import torch import torch.nn as nn import torch.nn.functional as F device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') class Attention(nn.Module): def __init__(self, input_size, hidden_size, num_classes): 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) 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(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(device) hidden = (torch.FloatTensor(batch_size, self.hidden_size).fill_(0).to(device), torch.FloatTensor(batch_size, self.hidden_size).fill_(0).to(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, alpha = 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(device) # [GO] token probs = torch.FloatTensor(batch_size, num_steps, self.num_classes).fill_(0).to(device) for i in range(num_steps): char_onehots = self._char_to_onehot(targets, onehot_dim=self.num_classes) hidden, alpha = 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