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author | Gustaf Rydholm <gustaf.rydholm@gmail.com> | 2021-03-20 18:09:06 +0100 |
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committer | Gustaf Rydholm <gustaf.rydholm@gmail.com> | 2021-03-20 18:09:06 +0100 |
commit | 7e8e54e84c63171e748bbf09516fd517e6821ace (patch) | |
tree | 996093f75a5d488dddf7ea1f159ed343a561ef89 /text_recognizer/networks/transformer/positional_encoding.py | |
parent | b0719d84138b6bbe5f04a4982dfca673aea1a368 (diff) |
Inital commit for refactoring to lightning
Diffstat (limited to 'text_recognizer/networks/transformer/positional_encoding.py')
-rw-r--r-- | text_recognizer/networks/transformer/positional_encoding.py | 32 |
1 files changed, 32 insertions, 0 deletions
diff --git a/text_recognizer/networks/transformer/positional_encoding.py b/text_recognizer/networks/transformer/positional_encoding.py new file mode 100644 index 0000000..1ba5537 --- /dev/null +++ b/text_recognizer/networks/transformer/positional_encoding.py @@ -0,0 +1,32 @@ +"""A positional encoding for the image features, as the transformer has no notation of the order of the sequence.""" +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) + self.max_len = max_len + + pe = torch.zeros(max_len, hidden_dim) + position = torch.arange(0, max_len).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, hidden_dim, 2) * -(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) + + def forward(self, x: Tensor) -> Tensor: + """Encodes the tensor with a postional embedding.""" + x = x + self.pe[:, : x.shape[1]] + return self.dropout(x) |