OCR深度学习模型CRNN+BiLSTM 模型2
OCR深度学习模型CRNN+BiLSTMimport torch
import torch.nn as nn
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class AsterBlock(nn.Module):
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(AsterBlock, self).__init__()
self.conv1 = conv1x1(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet_ASTER(nn.Module):
"""For aster or crnn"""
def __init__(self, num_class, with_lstm=False):
super(ResNet_ASTER, self).__init__()
self.with_lstm = with_lstm
in_channels = 1
self.layer0 = nn.Sequential(
nn.Conv2d(in_channels, 32, kernel_size=(3, 3), stride=1, padding=1, bias=False),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True))
self.inplanes = 32
self.layer1 = self._make_layer(32,3, ) #
self.layer2 = self._make_layer(64,4, ) #
self.layer3 = self._make_layer(128, 6, ) #
self.layer4 = self._make_layer(256, 6, ) #
self.layer5 = self._make_layer(512, 3, ) #
self.output_layer = nn.Linear(512,num_class)
if with_lstm:
self.rnn = nn.LSTM(512, 256, bidirectional=True, num_layers=2)
self.out_planes = 2 * 256
else:
self.out_planes = 512
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
nn.init.normal_(self.output_layer.weight,std=0.01)
nn.init.constant_(self.output_layer.bias,0)
def _make_layer(self, planes, blocks, stride):
downsample = None
if stride != or self.inplanes != planes:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes, stride),
nn.BatchNorm2d(planes))
layers = []
layers.append(AsterBlock(self.inplanes, planes, stride, downsample))
self.inplanes = planes
for _ in range(1, blocks):
layers.append(AsterBlock(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x0 = self.layer0(x)
x1 = self.layer1(x0)
x2 = self.layer2(x1)
x3 = self.layer3(x2)
x4 = self.layer4(x3)
x5 = self.layer5(x4)
cnn_feat = x5.squeeze(2) #
cnn_feat = cnn_feat.permute(2,0,1) #
if self.with_lstm:
rnn_feat, _ = self.rnn(cnn_feat)
T,b,h = rnn_feat.size()
output = rnn_feat.view(T*b,h)
output = self.output_layer(output)
output = output.view(T,b,-1)
output = nn.functional.log_softmax(output,dim=2)
return output
else:
return cnn_feat
def get_crnn(config):
assert config.MODEL.IMAGE_SIZE.H == 32, 'imgH has to be a multiple of 32'
return ResNet_ASTER(config.MODEL.NUM_CLASSES + 1, True)
if __name__ == "__main__":
from torchsummary import summary
model = ResNet_ASTER(35, True)
model.eval()
summary(model, (3, 32, 288))
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