File size: 4,015 Bytes
77f8d5f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 |
"""
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
from torch import nn
'''
Source - https://github.com/4uiiurz1/pytorch-nested-unet
An implementation of this paper - https://arxiv.org/abs/1807.10165
'''
class VGGBlock(nn.Module):
def __init__(self, in_channels, middle_channels, out_channels):
super().__init__()
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(in_channels, middle_channels, 3, padding=1)
self.bn1 = nn.BatchNorm2d(middle_channels)
self.conv2 = nn.Conv2d(middle_channels, out_channels, 3, padding=1)
self.bn2 = nn.BatchNorm2d(out_channels)
def forward(self, x):
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
return out
class NestedUNet(nn.Module):
def __init__(self, input_channels=1, out_channels=512):
super().__init__()
nb_filter = [32, 64, 128, 256, 512]
self.pool = nn.MaxPool2d(2, 2)
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.conv0_0 = VGGBlock(input_channels, nb_filter[0], nb_filter[0])
self.conv1_0 = VGGBlock(nb_filter[0], nb_filter[1], nb_filter[1])
self.conv2_0 = VGGBlock(nb_filter[1], nb_filter[2], nb_filter[2])
self.conv3_0 = VGGBlock(nb_filter[2], nb_filter[3], nb_filter[3])
self.conv4_0 = VGGBlock(nb_filter[3], nb_filter[4], nb_filter[4])
self.conv0_1 = VGGBlock(nb_filter[0]+nb_filter[1], nb_filter[0], nb_filter[0])
self.conv1_1 = VGGBlock(nb_filter[1]+nb_filter[2], nb_filter[1], nb_filter[1])
self.conv2_1 = VGGBlock(nb_filter[2]+nb_filter[3], nb_filter[2], nb_filter[2])
self.conv3_1 = VGGBlock(nb_filter[3]+nb_filter[4], nb_filter[3], nb_filter[3])
self.conv0_2 = VGGBlock(nb_filter[0]*2+nb_filter[1], nb_filter[0], nb_filter[0])
self.conv1_2 = VGGBlock(nb_filter[1]*2+nb_filter[2], nb_filter[1], nb_filter[1])
self.conv2_2 = VGGBlock(nb_filter[2]*2+nb_filter[3], nb_filter[2], nb_filter[2])
self.conv0_3 = VGGBlock(nb_filter[0]*3+nb_filter[1], nb_filter[0], nb_filter[0])
self.conv1_3 = VGGBlock(nb_filter[1]*3+nb_filter[2], nb_filter[1], nb_filter[1])
self.conv0_4 = VGGBlock(nb_filter[0]*4+nb_filter[1], nb_filter[0], nb_filter[0])
self.final = nn.Conv2d(nb_filter[0], out_channels, kernel_size=1)
def forward(self, input):
x0_0 = self.conv0_0(input)
x1_0 = self.conv1_0(self.pool(x0_0))
x0_1 = self.conv0_1(torch.cat([x0_0, self.up(x1_0)], 1))
x2_0 = self.conv2_0(self.pool(x1_0))
x1_1 = self.conv1_1(torch.cat([x1_0, self.up(x2_0)], 1))
x0_2 = self.conv0_2(torch.cat([x0_0, x0_1, self.up(x1_1)], 1))
x3_0 = self.conv3_0(self.pool(x2_0))
x2_1 = self.conv2_1(torch.cat([x2_0, self.up(x3_0)], 1))
x1_2 = self.conv1_2(torch.cat([x1_0, x1_1, self.up(x2_1)], 1))
x0_3 = self.conv0_3(torch.cat([x0_0, x0_1, x0_2, self.up(x1_2)], 1))
x4_0 = self.conv4_0(self.pool(x3_0))
x3_1 = self.conv3_1(torch.cat([x3_0, self.up(x4_0)], 1))
x2_2 = self.conv2_2(torch.cat([x2_0, x2_1, self.up(x3_1)], 1))
x1_3 = self.conv1_3(torch.cat([x1_0, x1_1, x1_2, self.up(x2_2)], 1))
x0_4 = self.conv0_4(torch.cat([x0_0, x0_1, x0_2, x0_3, self.up(x1_3)], 1))
output = self.final(x0_4)
return output
# x = torch.randn(1, 1, 32, 400)
# net = NestedUNet()
# out = net(x)
# print(out.shape) |