def __init__(self,
model_dim=None,
word_embedding_dim=None,
vocab_size=None,
use_product_feature=None,
use_difference_feature=None,
initial_embeddings=None,
fine_tune_loaded_embeddings=None,
num_classes=None,
embedding_keep_rate=None,
use_sentence_pair=False,
classifier_keep_rate=None,
mlp_dim=None,
num_mlp_layers=None,
mlp_ln=None,
composition_ln=None,
context_args=None,
trainable_temperature=None,
**kwargs):
super(EESC, self).__init__()
self.use_sentence_pair = use_sentence_pair
self.use_difference_feature = use_difference_feature
self.use_product_feature = use_product_feature
self.model_dim = model_dim
self.trainable_temperature = trainable_temperature
self.classifier_dropout_rate = 1. - classifier_keep_rate
self.embedding_dropout_rate = 1. - embedding_keep_rate
vocab = Vocab()
vocab.size = initial_embeddings.shape[
0] if initial_embeddings is not None else vocab_size
vocab.vectors = initial_embeddings
self.embed = Embed(word_embedding_dim,
vocab.size,
vectors=vocab.vectors,
fine_tune=fine_tune_loaded_embeddings)
self.binary_tree_lstm = BinaryTreeLSTM(
word_embedding_dim,
model_dim // 2,
False,
composition_ln=composition_ln,
trainable_temperature=trainable_temperature)
mlp_input_dim = self.get_features_dim()
self.mlp = MLP(mlp_input_dim, mlp_dim, num_classes, num_mlp_layers,
mlp_ln, self.classifier_dropout_rate)
self.encode = context_args.encoder
self.reshape_input = context_args.reshape_input
self.reshape_context = context_args.reshape_context
# For sample printing and logging
self.mask_memory = None
self.inverted_vocabulary = None
self.temperature_to_display = 0.0
def __init__(self,
model_dim=None,
word_embedding_dim=None,
vocab_size=None,
use_product_feature=None,
use_difference_feature=None,
initial_embeddings=None,
num_classes=None,
embedding_keep_rate=None,
use_sentence_pair=False,
classifier_keep_rate=None,
mlp_dim=None,
num_mlp_layers=None,
mlp_ln=None,
context_args=None,
bidirectional=None,
**kwargs):
super(RNNModel, self).__init__()
self.use_sentence_pair = use_sentence_pair
self.use_difference_feature = use_difference_feature
self.use_product_feature = use_product_feature
self.bidirectional = bidirectional
self.input_dim = context_args.input_dim
self.model_dim = model_dim
classifier_dropout_rate = 1. - classifier_keep_rate
self.embedding_dropout_rate = 1. - embedding_keep_rate
args = Args()
args.size = model_dim
vocab = Vocab()
vocab.size = initial_embeddings.shape[
0] if initial_embeddings is not None else vocab_size
vocab.vectors = initial_embeddings
self.embed = Embed(word_embedding_dim,
vocab.size,
vectors=vocab.vectors)
self.rnn = nn.LSTM(self.input_dim,
self.model_dim /
2 if self.bidirectional else self.model_dim,
num_layers=1,
bidirectional=self.bidirectional,
batch_first=True)
mlp_input_dim = self.get_features_dim()
self.mlp = MLP(mlp_input_dim, mlp_dim, num_classes, num_mlp_layers,
mlp_ln, classifier_dropout_rate)
self.encode = context_args.encoder
self.reshape_input = context_args.reshape_input
self.reshape_context = context_args.reshape_context
def __init__(self,
mlp_input_dim,
mlp_dim,
num_classes,
num_mlp_layers,
mlp_ln,
classifier_keep_rate=0.0):
super(Discriminator, self).__init__()
self.classifier_dropout_rate = 1. - classifier_keep_rate
self.discriminator = MLP(mlp_input_dim, mlp_dim, num_classes,
num_mlp_layers, mlp_ln, self.classifier_dropout_rate)
def __init__(self,
model_dim=None,
word_embedding_dim=None,
vocab_size=None,
initial_embeddings=None,
num_classes=None,
embedding_keep_rate=None,
use_sentence_pair=False,
classifier_keep_rate=None,
mlp_dim=None,
num_mlp_layers=None,
mlp_ln=None,
context_args=None,
gated=None,
selection_keep_rate=None,
pyramid_selection_keep_rate=None,
**kwargs):
super(Pyramid, self).__init__()
self.use_sentence_pair = use_sentence_pair
self.model_dim = model_dim
self.gated = gated
self.selection_keep_rate = selection_keep_rate
classifier_dropout_rate = 1. - classifier_keep_rate
args = Args()
args.size = model_dim
args.input_dropout_rate = 1. - embedding_keep_rate
vocab = Vocab()
vocab.size = initial_embeddings.shape[
0] if initial_embeddings is not None else vocab_size
vocab.vectors = initial_embeddings
self.embed = Embed(word_embedding_dim,
vocab.size,
vectors=vocab.vectors)
self.composition_fn = SimpleTreeLSTM(model_dim / 2,
composition_ln=False)
self.selection_fn = Linear(initializer=HeKaimingInitializer)(model_dim,
1)
# TODO: Set up layer norm.
mlp_input_dim = model_dim * 2 if use_sentence_pair else model_dim
self.mlp = MLP(mlp_input_dim, mlp_dim, num_classes, num_mlp_layers,
mlp_ln, classifier_dropout_rate)
self.encode = context_args.encoder
self.reshape_input = context_args.reshape_input
self.reshape_context = context_args.reshape_context
def __init__(self,
model_dim=None,
word_embedding_dim=None,
vocab_size=None,
initial_embeddings=None,
fine_tune_loaded_embeddings=None,
use_difference_feature=None,
use_product_feature=None,
num_classes=None,
embedding_keep_rate=None,
classifier_keep_rate=None,
mlp_dim=None,
num_mlp_layers=None,
mlp_ln=None,
use_sentence_pair=False,
context_args=None,
**kwargs
):
super(BaseModel, self).__init__()
self.use_sentence_pair = use_sentence_pair
self.use_difference_feature = use_difference_feature
self.use_product_feature = use_product_feature
self.model_dim = model_dim
classifier_dropout_rate = 1. - classifier_keep_rate
args = Args()
args.size = model_dim
vocab = Vocab()
vocab.size = initial_embeddings.shape[0] if initial_embeddings is not None else vocab_size
vocab.vectors = initial_embeddings
self.embed = Embed(
word_embedding_dim,
vocab.size,
vectors=vocab.vectors,
fine_tune=fine_tune_loaded_embeddings)
mlp_input_dim = self.get_features_dim()
self.mlp = MLP(mlp_input_dim, mlp_dim, num_classes,
num_mlp_layers, mlp_ln, classifier_dropout_rate)
self.encode = context_args.encoder
self.reshape_input = context_args.reshape_input
self.reshape_context = context_args.reshape_context
def __init__(self,
model_dim=None,
word_embedding_dim=None,
vocab_size=None,
initial_embeddings=None,
num_classes=None,
embedding_keep_rate=None,
use_sentence_pair=False,
classifier_keep_rate=None,
mlp_dim=None,
num_mlp_layers=None,
mlp_bn=None,
context_args=None,
**kwargs):
super(BaseModel, self).__init__()
self.use_sentence_pair = use_sentence_pair
self.model_dim = model_dim
classifier_dropout_rate = 1. - classifier_keep_rate
args = Args()
args.size = model_dim
args.input_dropout_rate = 1. - embedding_keep_rate
vocab = Vocab()
vocab.size = initial_embeddings.shape[
0] if initial_embeddings is not None else vocab_size
vocab.vectors = initial_embeddings
self.embed = Embed(word_embedding_dim,
vocab.size,
vectors=vocab.vectors)
self.rnn = nn.LSTM(args.size,
model_dim,
num_layers=1,
batch_first=True)
mlp_input_dim = model_dim * 2 if use_sentence_pair else model_dim
self.mlp = MLP(mlp_input_dim, mlp_dim, num_classes, num_mlp_layers,
mlp_bn, classifier_dropout_rate)
self.encode = context_args.encoder
self.reshape_input = context_args.reshape_input
self.reshape_context = context_args.reshape_context
def __init__(self,
rl_mu=None,
rl_baseline=None,
rl_reward=None,
rl_weight=None,
rl_whiten=None,
rl_valid=None,
rl_epsilon=None,
rl_catalan=None,
rl_catalan_backprop=None,
rl_transition_acc_as_reward=None,
rl_value_size=None,
rl_value_lstm=None,
**kwargs):
super(BaseModel, self).__init__(**kwargs)
self.kwargs = kwargs
self.rl_mu = rl_mu
self.rl_baseline = rl_baseline
self.rl_reward = rl_reward
self.rl_weight = rl_weight
self.rl_whiten = rl_whiten
self.rl_valid = rl_valid
self.rl_value_size = rl_value_size
self.rl_value_lstm = rl_value_lstm
self.spinn.catalan = rl_catalan
self.spinn.catalan_backprop = rl_catalan_backprop
self.rl_transition_acc_as_reward = rl_transition_acc_as_reward
if self.rl_baseline == "value":
num_outputs = 2 if self.use_sentence_pair else 1
self.v_dim = self.rl_value_lstm
self.v_rnn_dim = self.v_dim
self.v_mlp_dim = self.v_dim * num_outputs
self.v_rnn = nn.LSTM(self.input_dim,
self.v_rnn_dim,
num_layers=1,
batch_first=True)
self.v_mlp = MLP(self.v_mlp_dim,
mlp_dim=self.rl_value_size,
num_classes=1,
num_mlp_layers=2,
mlp_ln=True,
classifier_dropout_rate=0.1)
self.register_buffer('baseline', torch.FloatTensor([0.0]))
def __init__(self,
rl_mu=None,
rl_baseline=None,
rl_reward=None,
rl_weight=None,
rl_whiten=None,
rl_valid=None,
rl_epsilon=None,
rl_entropy=None,
rl_entropy_beta=None,
rl_catalan=None,
rl_transition_acc_as_reward=None,
**kwargs):
super(BaseModel, self).__init__(**kwargs)
self.kwargs = kwargs
self.rl_mu = rl_mu
self.rl_baseline = rl_baseline
self.rl_reward = rl_reward
self.rl_weight = rl_weight
self.rl_whiten = rl_whiten
self.rl_valid = rl_valid
self.rl_entropy = rl_entropy
self.rl_entropy_beta = rl_entropy_beta
self.spinn.epsilon = rl_epsilon
self.spinn.catalan = rl_catalan
self.rl_transition_acc_as_reward = rl_transition_acc_as_reward
if self.rl_baseline == "value":
self.v_dim = 100
self.v_rnn = nn.LSTM(self.input_dim,
self.v_dim,
num_layers=1,
batch_first=True)
self.v_mlp = MLP(self.v_dim,
mlp_dim=1024,
num_classes=1,
num_mlp_layers=2,
mlp_ln=True,
classifier_dropout_rate=0.1)
self.register_buffer('baseline', torch.FloatTensor([0.0]))
def __init__(self,
rl_mu=None,
rl_baseline=None,
rl_reward=None,
rl_value_reward=None,
rl_weight=None,
rl_whiten=None,
rl_valid=None,
rl_epsilon=None,
rl_catalan=None,
rl_catalan_backprop=None,
rl_transition_acc_as_reward=None,
rl_value_size=None,
rl_value_lstm=None,
rl_detach=None,
data_type=None,
**kwargs):
super(BaseModel, self).__init__(data_type=data_type, **kwargs)
# ^ To-do: The data_type addiiton doesn't seem kosher, make change. --Nikita
self.kwargs = kwargs
self.rl_mu = rl_mu
self.rl_baseline = rl_baseline
self.rl_reward = rl_reward
self.rl_value_reward = rl_value_reward
self.rl_weight = rl_weight
self.rl_whiten = rl_whiten
self.rl_valid = rl_valid
self.rl_value_size = rl_value_size
self.rl_value_lstm = rl_value_lstm
self.spinn.catalan = rl_catalan
self.spinn.catalan_backprop = rl_catalan_backprop
self.rl_transition_acc_as_reward = rl_transition_acc_as_reward
self.rl_detach = rl_detach
self.data_type = data_type
if self.rl_baseline == "value":
num_outputs = 2 if self.use_sentence_pair else 1
self.v_dim = self.rl_value_lstm
self.v_rnn_dim = self.v_dim
self.v_mlp_dim = self.v_dim * num_outputs
if data_type == "mt":
self.v_rnn = nn.LSTM(self.input_dim // 2,
self.v_rnn_dim,
num_layers=1,
batch_first=True)
else:
self.v_rnn = nn.LSTM(self.input_dim,
self.v_rnn_dim,
num_layers=1,
batch_first=True)
self.v_mlp = MLP(self.v_mlp_dim,
mlp_dim=self.rl_value_size,
num_classes=1,
num_mlp_layers=2,
mlp_ln=True,
classifier_dropout_rate=0.1)
elif self.rl_baseline == "shared":
self.v_mlp = MLP(self.input_dim // 2,
mlp_dim=self.rl_value_size,
num_classes=1,
num_mlp_layers=2,
mlp_ln=True,
classifier_dropout_rate=0.1)
elif self.rl_baseline == "lbtree":
num_outputs = 2 if self.use_sentence_pair else 1
# To-do: make new flag to replace rl_value_size
self.lb_mlp = MLP(self.model_dim // 2,
mlp_dim=self.rl_value_size,
num_classes=1,
num_mlp_layers=2,
mlp_ln=True,
classifier_dropout_rate=0.1)
self.register_buffer('baseline', torch.FloatTensor([0.0]))
def __init__(self,
model_dim=None,
word_embedding_dim=None,
vocab_size=None,
initial_embeddings=None,
fine_tune_loaded_embeddings=None,
num_classes=None,
embedding_keep_rate=None,
encode_reverse=None,
encode_bidirectional=None,
encode_num_layers=None,
use_sentence_pair=False,
use_difference_feature=False,
use_product_feature=False,
mlp_dim=None,
num_mlp_layers=None,
mlp_ln=None,
classifier_keep_rate=None,
context_args=None,
composition_args=None,
data_type=None,
**kwargs):
super(BaseModel, self).__init__()
self.use_sentence_pair = use_sentence_pair
self.use_difference_feature = use_difference_feature
self.use_product_feature = use_product_feature
self.hidden_dim = hidden_dim = model_dim
self.wrap_items = composition_args.wrap_items
self.extract_h = composition_args.extract_h
self.initial_embeddings = initial_embeddings
self.word_embedding_dim = word_embedding_dim
self.model_dim = model_dim
self.data_type = data_type
classifier_dropout_rate = 1. - classifier_keep_rate
vocab = Vocab()
vocab.size = initial_embeddings.shape[
0] if initial_embeddings is not None else vocab_size
vocab.vectors = initial_embeddings
# Build parsing component.
self.lms = self.build_lms(composition_args, vocab)
# Build classiifer.
if self.data_type != "mt":
features_dim = self.get_features_dim()
self.mlp = MLP(features_dim, mlp_dim, num_classes, num_mlp_layers,
mlp_ln, classifier_dropout_rate)
self.embedding_dropout_rate = 1. - embedding_keep_rate
# Create dynamic embedding layer.
self.embed = Embed(word_embedding_dim,
vocab.size,
vectors=vocab.vectors,
fine_tune=fine_tune_loaded_embeddings)
self.input_dim = context_args.input_dim
self.encode = context_args.encoder
self.reshape_input = context_args.reshape_input
self.reshape_context = context_args.reshape_context
self.inverted_vocabulary = None
# Create Lift layer
self.lift = Lift(context_args.input_dim, model_dim * model_dim)
def __init__(self,
model_dim=None,
word_embedding_dim=None,
vocab_size=None,
initial_embeddings=None,
num_classes=None,
embedding_keep_rate=None,
tracking_lstm_hidden_dim=4,
transition_weight=None,
encode_reverse=None,
encode_bidirectional=None,
encode_num_layers=None,
lateral_tracking=None,
tracking_ln=None,
use_tracking_in_composition=None,
predict_use_cell=None,
use_sentence_pair=False,
use_difference_feature=False,
use_product_feature=False,
mlp_dim=None,
num_mlp_layers=None,
mlp_ln=None,
classifier_keep_rate=None,
context_args=None,
composition_args=None,
detach=None,
evolution=None,
**kwargs):
super(SpinnBaseModel, self).__init__()
assert not (
use_tracking_in_composition and not lateral_tracking
), "Lateral tracking must be on to use tracking in composition."
self.use_sentence_pair = use_sentence_pair
self.use_difference_feature = use_difference_feature
self.use_product_feature = use_product_feature
self.hidden_dim = composition_args.size
self.wrap_items = composition_args.wrap_items
self.extract_h = composition_args.extract_h
self.initial_embeddings = initial_embeddings
self.word_embedding_dim = word_embedding_dim
self.model_dim = model_dim
classifier_dropout_rate = 1. - classifier_keep_rate
vocab = Vocab()
vocab.size = initial_embeddings.shape[
0] if initial_embeddings is not None else vocab_size
vocab.vectors = initial_embeddings
# Build parsing component.
self.spinn = self.build_rspinn(composition_args, vocab,
predict_use_cell)
# Build classiifer.
features_dim = self.get_features_dim() #same as spinn
self.mlp = MLP(features_dim, mlp_dim, num_classes, num_mlp_layers,
mlp_ln, classifier_dropout_rate)
self.embedding_dropout_rate = 1. - embedding_keep_rate
# Create dynamic embedding layer.
self.embed = Embed(word_embedding_dim,
vocab.size,
vectors=vocab.vectors)
self.input_dim = context_args.input_dim
self.encode = context_args.encoder
self.reshape_input = context_args.reshape_input
self.reshape_context = context_args.reshape_context
self.inverted_vocabulary = None
def __init__(self,
model_dim=None,
word_embedding_dim=None,
vocab_size=None,
use_product_feature=None,
use_difference_feature=None,
initial_embeddings=None,
num_classes=None,
embedding_keep_rate=None,
use_sentence_pair=False,
classifier_keep_rate=None,
mlp_dim=None,
num_mlp_layers=None,
mlp_ln=None,
composition_ln=None,
context_args=None,
trainable_temperature=None,
enforce_right=None,
parent_selection=None,
composition_args=None,
predict_use_cell=None,
low_dim=None,
topk=None,
cp_num=None,
multiproc=None,
**kwargs):
super(CatalanPyramid, self).__init__()
self.use_sentence_pair = use_sentence_pair
self.use_difference_feature = use_difference_feature
self.use_product_feature = use_product_feature
self.model_dim = model_dim
self.low_dim = low_dim
self.topk = topk
self.cp_num = cp_num
self.multiproc = multiproc
self.trainable_temperature = trainable_temperature
self.parent_selection = parent_selection
self.enforce_right = enforce_right
self.classifier_dropout_rate = 1. - classifier_keep_rate
self.embedding_dropout_rate = 1. - embedding_keep_rate
vocab = Vocab()
vocab.size = initial_embeddings.shape[
0] if initial_embeddings is not None else vocab_size
vocab.vectors = initial_embeddings
self.embed = Embed(word_embedding_dim,
vocab.size,
vectors=vocab.vectors)
self.chart_parser = ChartParser(
word_embedding_dim,
model_dim // 2,
low_dim,
multiproc,
composition_ln=composition_ln,
trainable_temperature=trainable_temperature,
parent_selection=parent_selection,
use_sentence_pair=use_sentence_pair)
# assert FLAGS.lateral_tracking == False
# TODO: move assertion flag to base.
self.spinn = self.build_spinn(composition_args, vocab,
predict_use_cell)
mlp_input_dim = self.get_features_dim()
self.mlp = MLP(mlp_input_dim, mlp_dim, num_classes, num_mlp_layers,
mlp_ln, self.classifier_dropout_rate)
# SPINN vars
self.encode = context_args.encoder
#self.encode = Linear()(word_embedding_dim, model_dim)
self.reshape_input = context_args.reshape_input
self.reshape_context = context_args.reshape_context
self.input_dim = context_args.input_dim
self.wrap_items = composition_args.wrap_items
self.extract_h = composition_args.extract_h
# For sample printing and logging
self.parse_memory = None
self.inverted_vocabulary = None
self.temperature_to_display = 0.0
def __init__(self, model_dim=None,
word_embedding_dim=None,
vocab_size=None,
initial_embeddings=None,
num_classes=None,
mlp_dim=None,
embedding_keep_rate=None,
classifier_keep_rate=None,
tracking_lstm_hidden_dim=4,
transition_weight=None,
encode_style=None,
encode_reverse=None,
encode_bidirectional=None,
encode_num_layers=None,
use_skips=False,
lateral_tracking=None,
use_tracking_in_composition=None,
use_sentence_pair=False,
use_difference_feature=False,
use_product_feature=False,
num_mlp_layers=None,
mlp_bn=None,
use_projection=None,
**kwargs
):
super(BaseModel, self).__init__()
self.use_sentence_pair = use_sentence_pair
self.use_difference_feature = use_difference_feature
self.use_product_feature = use_product_feature
self.hidden_dim = hidden_dim = model_dim / 2
args = Args()
args.lateral_tracking = lateral_tracking
args.use_tracking_in_composition = use_tracking_in_composition
args.size = model_dim/2
args.tracker_size = tracking_lstm_hidden_dim
args.transition_weight = transition_weight
self.initial_embeddings = initial_embeddings
self.word_embedding_dim = word_embedding_dim
self.model_dim = model_dim
classifier_dropout_rate = 1. - classifier_keep_rate
vocab = Vocab()
vocab.size = initial_embeddings.shape[0] if initial_embeddings is not None else vocab_size
vocab.vectors = initial_embeddings
# Build parsing component.
self.spinn = self.build_spinn(args, vocab, use_skips)
# Build classiifer.
features_dim = self.get_features_dim()
self.mlp = MLP(features_dim, mlp_dim, num_classes,
num_mlp_layers, mlp_bn, classifier_dropout_rate)
# The input embeddings represent the hidden and cell state, so multiply by 2.
self.embedding_dropout_rate = 1. - embedding_keep_rate
input_embedding_dim = args.size * 2
# Projection will effectively be done by the encoding network.
use_projection = True if encode_style is None else False
# Create dynamic embedding layer.
self.embed = Embed(input_embedding_dim, vocab.size, vectors=vocab.vectors, use_projection=use_projection)
# Optionally build input encoder.
if encode_style is not None:
self.encode = self.build_input_encoder(encode_style=encode_style,
word_embedding_dim=word_embedding_dim, model_dim=model_dim,
num_layers=encode_num_layers, bidirectional=encode_bidirectional, reverse=encode_reverse,
dropout=self.embedding_dropout_rate)
def __init__(self, model_dim=None,
word_embedding_dim=None,
vocab_size=None,
initial_embeddings=None,
fine_tune_loaded_embeddings=None,
num_classes=None,
embedding_keep_rate=None,
tracking_lstm_hidden_dim=4,
transition_weight=None,
encode_reverse=None,
encode_bidirectional=None,
encode_num_layers=None,
lateral_tracking=None,
tracking_ln=None,
use_tracking_in_composition=None,
predict_use_cell=None,
use_sentence_pair=False,
use_difference_feature=False,
use_product_feature=False,
mlp_dim=None,
num_mlp_layers=None,
mlp_ln=None,
classifier_keep_rate=None,
context_args=None,
composition_args=None,
with_attention=False,
data_type=None,
target_vocabulary=None,
onmt_module=None,
**kwargs
):
super(BaseModel, self).__init__()
assert not (
use_tracking_in_composition and not lateral_tracking), "Lateral tracking must be on to use tracking in composition."
self.use_sentence_pair = use_sentence_pair
self.use_difference_feature = use_difference_feature
self.use_product_feature = use_product_feature
self.hidden_dim = composition_args.size
self.wrap_items = composition_args.wrap_items
self.extract_h = composition_args.extract_h
if data_type == "mt":
self.post_projection= Linear()(context_args.input_dim, int(context_args.input_dim/2), bias=True)
self.initial_embeddings = initial_embeddings
self.word_embedding_dim = word_embedding_dim
self.model_dim = model_dim
self.data_type = data_type
classifier_dropout_rate = 1. - classifier_keep_rate
vocab = Vocab()
vocab.size = initial_embeddings.shape[0] if initial_embeddings is not None else vocab_size
vocab.vectors = initial_embeddings
# Build parsing component.
self.spinn = self.build_spinn(
composition_args, vocab, predict_use_cell)
# Build classiifer.
features_dim = self.get_features_dim()
if data_type != "mt":
self.mlp = MLP(features_dim, mlp_dim, num_classes,
num_mlp_layers, mlp_ln, classifier_dropout_rate)
#self.generator = nn.Sequential(nn.Linear(self.model_dim, len(self.target_vocabulary), nn.LogSoftmax())
self.embedding_dropout_rate = 1. - embedding_keep_rate
# Create dynamic embedding layer.
self.embed = Embed(
word_embedding_dim,
vocab.size,
vectors=vocab.vectors,
fine_tune=fine_tune_loaded_embeddings)
self.input_dim = context_args.input_dim
self.encode = context_args.encoder
self.reshape_input = context_args.reshape_input
self.reshape_context = context_args.reshape_context
self.inverted_vocabulary = None
def __init__(self, model_dim=None,
word_embedding_dim=None,
vocab_size=None,
initial_embeddings=None,
num_classes=None,
mlp_dim=None,
embedding_keep_rate=None,
classifier_keep_rate=None,
tracking_lstm_hidden_dim=4,
transition_weight=None,
use_encode=None,
encode_reverse=None,
encode_bidirectional=None,
encode_num_layers=None,
use_skips=False,
lateral_tracking=None,
use_tracking_in_composition=None,
# use_sentence_pair=False,
use_difference_feature=False,
use_product_feature=False,
num_mlp_layers=None,
mlp_bn=None,
model_specific_params={},
**kwargs
):
super(SentencePairModel, self).__init__()
logger.info('ATTSPINN SentencePairModel init...')
# self.use_sentence_pair = use_sentence_pair
self.use_difference_feature = use_difference_feature
self.use_product_feature = use_product_feature
self.hidden_dim = hidden_dim = model_dim / 2
# features_dim = hidden_dim * 2 if use_sentence_pair else hidden_dim
features_dim = model_dim
# [premise, hypothesis, diff, product]
if self.use_difference_feature:
features_dim += self.hidden_dim
if self.use_product_feature:
features_dim += self.hidden_dim
mlp_input_dim = features_dim
self.initial_embeddings = initial_embeddings
self.word_embedding_dim = word_embedding_dim
self.model_dim = model_dim
classifier_dropout_rate = 1. - classifier_keep_rate
args = Args()
args.lateral_tracking = lateral_tracking
args.use_tracking_in_composition = use_tracking_in_composition
args.size = model_dim/2
args.tracker_size = tracking_lstm_hidden_dim
args.transition_weight = transition_weight
args.using_diff_in_mlstm = model_specific_params['using_diff_in_mlstm']
args.using_prod_in_mlstm = model_specific_params['using_prod_in_mlstm']
args.using_null_in_attention = model_specific_params['using_null_in_attention']
vocab = Vocab()
vocab.size = initial_embeddings.shape[0] if initial_embeddings is not None else vocab_size
vocab.vectors = initial_embeddings
# The input embeddings represent the hidden and cell state, so multiply by 2.
self.embedding_dropout_rate = 1. - embedding_keep_rate
input_embedding_dim = args.size * 2
# Create dynamic embedding layer.
self.embed = Embed(input_embedding_dim, vocab.size, vectors=vocab.vectors)
self.use_encode = use_encode
if use_encode:
self.encode_reverse = encode_reverse
self.encode_bidirectional = encode_bidirectional
self.bi = 2 if self.encode_bidirectional else 1
self.encode_num_layers = encode_num_layers
self.encode = nn.LSTM(model_dim, model_dim / self.bi, num_layers=encode_num_layers,
batch_first=True,
bidirectional=self.encode_bidirectional,
dropout=self.embedding_dropout_rate)
self.spinn = self.build_spinn(args, vocab, use_skips)
self.attention = self.build_attention(args)
self.mlp = MLP(mlp_input_dim, mlp_dim, num_classes,
num_mlp_layers, mlp_bn, classifier_dropout_rate)
def __init__(self,
model_dim=None,
word_embedding_dim=None,
vocab_size=None,
use_product_feature=None,
use_difference_feature=None,
initial_embeddings=None,
num_classes=None,
embedding_keep_rate=None,
use_sentence_pair=False,
classifier_keep_rate=None,
mlp_dim=None,
num_mlp_layers=None,
mlp_ln=None,
composition_ln=None,
context_args=None,
trainable_temperature=None,
test_temperature_multiplier=None,
selection_dim=None,
gumbel=None,
**kwargs):
super(Pyramid, self).__init__()
self.use_sentence_pair = use_sentence_pair
self.use_difference_feature = use_difference_feature
self.use_product_feature = use_product_feature
self.model_dim = model_dim
self.test_temperature_multiplier = test_temperature_multiplier
self.trainable_temperature = trainable_temperature
self.gumbel = gumbel
self.selection_dim = selection_dim
self.classifier_dropout_rate = 1. - classifier_keep_rate
self.embedding_dropout_rate = 1. - embedding_keep_rate
vocab = Vocab()
vocab.size = initial_embeddings.shape[
0] if initial_embeddings is not None else vocab_size
vocab.vectors = initial_embeddings
self.embed = Embed(word_embedding_dim,
vocab.size,
vectors=vocab.vectors)
self.composition_fn = SimpleTreeLSTM(model_dim / 2,
composition_ln=composition_ln)
self.selection_fn_1 = Linear(initializer=HeKaimingInitializer)(
model_dim, selection_dim)
self.selection_fn_2 = Linear(initializer=HeKaimingInitializer)(
selection_dim, 1)
def selection_fn(selection_input):
selection_hidden = F.tanh(self.selection_fn_1(selection_input))
return self.selection_fn_2(selection_hidden)
self.selection_fn = selection_fn
mlp_input_dim = self.get_features_dim()
self.mlp = MLP(mlp_input_dim, mlp_dim, num_classes, num_mlp_layers,
mlp_ln, self.classifier_dropout_rate)
if self.trainable_temperature:
self.temperature = nn.Parameter(torch.ones(1, 1),
requires_grad=True)
self.encode = context_args.encoder
self.reshape_input = context_args.reshape_input
self.reshape_context = context_args.reshape_context
# For sample printing and logging
self.merge_sequence_memory = None
self.inverted_vocabulary = None
self.temperature_to_display = 0.0
