2022-12-06 16:47:02 +08:00
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# Copyright (c) OpenMMLab. All rights reserved.
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import torch.nn as nn
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from mmcv.cnn import ConvModule
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from mmengine.model import BaseModule, Sequential
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from mmocr.registry import MODELS
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@MODELS.register_module()
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class ABCNetRecBackbone(BaseModule):
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def __init__(self, init_cfg=None):
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super().__init__(init_cfg)
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self.convs = Sequential(
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ConvModule(
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in_channels=256,
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out_channels=256,
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kernel_size=3,
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padding=1,
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bias='auto',
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norm_cfg=dict(type='BN'),
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act_cfg=dict(type='ReLU')),
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ConvModule(
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in_channels=256,
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out_channels=256,
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kernel_size=3,
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padding=1,
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bias='auto',
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norm_cfg=dict(type='BN'),
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act_cfg=dict(type='ReLU')),
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ConvModule(
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in_channels=256,
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out_channels=256,
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kernel_size=3,
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padding=1,
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stride=(2, 1),
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bias='auto',
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norm_cfg=dict(type='GN', num_groups=32),
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act_cfg=dict(type='ReLU')),
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ConvModule(
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in_channels=256,
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out_channels=256,
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kernel_size=3,
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padding=1,
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stride=(2, 1),
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bias='auto',
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norm_cfg=dict(type='GN', num_groups=32),
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2023-01-18 18:37:19 +08:00
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act_cfg=dict(type='ReLU')), nn.AdaptiveAvgPool2d((1, None)))
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2022-12-06 16:47:02 +08:00
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def forward(self, x):
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return self.convs(x)
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