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|
# """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
# import torch.nn.functional as F
#
# from text_recognizer.networks.transformer.attention import MultiHeadAttention
# from text_recognizer.networks.util import activation_function
#
#
# class GEGLU(nn.Module):
# """GLU activation for improving feedforward activations."""
#
# def __init__(self, dim_in: int, dim_out: int) -> None:
# super().__init__()
# self.proj = nn.Linear(dim_in, dim_out * 2)
#
# def forward(self, x: Tensor) -> Tensor:
# """Forward propagation."""
# x, gate = self.proj(x).chunk(2, dim=-1)
# return x * F.gelu(gate)
#
#
# 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 FeedForward(nn.Module):
# def __init__(
# self,
# hidden_dim: int,
# expansion_dim: int,
# dropout_rate: float,
# activation: str = "relu",
# ) -> None:
# super().__init__()
#
# in_projection = (
# nn.Sequential(
# nn.Linear(hidden_dim, expansion_dim), activation_function(activation)
# )
# if activation != "glu"
# else GEGLU(hidden_dim, expansion_dim)
# )
#
# self.layer = nn.Sequential(
# in_projection,
# 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.mlp = FeedForward(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.
# mlp_out = self.mlp(out)
#
# # Add & norm.
# out = self.block2(mlp_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.mlp = FeedForward(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.mlp(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
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