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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, in_channels, with_lstm=True, n_group=1):
super(ResNet_ASTER, self).__init__()
self.with_lstm = with_lstm
self.n_group = n_group
self.out_channels = 512
if with_lstm:
self.out_channels = 512
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, [2, 2]) # [16, 50]
self.layer2 = self._make_layer(64, 4, [2, 2]) # [8, 25]
self.layer3 = self._make_layer(128, 6, [2, 1]) # [4, 25]
self.layer4 = self._make_layer(256, 6, [2, 1]) # [2, 25]
self.layer5 = self._make_layer(512, 3, [2, 1]) # [1, 25]
if with_lstm:
self.rnn = nn.LSTM(512,
256,
bidirectional=True,
num_layers=2,
batch_first=True)
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)
def _make_layer(self, planes, blocks, stride):
downsample = None
if stride != [1, 1] 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) # [N, c, w]
cnn_feat = cnn_feat.transpose(2, 1).contiguous()
if self.with_lstm:
rnn_feat, _ = self.rnn(cnn_feat)
return rnn_feat
else:
return cnn_feat
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