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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/) | |
| """ | |
| from torch import nn | |
| import torch.nn.functional as F | |
| # For Gated RCNN | |
| class GRCL(nn.Module): | |
| def __init__(self, input_channel, output_channel, num_iteration, kernel_size, pad): | |
| super(GRCL, self).__init__() | |
| self.wgf_u = nn.Conv2d(input_channel, output_channel, 1, 1, 0, bias=False) | |
| self.wgr_x = nn.Conv2d(output_channel, output_channel, 1, 1, 0, bias=False) | |
| self.wf_u = nn.Conv2d(input_channel, output_channel, kernel_size, 1, pad, bias=False) | |
| self.wr_x = nn.Conv2d(output_channel, output_channel, kernel_size, 1, pad, bias=False) | |
| self.BN_x_init = nn.BatchNorm2d(output_channel) | |
| self.num_iteration = num_iteration | |
| self.GRCL = [GRCL_unit(output_channel) for _ in range(num_iteration)] | |
| self.GRCL = nn.Sequential(*self.GRCL) | |
| def forward(self, input): | |
| """ The input of GRCL is consistant over time t, which is denoted by u(0) | |
| thus wgf_u / wf_u is also consistant over time t. | |
| """ | |
| wgf_u = self.wgf_u(input) | |
| wf_u = self.wf_u(input) | |
| x = F.relu(self.BN_x_init(wf_u)) | |
| for i in range(self.num_iteration): | |
| x = self.GRCL[i](wgf_u, self.wgr_x(x), wf_u, self.wr_x(x)) | |
| return x | |
| class GRCL_unit(nn.Module): | |
| def __init__(self, output_channel): | |
| super(GRCL_unit, self).__init__() | |
| self.BN_gfu = nn.BatchNorm2d(output_channel) | |
| self.BN_grx = nn.BatchNorm2d(output_channel) | |
| self.BN_fu = nn.BatchNorm2d(output_channel) | |
| self.BN_rx = nn.BatchNorm2d(output_channel) | |
| self.BN_Gx = nn.BatchNorm2d(output_channel) | |
| def forward(self, wgf_u, wgr_x, wf_u, wr_x): | |
| G_first_term = self.BN_gfu(wgf_u) | |
| G_second_term = self.BN_grx(wgr_x) | |
| G = F.sigmoid(G_first_term + G_second_term) | |
| x_first_term = self.BN_fu(wf_u) | |
| x_second_term = self.BN_Gx(self.BN_rx(wr_x) * G) | |
| x = F.relu(x_first_term + x_second_term) | |
| return x | |
| class RCNN(nn.Module): | |
| """ FeatureExtractor of GRCNN (https://papers.nips.cc/paper/6637-gated-recurrent-convolution-neural-network-for-ocr.pdf) """ | |
| def __init__(self, input_channel=1, output_channel=512): | |
| super(RCNN, self).__init__() | |
| self.output_channel = [int(output_channel / 8), int(output_channel / 4), | |
| int(output_channel / 2), output_channel] # [64, 128, 256, 512] | |
| self.ConvNet = nn.Sequential( | |
| nn.Conv2d(input_channel, self.output_channel[0], 3, 1, 1), nn.ReLU(True), | |
| nn.MaxPool2d(2, 2), # 64 x 16 x 50 | |
| GRCL(self.output_channel[0], self.output_channel[0], num_iteration=5, kernel_size=3, pad=1), | |
| nn.MaxPool2d(2, 2), # 64 x 8 x 25 | |
| GRCL(self.output_channel[0], self.output_channel[1], num_iteration=5, kernel_size=3, pad=1), | |
| nn.MaxPool2d(2, (2, 1), (0, 1)), # 128 x 4 x 26 | |
| GRCL(self.output_channel[1], self.output_channel[2], num_iteration=5, kernel_size=3, pad=1), | |
| nn.MaxPool2d(2, (2, 1), (0, 1)), # 256 x 2 x 27 | |
| nn.Conv2d(self.output_channel[2], self.output_channel[3], 2, 1, 0, bias=False), | |
| nn.BatchNorm2d(self.output_channel[3]), nn.ReLU(True)) # 512 x 1 x 26 | |
| def forward(self, input): | |
| return self.ConvNet(input) | |
| # import torch | |
| # x = torch.randn(1, 1, 32, 400) | |
| # net = RCNN() | |
| # out = net(x) | |
| # print(out.shape) |