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label_convertor = dict(
type='AttnConvertor', dict_type='DICT90', with_unknown=True)
model = dict(
type='MASTER',
backbone=dict(
type='ResNet',
in_channels=3,
stem_channels=[64, 128],
block_cfgs=dict(
type='BasicBlock',
plugins=dict(
cfg=dict(
type='GCAModule',
ratio=0.0625,
n_head=1,
pooling_type='att',
is_att_scale=False,
fusion_type='channel_add'),
position='after_conv2')),
arch_layers=[1, 2, 5, 3],
arch_channels=[256, 256, 512, 512],
strides=[1, 1, 1, 1],
plugins=[
dict(
cfg=dict(type='Maxpool2d', kernel_size=2, stride=(2, 2)),
stages=(True, True, False, False),
position='before_stage'),
dict(
cfg=dict(type='Maxpool2d', kernel_size=(2, 1), stride=(2, 1)),
stages=(False, False, True, False),
position='before_stage'),
dict(
cfg=dict(
type='ConvModule',
kernel_size=3,
stride=1,
padding=1,
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU')),
stages=(True, True, True, True),
position='after_stage')
],
init_cfg=[
dict(type='Kaiming', layer='Conv2d'),
dict(type='Constant', val=1, layer='BatchNorm2d'),
]),
encoder=None,
decoder=dict(
type='MasterDecoder',
d_model=512,
n_head=8,
attn_drop=0.,
ffn_drop=0.,
d_inner=2048,
n_layers=3,
feat_pe_drop=0.2,
feat_size=6 * 40),
loss=dict(type='TFLoss', reduction='mean'),
label_convertor=label_convertor,
max_seq_len=30)
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