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| thvl_textrecog_data_root = 'data/recog/synTH' | |
| thvl_textrecog_train = dict( | |
| type='OCRDataset', | |
| data_root='data/recog/synTH', | |
| ann_file='textrecog_train.json', | |
| pipeline=None) | |
| thvl_textrecog_test = dict( | |
| type='OCRDataset', | |
| data_root='data/recog/synTH', | |
| ann_file='textrecog_test.json', | |
| test_mode=True, | |
| pipeline=None) | |
| default_scope = 'mmocr' | |
| env_cfg = dict( | |
| cudnn_benchmark=True, | |
| mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), | |
| dist_cfg=dict(backend='nccl')) | |
| randomness = dict(seed=None) | |
| default_hooks = dict( | |
| timer=dict(type='IterTimerHook'), | |
| logger=dict(type='LoggerHook', interval=100), | |
| param_scheduler=dict(type='ParamSchedulerHook'), | |
| checkpoint=dict(type='CheckpointHook', interval=1), | |
| sampler_seed=dict(type='DistSamplerSeedHook'), | |
| sync_buffer=dict(type='SyncBuffersHook'), | |
| visualization=dict( | |
| type='VisualizationHook', | |
| interval=1, | |
| enable=False, | |
| show=False, | |
| draw_gt=False, | |
| draw_pred=False)) | |
| log_level = 'INFO' | |
| log_processor = dict(type='LogProcessor', window_size=10, by_epoch=True) | |
| load_from = None | |
| resume = False | |
| val_evaluator = dict( | |
| type='MultiDatasetsEvaluator', | |
| metrics=[ | |
| dict( | |
| type='WordMetric', | |
| mode=['exact', 'ignore_case', 'ignore_case_symbol']), | |
| dict(type='CharMetric') | |
| ], | |
| dataset_prefixes=None) | |
| test_evaluator = dict( | |
| type='MultiDatasetsEvaluator', | |
| metrics=[ | |
| dict( | |
| type='WordMetric', | |
| mode=['exact', 'ignore_case', 'ignore_case_symbol']), | |
| dict(type='CharMetric') | |
| ], | |
| dataset_prefixes=None) | |
| vis_backends = [dict(type='LocalVisBackend')] | |
| visualizer = dict( | |
| type='TextRecogLocalVisualizer', | |
| name='visualizer', | |
| vis_backends=[dict(type='LocalVisBackend')]) | |
| optim_wrapper = dict( | |
| type='OptimWrapper', optimizer=dict(type='Adam', lr=0.0003)) | |
| train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=50, val_interval=1) | |
| val_cfg = dict(type='ValLoop') | |
| test_cfg = dict(type='TestLoop') | |
| param_scheduler = [dict(type='MultiStepLR', milestones=[3, 4], end=6)] | |
| file_client_args = dict(backend='disk') | |
| dictionary = dict( | |
| type='Dictionary', | |
| dict_file= | |
| 'th_dict.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=dict( | |
| type='Dictionary', | |
| dict_file= | |
| 'th_dict.txt', | |
| with_padding=True, | |
| with_unknown=True, | |
| same_start_end=True, | |
| with_start=True, | |
| with_end=True), | |
| 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=dict(backend='disk'), | |
| 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=dict(backend='disk')), | |
| dict( | |
| type='RescaleToHeight', | |
| height=32, | |
| min_width=32, | |
| max_width=160, | |
| width_divisor=16), | |
| dict(type='PadToWidth', width=160), | |
| dict(type='LoadOCRAnnotations', with_text=True), | |
| dict( | |
| type='PackTextRecogInputs', | |
| meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio')) | |
| ] | |
| train_list = [ | |
| dict( | |
| type='OCRDataset', | |
| data_root='data/recog/synTH', | |
| ann_file='textrecog_train.json', | |
| pipeline=None) | |
| ] | |
| test_list = [ | |
| dict( | |
| type='OCRDataset', | |
| data_root='data/recog/synTH', | |
| ann_file='textrecog_test.json', | |
| test_mode=True, | |
| pipeline=None) | |
| ] | |
| train_dataset = dict( | |
| type='ConcatDataset', | |
| datasets=[ | |
| dict( | |
| type='OCRDataset', | |
| data_root='data/recog/synTH', | |
| ann_file='textrecog_train.json', | |
| pipeline=None) | |
| ], | |
| pipeline=[ | |
| dict( | |
| type='LoadImageFromFile', | |
| file_client_args=dict(backend='disk'), | |
| 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_dataset = dict( | |
| type='ConcatDataset', | |
| datasets=[ | |
| dict( | |
| type='OCRDataset', | |
| data_root='data/recog/synTH', | |
| ann_file='textrecog_test.json', | |
| test_mode=True, | |
| pipeline=None) | |
| ], | |
| pipeline=[ | |
| dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')), | |
| dict( | |
| type='RescaleToHeight', | |
| height=32, | |
| min_width=32, | |
| max_width=160, | |
| width_divisor=16), | |
| dict(type='PadToWidth', width=160), | |
| dict(type='LoadOCRAnnotations', with_text=True), | |
| dict( | |
| type='PackTextRecogInputs', | |
| meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio')) | |
| ]) | |
| train_dataloader = dict( | |
| batch_size=384, | |
| num_workers=24, | |
| persistent_workers=True, | |
| sampler=dict(type='DefaultSampler', shuffle=True), | |
| dataset=dict( | |
| type='ConcatDataset', | |
| datasets=[ | |
| dict( | |
| type='OCRDataset', | |
| data_root='data/recog/synTH', | |
| ann_file='textrecog_train.json', | |
| pipeline=None) | |
| ], | |
| pipeline=[ | |
| dict( | |
| type='LoadImageFromFile', | |
| file_client_args=dict(backend='disk'), | |
| 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_dataloader = dict( | |
| batch_size=1, | |
| num_workers=4, | |
| persistent_workers=True, | |
| drop_last=False, | |
| sampler=dict(type='DefaultSampler', shuffle=False), | |
| dataset=dict( | |
| type='ConcatDataset', | |
| datasets=[ | |
| dict( | |
| type='OCRDataset', | |
| data_root='data/recog/synTH', | |
| ann_file='textrecog_test.json', | |
| test_mode=True, | |
| pipeline=None) | |
| ], | |
| pipeline=[ | |
| dict( | |
| type='LoadImageFromFile', | |
| file_client_args=dict(backend='disk')), | |
| dict( | |
| type='RescaleToHeight', | |
| height=32, | |
| min_width=32, | |
| max_width=160, | |
| width_divisor=16), | |
| dict(type='PadToWidth', width=160), | |
| dict(type='LoadOCRAnnotations', with_text=True), | |
| dict( | |
| type='PackTextRecogInputs', | |
| meta_keys=('img_path', 'ori_shape', 'img_shape', | |
| 'valid_ratio')) | |
| ])) | |
| val_dataloader = dict( | |
| batch_size=1, | |
| num_workers=4, | |
| persistent_workers=True, | |
| drop_last=False, | |
| sampler=dict(type='DefaultSampler', shuffle=False), | |
| dataset=dict( | |
| type='ConcatDataset', | |
| datasets=[ | |
| dict( | |
| type='OCRDataset', | |
| data_root='data/recog/synTH', | |
| ann_file='textrecog_test.json', | |
| test_mode=True, | |
| pipeline=None) | |
| ], | |
| pipeline=[ | |
| dict( | |
| type='LoadImageFromFile', | |
| file_client_args=dict(backend='disk')), | |
| dict( | |
| type='RescaleToHeight', | |
| height=32, | |
| min_width=32, | |
| max_width=160, | |
| width_divisor=16), | |
| dict(type='PadToWidth', width=160), | |
| dict(type='LoadOCRAnnotations', with_text=True), | |
| dict( | |
| type='PackTextRecogInputs', | |
| meta_keys=('img_path', 'ori_shape', 'img_shape', | |
| 'valid_ratio')) | |
| ])) | |
| auto_scale_lr = dict(base_batch_size=384) | |
| launcher = 'none' | |
| work_dir = './work_dirs/nrtr_modality-transform_50e_thvl' | |