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