Spaces:
Running
Running
File size: 9,837 Bytes
9bf4bd7 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 |
# Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Dict, Optional, Sequence, Union
import torch
import torch.nn as nn
from mmocr.models.common.dictionary import Dictionary
from mmocr.models.textrecog.layers import DotProductAttentionLayer
from mmocr.registry import MODELS
from mmocr.structures import TextRecogDataSample
from .base import BaseDecoder
@MODELS.register_module()
class SequenceAttentionDecoder(BaseDecoder):
"""Sequence attention decoder for RobustScanner.
RobustScanner: `RobustScanner: Dynamically Enhancing Positional Clues for
Robust Text Recognition <https://arxiv.org/abs/2007.07542>`_
Args:
dictionary (dict or :obj:`Dictionary`): The config for `Dictionary` or
the instance of `Dictionary`.
module_loss (dict, optional): Config to build module_loss. Defaults
to None.
postprocessor (dict, optional): Config to build postprocessor.
Defaults to None.
rnn_layers (int): Number of RNN layers. Defaults to 2.
dim_input (int): Dimension :math:`D_i` of input vector ``feat``.
Defaults to 512.
dim_model (int): Dimension :math:`D_m` of the model. Should also be the
same as encoder output vector ``out_enc``. Defaults to 128.
max_seq_len (int): Maximum output sequence length :math:`T`.
Defaults to 40.
mask (bool): Whether to mask input features according to
``data_sample.valid_ratio``. Defaults to True.
dropout (float): Dropout rate for LSTM layer. Defaults to 0.
return_feature (bool): Return feature or logic as the result.
Defaults to True.
encode_value (bool): Whether to use the output of encoder ``out_enc``
as `value` of attention layer. If False, the original feature
``feat`` will be used. Defaults to False.
init_cfg (dict or list[dict], optional): Initialization configs.
Defaults to None.
"""
def __init__(self,
dictionary: Union[Dictionary, Dict],
module_loss: Optional[Dict] = None,
postprocessor: Optional[Dict] = None,
rnn_layers: int = 2,
dim_input: int = 512,
dim_model: int = 128,
max_seq_len: int = 40,
mask: bool = True,
dropout: int = 0,
return_feature: bool = True,
encode_value: bool = False,
init_cfg: Optional[Union[Dict,
Sequence[Dict]]] = None) -> None:
super().__init__(
dictionary=dictionary,
module_loss=module_loss,
postprocessor=postprocessor,
max_seq_len=max_seq_len,
init_cfg=init_cfg)
self.dim_input = dim_input
self.dim_model = dim_model
self.return_feature = return_feature
self.encode_value = encode_value
self.mask = mask
self.embedding = nn.Embedding(
self.dictionary.num_classes,
self.dim_model,
padding_idx=self.dictionary.padding_idx)
self.sequence_layer = nn.LSTM(
input_size=dim_model,
hidden_size=dim_model,
num_layers=rnn_layers,
batch_first=True,
dropout=dropout)
self.attention_layer = DotProductAttentionLayer()
self.prediction = None
if not self.return_feature:
self.prediction = nn.Linear(
dim_model if encode_value else dim_input,
self.dictionary.num_classes)
self.softmax = nn.Softmax(dim=-1)
def forward_train(
self,
feat: torch.Tensor,
out_enc: torch.Tensor,
data_samples: Optional[Sequence[TextRecogDataSample]] = None
) -> torch.Tensor:
"""
Args:
feat (Tensor): Tensor of shape :math:`(N, D_i, H, W)`.
out_enc (Tensor): Encoder output of shape
:math:`(N, D_m, H, W)`.
targets_dict (dict): A dict with the key ``padded_targets``, a
tensor of shape :math:`(N, T)`. Each element is the index of a
character.
data_samples (list[TextRecogDataSample], optional): Batch of
TextRecogDataSample, containing gt_text information. Defaults
to None.
Returns:
Tensor: A raw logit tensor of shape :math:`(N, T, C)` if
``return_feature=False``. Otherwise it would be the hidden feature
before the prediction projection layer, whose shape is
:math:`(N, T, D_m)`.
"""
valid_ratios = [
data_sample.get('valid_ratio', 1.0) for data_sample in data_samples
] if self.mask else None
padded_targets = [
data_sample.gt_text.padded_indexes for data_sample in data_samples
]
padded_targets = torch.stack(padded_targets, dim=0).to(feat.device)
tgt_embedding = self.embedding(padded_targets)
n, c_enc, h, w = out_enc.size()
assert c_enc == self.dim_model
_, c_feat, _, _ = feat.size()
assert c_feat == self.dim_input
_, len_q, c_q = tgt_embedding.size()
assert c_q == self.dim_model
assert len_q <= self.max_seq_len
query, _ = self.sequence_layer(tgt_embedding)
query = query.permute(0, 2, 1).contiguous()
key = out_enc.view(n, c_enc, h * w)
if self.encode_value:
value = key
else:
value = feat.view(n, c_feat, h * w)
mask = None
if valid_ratios is not None:
mask = query.new_zeros((n, h, w))
for i, valid_ratio in enumerate(valid_ratios):
valid_width = min(w, math.ceil(w * valid_ratio))
mask[i, :, valid_width:] = 1
mask = mask.bool()
mask = mask.view(n, h * w)
attn_out = self.attention_layer(query, key, value, mask)
attn_out = attn_out.permute(0, 2, 1).contiguous()
if self.return_feature:
return attn_out
out = self.prediction(attn_out)
return out
def forward_test(self, feat: torch.Tensor, out_enc: torch.Tensor,
data_samples: Optional[Sequence[TextRecogDataSample]]
) -> torch.Tensor:
"""
Args:
feat (Tensor): Tensor of shape :math:`(N, D_i, H, W)`.
out_enc (Tensor): Encoder output of shape
:math:`(N, D_m, H, W)`.
data_samples (list[TextRecogDataSample], optional): Batch of
TextRecogDataSample, containing gt_text information. Defaults
to None.
Returns:
Tensor: Character probabilities. of shape
:math:`(N, self.max_seq_len, C)` where :math:`C` is
``num_classes``.
"""
seq_len = self.max_seq_len
batch_size = feat.size(0)
decode_sequence = (feat.new_ones(
(batch_size, seq_len)) * self.dictionary.start_idx).long()
assert not self.return_feature
outputs = []
for i in range(seq_len):
step_out = self.forward_test_step(feat, out_enc, decode_sequence,
i, data_samples)
outputs.append(step_out)
_, max_idx = torch.max(step_out, dim=1, keepdim=False)
if i < seq_len - 1:
decode_sequence[:, i + 1] = max_idx
outputs = torch.stack(outputs, 1)
return self.softmax(outputs)
def forward_test_step(self, feat: torch.Tensor, out_enc: torch.Tensor,
decode_sequence: torch.Tensor, current_step: int,
data_samples: Sequence[TextRecogDataSample]
) -> torch.Tensor:
"""
Args:
feat (Tensor): Tensor of shape :math:`(N, D_i, H, W)`.
out_enc (Tensor): Encoder output of shape
:math:`(N, D_m, H, W)`.
decode_sequence (Tensor): Shape :math:`(N, T)`. The tensor that
stores history decoding result.
current_step (int): Current decoding step.
data_samples (list[TextRecogDataSample], optional): Batch of
TextRecogDataSample, containing gt_text information. Defaults
to None.
Returns:
Tensor: Shape :math:`(N, C)`. The logit tensor of predicted
tokens at current time step.
"""
valid_ratios = [
img_meta.get('valid_ratio', 1.0) for img_meta in data_samples
] if self.mask else None
embed = self.embedding(decode_sequence)
n, c_enc, h, w = out_enc.size()
assert c_enc == self.dim_model
_, c_feat, _, _ = feat.size()
assert c_feat == self.dim_input
_, _, c_q = embed.size()
assert c_q == self.dim_model
query, _ = self.sequence_layer(embed)
query = query.permute(0, 2, 1).contiguous()
key = out_enc.view(n, c_enc, h * w)
if self.encode_value:
value = key
else:
value = feat.view(n, c_feat, h * w)
mask = None
if valid_ratios is not None:
mask = query.new_zeros((n, h, w))
for i, valid_ratio in enumerate(valid_ratios):
valid_width = min(w, math.ceil(w * valid_ratio))
mask[i, :, valid_width:] = 1
mask = mask.bool()
mask = mask.view(n, h * w)
# [n, c, l]
attn_out = self.attention_layer(query, key, value, mask)
out = attn_out[:, :, current_step]
if not self.return_feature:
out = self.prediction(out)
return out
|