def __init__(self, vocab, num_classes, char_alphabet):
super(CNNCNN, self).__init__()
self.embedding = vocab.init_embed_layer()
self.hidden_size = opt.hidden_size
# charcnn
self.char_hidden_dim = 10
self.char_embedding_dim = 20
self.char_feature = CharCNN(len(char_alphabet), None,
self.char_embedding_dim,
self.char_hidden_dim, opt.dropout, opt.gpu)
D = self.embedding.weight.size(1)
self.hidden_size = opt.hidden_size
D = D + self.char_hidden_dim
#mention cnn
Ci = 1
Co = opt.kernel_num
Ks = [int(k) for k in list(opt.kernel_sizes) if k != ","]
self.convs1 = nn.ModuleList([
nn.Conv2d(in_channels=Ci,
out_channels=Co,
kernel_size=(K, D),
stride=(1, 1),
padding=(K // 2, 0),
dilation=1,
bias=False) for K in Ks
])
self.hidden = nn.Linear(len(Ks) * Co, self.hidden_size)
self.out = nn.Linear(self.hidden_size, num_classes)
self.dropout = nn.Dropout(opt.dropout)
def __init__(self, vocab, num_classes, char_alphabet): super(CNNCNN_SentLSTM,self).__init__() self.embedding = vocab.init_embed_layer() self.hidden_size = opt.hidden_size # charcnn self.char_hidden_dim = 10 self.char_embedding_dim = 20 self.char_feature = CharCNN(len(char_alphabet), None, self.char_embedding_dim, self.char_hidden_dim, opt.dropout, opt.gpu) self.embedding_size = self.embedding.weight.size(1) self.hidden_size = opt.hidden_size Ci = 1 Co = opt.kernel_num Ks = [int(k) for k in list(opt.kernel_sizes) if k != ","] # mention char_cnn D = self.embedding_size + self.char_hidden_dim self.convs1 = nn.ModuleList([nn.Conv2d(in_channels=Ci, out_channels=Co, kernel_size=(K, D), stride=(1, 1), padding=(K // 2, 0), dilation=1, bias=False) for K in Ks]) self.mention_hidden = nn.Linear(len(Ks) * Co, self.hidden_size) #sentence lstm self.lstm_hidden = opt.hidden_size self.lstm = nn.GRU(self.embedding_size, self.lstm_hidden, num_layers=1, batch_first=True, bidirectional=True) self.sent_hidden_size = opt.sent_hidden_size self.sent_hidden = nn.Linear(self.lstm_hidden*2, self.sent_hidden_size) self.hidden = nn.Linear(self.hidden_size + self.sent_hidden_size, self.hidden_size) # mention_hidden_size + sentence_hidden_size self.out = nn.Linear(self.hidden_size, num_classes) self.dropout = nn.Dropout(opt.dropout)
def __init__(self, vocab, num_classes, char_alphabet): super(AttenCNN,self).__init__() self.embed_size = opt.word_emb_size self.embedding = vocab.init_embed_layer() self.hidden_size = opt.hidden_size self.char_hidden_dim = 10 self.char_embedding_dim = 20 self.char_feature = CharCNN(len(char_alphabet), None, self.char_embedding_dim, self.char_hidden_dim, opt.dropout, opt.gpu) self.input_size = self.embed_size + self.char_hidden_dim self.W = nn.Linear(self.input_size, 1, bias=False) self.hidden = nn.Linear(self.input_size, self.hidden_size) self.out = nn.Linear(self.hidden_size, num_classes) self.dropout = nn.Dropout(opt.dropout)
def __init__(self, data):
super(WordRep, self).__init__()
self.char_hidden_dim = data.char_hidden_dim # 50
self.char_embedding_dim = data.char_emb_dim # 300
self.char_feature = CharCNN(data.char_alphabet_size,
data.pretrain_char_embedding,
self.char_embedding_dim,
self.char_hidden_dim, data.dropout)
self.drop = nn.Dropout(data.dropout)
self.word_embedding = nn.Embedding(data.word_alphabet_size,
data.word_emb_dim)
self.word_embedding.weight.data.copy_(
torch.from_numpy(
self.random_embedding(data.word_alphabet_size,
data.word_emb_dim)))
self.feature_embedding = nn.Embedding(data.feat_alphabet_size,
data.feature_emb_dim)
self.feature_embedding.weight.data.copy_(
torch.from_numpy(data.pretrain_feature_embeddings))
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.w = nn.Linear(data.word_emb_dim, data.HP_trans_hidden_dim)
if self.use_trans:
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)
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,
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, 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()
def __init__(self, data, use_position, use_cap, use_postag, use_char):
super(WordRep, self).__init__()
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
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.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 __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.bilstm_flag = False
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)))
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
print("********************use_lattice",self.use_gaz)
if self.use_gaz:
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)
else:
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)
self.hidden2tag_ner = nn.Linear(data.HP_hidden_dim, data.label_alphabet_size_ner)
self.hidden2tag_general = nn.Linear(data.HP_hidden_dim, data.label_alphabet_size_general)
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()
if self.use_gaz:
self.forward_lstm = self.forward_lstm.cuda()
if self.bilstm_flag:
self.backward_lstm = self.backward_lstm.cuda()
else:
self.lstm = self.lstm.cuda()
self.hidden2tag = self.hidden2tag.cuda()
self.hidden2tag_ner = self.hidden2tag_ner.cuda()
self.hidden2tag_general = self.hidden2tag_general.cuda()
def __init__(self,
rnn_type,
vocab_size,
embedding_dim,
hidden_dim,
num_layers,
tie_weights,
dropout,
device,
pretrain_emb=None,
use_ch=False,
use_he=False,
use_i=False,
use_h=False,
use_g=True,
**kwargs):
super(RNNModel, self).__init__()
self.rnn_type = rnn_type
self.n_layers = num_layers
self.hi_dim = hidden_dim
self.device = device
self.use_i = use_i
self.use_h = use_h
self.use_g = use_g
self.use_ch = use_ch
self.use_he = use_he
self.drop = nn.Dropout(dropout)
char_hid_dim = 0
char_len = 0
he_dim = 0
self.embedding = nn.Embedding(vocab_size, embedding_dim)
if pretrain_emb is not None:
self.embedding.weight.data.copy_(torch.from_numpy(pretrain_emb))
else:
self.embedding.weight.data.copy_(
torch.from_numpy(
self.random_embedding(vocab_size, embedding_dim)))
self.embedding.weight.requires_grad = False
# ch
if use_ch:
char_vocab_size = kwargs['char_vocab_size']
char_emb_dim = kwargs['char_emb_dim']
char_hid_dim = kwargs['char_hid_dim']
char_len = kwargs['char_len']
self.ch = CharCNN(char_vocab_size, None, char_emb_dim,
char_hid_dim, dropout).to(device)
# he
if use_he:
print("Build Hypernym Embeddings...")
he_dim = embedding_dim
self.he = Hypernym(embedding_dim, self.embedding, device)
concat_embedding_dim = embedding_dim + char_len * char_hid_dim + he_dim
if self.use_i:
embedding_dim = embedding_dim + concat_embedding_dim
if self.use_h:
self.h_linear = nn.Linear(concat_embedding_dim + hidden_dim,
hidden_dim)
if self.use_g:
self.zt_linear = nn.Linear(concat_embedding_dim + hidden_dim,
hidden_dim)
self.rt_linear = nn.Linear(concat_embedding_dim + hidden_dim,
concat_embedding_dim)
self.ht_linear = nn.Linear(concat_embedding_dim + hidden_dim,
hidden_dim)
if rnn_type in ['LSTM', 'GRU']:
self.rnn = getattr(nn, rnn_type)(embedding_dim,
hidden_dim,
num_layers,
dropout=dropout)
else:
try:
nonlinearity = {
'RNN_TANH': 'tanh',
'RNN_RELU': 'relu'
}[rnn_type]
except KeyError:
raise ValueError(
"""An invalid option for `--model` was supplied,
options are ['LSTM', 'GRU', 'RNN_TANH' or 'RNN_RELU']"""
)
self.rnn = nn.RNN(embedding_dim,
hidden_dim,
num_layers,
nonlinearity=nonlinearity,
dropout=dropout)
self.word2hidden = nn.Linear(concat_embedding_dim, hidden_dim)
self.decoder = nn.Linear(hidden_dim, vocab_size)
if tie_weights:
if hidden_dim != embedding_dim:
raise ValueError(
'When using the tied flag, nhid must be equal to emsize')
self.decoder.weight = self.embedding.weight
self.init_weights()
def __init__(self,
vocab_size,
emb_dim,
hid_dim,
device,
pretrain_emb=None,
dropout=0,
use_i=False,
use_h=False,
use_g=True,
use_ch=True,
use_he=False,
**kwargs):
super(DefSeq, self).__init__()
self.device = device
self.use_i = use_i
self.use_h = use_h
self.use_g = use_g
self.use_ch = use_ch
self.use_he = use_he
char_emb_dim = 0
char_hid_dim = 0
char_len = 0
he_dim = 0
def weight_init(m):
if isinstance(m, nn.Embedding):
nn.init.orthogonal_(m.weight.data)
if isinstance(m, nn.Linear):
nn.init.orthogonal_(m.weight.data)
nn.init.constant_(m.bias.data, 0.5)
if isinstance(m, nn.LSTMCell):
nn.init.orthogonal_(m.weight_hh.data)
nn.init.orthogonal_(m.weight_ih.data)
nn.init.constant_(m.bias_hh.data, 0.5)
nn.init.constant_(m.bias_ih.data, 0.5)
self.embedding = nn.Embedding(vocab_size, emb_dim)
if pretrain_emb is not None:
# self.embedding.weight.data.copy_(pretrain_emb)
self.embedding.from_pretrained(pretrain_emb, freeze=True)
# self.embedding.weight.requires_grad = False
else:
weight_init(self.embedding)
if self.use_ch:
print("build char sequence feature extractor: CNN ...")
char_vocab_size = kwargs['char_vocab_size']
char_emb_dim = kwargs['char_emb_dim']
char_hid_dim = kwargs['char_hid_dim']
char_len = kwargs['char_len']
self.ch = CharCNN(char_vocab_size, None, char_emb_dim,
char_hid_dim, dropout, device)
if self.use_he:
print("build Hypernym Embeddings...")
he_dim = emb_dim
self.he = Hypernym(emb_dim, self.embedding, device)
final_word_dim = emb_dim + char_hid_dim * char_len + he_dim
self.word_linear = nn.Linear(final_word_dim, hid_dim)
weight_init(self.word_linear)
self.s_lstm = nn.LSTMCell(emb_dim, hid_dim)
weight_init(self.s_lstm)
if self.use_i:
self.i_lstm = nn.LSTMCell(final_word_dim + emb_dim, hid_dim)
weight_init(self.i_lstm)
if self.use_h:
self.h_linear = nn.Linear(final_word_dim + hid_dim, hid_dim)
weight_init(self.h_linear)
if self.use_g:
self.g_zt_linear = nn.Linear(final_word_dim + hid_dim, hid_dim)
weight_init(self.g_zt_linear)
self.g_rt_linear = nn.Linear(final_word_dim + hid_dim,
final_word_dim)
weight_init(self.g_rt_linear)
self.g_ht_linear = nn.Linear(final_word_dim + hid_dim, hid_dim)
weight_init(self.g_ht_linear)
self.hidden2tag_linear = nn.Linear(hid_dim, vocab_size)
weight_init(self.hidden2tag_linear)
self.dropout = nn.Dropout(p=dropout)