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
|
# """A Transformer with a cnn backbone.
#
# The network encodes a image with a convolutional backbone to a latent representation,
# i.e. feature maps. A 2d positional encoding is applied to the feature maps for
# spatial information. The resulting feature are then set to a transformer decoder
# together with the target tokens.
#
# TODO: Local attention for lower layer in attention.
#
# """
# import importlib
# import math
# from typing import Dict, Optional, Union, Sequence, Type
#
# from einops import rearrange
# from omegaconf import DictConfig, OmegaConf
# import torch
# from torch import nn
# from torch import Tensor
#
# from text_recognizer.data.emnist import NUM_SPECIAL_TOKENS
# from text_recognizer.networks.transformer import (
# Decoder,
# DecoderLayer,
# PositionalEncoding,
# PositionalEncoding2D,
# target_padding_mask,
# )
#
# NUM_WORD_PIECES = 1000
#
#
# class CNNTransformer(nn.Module):
# def __init__(
# self,
# input_dim: Sequence[int],
# output_dims: Sequence[int],
# encoder: Union[DictConfig, Dict],
# vocab_size: Optional[int] = None,
# num_decoder_layers: int = 4,
# hidden_dim: int = 256,
# num_heads: int = 4,
# expansion_dim: int = 1024,
# dropout_rate: float = 0.1,
# transformer_activation: str = "glu",
# *args,
# **kwargs,
# ) -> None:
# super().__init__()
# self.vocab_size = (
# NUM_WORD_PIECES + NUM_SPECIAL_TOKENS if vocab_size is None else vocab_size
# )
# self.pad_index = 3 # TODO: fix me
# self.hidden_dim = hidden_dim
# self.max_output_length = output_dims[0]
#
# # Image backbone
# self.encoder = self._configure_encoder(encoder)
# self.encoder_proj = nn.Conv2d(256, hidden_dim, kernel_size=1)
# self.feature_map_encoding = PositionalEncoding2D(
# hidden_dim=hidden_dim, max_h=input_dim[1], max_w=input_dim[2]
# )
#
# # Target token embedding
# self.trg_embedding = nn.Embedding(self.vocab_size, hidden_dim)
# self.trg_position_encoding = PositionalEncoding(
# hidden_dim, dropout_rate, max_len=output_dims[0]
# )
#
# # Transformer decoder
# self.decoder = Decoder(
# decoder_layer=DecoderLayer(
# hidden_dim=hidden_dim,
# num_heads=num_heads,
# expansion_dim=expansion_dim,
# dropout_rate=dropout_rate,
# activation=transformer_activation,
# ),
# num_layers=num_decoder_layers,
# norm=nn.LayerNorm(hidden_dim),
# )
#
# # Classification head
# self.head = nn.Linear(hidden_dim, self.vocab_size)
#
# # Initialize weights
# self._init_weights()
#
# def _init_weights(self) -> None:
# """Initialize network weights."""
# self.trg_embedding.weight.data.uniform_(-0.1, 0.1)
# self.head.bias.data.zero_()
# self.head.weight.data.uniform_(-0.1, 0.1)
#
# nn.init.kaiming_normal_(
# self.encoder_proj.weight.data,
# a=0,
# mode="fan_out",
# nonlinearity="relu",
# )
# if self.encoder_proj.bias is not None:
# _, fan_out = nn.init._calculate_fan_in_and_fan_out(
# self.encoder_proj.weight.data
# )
# bound = 1 / math.sqrt(fan_out)
# nn.init.normal_(self.encoder_proj.bias, -bound, bound)
#
# @staticmethod
# def _configure_encoder(encoder: Union[DictConfig, Dict]) -> Type[nn.Module]:
# encoder = OmegaConf.create(encoder)
# args = encoder.args or {}
# network_module = importlib.import_module("text_recognizer.networks")
# encoder_class = getattr(network_module, encoder.type)
# return encoder_class(**args)
#
# def encode(self, image: Tensor) -> Tensor:
# """Extracts image features with backbone.
#
# Args:
# image (Tensor): Image(s) of handwritten text.
#
# Retuns:
# Tensor: Image features.
#
# Shapes:
# - image: :math: `(B, C, H, W)`
# - latent: :math: `(B, T, C)`
#
# """
# # Extract image features.
# image_features = self.encoder(image)
# image_features = self.encoder_proj(image_features)
#
# # Add 2d encoding to the feature maps.
# image_features = self.feature_map_encoding(image_features)
#
# # Collapse features maps height and width.
# image_features = rearrange(image_features, "b c h w -> b (h w) c")
# return image_features
#
# def decode(self, memory: Tensor, trg: Tensor) -> Tensor:
# """Decodes image features with transformer decoder."""
# trg_mask = target_padding_mask(trg=trg, pad_index=self.pad_index)
# trg = self.trg_embedding(trg) * math.sqrt(self.hidden_dim)
# trg = rearrange(trg, "b t d -> t b d")
# trg = self.trg_position_encoding(trg)
# trg = rearrange(trg, "t b d -> b t d")
# out = self.decoder(trg=trg, memory=memory, trg_mask=trg_mask, memory_mask=None)
# logits = self.head(out)
# return logits
#
# def forward(self, image: Tensor, trg: Tensor) -> Tensor:
# image_features = self.encode(image)
# output = self.decode(image_features, trg)
# output = rearrange(output, "b t c -> b c t")
# return output
#
# def predict(self, image: Tensor) -> Tensor:
# """Transcribes text in image(s)."""
# bsz = image.shape[0]
# image_features = self.encode(image)
#
# output_tokens = (
# (torch.ones((bsz, self.max_output_length)) * self.pad_index)
# .type_as(image)
# .long()
# )
# output_tokens[:, 0] = self.start_index
# for i in range(1, self.max_output_length):
# trg = output_tokens[:, :i]
# output = self.decode(image_features, trg)
# output = torch.argmax(output, dim=-1)
# output_tokens[:, i] = output[-1:]
#
# # Set all tokens after end token to be padding.
# for i in range(1, self.max_output_length):
# indices = output_tokens[:, i - 1] == self.end_index | (
# output_tokens[:, i - 1] == self.pad_index
# )
# output_tokens[indices, i] = self.pad_index
#
# return output_tokens
|
