diff options
Diffstat (limited to 'text_recognizer')
| -rw-r--r-- | text_recognizer/networks/transformer/positional_encoding.py | 50 |
1 files changed, 44 insertions, 6 deletions
diff --git a/text_recognizer/networks/transformer/positional_encoding.py b/text_recognizer/networks/transformer/positional_encoding.py index 1ba5537..d03f630 100644 --- a/text_recognizer/networks/transformer/positional_encoding.py +++ b/text_recognizer/networks/transformer/positional_encoding.py @@ -1,4 +1,5 @@ """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 @@ -13,20 +14,57 @@ class PositionalEncoding(nn.Module): ) -> None: super().__init__() self.dropout = nn.Dropout(p=dropout_rate) - self.max_len = max_len - + 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).unsqueeze(1) + position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) div_term = torch.exp( - torch.arange(0, hidden_dim, 2) * -(np.log(10000.0) / hidden_dim) + 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(0) - self.register_buffer("pe", pe) + pe = pe.unsqueeze(1) + return pe def forward(self, x: Tensor) -> Tensor: """Encodes the tensor with a postional embedding.""" x = x + self.pe[:, : x.shape[1]] 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) + + 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_h) # [W, 1, D // 2] + pe_w = repeat(pe_w, "h w 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 + + |