pipeline/mmocr/configs/textrecog/_base_/default_runtime.py

default_scope = 'mmocr'
env_cfg = dict(
    cudnn_benchmark=False,
    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),
)
# Logging
log_level = 'INFO'
log_processor = dict(type='LogProcessor', window_size=10, by_epoch=True)

load_from = None
resume = False

# Evaluation
val_evaluator = dict(
    type='MultiDatasetsEvaluator',
    metrics=[
        dict(
            type='WordMetric',
            mode=['exact', 'ignore_case', 'ignore_case_symbol']),
        dict(type='CharMetric')
    ],
    dataset_prefixes=None)
test_evaluator = val_evaluator

# Visualization
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
    type='TextRecogLocalVisualizer',
    name='visualizer',
    vis_backends=vis_backends)

tta_model = dict(type='EncoderDecoderRecognizerTTAModel')