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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 | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| ''' | |
| Source - https://github.com/NYUMedML/DARTS/blob/master/DARTS/models/dense_unet_model.py | |
| An implementation of this paper - https://arxiv.org/abs/1608.06993 | |
| ''' | |
| class Single_level_densenet(nn.Module): | |
| def __init__(self,filters, num_conv = 4): | |
| super(Single_level_densenet, self).__init__() | |
| self.num_conv = num_conv | |
| self.conv_list = nn.ModuleList() | |
| self.bn_list = nn.ModuleList() | |
| for i in range(self.num_conv): | |
| self.conv_list.append(nn.Conv2d(filters,filters,3, padding = 1)) | |
| self.bn_list.append(nn.BatchNorm2d(filters)) | |
| def forward(self,x): | |
| outs = [] | |
| outs.append(x) | |
| for i in range(self.num_conv): | |
| temp_out = self.conv_list[i](outs[i]) | |
| if i > 0: | |
| for j in range(i): | |
| temp_out += outs[j] | |
| outs.append(F.relu(self.bn_list[i](temp_out))) | |
| out_final = outs[-1] | |
| del outs | |
| return out_final | |
| class Down_sample(nn.Module): | |
| def __init__(self,kernel_size = 2, stride = 2): | |
| super(Down_sample, self).__init__() | |
| self.down_sample_layer = nn.MaxPool2d(kernel_size, stride) | |
| def forward(self,x): | |
| y = self.down_sample_layer(x) | |
| return y,x | |
| class Upsample_n_Concat(nn.Module): | |
| def __init__(self,filters): | |
| super(Upsample_n_Concat, self).__init__() | |
| self.upsample_layer = nn.ConvTranspose2d(filters, filters, 4, padding = 1, stride = 2) | |
| self.conv = nn.Conv2d(2*filters,filters,3, padding = 1) | |
| self.bn = nn.BatchNorm2d(filters) | |
| def forward(self,x,y): | |
| x = self.upsample_layer(x) | |
| x = torch.cat([x,y],dim = 1) | |
| x = F.relu(self.bn(self.conv(x))) | |
| return x | |
| class DenseNet(nn.Module): | |
| def __init__(self, in_chan=1, out_chan=512, filters=256, num_conv = 4): | |
| super(DenseNet, self).__init__() | |
| self.conv1 = nn.Conv2d(in_chan,filters,1) | |
| self.d1 = Single_level_densenet(filters,num_conv ) | |
| self.down1 = Down_sample() | |
| self.d2 = Single_level_densenet(filters,num_conv ) | |
| self.down2 = Down_sample() | |
| self.d3 = Single_level_densenet(filters,num_conv ) | |
| self.down3 = Down_sample() | |
| self.d4 = Single_level_densenet(filters,num_conv ) | |
| self.down4 = Down_sample() | |
| self.bottom = Single_level_densenet(filters,num_conv ) | |
| self.up4 = Upsample_n_Concat(filters) | |
| self.u4 = Single_level_densenet(filters,num_conv ) | |
| self.up3 = Upsample_n_Concat(filters) | |
| self.u3 = Single_level_densenet(filters,num_conv ) | |
| self.up2 = Upsample_n_Concat(filters) | |
| self.u2 = Single_level_densenet(filters,num_conv ) | |
| self.up1 = Upsample_n_Concat(filters) | |
| self.u1 = Single_level_densenet(filters,num_conv ) | |
| self.outconv = nn.Conv2d(filters,out_chan, 1) | |
| # self.outconvp1 = nn.Conv2d(filters,out_chan, 1) | |
| # self.outconvm1 = nn.Conv2d(filters,out_chan, 1) | |
| def forward(self,x): | |
| bsz = x.shape[0] | |
| x = self.conv1(x) | |
| x,y1 = self.down1(self.d1(x)) | |
| x,y2 = self.down1(self.d2(x)) | |
| x,y3 = self.down1(self.d3(x)) | |
| x,y4 = self.down1(self.d4(x)) | |
| x = self.bottom(x) | |
| x = self.u4(self.up4(x,y4)) | |
| x = self.u3(self.up3(x,y3)) | |
| x = self.u2(self.up2(x,y2)) | |
| x = self.u1(self.up1(x,y1)) | |
| x1 = self.outconv(x) | |
| # xm1 = self.outconvm1(x) | |
| # xp1 = self.outconvp1(x) | |
| return x1 | |
| # # x = torch.randn(1, 1, 32, 400) | |
| # model = DenseNet(1, 512) | |
| # # out = model(x) | |
| # # print(out.shape) |