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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
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
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