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[ASTER] Add ASTER decoder (#1625) 

* add aster decoder

* aster decoder

* decoder

Co-authored-by: gaotongxiao <gaotongxiao@gmail.com>
pull/1623/head
Qing Jiang 2022-12-15 14:53:17 +08:00 committed by GitHub
parent 0bd62d67c8
commit 419f98d8a4
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 282 additions and 1 deletions
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3
mmocr/models/textrecog/decoders/__init__.py
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@ -2,6 +2,7 @@
from .abi_fuser import ABIFuser
from .abi_language_decoder import ABILanguageDecoder
from .abi_vision_decoder import ABIVisionDecoder
from .aster_decoder import ASTERDecoder
from .base import BaseDecoder
from .crnn_decoder import CRNNDecoder
from .master_decoder import MasterDecoder
@ -17,5 +18,5 @@ __all__ = [
'ParallelSARDecoderWithBS', 'NRTRDecoder', 'BaseDecoder',
'SequenceAttentionDecoder', 'PositionAttentionDecoder',
'ABILanguageDecoder', 'ABIVisionDecoder', 'MasterDecoder',
'RobustScannerFuser', 'ABIFuser'
'RobustScannerFuser', 'ABIFuser', 'ASTERDecoder'
]

180
mmocr/models/textrecog/decoders/aster_decoder.py 100644
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@ -0,0 +1,180 @@
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, Optional, Sequence, Tuple, Union
import torch
import torch.nn as nn
from mmocr.models.common.dictionary import Dictionary
from mmocr.registry import MODELS
from mmocr.structures import TextRecogDataSample
from .base import BaseDecoder
@MODELS.register_module()
class ASTERDecoder(BaseDecoder):
"""Implement attention decoder.
Args:
in_channels (int): Number of input channels.
emb_dims (int): Dims of char embedding. Defaults to 512.
attn_dims (int): Dims of attention. Both hidden states and features
will be projected to this dims. Defaults to 512.
hidden_size (int): Dims of hidden state for GRU. Defaults to 512.
dictionary (dict or :obj:`Dictionary`): The config for `Dictionary` or
the instance of `Dictionary`. Defaults to None.
max_seq_len (int): Maximum output sequence length :math:`T`. Defaults
to 25.
module_loss (dict, optional): Config to build loss. Defaults to None.
postprocessor (dict, optional): Config to build postprocessor.
Defaults to None.
init_cfg (dict or list[dict], optional): Initialization configs.
Defaults to None.
"""
def __init__(self,
in_channels: int,
emb_dims: int = 512,
attn_dims: int = 512,
hidden_size: int = 512,
dictionary: Union[Dictionary, Dict] = None,
max_seq_len: int = 25,
module_loss: Dict = None,
postprocessor: Dict = None,
init_cfg=dict(type='Xavier', layer='Conv2d')):
super().__init__(
init_cfg=init_cfg,
dictionary=dictionary,
module_loss=module_loss,
postprocessor=postprocessor,
max_seq_len=max_seq_len)
self.start_idx = self.dictionary.start_idx
self.num_classes = self.dictionary.num_classes
self.in_channels = in_channels
self.embedding_dim = emb_dims
self.att_dims = attn_dims
self.hidden_size = hidden_size
# Projection layers
self.proj_feat = nn.Linear(in_channels, attn_dims)
self.proj_hidden = nn.Linear(hidden_size, attn_dims)
self.proj_sum = nn.Linear(attn_dims, 1)
# Decoder input embedding
self.embedding = nn.Embedding(self.num_classes, self.att_dims)
# GRU
self.gru = nn.GRU(
input_size=self.in_channels + self.embedding_dim,
hidden_size=self.hidden_size,
batch_first=True)
# Prediction layer
self.fc = nn.Linear(hidden_size, self.dictionary.num_classes)
def _attention(self, feat: torch.Tensor, prev_hidden: torch.Tensor,
prev_char: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Implement the attention mechanism.
Args:
feat (Tensor): Feature map from encoder of shape :math:`(N, T, C)`.
prev_hidden (Tensor): Previous hidden state from GRU of shape
:math:`(1, N, self.hidden_size)`.
prev_char (Tensor): Previous predicted character of shape
:math:`(N, )`.
Returns:
tuple(Tensor, Tensor):
- output (Tensor): Predicted character of current time step of
shape :math:`(N, 1)`.
- state (Tensor): Hidden state from GRU of current time step of
shape :math:`(N, self.hidden_size)`.
"""
# Calculate the attention weights
B, T, _ = feat.size()
feat_proj = self.proj_feat(feat) # [N, T, attn_dims]
hidden_proj = self.proj_hidden(prev_hidden) # [1, N, attn_dims]
hidden_proj = hidden_proj.squeeze(0).unsqueeze(1) # [N, 1, attn_dims]
hidden_proj = hidden_proj.expand(B, T,
self.att_dims) # [N, T, attn_dims]
sum_tanh = torch.tanh(feat_proj + hidden_proj) # [N, T, attn_dims]
sum_proj = self.proj_sum(sum_tanh).squeeze(-1) # [N, T]
attn_weights = torch.softmax(sum_proj, dim=1) # [N, T]
# GRU forward
context = torch.bmm(attn_weights.unsqueeze(1), feat).squeeze(1)
char_embed = self.embedding(prev_char.long()) # [N, emb_dims]
output, state = self.gru(
torch.cat([char_embed, context], 1).unsqueeze(1), prev_hidden)
output = output.squeeze(1)
output = self.fc(output)
return output, state
def forward_train(
self,
feat: torch.Tensor = None,
out_enc: Optional[torch.Tensor] = None,
data_samples: Optional[Sequence[TextRecogDataSample]] = None
) -> torch.Tensor:
"""
Args:
feat (Tensor): Feature from backbone. Unused in this decoder.
out_enc (torch.Tensor, optional): Encoder output. Defaults to None.
data_samples (list[TextRecogDataSample], optional): Batch of
TextRecogDataSample, containing gt_text information. Defaults
to None.
Returns:
Tensor: The raw logit tensor. Shape :math:`(N, T, C)` where
:math:`C` is ``num_classes``.
"""
B = out_enc.shape[0]
state = torch.zeros(1, B, self.hidden_size).to(out_enc.device)
padded_targets = [
data_sample.gt_text.padded_indexes for data_sample in data_samples
]
padded_targets = torch.stack(padded_targets, dim=0).to(out_enc.device)
outputs = []
for i in range(self.max_seq_len):
prev_char = padded_targets[:, i].to(out_enc.device)
output, state = self._attention(out_enc, state, prev_char)
outputs.append(output)
outputs = torch.cat([_.unsqueeze(1) for _ in outputs], 1)
return outputs
def forward_test(
self,
feat: Optional[torch.Tensor] = None,
out_enc: Optional[torch.Tensor] = None,
data_samples: Optional[Sequence[TextRecogDataSample]] = None
) -> torch.Tensor:
"""
Args:
feat (Tensor): Feature from backbone. Unused in this decoder.
out_enc (torch.Tensor, optional): Encoder output. Defaults to None.
data_samples (list[TextRecogDataSample], optional): Batch of
TextRecogDataSample, containing gt_text information. Defaults
to None. Unused in this decoder.
Returns:
Tensor: The raw logit tensor. Shape :math:`(N, T, C)` where
:math:`C` is ``num_classes``.
"""
B = out_enc.shape[0]
predicted = []
state = torch.zeros(1, B, self.hidden_size).to(out_enc.device)
outputs = []
for i in range(self.max_seq_len):
if i == 0:
prev_char = torch.zeros(B).fill_(self.start_idx).to(
out_enc.device)
else:
prev_char = predicted
output, state = self._attention(out_enc, state, prev_char)
outputs.append(output)
_, predicted = output.max(-1)
outputs = torch.cat([_.unsqueeze(1) for _ in outputs], 1)
return outputs

100
tests/test_models/test_textrecog/test_decoders/test_aster_decoder.py 100644
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@ -0,0 +1,100 @@
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
from unittest import TestCase
import torch
from mmengine.structures import LabelData
from mmocr.models.textrecog.decoders import ASTERDecoder
from mmocr.structures import TextRecogDataSample
class TestASTERDecoder(TestCase):
def setUp(self):
gt_text_sample1 = TextRecogDataSample()
gt_text = LabelData()
gt_text.item = 'Hello'
gt_text_sample1.gt_text = gt_text
gt_text_sample2 = TextRecogDataSample()
gt_text = LabelData()
gt_text = LabelData()
gt_text.item = 'World1'
gt_text_sample2.gt_text = gt_text
self.data_info = [gt_text_sample1, gt_text_sample2]
def _create_dummy_dict_file(
self, dict_file,
chars=list('0123456789abcdefghijklmnopqrstuvwxyz')): # NOQA
with open(dict_file, 'w') as f:
for char in chars:
f.write(char + '\n')
def test_init(self):
tmp_dir = tempfile.TemporaryDirectory()
dict_file = osp.join(tmp_dir.name, 'fake_chars.txt')
self._create_dummy_dict_file(dict_file)
dict_cfg = dict(
type='Dictionary',
dict_file=dict_file,
with_start=True,
with_end=True,
same_start_end=True,
with_padding=True,
with_unknown=True)
loss_cfg = dict(type='CEModuleLoss')
ASTERDecoder(
in_channels=512, dictionary=dict_cfg, module_loss=loss_cfg)
tmp_dir.cleanup()
def test_forward_train(self):
encoder_out = torch.randn(2, 25, 512)
tmp_dir = tempfile.TemporaryDirectory()
dict_file = osp.join(tmp_dir.name, 'fake_chars.txt')
self._create_dummy_dict_file(dict_file)
# test diction cfg
dict_cfg = dict(
type='Dictionary',
dict_file=dict_file,
with_start=True,
with_end=True,
same_start_end=True,
with_padding=True,
with_unknown=True)
loss_cfg = dict(type='CEModuleLoss')
decoder = ASTERDecoder(
in_channels=512,
dictionary=dict_cfg,
module_loss=loss_cfg,
max_seq_len=25)
data_samples = decoder.module_loss.get_targets(self.data_info)
output = decoder.forward_train(
out_enc=encoder_out, data_samples=data_samples)
self.assertTupleEqual(tuple(output.shape), (2, 25, 39))
def test_forward_test(self):
encoder_out = torch.randn(2, 25, 512)
tmp_dir = tempfile.TemporaryDirectory()
dict_file = osp.join(tmp_dir.name, 'fake_chars.txt')
self._create_dummy_dict_file(dict_file)
# test diction cfg
dict_cfg = dict(
type='Dictionary',
dict_file=dict_file,
with_start=True,
with_end=True,
same_start_end=True,
with_padding=True,
with_unknown=True)
loss_cfg = dict(type='CEModuleLoss')
decoder = ASTERDecoder(
in_channels=512,
dictionary=dict_cfg,
module_loss=loss_cfg,
max_seq_len=25)
output = decoder.forward_test(
out_enc=encoder_out, data_samples=self.data_info)
self.assertTupleEqual(tuple(output.shape), (2, 25, 39))