|
_base_ = [ |
|
'../../_base_/default_runtime.py', |
|
'../../_base_/recog_pipelines/satrn_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='SATRN', |
|
backbone=dict(type='ShallowCNN', input_channels=3, hidden_dim=512), |
|
encoder=dict( |
|
type='SatrnEncoder', |
|
n_layers=12, |
|
n_head=8, |
|
d_k=512 // 8, |
|
d_v=512 // 8, |
|
d_model=512, |
|
n_position=100, |
|
d_inner=512 * 4, |
|
dropout=0.1), |
|
decoder=dict( |
|
type='NRTRDecoder', |
|
n_layers=6, |
|
d_embedding=512, |
|
n_head=8, |
|
d_model=512, |
|
d_inner=512 * 4, |
|
d_k=512 // 8, |
|
d_v=512 // 8), |
|
loss=dict(type='TFLoss'), |
|
label_convertor=label_convertor, |
|
max_seq_len=25) |
|
|
|
|
|
optimizer = dict(type='Adam', lr=3e-4) |
|
optimizer_config = dict(grad_clip=None) |
|
|
|
lr_config = dict(policy='step', step=[3, 4]) |
|
total_epochs = 6 |
|
|
|
data = dict( |
|
samples_per_gpu=64, |
|
workers_per_gpu=4, |
|
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') |
|
|
