chore: remove axial attention

chore: remove axial attention

5 files changed, 0 insertions, 209 deletions

diff --git a/text_recognizer/networks/conv_transformer.py b/text_recognizer/networks/conv_transformer.py
index a068ea3..5b29362 100644
--- a/text_recognizer/networks/conv_transformer.py
+++ b/text_recognizer/networks/conv_transformer.py
@@ -6,7 +6,6 @@ from loguru import logger as log
 from torch import nn, Tensor
 
 from text_recognizer.networks.base import BaseTransformer
-from text_recognizer.networks.transformer.axial_attention.encoder import AxialEncoder
 from text_recognizer.networks.transformer.decoder import Decoder
 from text_recognizer.networks.transformer.embeddings.axial import (
     AxialPositionalEmbedding,
@@ -24,7 +23,6 @@ class ConvTransformer(BaseTransformer):
         pad_index: Tensor,
         encoder: Type[nn.Module],
         decoder: Decoder,
-        axial_encoder: Optional[AxialEncoder],
         pixel_pos_embedding: AxialPositionalEmbedding,
         token_pos_embedding: Optional[Type[nn.Module]] = None,
     ) -> None:
@@ -39,7 +37,6 @@ class ConvTransformer(BaseTransformer):
         )
 
         self.pixel_pos_embedding = pixel_pos_embedding
-        self.axial_encoder = axial_encoder
 
         # Latent projector for down sampling number of filters and 2d
         # positional encoding.
@@ -79,7 +76,6 @@ class ConvTransformer(BaseTransformer):
         z = self.encoder(x)
         z = self.conv(z)
         z = self.pixel_pos_embedding(z)
-        z = self.axial_encoder(z) if self.axial_encoder is not None else z
         z = z.flatten(start_dim=2)
 
         # Permute tensor from [B, E, Ho * Wo] to [B, Sx, E]
diff --git a/text_recognizer/networks/transformer/axial_attention/__init__.py b/text_recognizer/networks/transformer/axial_attention/__init__.py
deleted file mode 100644
index e69de29..0000000
--- a/text_recognizer/networks/transformer/axial_attention/__init__.py
+++ /dev/null
diff --git a/text_recognizer/networks/transformer/axial_attention/encoder.py b/text_recognizer/networks/transformer/axial_attention/encoder.py
deleted file mode 100644
index 3082bd6..0000000
--- a/text_recognizer/networks/transformer/axial_attention/encoder.py
+++ /dev/null
@@ -1,80 +0,0 @@
-"""Axial transformer encoder."""
-
-from typing import List
-
-import attr
-from torch import nn, Tensor
-
-from text_recognizer.networks.transformer.axial_attention.self_attention import (
-    SelfAttention,
-)
-from text_recognizer.networks.transformer.axial_attention.utils import (
-    calculate_permutations,
-    PermuteToForm,
-    Sequential,
-)
-from text_recognizer.networks.transformer.norm import PreNorm
-
-
-@attr.s(eq=False)
-class AxialEncoder(nn.Module):
-    """Axial transfomer encoder."""
-
-    def __attrs_pre_init__(self) -> None:
-        super().__init__()
-
-    shape: List[int] = attr.ib()
-    dim: int = attr.ib()
-    depth: int = attr.ib()
-    heads: int = attr.ib()
-    dim_head: int = attr.ib()
-    dim_index: int = attr.ib()
-    fn: nn.Sequential = attr.ib(init=False)
-
-    def __attrs_post_init__(self) -> None:
-        self._build()
-
-    def _build(self) -> None:
-        permutations = calculate_permutations(2, self.dim_index)
-        get_ff = lambda: nn.Sequential(
-            nn.LayerNorm([self.dim, *self.shape]),
-            nn.Conv2d(
-                in_channels=self.dim,
-                out_channels=4 * self.dim,
-                kernel_size=3,
-                padding=1,
-            ),
-            nn.Mish(inplace=True),
-            nn.Conv2d(
-                in_channels=4 * self.dim,
-                out_channels=self.dim,
-                kernel_size=3,
-                padding=1,
-            ),
-        )
-
-        layers = nn.ModuleList([])
-        for _ in range(self.depth):
-            attns = nn.ModuleList(
-                [
-                    PermuteToForm(
-                        permutation=permutation,
-                        fn=PreNorm(
-                            self.dim,
-                            SelfAttention(
-                                dim=self.dim, heads=self.heads, dim_head=self.dim_head
-                            ),
-                        ),
-                    )
-                    for permutation in permutations
-                ]
-            )
-            convs = nn.ModuleList([get_ff(), get_ff()])
-            layers.append(attns)
-            layers.append(convs)
-
-        self.fn = Sequential(layers)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Applies fn to input."""
-        return self.fn(x)
diff --git a/text_recognizer/networks/transformer/axial_attention/self_attention.py b/text_recognizer/networks/transformer/axial_attention/self_attention.py
deleted file mode 100644
index ba162be..0000000
--- a/text_recognizer/networks/transformer/axial_attention/self_attention.py
+++ /dev/null
@@ -1,45 +0,0 @@
-"""Axial self attention module."""
-
-import attr
-import torch
-from torch import nn
-from torch import Tensor
-
-
-@attr.s(eq=False)
-class SelfAttention(nn.Module):
-    """Axial self attention module."""
-
-    def __attrs_pre_init__(self) -> None:
-        super().__init__()
-
-    dim: int = attr.ib()
-    dim_head: int = attr.ib()
-    heads: int = attr.ib()
-    dim_hidden: int = attr.ib(init=False)
-    to_q: nn.Linear = attr.ib(init=False)
-    to_kv: nn.Linear = attr.ib(init=False)
-    to_out: nn.Linear = attr.ib(init=False)
-
-    def __attrs_post_init__(self) -> None:
-        self.dim_hidden = self.heads * self.dim_head
-        self.to_q = nn.Linear(self.dim, self.dim_hidden, bias=False)
-        self.to_kv = nn.Linear(self.dim, 2 * self.dim_hidden, bias=False)
-        self.to_out = nn.Linear(self.dim_hidden, self.dim)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Applies self attention."""
-        q, k, v = (self.to_q(x), *self.to_kv(x).chunk(2, dim=-1))
-        b, _, d, h, e = *q.shape, self.heads, self.dim_head
-
-        merge_heads = (
-            lambda x: x.reshape(b, -1, h, e).transpose(1, 2).reshape(b * h, -1, e)
-        )
-        q, k, v = map(merge_heads, (q, k, v))
-
-        energy = torch.einsum("bie,bje->bij", q, k) * (e ** -0.5)
-        energy = energy.softmax(dim=-1)
-        attn = torch.einsum("bij,bje->bie", energy, v)
-
-        out = attn.reshape(b, h, -1, e).transpose(1, 2).reshape(b, -1, d)
-        return self.to_out(out)
diff --git a/text_recognizer/networks/transformer/axial_attention/utils.py b/text_recognizer/networks/transformer/axial_attention/utils.py
deleted file mode 100644
index 534ef4e..0000000
--- a/text_recognizer/networks/transformer/axial_attention/utils.py
+++ /dev/null
@@ -1,80 +0,0 @@
-"""Helper functions for axial attention."""
-from operator import itemgetter
-from typing import Callable, List, Tuple
-
-import attr
-from torch import nn, Tensor
-
-
-def _map_el_ind(arr: Tensor, ind: int) -> List:
-    return list(map(itemgetter(ind), arr))
-
-
-def _sort_indices(arr: Tensor) -> Tuple[List[int], List[int]]:
-    indices = [i for i in range(len(arr))]
-    arr = zip(arr, indices)
-    arr = sorted(arr)
-    return _map_el_ind(arr, 0), _map_el_ind(arr, 1)
-
-
-def calculate_permutations(num_dims: int, emb_dim: int) -> List[List[int]]:
-    """Returns permutations of tensor."""
-    total_dims = num_dims + 2
-    axial_dims = [i for i in range(1, total_dims) if i != emb_dim]
-
-    permutations = []
-
-    for axial_dim in axial_dims:
-        last_two_dims = [axial_dim, emb_dim]
-        dims_rest = set(range(0, total_dims)) - set(last_two_dims)
-        permutation = [*dims_rest, *last_two_dims]
-        permutations.append(permutation)
-
-    return permutations
-
-
-@attr.s(eq=False)
-class PermuteToForm(nn.Module):
-    """Helper class for applying axial attention."""
-
-    def __attrs_pre_init__(self) -> None:
-        super().__init__()
-
-    fn: Callable = attr.ib()
-    permutation: List[List[int]] = attr.ib()
-    inv_permutation: List[List[int]] = attr.ib(init=False)
-
-    def __attrs_post_init__(self) -> None:
-        _, self.inv_permutation = _sort_indices(self.permutation)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Permutes tensor, applies axial attention, permutes tensor back."""
-        x = x.permute(*self.permutation).contiguous()
-        shape = x.shape
-        *_, t, d = shape
-
-        # Merge all but axial dimension
-        x = x.reshape(-1, t, d)
-
-        # Apply attention
-        x = self.fn(x)
-
-        # Restore original shape and permutation
-        x = x.reshape(*shape)
-        x = x.permute(*self.inv_permutation).contiguous()
-        return x
-
-
-class Sequential(nn.Module):
-    """Applies a list of paired functions to input."""
-
-    def __init__(self, fns: nn.ModuleList) -> None:
-        super().__init__()
-        self.fns = fns
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Applies blocks to input."""
-        for f, g in self.fns:
-            x = x + f(x)
-            x = x + g(x)
-        return x