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