#the model
from typing import List, Optional, Tuple
import torch
from torch import Tensor
from torch import nn
from transformers import RobertaModel
from faknow.model.layers.layer import TextCNNLayer
from faknow.model.model import AbstractModel
from faknow.data.process.text_process import TokenizerFromPreTrained
import pandas as pd
import gdown
import os
class _MLP(nn.Module):
def __init__(self,
input_dim: int,
embed_dims: List[int],
dropout_rate: float,
output_layer=True):
super().__init__()
layers = list()
for embed_dim in embed_dims:
layers.append(nn.Linear(input_dim, embed_dim))
layers.append(nn.BatchNorm1d(embed_dim))
layers.append(nn.ReLU())
layers.append(nn.Dropout(p=dropout_rate))
input_dim = embed_dim
if output_layer:
layers.append(torch.nn.Linear(input_dim, 1))
self.mlp = torch.nn.Sequential(*layers)
def forward(self, x: Tensor) -> Tensor:
"""
Args:
x (Tensor): shared feature from domain and text, shape=(batch_size, embed_dim)
"""
return self.mlp(x)
class _MaskAttentionLayer(torch.nn.Module):
"""
Compute attention layer
"""
def __init__(self, input_size: int):
super(_MaskAttentionLayer, self).__init__()
self.attention_layer = torch.nn.Linear(input_size, 1)
def forward(self,
inputs: Tensor,
mask: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]:
weights = self.attention_layer(inputs).view(-1, inputs.size(1))
if mask is not None:
weights = weights.masked_fill(mask == 0, float("-inf"))
weights = torch.softmax(weights, dim=-1).unsqueeze(1)
outputs = torch.matmul(weights, inputs).squeeze(1)
return outputs, weights
class MDFEND(AbstractModel):
r"""
MDFEND: Multi-domain Fake News Detection, CIKM 2021
paper: https://dl.acm.org/doi/10.1145/3459637.3482139
code: https://github.com/kennqiang/MDFEND-Weibo21
"""
def __init__(self,
pre_trained_bert_name: str,
domain_num: int,
mlp_dims: Optional[List[int]] = None,
dropout_rate=0.2,
expert_num=5):
"""
Args:
pre_trained_bert_name (str): the name or local path of pre-trained bert model
domain_num (int): total number of all domains
mlp_dims (List[int]): a list of the dimensions in MLP layer, if None, [384] will be taken as default, default=384
dropout_rate (float): rate of Dropout layer, default=0.2
expert_num (int): number of experts also called TextCNNLayer, default=5
"""
super(MDFEND, self).__init__()
self.domain_num = domain_num
self.expert_num = expert_num
self.bert = RobertaModel.from_pretrained(
pre_trained_bert_name).requires_grad_(False)
self.embedding_size = self.bert.config.hidden_size
self.loss_func = nn.BCELoss()
if mlp_dims is None:
mlp_dims = [384]
filter_num = 64
filter_sizes = [1, 2, 3, 5, 10]
experts = [
TextCNNLayer(self.embedding_size, filter_num, filter_sizes)
for _ in range(self.expert_num)
]
self.experts = nn.ModuleList(experts)
self.gate = nn.Sequential(
nn.Linear(self.embedding_size * 2, mlp_dims[-1]), nn.ReLU(),
nn.Linear(mlp_dims[-1], self.expert_num), nn.Softmax(dim=1))
self.attention = _MaskAttentionLayer(self.embedding_size)
self.domain_embedder = nn.Embedding(num_embeddings=self.domain_num,
embedding_dim=self.embedding_size)
self.classifier = _MLP(320, mlp_dims, dropout_rate)
def forward(self, token_id: Tensor, mask: Tensor,
domain: Tensor) -> Tensor:
"""
Args:
token_id (Tensor): token ids from bert tokenizer, shape=(batch_size, max_len)
mask (Tensor): mask from bert tokenizer, shape=(batch_size, max_len)
domain (Tensor): domain id, shape=(batch_size,)
Returns:
FloatTensor: the prediction of being fake, shape=(batch_size,)
"""
text_embedding = self.bert(token_id,
attention_mask=mask).last_hidden_state
attention_feature, _ = self.attention(text_embedding, mask)
domain_embedding = self.domain_embedder(domain.view(-1, 1)).squeeze(1)
gate_input = torch.cat([domain_embedding, attention_feature], dim=-1)
gate_output = self.gate(gate_input)
shared_feature = 0
for i in range(self.expert_num):
expert_feature = self.experts[i](text_embedding)
shared_feature += (expert_feature * gate_output[:, i].unsqueeze(1))
label_pred = self.classifier(shared_feature)
return torch.sigmoid(label_pred.squeeze(1))
def calculate_loss(self, data) -> Tensor:
"""
calculate loss via BCELoss
Args:
data (dict): batch data dict
Returns:
loss (Tensor): loss value
"""
token_ids = data['text']['token_id']
masks = data['text']['mask']
domains = data['domain']
labels = data['label']
output = self.forward(token_ids, masks, domains)
return self.loss_func(output, labels.float())
def predict(self, data_without_label) -> Tensor:
"""
predict the probability of being fake news
Args:
data_without_label (Dict[str, Any]): batch data dict
Returns:
Tensor: one-hot probability, shape=(batch_size, 2)
"""
token_ids = data_without_label['text']['token_id']
masks = data_without_label['text']['mask']
domains = data_without_label['domain']
# shape=(n,), data = 1 or 0
round_pred = torch.round(self.forward(token_ids, masks,
domains)).long()
# after one hot: shape=(n,2), data = [0,1] or [1,0]
one_hot_pred = torch.nn.functional.one_hot(round_pred, num_classes=2)
return one_hot_pred
def download_from_gdrive(file_id, output_path):
output = os.path.join(output_path)
# Check if the file already exists
if not os.path.exists(output):
gdown.download(id=file_id, output=output, quiet=False)
return output
def loading_model_and_tokenizer():
max_len, bert = 160, 'FacebookAI/xlm-roberta-base'
#https://drive.google.com/file/d/1--6GB3Ff81sILwtuvVTuAW3shGW_5VWC/view
file_id = "1--6GB3Ff81sILwtuvVTuAW3shGW_5VWC"
model_path = '/content/drive/MyDrive/models/last-epoch-model-2024-03-17-01_00_32_1.pth'
MODEL_SAVE_PATH = download_from_gdrive(file_id, model_path)
domain_num = 4
tokenizer = TokenizerFromPreTrained(max_len, bert)
model = MDFEND(bert, domain_num , expert_num=12 , mlp_dims = [3010, 2024 ,1012 ,606 , 400])
model.load_state_dict(torch.load(f=MODEL_SAVE_PATH , map_location=torch.device('cpu')))
model.requires_grad_(False)
return tokenizer , model