def __init__(self, config, tokenizer):
super(RUBER, self).__init__()
# Attributes
# Attributes from config
self.word_embedding_dim = config.word_embedding_dim
self.attr_embedding_dim = config.attr_embedding_dim
self.sent_encoder_hidden_dim = config.sent_encoder_hidden_dim
self.n_sent_encoder_layers = config.n_sent_encoder_layers
self.dial_encoder_hidden_dim = config.dial_encoder_hidden_dim
self.n_dial_encoder_layers = config.n_dial_encoder_layers
self.rnn_type = config.rnn_type
self.word_embedding_path = config.word_embedding_path
self.floor_encoder_type = config.floor_encoder
self.metric = config.metric_type
# Optional attributes from config
self.dropout = config.dropout if hasattr(config, "dropout") else 0.0
self.use_pretrained_word_embedding = config.use_pretrained_word_embedding if hasattr(config, "use_pretrained_word_embedding") else False
# Other attributes
self.id2word = tokenizer.id2word
self.vocab_size = len(tokenizer.word2id)
self.pad_token_id = tokenizer.pad_token_id
self.margin = 0.5
# Input components
self.word_embedding = nn.Embedding(
self.vocab_size,
self.word_embedding_dim,
padding_idx=self.pad_token_id,
_weight=init_word_embedding(
load_pretrained_word_embedding=self.use_pretrained_word_embedding,
pretrained_word_embedding_path=self.word_embedding_path,
id2word=self.id2word,
word_embedding_dim=self.word_embedding_dim,
vocab_size=self.vocab_size,
pad_token_id=self.pad_token_id
),
)
# Encoding components
self.sent_encoder = EncoderRNN(
input_dim=self.word_embedding_dim,
hidden_dim=self.sent_encoder_hidden_dim,
n_layers=self.n_sent_encoder_layers,
dropout_emb=self.dropout,
dropout_input=self.dropout,
dropout_hidden=self.dropout,
dropout_output=self.dropout,
bidirectional=True,
embedding=self.word_embedding,
rnn_type=self.rnn_type,
)
self.dial_encoder = EncoderRNN(
input_dim=self.sent_encoder_hidden_dim,
hidden_dim=self.dial_encoder_hidden_dim,
n_layers=self.n_dial_encoder_layers,
dropout_emb=self.dropout,
dropout_input=self.dropout,
dropout_hidden=self.dropout,
dropout_output=self.dropout,
bidirectional=True,
rnn_type=self.rnn_type,
)
# Output components
# regressor for unsupervised training of is-next-sentence prediction
self.M = nn.Parameter(
torch.FloatTensor(self.dial_encoder_hidden_dim, self.sent_encoder_hidden_dim)
)
self.unref_fc = nn.ModuleList(
[
nn.Dropout(self.dropout),
nn.Linear(self.dial_encoder_hidden_dim+self.sent_encoder_hidden_dim+1, self.dial_encoder_hidden_dim),
nn.Tanh(),
nn.Dropout(self.dropout),
nn.Linear(self.dial_encoder_hidden_dim, 1),
nn.Sigmoid()
]
)
# Extra components
# floor encoding
if self.floor_encoder_type == "abs":
self.floor_encoder = AbsFloorEmbEncoder(
input_dim=self.sent_encoder_hidden_dim,
embedding_dim=self.attr_embedding_dim
)
elif self.floor_encoder_type == "rel":
self.floor_encoder = RelFloorEmbEncoder(
input_dim=self.sent_encoder_hidden_dim,
embedding_dim=self.attr_embedding_dim
)
else:
self.floor_encoder = None
# Initialization
self._init_weights()
def __init__(self, config, tokenizer):
super(HRED, self).__init__()
# Attributes
# Attributes from config
self.word_embedding_dim = config.word_embedding_dim
self.attr_embedding_dim = config.attr_embedding_dim
self.sent_encoder_hidden_dim = config.sent_encoder_hidden_dim
self.n_sent_encoder_layers = config.n_sent_encoder_layers
self.dial_encoder_hidden_dim = config.dial_encoder_hidden_dim
self.n_dial_encoder_layers = config.n_dial_encoder_layers
self.decoder_hidden_dim = config.decoder_hidden_dim
self.n_decoder_layers = config.n_decoder_layers
self.use_attention = config.use_attention
self.decode_max_len = config.decode_max_len
self.tie_weights = config.tie_weights
self.rnn_type = config.rnn_type
self.gen_type = config.gen_type
self.top_k = config.top_k
self.top_p = config.top_p
self.temp = config.temp
self.word_embedding_path = config.word_embedding_path
self.floor_encoder_type = config.floor_encoder
# Optional attributes from config
self.dropout_emb = config.dropout if hasattr(config,
"dropout") else 0.0
self.dropout_input = config.dropout if hasattr(config,
"dropout") else 0.0
self.dropout_hidden = config.dropout if hasattr(config,
"dropout") else 0.0
self.dropout_output = config.dropout if hasattr(config,
"dropout") else 0.0
self.use_pretrained_word_embedding = config.use_pretrained_word_embedding if hasattr(
config, "use_pretrained_word_embedding") else False
# Other attributes
self.word2id = tokenizer.word2id
self.id2word = tokenizer.id2word
self.vocab_size = len(tokenizer.word2id)
self.pad_token_id = tokenizer.pad_token_id
self.bos_token_id = tokenizer.bos_token_id
self.eos_token_id = tokenizer.eos_token_id
# Input components
self.word_embedding = nn.Embedding(
self.vocab_size,
self.word_embedding_dim,
padding_idx=self.pad_token_id,
_weight=init_word_embedding(
load_pretrained_word_embedding=self.
use_pretrained_word_embedding,
pretrained_word_embedding_path=self.word_embedding_path,
id2word=self.id2word,
word_embedding_dim=self.word_embedding_dim,
vocab_size=self.vocab_size,
pad_token_id=self.pad_token_id),
)
# Encoding components
self.sent_encoder = EncoderRNN(
input_dim=self.word_embedding_dim,
hidden_dim=self.sent_encoder_hidden_dim,
n_layers=self.n_sent_encoder_layers,
dropout_emb=self.dropout_emb,
dropout_input=self.dropout_input,
dropout_hidden=self.dropout_hidden,
dropout_output=self.dropout_output,
bidirectional=True,
embedding=self.word_embedding,
rnn_type=self.rnn_type,
)
self.dial_encoder = EncoderRNN(
input_dim=self.sent_encoder_hidden_dim,
hidden_dim=self.dial_encoder_hidden_dim,
n_layers=self.n_dial_encoder_layers,
dropout_emb=self.dropout_emb,
dropout_input=self.dropout_input,
dropout_hidden=self.dropout_hidden,
dropout_output=self.dropout_output,
bidirectional=False,
rnn_type=self.rnn_type,
)
# Decoding components
self.enc2dec_hidden_fc = nn.Linear(
self.dial_encoder_hidden_dim,
self.n_decoder_layers * self.decoder_hidden_dim if self.rnn_type
== "gru" else self.n_decoder_layers * self.decoder_hidden_dim * 2)
self.decoder = DecoderRNN(vocab_size=len(self.word2id),
input_dim=self.word_embedding_dim,
hidden_dim=self.decoder_hidden_dim,
feat_dim=self.dial_encoder_hidden_dim,
n_layers=self.n_decoder_layers,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
max_len=self.decode_max_len,
dropout_emb=self.dropout_emb,
dropout_input=self.dropout_input,
dropout_hidden=self.dropout_hidden,
dropout_output=self.dropout_output,
embedding=self.word_embedding,
tie_weights=self.tie_weights,
rnn_type=self.rnn_type,
use_attention=self.use_attention,
attn_dim=self.sent_encoder_hidden_dim)
# Extra components
# floor encoding
if self.floor_encoder_type == "abs":
self.floor_encoder = AbsFloorEmbEncoder(
input_dim=self.sent_encoder_hidden_dim,
embedding_dim=self.attr_embedding_dim)
elif self.floor_encoder_type == "rel":
self.floor_encoder = RelFloorEmbEncoder(
input_dim=self.sent_encoder_hidden_dim,
embedding_dim=self.attr_embedding_dim)
else:
self.floor_encoder = None
# Initialization
self._init_weights()
def __init__(self, config, tokenizer):
super(RNNLM, self).__init__()
# Attributes
# Attributes from config
self.word_embedding_dim = config.word_embedding_dim
self.decoder_hidden_dim = config.decoder_hidden_dim
self.n_decoder_layers = config.n_decoder_layers
self.decode_max_len = config.decode_max_len
self.tie_weights = config.tie_weights
self.rnn_type = config.rnn_type
# Optional attributes from config
self.gen_type = config.gen_type if hasattr(config, "gen_type") else "greedy"
self.top_k = config.top_k if hasattr(config, "top_k") else 0
self.top_p = config.top_p if hasattr(config, "top_p") else 0.0
self.temp = config.temp if hasattr(config, "temp") else 1.0
self.dropout_emb = config.dropout if hasattr(config, "dropout") else 0.0
self.dropout_input = config.dropout if hasattr(config, "dropout") else 0.0
self.dropout_hidden = config.dropout if hasattr(config, "dropout") else 0.0
self.dropout_output = config.dropout if hasattr(config, "dropout") else 0.0
self.use_pretrained_word_embedding = config.use_pretrained_word_embedding if hasattr(config, "use_pretrained_word_embedding") else False
self.word_embedding_path = config.word_embedding_path if hasattr(config, "word_embedding_path") else None
# Other attributes
self.tokenizer = tokenizer
self.word2id = tokenizer.word2id
self.id2word = tokenizer.id2word
self.vocab_size = len(tokenizer.word2id)
self.pad_token_id = tokenizer.pad_token_id
self.bos_token_id = tokenizer.bos_token_id
self.eos_token_id = tokenizer.eos_token_id
# Components
self.word_embedding = nn.Embedding(
self.vocab_size,
self.word_embedding_dim,
padding_idx=self.pad_token_id,
_weight=init_word_embedding(
load_pretrained_word_embedding=self.use_pretrained_word_embedding,
pretrained_word_embedding_path=self.word_embedding_path,
id2word=self.id2word,
word_embedding_dim=self.word_embedding_dim,
vocab_size=self.vocab_size,
pad_token_id=self.pad_token_id
),
)
self.decoder = DecoderRNN(
vocab_size=len(self.word2id),
input_dim=self.word_embedding_dim,
hidden_dim=self.decoder_hidden_dim,
n_layers=self.n_decoder_layers,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
max_len=self.decode_max_len,
dropout_emb=self.dropout_emb,
dropout_input=self.dropout_input,
dropout_hidden=self.dropout_hidden,
dropout_output=self.dropout_output,
embedding=self.word_embedding,
tie_weights=self.tie_weights,
rnn_type=self.rnn_type,
)
# Initialization
self._init_weights()
def __init__(self, config, tokenizer):
super(VHRED, self).__init__()
# Attributes
# Attributes from config
self.word_embedding_dim = config.word_embedding_dim
self.attr_embedding_dim = config.attr_embedding_dim
self.sent_encoder_hidden_dim = config.sent_encoder_hidden_dim
self.n_sent_encoder_layers = config.n_sent_encoder_layers
self.dial_encoder_hidden_dim = config.dial_encoder_hidden_dim
self.n_dial_encoder_layers = config.n_dial_encoder_layers
self.latent_variable_dim = config.latent_dim
self.decoder_hidden_dim = config.decoder_hidden_dim
self.n_decoder_layers = config.n_decoder_layers
self.use_attention = config.use_attention
self.decode_max_len = config.decode_max_len
self.tie_weights = config.tie_weights
self.rnn_type = config.rnn_type
self.gen_type = config.gen_type
self.top_k = config.top_k
self.top_p = config.top_p
self.temp = config.temp
self.word_embedding_path = config.word_embedding_path
self.floor_encoder_type = config.floor_encoder
self.gaussian_mix_type = config.gaussian_mix_type
# Optional attributes from config
self.use_bow_loss = config.use_bow_loss if hasattr(
config, "use_bow_loss") else True
self.dropout = config.dropout if hasattr(config, "dropout") else 0.0
self.dropout_emb = config.dropout if hasattr(config,
"dropout") else 0.0
self.dropout_input = config.dropout if hasattr(config,
"dropout") else 0.0
self.dropout_hidden = config.dropout if hasattr(config,
"dropout") else 0.0
self.dropout_output = config.dropout if hasattr(config,
"dropout") else 0.0
self.use_pretrained_word_embedding = config.use_pretrained_word_embedding if hasattr(
config, "use_pretrained_word_embedding") else False
self.n_step_annealing = config.n_step_annealing if hasattr(
config, "n_step_annealing") else 1
self.n_components = config.n_components if hasattr(
config, "n_components") else 1
# Other attributes
self.vocab_size = len(tokenizer.word2id)
self.word2id = tokenizer.word2id
self.id2word = tokenizer.id2word
self.pad_token_id = tokenizer.pad_token_id
self.bos_token_id = tokenizer.bos_token_id
self.eos_token_id = tokenizer.eos_token_id
# Input components
self.word_embedding = nn.Embedding(
self.vocab_size,
self.word_embedding_dim,
padding_idx=self.pad_token_id,
_weight=init_word_embedding(
load_pretrained_word_embedding=self.
use_pretrained_word_embedding,
pretrained_word_embedding_path=self.word_embedding_path,
id2word=self.id2word,
word_embedding_dim=self.word_embedding_dim,
vocab_size=self.vocab_size,
pad_token_id=self.pad_token_id),
)
# Encoding components
self.sent_encoder = EncoderRNN(
input_dim=self.word_embedding_dim,
hidden_dim=self.sent_encoder_hidden_dim,
n_layers=self.n_sent_encoder_layers,
dropout_emb=self.dropout_emb,
dropout_input=self.dropout_input,
dropout_hidden=self.dropout_hidden,
dropout_output=self.dropout_output,
bidirectional=True,
embedding=self.word_embedding,
rnn_type=self.rnn_type,
)
self.dial_encoder = EncoderRNN(
input_dim=self.sent_encoder_hidden_dim,
hidden_dim=self.dial_encoder_hidden_dim,
n_layers=self.n_dial_encoder_layers,
dropout_emb=self.dropout_emb,
dropout_input=self.dropout_input,
dropout_hidden=self.dropout_hidden,
dropout_output=self.dropout_output,
bidirectional=False,
rnn_type=self.rnn_type,
)
# Variational components
if config.n_components == 1:
self.prior_net = GaussianVariation(
input_dim=self.dial_encoder_hidden_dim,
z_dim=self.latent_variable_dim,
# large_mlp=True
)
elif config.n_components > 1:
if self.gaussian_mix_type == "gmm":
self.prior_net = GMMVariation(
input_dim=self.dial_encoder_hidden_dim,
z_dim=self.latent_variable_dim,
n_components=self.n_components,
)
elif self.gaussian_mix_type == "lgm":
self.prior_net = LGMVariation(
input_dim=self.dial_encoder_hidden_dim,
z_dim=self.latent_variable_dim,
n_components=self.n_components,
)
self.post_net = GaussianVariation(
input_dim=self.sent_encoder_hidden_dim +
self.dial_encoder_hidden_dim,
z_dim=self.latent_variable_dim,
)
self.latent_to_bow = nn.Sequential(
nn.Linear(self.latent_variable_dim + self.dial_encoder_hidden_dim,
self.latent_variable_dim), nn.Tanh(),
nn.Dropout(self.dropout),
nn.Linear(self.latent_variable_dim, self.vocab_size))
self.ctx_fc = nn.Sequential(
nn.Linear(
self.latent_variable_dim + self.dial_encoder_hidden_dim,
self.dial_encoder_hidden_dim,
), nn.Tanh(), nn.Dropout(self.dropout))
# Decoding components
self.enc2dec_hidden_fc = nn.Linear(
self.dial_encoder_hidden_dim,
self.n_decoder_layers * self.decoder_hidden_dim if self.rnn_type
== "gru" else self.n_decoder_layers * self.decoder_hidden_dim * 2)
self.decoder = DecoderRNN(vocab_size=len(self.word2id),
input_dim=self.word_embedding_dim,
hidden_dim=self.decoder_hidden_dim,
feat_dim=self.dial_encoder_hidden_dim,
n_layers=self.n_decoder_layers,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
max_len=self.decode_max_len,
dropout_emb=self.dropout_emb,
dropout_input=self.dropout_input,
dropout_hidden=self.dropout_hidden,
dropout_output=self.dropout_output,
embedding=self.word_embedding,
tie_weights=self.tie_weights,
rnn_type=self.rnn_type,
use_attention=self.use_attention,
attn_dim=self.sent_encoder_hidden_dim)
# Extra components
# Floor encoding
if self.floor_encoder_type == "abs":
self.floor_encoder = AbsFloorEmbEncoder(
input_dim=self.sent_encoder_hidden_dim,
embedding_dim=self.attr_embedding_dim)
elif self.floor_encoder_type == "rel":
self.floor_encoder = RelFloorEmbEncoder(
input_dim=self.sent_encoder_hidden_dim,
embedding_dim=self.attr_embedding_dim)
else:
self.floor_encoder = None
# Initialization
self._init_weights()
def __init__(self, config, tokenizer):
super(HRESepUttrEnc, self).__init__()
# Attributes
# Attributes from config
self.num_labels = len(config.dialog_acts)
self.word_embedding_dim = config.word_embedding_dim
self.attr_embedding_dim = config.attr_embedding_dim
self.sent_encoder_hidden_dim = config.sent_encoder_hidden_dim
self.n_sent_encoder_layers = config.n_sent_encoder_layers
self.dial_encoder_hidden_dim = config.dial_encoder_hidden_dim
self.n_dial_encoder_layers = config.n_dial_encoder_layers
self.rnn_type = config.rnn_type
self.word_embedding_path = config.word_embedding_path
self.floor_encoder_type = config.floor_encoder
# Optional attributes from config
self.dropout_emb = config.dropout if hasattr(config,
"dropout") else 0.0
self.dropout_input = config.dropout if hasattr(config,
"dropout") else 0.0
self.dropout_hidden = config.dropout if hasattr(config,
"dropout") else 0.0
self.dropout_output = config.dropout if hasattr(config,
"dropout") else 0.0
self.use_pretrained_word_embedding = config.use_pretrained_word_embedding if hasattr(
config, "use_pretrained_word_embedding") else False
# Other attributes
self.word2id = tokenizer.word2id
self.id2word = tokenizer.id2word
self.vocab_size = len(tokenizer.word2id)
self.pad_token_id = tokenizer.pad_token_id
# Input components
self.word_embedding = nn.Embedding(
self.vocab_size,
self.word_embedding_dim,
padding_idx=self.pad_token_id,
_weight=init_word_embedding(
load_pretrained_word_embedding=self.
use_pretrained_word_embedding,
pretrained_word_embedding_path=self.word_embedding_path,
id2word=self.id2word,
word_embedding_dim=self.word_embedding_dim,
vocab_size=self.vocab_size,
pad_token_id=self.pad_token_id),
)
# Encoding components
self.own_sent_encoder = EncoderRNN(
input_dim=self.word_embedding_dim,
hidden_dim=self.sent_encoder_hidden_dim,
n_layers=self.n_sent_encoder_layers,
dropout_emb=self.dropout_emb,
dropout_input=self.dropout_input,
dropout_hidden=self.dropout_hidden,
dropout_output=self.dropout_output,
bidirectional=True,
embedding=self.word_embedding,
rnn_type=self.rnn_type,
)
self.oth_sent_encoder = EncoderRNN(
input_dim=self.word_embedding_dim,
hidden_dim=self.sent_encoder_hidden_dim,
n_layers=self.n_sent_encoder_layers,
dropout_emb=self.dropout_emb,
dropout_input=self.dropout_input,
dropout_hidden=self.dropout_hidden,
dropout_output=self.dropout_output,
bidirectional=True,
embedding=self.word_embedding,
rnn_type=self.rnn_type,
)
self.dial_encoder = EncoderRNN(
input_dim=self.sent_encoder_hidden_dim,
hidden_dim=self.dial_encoder_hidden_dim,
n_layers=self.n_dial_encoder_layers,
dropout_emb=self.dropout_emb,
dropout_input=self.dropout_input,
dropout_hidden=self.dropout_hidden,
dropout_output=self.dropout_output,
bidirectional=False,
rnn_type=self.rnn_type,
)
# Classification components
self.output_fc = nn.Linear(self.dial_encoder_hidden_dim,
self.num_labels)
# Initialization
self._init_weights()
def __init__(self, config, tokenizer):
super(VHCR, self).__init__()
# Attributes
# Attributes from config
self.word_embedding_dim = config.word_embedding_dim
self.attr_embedding_dim = config.attr_embedding_dim
self.sent_encoder_hidden_dim = config.sent_encoder_hidden_dim
self.n_sent_encoder_layers = config.n_sent_encoder_layers
self.dial_encoder_hidden_dim = config.dial_encoder_hidden_dim
self.n_dial_encoder_layers = config.n_dial_encoder_layers
self.latent_variable_dim = config.latent_dim
self.decoder_hidden_dim = config.decoder_hidden_dim
self.n_decoder_layers = config.n_decoder_layers
self.use_attention = config.use_attention
self.decode_max_len = config.decode_max_len
self.tie_weights = config.tie_weights
self.rnn_type = config.rnn_type
self.gen_type = config.gen_type
self.top_k = config.top_k
self.top_p = config.top_p
self.temp = config.temp
self.word_embedding_path = config.word_embedding_path
self.floor_encoder_type = config.floor_encoder
# Optional attributes from config
self.dropout_emb = config.dropout if hasattr(config, "dropout") else 0.0
self.dropout_input = config.dropout if hasattr(config, "dropout") else 0.0
self.dropout_hidden = config.dropout if hasattr(config, "dropout") else 0.0
self.dropout_output = config.dropout if hasattr(config, "dropout") else 0.0
self.use_pretrained_word_embedding = config.use_pretrained_word_embedding if hasattr(config, "use_pretrained_word_embedding") else False
self.n_step_annealing = config.n_step_annealing if hasattr(config, "n_step_annealing") else 0
# Other attributes
self.vocab_size = len(tokenizer.word2id)
self.word2id = tokenizer.word2id
self.id2word = tokenizer.id2word
self.pad_token_id = tokenizer.pad_token_id
self.bos_token_id = tokenizer.bos_token_id
self.eos_token_id = tokenizer.eos_token_id
self.dropout_sent = 0.25
# Input components
self.word_embedding = nn.Embedding(
self.vocab_size,
self.word_embedding_dim,
padding_idx=self.pad_token_id,
_weight=init_word_embedding(
load_pretrained_word_embedding=self.use_pretrained_word_embedding,
pretrained_word_embedding_path=self.word_embedding_path,
id2word=self.id2word,
word_embedding_dim=self.word_embedding_dim,
vocab_size=self.vocab_size,
pad_token_id=self.pad_token_id
),
)
# Encoding components
self.sent_encoder = EncoderRNN(
input_dim=self.word_embedding_dim,
hidden_dim=self.sent_encoder_hidden_dim,
n_layers=self.n_sent_encoder_layers,
dropout_emb=self.dropout_emb,
dropout_input=self.dropout_input,
dropout_hidden=self.dropout_hidden,
dropout_output=self.dropout_output,
bidirectional=True,
embedding=self.word_embedding,
rnn_type=self.rnn_type,
)
self.dial_encoder = EncoderRNN(
input_dim=self.sent_encoder_hidden_dim+self.latent_variable_dim,
hidden_dim=self.dial_encoder_hidden_dim,
n_layers=self.n_dial_encoder_layers,
dropout_emb=self.dropout_emb,
dropout_input=self.dropout_input,
dropout_hidden=self.dropout_hidden,
dropout_output=self.dropout_output,
bidirectional=False,
rnn_type=self.rnn_type,
)
self.dial_infer_encoder = EncoderRNN(
input_dim=self.sent_encoder_hidden_dim,
hidden_dim=self.dial_encoder_hidden_dim,
n_layers=self.n_dial_encoder_layers,
dropout_emb=self.dropout_emb,
dropout_input=self.dropout_input,
dropout_hidden=self.dropout_hidden,
dropout_output=self.dropout_output,
bidirectional=True,
rnn_type=self.rnn_type,
)
# Variational components
self.dial_post_net = GaussianVariation(
input_dim=self.dial_encoder_hidden_dim,
z_dim=self.latent_variable_dim
)
self.sent_prior_net = GaussianVariation(
input_dim=self.dial_encoder_hidden_dim+self.latent_variable_dim,
z_dim=self.latent_variable_dim
)
self.sent_post_net = GaussianVariation(
input_dim=self.sent_encoder_hidden_dim+self.dial_encoder_hidden_dim+self.latent_variable_dim,
z_dim=self.latent_variable_dim
)
self.unk_sent_vec = nn.Parameter(torch.randn(self.sent_encoder_hidden_dim)).to(DEVICE)
# Decoding components
self.ctx_fc = nn.Linear(
2*self.latent_variable_dim+self.dial_encoder_hidden_dim,
self.dial_encoder_hidden_dim
)
self.enc2dec_hidden_fc = nn.Linear(
self.dial_encoder_hidden_dim,
self.n_decoder_layers*self.decoder_hidden_dim if self.rnn_type == "gru" else self.n_decoder_layers*self.decoder_hidden_dim*2
)
self.decoder = DecoderRNN(
vocab_size=len(self.word2id),
input_dim=self.word_embedding_dim,
hidden_dim=self.decoder_hidden_dim,
feat_dim=self.dial_encoder_hidden_dim,
n_layers=self.n_decoder_layers,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
max_len=self.decode_max_len,
dropout_emb=self.dropout_emb,
dropout_input=self.dropout_input,
dropout_hidden=self.dropout_hidden,
dropout_output=self.dropout_output,
embedding=self.word_embedding,
tie_weights=self.tie_weights,
rnn_type=self.rnn_type,
use_attention=self.use_attention,
attn_dim=self.sent_encoder_hidden_dim
)
# Extra components
# Floor encoding
if self.floor_encoder_type == "abs":
self.floor_encoder = AbsFloorEmbEncoder(
input_dim=self.sent_encoder_hidden_dim,
embedding_dim=self.attr_embedding_dim
)
elif self.floor_encoder_type == "rel":
self.floor_encoder = RelFloorEmbEncoder(
input_dim=self.sent_encoder_hidden_dim,
embedding_dim=self.attr_embedding_dim
)
else:
self.floor_encoder = None
# Hidden initialization
self.dial_z2dial_enc_hidden_fc = nn.Linear(
self.latent_variable_dim,
self.n_dial_encoder_layers*self.dial_encoder_hidden_dim if self.rnn_type == "gru" else self.n_dial_encoder_layers*self.dial_encoder_hidden_dim*2
)
# Initialization
self._init_weights()
def __init__(self, config, tokenizer):
super(ADEM, self).__init__()
# Attributes
# Attributes from config
self.word_embedding_dim = config.word_embedding_dim
self.attr_embedding_dim = config.attr_embedding_dim
self.sent_encoder_hidden_dim = config.sent_encoder_hidden_dim
self.n_sent_encoder_layers = config.n_sent_encoder_layers
self.dial_encoder_hidden_dim = config.dial_encoder_hidden_dim
self.n_dial_encoder_layers = config.n_dial_encoder_layers
assert self.sent_encoder_hidden_dim == self.dial_encoder_hidden_dim
self.latent_variable_dim = config.latent_dim
self.tie_weights = config.tie_weights
self.rnn_type = config.rnn_type
self.word_embedding_path = config.word_embedding_path
self.floor_encoder_type = config.floor_encoder
self.metric = config.metric_type
# Optional attributes from config
self.dropout_emb = config.dropout if hasattr(config,
"dropout") else 0.0
self.dropout_input = config.dropout if hasattr(config,
"dropout") else 0.0
self.dropout_hidden = config.dropout if hasattr(config,
"dropout") else 0.0
self.dropout_output = config.dropout if hasattr(config,
"dropout") else 0.0
self.use_pretrained_word_embedding = config.use_pretrained_word_embedding if hasattr(
config, "use_pretrained_word_embedding") else False
# Other attributes
self.id2word = tokenizer.id2word
self.vocab_size = len(tokenizer.word2id)
self.pad_token_id = tokenizer.pad_token_id
self.n_pca_components = 50
# Input components
self.word_embedding = nn.Embedding(
self.vocab_size,
self.word_embedding_dim,
padding_idx=self.pad_token_id,
_weight=init_word_embedding(
load_pretrained_word_embedding=self.
use_pretrained_word_embedding,
pretrained_word_embedding_path=self.word_embedding_path,
id2word=self.id2word,
word_embedding_dim=self.word_embedding_dim,
vocab_size=self.vocab_size,
pad_token_id=self.pad_token_id),
)
# Encoding components
self.sent_encoder = EncoderRNN(
input_dim=self.word_embedding_dim,
hidden_dim=self.sent_encoder_hidden_dim,
n_layers=self.n_sent_encoder_layers,
dropout_emb=self.dropout_emb,
dropout_input=self.dropout_input,
dropout_hidden=self.dropout_hidden,
dropout_output=self.dropout_output,
bidirectional=True,
embedding=self.word_embedding,
rnn_type=self.rnn_type,
)
self.dial_encoder = EncoderRNN(
input_dim=self.sent_encoder_hidden_dim,
hidden_dim=self.dial_encoder_hidden_dim,
n_layers=self.n_dial_encoder_layers,
dropout_emb=self.dropout_emb,
dropout_input=self.dropout_input,
dropout_hidden=self.dropout_hidden,
dropout_output=self.dropout_output,
bidirectional=True,
rnn_type=self.rnn_type,
)
self.pca = PCA(n_components=self.n_pca_components)
# Scoring components
self.M = nn.Linear(self.n_pca_components, self.n_pca_components)
self.N = nn.Linear(self.n_pca_components, self.n_pca_components)
self.alpha = None
self.beta = None
# Extra components
# floor encoding
if self.floor_encoder_type == "abs":
self.floor_encoder = AbsFloorEmbEncoder(
input_dim=self.sent_encoder_hidden_dim,
embedding_dim=self.attr_embedding_dim)
elif self.floor_encoder_type == "rel":
self.floor_encoder = RelFloorEmbEncoder(
input_dim=self.sent_encoder_hidden_dim,
embedding_dim=self.attr_embedding_dim)
else:
self.floor_encoder = None
# Initialization
self._init_weights()
