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