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# num_chars depends on the configuration of label_convertor. The actual
# dictionary size is 36 + 1 (<BOS/EOS>).
# TODO: Automatically update num_chars based on the configuration of
# label_convertor
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')),
),
decoder=dict(
type='ABILanguageDecoder',
d_model=512,
n_head=8,
d_inner=2048,
n_layers=4,
dropout=0.1,
detach_tokens=True,
use_self_attn=False,
pad_idx=num_chars - 1,
num_chars=num_chars,
max_seq_len=max_seq_len,
init_cfg=None),
fuser=dict(
type='ABIFuser',
d_model=512,
num_chars=num_chars,
init_cfg=None,
max_seq_len=max_seq_len,
),
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=3)
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