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# A simplified version of the original code - https://github.com/abdur75648/UTRNet-High-Resolution-Urdu-Text-Recognition
import torch
import torch.nn as nn
import torch.nn.functional as F
# Code For UNet Feature Extractor - Source - https://github.com/milesial/Pytorch-UNet
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, bias=False),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False),
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 Up(nn.Module):
"""Upscaling then double conv"""
def __init__(self, in_channels, out_channels):
super().__init__()
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):
x1 = self.up(x1)
# input is CHW
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([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 UNet(nn.Module):
def __init__(self, n_channels=1, n_classes=512):
super(UNet, self).__init__()
self.n_channels = n_channels
self.n_classes = n_classes
self.inc = DoubleConv(n_channels, 32)
self.down1 = Down(32, 64)
self.down2 = Down(64, 128)
self.down3 = Down(128, 256)
self.down4 = Down(256, 512)
self.up1 = Up(512, 256)
self.up2 = Up(256, 128)
self.up3 = Up(128, 64)
self.up4 = Up(64, 32)
self.outc = OutConv(32, n_classes)
def forward(self, x):
# print(x.shape) # torch.Size([1, 1, 32, 400])
x1 = self.inc(x)
# print(x1.shape) # torch.Size([1, 32, 32, 400])
x2 = self.down1(x1)
# print(x2.shape) # torch.Size([1, 64, 16, 200])
x3 = self.down2(x2)
# print(x3.shape) # torch.Size([1, 128, 8, 100])
x4 = self.down3(x3)
# print(x4.shape) # torch.Size([1, 256, 4, 50])
x5 = self.down4(x4)
# print(x5.shape) # torch.Size([1, 512, 2, 25])
# print("Upscaling...")
x = self.up1(x5, x4)
# print(x.shape) # torch.Size([1, 256, 4, 50])
x = self.up2(x, x3)
# print(x.shape) # torch.Size([1, 128, 8, 100])
x = self.up3(x, x2)
# print(x.shape) # torch.Size([1, 64, 16, 200])
x = self.up4(x, x1)
# print(x.shape) # torch.Size([1, 32, 32, 400])
logits = self.outc(x)
# print(logits.shape) # torch.Size([1, 512, 32, 400])
return logits
# x = torch.randn(1, 1, 32, 400)
# net = UNet()
# out = net(x)
# print(out.shape) |