def __init__(self,
text_processor: TextProcessor,
config: BertConfig = None,
encoder: BertModel = None,
enc_layer: int = 6,
embed_dim: int = 768,
intermediate_dim: int = 3072):
super(LM, self).__init__()
self.text_processor: TextProcessor = text_processor
if config is not None:
self.config = config
else:
self.config = lm_config.get_config(
vocab_size=text_processor.tokenizer.get_vocab_size(),
pad_token_id=text_processor.pad_token_id(),
bos_token_id=text_processor.bos_token_id(),
eos_token_id=text_processor.sep_token_id(),
enc_layer=enc_layer,
embed_dim=embed_dim,
intermediate_dim=intermediate_dim)
self.config["type_vocab_size"] = len(text_processor.languages)
self.config = BertConfig(**self.config)
self.masked_lm = BertOutputLayer(self.config)
if encoder is None:
self.encoder: BertModel = BertModel(self.config)
self.encoder.init_weights()
else:
self.encoder = encoder
self.encoder._tie_or_clone_weights(
self.masked_lm.decoder, self.encoder.embeddings.word_embeddings)
def __init__(self,
text_processor: TextProcessor,
enc_layer: int = 6,
embed_dim: int = 768,
intermediate_dim: int = 3072):
super(SenSim, self).__init__()
self.text_processor: TextProcessor = text_processor
self.config = lm_config.get_config(
vocab_size=text_processor.tokenizer.get_vocab_size(),
pad_token_id=text_processor.pad_token_id(),
bos_token_id=text_processor.bos_token_id(),
eos_token_id=text_processor.sep_token_id(),
enc_layer=enc_layer,
embed_dim=embed_dim,
intermediate_dim=intermediate_dim)
self.enc_layer = enc_layer
self.embed_dim = embed_dim
self.intermediate_dim = intermediate_dim
self.config["type_vocab_size"] = len(text_processor.languages)
self.config = BertConfig(**self.config)
self.encoder = BertEncoderModel(self.config)
self.encoder.init_weights()
self.input_attention = nn.Linear(self.config.hidden_size, 1)
def __init__(self,
text_processor: TextProcessor,
lang_dec: bool = True,
use_proposals=False,
tie_embed=False,
enc_layer: int = 6,
dec_layer: int = 3,
embed_dim: int = 768,
intermediate_dim: int = 3072,
freeze_image: bool = False,
resnet_depth: int = 1):
super(Seq2Seq, self).__init__()
self.text_processor: TextProcessor = text_processor
self.config = lm_config.get_config(
vocab_size=text_processor.tokenizer.get_vocab_size(),
pad_token_id=text_processor.pad_token_id(),
bos_token_id=text_processor.bos_token_id(),
eos_token_id=text_processor.sep_token_id(),
enc_layer=enc_layer,
embed_dim=embed_dim,
intermediate_dim=intermediate_dim)
self.enc_layer = enc_layer
self.dec_layer = dec_layer
self.embed_dim = embed_dim
self.intermediate_dim = intermediate_dim
self.config["type_vocab_size"] = len(text_processor.languages)
self.config = BertConfig(**self.config)
dec_config = copy.deepcopy(self.config)
dec_config.num_hidden_layers = self.dec_layer
self.encoder = BertEncoderModel(self.config)
self.encoder.init_weights()
self.lang_dec = lang_dec
self.tie_embed = tie_embed
if not lang_dec:
self.decoder = BertDecoderModel(dec_config)
self.encoder._tie_or_clone_weights(
self.encoder.embeddings.position_embeddings,
self.decoder.embeddings.position_embeddings)
self.encoder._tie_or_clone_weights(
self.encoder.embeddings.token_type_embeddings,
self.decoder.embeddings.token_type_embeddings)
self.encoder._tie_or_clone_weights(
self.encoder.embeddings.word_embeddings,
self.decoder.embeddings.word_embeddings)
if tie_embed:
self.output_layer = BertOutputLayer(dec_config)
self.encoder._tie_or_clone_weights(
self.output_layer, self.encoder.embeddings.word_embeddings)
self.encoder._tie_or_clone_weights(
self.encoder.embeddings.position_embeddings,
self.decoder.embeddings.position_embeddings)
self.decoder._tie_or_clone_weights(
self.output_layer, self.decoder.embeddings.word_embeddings)
else:
self.output_layer = nn.ModuleList([
BertOutputLayer(dec_config)
for _ in text_processor.languages
])
if len(self.encoder.encoder.layer) == len(
self.decoder.decoder.layer):
for i in range(len(self.encoder.encoder.layer)):
self.decoder.decoder.layer[
i].attention = self.encoder.encoder.layer[i].attention
else:
dec = BertDecoderModel(dec_config)
self.decoder = nn.ModuleList(
[copy.deepcopy(dec) for _ in text_processor.languages])
self.output_layer = nn.ModuleList([
BertOutputLayer(dec_config) for _ in text_processor.languages
])
for i, dec in enumerate(self.decoder):
if tie_embed:
self.encoder._tie_or_clone_weights(
self.output_layer[i],
self.encoder.embeddings.word_embeddings)
dec.embeddings.position_embeddings = self.encoder.embeddings.position_embeddings
dec._tie_or_clone_weights(self.output_layer[i],
dec.embeddings.word_embeddings)
dec._tie_or_clone_weights(
self.encoder.embeddings.token_type_embeddings,
dec.embeddings.token_type_embeddings)
self.use_proposals = use_proposals
if self.use_proposals:
self.proposal_embedding = self.encoder.embeddings.word_embeddings
self.lexical_gate = nn.Parameter(torch.zeros(
1, self.config.hidden_size).fill_(0.1),
requires_grad=True)
self.lexical_layer_norm = nn.LayerNorm(
self.config.hidden_size, eps=self.config.layer_norm_eps)
self.freeze_image = freeze_image
self.resnet_depth = resnet_depth