mmocr/configs/textrecog/sar/sar_r31_sequential_decoder_academic.py

_base_ = [
    'sar.py',
    '../../_base_/recog_datasets/ST_SA_MJ_real_train.py',
    '../../_base_/recog_datasets/academic_test.py',
    '../../_base_/default_runtime.py',
    '../../_base_/schedules/schedule_adam_step_5e.py',
]

# dataset settings
file_client_args = dict(backend='disk')
default_hooks = dict(logger=dict(type='LoggerHook', interval=100))

train_pipeline = [
    dict(
        type='LoadImageFromFile',
        file_client_args=file_client_args,
        ignore_empty=True,
        min_size=5),
    dict(type='LoadOCRAnnotations', with_text=True),
    dict(type='Resize', scale=(160, 48), keep_ratio=False),
    dict(
        type='PackTextRecogInputs',
        meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio'))
]

test_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=file_client_args),
    dict(
        type='RescaleToHeight',
        height=48,
        min_width=48,
        max_width=160,
        width_divisor=4),
    dict(type='PadToWidth', width=160),
    dict(
        type='PackTextRecogInputs',
        meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio',
                   'instances'))
]

# dataset settings
ic11_rec_train = _base_.ic11_rec_train
ic13_rec_train = _base_.ic13_rec_train
ic15_rec_train = _base_.ic15_rec_train
cocov1_rec_train = _base_.cocov1_rec_train
iiit5k_rec_train = _base_.iiit5k_rec_train
st_add_rec_train = _base_.st_add_rec_train
st_rec_train = _base_.st_rec_train
mj_rec_trian = _base_.mj_rec_trian

ic11_rec_train.pipeline = train_pipeline
ic13_rec_train.pipeline = train_pipeline
ic15_rec_train.pipeline = train_pipeline
cocov1_rec_train.pipeline = train_pipeline
iiit5k_rec_train.pipeline = train_pipeline
st_add_rec_train.pipeline = train_pipeline
st_rec_train.pipeline = train_pipeline
mj_rec_trian.pipeline = train_pipeline
repeat_ic11 = dict(type='RepeatDataset', dataset=ic11_rec_train, times=20)
repeat_ic13 = dict(type='RepeatDataset', dataset=ic13_rec_train, times=20)
repeat_ic15 = dict(type='RepeatDataset', dataset=ic15_rec_train, times=20)
repeat_cocov1 = dict(type='RepeatDataset', dataset=cocov1_rec_train, times=20)
repeat_iiit5k = dict(type='RepeatDataset', dataset=iiit5k_rec_train, times=20)

train_dataloader = dict(
    batch_size=64,
    num_workers=16,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=dict(
        type='ConcatDataset',
        datasets=[
            repeat_ic11, repeat_ic13, repeat_ic15, repeat_cocov1,
            repeat_iiit5k, st_add_rec_train, st_rec_train, mj_rec_trian
        ]))

test_cfg = dict(type='MultiTestLoop')
val_cfg = dict(type='MultiValLoop')
val_dataloader = _base_.val_dataloader
test_dataloader = _base_.test_dataloader
for dataloader in test_dataloader:
    dataloader['dataset']['pipeline'] = test_pipeline
for dataloader in val_dataloader:
    dataloader['dataset']['pipeline'] = test_pipeline
visualizer = dict(type='TextRecogLocalVisualizer', name='visualizer')

dictionary = dict(
    type='Dictionary',
    dict_file='dicts/english_digits_symbols.txt',
    with_start=True,
    with_end=True,
    same_start_end=True,
    with_padding=True,
    with_unknown=True)
model = dict(
    type='SARNet',
    backbone=dict(type='ResNet31OCR'),
    encoder=dict(
        type='SAREncoder',
        enc_bi_rnn=False,
        enc_do_rnn=0.1,
        enc_gru=False,
    ),
    decoder=dict(
        type='SequentialSARDecoder',
        enc_bi_rnn=False,
        dec_bi_rnn=False,
        dec_do_rnn=0,
        dec_gru=False,
        pred_dropout=0.1,
        d_k=512,
        pred_concat=True,
        postprocessor=dict(type='AttentionPostprocessor'),
        module_loss=dict(
            type='CEModuleLoss', ignore_first_char=True, reduction='mean'),
        dictionary=dictionary,
        max_seq_len=30))