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_base_ = [
'../../_base_/default_runtime.py',
'../../_base_/schedules/schedule_adam_step_20e.py',
'../../_base_/recog_pipelines/abinet_pipeline.py',
'../../_base_/recog_datasets/ST_MJ_alphanumeric_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}}
# Model
num_chars = 37
max_seq_len = 26
label_convertor = dict(
type='ABIConvertor',
dict_type='DICT36',
with_unknown=False,
with_padding=False,
lower=True,
)
model = dict(
type='ABINet',
backbone=dict(type='ResNetABI'),
encoder=dict(
type='ABIVisionModel',
encoder=dict(
type='TransformerEncoder',
n_layers=3,
n_head=8,
d_model=512,
d_inner=2048,
dropout=0.1,
max_len=8 * 32,
),
decoder=dict(
type='ABIVisionDecoder',
in_channels=512,
num_channels=64,
attn_height=8,
attn_width=32,
attn_mode='nearest',
use_result='feature',
num_chars=num_chars,
max_seq_len=max_seq_len,
init_cfg=dict(type='Xavier', layer='Conv2d')),
),
loss=dict(
type='ABILoss',
enc_weight=1.0,
dec_weight=1.0,
fusion_weight=1.0,
num_classes=num_chars),
label_convertor=label_convertor,
max_seq_len=max_seq_len,
iter_size=1)
data = dict(
samples_per_gpu=192,
workers_per_gpu=8,
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')
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