def __init__(self, corpus, config):
super(Checklist, self).__init__(config)
self.vocab = corpus.vocab
self.vocab_dict = corpus.vocab_dict
self.vocab_size = len(self.vocab)
self.goal_vocab = corpus.goal_vocab
self.goal_vocab_dict = corpus.goal_vocab_dict
self.goal_vocab_size = len(self.goal_vocab)
self.outcome_vocab = corpus.outcome_vocab
self.outcome_vocab_dict = corpus.outcome_vocab_dict
self.outcome_vocab_size = len(self.outcome_vocab)
self.sys_id = self.vocab_dict[SYS]
self.eos_id = self.vocab_dict[EOS]
self.pad_id = self.vocab_dict[PAD]
self.simple_posterior = False
assert not config.simple_posterior
self.goal_encoder = MlpGoalEncoder(
goal_vocab_size=self.goal_vocab_size,
k=config.k,
nembed=config.goal_embed_size,
nhid=config.goal_nhid,
init_range=config.init_range)
self.z_size = config.z_size
self.item_emb = nn.Embedding(11, 32)
self.res_layer = nn_lib.ResidualLayer(3 * 32, 64)
self.w_pz0 = nn.Linear(64, 64, bias=False)
self.embedding = nn.Embedding(self.vocab_size,
config.embed_size,
padding_idx=self.pad_id)
self.utt_encoder = RnnUttEncoder(
vocab_size=self.vocab_size,
embedding_dim=config.embed_size,
feat_size=0,
goal_nhid=config.goal_nhid,
rnn_cell=config.utt_rnn_cell,
utt_cell_size=config.utt_cell_size,
num_layers=config.num_layers,
input_dropout_p=config.dropout,
output_dropout_p=config.dropout,
bidirectional=config.bi_utt_cell,
variable_lengths=
False, # means it looks at padding and 20 tokens every time
use_attn=config.enc_use_attn,
embedding=self.embedding)
self.ctx_encoder = EncoderRNN(
input_dropout_p=0.0,
rnn_cell=config.ctx_rnn_cell,
# input_size=self.utt_encoder.output_size+config.goal_nhid,
input_size=self.utt_encoder.output_size,
hidden_size=config.ctx_cell_size,
num_layers=config.num_layers,
output_dropout_p=config.dropout,
bidirectional=config.bi_ctx_cell,
variable_lengths=False)
# mu and logvar projector
self.c2z = nn_lib.Hidden2DiscreteDeal(
self.ctx_encoder.output_size,
config.z_size,
is_lstm=config.ctx_rnn_cell == 'lstm',
)
self.xc2z = nn_lib.Hidden2DiscreteDeal(
self.ctx_encoder.output_size + self.utt_encoder.output_size,
config.z_size,
is_lstm=False,
)
self.gumbel_connector = nn_lib.GumbelConnector(config.use_gpu)
#self.z_embedding = nn.Linear(config.z_size, config.dec_cell_size, bias=False)
self.z_embedding = nn.Embedding(config.z_size, config.dec_cell_size)
self.decoder = DecoderRNN(input_dropout_p=config.dropout,
rnn_cell=config.dec_rnn_cell,
input_size=config.embed_size +
config.goal_nhid,
hidden_size=config.dec_cell_size,
num_layers=config.num_layers,
output_dropout_p=config.dropout,
bidirectional=False,
vocab_size=self.vocab_size,
use_attn=config.dec_use_attn,
ctx_cell_size=self.ctx_encoder.output_size,
attn_mode=config.dec_attn_mode,
sys_id=self.sys_id,
eos_id=self.eos_id,
use_gpu=config.use_gpu,
max_dec_len=config.max_dec_len,
embedding=self.embedding)
self.nll = NLLEntropy(self.pad_id, config.avg_type)
self.cat_kl_loss = criterions.CatKLLoss()
self.entropy_loss = criterions.Entropy()
# ?
self.log_uniform_z = th.log(th.ones(1) / config.z_size)
if self.use_gpu:
self.log_uniform_z = self.log_uniform_z.cuda()
def __init__(self, corpus, config):
super(CatHRED, self).__init__(config)
self.vocab = corpus.vocab
self.vocab_dict = corpus.vocab_dict
self.vocab_size = len(self.vocab)
self.goal_vocab = corpus.goal_vocab
self.goal_vocab_dict = corpus.goal_vocab_dict
self.goal_vocab_size = len(self.goal_vocab)
self.outcome_vocab = corpus.outcome_vocab
self.outcome_vocab_dict = corpus.outcome_vocab_dict
self.outcome_vocab_size = len(self.outcome_vocab)
self.sys_id = self.vocab_dict[SYS]
self.eos_id = self.vocab_dict[EOS]
self.pad_id = self.vocab_dict[PAD]
self.simple_posterior = config.simple_posterior
self.goal_encoder = MlpGoalEncoder(goal_vocab_size=self.goal_vocab_size,
k=config.k,
nembed=config.goal_embed_size,
nhid=config.goal_nhid,
init_range=config.init_range)
self.embedding = nn.Embedding(self.vocab_size, config.embed_size, padding_idx=self.pad_id)
self.utt_encoder = RnnUttEncoder(vocab_size=self.vocab_size,
embedding_dim=config.embed_size,
feat_size=0,
goal_nhid=config.goal_nhid,
rnn_cell=config.utt_rnn_cell,
utt_cell_size=config.utt_cell_size,
num_layers=config.num_layers,
input_dropout_p=config.dropout,
output_dropout_p=config.dropout,
bidirectional=config.bi_utt_cell,
variable_lengths=False,
use_attn=config.enc_use_attn,
embedding=self.embedding)
self.ctx_encoder = EncoderRNN(input_dropout_p=0.0,
rnn_cell=config.ctx_rnn_cell,
# input_size=self.utt_encoder.output_size+config.goal_nhid,
input_size=self.utt_encoder.output_size,
hidden_size=config.ctx_cell_size,
num_layers=config.num_layers,
output_dropout_p=config.dropout,
bidirectional=config.bi_ctx_cell,
variable_lengths=False)
# mu and logvar projector
self.c2z = nn_lib.Hidden2Discrete(self.ctx_encoder.output_size, config.y_size, config.k_size,
is_lstm=config.ctx_rnn_cell == 'lstm')
if not self.simple_posterior:
self.xc2z = nn_lib.Hidden2Discrete(self.ctx_encoder.output_size + self.utt_encoder.output_size,
config.y_size, config.k_size, is_lstm=False)
self.gumbel_connector = nn_lib.GumbelConnector(config.use_gpu)
self.z_embedding = nn.Linear(config.y_size * config.k_size, config.dec_cell_size, bias=False)
self.decoder = DecoderRNN(input_dropout_p=config.dropout,
rnn_cell=config.dec_rnn_cell,
input_size=config.embed_size + config.goal_nhid,
hidden_size=config.dec_cell_size,
num_layers=config.num_layers,
output_dropout_p=config.dropout,
bidirectional=False,
vocab_size=self.vocab_size,
use_attn=config.dec_use_attn,
ctx_cell_size=self.ctx_encoder.output_size,
attn_mode=config.dec_attn_mode,
sys_id=self.sys_id,
eos_id=self.eos_id,
use_gpu=config.use_gpu,
max_dec_len=config.max_dec_len,
embedding=self.embedding)
self.nll = NLLEntropy(self.pad_id, config.avg_type)
self.cat_kl_loss = criterions.CatKLLoss()
self.entropy_loss = criterions.Entropy()
self.log_uniform_y = Variable(th.log(th.ones(1) / config.k_size))
if self.use_gpu:
self.log_uniform_y = self.log_uniform_y.cuda()
def __init__(self, corpus, config):
super(SysPerfectBD2Cat, self).__init__(config)
self.vocab = corpus.vocab
self.vocab_dict = corpus.vocab_dict
self.vocab_size = len(self.vocab)
self.bos_id = self.vocab_dict[BOS]
self.eos_id = self.vocab_dict[EOS]
self.pad_id = self.vocab_dict[PAD]
self.bs_size = corpus.bs_size
self.db_size = corpus.db_size
self.k_size = config.k_size
self.y_size = config.y_size
self.simple_posterior = config.simple_posterior
self.contextual_posterior = config.contextual_posterior
self.embedding = None
self.utt_encoder = RnnUttEncoder(vocab_size=self.vocab_size,
embedding_dim=config.embed_size,
feat_size=0,
goal_nhid=0,
rnn_cell=config.utt_rnn_cell,
utt_cell_size=config.utt_cell_size,
num_layers=config.num_layers,
input_dropout_p=config.dropout,
output_dropout_p=config.dropout,
bidirectional=config.bi_utt_cell,
variable_lengths=False,
use_attn=config.enc_use_attn,
embedding=self.embedding)
self.c2z = nn_lib.Hidden2Discrete(self.utt_encoder.output_size + self.db_size + self.bs_size,
config.y_size, config.k_size, is_lstm=False)
self.z_embedding = nn.Linear(self.y_size * self.k_size, config.dec_cell_size, bias=False)
self.gumbel_connector = nn_lib.GumbelConnector(config.use_gpu)
if not self.simple_posterior:
if self.contextual_posterior:
self.xc2z = nn_lib.Hidden2Discrete(self.utt_encoder.output_size * 2 + self.db_size + self.bs_size,
config.y_size, config.k_size, is_lstm=False)
else:
self.xc2z = nn_lib.Hidden2Discrete(self.utt_encoder.output_size, config.y_size, config.k_size, is_lstm=False)
self.decoder = DecoderRNN(input_dropout_p=config.dropout,
rnn_cell=config.dec_rnn_cell,
input_size=config.embed_size,
hidden_size=config.dec_cell_size,
num_layers=config.num_layers,
output_dropout_p=config.dropout,
bidirectional=False,
vocab_size=self.vocab_size,
use_attn=config.dec_use_attn,
ctx_cell_size=config.dec_cell_size,
attn_mode=config.dec_attn_mode,
sys_id=self.bos_id,
eos_id=self.eos_id,
use_gpu=config.use_gpu,
max_dec_len=config.max_dec_len,
embedding=self.embedding)
self.nll = NLLEntropy(self.pad_id, config.avg_type)
self.cat_kl_loss = CatKLLoss()
self.entropy_loss = Entropy()
self.log_uniform_y = Variable(th.log(th.ones(1) / config.k_size))
self.eye = Variable(th.eye(self.config.y_size).unsqueeze(0))
self.beta = self.config.beta if hasattr(self.config, 'beta') else 0.0
if self.use_gpu:
self.log_uniform_y = self.log_uniform_y.cuda()
self.eye = self.eye.cuda()