class EncoderDecoder(nn.Module):
def __init__(self, embedding_size, hidden_size, vocab_size, gru_layers):
super(EncoderDecoder, self).__init__()
self.encoder = Encoder(embedding_size)
self.decoder = Decoder(embedding_size, hidden_size, vocab_size,
gru_layers)
def forward(self, images, captions):
features = self.encoder(images)
outputs = self.decoder(features, captions)
return outputs
def caption_image(self, image, vocabulary, max_length=50):
result_caption = []
with torch.no_grad():
x = self.encoder(image).unsqueeze(0)
states = None
for _ in range(max_length):
hiddens, states = self.decoder.gru(x, states)
output = self.decoder.linear(hiddens.squeeze(0))
predicted = output.argmax(1)
item = predicted.item()
result_caption.append(item)
x = self.decoder.embed(predicted).unsqueeze(0)
if vocabulary.itos[item] == "<end>":
break
return [vocabulary.itos[idx] for idx in result_caption]
class ParaphraseModel(nn.Module):
'''
dicstionary_size = the size of the dictionary in the dataset
embedding_dim = each word in the dictionary is embedded in a vector space with that dimension
rnn_hidden_size =
rnn_num_layers = the numbers of the layers in the each LSTM in the model
z_dim = the encoder encodes the sentence to a z-vector space with that dimension
'''
def __init__(self,
dictionary_size=100,
embedding_dim=1100,
rnn_hidden_size=600,
rnn_num_layers=2,
z_dim=1100): #Does embedding_dim should be the same as z_dim?
super(ParaphraseModel, self).__init__()
self.embedding = nn.Embedding(
dictionary_size,
embedding_dim) #should be replaced in word embedding like word2vec
self.encoder = Encoder(embedding_dim, rnn_hidden_size, rnn_num_layers,
z_dim)
self.decoder = Decoder(embedding_dim, rnn_hidden_size, rnn_num_layers,
dictionary_size)
self.cel = nn.CrossEntropyLoss(ignore_index=-1) #cross entrpoy
self.dictionary_size = dictionary_size
self.embedding_dim = embedding_dim
def train_model(self, xo, xp, xo_len, xp_len, kld_coef=1):
logits, z, mu, logvar = self.AE_forward(xo, xp, xo_len, xp_len)
cel_loss = self.cel(
logits.view(-1, self.dictionary_size).contiguous(),
xp.cuda().view(-1))
kl_loss = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp()).sum(1).mean()
total_loss = cel_loss + kld_coef * kl_loss
#print(cel_loss, kl_loss)
return total_loss
def AE_forward(self, xo, xp, xo_len, xp_len):
xo_embed = self.embedding(xo.cuda())
xp_embed = self.embedding(xp.cuda())
mu, logvar = self.encoder(xo_embed, xp_embed, xo_len, xp_len)
std = torch.exp(0.5 * logvar)
nd = Normal(torch.ones_like(mu), torch.zeros_like(std))
z = nd.sample() * std + mu
logits = self.decoder(xo_embed, z, xo_len, xp_len)
return logits, z, mu, logvar
def infer(self, xo, xo_len):
xo_embed = self.embedding(xo.cuda())
_, (hT, cT) = self.decoder.ose(xo_embed, xo_len)
completed_sentences = torch.zeros(len(xo_embed))
sentences = []
mu, sigma = torch.zeros(len(xo), self.embedding_dim), torch.ones(
len(xo), self.embedding_dim)
nd = Normal(mu, sigma)
z = nd.sample().cuda()
out = hT[-1]
steps = 0
while not all(completed_sentences):
real_inp = torch.cat((z, out), 1).unsqueeze(1)
output, (hT, cT) = self.decoder.pse(real_inp,
torch.tensor([1] * len(z)),
h0=hT,
c0=cT)
out = hT[-1]
probs = self.decoder.linear(out)
topwords = [word_probs.topk(1)[1] for word_probs in probs]
for j, result in enumerate(topwords):
if int(result) == EOS_TOKEN:
completed_sentences[j] = 1
sentences.append(topwords)
steps += 1
if steps == MAX_PARA_LENGTH:
break
return sentences
