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dataset.py
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import torch
from torch.utils.data import Dataset
from torch.nn.utils.rnn import pad_sequence
import pickle, pandas as pd
import json
import numpy as np
import random
from pandas import DataFrame
from transformers import AutoTokenizer, AutoModelForMaskedLM
class IEMOCAPDataset(Dataset):
def __init__(self, dataset_name='IEMOCAP', split='train', speaker_vocab=None, label_vocab=None, args=None,
tokenizer=None):
self.speaker_vocab = speaker_vocab
self.label_vocab = label_vocab
self.args = args
self.data = self.read(dataset_name, split, tokenizer)
print(len(self.data))
self.len = len(self.data)
def read(self, dataset_name, split, tokenizer):
with open('./data/%s/%s_data_roberta_v2.json.feature' % (dataset_name, split), encoding='utf-8') as f:
raw_data = json.load(f)
# process dialogue
dialogs = []
# raw_data = sorted(raw_data, key=lambda x:len(x))
for d in raw_data:
# if len(d) < 5 or len(d) > 6:
# continue
utterances = []
labels = []
speakers = []
features = []
for i, u in enumerate(d):
utterances.append(u['text'])
labels.append(self.label_vocab['stoi'][u['label']] if 'label' in u.keys() else -1)
speakers.append(self.speaker_vocab['stoi'][u['speaker']])
features.append(u['cls'])
dialogs.append({
'utterances': utterances,
'labels': labels,
'speakers': speakers,
'features': features
})
random.shuffle(dialogs)
return dialogs
def __getitem__(self, index):
'''
:param index:
:return:
feature,
label
speaker
length
text
'''
return torch.FloatTensor(self.data[index]['features']), \
torch.LongTensor(self.data[index]['labels']), \
self.data[index]['speakers'], \
len(self.data[index]['labels']), \
self.data[index]['utterances']
def __len__(self):
return self.len
def collate_fn(self, data):
'''
:param data:
features, labels, speakers, length, utterances
:return:
features: (B, N, D) padded
labels: (B, N) padded
adj: (B, N, N) adj[:,i,:] means the direct predecessors of node i
s_mask: (B, N, N) s_mask[:,i,:] means the speaker informations for predecessors of node i, where 1 denotes the same speaker, 0 denotes the different speaker
lengths: (B, )
utterances: not a tensor
'''
max_dialog_len = max([d[3] for d in data])
feaures = pad_sequence([d[0] for d in data], batch_first=True) # (B, N, D)
labels = pad_sequence([d[1] for d in data], batch_first=True, padding_value=-1) # (B, N )
# adj = self.get_adj_v1([d[2] for d in data], max_dialog_len)
# s_mask, s_mask_onehot = self.get_s_mask([d[2] for d in data], max_dialog_len)
lengths = torch.LongTensor([d[3] for d in data])
speakers = pad_sequence([torch.LongTensor(d[2]) for d in data], batch_first=True, padding_value=-1) # B x N
utterances = [d[4] for d in data]
return feaures, labels, speakers, lengths, utterances
class IEMOCAPDataset2(Dataset):
def __init__(self, dataset_name='IEMOCAP', split='train', speaker_vocab=None, label_vocab=None, args=None,
tokenizer=None):
self.speaker_vocab = speaker_vocab
self.label_vocab = label_vocab
self.args = args
self.data = self.read(dataset_name, split, tokenizer)
print(f"dataset size is:{len(self.data)}")
# self.tokenizer = AutoTokenizer.from_pretrained("hfl/chinese-roberta-wwm-ext-large")
self.tokenizer = AutoTokenizer.from_pretrained("roberta-base")
self.len = len(self.data)
def read(self, dataset_name, split, tokenizer):
if dataset_name in ['MELD', 'EmoryNLP']:
version = 9
else:
version = 2
with open(f'./data/%s/%s_data_roberta_v{version}.json.feature' % (dataset_name, split), encoding='utf-8') as f:
raw_data = json.load(f)
# process dialogue
dialogs = []
# raw_data = sorted(raw_data, key=lambda x:len(x))
for d in raw_data:
# if len(d) < 5 or len(d) > 6:
# continue
utterances = []
labels = []
speakers = []
for i, u in enumerate(d):
utterances.append(u['text'])
labels.append(self.label_vocab['stoi'][u['label']] if 'label' in u.keys() else -1)
speakers.append(self.speaker_vocab['stoi'][u['speaker']])
dialogs.append({
'utterances': utterances,
'labels': labels,
'speakers': speakers,
})
random.shuffle(dialogs)
return dialogs
def __getitem__(self, index):
'''
:param index:
:return:
label
speaker
length
utterances
'''
return torch.LongTensor(self.data[index]['labels']), \
self.data[index]['speakers'], \
len(self.data[index]['labels']), \
self.data[index]['utterances']
def __len__(self):
return self.len
def collate_fn(self, data):
'''
:param data:
a list of dialogues, d: (labels, speakers, lengths, utterances)
:return:
features: (B, N, D) padded
labels: (B, N) padded
adj: (B, N, N) adj[:,i,:] means the direct predecessors of node i
s_mask: (B, N, N) s_mask[:,i,:] means the speaker informations for predecessors of node i, where 1 denotes the same speaker, 0 denotes the different speaker
lengths: (B, )
utterances: not a tensor
'''
labels = pad_sequence([d[0] for d in data], batch_first=True, padding_value=-1) # (B, N )
lengths = torch.LongTensor([d[2] for d in data])
speakers = pad_sequence([torch.LongTensor(d[1]) for d in data], batch_first=True, padding_value=-1) # B x N
utterances = [d[3] for d in data]
utts = []
att_mask = []
for d in data:
encoded_utts = self.tokenizer(d[3], padding='max_length', truncation=True, return_tensors='pt',
max_length=32)
utts.append(encoded_utts["input_ids"]) # S x M
att_mask.append(encoded_utts["attention_mask"]) # S x M
utts = pad_sequence(utts, batch_first=True, padding_value=1) # <pad> -> 1 # B x S x M
att_mask = pad_sequence(att_mask, batch_first=True, padding_value=0) # B x S x M
return labels, speakers, lengths, utterances, utts, att_mask