def build(self, source_vectors=None, target_vectors=None):
hidden_size = self.config.hidden_sizes[0]
nb_classes = self.config.nb_classes
dropout = self.config.dropout
weight = make_loss_weights(nb_classes, const.BAD_ID,
self.config.bad_weight)
self._loss = nn.CrossEntropyLoss(weight=weight,
ignore_index=const.PAD_TAGS_ID)
# Embeddings layers:
self._build_embeddings(source_vectors, target_vectors)
feature_set_size = (
self.config.source_embeddings_size +
self.config.target_embeddings_size) * self.config.window_size
self.linear = nn.Linear(feature_set_size, hidden_size)
self.linear_out = nn.Linear(hidden_size, nb_classes)
self.dropout = nn.Dropout(dropout)
torch.nn.init.xavier_uniform_(self.linear.weight)
torch.nn.init.xavier_uniform_(self.linear_out.weight)
torch.nn.init.constant_(self.linear.bias, 0.0)
torch.nn.init.constant_(self.linear_out.bias, 0.0)
self.is_built = True
def __init__(self,
vocabs,
predictor_tgt=None,
predictor_src=None,
**kwargs):
super().__init__(vocabs=vocabs, ConfigCls=EstimatorConfig, **kwargs)
if predictor_src:
self.config.update(predictor_src.config)
elif predictor_tgt:
self.config.update(predictor_tgt.config)
# Predictor Settings #
predict_tgt = (self.config.predict_target or self.config.predict_gaps
or self.config.sentence_level)
if predict_tgt and not predictor_tgt:
predictor_tgt = Predictor(
vocabs=vocabs,
predict_inverse=False,
hidden_pred=self.config.hidden_pred,
rnn_layers_pred=self.config.rnn_layers_pred,
dropout_pred=self.config.dropout_pred,
target_embeddings_size=self.config.target_embeddings_size,
source_embeddings_size=self.config.source_embeddings_size,
out_embeddings_size=self.config.out_embeddings_size,
)
if self.config.predict_source and not predictor_src:
predictor_src = Predictor(
vocabs=vocabs,
predict_inverse=True,
hidden_pred=self.config.hidden_pred,
rnn_layers_pred=self.config.rnn_layers_pred,
dropout_pred=self.config.dropout_pred,
target_embeddings_size=self.config.target_embeddings_size,
source_embeddings_size=self.config.source_embeddings_size,
out_embeddings_size=self.config.out_embeddings_size,
)
# Update the predictor vocabs if token level == True
# Required by `get_mask` call in predictor forward with `pe` side
# to determine padding IDs.
if self.config.token_level:
if predictor_src:
predictor_src.vocabs = vocabs
if predictor_tgt:
predictor_tgt.vocabs = vocabs
self.predictor_tgt = predictor_tgt
self.predictor_src = predictor_src
predictor_hidden = self.config.hidden_pred
embedding_size = self.config.out_embeddings_size
input_size = 2 * predictor_hidden + embedding_size
self.nb_classes = len(const.LABELS)
self.lstm_input_size = input_size
self.mlp = None
self.sentence_pred = None
self.sentence_sigma = None
self.binary_pred = None
self.binary_scale = None
# Build Model #
if self.config.start_stop:
self.start_PreQEFV = nn.Parameter(torch.zeros(
1, 1, embedding_size))
self.end_PreQEFV = nn.Parameter(torch.zeros(1, 1, embedding_size))
if self.config.mlp_est:
self.mlp = nn.Sequential(
nn.Linear(input_size, self.config.hidden_est), nn.Tanh())
self.lstm_input_size = self.config.hidden_est
self.lstm = nn.LSTM(
input_size=self.lstm_input_size,
hidden_size=self.config.hidden_est,
num_layers=self.config.rnn_layers_est,
batch_first=True,
dropout=self.config.dropout_est,
bidirectional=True,
)
self.embedding_out = nn.Linear(2 * self.config.hidden_est,
self.nb_classes)
if self.config.predict_gaps:
self.embedding_out_gaps = nn.Linear(4 * self.config.hidden_est,
self.nb_classes)
self.dropout = None
if self.config.dropout_est:
self.dropout = nn.Dropout(self.config.dropout_est)
# Multitask Learning Objectives #
sentence_input_size = (2 * self.config.rnn_layers_est *
self.config.hidden_est)
if self.config.sentence_level:
self.sentence_pred = nn.Sequential(
nn.Linear(sentence_input_size, sentence_input_size // 2),
nn.Sigmoid(),
nn.Linear(sentence_input_size // 2, sentence_input_size // 4),
nn.Sigmoid(),
nn.Linear(sentence_input_size // 4, 1),
)
self.sentence_sigma = None
if self.config.sentence_ll:
# Predict truncated Gaussian distribution
self.sentence_sigma = nn.Sequential(
nn.Linear(sentence_input_size, sentence_input_size // 2),
nn.Sigmoid(),
nn.Linear(sentence_input_size // 2,
sentence_input_size // 4),
nn.Sigmoid(),
nn.Linear(sentence_input_size // 4, 1),
nn.Sigmoid(),
)
if self.config.binary_level:
self.binary_pred = nn.Sequential(
nn.Linear(sentence_input_size, sentence_input_size // 2),
nn.Tanh(),
nn.Linear(sentence_input_size // 2, sentence_input_size // 4),
nn.Tanh(),
nn.Linear(sentence_input_size // 4, 2),
)
# Build Losses #
# FIXME: Remove dependency on magic numbers
self.xents = nn.ModuleDict()
weight = make_loss_weights(self.nb_classes, const.BAD_ID,
self.config.target_bad_weight)
self.xents[const.TARGET_TAGS] = nn.CrossEntropyLoss(
reduction='sum', ignore_index=const.PAD_TAGS_ID, weight=weight)
if self.config.predict_source:
weight = make_loss_weights(self.nb_classes, const.BAD_ID,
self.config.source_bad_weight)
self.xents[const.SOURCE_TAGS] = nn.CrossEntropyLoss(
reduction='sum', ignore_index=const.PAD_TAGS_ID, weight=weight)
if self.config.predict_gaps:
weight = make_loss_weights(self.nb_classes, const.BAD_ID,
self.config.gaps_bad_weight)
self.xents[const.GAP_TAGS] = nn.CrossEntropyLoss(
reduction='sum', ignore_index=const.PAD_TAGS_ID, weight=weight)
if self.config.sentence_level and not self.config.sentence_ll:
self.mse_loss = nn.MSELoss(reduction='sum')
if self.config.binary_level:
self.xent_binary = nn.CrossEntropyLoss(reduction='sum')
def build(self, source_vectors=None, target_vectors=None):
nb_classes = self.config.nb_classes
# FIXME: Remove dependency on magic number
weight = make_loss_weights(nb_classes, const.BAD_ID, self.config.bad_weight)
'''start:将词表中的每个token用bert生成embedding表示，修改格式，分别保存在self.source_bert和self.target_bert中'''
# path = os.getcwd()
# source_bert, target_bert = [], []
# src_bert = np.load(path + '/data/exp2/en_emb.npy', allow_pickle=True).item()
# tgt_bert = np.load(path + '/data/exp2/de_emb.npy', allow_pickle=True).item()
# for s in src_bert:
# source_bert.append(list(src_bert[s].squeeze(0)))
# for t in tgt_bert:
# target_bert.append(list(tgt_bert[t].squeeze(0)))
# self.source_bert = torch.Tensor(source_bert)
# self.target_bert = torch.Tensor(target_bert)
'''end'''
self._loss = nn.CrossEntropyLoss(weight=weight, ignore_index=self.config.tags_pad_id, reduction='sum')
# Embeddings layers:
self._build_embeddings(source_vectors, target_vectors)
feature_set_size = (self.config.source_embeddings_size + self.config.target_embeddings_size) * self.config.window_size
l1_dim = self.config.hidden_sizes[0]
l2_dim = self.config.hidden_sizes[1]
l3_dim = self.config.hidden_sizes[2]
l4_dim = self.config.hidden_sizes[3]
nb_classes = self.config.nb_classes
dropout = self.config.dropout
# Linear layers
self.linear_1 = nn.Linear(768, l1_dim)
self.linear_2 = nn.Linear(l1_dim, l1_dim)
self.linear_3 = nn.Linear(2 * l2_dim, l2_dim)
self.linear_4 = nn.Linear(l2_dim, l2_dim)
self.linear_5 = nn.Linear(2 * l2_dim, l3_dim)
self.linear_6 = nn.Linear(l3_dim, l4_dim)
# Output layer
self.linear_out = nn.Linear(l4_dim, nb_classes)
# Recurrent Layers
self.gru_1 = nn.GRU(l1_dim, l2_dim, bidirectional=True, batch_first=True)
self.gru_2 = nn.GRU(l2_dim, l2_dim, bidirectional=True, batch_first=True)
# Dropout after linear layers
self.dropout_in = nn.Dropout(dropout)
self.dropout_out = nn.Dropout(dropout)
# Explicit initializations
nn.init.xavier_uniform_(self.linear_1.weight)
nn.init.xavier_uniform_(self.linear_2.weight)
nn.init.xavier_uniform_(self.linear_3.weight)
nn.init.xavier_uniform_(self.linear_4.weight)
nn.init.xavier_uniform_(self.linear_5.weight)
nn.init.xavier_uniform_(self.linear_6.weight)
# nn.init.xavier_uniform_(self.linear_out.weight)
nn.init.constant_(self.linear_1.bias, 0.0)
nn.init.constant_(self.linear_2.bias, 0.0)
nn.init.constant_(self.linear_3.bias, 0.0)
nn.init.constant_(self.linear_4.bias, 0.0)
nn.init.constant_(self.linear_5.bias, 0.0)
nn.init.constant_(self.linear_6.bias, 0.0)
# nn.init.constant_(self.linear_out.bias, 0.0)
self.is_built = True
def build(self, source_vectors=None, target_vectors=None):
nb_classes = self.config.nb_classes
# FIXME: Remove dependency on magic number
weight = make_loss_weights(nb_classes, const.BAD_ID,
self.config.bad_weight)
self._loss = nn.CrossEntropyLoss(weight=weight,
ignore_index=self.config.tags_pad_id,
reduction='sum')
# Embeddings layers:
self._build_embeddings(source_vectors, target_vectors)
feature_set_size = (
self.config.source_embeddings_size +
self.config.target_embeddings_size) * self.config.window_size
l1_dim = self.config.hidden_sizes[0]
l2_dim = self.config.hidden_sizes[1]
l3_dim = self.config.hidden_sizes[2]
l4_dim = self.config.hidden_sizes[3]
nb_classes = self.config.nb_classes
dropout = self.config.dropout
# Linear layers
self.linear_1 = nn.Linear(feature_set_size, l1_dim)
self.linear_2 = nn.Linear(l1_dim, l1_dim)
self.linear_3 = nn.Linear(2 * l2_dim, l2_dim)
self.linear_4 = nn.Linear(l2_dim, l2_dim)
self.linear_5 = nn.Linear(2 * l2_dim, l3_dim)
self.linear_6 = nn.Linear(l3_dim, l4_dim)
# Output layer
self.linear_out = nn.Linear(l4_dim, nb_classes)
# Recurrent Layers
self.gru_1 = nn.GRU(l1_dim,
l2_dim,
bidirectional=True,
batch_first=True)
self.gru_2 = nn.GRU(l2_dim,
l2_dim,
bidirectional=True,
batch_first=True)
# Dropout after linear layers
self.dropout_in = nn.Dropout(dropout)
self.dropout_out = nn.Dropout(dropout)
# Explicit initializations
nn.init.xavier_uniform_(self.linear_1.weight)
nn.init.xavier_uniform_(self.linear_2.weight)
nn.init.xavier_uniform_(self.linear_3.weight)
nn.init.xavier_uniform_(self.linear_4.weight)
nn.init.xavier_uniform_(self.linear_5.weight)
nn.init.xavier_uniform_(self.linear_6.weight)
# nn.init.xavier_uniform_(self.linear_out)
nn.init.constant_(self.linear_1.bias, 0.0)
nn.init.constant_(self.linear_2.bias, 0.0)
nn.init.constant_(self.linear_3.bias, 0.0)
nn.init.constant_(self.linear_4.bias, 0.0)
nn.init.constant_(self.linear_5.bias, 0.0)
nn.init.constant_(self.linear_6.bias, 0.0)
# nn.init.constant_(self.linear_out.bias, 0.)
self.is_built = True
