1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
|
"""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]:
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
|
