file_client_args = dict(backend='disk') 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='NRTR', backbone=dict(type='NRTRModalityTransform'), encoder=dict(type='NRTREncoder', n_layers=12), decoder=dict( type='NRTRDecoder', module_loss=dict( type='CEModuleLoss', ignore_first_char=True, flatten=True), postprocessor=dict(type='AttentionPostprocessor'), dictionary=dictionary, max_seq_len=30), data_preprocessor=dict( type='TextRecogDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375])) train_pipeline = [ dict( type='LoadImageFromFile', file_client_args=file_client_args, ignore_empty=True, min_size=2), dict(type='LoadOCRAnnotations', with_text=True), dict( type='RescaleToHeight', height=32, min_width=32, max_width=160, width_divisor=4), dict(type='PadToWidth', width=160), 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=32, min_width=32, max_width=160, width_divisor=16), dict(type='PadToWidth', width=160), # 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', file_client_args=file_client_args), 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]