def __init__(
self,
vocab: Vocabulary,
embedding_size: int,
encoder_hidden_size: int,
encoder_num_layers: int,
decoder: DecoderNet,
decoder_type: str = "lstm",
decoder_num_layers: int = 1,
share_decoder_params:
bool = True, # only valid when decoder_type == `transformer`
text_field_embedder: TextFieldEmbedder = None,
start_token: str = "[CLS]",
end_token: str = "[SEP]",
index_name: str = "tokens",
beam_size: int = 4,
max_turn_len: int = 3,
min_dec_len: int = 4,
max_dec_len: int = 30,
coverage_factor: float = 0.0,
device: Union[int, str, List[int]] = -1,
metrics: Optional[List[Metric]] = None,
valid_metric_keys: List[str] = None,
dropout_rate: float = 0.1,
seed: int = 42,
initializer: InitializerApplicator = InitializerApplicator(),
regularizer: RegularizerApplicator = None):
# 初始化vocab和regularizer
Model.__init__(self, vocab, regularizer)
# ----------- 定义embedding和编码器 ---------------
# 获取单词序列的embedding
self._text_field_embedder = text_field_embedder
# 定义编码器
self.encoder = torch.nn.LSTM(input_size=embedding_size,
hidden_size=encoder_hidden_size,
num_layers=encoder_num_layers,
batch_first=True,
dropout=dropout_rate,
bidirectional=True)
self.encoder_num_layers = encoder_num_layers
# 由于编码器是双向的，而解码器是单向的
# 所有将编码器的输出转换成单向的维度
self.bi2uni_dec_init_state = torch.nn.Linear(2 * encoder_hidden_size,
encoder_hidden_size)
self.encoder_output_dim = encoder_hidden_size
# ------------- 通用初始化过程 ---------------------
self.common_init(self.encoder_output_dim, decoder, decoder_type,
decoder_num_layers, share_decoder_params, start_token,
end_token, index_name, beam_size, min_dec_len,
max_dec_len, coverage_factor, device, metrics,
valid_metric_keys, seed, initializer)
# -------------- 不同编码器不同的初始化过程 ---------------
# 获取embedding的维度
embedding_size = self._text_field_embedder.get_output_dim()
self.turn_embedding = torch.nn.Embedding(max_turn_len, embedding_size)
def __init__(self,
vocab: Vocabulary,
encoder: Seq2SeqEncoder,
decoder: DecoderNet,
decoder_type: str = "lstm",
encoder_num_layers: int = 1,
decoder_num_layers: int = 1,
share_encoder_params: bool = True,
share_decoder_params: bool = True,
text_field_embedder: TextFieldEmbedder = None,
start_token: str = "[CLS]",
end_token: str = "[SEP]",
index_name: str = "tokens",
beam_size: int = 4,
max_turn_len: int = 3,
min_dec_len: int = 4,
max_dec_len: int = 30,
coverage_factor: float = 0.0,
device: Union[int, str, List[int]] = -1,
metrics: Optional[List[Metric]] = None,
valid_metric_keys: List[str] = None,
seed: int = 42,
initializer: InitializerApplicator = InitializerApplicator(),
regularizer: RegularizerApplicator = None):
# 初始化vocab和regularizer
Model.__init__(self, vocab, regularizer)
# ---------- 定义embedding和编码器 -----------------
# 获取单词序列的embedding
# 通常是Embedding这个类
self._text_field_embedder = text_field_embedder
# 定义编码器
self.encoder = encoder
# 获取编码器的输出维度
self.encoder_output_dim = self.encoder.get_output_dim()
# ---------- 通用初始化过程 -------------
self.common_init(self.encoder_output_dim, decoder, decoder_type,
decoder_num_layers, share_decoder_params, start_token,
end_token, index_name, beam_size, min_dec_len,
max_dec_len, coverage_factor, device, metrics,
valid_metric_keys, seed, initializer)
# --------- 不同编码器不同的初始化过程 -------
# 获取embedding的维度
embedding_size = self._text_field_embedder.get_output_dim()
self.turn_embedding = torch.nn.Embedding(max_turn_len, embedding_size)
self.encoder_num_layers = encoder_num_layers
self._share_encoder_params = share_encoder_params
# 如果解码器是LSTM，则需要使用attention初始化LSTM的初始状态
# 如果编码器也是LSTM，则不需要
if self.params["decoder_type"] == "lstm":
self.h_query = torch.nn.Parameter(torch.randn(
[self.encoder_output_dim]),
requires_grad=True)
self.c_query = torch.nn.Parameter(torch.randn(
[self.encoder_output_dim]),
requires_grad=True)
self.init_attention = DotProductAttention()
