Make rotary pos encoding mandatory

2 files changed, 0 insertions, 70 deletions

diff --git a/text_recognizer/networks/transformer/embeddings/absolute.py b/text_recognizer/networks/transformer/embeddings/absolute.py
deleted file mode 100644
index 9274b55..0000000
--- a/text_recognizer/networks/transformer/embeddings/absolute.py
+++ /dev/null
@@ -1,34 +0,0 @@
-"""Absolute positional embedding."""
-
-import torch
-import torch.nn.functional as F
-from einops import rearrange
-from torch import nn
-
-
-def l2norm(t, groups=1):
-    t = rearrange(t, "... (g d) -> ... g d", g=groups)
-    t = F.normalize(t, p=2, dim=-1)
-    return rearrange(t, "... g d -> ... (g d)")
-
-
-class AbsolutePositionalEmbedding(nn.Module):
-    def __init__(self, dim, max_seq_len, l2norm_embed=False):
-        super().__init__()
-        self.scale = dim**-0.5 if not l2norm_embed else 1.0
-        self.max_seq_len = max_seq_len
-        self.l2norm_embed = l2norm_embed
-        self.emb = nn.Embedding(max_seq_len, dim)
-
-    def forward(self, x, pos=None):
-        seq_len = x.shape[1]
-        assert (
-            seq_len <= self.max_seq_len
-        ), f"you are passing in a sequence length of {seq_len} but your absolute positional embedding has a max sequence length of {self.max_seq_len}"
-
-        if pos is None:
-            pos = torch.arange(seq_len, device=x.device)
-
-        pos_emb = self.emb(pos)
-        pos_emb = pos_emb * self.scale
-        return l2norm(pos_emb) if self.l2norm_embed else pos_emb
diff --git a/text_recognizer/networks/transformer/embeddings/fourier.py b/text_recognizer/networks/transformer/embeddings/fourier.py
deleted file mode 100644
index 28da7a1..0000000
--- a/text_recognizer/networks/transformer/embeddings/fourier.py
+++ /dev/null
@@ -1,36 +0,0 @@
-"""Fourier positional embedding."""
-import numpy as np
-import torch
-from torch import Tensor, nn
-
-
-class PositionalEncoding(nn.Module):
-    """Encodes a sense of distance or time for transformer networks."""
-
-    def __init__(self, dim: int, dropout_rate: float, max_len: int = 1000) -> None:
-        super().__init__()
-        self.dropout = nn.Dropout(p=dropout_rate)
-        pe = self.make_pe(dim, max_len)
-        self.register_buffer("pe", pe)
-
-    @staticmethod
-    def make_pe(hidden_dim: int, max_len: int) -> Tensor:
-        """Returns positional encoding."""
-        pe = torch.zeros(max_len, hidden_dim)
-        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
-        div_term = torch.exp(
-            torch.arange(0, hidden_dim, 2).float() * (-np.log(10000.0) / hidden_dim)
-        )
-
-        pe[:, 0::2] = torch.sin(position * div_term)
-        pe[:, 1::2] = torch.cos(position * div_term)
-        pe = pe.unsqueeze(1)
-        return pe
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Encodes the tensor with a postional embedding."""
-        # [T, B, D]
-        if x.shape[2] != self.pe.shape[2]:
-            raise ValueError("x shape does not match pe in the 3rd dim.")
-        x = x + self.pe[: x.shape[0]]
-        return self.dropout(x)