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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/)
"""
'''
Source - https://github.com/shuuchen/HRNet/blob/master/hrnet.py
An implementation of this paper - https://arxiv.org/pdf/1908.07919.pdf
'''
import torch
from torch import nn
# from utils import draw_feature_map
BN_MOMENTUM = 0.1
# Conv Module - Does not change the shape of the input, only the number of channels
class Conv(nn.Module):
def __init__(self, in_ch, out_ch, kernel_size=3, stride=1, relued=True):
super(Conv, self).__init__()
padding = (kernel_size - 1) // 2
self.conv_bn = nn.Sequential(
nn.Conv2d(in_ch, out_ch, kernel_size, stride, padding, bias=False),
nn.BatchNorm2d(out_ch, momentum=BN_MOMENTUM))
self.relu = nn.ReLU()
self.relued = relued
def forward(self, x):
x = self.conv_bn(x)
if self.relued:
x = self.relu(x)
return x
# BasicBlock - Does not change shape or size of the input at all
class BasicBlock(nn.Module):
def __init__(self, in_ch, out_ch):
super(BasicBlock, self).__init__()
self.conv = nn.Sequential(
Conv(in_ch, out_ch),
Conv(in_ch, out_ch, relued=False))
self.relu = nn.ReLU()
def forward(self, x):
identity = x
x = self.conv(x)
x = x + identity
return self.relu(x)
# Bottleneck - Does not change shape of the input, increases channels to 4*out_ch (instead of out_ch)
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_ch, out_ch, downsampling=None):
super(Bottleneck, self).__init__()
self.conv = nn.Sequential(
Conv(in_ch, out_ch, kernel_size=1),
Conv(out_ch, out_ch),
Conv(out_ch, out_ch * self.expansion, kernel_size=1, relued=False))
self.relu = nn.ReLU()
self.downsampling = downsampling
def forward(self, x):
identity = x
x = self.conv(x)
if self.downsampling:
identity = self.downsampling(identity)
x = x + identity
return self.relu(x)
# UpSampling - Reduces the number of channels to ch // up_factor and increases input size up_factor times
class UpSampling(nn.Module):
def __init__(self, ch, up_factor):
super(UpSampling, self).__init__()
self.up_sampling = nn.Sequential(
nn.Upsample(scale_factor=up_factor, mode='bilinear', align_corners=False),
Conv(ch, ch // up_factor, 1, relued=False))
def forward(self, x):
return self.up_sampling(x)
# DownSampling - Increases the number of channels and reduces input size by a factor of (2^num_samplings)
class DownSampling(nn.Module):
def __init__(self, ch, num_samplings):
super(DownSampling, self).__init__()
convs = []
for i in range(num_samplings):
relued = True if i < num_samplings - 1 else False
convs.append(Conv(ch, ch * 2, 3, 2, relued=relued))
ch *= 2
self.down_sampling = nn.Sequential(*convs)
def forward(self, x):
return self.down_sampling(x)
class HRBlock(nn.Module):
def __init__(self, ch, index, last_stage, block, num_conv_block_per_list=4):
super(HRBlock, self).__init__()
self.index = index
self.last_stage = last_stage
self.num_conv_block_per_list = num_conv_block_per_list
self.relu = nn.ReLU()
self.parallel_conv_lists = nn.ModuleList()
for i in range(index):
ch_i = ch * 2**i
conv_list = []
for j in range(num_conv_block_per_list):
conv_list.append(block(ch_i, ch_i))
self.parallel_conv_lists.append(nn.Sequential(*conv_list))
self.up_conv_lists = nn.ModuleList()
for i in range(index - 1):
conv_list = nn.ModuleList()
for j in range(i + 1, index):
up_factor = 2 ** (j-i)
ch_j = ch * 2**j
conv_list.append(UpSampling(ch_j, up_factor))
self.up_conv_lists.append(conv_list)
self.down_conv_lists = nn.ModuleList()
for i in range(1, index if last_stage else index + 1):
conv_list = nn.ModuleList()
for j in range(i):
ch_j = ch * 2**j
conv_list.append(DownSampling(ch_j, i - j))
self.down_conv_lists.append(conv_list)
def forward(self, x_list):
parallel_res_list = []
for i in range(self.index):
x = x_list[i]
x = self.parallel_conv_lists[i](x)
parallel_res_list.append(x)
final_res_list = []
for i in range(self.index if self.last_stage else self.index + 1):
# Downsampling all streams to a dimension just lower than the lowest stream, for next stage (Don't do for last stage i.e. index = 4 obviously)
if i == self.index:
x = 0
for t, m in zip(parallel_res_list, self.down_conv_lists[-1]):
x = x + m(t)
else:
x = parallel_res_list[i]
# Upsampling all streams (except the uppermost), to all possible dimensions above it till the highest stream
if i != self.index - 1:
res_list = parallel_res_list[i+1:]
up_x = 0
for t, m in zip(res_list, self.up_conv_lists[i]):
up_x = up_x + m(t)
x = x + up_x
# Downsampling all streams (except the lowest) to all possible dimensions below it till the lowest stream dimension
if i != 0:
res_list = parallel_res_list[:i]
down_x = 0
for t, m in zip(res_list, self.down_conv_lists[i - 1]):
down_x = down_x + m(t)
x = x + down_x
x = self.relu(x)
final_res_list.append(x)
return final_res_list
class HRNet(nn.Module):
def __init__(self, in_ch=1, out_ch=32, mid_ch=64, num_stage=4):
super(HRNet, self).__init__()
self.init_conv = nn.Sequential(
Conv(in_ch, 64, 1),
Conv(64, 64, 1))
self.head = nn.Sequential(
Conv(mid_ch * (1 + 2 + 4 + 8), mid_ch * (1 + 2 + 4 + 8), 1),
nn.Conv2d(mid_ch * (1 + 2 + 4 + 8), out_ch, 1))
self.first_layer = self._make_layer(64, 64, Bottleneck, 4)
self.first_transition = self._make_transition_layer(256, mid_ch, 1)
self.num_stage = num_stage
self.hr_blocks = nn.ModuleList()
for i in range(1, num_stage):
self.hr_blocks.append(HRBlock(mid_ch, i + 1, True if i == num_stage - 1 else False, BasicBlock))
self.up_samplings = nn.ModuleList()
for i in range(num_stage - 1):
up_factor = 2 ** (i + 1)
up = nn.Upsample(scale_factor=up_factor, mode='bilinear')
self.up_samplings.append(up)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.normal_(m.weight, std=0.001)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def _make_layer(self, in_ch, ch, block, num):
downsampling = None
if in_ch != ch * block.expansion:
downsampling = Conv(in_ch, ch * block.expansion, 1, relued=False)
layers = []
layers.append(block(in_ch, ch, downsampling))
for i in range(1, num):
layers.append(block(ch * block.expansion, ch))
return nn.Sequential(*layers)
def _make_transition_layer(self, in_ch, out_ch, stage):
layers = nn.ModuleList()
layers.append(Conv(in_ch, out_ch, 1))
layers.append(Conv(in_ch, out_ch * 2, 3, 2))
return layers
def forward(self, x):
x = self.init_conv(x)
# Save visual_features from any 10 random channels for visualization # For image at index 0 in batch
# draw_feature_map(x,"vis_feature_maps/initial_layer", num_channel=25)
# if os.path.exists('vis_feature_maps/initial_layer'):
x = self.first_layer(x)
x_list = [m(x) for m in self.first_transition]
for i in range(self.num_stage - 1):
x_list = self.hr_blocks[i](x_list)
# Visualization from any 10 random channels for visualization # For image at index 0 in batch
# if i==2: # Last stage
# draw_feature_map( x_list[-1],"vis_feature_maps/lower_layers",25)
res_list = [x_list[0]]
for t, m in zip(x_list[1:], self.up_samplings):
res_list.append(m(t))
x = torch.cat(res_list, dim=1)
x = self.head(x)
# draw_feature_map(x,"vis_feature_maps/output_layer", num_channel=25)
return x
# x = [torch.randn(1, 64, 32, 400),torch.randn(1, 128, 16, 200), torch.rand(1, 256, 8, 100)]
# model = HRBlock(ch=64,index=3,last_stage=False,block=BasicBlock) # index = 2,3,4
# x = torch.randn(1, 1, 32, 400)
# model = HRNet()
# out = model(x)
# print(out.shape)
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