| """ |
| 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: |
| 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) |
| |
| 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 = self.up2(x, x3, block3) |
| |
| x = self.up3(x, x2, block2) |
| |
| x = self.up4(x, x1, block1) |
| |
| logits = self.outc(x) |
| return logits |
|
|
| |
| |
| |
| |
