1 files changed, 2 insertions, 48 deletions
diff --git a/text_recognizer/networks/transformer/embeddings/fourier.py b/text_recognizer/networks/transformer/embeddings/fourier.py
index c50afc3..ade589c 100644
--- a/text_recognizer/networks/transformer/embeddings/fourier.py
+++ b/text_recognizer/networks/transformer/embeddings/fourier.py
@@ -1,5 +1,4 @@
-"""A positional encoding for the image features, as the transformer has no notation of the order of the sequence."""
-from einops import repeat
+"""Fourier positional embedding."""
 import numpy as np
 import torch
 from torch import nn
@@ -35,51 +34,6 @@ class PositionalEncoding(nn.Module):
         """Encodes the tensor with a postional embedding."""
         # [T, B, D]
         if x.shape[2] != self.pe.shape[2]:
-            raise ValueError(f"x shape does not match pe in the 3rd dim.")
+            raise ValueError("x shape does not match pe in the 3rd dim.")
         x = x + self.pe[: x.shape[0]]
         return self.dropout(x)
-
-
-class PositionalEncoding2D(nn.Module):
-    """Positional encodings for feature maps."""
-
-    def __init__(self, hidden_dim: int, max_h: int = 2048, max_w: int = 2048) -> None:
-        super().__init__()
-        if hidden_dim % 2 != 0:
-            raise ValueError(f"Embedding depth {hidden_dim} is not even!")
-        self.hidden_dim = hidden_dim
-        pe = self.make_pe(hidden_dim, max_h, max_w)
-        self.register_buffer("pe", pe)
-
-    @staticmethod
-    def make_pe(hidden_dim: int, max_h: int, max_w: int) -> Tensor:
-        """Returns 2d postional encoding."""
-        pe_h = PositionalEncoding.make_pe(
-            hidden_dim // 2, max_len=max_h
-        )  # [H, 1, D // 2]
-        pe_h = repeat(pe_h, "h w d -> d h (w tile)", tile=max_w)
-
-        pe_w = PositionalEncoding.make_pe(
-            hidden_dim // 2, max_len=max_w
-        )  # [W, 1, D // 2]
-        pe_w = repeat(pe_w, "w h d -> d (h tile) w", tile=max_h)
-
-        pe = torch.cat([pe_h, pe_w], dim=0)  # [D, H, W]
-        return pe
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Adds 2D postional encoding to input tensor."""
-        # Assumes x hase shape [B, D, H, W]
-        if x.shape[1] != self.pe.shape[0]:
-            raise ValueError("Hidden dimensions does not match.")
-        x += self.pe[:, : x.shape[2], : x.shape[3]]
-        return x
-
-
-def target_padding_mask(trg: Tensor, pad_index: int) -> Tensor:
-    """Returns causal target mask."""
-    trg_pad_mask = (trg != pad_index)[:, None, None]
-    trg_len = trg.shape[1]
-    trg_sub_mask = torch.tril(torch.ones((trg_len, trg_len), device=trg.device)).bool()
-    trg_mask = trg_pad_mask & trg_sub_mask
-    return trg_mask