dictionary = dict(
    type='Dictionary',
    dict_file='{{ fileDirname }}/../../../dicts/english_digits_symbols.txt',
    with_padding=True,
    with_unknown=True,
    same_start_end=True,
    with_start=True,
    with_end=True)

model = dict(
    type='SATRN',
    backbone=dict(type='ShallowCNN', input_channels=3, hidden_dim=512),
    encoder=dict(
        type='SATRNEncoder',
        n_layers=12,
        n_head=8,
        d_k=512 // 8,
        d_v=512 // 8,
        d_model=512,
        n_position=100,
        d_inner=512 * 4,
        dropout=0.1),
    decoder=dict(
        type='NRTRDecoder',
        n_layers=6,
        d_embedding=512,
        n_head=8,
        d_model=512,
        d_inner=512 * 4,
        d_k=512 // 8,
        d_v=512 // 8,
        module_loss=dict(
            type='CEModuleLoss', flatten=True, ignore_first_char=True),
        dictionary=dictionary,
        max_seq_len=25,
        postprocessor=dict(type='AttentionPostprocessor')),
    data_preprocessor=dict(
        type='TextRecogDataPreprocessor',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375]))

train_pipeline = [
    dict(type='LoadImageFromFile', ignore_empty=True, min_size=0),
    dict(type='LoadOCRAnnotations', with_text=True),
    dict(type='Resize', scale=(100, 32), keep_ratio=False),
    dict(
        type='PackTextRecogInputs',
        meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio'))
]

test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='Resize', scale=(100, 32), keep_ratio=False),
    # add loading annotation after ``Resize`` because ground truth
    # does not need to do resize data transform
    dict(type='LoadOCRAnnotations', with_text=True),
    dict(
        type='PackTextRecogInputs',
        meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio'))
]

tta_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='TestTimeAug',
        transforms=[
            [
                dict(
                    type='ConditionApply',
                    true_transforms=[
                        dict(
                            type='ImgAugWrapper',
                            args=[dict(cls='Rot90', k=0, keep_size=False)])
                    ],
                    condition="results['img_shape'][1]<results['img_shape'][0]"
                ),
                dict(
                    type='ConditionApply',
                    true_transforms=[
                        dict(
                            type='ImgAugWrapper',
                            args=[dict(cls='Rot90', k=1, keep_size=False)])
                    ],
                    condition="results['img_shape'][1]<results['img_shape'][0]"
                ),
                dict(
                    type='ConditionApply',
                    true_transforms=[
                        dict(
                            type='ImgAugWrapper',
                            args=[dict(cls='Rot90', k=3, keep_size=False)])
                    ],
                    condition="results['img_shape'][1]<results['img_shape'][0]"
                ),
            ],
            [dict(type='Resize', scale=(100, 32), keep_ratio=False)],
            # add loading annotation after ``Resize`` because ground truth
            # does not need to do resize data transform
            [dict(type='LoadOCRAnnotations', with_text=True)],
            [
                dict(
                    type='PackTextRecogInputs',
                    meta_keys=('img_path', 'ori_shape', 'img_shape',
                               'valid_ratio'))
            ]
        ])
]