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import torch
import torch.nn as nn
from modules.build import HEADS_REGISTRY
from modules.utils import get_activation_fn
class BertPredictionHeadTransform(nn.Module):
def __init__(self, hidden_size, hidden_act='gelu'):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.transform_act_fn = get_activation_fn(hidden_act)
self.LayerNorm = nn.LayerNorm(hidden_size)
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
class BertLMPredictionHead(nn.Module):
def __init__(self, hidden_size, vocab_size):
super().__init__()
self.transform = BertPredictionHeadTransform(hidden_size=hidden_size, hidden_act='gelu')
self.decoder = nn.Linear(hidden_size, vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(vocab_size))
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states) + self.bias
return hidden_states
@HEADS_REGISTRY.register()
class PretrainHeadV1(nn.Module):
def __init__(self, cfg, hidden_size=768, vocab_size=30522):
super().__init__()
self.lm_pred_head = BertLMPredictionHead(hidden_size, vocab_size)
def forward(self, txt_embeds, **kwargs):
txt_lm_cls_logits = self.lm_pred_head(txt_embeds)
return txt_lm_cls_logits
@HEADS_REGISTRY.register()
class OVPretrainHead(nn.Module):
def __init__(self, cfg, hidden_size=768, vocab_size=30522, obj_vocab_size=607):
super().__init__()
self.lm_pred_head = BertLMPredictionHead(hidden_size, vocab_size)
self.obj_pred_head = BertLMPredictionHead(hidden_size, obj_vocab_size)
def forward(self, txt_embeds, obj_embeds, **kwargs):
txt_lm_cls_logits = self.lm_pred_head(txt_embeds)
obj_lm_cls_logits = self.obj_pred_head(obj_embeds)
return (txt_lm_cls_logits, obj_lm_cls_logits) |