def __init__(self, config):
super(HRED, self).__init__()
self.config = config
self.encoder = layers.EncoderRNN(config.vocab_size,
config.embedding_size,
config.encoder_hidden_size,
config.rnn, config.num_layers,
config.bidirectional, config.dropout)
context_input_size = (config.num_layers * config.encoder_hidden_size *
self.encoder.num_directions)
self.context_encoder = layers.ContextRNN(context_input_size,
config.context_size,
config.rnn, config.num_layers,
config.dropout)
self.decoder = layers.DecoderRNN(
config.vocab_size, config.embedding_size,
config.decoder_hidden_size, config.rnncell, config.num_layers,
config.dropout, config.word_drop, config.max_unroll, config.sample,
config.temperature, config.beam_size)
self.context2decoder = layers.FeedForward(config.context_size,
config.num_layers *
config.decoder_hidden_size,
num_layers=1,
activation=config.activation)
if config.tie_embedding:
self.decoder.embedding = self.encoder.embedding
def __init__(self, config):
super(VHRED, self).__init__()
self.config = config
self.encoder = layers.EncoderRNN(config.vocab_size, config.embedding_size, config.encoder_hidden_size,
config.rnn, config.num_layers, config.bidirectional, config.dropout,
pretrained_wv_path=config.pretrained_wv_path)
context_input_size = (config.num_layers * config.encoder_hidden_size * self.encoder.num_directions)
self.context_encoder = layers.ContextRNN(context_input_size, config.context_size, config.rnn,
config.num_layers, config.dropout)
self.decoder = layers.DecoderRNN(config.vocab_size, config.embedding_size, config.decoder_hidden_size,
config.rnncell, config.num_layers, config.dropout, config.word_drop,
config.max_unroll, config.sample, config.temperature, config.beam_size)
self.context2decoder = layers.FeedForward(config.context_size + config.z_utter_size,
config.num_layers * config.decoder_hidden_size,
num_layers=1, activation=config.activation)
self.softplus = nn.Softplus()
self.prior_h = layers.FeedForward(config.context_size, config.context_size, num_layers=2,
hidden_size=config.context_size, activation=config.activation)
self.prior_mu = nn.Linear(config.context_size, config.z_utter_size)
self.prior_var = nn.Linear(config.context_size, config.z_utter_size)
self.posterior_h = layers.FeedForward(config.encoder_hidden_size * self.encoder.num_directions * config.num_layers + config.context_size,
config.context_size, num_layers=2, hidden_size=config.context_size,
activation=config.activation)
self.posterior_mu = nn.Linear(config.context_size, config.z_utter_size)
self.posterior_var = nn.Linear(config.context_size, config.z_utter_size)
if config.tie_embedding:
self.decoder.embedding = self.encoder.embedding
def __init__(self,
word_vectors,
hidden_size,
output_size,
device,
drop_prob=0.,
num_layers=1):
super(Seq2Seq, self).__init__()
self.hidden_size = hidden_size
self.device = device
self.word_vectors = word_vectors
self.model_type = 'seq2seq'
#self.emb = nn.Embedding(num_embeddings=output_size, embedding_dim=hidden_size)
#self.dropout = nn.Dropout(p=drop_prob)
self.emb = layers.Embedding(word_vectors,
hidden_size,
drop_prob=drop_prob)
self.encoder = layers.EncoderRNN(input_size=hidden_size,
hidden_size=hidden_size,
num_layers=num_layers,
drop_prob=drop_prob)
self.decoder = layers.DecoderRNN(input_size=hidden_size,
hidden_size=hidden_size,
num_layers=num_layers,
drop_prob=drop_prob)
self.generator = layers.Generator(hidden_size, output_size)
def __init__(self, config):
super(Seq2Seq, self).__init__()
self.config = config
self.encoder = layers.EncoderRNN(config.vocab_size,
config.embedding_size,
config.encoder_hidden_size,
config.rnn, config.num_layers, False,
config.dropout)
self.decoder = layers.DecoderRNN(
config.vocab_size, config.embedding_size,
config.decoder_hidden_size, config.rnncell, config.num_layers,
config.dropout, config.word_drop, config.max_unroll, config.sample,
config.temperature, config.beam_size)
if config.tie_embedding:
self.decoder.embedding = self.encoder.embedding
def __init__(self,
word_vectors,
hidden_size,
output_size,
device,
drop_prob=0.,
num_layers=1):
super(Seq2SeqAttn, self).__init__()
self.hidden_size = hidden_size
self.word_vectors = word_vectors
self.device = device
self.enc_hiddens = None
self.enc_masks = None
self.model_type = 'seq2seq_attn'
#self.emb = nn.Embedding(num_embeddings=output_size, embedding_dim=hidden_size)
self.emb = layers.Embedding(word_vectors, hidden_size)
self.encoder = layers.EncoderRNN(input_size=hidden_size,
hidden_size=hidden_size,
num_layers=num_layers,
drop_prob=drop_prob)
self.decoder = layers.DecoderRNN(input_size=2 * hidden_size,
hidden_size=hidden_size,
num_layers=num_layers,
drop_prob=drop_prob)
self.att_projection = nn.Linear(in_features=2 * hidden_size,
out_features=hidden_size,
bias=False)
self.combined_output_projection = nn.Linear(in_features=3 *
hidden_size,
out_features=hidden_size,
bias=False)
self.generator = layers.Generator(hidden_size, output_size)
self.dropout = nn.Dropout(p=drop_prob)
def __init__(self, config):
super(VHCR, self).__init__()
self.config = config
self.encoder = layers.EncoderRNN(config.vocab_size,
config.embedding_size,
config.encoder_hidden_size,
config.rnn, config.num_layers,
config.bidirectional, config.dropout)
context_input_size = (config.num_layers * config.encoder_hidden_size *
self.encoder.num_directions + config.z_conv_size)
self.context_encoder = layers.ContextRNN(context_input_size,
config.context_size,
config.rnn, config.num_layers,
config.dropout)
self.unk_sent = nn.Parameter(
torch.randn(context_input_size - config.z_conv_size))
self.z_conv2context = layers.FeedForward(config.z_conv_size,
config.num_layers *
config.context_size,
num_layers=1,
activation=config.activation)
context_input_size = (config.num_layers * config.encoder_hidden_size *
self.encoder.num_directions)
self.context_inference = layers.ContextRNN(context_input_size,
config.context_size,
config.rnn,
config.num_layers,
config.dropout,
bidirectional=True)
self.decoder = layers.DecoderRNN(
config.vocab_size, config.embedding_size,
config.decoder_hidden_size, config.rnncell, config.num_layers,
config.dropout, config.word_drop, config.max_unroll, config.sample,
config.temperature, config.beam_size)
self.context2decoder = layers.FeedForward(
config.context_size + config.z_sent_size + config.z_conv_size,
config.num_layers * config.decoder_hidden_size,
num_layers=1,
activation=config.activation)
self.softplus = nn.Softplus()
self.conv_posterior_h = layers.FeedForward(
config.num_layers * self.context_inference.num_directions *
config.context_size,
config.context_size,
num_layers=2,
hidden_size=config.context_size,
activation=config.activation)
self.conv_posterior_mu = nn.Linear(config.context_size,
config.z_conv_size)
self.conv_posterior_var = nn.Linear(config.context_size,
config.z_conv_size)
self.sent_prior_h = layers.FeedForward(config.context_size +
config.z_conv_size,
config.context_size,
num_layers=1,
hidden_size=config.z_sent_size,
activation=config.activation)
self.sent_prior_mu = nn.Linear(config.context_size, config.z_sent_size)
self.sent_prior_var = nn.Linear(config.context_size,
config.z_sent_size)
self.sent_posterior_h = layers.FeedForward(
config.z_conv_size + config.encoder_hidden_size *
self.encoder.num_directions * config.num_layers +
config.context_size,
config.context_size,
num_layers=2,
hidden_size=config.context_size,
activation=config.activation)
self.sent_posterior_mu = nn.Linear(config.context_size,
config.z_sent_size)
self.sent_posterior_var = nn.Linear(config.context_size,
config.z_sent_size)
if config.tie_embedding:
self.decoder.embedding = self.encoder.embedding
def __init__(self, config):
super(VHUCM, self).__init__()
self.config = config
# user embedding
if config.pretrained_uv_path is None:
self.user_embedding = nn.Embedding(config.user_size, config.embedding_size)
self.user_embedding.weight.requires_grad = True
else:
with open(config.pretrained_uv_path, 'rb') as f:
weight_tensor = to_var(torch.FloatTensor(pickle.load(f)))
self.user_embedding = nn.Embedding.from_pretrained(weight_tensor, freeze=False)
self.encoder = layers.EncoderRNN(config.vocab_size,
config.embedding_size,
config.encoder_hidden_size,
config.rnn,
config.num_layers,
config.bidirectional,
config.dropout,
pretrained_wv_path=config.pretrained_wv_path)
context_input_size = (config.num_layers
* config.encoder_hidden_size
* self.encoder.num_directions + config.z_conv_size + config.embedding_size)
self.context_encoder = layers.ContextRNN(context_input_size,
config.context_size,
config.rnn,
config.num_layers,
config.dropout)
self.unk_sent = nn.Parameter(torch.randn(context_input_size - (config.z_conv_size + config.embedding_size)))
self.z_conv2context = layers.FeedForward(config.z_conv_size,
config.num_layers * config.context_size,
num_layers=1,
activation=config.activation)
context_input_size = (config.num_layers
* config.encoder_hidden_size
* self.encoder.num_directions)
self.context_inference = layers.ContextRNN(context_input_size,
config.context_size,
config.rnn,
config.num_layers,
config.dropout,
bidirectional=True)
self.decoder = layers.DecoderRNN(config.vocab_size,
config.embedding_size,
config.decoder_hidden_size,
config.rnncell,
config.num_layers,
config.dropout,
config.word_drop,
config.max_unroll,
config.sample,
config.temperature,
config.beam_size)
self.context2decoder = layers.FeedForward(config.context_size + config.z_sent_size + config.z_conv_size,
config.num_layers * config.decoder_hidden_size,
num_layers=1,
activation=config.activation)
self.softplus = nn.Softplus()
self.conv_prior_h = layers.FeedForward(2*config.embedding_size,
config.embedding_size, num_layers=1, hidden_size=config.z_conv_size,
activation=config.activation)
self.conv_prior_mu = nn.Linear(config.embedding_size, config.z_conv_size)
self.conv_prior_var = nn.Linear(config.embedding_size, config.z_conv_size)
self.conv_posterior_h = layers.FeedForward(config.num_layers * self.context_inference.num_directions * config.context_size + 2*config.embedding_size,
config.context_size,
num_layers=2,
hidden_size=config.context_size,
activation=config.activation)
self.conv_posterior_mu = nn.Linear(config.context_size,
config.z_conv_size)
self.conv_posterior_var = nn.Linear(config.context_size,
config.z_conv_size)
self.sent_prior_h = layers.FeedForward(config.context_size + config.z_conv_size,
config.context_size,
num_layers=1,
hidden_size=config.z_sent_size,
activation=config.activation)
self.sent_prior_mu = nn.Linear(config.context_size,
config.z_sent_size)
self.sent_prior_var = nn.Linear(config.context_size,
config.z_sent_size)
self.sent_posterior_h = layers.FeedForward(config.z_conv_size + config.encoder_hidden_size * self.encoder.num_directions * config.num_layers + config.context_size,
config.context_size,
num_layers=2,
hidden_size=config.context_size,
activation=config.activation)
self.sent_posterior_mu = nn.Linear(config.context_size,
config.z_sent_size)
self.sent_posterior_var = nn.Linear(config.context_size,
config.z_sent_size)
if config.tie_embedding:
self.decoder.embedding = self.encoder.embedding
if config.bow:
self.bow_h = layers.FeedForward(config.z_sent_size,
config.decoder_hidden_size,
num_layers=1,
hidden_size=config.decoder_hidden_size,
activation=config.activation)
self.bow_predict = nn.Linear(config.decoder_hidden_size, config.vocab_size)
