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Diffstat (limited to 'text_recognizer/networks/transformer/embeddings')
4 files changed, 145 insertions, 0 deletions
diff --git a/text_recognizer/networks/transformer/embeddings/__init__.py b/text_recognizer/networks/transformer/embeddings/__init__.py new file mode 100644 index 0000000..bb3f904 --- /dev/null +++ b/text_recognizer/networks/transformer/embeddings/__init__.py @@ -0,0 +1 @@ +"""Positional encodings for transformers.""" diff --git a/text_recognizer/networks/transformer/embeddings/absolute.py b/text_recognizer/networks/transformer/embeddings/absolute.py new file mode 100644 index 0000000..7140537 --- /dev/null +++ b/text_recognizer/networks/transformer/embeddings/absolute.py @@ -0,0 +1,17 @@ +"""Absolute positional embedding.""" +import torch +from torch import nn, Tensor + + +class AbsolutePositionalEmbedding(nn.Module): + def __init__(self, dim: int, max_seq_len: int) -> None: + super().__init__() + self.emb = nn.Embedding(max_seq_len, dim) + self._weight_init() + + def _weight_init(self) -> None: + nn.init.normal_(self.emb.weight, std=0.02) + + def forward(self, x: Tensor) -> Tensor: + n = torch.arange(x.shape[1], device=x.device) + return self.emb(n)[None, :, :] 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 diff --git a/text_recognizer/networks/transformer/embeddings/rotary.py b/text_recognizer/networks/transformer/embeddings/rotary.py new file mode 100644 index 0000000..2f58964 --- /dev/null +++ b/text_recognizer/networks/transformer/embeddings/rotary.py @@ -0,0 +1,42 @@ +"""Roatary embedding. + +Stolen from lucidrains: + https://github.com/lucidrains/rotary-embedding-torch + +Explanation of roatary: + https://blog.eleuther.ai/rotary-embeddings/ +""" +from typing import Tuple + +from einops import rearrange +import torch +from torch import nn +from torch import Tensor + + +class RotaryEmbedding(nn.Module): + """Rotary positional embedding.""" + + def __init__(self, dim: int): + super().__init__() + inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim)) + self.register_buffer("inv_freq", inv_freq) + + def forward(self, x: Tensor, seq_dim: int = 1) -> Tensor: + """Encodes tensor x with rotary embeddings.""" + t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + freqs = torch.einsum("i , j -> i j", t, self.inv_freq) + emb = torch.cat((freqs, freqs), dim=-1) + return rearrange(emb, "n d -> () () n d") + + +def rotate_half(x: Tensor) -> Tensor: + x = rearrange(x, "... (j d) -> ... j d", j=2) + x1, x2 = x.unbind(dim=-2) + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(t: Tensor, freqs: Tensor) -> Tensor: + seq_len = t.shape[-2] + freqs = freqs[:, :, -seq_len:] + return (t * freqs.cos()) + (rotate_half(t) * freqs.sin()) |