Rename transformer embeddings

1 files changed, 85 insertions, 0 deletions

diff --git a/text_recognizer/networks/transformer/embeddings/fourier.py b/text_recognizer/networks/transformer/embeddings/fourier.py
new file mode 100644
index 0000000..c50afc3
--- /dev/null
+++ b/text_recognizer/networks/transformer/embeddings/fourier.py
@@ -0,0 +1,85 @@
+"""A positional encoding for the image features, as the transformer has no notation of the order of the sequence."""
+from einops import repeat
+import numpy as np
+import torch
+from torch import nn
+from torch import Tensor
+
+
+class PositionalEncoding(nn.Module):
+    """Encodes a sense of distance or time for transformer networks."""
+
+    def __init__(
+        self, hidden_dim: int, dropout_rate: float, max_len: int = 1000
+    ) -> None:
+        super().__init__()
+        self.dropout = nn.Dropout(p=dropout_rate)
+        pe = self.make_pe(hidden_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(f"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