configs/textrecog/crnn/crnn_toy_dataset.py

_base_ = [
    '../../_base_/default_runtime.py',
    '../../_base_/recog_pipelines/crnn_pipeline.py',
    '../../_base_/recog_datasets/toy_data.py',
    '../../_base_/schedules/schedule_adadelta_5e.py'
]

label_convertor = dict(
    type='CTCConvertor', dict_type='DICT36', with_unknown=True, lower=True)

model = dict(
    type='CRNNNet',
    preprocessor=None,
    backbone=dict(type='VeryDeepVgg', leaky_relu=False, input_channels=1),
    encoder=None,
    decoder=dict(type='CRNNDecoder', in_channels=512, rnn_flag=True),
    loss=dict(type='CTCLoss'),
    label_convertor=label_convertor,
    pretrained=None)

train_list = {{_base_.train_list}}
test_list = {{_base_.test_list}}

train_pipeline = {{_base_.train_pipeline}}
test_pipeline = {{_base_.test_pipeline}}

data = dict(
    samples_per_gpu=32,
    workers_per_gpu=2,
    val_dataloader=dict(samples_per_gpu=1),
    test_dataloader=dict(samples_per_gpu=1),
    train=dict(
        type='UniformConcatDataset',
        datasets=train_list,
        pipeline=train_pipeline),
    val=dict(
        type='UniformConcatDataset',
        datasets=test_list,
        pipeline=test_pipeline),
    test=dict(
        type='UniformConcatDataset',
        datasets=test_list,
        pipeline=test_pipeline))

evaluation = dict(interval=1, metric='acc')

cudnn_benchmark = True