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_base_ = [ | |
'../../_base_/default_runtime.py', | |
'../../_base_/schedules/schedule_adam_step_6e.py', | |
'../../_base_/recog_pipelines/nrtr_pipeline.py', | |
'../../_base_/recog_datasets/ST_MJ_train.py', | |
'../../_base_/recog_datasets/academic_test.py' | |
] | |
train_list = {{_base_.train_list}} | |
test_list = {{_base_.test_list}} | |
train_pipeline = {{_base_.train_pipeline}} | |
test_pipeline = {{_base_.test_pipeline}} | |
label_convertor = dict( | |
type='AttnConvertor', dict_type='DICT90', with_unknown=True) | |
model = dict( | |
type='NRTR', | |
backbone=dict( | |
type='ResNet31OCR', | |
layers=[1, 2, 5, 3], | |
channels=[32, 64, 128, 256, 512, 512], | |
stage4_pool_cfg=dict(kernel_size=(2, 1), stride=(2, 1)), | |
last_stage_pool=False), | |
encoder=dict(type='NRTREncoder'), | |
decoder=dict(type='NRTRDecoder'), | |
loss=dict(type='TFLoss'), | |
label_convertor=label_convertor, | |
max_seq_len=40) | |
data = dict( | |
samples_per_gpu=64, | |
workers_per_gpu=4, | |
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') | |