Spaces:
Sleeping
Sleeping
File size: 1,330 Bytes
9c06ac7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 |
_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
|