MMOCR / configs /textrecog /nrtr /nrtr_r31_1by8_1by4_academic.py
tomofi's picture
Add application file
2366e36
raw
history blame
1.37 kB
_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')