new updates

1 files changed, 242 insertions, 0 deletions

diff --git a/src/text_recognizer/networks/transformer/transformer.py b/src/text_recognizer/networks/transformer/transformer.py
new file mode 100644
index 0000000..c6e943e
--- /dev/null
+++ b/src/text_recognizer/networks/transformer/transformer.py
@@ -0,0 +1,242 @@
+"""Transfomer module."""
+import copy
+from typing import Dict, Optional, Type, Union
+
+import numpy as np
+import torch
+from torch import nn
+from torch import Tensor
+
+from text_recognizer.networks.transformer.attention import MultiHeadAttention
+from text_recognizer.networks.util import activation_function
+
+
+def _get_clones(module: Type[nn.Module], num_layers: int) -> nn.ModuleList:
+    return nn.ModuleList([copy.deepcopy(module) for _ in range(num_layers)])
+
+
+class _IntraLayerConnection(nn.Module):
+    """Preforms the residual connection inside the transfomer blocks and applies layernorm."""
+
+    def __init__(self, dropout_rate: float, hidden_dim: int) -> None:
+        super().__init__()
+        self.norm = nn.LayerNorm(normalized_shape=hidden_dim)
+        self.dropout = nn.Dropout(p=dropout_rate)
+
+    def forward(self, src: Tensor, residual: Tensor) -> Tensor:
+        return self.norm(self.dropout(src) + residual)
+
+
+class _ConvolutionalLayer(nn.Module):
+    def __init__(
+        self,
+        hidden_dim: int,
+        expansion_dim: int,
+        dropout_rate: float,
+        activation: str = "relu",
+    ) -> None:
+        super().__init__()
+        self.layer = nn.Sequential(
+            nn.Linear(in_features=hidden_dim, out_features=expansion_dim),
+            activation_function(activation),
+            nn.Dropout(p=dropout_rate),
+            nn.Linear(in_features=expansion_dim, out_features=hidden_dim),
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.layer(x)
+
+
+class EncoderLayer(nn.Module):
+    """Transfomer encoding layer."""
+
+    def __init__(
+        self,
+        hidden_dim: int,
+        num_heads: int,
+        expansion_dim: int,
+        dropout_rate: float,
+        activation: str = "relu",
+    ) -> None:
+        super().__init__()
+        self.self_attention = MultiHeadAttention(hidden_dim, num_heads, dropout_rate)
+        self.cnn = _ConvolutionalLayer(
+            hidden_dim, expansion_dim, dropout_rate, activation
+        )
+        self.block1 = _IntraLayerConnection(dropout_rate, hidden_dim)
+        self.block2 = _IntraLayerConnection(dropout_rate, hidden_dim)
+
+    def forward(self, src: Tensor, mask: Optional[Tensor] = None) -> Tensor:
+        """Forward pass through the encoder."""
+        # First block.
+        # Multi head attention.
+        out, _ = self.self_attention(src, src, src, mask)
+
+        # Add & norm.
+        out = self.block1(out, src)
+
+        # Second block.
+        # Apply 1D-convolution.
+        cnn_out = self.cnn(out)
+
+        # Add & norm.
+        out = self.block2(cnn_out, out)
+
+        return out
+
+
+class Encoder(nn.Module):
+    """Transfomer encoder module."""
+
+    def __init__(
+        self,
+        num_layers: int,
+        encoder_layer: Type[nn.Module],
+        norm: Optional[Type[nn.Module]] = None,
+    ) -> None:
+        super().__init__()
+        self.layers = _get_clones(encoder_layer, num_layers)
+        self.norm = norm
+
+    def forward(self, src: Tensor, src_mask: Optional[Tensor] = None) -> Tensor:
+        """Forward pass through all encoder layers."""
+        for layer in self.layers:
+            src = layer(src, src_mask)
+
+        if self.norm is not None:
+            src = self.norm(src)
+
+        return src
+
+
+class DecoderLayer(nn.Module):
+    """Transfomer decoder layer."""
+
+    def __init__(
+        self,
+        hidden_dim: int,
+        num_heads: int,
+        expansion_dim: int,
+        dropout_rate: float = 0.0,
+        activation: str = "relu",
+    ) -> None:
+        super().__init__()
+        self.hidden_dim = hidden_dim
+        self.self_attention = MultiHeadAttention(hidden_dim, num_heads, dropout_rate)
+        self.multihead_attention = MultiHeadAttention(
+            hidden_dim, num_heads, dropout_rate
+        )
+        self.cnn = _ConvolutionalLayer(
+            hidden_dim, expansion_dim, dropout_rate, activation
+        )
+        self.block1 = _IntraLayerConnection(dropout_rate, hidden_dim)
+        self.block2 = _IntraLayerConnection(dropout_rate, hidden_dim)
+        self.block3 = _IntraLayerConnection(dropout_rate, hidden_dim)
+
+    def forward(
+        self,
+        trg: Tensor,
+        memory: Tensor,
+        trg_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        """Forward pass of the layer."""
+        out, _ = self.self_attention(trg, trg, trg, trg_mask)
+        trg = self.block1(out, trg)
+
+        out, _ = self.multihead_attention(trg, memory, memory, memory_mask)
+        trg = self.block2(out, trg)
+
+        out = self.cnn(trg)
+        out = self.block3(out, trg)
+
+        return out
+
+
+class Decoder(nn.Module):
+    """Transfomer decoder module."""
+
+    def __init__(
+        self,
+        decoder_layer: Type[nn.Module],
+        num_layers: int,
+        norm: Optional[Type[nn.Module]] = None,
+    ) -> None:
+        super().__init__()
+        self.layers = _get_clones(decoder_layer, num_layers)
+        self.num_layers = num_layers
+        self.norm = norm
+
+    def forward(
+        self,
+        trg: Tensor,
+        memory: Tensor,
+        trg_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        """Forward pass through the decoder."""
+        for layer in self.layers:
+            trg = layer(trg, memory, trg_mask, memory_mask)
+
+        if self.norm is not None:
+            trg = self.norm(trg)
+
+        return trg
+
+
+class Transformer(nn.Module):
+    """Transformer network."""
+
+    def __init__(
+        self,
+        num_encoder_layers: int,
+        num_decoder_layers: int,
+        hidden_dim: int,
+        num_heads: int,
+        expansion_dim: int,
+        dropout_rate: float,
+        activation: str = "relu",
+    ) -> None:
+        super().__init__()
+
+        # Configure encoder.
+        encoder_norm = nn.LayerNorm(hidden_dim)
+        encoder_layer = EncoderLayer(
+            hidden_dim, num_heads, expansion_dim, dropout_rate, activation
+        )
+        self.encoder = Encoder(num_encoder_layers, encoder_layer, encoder_norm)
+
+        # Configure decoder.
+        decoder_norm = nn.LayerNorm(hidden_dim)
+        decoder_layer = DecoderLayer(
+            hidden_dim, num_heads, expansion_dim, dropout_rate, activation
+        )
+        self.decoder = Decoder(decoder_layer, num_decoder_layers, decoder_norm)
+
+        self._reset_parameters()
+
+    def _reset_parameters(self) -> None:
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+
+    def forward(
+        self,
+        src: Tensor,
+        trg: Tensor,
+        src_mask: Optional[Tensor] = None,
+        trg_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        """Forward pass through the transformer."""
+        if src.shape[0] != trg.shape[0]:
+            print(trg.shape)
+            raise RuntimeError("The batch size of the src and trg must be the same.")
+        if src.shape[2] != trg.shape[2]:
+            raise RuntimeError(
+                "The number of features for the src and trg must be the same."
+            )
+
+        memory = self.encoder(src, src_mask)
+        output = self.decoder(trg, memory, trg_mask, memory_mask)
+        return output