B, C, Sy]
-        return logits
diff --git a/text_recognizer/networks/transformer/__init__.py b/text_recognizer/networks/transformer/__init__.py
deleted file mode 100644
index 0d17deb..0000000
--- a/text_recognizer/networks/transformer/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-"""Transformer modules."""
-from text_recognizer.networks.transformer.attention import Attention
-from text_recognizer.networks.transformer.decoder import Decoder, DecoderBlock
-from text_recognizer.networks.transformer.embeddings.rotary import RotaryEmbedding
-from text_recognizer.networks.transformer.ff import FeedForward
-from text_recognizer.networks.transformer.norm import RMSNorm
diff --git a/text_recognizer/networks/transformer/attention.py b/text_recognizer/networks/transformer/attention.py
deleted file mode 100644
index 85f513e..0000000
--- a/text_recognizer/networks/transformer/attention.py
+++ /dev/null
@@ -1,109 +0,0 @@
-"""Implementes the attention module for the transformer."""
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-from einops import rearrange
-from torch import Tensor, einsum, nn
-
-from text_recognizer.networks.transformer.embeddings.rotary import (
-    RotaryEmbedding,
-)
-
-
-class Attention(nn.Module):
-    """Standard attention."""
-
-    def __init__(
-        self,
-        dim: int,
-        num_heads: int,
-        causal: bool = False,
-        dim_head: int = 64,
-        dropout_rate: float = 0.0,
-    ) -> None:
-        super().__init__()
-        self.dim = dim
-        self.scale = self.dim**-0.5
-        self.num_heads = num_heads
-        self.dim_head = dim_head
-
-        self.causal = causal
-        self.dropout_rate = dropout_rate
-
-        # Single key/value head
-        k_dim = dim_head
-        v_dim = dim_head
-
-        out_dim = self.num_heads * self.dim_head
-
-        self.to_q = nn.Linear(self.dim, out_dim, bias=False)
-        self.to_k = nn.Linear(self.dim, k_dim, bias=False)
-        self.to_v = nn.Linear(self.dim, v_dim, bias=False)
-
-        self.dropout = nn.Dropout(p=self.dropout_rate)
-
-        # Feedforward
-        self.fc = nn.Linear(out_dim, self.dim)
-
-    def forward(
-        self,
-        x: Tensor,
-        context: Optional[Tensor] = None,
-        mask: Optional[Tensor] = None,
-        rotary_embedding: Optional[RotaryEmbedding] = None,
-    ) -> Tensor:
-        """Computes the attention."""
-        b, device = x.shape[0], x.device
-
-        q = self.to_q(x)
-        q = rearrange(q, "b n (h d) -> b h n d", h=self.num_heads)
-        k = self.to_k(context) if context is not None else self.to_k(x)
-        v = self.to_v(context) if context is not None else self.to_v(x)
-
-        if rotary_embedding is not None:
-            q, k, v = map(lambda t: rotary_embedding.rotate(t), (q, k, v))
-
-        energy = einsum("b h i d, b j d -> b h i j", q, k) * self.scale
-        mask_value = -torch.finfo(energy.dtype).max
-        energy = apply_input_mask(b, k, energy, mask, mask_value, device)
-        if self.causal:
-            energy = apply_causal_mask(energy, mask, mask_value, device)
-
-        attn = F.softmax(energy, dim=-1)
-        attn = self.dropout(attn)
-        out = einsum("b h i j, b j d -> b h i d", attn, v)
-        out = rearrange(out, "b h n d -> b n (h d)")
-        out = self.fc(out)
-        return out
-
-
-def apply_input_mask(
-    b: int,
-    k: Tensor,
-    energy: Tensor,
-    mask: Optional[Tensor],
-    mask_value: Tensor,
-    device: str,
-) -> Tensor:
-    """Applies an input mask."""
-    if mask is not None:
-        k_mask = torch.ones((b, k.shape[-2]), device=device).bool()
-        q_mask = rearrange(mask, "b i -> b () i ()")
-        k_mask = rearrange(k_mask, "b j -> b () () j")
-        input_mask = q_mask * k_mask
-
-        energy = energy.masked_fill_(~input_mask, mask_value)
-    return energy
-
-
-def apply_causal_mask(
-    energy: Tensor, mask: Tensor, mask_value: Tensor, device: str
-) -> Tensor:
-    """Applies a causal mask to the energy tensor."""
-    i, j = energy.shape[-2:]
-    r = torch.arange(i, device=device)
-    mask = rearrange(r, "i -> () () i ()") < rearrange(r, "j -> () () () j")
-    mask = F.pad(mask, (j - i, 0), value=False)
-    energy.masked_fill_(mask, mask_value)
-    return energy
diff --git a/text_recognizer/networks/transformer/decoder.py b/text_recognizer/networks/transformer/decoder.py
deleted file mode 100644
index 826bc13..0000000
--- a/text_recognizer/networks/transformer/decoder.py
+++ /dev/null
@@ -1,41 +0,0 @@
-"""Transformer decoder module."""
-from copy import deepcopy
-from typing import Optional
-
-from torch import Tensor, nn
-
-from text_recognizer.networks.transformer.decoder_block import DecoderBlock
-from text_recognizer.networks.transformer.embeddings.rotary import RotaryEmbedding
-
-
-class Decoder(nn.Module):
-    """Decoder Network."""
-
-    def __init__(
-        self,
-        depth: int,
-        dim: int,
-        block: DecoderBlock,
-        rotary_embedding: RotaryEmbedding,
-    ) -> None:
-        super().__init__()
-        self.depth = depth
-        self.rotary_embedding = rotary_embedding
-        self.blocks = nn.ModuleList([deepcopy(block) for _ in range(self.depth)])
-        self.ln = nn.LayerNorm(dim)
-
-    def forward(
-        self,
-        x: Tensor,
-        context: Optional[Tensor] = None,
-        mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        """Applies attention blocks."""
-        for block in self.blocks:
-            x = block(
-                x=x,
-                context=context,
-                mask=mask,
-                rotary_embedding=self.rotary_embedding,
-            )
-        return self.ln(x)
diff --git a/text_recognizer/networks/transformer/decoder_block.py b/text_recognizer/networks/transformer/decoder_block.py
deleted file mode 100644
index b8eb5c4..0000000
--- a/text_recognizer/networks/transformer/decoder_block.py
+++ /dev/null
@@ -1,44 +0,0 @@
-"""Transformer decoder module."""
-from copy import deepcopy
-from typing import Optional, Type
-
-from torch import Tensor, nn
-
-from text_recognizer.networks.transformer.attention import Attention
-from text_recognizer.networks.transformer.embeddings.rotary import RotaryEmbedding
-from text_recognizer.networks.transformer.ff import FeedForward
-
-
-class DecoderBlock(nn.Module):
-    """Residual decoder block."""
-
-    def __init__(
-        self,
-        self_attn: Attention,
-        norm: Type[nn.Module],
-        ff: FeedForward,
-        cross_attn: Optional[Attention] = None,
-    ) -> None:
-        super().__init__()
-        self.ln_attn = norm
-        self.attn = self_attn
-        self.ln_cross_attn = deepcopy(norm)
-        self.cross_attn = cross_attn
-        self.ln_ff = deepcopy(norm)
-        self.ff = ff
-
-    def forward(
-        self,
-        x: Tensor,
-        rotary_embedding: RotaryEmbedding,
-        context: Optional[Tensor] = None,
-        mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        """Applies decoder block on input signals."""
-        x = x + self.attn(self.ln_attn(x), mask=mask, rotary_embedding=rotary_embedding)
-        x = x + self.cross_attn(
-            x=self.ln_cross_attn(x),
-            context=context,
-        )
-        x = x + self.ff(self.ln_ff(x))
-        return x
diff --git a/text_recognizer/networks/transformer/embeddings/__init__.py b/text_recognizer/networks/transformer/embeddings/__init__.py
deleted file mode 100644
index bb3f904..0000000
--- a/text_recognizer/networks/transformer/embeddings/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Positional encodings for transformers."""
diff --git a/text_recognizer/networks/transformer/embeddings/axial.py b/text_recognizer/networks/transformer/embeddings/axial.py
deleted file mode 100644
index 9b872a9..0000000
--- a/text_recognizer/networks/transformer/embeddings/axial.py
+++ /dev/null
@@ -1,104 +0,0 @@
-"""Axial attention for multi-dimensional data.
-
-Stolen from:
-    https://github.com/lucidrains/axial-attention/blob/
-    eff2c10c2e76c735a70a6b995b571213adffbbb7/axial_attention/axial_attention.py#L100
-"""
-from functools import reduce
-from operator import mul
-from typing import Optional, Sequence
-
-import torch
-from torch import Tensor, nn
-
-
-class AxialPositionalEmbedding(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        axial_shape: Sequence[int],
-        axial_dims: Optional[Sequence[int]] = None,
-    ) -> None:
-        super().__init__()
-
-        self.dim = dim
-        self.shape = axial_shape
-        self.max_seq_len = reduce(mul, axial_shape, 1)
-
-        self.summed = axial_dims is None
-        axial_dims = ((dim,) * len(axial_shape)) if self.summed else axial_dims
-
-        assert len(self.shape) == len(
-            axial_dims
-        ), "number of axial dimensions must equal the number of dimensions in the shape"
-        assert (
-            self.summed or not self.summed and sum(axial_dims) == dim
-        ), f"axial dimensions must sum up to the target dimension {dim}"
-
-        self.weights = ParameterList(self, "weights", len(axial_shape))
-
-        for ind, (shape, axial_dim) in enumerate(zip(self.shape, axial_dims)):
-            ax_shape = [1] * len(self.shape)
-            ax_shape[ind] = shape
-            ax_shape = (1, *ax_shape, axial_dim)
-            ax_emb = nn.Parameter(torch.zeros(ax_shape).normal_(0, 1))
-            self.weights.append(ax_emb)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Returns axial positional embedding."""
-        b, t, _ = x.shape
-        assert (
-            t <= self.max_seq_len
-        ), f"Sequence length ({t}) must be less than the maximum sequence length allowed ({self.max_seq_len})"
-        embs = []
-
-        for ax_emb in self.weights.to_list():
-            axial_dim = ax_emb.shape[-1]
-            expand_shape = (b, *self.shape, axial_dim)
-            emb = ax_emb.expand(expand_shape).reshape(b, self.max_seq_len, axial_dim)
-            embs.append(emb)
-
-        pos_emb = sum(embs) if self.summed else torch.cat(embs, dim=-1)
-        return pos_emb[:, :t].to(x)
-
-
-# a mock parameter list object until below issue is resolved
-# https://github.com/pytorch/pytorch/issues/36035
-class ParameterList(object):
-    def __init__(self, kls, prefix, length):
-        self.ind = 0
-        self.kls = kls
-        self.prefix = prefix
-        self.length = length
-
-    def _keyname(self, prefix, ind):
-        return f"{prefix}_{ind}"
-
-    def append(self, x):
-        setattr(self.kls, self._keyname(self.prefix, self.ind), x)
-        self.ind += 1
-
-    def to_list(self):
-        return [
-            getattr(self.kls, self._keyname(self.prefix, i)) for i in range(self.length)
-        ]
-
-
-class AxialPositionalEmbeddingImage(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        axial_shape: Sequence[int],
-        axial_dims: Optional[Sequence[int]] = None,
-    ) -> None:
-        super().__init__()
-        axial_dims = (dim // 2, dim // 2) if axial_dims is None else axial_dims
-        assert len(axial_shape) == 2, "Axial shape must have 2 dimensions for images"
-        self.dim = dim
-        self.pos_emb = AxialPositionalEmbedding(dim, axial_shape, axial_dims)
-
-    def forward(self, img):
-        b, c, h, w = img.shape
-        img = img.permute(0, 2, 3, 1).reshape(b, h * w, c)
-        pos_emb = self.pos_emb(img)
-        return pos_emb.reshape(b, h, w, self.dim).permute(0, 3, 1, 2)
diff --git a/text_recognizer/networks/transformer/embeddings/rotary.py b/text_recognizer/networks/transformer/embeddings/rotary.py
deleted file mode 100644
index ca0a260..0000000
--- a/text_recognizer/networks/transformer/embeddings/rotary.py
+++ /dev/null
@@ -1,67 +0,0 @@
-"""Roatary embedding.
-
-Stolen from lucidrains:
-    https://github.com/lucidrains/rotary-embedding-torch
-
-Explanation of roatary:
-    https://blog.eleuther.ai/rotary-embeddings/
-"""
-from inspect import isfunction
-
-from einops import rearrange, repeat
-import torch
-from torch import Tensor, nn
-
-
-class RotaryEmbedding(nn.Module):
-    """Rotary positional embedding."""
-
-    def __init__(self, dim: int) -> None:
-        super().__init__()
-        inv_freqs = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
-        self.register_buffer("inv_freqs", inv_freqs)
-        self.cache = {}
-
-    def rotate(self, t: Tensor, dim: int = -2) -> Tensor:
-        """Rotate vector."""
-        device, n = t.device, t.shape[dim]
-        freqs = self.forward(lambda: torch.arange(n, device=device), cache_key=n)
-        return apply_rotary_emb(t, freqs)
-
-    def forward(self, t: Tensor, cache_key: int) -> Tensor:
-        """Encodes tensor x with rotary embeddings."""
-        if cache_key in self.cache:
-            return self.cache[cache_key]
-
-        if isfunction(t):
-            t = t()
-
-        freqs = self.inv_freqs
-        freqs = torch.einsum("..., f -> ... f", t.type(freqs.dtype), freqs)
-        freqs = repeat(freqs, "... n -> ... (n r)", r=2)
-        self.cache[cache_key] = freqs
-        return freqs
-
-
-def rotate_half(x: Tensor) -> Tensor:
-    x = rearrange(x, "... (d r) -> ... d r", r=2)
-    x1, x2 = x.unbind(dim=-1)
-    x = torch.stack((-x2, x1), dim=-1)
-    return rearrange(x, "... d r -> ... (d r)")
-
-
-def apply_rotary_emb(t: Tensor, freqs: Tensor, start_index: int = 0) -> Tensor:
-    freqs = freqs.to(t)
-    rot_dim = freqs.shape[-1]
-    end_index = start_index + rot_dim
-    assert rot_dim <= t.shape[-1], (
-        f"feature dimension {t.shape[-1]} is not of sufficient size to rotate"
-        f"in all the positions {rot_dim}"
-    )
-    t_left, t, t_right = (
-        t[..., :start_index],
-        t[..., start_index:end_index],
-        t[..., end_index:],
-    )
-    t = (t * freqs.cos()) + (rotate_half(t) * freqs.sin())
-    return torch.cat((t_left, t, t_right), dim=-1)
diff --git a/text_recognizer/networks/transformer/ff.py b/text_recognizer/networks/transformer/ff.py
deleted file mode 100644
index 3ccf5b5..0000000
--- a/text_recognizer/networks/transformer/ff.py
+++ /dev/null
@@ -1,45 +0,0 @@
-"""Feedforward layer in transformer.
-
-Stolen from lucidrains:
-    https://github.com/lucidrains/x-transformers/blob/main/x_transformers/x_transformers.py
-"""
-from typing import Optional
-
-import torch.nn.functional as F
-from torch import Tensor, nn
-
-
-class GEGLU(nn.Module):
-    def __init__(self, dim_in: int, dim_out: int) -> None:
-        super().__init__()
-        self.fc = nn.Linear(dim_in, dim_out * 2)
-
-    def forward(self, x: Tensor) -> Tensor:
-        x, gate = self.fc(x).chunk(2, dim=-1)
-        return x * F.gelu(gate)
-
-
-class FeedForward(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        dim_out: Optional[int] = None,
-        expansion_factor: int = 4,
-        glu: bool = True,
-        dropout_rate: float = 0.0,
-    ) -> None:
-        super().__init__()
-        inner_dim = dim * expansion_factor
-        dim_out = dim_out if dim_out is not None else dim
-        in_projection = (
-            nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
-            if not glu
-            else GEGLU(dim, inner_dim)
-        )
-
-        self.mlp = nn.Sequential(
-            in_projection, nn.Dropout(dropout_rate), nn.Linear(inner_dim, dim_out)
-        )
-
-    def forward(self, x: Tensor) -> Tensor:
-        return self.mlp(x)
diff --git a/text_recognizer/networks/transformer/norm.py b/text_recognizer/networks/transformer/norm.py
deleted file mode 100644
index 1431327..0000000
--- a/text_recognizer/networks/transformer/norm.py
+++ /dev/null
@@ -1,51 +0,0 @@
-"""Normalization layers for transfromers.
-
-Copied from lucidrains:
-    https://github.com/lucidrains/x-transformers/blob/main/x_transformers/x_transformers.py
-
-"""
-from typing import Optional, Type
-
-import torch
-from torch import Tensor, nn
-
-
-class RMSNorm(nn.Module):
-    """Root mean square layer normalization."""
-
-    def __init__(self, dim: int, eps: float = 1e-8) -> None:
-        super().__init__()
-        self.scale = dim**-0.5
-        self.eps = eps
-        self.g = nn.Parameter(torch.ones(dim))
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Applies normalization."""
-        norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
-        return x / norm.clamp(min=self.eps) * self.g
-
-
-class PreNorm(nn.Module):
-    """Applies layer normalization then function."""
-
-    def __init__(
-        self,
-        normalized_shape: int,
-        fn: Type[nn.Module],
-        context_dim: Optional[int] = None,
-    ) -> None:
-        super().__init__()
-        self.norm = nn.LayerNorm(normalized_shape)
-        self.fn = fn
-        self.norm_context = (
-            nn.LayerNorm(context_dim) if context_dim is not None else None
-        )
-
-    def forward(self, x: Tensor, **kwargs) -> Tensor:
-        """Applies pre norm."""
-        x = self.norm(x)
-        if self.norm_context is not None:
-            context = kwargs["context"]
-            normed_context = self.norm_context(context)
-            kwargs.update(context=normed_context)
-        return self.fn(x, **kwargs)