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# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.runner import BaseModule
from mmocr.models.builder import BACKBONES
@BACKBONES.register_module()
class NRTRModalityTransform(BaseModule):
def __init__(self,
input_channels=3,
init_cfg=[
dict(type='Kaiming', layer='Conv2d'),
dict(type='Uniform', layer='BatchNorm2d')
]):
super().__init__(init_cfg=init_cfg)
self.conv_1 = nn.Conv2d(
in_channels=input_channels,
out_channels=32,
kernel_size=3,
stride=2,
padding=1)
self.relu_1 = nn.ReLU(True)
self.bn_1 = nn.BatchNorm2d(32)
self.conv_2 = nn.Conv2d(
in_channels=32,
out_channels=64,
kernel_size=3,
stride=2,
padding=1)
self.relu_2 = nn.ReLU(True)
self.bn_2 = nn.BatchNorm2d(64)
self.linear = nn.Linear(512, 512)
def forward(self, x):
x = self.conv_1(x)
x = self.relu_1(x)
x = self.bn_1(x)
x = self.conv_2(x)
x = self.relu_2(x)
x = self.bn_2(x)
n, c, h, w = x.size()
x = x.permute(0, 3, 2, 1).contiguous().view(n, w, h * c)
x = self.linear(x)
x = x.permute(0, 2, 1).contiguous().view(n, -1, 1, w)
return x
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