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Source code for mmocr.models.textrecog.recognizer.abinet
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch
from mmocr.models.builder import (RECOGNIZERS, build_backbone, build_convertor,
build_decoder, build_encoder, build_fuser,
build_loss, build_preprocessor)
from .encode_decode_recognizer import EncodeDecodeRecognizer
[docs]@RECOGNIZERS.register_module()
class ABINet(EncodeDecodeRecognizer):
"""Implementation of `Read Like Humans: Autonomous, Bidirectional and
Iterative LanguageModeling for Scene Text Recognition.
<https://arxiv.org/pdf/2103.06495.pdf>`_
"""
def __init__(self,
preprocessor=None,
backbone=None,
encoder=None,
decoder=None,
iter_size=1,
fuser=None,
loss=None,
label_convertor=None,
train_cfg=None,
test_cfg=None,
max_seq_len=40,
pretrained=None,
init_cfg=None):
super(EncodeDecodeRecognizer, self).__init__(init_cfg=init_cfg)
# Label convertor (str2tensor, tensor2str)
assert label_convertor is not None
label_convertor.update(max_seq_len=max_seq_len)
self.label_convertor = build_convertor(label_convertor)
# Preprocessor module, e.g., TPS
self.preprocessor = None
if preprocessor is not None:
self.preprocessor = build_preprocessor(preprocessor)
# Backbone
assert backbone is not None
self.backbone = build_backbone(backbone)
# Encoder module
self.encoder = None
if encoder is not None:
self.encoder = build_encoder(encoder)
# Decoder module
self.decoder = None
if decoder is not None:
decoder.update(num_classes=self.label_convertor.num_classes())
decoder.update(start_idx=self.label_convertor.start_idx)
decoder.update(padding_idx=self.label_convertor.padding_idx)
decoder.update(max_seq_len=max_seq_len)
self.decoder = build_decoder(decoder)
# Loss
assert loss is not None
self.loss = build_loss(loss)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.max_seq_len = max_seq_len
if pretrained is not None:
warnings.warn('DeprecationWarning: pretrained is a deprecated \
key, please consider using init_cfg')
self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
self.iter_size = iter_size
self.fuser = None
if fuser is not None:
self.fuser = build_fuser(fuser)
[docs] def forward_train(self, img, img_metas):
"""
Args:
img (tensor): Input images of shape (N, C, H, W).
Typically these should be mean centered and std scaled.
img_metas (list[dict]): A list of image info dict where each dict
contains: 'img_shape', 'filename', and may also contain
'ori_shape', and 'img_norm_cfg'.
For details on the values of these keys see
:class:`mmdet.datasets.pipelines.Collect`.
Returns:
dict[str, tensor]: A dictionary of loss components.
"""
for img_meta in img_metas:
valid_ratio = 1.0 * img_meta['resize_shape'][1] / img.size(-1)
img_meta['valid_ratio'] = valid_ratio
feat = self.extract_feat(img)
gt_labels = [img_meta['text'] for img_meta in img_metas]
targets_dict = self.label_convertor.str2tensor(gt_labels)
text_logits = None
out_enc = None
if self.encoder is not None:
out_enc = self.encoder(feat)
text_logits = out_enc['logits']
out_decs = []
out_fusers = []
for _ in range(self.iter_size):
if self.decoder is not None:
out_dec = self.decoder(
feat,
text_logits,
targets_dict,
img_metas,
train_mode=True)
out_decs.append(out_dec)
if self.fuser is not None:
out_fuser = self.fuser(out_enc['feature'], out_dec['feature'])
text_logits = out_fuser['logits']
out_fusers.append(out_fuser)
outputs = dict(
out_enc=out_enc, out_decs=out_decs, out_fusers=out_fusers)
losses = self.loss(outputs, targets_dict, img_metas)
return losses
[docs] def simple_test(self, img, img_metas, **kwargs):
"""Test function with test time augmentation.
Args:
imgs (torch.Tensor): Image input tensor.
img_metas (list[dict]): List of image information.
Returns:
list[str]: Text label result of each image.
"""
for img_meta in img_metas:
valid_ratio = 1.0 * img_meta['resize_shape'][1] / img.size(-1)
img_meta['valid_ratio'] = valid_ratio
feat = self.extract_feat(img)
text_logits = None
out_enc = None
if self.encoder is not None:
out_enc = self.encoder(feat)
text_logits = out_enc['logits']
out_decs = []
out_fusers = []
for _ in range(self.iter_size):
if self.decoder is not None:
out_dec = self.decoder(
feat, text_logits, img_metas=img_metas, train_mode=False)
out_decs.append(out_dec)
if self.fuser is not None:
out_fuser = self.fuser(out_enc['feature'], out_dec['feature'])
text_logits = out_fuser['logits']
out_fusers.append(out_fuser)
if len(out_fusers) > 0:
ret = out_fusers[-1]
elif len(out_decs) > 0:
ret = out_decs[-1]
else:
ret = out_enc
# early return to avoid post processing
if torch.onnx.is_in_onnx_export():
return ret['logits']
label_indexes, label_scores = self.label_convertor.tensor2idx(
ret['logits'].softmax(dim=-1), img_metas)
label_strings = self.label_convertor.idx2str(label_indexes)
# flatten batch results
results = []
for string, score in zip(label_strings, label_scores):
results.append(dict(text=string, score=score))
return results