Spaces:
Running
Running
File size: 9,375 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 |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List, Optional, Sequence, Tuple, Union
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmocr.models.common.dictionary import Dictionary
from mmocr.models.common.modules import PositionalEncoding
from mmocr.registry import MODELS
from mmocr.structures import TextRecogDataSample
from .base import BaseDecoder
@MODELS.register_module()
class ABIVisionDecoder(BaseDecoder):
"""Converts visual features into text characters.
Implementation of VisionEncoder in
`ABINet <https://arxiv.org/abs/2103.06495>`_.
Args:
dictionary (dict or :obj:`Dictionary`): The config for `Dictionary` or
the instance of `Dictionary`.
in_channels (int): Number of channels :math:`E` of input vector.
Defaults to 512.
num_channels (int): Number of channels of hidden vectors in mini U-Net.
Defaults to 64.
attn_height (int): Height :math:`H` of input image features. Defaults
to 8.
attn_width (int): Width :math:`W` of input image features. Defaults to
32.
attn_mode (str): Upsampling mode for :obj:`torch.nn.Upsample` in mini
U-Net. Defaults to 'nearest'.
module_loss (dict, optional): Config to build loss. Defaults to None.
postprocessor (dict, optional): Config to build postprocessor.
Defaults to None.
max_seq_len (int): Maximum sequence length. The
sequence is usually generated from decoder. Defaults to 40.
init_cfg (dict or list[dict], optional): Initialization configs.
Defaults to dict(type='Xavier', layer='Conv2d').
"""
def __init__(self,
dictionary: Union[Dict, Dictionary],
in_channels: int = 512,
num_channels: int = 64,
attn_height: int = 8,
attn_width: int = 32,
attn_mode: str = 'nearest',
module_loss: Optional[Dict] = None,
postprocessor: Optional[Dict] = None,
max_seq_len: int = 40,
init_cfg: Optional[Union[Dict, List[Dict]]] = dict(
type='Xavier', layer='Conv2d'),
**kwargs) -> None:
super().__init__(
dictionary=dictionary,
module_loss=module_loss,
postprocessor=postprocessor,
max_seq_len=max_seq_len,
init_cfg=init_cfg)
# For mini-Unet
self.k_encoder = nn.Sequential(
self._encoder_layer(in_channels, num_channels, stride=(1, 2)),
self._encoder_layer(num_channels, num_channels, stride=(2, 2)),
self._encoder_layer(num_channels, num_channels, stride=(2, 2)),
self._encoder_layer(num_channels, num_channels, stride=(2, 2)))
self.k_decoder = nn.Sequential(
self._decoder_layer(
num_channels, num_channels, scale_factor=2, mode=attn_mode),
self._decoder_layer(
num_channels, num_channels, scale_factor=2, mode=attn_mode),
self._decoder_layer(
num_channels, num_channels, scale_factor=2, mode=attn_mode),
self._decoder_layer(
num_channels,
in_channels,
size=(attn_height, attn_width),
mode=attn_mode))
self.pos_encoder = PositionalEncoding(in_channels, max_seq_len)
self.project = nn.Linear(in_channels, in_channels)
self.cls = nn.Linear(in_channels, self.dictionary.num_classes)
def forward_train(
self,
feat: Optional[torch.Tensor] = None,
out_enc: torch.Tensor = None,
data_samples: Optional[Sequence[TextRecogDataSample]] = None
) -> Dict:
"""
Args:
feat (Tensor, optional): Image features of shape (N, E, H, W).
Defaults to None.
out_enc (torch.Tensor): Encoder output. Defaults to None.
data_samples (list[TextRecogDataSample], optional): Batch of
TextRecogDataSample, containing gt_text information. Defaults
to None.
Returns:
dict: A dict with keys ``feature``, ``logits`` and ``attn_scores``.
- feature (Tensor): Shape (N, T, E). Raw visual features for
language decoder.
- logits (Tensor): Shape (N, T, C). The raw logits for
characters.
- attn_scores (Tensor): Shape (N, T, H, W). Intermediate result
for vision-language aligner.
"""
# Position Attention
N, E, H, W = out_enc.size()
k, v = out_enc, out_enc # (N, E, H, W)
# Apply mini U-Net on k
features = []
for i in range(len(self.k_encoder)):
k = self.k_encoder[i](k)
features.append(k)
for i in range(len(self.k_decoder) - 1):
k = self.k_decoder[i](k)
k = k + features[len(self.k_decoder) - 2 - i]
k = self.k_decoder[-1](k)
# q = positional encoding
zeros = out_enc.new_zeros((N, self.max_seq_len, E)) # (N, T, E)
q = self.pos_encoder(zeros) # (N, T, E)
q = self.project(q) # (N, T, E)
# Attention encoding
attn_scores = torch.bmm(q, k.flatten(2, 3)) # (N, T, (H*W))
attn_scores = attn_scores / (E**0.5)
attn_scores = torch.softmax(attn_scores, dim=-1)
v = v.permute(0, 2, 3, 1).view(N, -1, E) # (N, (H*W), E)
attn_vecs = torch.bmm(attn_scores, v) # (N, T, E)
out_enc = self.cls(attn_vecs)
result = {
'feature': attn_vecs,
'logits': out_enc,
'attn_scores': attn_scores.view(N, -1, H, W)
}
return result
def forward_test(
self,
feat: Optional[torch.Tensor] = None,
out_enc: torch.Tensor = None,
data_samples: Optional[Sequence[TextRecogDataSample]] = None
) -> Dict:
"""
Args:
feat (torch.Tensor, optional): Image features of shape
(N, E, H, W). Defaults to None.
out_enc (torch.Tensor): Encoder output. Defaults to None.
data_samples (list[TextRecogDataSample], optional): Batch of
TextRecogDataSample, containing gt_text information. Defaults
to None.
Returns:
dict: A dict with keys ``feature``, ``logits`` and ``attn_scores``.
- feature (Tensor): Shape (N, T, E). Raw visual features for
language decoder.
- logits (Tensor): Shape (N, T, C). The raw logits for
characters.
- attn_scores (Tensor): Shape (N, T, H, W). Intermediate result
for vision-language aligner.
"""
return self.forward_train(
feat, out_enc=out_enc, data_samples=data_samples)
def _encoder_layer(self,
in_channels: int,
out_channels: int,
kernel_size: int = 3,
stride: int = 2,
padding: int = 1) -> nn.Sequential:
"""Generate encoder layer.
Args:
in_channels (int): Input channels.
out_channels (int): Output channels.
kernel_size (int, optional): Kernel size. Defaults to 3.
stride (int, optional): Stride. Defaults to 2.
padding (int, optional): Padding. Defaults to 1.
Returns:
nn.Sequential: Encoder layer.
"""
return ConvModule(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'))
def _decoder_layer(self,
in_channels: int,
out_channels: int,
kernel_size: int = 3,
stride: int = 1,
padding: int = 1,
mode: str = 'nearest',
scale_factor: Optional[int] = None,
size: Optional[Tuple[int, int]] = None):
"""Generate decoder layer.
Args:
in_channels (int): Input channels.
out_channels (int): Output channels.
kernel_size (int): Kernel size. Defaults to 3.
stride (int): Stride. Defaults to 1.
padding (int): Padding. Defaults to 1.
mode (str): Interpolation mode. Defaults to 'nearest'.
scale_factor (int, optional): Scale factor for upsampling.
size (Tuple[int, int], optional): Output size. Defaults to None.
"""
align_corners = None if mode == 'nearest' else True
return nn.Sequential(
nn.Upsample(
size=size,
scale_factor=scale_factor,
mode=mode,
align_corners=align_corners),
ConvModule(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU')))
|