def __init__(self, data):
super(BiLSTM, self).__init__()
print "build batched bilstm..."
self.gpu = data.HP_gpu
self.use_char = data.HP_use_char
self.batch_size = data.HP_batch_size
self.char_hidden_dim = 0
self.average_batch = data.HP_average_batch_loss
if self.use_char:
self.char_hidden_dim = data.HP_char_hidden_dim
self.char_embedding_dim = data.char_emb_dim
if data.char_features == "CNN":
self.char_feature = CharCNN(data.char_alphabet.size(),
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
elif data.char_features == "LSTM":
self.char_feature = CharBiLSTM(data.char_alphabet.size(),
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
else:
print "Error char feature selection, please check parameter data.char_features (either CNN or LSTM)."
exit(0)
self.embedding_dim = data.word_emb_dim
self.hidden_dim = data.HP_hidden_dim
self.drop = nn.Dropout(data.HP_dropout)
self.droplstm = nn.Dropout(data.HP_dropout)
self.word_embeddings = nn.Embedding(data.word_alphabet.size(),
self.embedding_dim)
self.bilstm_flag = data.HP_bilstm
self.lstm_layer = data.HP_lstm_layer
if data.pretrain_word_embedding is not None:
self.word_embeddings.weight.data.copy_(
torch.from_numpy(data.pretrain_word_embedding))
else:
self.word_embeddings.weight.data.copy_(
torch.from_numpy(
self.random_embedding(data.word_alphabet.size(),
self.embedding_dim)))
# The LSTM takes word embeddings as inputs, and outputs hidden states
# with dimensionality hidden_dim.
if self.bilstm_flag:
lstm_hidden = data.HP_hidden_dim // 2
else:
lstm_hidden = data.HP_hidden_dim
self.lstm = nn.LSTM(self.embedding_dim + self.char_hidden_dim,
lstm_hidden,
num_layers=self.lstm_layer,
batch_first=True,
bidirectional=self.bilstm_flag)
# The linear layer that maps from hidden state space to tag space
self.hidden2tag = nn.Linear(self.hidden_dim, data.label_alphabet_size)
if self.gpu:
self.drop = self.drop.cuda()
self.droplstm = self.droplstm.cuda()
self.word_embeddings = self.word_embeddings.cuda()
self.lstm = self.lstm.cuda()
self.hidden2tag = self.hidden2tag.cuda()
def __init__(self, args, model_params):
super(WordEmbedding, self).__init__()
self.gpu = args.ifgpu
self.use_char = args.useChar
self.char_hidden_dim = args.char_hidden_dim
self.char_embedding_dim = args.char_embedding_dim
self.embedding_dim = model_params.embedding_dim
self.drop = nn.Dropout(args.dropout)
#char Embedding
if self.use_char:
if args.charExtractor == "CNN":
self.char_feature = CharCNN(
model_params.char_alphabet.size(),
model_params.pretrain_char_embedding,
self.char_embedding_dim, self.char_hidden_dim,
args.dropout, self.gpu)
elif args.charExtractor == "LSTM":
self.char_feature = CharBiLSTM(
model_params.char_alphabet.size(),
model_params.pretrain_char_embedding,
self.char_embedding_dim, self.char_hidden_dim,
args.dropout, self.gpu)
else:
print(
"Error char feature selection, please check parameter data.char_feature_extractor (CNN/LSTM)."
)
exit(0)
#word Embedding
self.word_embedding = nn.Embedding(model_params.word_alphabet.size(),
self.embedding_dim)
if model_params.pretrain_word_embedding is not None:
self.word_embedding.weight.data.copy_(
torch.from_numpy(model_params.pretrain_word_embedding))
else:
self.word_embedding.weight.data.copy_(
torch.from_numpy(
self.random_embedding(model_params.word_alphabet.size(),
self.embedding_dim)))
def __init__(self, data, use_position, use_cap, use_postag, use_char):
super(WordRep, self).__init__()
print "build word representation..."
self.gpu = data.HP_gpu
self.use_char = use_char
self.batch_size = data.HP_batch_size
self.char_hidden_dim = 0
self.char_all_feature = False
if self.use_char:
self.char_hidden_dim = data.HP_char_hidden_dim
self.char_embedding_dim = data.char_emb_dim
if data.char_feature_extractor == "CNN":
self.char_feature = CharCNN(data.char_alphabet.size(),
data.pretrain_char_embedding,
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
elif data.char_feature_extractor == "LSTM":
self.char_feature = CharBiLSTM(data.char_alphabet.size(),
data.pretrain_char_embedding,
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
elif data.char_feature_extractor == "GRU":
self.char_feature = CharBiGRU(data.char_alphabet.size(),
data.pretrain_char_embedding,
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
elif data.char_feature_extractor == "ALL":
self.char_all_feature = True
self.char_feature = CharCNN(data.char_alphabet.size(),
data.pretrain_char_embedding,
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
self.char_feature_extra = CharBiLSTM(
data.char_alphabet.size(), data.pretrain_char_embedding,
self.char_embedding_dim, self.char_hidden_dim,
data.HP_dropout, self.gpu)
else:
print "Error char feature selection, please check parameter data.char_feature_extractor (CNN/LSTM/GRU/ALL)."
exit(0)
self.embedding_dim = data.word_emb_dim
self.drop = nn.Dropout(data.HP_dropout)
self.word_embedding = nn.Embedding(data.word_alphabet.size(),
self.embedding_dim)
if data.pretrain_word_embedding is not None:
self.word_embedding.weight.data.copy_(
torch.from_numpy(data.pretrain_word_embedding))
else:
self.word_embedding.weight.data.copy_(
torch.from_numpy(
self.random_embedding(data.word_alphabet.size(),
self.embedding_dim)))
self.feature_num = 0
self.feature_embedding_dims = data.feature_emb_dims
self.feature_embeddings = nn.ModuleList()
if use_cap:
self.feature_num += 1
alphabet_id = data.feature_name2id['[Cap]']
emb = nn.Embedding(data.feature_alphabets[alphabet_id].size(),
self.feature_embedding_dims[alphabet_id])
emb.weight.data.copy_(
torch.from_numpy(
self.random_embedding(
data.feature_alphabets[alphabet_id].size(),
self.feature_embedding_dims[alphabet_id])))
self.feature_embeddings.append(emb)
if use_postag:
self.feature_num += 1
alphabet_id = data.feature_name2id['[POS]']
emb = nn.Embedding(data.feature_alphabets[alphabet_id].size(),
self.feature_embedding_dims[alphabet_id])
emb.weight.data.copy_(
torch.from_numpy(
self.random_embedding(
data.feature_alphabets[alphabet_id].size(),
self.feature_embedding_dims[alphabet_id])))
self.feature_embeddings.append(emb)
self.use_position = use_position
if self.use_position:
position_alphabet_id = data.re_feature_name2id['[POSITION]']
self.position_embedding_dim = data.re_feature_emb_dims[
position_alphabet_id]
self.position1_emb = nn.Embedding(
data.re_feature_alphabet_sizes[position_alphabet_id],
self.position_embedding_dim, data.pad_idx)
self.position1_emb.weight.data.copy_(
torch.from_numpy(
self.random_embedding(
data.re_feature_alphabet_sizes[position_alphabet_id],
self.position_embedding_dim)))
self.position2_emb = nn.Embedding(
data.re_feature_alphabet_sizes[position_alphabet_id],
self.position_embedding_dim, data.pad_idx)
self.position2_emb.weight.data.copy_(
torch.from_numpy(
self.random_embedding(
data.re_feature_alphabet_sizes[position_alphabet_id],
self.position_embedding_dim)))
if torch.cuda.is_available():
self.drop = self.drop.cuda(self.gpu)
self.word_embedding = self.word_embedding.cuda(self.gpu)
for idx in range(self.feature_num):
self.feature_embeddings[idx] = self.feature_embeddings[
idx].cuda(self.gpu)
if self.use_position:
self.position1_emb = self.position1_emb.cuda(self.gpu)
self.position2_emb = self.position2_emb.cuda(self.gpu)
class WordRep(nn.Module):
def __init__(self, data, use_position, use_cap, use_postag, use_char):
super(WordRep, self).__init__()
print "build word representation..."
self.gpu = data.HP_gpu
self.use_char = use_char
self.batch_size = data.HP_batch_size
self.char_hidden_dim = 0
self.char_all_feature = False
if self.use_char:
self.char_hidden_dim = data.HP_char_hidden_dim
self.char_embedding_dim = data.char_emb_dim
if data.char_feature_extractor == "CNN":
self.char_feature = CharCNN(data.char_alphabet.size(),
data.pretrain_char_embedding,
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
elif data.char_feature_extractor == "LSTM":
self.char_feature = CharBiLSTM(data.char_alphabet.size(),
data.pretrain_char_embedding,
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
elif data.char_feature_extractor == "GRU":
self.char_feature = CharBiGRU(data.char_alphabet.size(),
data.pretrain_char_embedding,
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
elif data.char_feature_extractor == "ALL":
self.char_all_feature = True
self.char_feature = CharCNN(data.char_alphabet.size(),
data.pretrain_char_embedding,
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
self.char_feature_extra = CharBiLSTM(
data.char_alphabet.size(), data.pretrain_char_embedding,
self.char_embedding_dim, self.char_hidden_dim,
data.HP_dropout, self.gpu)
else:
print "Error char feature selection, please check parameter data.char_feature_extractor (CNN/LSTM/GRU/ALL)."
exit(0)
self.embedding_dim = data.word_emb_dim
self.drop = nn.Dropout(data.HP_dropout)
self.word_embedding = nn.Embedding(data.word_alphabet.size(),
self.embedding_dim)
if data.pretrain_word_embedding is not None:
self.word_embedding.weight.data.copy_(
torch.from_numpy(data.pretrain_word_embedding))
else:
self.word_embedding.weight.data.copy_(
torch.from_numpy(
self.random_embedding(data.word_alphabet.size(),
self.embedding_dim)))
self.feature_num = 0
self.feature_embedding_dims = data.feature_emb_dims
self.feature_embeddings = nn.ModuleList()
if use_cap:
self.feature_num += 1
alphabet_id = data.feature_name2id['[Cap]']
emb = nn.Embedding(data.feature_alphabets[alphabet_id].size(),
self.feature_embedding_dims[alphabet_id])
emb.weight.data.copy_(
torch.from_numpy(
self.random_embedding(
data.feature_alphabets[alphabet_id].size(),
self.feature_embedding_dims[alphabet_id])))
self.feature_embeddings.append(emb)
if use_postag:
self.feature_num += 1
alphabet_id = data.feature_name2id['[POS]']
emb = nn.Embedding(data.feature_alphabets[alphabet_id].size(),
self.feature_embedding_dims[alphabet_id])
emb.weight.data.copy_(
torch.from_numpy(
self.random_embedding(
data.feature_alphabets[alphabet_id].size(),
self.feature_embedding_dims[alphabet_id])))
self.feature_embeddings.append(emb)
self.use_position = use_position
if self.use_position:
position_alphabet_id = data.re_feature_name2id['[POSITION]']
self.position_embedding_dim = data.re_feature_emb_dims[
position_alphabet_id]
self.position1_emb = nn.Embedding(
data.re_feature_alphabet_sizes[position_alphabet_id],
self.position_embedding_dim, data.pad_idx)
self.position1_emb.weight.data.copy_(
torch.from_numpy(
self.random_embedding(
data.re_feature_alphabet_sizes[position_alphabet_id],
self.position_embedding_dim)))
self.position2_emb = nn.Embedding(
data.re_feature_alphabet_sizes[position_alphabet_id],
self.position_embedding_dim, data.pad_idx)
self.position2_emb.weight.data.copy_(
torch.from_numpy(
self.random_embedding(
data.re_feature_alphabet_sizes[position_alphabet_id],
self.position_embedding_dim)))
if torch.cuda.is_available():
self.drop = self.drop.cuda(self.gpu)
self.word_embedding = self.word_embedding.cuda(self.gpu)
for idx in range(self.feature_num):
self.feature_embeddings[idx] = self.feature_embeddings[
idx].cuda(self.gpu)
if self.use_position:
self.position1_emb = self.position1_emb.cuda(self.gpu)
self.position2_emb = self.position2_emb.cuda(self.gpu)
def random_embedding(self, vocab_size, embedding_dim):
pretrain_emb = np.empty([vocab_size, embedding_dim])
scale = np.sqrt(3.0 / embedding_dim)
for index in range(vocab_size):
pretrain_emb[index, :] = np.random.uniform(-scale, scale,
[1, embedding_dim])
return pretrain_emb
def forward(self, word_inputs, feature_inputs, word_seq_lengths,
char_inputs, char_seq_lengths, char_seq_recover,
position1_inputs, position2_inputs):
"""
input:
word_inputs: (batch_size, sent_len)
features: list [(batch_size, sent_len), (batch_len, sent_len),...]
word_seq_lengths: list of batch_size, (batch_size,1)
char_inputs: (batch_size*sent_len, word_length)
char_seq_lengths: list of whole batch_size for char, (batch_size*sent_len, 1)
char_seq_recover: variable which records the char order information, used to recover char order
output:
Variable(batch_size, sent_len, hidden_dim)
"""
batch_size = word_inputs.size(0)
sent_len = word_inputs.size(1)
word_embs = self.word_embedding(word_inputs)
word_list = [word_embs]
for idx in range(self.feature_num):
word_list.append(self.feature_embeddings[idx](feature_inputs[idx]))
if self.use_char:
## calculate char lstm last hidden
char_features = self.char_feature.get_last_hiddens(
char_inputs,
char_seq_lengths.cpu().numpy())
char_features = char_features[char_seq_recover]
char_features = char_features.view(batch_size, sent_len, -1)
## concat word and char together
word_list.append(char_features)
word_embs = torch.cat([word_embs, char_features], 2)
if self.char_all_feature:
char_features_extra = self.char_feature_extra.get_last_hiddens(
char_inputs,
char_seq_lengths.cpu().numpy())
char_features_extra = char_features_extra[char_seq_recover]
char_features_extra = char_features_extra.view(
batch_size, sent_len, -1)
## concat word and char together
word_list.append(char_features_extra)
if self.use_position:
position1_feature = self.position1_emb(position1_inputs)
position2_feature = self.position2_emb(position2_inputs)
word_list.append(position1_feature)
word_list.append(position2_feature)
word_embs = torch.cat(word_list, 2)
word_represent = self.drop(word_embs)
return word_represent
def __init__(self, data):
super(WordRep, self).__init__()
print "build word representation..."
self.gpu = data.HP_gpu
self.use_char = data.use_char
self.batch_size = data.HP_batch_size
self.char_hidden_dim = 0
self.char_all_feature = False
if self.use_char:
self.char_hidden_dim = data.HP_char_hidden_dim
self.char_embedding_dim = data.char_emb_dim
if data.char_seq_feature == "CNN":
self.char_feature = CharCNN(data.char_alphabet.size(),
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
elif data.char_seq_feature == "LSTM":
self.char_feature = CharBiLSTM(data.char_alphabet.size(),
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
elif data.char_seq_feature == "GRU":
self.char_feature = CharBiGRU(data.char_alphabet.size(),
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
elif data.char_seq_feature == "ALL":
self.char_all_feature = True
self.char_feature = CharCNN(data.char_alphabet.size(),
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
self.char_feature_extra = CharBiLSTM(data.char_alphabet.size(),
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
else:
print "Error char feature selection, please check parameter data.char_seq_feature (CNN/LSTM/GRU/ALL)."
exit(0)
self.embedding_dim = data.word_emb_dim
self.drop = nn.Dropout(data.HP_dropout)
self.word_embedding = nn.Embedding(data.word_alphabet.size(),
self.embedding_dim)
if data.pretrain_word_embedding is not None:
self.word_embedding.weight.data.copy_(
torch.from_numpy(data.pretrain_word_embedding))
else:
self.word_embedding.weight.data.copy_(
torch.from_numpy(
self.random_embedding(data.word_alphabet.size(),
self.embedding_dim)))
self.feature_num = data.feature_num
self.feature_embedding_dims = data.feature_emb_dims
self.feature_embeddings = nn.ModuleList()
for idx in range(self.feature_num):
self.feature_embeddings.append(
nn.Embedding(data.feature_alphabets[idx].size(),
self.feature_embedding_dims[idx]))
for idx in range(self.feature_num):
if data.pretrain_feature_embeddings[idx] is not None:
self.feature_embeddings[idx].weight.data.copy_(
torch.from_numpy(data.pretrain_feature_embeddings[idx]))
else:
self.feature_embeddings[idx].weight.data.copy_(
torch.from_numpy(
self.random_embedding(
data.feature_alphabets[idx].size(),
self.feature_embedding_dims[idx])))
if self.gpu:
self.drop = self.drop.cuda()
self.word_embedding = self.word_embedding.cuda()
for idx in range(self.feature_num):
self.feature_embeddings[idx] = self.feature_embeddings[
idx].cuda()
class BiLSTM(nn.Module):
def __init__(self, data):
super(BiLSTM, self).__init__()
print "build batched bilstm..."
self.use_bigram = data.use_bigram
self.gpu = data.HP_gpu
self.use_char = data.HP_use_char
self.use_gaz = data.HP_use_gaz
self.batch_size = data.HP_batch_size
self.char_hidden_dim = 0
if self.use_char:
self.char_hidden_dim = data.HP_char_hidden_dim
self.char_embedding_dim = data.char_emb_dim
if data.char_features == "CNN":
self.char_feature = CharCNN(data.char_alphabet.size(),
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
elif data.char_features == "LSTM":
self.char_feature = CharBiLSTM(data.char_alphabet.size(),
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
else:
print "Error char feature selection, please check parameter data.char_features (either CNN or LSTM)."
exit(0)
self.embedding_dim = data.word_emb_dim
self.hidden_dim = data.HP_hidden_dim
self.drop = nn.Dropout(data.HP_dropout)
self.droplstm = nn.Dropout(data.HP_dropout)
self.word_embeddings = nn.Embedding(data.word_alphabet.size(),
self.embedding_dim)
self.biword_embeddings = nn.Embedding(data.biword_alphabet.size(),
data.biword_emb_dim)
self.bilstm_flag = data.HP_bilstm
self.lstm_layer = data.HP_lstm_layer
# 添加radical embedding
if data.pretrain_word_embedding is not None:
self.word_embeddings.weight.data.copy_(
torch.from_numpy(data.pretrain_word_embedding))
else:
self.word_embeddings.weight.data.copy_(
torch.from_numpy(
self.random_embedding(data.word_alphabet.size(),
self.embedding_dim)))
if data.pretrain_biword_embedding is not None:
self.biword_embeddings.weight.data.copy_(
torch.from_numpy(data.pretrain_biword_embedding))
else:
self.biword_embeddings.weight.data.copy_(
torch.from_numpy(
self.random_embedding(data.biword_alphabet.size(),
data.biword_emb_dim)))
# The LSTM takes word embeddings as inputs, and outputs hidden states with dimensionality hidden_dim.
if self.bilstm_flag:
lstm_hidden = data.HP_hidden_dim // 2
else:
lstm_hidden = data.HP_hidden_dim
lstm_input = self.embedding_dim + self.char_hidden_dim
if self.use_bigram:
lstm_input += data.biword_emb_dim
# 添加gaz embedding
self.forward_lstm = LatticeLSTM(lstm_input, lstm_hidden,
data.gaz_dropout,
data.gaz_alphabet.size(),
data.gaz_emb_dim,
data.pretrain_gaz_embedding, True,
data.HP_fix_gaz_emb, self.gpu)
if self.bilstm_flag:
self.backward_lstm = LatticeLSTM(lstm_input, lstm_hidden,
data.gaz_dropout,
data.gaz_alphabet.size(),
data.gaz_emb_dim,
data.pretrain_gaz_embedding,
False, data.HP_fix_gaz_emb,
self.gpu)
# self.lstm = nn.LSTM(lstm_input, lstm_hidden, num_layers=self.lstm_layer, batch_first=True, bidirectional=self.bilstm_flag)
# The linear layer that maps from hidden state space to tag space
self.hidden2tag = nn.Linear(data.HP_hidden_dim,
data.label_alphabet_size)
if self.gpu:
self.drop = self.drop.cuda()
self.droplstm = self.droplstm.cuda()
self.word_embeddings = self.word_embeddings.cuda()
self.biword_embeddings = self.biword_embeddings.cuda()
self.forward_lstm = self.forward_lstm.cuda()
if self.bilstm_flag:
self.backward_lstm = self.backward_lstm.cuda()
self.hidden2tag = self.hidden2tag.cuda()
def random_embedding(self, vocab_size, embedding_dim):
pretrain_emb = np.empty([vocab_size, embedding_dim])
scale = np.sqrt(3.0 / embedding_dim)
for index in range(vocab_size):
pretrain_emb[index, :] = np.random.uniform(-scale, scale,
[1, embedding_dim])
return pretrain_emb
def get_lstm_features(self, gaz_list, word_inputs, biword_inputs,
word_seq_lengths, char_inputs, char_seq_lengths,
char_seq_recover):
"""
input:
word_inputs: (batch_size, sent_len)
gaz_list:
word_seq_lengths: list of batch_size, (batch_size,1)
char_inputs: (batch_size*sent_len, word_length)
char_seq_lengths: list of whole batch_size for char, (batch_size*sent_len, 1)
char_seq_recover: variable which records the char order information, used to recover char order
output:
Variable(sent_len, batch_size, hidden_dim)
"""
batch_size = word_inputs.size(0)
sent_len = word_inputs.size(1)
word_embs = self.word_embeddings(word_inputs)
if self.use_bigram:
biword_embs = self.biword_embeddings(biword_inputs)
word_embs = torch.cat([word_embs, biword_embs], 2)
if self.use_char:
# calculate char lstm last hidden
char_features = self.char_feature.get_last_hiddens(
char_inputs,
char_seq_lengths.cpu().numpy())
char_features = char_features[char_seq_recover]
char_features = char_features.view(batch_size, sent_len, -1)
# concat word and char together
word_embs = torch.cat([word_embs, char_features], 2)
word_embs = self.drop(word_embs)
# packed_words = pack_padded_sequence(word_embs, word_seq_lengths.cpu().numpy(), True)
hidden = None
lstm_out, hidden = self.forward_lstm(word_embs, gaz_list, hidden)
if self.bilstm_flag:
backward_hidden = None
backward_lstm_out, backward_hidden = self.backward_lstm(
word_embs, gaz_list, backward_hidden)
lstm_out = torch.cat([lstm_out, backward_lstm_out], 2)
# lstm_out, _ = pad_packed_sequence(lstm_out)
lstm_out = self.droplstm(lstm_out)
return lstm_out
def get_output_score(self, gaz_list, word_inputs, biword_inputs,
word_seq_lengths, char_inputs, char_seq_lengths,
char_seq_recover):
lstm_out = self.get_lstm_features(gaz_list, word_inputs, biword_inputs,
word_seq_lengths, char_inputs,
char_seq_lengths, char_seq_recover)
# lstm_out (batch_size, sent_len, hidden_dim)
outputs = self.hidden2tag(lstm_out)
return outputs
def neg_log_likelihood_loss(self, gaz_list, word_inputs, biword_inputs,
word_seq_lengths, char_inputs,
char_seq_lengths, char_seq_recover,
batch_label, mask):
# mask is not used
batch_size = word_inputs.size(0)
seq_len = word_inputs.size(1)
total_word = batch_size * seq_len
loss_function = nn.NLLLoss(ignore_index=0, size_average=False)
outs = self.get_output_score(gaz_list, word_inputs, biword_inputs,
word_seq_lengths, char_inputs,
char_seq_lengths, char_seq_recover)
# outs (batch_size, seq_len, label_vocab)
outs = outs.view(total_word, -1)
score = F.log_softmax(outs, 1)
loss = loss_function(score, batch_label.view(total_word))
_, tag_seq = torch.max(score, 1)
tag_seq = tag_seq.view(batch_size, seq_len)
return loss, tag_seq
def forward(self, gaz_list, word_inputs, biword_inputs, word_seq_lengths,
char_inputs, char_seq_lengths, char_seq_recover, mask):
batch_size = word_inputs.size(0)
seq_len = word_inputs.size(1)
total_word = batch_size * seq_len
outs = self.get_output_score(gaz_list, word_inputs, biword_inputs,
word_seq_lengths, char_inputs,
char_seq_lengths, char_seq_recover)
outs = outs.view(total_word, -1)
_, tag_seq = torch.max(outs, 1)
tag_seq = tag_seq.view(batch_size, seq_len)
# filter padded position with zero
decode_seq = mask.long() * tag_seq
return decode_seq
class WordEmbedding(nn.Module):
def __init__(self, args, model_params):
super(WordEmbedding, self).__init__()
self.gpu = args.ifgpu
self.use_char = args.useChar
self.char_hidden_dim = args.char_hidden_dim
self.char_embedding_dim = args.char_embedding_dim
self.embedding_dim = model_params.embedding_dim
self.drop = nn.Dropout(args.dropout)
#char Embedding
if self.use_char:
if args.charExtractor == "CNN":
self.char_feature = CharCNN(
model_params.char_alphabet.size(),
model_params.pretrain_char_embedding,
self.char_embedding_dim, self.char_hidden_dim,
args.dropout, self.gpu)
elif args.charExtractor == "LSTM":
self.char_feature = CharBiLSTM(
model_params.char_alphabet.size(),
model_params.pretrain_char_embedding,
self.char_embedding_dim, self.char_hidden_dim,
args.dropout, self.gpu)
else:
print(
"Error char feature selection, please check parameter data.char_feature_extractor (CNN/LSTM)."
)
exit(0)
#word Embedding
self.word_embedding = nn.Embedding(model_params.word_alphabet.size(),
self.embedding_dim)
if model_params.pretrain_word_embedding is not None:
self.word_embedding.weight.data.copy_(
torch.from_numpy(model_params.pretrain_word_embedding))
else:
self.word_embedding.weight.data.copy_(
torch.from_numpy(
self.random_embedding(model_params.word_alphabet.size(),
self.embedding_dim)))
def random_embedding(self, vocab_size, embedding_dim):
pretrain_emb = np.empty([vocab_size, embedding_dim])
scale = np.sqrt(3.0 / embedding_dim)
for index in range(vocab_size):
pretrain_emb[index, :] = np.random.uniform(-scale, scale,
[1, embedding_dim])
return pretrain_emb
def forward(self, word_inputs, char_inputs, char_seq_lengths,
char_seq_recover):
"""
input:
word_inputs: (batch_size, sent_len)
char_inputs: (batch_size*sent_len, word_length)
char_seq_lengths: list of whole batch_size for char, (batch_size*sent_len, 1)
char_seq_recover: variable which records the char order information, used to recover char order
output:
Variable(batch_size, sent_len, hidden_dim)
"""
batch_size = word_inputs.size(0)
sent_len = word_inputs.size(1)
word_embs = self.word_embedding(word_inputs)
word_list = [word_embs]
if self.use_char:
## calculate char lstm last hidden
char_features = self.char_feature.get_last_hiddens(
char_inputs,
char_seq_lengths.cpu().numpy())
char_features = char_features[char_seq_recover]
char_features = char_features.view(batch_size, sent_len, -1)
## concat word and char together
word_list.append(char_features)
word_embs = torch.cat(word_list, 2)
word_represent = self.drop(word_embs)
return word_represent
def __init__(self, data):
super(WordRep, self).__init__()
print "Build word representation..."
self.gpu = data.HP_gpu
self.use_char = data.use_char
self.use_trans = data.use_trans
self.batch_size = data.HP_batch_size
self.char_hidden_dim = 0
self.char_all_feature = False
self.use_mapping = data.use_mapping
self.mapping_func = data.mapping_func
# character-level
if self.use_trans:
if self.use_mapping:
# linear mapping
self.w = nn.Linear(data.word_emb_dim, data.HP_trans_hidden_dim)
# non-linear mapping:w + tanh or w + sigmoid
if self.mapping_func:
if self.mapping_func == "tanh":
self.non_linear = nn.Tanh()
elif self.mapping_func == "sigmoid":
self.non_linear = nn.Sigmoid()
self.trans_hidden_dim = data.HP_trans_hidden_dim
self.trans_embedding_dim = data.trans_emb_dim
self.trans_feature = TransBiLSTM(data.translation_alphabet.size(), self.trans_embedding_dim,
self.trans_hidden_dim,
data.HP_dropout, data.pretrain_trans_embedding, self.gpu)
# word-level
if self.use_char:
self.char_hidden_dim = data.HP_char_hidden_dim
self.char_embedding_dim = data.char_emb_dim
if data.char_seq_feature == "CNN":
self.char_feature = CharCNN(data.char_alphabet.size(), self.char_embedding_dim, self.char_hidden_dim,
data.HP_dropout, data.pretrain_char_embedding, self.gpu)
elif data.char_seq_feature == "LSTM":
self.char_feature = CharBiLSTM(data.char_alphabet.size(), self.char_embedding_dim, self.char_hidden_dim,
data.HP_dropout, data.pretrain_char_embedding, self.gpu)
elif data.char_seq_feature == "GRU":
self.char_feature = CharBiGRU(data.char_alphabet.size(), self.char_embedding_dim, self.char_hidden_dim,
data.HP_dropout, self.gpu)
elif data.char_seq_feature == "ALL":
self.char_all_feature = True
self.char_feature = CharCNN(data.char_alphabet.size(), self.char_embedding_dim, self.char_hidden_dim,
data.HP_dropout, data.pretrain_char_embedding, self.gpu)
self.char_feature_extra = CharBiLSTM(data.char_alphabet.size(), self.char_embedding_dim,
self.char_hidden_dim, data.HP_dropout, self.gpu)
else:
print "Error char feature selection, please check parameter data.char_seq_feature (CNN/LSTM/GRU/ALL)."
exit(0)
# Word embedding
self.embedding_dim = data.word_emb_dim
self.drop = nn.Dropout(data.HP_dropout)
self.word_embedding = nn.Embedding(data.word_alphabet.size(), self.embedding_dim)
if data.pretrain_word_embedding is not None:
self.word_embedding.weight.data.copy_(torch.from_numpy(data.pretrain_word_embedding))
else:
self.word_embedding.weight.data.copy_(
torch.from_numpy(self.random_embedding(data.word_alphabet.size(), self.embedding_dim)))
# not use
self.feature_num = data.feature_num
self.feature_embedding_dims = data.feature_emb_dims
self.feature_embeddings = nn.ModuleList()
for idx in range(self.feature_num):
self.feature_embeddings.append(
nn.Embedding(data.feature_alphabets[idx].size(), self.feature_embedding_dims[idx]))
for idx in range(self.feature_num):
if data.pretrain_feature_embeddings[idx] is not None:
self.feature_embeddings[idx].weight.data.copy_(torch.from_numpy(data.pretrain_feature_embeddings[idx]))
else:
self.feature_embeddings[idx].weight.data.copy_(torch.from_numpy(
self.random_embedding(data.feature_alphabets[idx].size(), self.feature_embedding_dims[idx])))
if self.gpu:
self.drop = self.drop.cuda()
self.word_embedding = self.word_embedding.cuda()
for idx in range(self.feature_num):
self.feature_embeddings[idx] = self.feature_embeddings[idx].cuda()
class WordRep(nn.Module):
def __init__(self, data):
super(WordRep, self).__init__()
print "Build word representation..."
self.gpu = data.HP_gpu
self.use_char = data.use_char
self.use_trans = data.use_trans
self.batch_size = data.HP_batch_size
self.char_hidden_dim = 0
self.char_all_feature = False
self.use_mapping = data.use_mapping
self.mapping_func = data.mapping_func
# character-level
if self.use_trans:
if self.use_mapping:
# linear mapping
self.w = nn.Linear(data.word_emb_dim, data.HP_trans_hidden_dim)
# non-linear mapping:w + tanh or w + sigmoid
if self.mapping_func:
if self.mapping_func == "tanh":
self.non_linear = nn.Tanh()
elif self.mapping_func == "sigmoid":
self.non_linear = nn.Sigmoid()
self.trans_hidden_dim = data.HP_trans_hidden_dim
self.trans_embedding_dim = data.trans_emb_dim
self.trans_feature = TransBiLSTM(data.translation_alphabet.size(), self.trans_embedding_dim,
self.trans_hidden_dim,
data.HP_dropout, data.pretrain_trans_embedding, self.gpu)
# word-level
if self.use_char:
self.char_hidden_dim = data.HP_char_hidden_dim
self.char_embedding_dim = data.char_emb_dim
if data.char_seq_feature == "CNN":
self.char_feature = CharCNN(data.char_alphabet.size(), self.char_embedding_dim, self.char_hidden_dim,
data.HP_dropout, data.pretrain_char_embedding, self.gpu)
elif data.char_seq_feature == "LSTM":
self.char_feature = CharBiLSTM(data.char_alphabet.size(), self.char_embedding_dim, self.char_hidden_dim,
data.HP_dropout, data.pretrain_char_embedding, self.gpu)
elif data.char_seq_feature == "GRU":
self.char_feature = CharBiGRU(data.char_alphabet.size(), self.char_embedding_dim, self.char_hidden_dim,
data.HP_dropout, self.gpu)
elif data.char_seq_feature == "ALL":
self.char_all_feature = True
self.char_feature = CharCNN(data.char_alphabet.size(), self.char_embedding_dim, self.char_hidden_dim,
data.HP_dropout, data.pretrain_char_embedding, self.gpu)
self.char_feature_extra = CharBiLSTM(data.char_alphabet.size(), self.char_embedding_dim,
self.char_hidden_dim, data.HP_dropout, self.gpu)
else:
print "Error char feature selection, please check parameter data.char_seq_feature (CNN/LSTM/GRU/ALL)."
exit(0)
# Word embedding
self.embedding_dim = data.word_emb_dim
self.drop = nn.Dropout(data.HP_dropout)
self.word_embedding = nn.Embedding(data.word_alphabet.size(), self.embedding_dim)
if data.pretrain_word_embedding is not None:
self.word_embedding.weight.data.copy_(torch.from_numpy(data.pretrain_word_embedding))
else:
self.word_embedding.weight.data.copy_(
torch.from_numpy(self.random_embedding(data.word_alphabet.size(), self.embedding_dim)))
# not use
self.feature_num = data.feature_num
self.feature_embedding_dims = data.feature_emb_dims
self.feature_embeddings = nn.ModuleList()
for idx in range(self.feature_num):
self.feature_embeddings.append(
nn.Embedding(data.feature_alphabets[idx].size(), self.feature_embedding_dims[idx]))
for idx in range(self.feature_num):
if data.pretrain_feature_embeddings[idx] is not None:
self.feature_embeddings[idx].weight.data.copy_(torch.from_numpy(data.pretrain_feature_embeddings[idx]))
else:
self.feature_embeddings[idx].weight.data.copy_(torch.from_numpy(
self.random_embedding(data.feature_alphabets[idx].size(), self.feature_embedding_dims[idx])))
if self.gpu:
self.drop = self.drop.cuda()
self.word_embedding = self.word_embedding.cuda()
for idx in range(self.feature_num):
self.feature_embeddings[idx] = self.feature_embeddings[idx].cuda()
def random_embedding(self, vocab_size, embedding_dim):
pretrain_emb = np.empty([vocab_size, embedding_dim])
scale = np.sqrt(3.0 / embedding_dim)
for index in range(vocab_size):
pretrain_emb[index, :] = np.random.uniform(-scale, scale, [1, embedding_dim])
return pretrain_emb
def forward(self, word_inputs, feature_inputs, word_seq_lengths, char_inputs, char_seq_lengths, char_seq_recover,
trans_inputs, trans_seq_length, trans_seq_recover):
"""
input:
word_inputs: (batch_size, sent_len)
features: list [(batch_size, sent_len), (batch_len, sent_len),...]
word_seq_lengths: list of batch_size, (batch_size,1)
char_inputs: (batch_size*sent_len, word_length)
char_seq_lengths: list of whole batch_size for char, (batch_size*sent_len, 1)
char_seq_recover: variable which records the char order information, used to recover char order
output:
Variable(batch_size, sent_len, hidden_dim)
"""
batch_size = word_inputs.size(0)
sent_len = word_inputs.size(1)
word_embs = self.word_embedding(word_inputs)
word_list = [word_embs]
for idx in range(self.feature_num):
word_list.append(self.feature_embeddings[idx](feature_inputs[idx]))
if self.use_char:
# calculate char lstm last hidden
char_features, _ = self.char_feature.get_last_hiddens(char_inputs, char_seq_lengths.cpu().numpy())
char_features = char_features[char_seq_recover]
char_features = char_features.view(batch_size, sent_len, -1)
word_list.append(char_features)
if self.char_all_feature:
char_features_extra, _ = self.char_feature_extra.get_last_hiddens(char_inputs,
char_seq_lengths.cpu().numpy())
char_features_extra = char_features_extra[char_seq_recover]
char_features_extra = char_features_extra.view(batch_size, sent_len, -1)
# concat word and char together
word_list.append(char_features_extra)
trans_features_wc_temp = None
word_embed_mapping = None
word_embs_temp = word_embs.view(batch_size * sent_len, -1)
if self.use_trans:
trans_features, trans_rnn_length = self.trans_feature.get_last_hiddens(trans_inputs,
trans_seq_length.cpu().numpy())
if self.use_mapping:
trans_features_wc = trans_features
if self.gpu:
trans_features_wc.cuda()
trans_features_wc = trans_features_wc[trans_seq_recover]
trans_inputs = trans_inputs[trans_seq_recover]
for index, line in enumerate(trans_inputs):
if line[0].data.cpu().numpy()[0] == 0:
if self.mapping_func:
trans_features_wc[index] = self.non_linear(self.w(word_embs_temp[index]))
else:
trans_features_wc[index] = self.w(word_embs_temp[index])
trans_features_wc_temp = trans_features_wc
trans_features_wc = trans_features_wc.view(batch_size, sent_len, -1)
word_list.append(trans_features_wc)
if self.mapping_func:
word_embed_mapping = self.non_linear(self.w(word_embs_temp))
else:
word_embed_mapping = self.w(word_embs_temp)
else:
trans_features = trans_features[trans_seq_recover]
trans_features = trans_features.view(batch_size, sent_len, -1)
word_list.append(trans_features)
word_embs = torch.cat(word_list, 2)
word_represent = self.drop(word_embs)
return word_represent, word_embed_mapping, trans_features_wc_temp
class Examiner(nn.Module):
def __init__(self, data):
super(Examiner, self).__init__()
print "build batched bilstm..."
self.gpu = data.HP_gpu
self.use_char = data.HP_use_char
self.batch_size = data.HP_batch_size
self.char_hidden_dim = 0
self.average_batch = data.HP_average_batch_loss
if self.use_char:
self.char_hidden_dim = data.HP_char_hidden_dim
self.char_embedding_dim = data.char_emb_dim
if data.char_features == "CNN":
self.char_feature = CharCNN(data.char_alphabet.size(),
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
elif data.char_features == "LSTM":
self.char_feature = CharBiLSTM(data.char_alphabet.size(),
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
else:
print "Error char feature selection, please check parameter data.char_features (either CNN or LSTM)."
exit(0)
self.embedding_dim = data.word_emb_dim
self.hidden_dim = data.HP_hidden_dim
self.drop = nn.Dropout(data.HP_dropout)
self.droplstm = nn.Dropout(data.HP_dropout)
self.word_embeddings = nn.Embedding(data.word_alphabet.size(),
self.embedding_dim)
self.bilstm_flag = data.HP_bilstm
self.lstm_layer = data.HP_lstm_layer
self.tag_size = data.label_alphabet_size
if data.pretrain_word_embedding is not None:
self.word_embeddings.weight.data.copy_(
torch.from_numpy(data.pretrain_word_embedding))
else:
self.word_embeddings.weight.data.copy_(
torch.from_numpy(
self.random_embedding(data.word_alphabet.size(),
self.embedding_dim)))
# The LSTM takes word embeddings as inputs, and outputs hidden states
# with dimensionality hidden_dim.
if self.bilstm_flag:
lstm_hidden = data.HP_hidden_dim // 2
else:
lstm_hidden = data.HP_hidden_dim
self.lstm = nn.LSTM(self.embedding_dim + self.char_hidden_dim +
data.label_alphabet_size,
lstm_hidden,
num_layers=self.lstm_layer,
batch_first=True,
bidirectional=self.bilstm_flag)
# The linear layer that maps from hidden state space to tag space
self.hidden2tag = nn.Linear(self.hidden_dim, 2)
self.topk = 50
if self.gpu:
self.drop = self.drop.cuda()
self.droplstm = self.droplstm.cuda()
self.word_embeddings = self.word_embeddings.cuda()
self.lstm = self.lstm.cuda()
self.hidden2tag = self.hidden2tag.cuda()
def random_embedding(self, vocab_size, embedding_dim):
pretrain_emb = np.empty([vocab_size, embedding_dim])
scale = np.sqrt(3.0 / embedding_dim)
for index in range(vocab_size):
pretrain_emb[index, :] = np.random.uniform(-scale, scale,
[1, embedding_dim])
return pretrain_emb
def get_lstm_features(self, word_inputs, word_seq_lengths, char_inputs,
char_seq_lengths, char_seq_recover, tag_prob,
tag_size):
"""
input:
word_inputs: (batch_size, sent_len)
word_seq_lengths: list of batch_size, (batch_size,1)
char_inputs: (batch_size*sent_len, word_length)
char_seq_lengths: list of whole batch_size for char, (batch_size*sent_len, 1)
char_seq_recover: variable which records the char order information, used to recover char order
output:
Variable(batch_size, sent_len, hidden_dim)
"""
batch_size = word_inputs.size(0)
sent_len = word_inputs.size(1)
word_embs = self.word_embeddings(word_inputs)
if self.use_char:
## calculate char lstm last hidden
char_features = self.char_feature.get_last_hiddens(
char_inputs,
char_seq_lengths.cpu().numpy())
char_features = char_features[char_seq_recover]
char_features = char_features.view(batch_size, sent_len, -1)
word_embs = torch.cat([word_embs, char_features], 2)
tag_feature = tag_prob #torch.zeros(batch_size, sent_len, tag_size).cuda().scatter_(2,tag_seq.unsqueeze(2).cuda(),1.0)
#print("tag_feature")
#print(tag_feature)
## concat word and char together
word_embs = self.drop(word_embs)
word_embs = torch.cat([word_embs, tag_feature], 2)
packed_words = pack_padded_sequence(word_embs,
word_seq_lengths.cpu().numpy(),
True)
hidden = None
packed_words.requires_grad = False
lstm_out, hidden = self.lstm(packed_words, hidden)
lstm_out, _ = pad_packed_sequence(lstm_out)
lstm_out = self.droplstm(lstm_out.transpose(1, 0))
## lstm_out (batch_size, seq_len, hidden_size)
return lstm_out
def get_output_score(self, word_inputs, word_seq_lengths, char_inputs,
char_seq_lengths, char_seq_recover, tag_seq):
lstm_out = self.get_lstm_features(word_inputs, word_seq_lengths,
char_inputs, char_seq_lengths,
char_seq_recover, tag_seq,
self.tag_size)
outputs = self.hidden2tag(lstm_out)
return outputs
def neg_log_likelihood_loss(self, word_inputs, word_seq_lengths,
char_inputs, char_seq_lengths,
char_seq_recover, batch_label, tag_seq,
tag_prob, mask):
batch_size = word_inputs.size(0)
seq_len = word_inputs.size(1)
total_word = batch_size * seq_len
outs = self.get_output_score(word_inputs, word_seq_lengths,
char_inputs, char_seq_lengths,
char_seq_recover, tag_prob)
# outs (batch_size, seq_len, 2)
outs = outs.view(total_word, -1)
score = F.softmax(outs, 1)
# score: The score for choosing each position
score = score[:, 0]
score = score.contiguous().view(batch_size, seq_len)
score = mask.float() * score #the score is always positive
score = F.softmax(score, 1)
if seq_len >= self.topk:
topk, indices = score.topk(self.topk, dim=1)
else:
topk, indices = score.topk(seq_len, dim=1)
tag_mask = Variable(torch.ones(batch_size, seq_len).cuda())
tag_mask = tag_mask.scatter(1, indices, 0).long()
# tag mask: selected positons as mask vector
topk = torch.log(topk)
#topk: the topk scores
info_tensor = (
1 -
(-torch.abs(Variable(tag_seq).cuda() - batch_label)).ge(0).float()
) #inequal if one
_sum = info_tensor.sum().long()[0]
full_loss = -torch.log(score) * (
1 -
(-torch.abs(Variable(tag_seq).cuda() - batch_label)).ge(0).float())
partial_reward = score * (
1 -
(-torch.abs(Variable(tag_seq).cuda() - batch_label)).ge(0).float())
#full_loss: the supervised loss
#partial_loss: the partial labeled supervised reward
return indices, tag_mask, topk.mean(1), full_loss, partial_reward
class Reformulator(nn.Module):
def __init__(self, data):
super(Reformulator, self).__init__()
print "build batched bilstm..."
self.gpu = data.HP_gpu
self.use_char = data.HP_use_char
self.batch_size = data.HP_batch_size
self.char_hidden_dim = 0
self.average_batch = data.HP_average_batch_loss
if self.use_char:
self.char_hidden_dim = data.HP_char_hidden_dim
self.char_embedding_dim = data.char_emb_dim
if data.char_features == "CNN":
self.char_feature = CharCNN(data.char_alphabet.size(),
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
elif data.char_features == "LSTM":
self.char_feature = CharBiLSTM(data.char_alphabet.size(),
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
else:
print "Error char feature selection, please check parameter data.char_features (either CNN or LSTM)."
exit(0)
self.embedding_dim = data.word_emb_dim
self.hidden_dim = data.HP_hidden_dim
self.drop = nn.Dropout(data.HP_dropout)
self.droplstm = nn.Dropout(data.HP_dropout)
self.word_embeddings = nn.Embedding(data.word_alphabet.size(),
self.embedding_dim)
self.bilstm_flag = data.HP_bilstm
self.lstm_layer = data.HP_lstm_layer
self.tag_size = data.label_alphabet_size
if data.pretrain_word_embedding is not None:
self.word_embeddings.weight.data.copy_(
torch.from_numpy(data.pretrain_word_embedding))
else:
self.word_embeddings.weight.data.copy_(
torch.from_numpy(
self.random_embedding(data.word_alphabet.size(),
self.embedding_dim)))
# The LSTM takes word embeddings as inputs, and outputs hidden states
# with dimensionality hidden_dim.
if self.bilstm_flag:
lstm_hidden = data.HP_hidden_dim // 2
else:
lstm_hidden = data.HP_hidden_dim
self.lstm = nn.LSTM(self.embedding_dim + self.char_hidden_dim +
data.label_alphabet_size,
lstm_hidden,
num_layers=self.lstm_layer,
batch_first=True,
bidirectional=self.bilstm_flag)
# The linear layer that maps from hidden state space to tag space
self.hidden2tag = nn.Linear(self.hidden_dim, 2)
self.topk = 50
if self.gpu:
self.drop = self.drop.cuda()
self.droplstm = self.droplstm.cuda()
self.word_embeddings = self.word_embeddings.cuda()
self.lstm = self.lstm.cuda()
self.hidden2tag = self.hidden2tag.cuda()
def random_embedding(self, vocab_size, embedding_dim):
pretrain_emb = np.empty([vocab_size, embedding_dim])
scale = np.sqrt(3.0 / embedding_dim)
for index in range(vocab_size):
pretrain_emb[index, :] = np.random.uniform(-scale, scale,
[1, embedding_dim])
return pretrain_emb
def get_lstm_features(self, word_inputs, word_seq_lengths, char_inputs,
char_seq_lengths, char_seq_recover, tag_seq,
tag_size):
"""
input:
word_inputs: (batch_size, sent_len)
word_seq_lengths: list of batch_size, (batch_size,1)
char_inputs: (batch_size*sent_len, word_length)
char_seq_lengths: list of whole batch_size for char, (batch_size*sent_len, 1)
char_seq_recover: variable which records the char order information, used to recover char order
output:
Variable(batch_size, sent_len, hidden_dim)
"""
batch_size = word_inputs.size(0)
sent_len = word_inputs.size(1)
word_embs = self.word_embeddings(word_inputs)
if self.use_char:
## calculate char lstm last hidden
char_features = self.char_feature.get_last_hiddens(
char_inputs,
char_seq_lengths.cpu().numpy())
char_features = char_features[char_seq_recover]
char_features = char_features.view(batch_size, sent_len, -1)
word_embs = torch.cat([word_embs, char_features], 2)
tag_feature = torch.zeros(batch_size, sent_len,
tag_size).cuda().scatter_(
2,
tag_seq.unsqueeze(2).cuda(), 1.0)
## concat word and char together
word_embs = self.drop(word_embs)
word_embs = torch.cat([word_embs, Variable(tag_feature)], 2)
packed_words = pack_padded_sequence(word_embs,
word_seq_lengths.cpu().numpy(),
True)
hidden = None
packed_words.requires_grad = False
lstm_out, hidden = self.lstm(packed_words, hidden)
lstm_out, _ = pad_packed_sequence(lstm_out)
lstm_out = self.droplstm(lstm_out.transpose(1, 0))
## lstm_out (batch_size, seq_len, hidden_size)
return lstm_out
def get_output_score(self, word_inputs, word_seq_lengths, char_inputs,
char_seq_lengths, char_seq_recover, tag_seq):
lstm_out = self.get_lstm_features(word_inputs, word_seq_lengths,
char_inputs, char_seq_lengths,
char_seq_recover, tag_seq,
self.tag_size)
outputs = self.hidden2tag(lstm_out)
return outputs
def neg_log_likelihood_loss(self, word_inputs, word_seq_lengths,
char_inputs, char_seq_lengths,
char_seq_recover, batch_label, tag_seq, mask):
## mask is not used
batch_size = word_inputs.size(0)
seq_len = word_inputs.size(1)
total_word = batch_size * seq_len
#loss_function = nn.CrossEntropyLoss()
outs = self.get_output_score(word_inputs, word_seq_lengths,
char_inputs, char_seq_lengths,
char_seq_recover, tag_seq)
# outs (batch_size, seq_len, 2)
outs = outs.view(total_word, -1)
score = F.softmax(outs, 1)
#loss = loss_function(score, batch_label.view(total_word))
#if self.average_batch:
# loss = loss / batch_size
#print('score0',loss)
_ = score[:, 0]
_ = _.contiguous().view(batch_size, seq_len)
#print(_)
_ = mask.float() * _ #the score is always positive
#print(mask)
#print("_",_)
_ = F.softmax(_, 1)
if seq_len >= self.topk:
#print(self.topk)
topk, indices = _.topk(self.topk, dim=1)
else:
#print(seq_len)
topk, indices = _.topk(seq_len, dim=1)
tag_mask = Variable(torch.ones(batch_size, seq_len).cuda())
tag_mask = tag_mask.scatter(1, indices, 0).long()
#print("topk",topk)
#topk=F.softmax(topk, 1)
topk = torch.log(topk)
#print(topk)
#topk=torch.log(_)*(1-(-torch.abs(Variable(tag_seq).cuda()-batch_label)).ge(0).float())
info_tensor = (
1 -
(-torch.abs(Variable(tag_seq).cuda() - batch_label)).ge(0).float()
) #inequal if one
_sum = info_tensor.sum().long()[0]
ans = -torch.log(_) * (
1 -
(-torch.abs(Variable(tag_seq).cuda() - batch_label)).ge(0).float())
correct = _ * (
1 -
(-torch.abs(Variable(tag_seq).cuda() - batch_label)).ge(0).float())
#ans=0.0
#for i in range(_sum):
# ans+=torch.index_select(info_tensor[0],0,indices[0][:i+1]).sum()/float(i+1)
#print(batch_label)
#print(Variable(tag_seq).cuda())
return indices, tag_mask, topk.mean(1), ans, correct
# tag_seq = autograd.Variable(torch.zeros(batch_size, seq_len)).long()
# total_loss = 0
# for idx in range(batch_size):
# score = F.log_softmax(outs[idx])
# loss = loss_function(score, batch_label[idx])
# # tag_seq[idx] = score.cpu().data.numpy().argmax(axis=1)
# _, tag_seq[idx] = torch.max(score, 1)
# total_loss += loss
# return total_loss, tag_seq
def forward(self, word_inputs, word_seq_lengths, char_inputs,
char_seq_lengths, char_seq_recover, mask, tag_seq):
batch_size = word_inputs.size(0)
seq_len = word_inputs.size(1)
total_word = batch_size * seq_len
outs = self.get_output_score(word_inputs, word_seq_lengths,
char_inputs, char_seq_lengths,
char_seq_recover, tag_seq)
outs = outs.view(total_word, -1)
_, tag_seq = torch.max(outs, 1)
tag_seq = tag_seq.view(batch_size, seq_len)
## filter padded position with zero
decode_seq = mask.long() * tag_seq
#print('outs',out)
return decode_seq
class CoveBiLSTM(nn.Module):
def __init__(self, data):
super(CoveBiLSTM, self).__init__()
print "build batched bilstm..."
self.gpu = data.HP_gpu
self.use_char = data.HP_use_char
self.batch_size = data.HP_batch_size
self.char_hidden_dim = 0
self.average_batch = data.HP_average_batch_loss
if self.use_char:
self.char_hidden_dim = data.HP_char_hidden_dim
self.char_embedding_dim = data.char_emb_dim
if data.char_features == "CNN":
self.char_feature = CharCNN(data.char_alphabet.size(),
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
elif data.char_features == "LSTM":
self.char_feature = CharBiLSTM(data.char_alphabet.size(),
self.char_embedding_dim,
self.char_hidden_dim,
data.HP_dropout, self.gpu)
else:
print "Error char feature selection, please check parameter data.char_features (either CNN or LSTM)."
exit(0)
self.embedding_dim = data.word_emb_dim
self.hidden_dim = data.HP_hidden_dim
self.drop = nn.Dropout(data.HP_dropout)
self.droplstm = nn.Dropout(data.HP_dropout)
self.word_embeddings = nn.Embedding(data.word_alphabet.size(),
self.embedding_dim)
self.bilstm_flag = data.HP_bilstm
self.lstm_layer = data.HP_lstm_layer
if data.pretrain_word_embedding is not None:
self.word_embeddings.weight.data.copy_(
torch.from_numpy(data.pretrain_word_embedding))
else:
self.word_embeddings.weight.data.copy_(
torch.from_numpy(
self.random_embedding(data.word_alphabet.size(),
self.embedding_dim)))
# The LSTM takes word embeddings as inputs, and outputs hidden states
# with dimensionality hidden_dim.
if self.bilstm_flag:
lstm_hidden = data.HP_hidden_dim // 2
else:
lstm_hidden = data.HP_hidden_dim
self.cove = nn.LSTM(300,
300,
num_layers=2,
bidirectional=True,
batch_first=True)
self.cove.load_state_dict(
model_zoo.load_url(model_urls['wmt-lstm'], model_dir=model_cache))
self.lstm = nn.LSTM(self.embedding_dim + self.char_hidden_dim + 600,
lstm_hidden,
num_layers=self.lstm_layer,
batch_first=True,
bidirectional=self.bilstm_flag)
## catner
# self.lstm = nn.LSTM(self.char_hidden_dim + 300, lstm_hidden, num_layers=self.lstm_layer,
# batch_first=True, bidirectional=self.bilstm_flag)
# The linear layer that maps from hidden state space to tag space
self.hidden2tag = nn.Linear(self.hidden_dim, data.label_alphabet_size)
if self.gpu:
self.drop = self.drop.cuda()
self.droplstm = self.droplstm.cuda()
self.word_embeddings = self.word_embeddings.cuda()
self.cove = self.cove.cuda()
self.lstm = self.lstm.cuda()
self.hidden2tag = self.hidden2tag.cuda()
def random_embedding(self, vocab_size, embedding_dim):
pretrain_emb = np.empty([vocab_size, embedding_dim])
scale = np.sqrt(3.0 / embedding_dim)
for index in range(vocab_size):
pretrain_emb[index, :] = np.random.uniform(-scale, scale,
[1, embedding_dim])
return pretrain_emb
def get_lstm_features(self, word_inputs, word_seq_lengths, char_inputs,
char_seq_lengths, char_seq_recover):
"""
input:
word_inputs: (batch_size, sent_len)
word_seq_lengths: list of batch_size, (batch_size,1)
char_inputs: (batch_size*sent_len, word_length)
char_seq_lengths: list of whole batch_size for char, (batch_size*sent_len, 1)
char_seq_recover: variable which records the char order information, used to recover char order
output:
Variable(batch_size, sent_len, hidden_dim)
"""
batch_size = word_inputs.size(0)
sent_len = word_inputs.size(1)
word_embs = self.word_embeddings(word_inputs)
## cove
cove_hidden = None
packed_words = pack_padded_sequence(word_embs,
word_seq_lengths.cpu().numpy(),
True)
outputs, hidden_t = self.cove(packed_words, cove_hidden)
outputs = pad_packed_sequence(outputs, batch_first=True)[0]
# _, _indices = torch.sort(indices, 0)
# outputs = outputs[_indices]
# outputs = outputs[0]
outputs.contiguous()
outputs = outputs.view(batch_size, sent_len, -1)
## catner
word_embs = torch.cat([word_embs, outputs], 2)
# word_embs = outputs
if self.use_char:
## calculate char lstm last hidden
char_features = self.char_feature.get_last_hiddens(
char_inputs,
char_seq_lengths.cpu().numpy())
char_features = char_features[char_seq_recover]
char_features = char_features.view(batch_size, sent_len, -1)
## concat word and char together
word_embs = torch.cat([word_embs, char_features], 2)
# cove_hidden = None
# cove_words = pack_padded_sequence(word_embs, word_seq_lengths.cpu().numpy(), True)
# outputs, hidden_t = self.cove(cove_words, cove_hidden)
# outputs = pad_packed_sequence(outputs, batch_first=True)[0]
# _, _indices = torch.sort(indices, 0)
# outputs = outputs[_indices]
word_embs = self.drop(word_embs)
## word_embs (batch_size, seq_len, embed_size)
packed_words = pack_padded_sequence(word_embs,
word_seq_lengths.cpu().numpy(),
True)
hidden = None
lstm_out, hidden = self.lstm(packed_words, hidden)
lstm_out, _ = pad_packed_sequence(lstm_out)
## lstm_out (seq_len, batch_size, hidden_size)
lstm_out = self.droplstm(lstm_out.transpose(1, 0))
## lstm_out (batch_size, seq_len, hidden_size)
return lstm_out
def get_output_score(self, word_inputs, word_seq_lengths, char_inputs,
char_seq_lengths, char_seq_recover):
lstm_out = self.get_lstm_features(word_inputs, word_seq_lengths,
char_inputs, char_seq_lengths,
char_seq_recover)
outputs = self.hidden2tag(lstm_out)
return outputs
def neg_log_likelihood_loss(self, word_inputs, word_seq_lengths,
char_inputs, char_seq_lengths,
char_seq_recover, batch_label, mask):
## mask is not used
batch_size = word_inputs.size(0)
seq_len = word_inputs.size(1)
total_word = batch_size * seq_len
loss_function = nn.NLLLoss(ignore_index=0, size_average=False)
outs = self.get_output_score(word_inputs, word_seq_lengths,
char_inputs, char_seq_lengths,
char_seq_recover)
# outs (batch_size, seq_len, label_vocab)
outs = outs.view(total_word, -1)
score = F.log_softmax(outs, 1)
loss = loss_function(score, batch_label.view(total_word))
if self.average_batch:
loss = loss / batch_size
_, tag_seq = torch.max(score, 1)
tag_seq = tag_seq.view(batch_size, seq_len)
return loss, tag_seq
# tag_seq = autograd.Variable(torch.zeros(batch_size, seq_len)).long()
# total_loss = 0
# for idx in range(batch_size):
# score = F.log_softmax(outs[idx])
# loss = loss_function(score, batch_label[idx])
# # tag_seq[idx] = score.cpu().data.numpy().argmax(axis=1)
# _, tag_seq[idx] = torch.max(score, 1)
# total_loss += loss
# return total_loss, tag_seq
def forward(self, word_inputs, word_seq_lengths, char_inputs,
char_seq_lengths, char_seq_recover, mask):
batch_size = word_inputs.size(0)
seq_len = word_inputs.size(1)
total_word = batch_size * seq_len
outs = self.get_output_score(word_inputs, word_seq_lengths,
char_inputs, char_seq_lengths,
char_seq_recover)
outs = outs.view(total_word, -1)
_, tag_seq = torch.max(outs, 1)
tag_seq = tag_seq.view(batch_size, seq_len)
## filter padded position with zero
decode_seq = mask.long() * tag_seq
return decode_seq
