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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
class Attention(nn.Module):
def __init__(self, input_size, hidden_size, num_classes, device):
super(Attention, self).__init__()
self.attention_cell = AttentionCell(input_size, hidden_size, num_classes)
self.hidden_size = hidden_size
self.num_classes = num_classes
self.generator = nn.Linear(hidden_size, num_classes)
self.device = device
def _char_to_onehot(self, input_char, onehot_dim=38):
input_char = input_char.unsqueeze(1)
batch_size = input_char.size(0)
one_hot = torch.FloatTensor(batch_size, onehot_dim).zero_().to(self.device)
one_hot = one_hot.scatter_(1, input_char, 1)
return one_hot
def forward(self, batch_H, text, is_train=True, batch_max_length=25):
"""
input:
batch_H : contextual_feature H = hidden state of encoder. [batch_size x num_steps x contextual_feature_channels]
text : the text-index of each image. [batch_size x (max_length+1)]. +1 for [GO] token. text[:, 0] = [GO].
output: probability distribution at each step [batch_size x num_steps x num_classes]
"""
batch_size = batch_H.size(0)
num_steps = batch_max_length + 1 # +1 for [s] at end of sentence.
output_hiddens = torch.FloatTensor(batch_size, num_steps, self.hidden_size).fill_(0).to(self.device)
hidden = (torch.FloatTensor(batch_size, self.hidden_size).fill_(0).to(self.device),
torch.FloatTensor(batch_size, self.hidden_size).fill_(0).to(self.device))
if is_train:
for i in range(num_steps):
# one-hot vectors for a i-th char. in a batch
char_onehots = self._char_to_onehot(text[:, i], onehot_dim=self.num_classes)
# hidden : decoder's hidden s_{t-1}, batch_H : encoder's hidden H, char_onehots : one-hot(y_{t-1})
hidden, _ = self.attention_cell(hidden, batch_H, char_onehots)
output_hiddens[:, i, :] = hidden[0] # LSTM hidden index (0: hidden, 1: Cell)
probs = self.generator(output_hiddens)
else:
targets = torch.LongTensor(batch_size).fill_(0).to(self.device) # [GO] token
probs = torch.FloatTensor(batch_size, num_steps, self.num_classes).fill_(0).to(self.device)
for i in range(num_steps):
char_onehots = self._char_to_onehot(targets, onehot_dim=self.num_classes)
hidden, _ = self.attention_cell(hidden, batch_H, char_onehots)
probs_step = self.generator(hidden[0])
probs[:, i, :] = probs_step
_, next_input = probs_step.max(1)
targets = next_input
return probs # batch_size x num_steps x num_classes
class AttentionCell(nn.Module):
def __init__(self, input_size, hidden_size, num_embeddings):
super(AttentionCell, self).__init__()
self.i2h = nn.Linear(input_size, hidden_size, bias=False)
self.h2h = nn.Linear(hidden_size, hidden_size) # either i2i or h2h should have bias
self.score = nn.Linear(hidden_size, 1, bias=False)
self.rnn = nn.LSTMCell(input_size + num_embeddings, hidden_size)
self.hidden_size = hidden_size
def forward(self, prev_hidden, batch_H, char_onehots):
# [batch_size x num_encoder_step x num_channel] -> [batch_size x num_encoder_step x hidden_size]
batch_H_proj = self.i2h(batch_H)
prev_hidden_proj = self.h2h(prev_hidden[0]).unsqueeze(1)
e = self.score(torch.tanh(batch_H_proj + prev_hidden_proj)) # batch_size x num_encoder_step * 1
alpha = F.softmax(e, dim=1)
context = torch.bmm(alpha.permute(0, 2, 1), batch_H).squeeze(1) # batch_size x num_channel
concat_context = torch.cat([context, char_onehots], 1) # batch_size x (num_channel + num_embedding)
cur_hidden = self.rnn(concat_context, prev_hidden)
return cur_hidden, alpha
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