mirror of
https://github.com/open-mmlab/mmocr.git
synced 2025-06-03 21:54:47 +08:00
061d353b2a
* fix #794: add MASTER * fix conflict add MASTER * fix conflict add MASTER * fix conflict add MASTER * fix conflict add MASTER * fix conflict add MASTER * fix conflict add MASTER * fix conflict add MASTER * Fix linting * after git rebase main * after git rebase main * fix conflict add MASTER * fix conflict add MASTER * after git rebase main * fix conflict add MASTER * fix conflict add MASTER * fix conflict add MASTER * after git rebase main * add GCAModule to plugins * coexist master and master_old * fix merge mmocr 0.5.0 conflict * fix lint error * update * [fix] remove remains in __init__ * [update] update code in review * update readme for master * Add docstr to MasterDecoder, refined MasterDecoder, remove MASTERLoss * Unify the output length of MasterDecoder in train and test mode; add test for it, remove MasterLoss * update readme * update * update metafile,README,demo/README,config,ocr.py * Update mmocr/utils/ocr.py * update Co-authored-by: gaotongxiao <gaotongxiao@gmail.com> Co-authored-by: Mountchicken <mountchicken@outlook.com>
62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
label_convertor = dict(
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type='AttnConvertor', dict_type='DICT90', with_unknown=True)
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model = dict(
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type='MASTER',
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backbone=dict(
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type='ResNet',
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in_channels=3,
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stem_channels=[64, 128],
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block_cfgs=dict(
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type='BasicBlock',
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plugins=dict(
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cfg=dict(
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type='GCAModule',
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ratio=0.0625,
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headers=1,
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pooling_type='att',
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is_att_scale=False,
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fusion_type='channel_add'),
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position='after_conv2')),
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arch_layers=[1, 2, 5, 3],
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arch_channels=[256, 256, 512, 512],
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strides=[1, 1, 1, 1],
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plugins=[
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dict(
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cfg=dict(type='Maxpool2d', kernel_size=2, stride=(2, 2)),
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stages=(True, True, False, False),
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position='before_stage'),
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dict(
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cfg=dict(type='Maxpool2d', kernel_size=(2, 1), stride=(2, 1)),
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stages=(False, False, True, False),
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position='before_stage'),
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dict(
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cfg=dict(
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type='ConvModule',
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kernel_size=3,
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stride=1,
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padding=1,
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norm_cfg=dict(type='BN'),
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act_cfg=dict(type='ReLU')),
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stages=(True, True, True, True),
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position='after_stage')
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],
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init_cfg=[
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dict(type='Kaiming', layer='Conv2d'),
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dict(type='Constant', val=1, layer='BatchNorm2d'),
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]),
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encoder=None,
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decoder=dict(
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type='MasterDecoder',
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d_model=512,
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n_head=8,
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attn_drop=0.,
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ffn_drop=0.,
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d_inner=2048,
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n_layers=3,
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feat_pe_drop=0.2,
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feat_size=6 * 40),
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loss=dict(type='TFLoss', reduction='mean'),
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label_convertor=label_convertor,
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max_seq_len=30)
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