From ffec11ce67d8fe75ea0d5dde5ddf17eb1017fa7d Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Sun, 2 Oct 2022 01:45:34 +0200
Subject: Add comments

---
 .../networks/transformer/embeddings/axial.py       | 24 +++++++++++++++++-----
 1 file changed, 19 insertions(+), 5 deletions(-)

(limited to 'text_recognizer/networks/transformer')

diff --git a/text_recognizer/networks/transformer/embeddings/axial.py b/text_recognizer/networks/transformer/embeddings/axial.py
index 25d8f60..9b872a9 100644
--- a/text_recognizer/networks/transformer/embeddings/axial.py
+++ b/text_recognizer/networks/transformer/embeddings/axial.py
@@ -1,17 +1,24 @@
 """Axial attention for multi-dimensional data.
 
 Stolen from:
-    https://github.com/lucidrains/axial-attention/blob/eff2c10c2e76c735a70a6b995b571213adffbbb7/axial_attention/axial_attention.py#L100
+    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 nn
+from torch import Tensor, nn
 
 
 class AxialPositionalEmbedding(nn.Module):
-    def __init__(self, dim, axial_shape, axial_dims=None):
+    def __init__(
+        self,
+        dim: int,
+        axial_shape: Sequence[int],
+        axial_dims: Optional[Sequence[int]] = None,
+    ) -> None:
         super().__init__()
 
         self.dim = dim
@@ -37,7 +44,8 @@ class AxialPositionalEmbedding(nn.Module):
             ax_emb = nn.Parameter(torch.zeros(ax_shape).normal_(0, 1))
             self.weights.append(ax_emb)
 
-    def forward(self, x):
+    def forward(self, x: Tensor) -> Tensor:
+        """Returns axial positional embedding."""
         b, t, _ = x.shape
         assert (
             t <= self.max_seq_len
@@ -77,8 +85,14 @@ class ParameterList(object):
 
 
 class AxialPositionalEmbeddingImage(nn.Module):
-    def __init__(self, dim, axial_shape, axial_dims=None):
+    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)
-- 
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