import torch.nn as nn
import torch.nn.functional as F
class VGG_FeatureExtractor(nn.Module):
""" FeatureExtractor of CRNN (https://arxiv.org/pdf/1507.05717.pdf) """
def __init__(self, input_channel, output_channel=512):
super(VGG_FeatureExtractor, self).__init__()
self.output_channel = [int(output_channel / 8), int(output_channel / 4),
int(output_channel / 2), output_channel] # [64, 128, 256, 512]
self.ConvNet = nn.Sequential(
nn.Conv2d(input_channel, self.output_channel[0], 3, 1, 1), nn.ReLU(True),
nn.MaxPool2d(2, 2), # 64x16x50
nn.Conv2d(self.output_channel[0], self.output_channel[1], 3, 1, 1), nn.ReLU(True),
nn.MaxPool2d(2, 2), # 128x8x25
nn.Conv2d(self.output_channel[1], self.output_channel[2], 3, 1, 1), nn.ReLU(True), # 256x8x25
nn.Conv2d(self.output_channel[2], self.output_channel[2], 3, 1, 1), nn.ReLU(True),
nn.MaxPool2d((2, 1), (2, 1)), # 256x4x25
nn.Conv2d(self.output_channel[2], self.output_channel[3], 3, 1, 1, bias=False),
nn.BatchNorm2d(self.output_channel[3]), nn.ReLU(True), # 512x4x25
nn.Conv2d(self.output_channel[3], self.output_channel[3], 3, 1, 1, bias=False),
nn.BatchNorm2d(self.output_channel[3]), nn.ReLU(True),
nn.MaxPool2d((2, 1), (2, 1)), # 512x2x25
nn.Conv2d(self.output_channel[3], self.output_channel[3], 2, 1, 0), nn.ReLU(True)) # 512x1x24
def forward(self, input):
return self.ConvNet(input)
class RCNN_FeatureExtractor(nn.Module):
""" FeatureExtractor of GRCNN (https://papers.nips.cc/paper/6637-gated-recurrent-convolution-neural-network-for-ocr.pdf) """
def __init__(self, input_channel, output_channel=512):
super(RCNN_FeatureExtractor, self).__init__()
self.output_channel = [int(output_channel / 8), int(output_channel / 4),
int(output_channel / 2), output_channel] # [64, 128, 256, 512]
self.ConvNet = nn.Sequential(
nn.Conv2d(input_channel, self.output_channel[0], 3, 1, 1), nn.ReLU(True),
nn.MaxPool2d(2, 2), # 64 x 16 x 50
GRCL(self.output_channel[0], self.output_channel[0], num_iteration=5, kernel_size=3, pad=1),
nn.MaxPool2d(2, 2), # 64 x 8 x 25
GRCL(self.output_channel[0], self.output_channel[1], num_iteration=5, kernel_size=3, pad=1),
nn.MaxPool2d(2, (2, 1), (0, 1)), # 128 x 4 x 26
GRCL(self.output_channel[1], self.output_channel[2], num_iteration=5, kernel_size=3, pad=1),
nn.MaxPool2d(2, (2, 1), (0, 1)), # 256 x 2 x 27
nn.Conv2d(self.output_channel[2], self.output_channel[3], 2, 1, 0, bias=False),
nn.BatchNorm2d(self.output_channel[3]), nn.ReLU(True)) # 512 x 1 x 26
def forward(self, input):
return self.ConvNet(input)
class ResNet_FeatureExtractor(nn.Module):
""" FeatureExtractor of FAN (http://openaccess.thecvf.com/content_ICCV_2017/papers/Cheng_Focusing_Attention_Towards_ICCV_2017_paper.pdf) """
def __init__(self, input_channel, output_channel=512):
super(ResNet_FeatureExtractor, self).__init__()
self.ConvNet = ResNet(input_channel, output_channel, BasicBlock, [1, 2, 5, 3])
def forward(self, input):
return self.ConvNet(input)
# For Gated RCNN
class GRCL(nn.Module):
def __init__(self, input_channel, output_channel, num_iteration, kernel_size, pad):
super(GRCL, self).__init__()
self.wgf_u = nn.Conv2d(input_channel, output_channel, 1, 1, 0, bias=False)
self.wgr_x = nn.Conv2d(output_channel, output_channel, 1, 1, 0, bias=False)
self.wf_u = nn.Conv2d(input_channel, output_channel, kernel_size, 1, pad, bias=False)
self.wr_x = nn.Conv2d(output_channel, output_channel, kernel_size, 1, pad, bias=False)
self.BN_x_init = nn.BatchNorm2d(output_channel)
self.num_iteration = num_iteration
self.GRCL = [GRCL_unit(output_channel) for _ in range(num_iteration)]
self.GRCL = nn.Sequential(*self.GRCL)
def forward(self, input):
""" The input of GRCL is consistant over time t, which is denoted by u(0)
thus wgf_u / wf_u is also consistant over time t.
"""
wgf_u = self.wgf_u(input)
wf_u = self.wf_u(input)
x = F.relu(self.BN_x_init(wf_u))
for i in range(self.num_iteration):
x = self.GRCL[i](wgf_u, self.wgr_x(x), wf_u, self.wr_x(x))
return x
class GRCL_unit(nn.Module):
def __init__(self, output_channel):
super(GRCL_unit, self).__init__()
self.BN_gfu = nn.BatchNorm2d(output_channel)
self.BN_grx = nn.BatchNorm2d(output_channel)
self.BN_fu = nn.BatchNorm2d(output_channel)
self.BN_rx = nn.BatchNorm2d(output_channel)
self.BN_Gx = nn.BatchNorm2d(output_channel)
def forward(self, wgf_u, wgr_x, wf_u, wr_x):
G_first_term = self.BN_gfu(wgf_u)
G_second_term = self.BN_grx(wgr_x)
G = F.sigmoid(G_first_term + G_second_term)
x_first_term = self.BN_fu(wf_u)
x_second_term = self.BN_Gx(self.BN_rx(wr_x) * G)
x = F.relu(x_first_term + x_second_term)
return x
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = self._conv3x3(inplanes, planes)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = self._conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU()
self.relu2 = nn.ReLU()
self.downsample = downsample
self.stride = stride
def _conv3x3(self, 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 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.relu2(out)
return out
class ResNet(nn.Module):
def __init__(self, input_channel, output_channel, block, layers):
super(ResNet, self).__init__()
self.output_channel_block = [int(output_channel / 4), int(output_channel / 2), output_channel, output_channel]
self.inplanes = int(output_channel / 8)
self.conv0_1 = nn.Conv2d(input_channel, int(output_channel / 16),
kernel_size=3, stride=1, padding=1, bias=False)
self.bn0_1 = nn.BatchNorm2d(int(output_channel / 16))
self.conv0_2 = nn.Conv2d(int(output_channel / 16), self.inplanes,
kernel_size=3, stride=1, padding=1, bias=False)
self.bn0_2 = nn.BatchNorm2d(self.inplanes)
self.relu = nn.ReLU()
self.relu1 = nn.ReLU()
self.relu2 = nn.ReLU()
self.relu3 = nn.ReLU()
self.relu4 = nn.ReLU()
self.relu5 = nn.ReLU()
self.relu6 = nn.ReLU()
self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.layer1 = self._make_layer(block, self.output_channel_block[0], layers[0])
self.conv1 = nn.Conv2d(self.output_channel_block[0], self.output_channel_block[
0], kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(self.output_channel_block[0])
self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.layer2 = self._make_layer(block, self.output_channel_block[1], layers[1], stride=1)
self.conv2 = nn.Conv2d(self.output_channel_block[1], self.output_channel_block[
1], kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(self.output_channel_block[1])
self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=(2, 1), padding=(0, 1))
self.layer3 = self._make_layer(block, self.output_channel_block[2], layers[2], stride=1)
self.conv3 = nn.Conv2d(self.output_channel_block[2], self.output_channel_block[
2], kernel_size=3, stride=1, padding=1, bias=False)
self.bn3 = nn.BatchNorm2d(self.output_channel_block[2])
self.layer4 = self._make_layer(block, self.output_channel_block[3], layers[3], stride=1)
self.conv4_1 = nn.Conv2d(self.output_channel_block[3], self.output_channel_block[
3], kernel_size=2, stride=(2, 1), padding=(0, 1), bias=False)
self.bn4_1 = nn.BatchNorm2d(self.output_channel_block[3])
self.conv4_2 = nn.Conv2d(self.output_channel_block[3], self.output_channel_block[
3], kernel_size=2, stride=1, padding=0, bias=False)
self.bn4_2 = nn.BatchNorm2d(self.output_channel_block[3])
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv0_1(x)
x = self.bn0_1(x)
x = self.relu(x)
x = self.conv0_2(x)
x = self.bn0_2(x)
x = self.relu1(x)
x = self.maxpool1(x)
x = self.layer1(x)
x = self.conv1(x)
x = self.bn1(x)
x = self.relu2(x)
x = self.maxpool2(x)
x = self.layer2(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu3(x)
x = self.maxpool3(x)
x = self.layer3(x)
x = self.conv3(x)
x = self.bn3(x)
x = self.relu4(x)
x = self.layer4(x)
x = self.conv4_1(x)
x = self.bn4_1(x)
x = self.relu5(x)
x = self.conv4_2(x)
x = self.bn4_2(x)
x = self.relu6(x)
return x