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"""
Paper: "UTRNet: High-Resolution Urdu Text Recognition In Printed Documents" presented at ICDAR 2023
Authors: Abdur Rahman, Arjun Ghosh, Chetan Arora
GitHub Repository: https://github.com/abdur75648/UTRNet-High-Resolution-Urdu-Text-Recognition
Project Website: https://abdur75648.github.io/UTRNet/
Copyright (c) 2023-present: This work is licensed under the Creative Commons Attribution-NonCommercial
4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/)
"""
import torch.nn.functional as F
import torch.nn as nn
import torch
'''
Source - https://github.com/mribrahim/Pytorch-UNet-and-Inception/blob/e627658ee84e26ef3befd1ded4904048997e84f8/unet/inception.py
An implementation of this paper - https://dl.acm.org/doi/abs/10.1145/3376922
'''
class InceptionConv(nn.Module):
"""(convolution => [BN] => ReLU) * 2"""
def __init__(self, in_channels, out_channels, mid_channels=None):
super().__init__()
if not mid_channels:
mid_channels = out_channels
self.double_conv1 = nn.Sequential(
nn.MaxPool2d(2),
nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
self.double_conv2 = nn.Sequential(
nn.MaxPool2d(2),
nn.Conv2d(in_channels, mid_channels, kernel_size=5, padding=2),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels, out_channels, kernel_size=5, padding=2),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
self.double_conv3 = nn.Sequential(
nn.MaxPool2d(2),
nn.Conv2d(in_channels, mid_channels, kernel_size=1, padding=0),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
)
self.double_conv4 = nn.Sequential(
nn.MaxPool2d(2),
nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels, out_channels, kernel_size=1, padding=0),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def forward(self, x):
outputs = [self.double_conv1(x), self.double_conv2(x), self.double_conv3(x), self.double_conv4(x)]
return torch.cat(outputs, 1)
class DoubleConv(nn.Module):
"""(convolution => [BN] => ReLU) * 2"""
def __init__(self, in_channels, out_channels, mid_channels=None):
super().__init__()
if not mid_channels:
mid_channels = out_channels
self.double_conv = nn.Sequential(
nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.double_conv(x)
class Down(nn.Module):
"""Downscaling with maxpool then double conv"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.maxpool_conv = nn.Sequential(
nn.MaxPool2d(2),
DoubleConv(in_channels, out_channels)
)
def forward(self, x):
return self.maxpool_conv(x)
class UpInception(nn.Module):
"""Upscaling then double conv"""
def __init__(self, in_channels, out_channels, bilinear=True):
super().__init__()
# if bilinear, use the normal convolutions to reduce the number of channels
if bilinear:
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
else:
self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2)
self.conv = DoubleConv(in_channels, out_channels)
def forward(self, x1, x2, x3):
x1 = self.up(x1)
x3 = self.up(x3)
# input is CHW
diffY = x2.size()[2] - x1.size()[2]
diffX = x2.size()[3] - x1.size()[3]
x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
diffY // 2, diffY - diffY // 2])
x = torch.cat([x3, x2, x1], dim=1)
return self.conv(x)
class OutConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(OutConv, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
def forward(self, x):
return self.conv(x)
class InceptionUNet(nn.Module):
def __init__(self, n_channels=1, out_channels=512, bilinear=True):
super(InceptionUNet, self).__init__()
self.n_channels = n_channels
self.out_channels = out_channels
self.bilinear = bilinear
self.block1 = InceptionConv(64, 32)
self.block2 = InceptionConv(128, 64)
self.block3 = InceptionConv(256, 128)
self.block4 = InceptionConv(512, 128)
self.inc = DoubleConv(n_channels, 64)
self.down1 = Down(64, 128)
self.down2 = Down(128, 256)
self.down3 = Down(256, 512)
factor = 2 if bilinear else 1
self.down4 = Down(512, 1024 // factor)
self.up1 = UpInception(1024+512, 256 // factor, bilinear)
self.up2 = UpInception(896, 128 // factor, bilinear)
self.up3 = UpInception(448, 32 // factor, bilinear)
self.up4 = UpInception(208, 16, bilinear)
self.outc = OutConv(16, out_channels)
def forward(self, x):
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
block1 = self.block1(x1)
block2 = self.block2(block1)
block3 = self.block3(block2)
block4 = self.block4(block3)
x = self.up1(x5, x4, block4)
# x = torch.cat(x, block4)
x = self.up2(x, x3, block3)
# x = torch.cat(x, block3)
x = self.up3(x, x2, block2)
# x = torch.cat(x, block2)
x = self.up4(x, x1, block1)
# x = torch.cat(x, block1)
logits = self.outc(x)
return logits
# x = torch.randn(1, 1, 32, 400)
# net = InceptionUNet()
# out = net(x)
# print(out.shape) |