1 files changed, 0 insertions, 104 deletions
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)