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ERC_dataset.py
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from torch.utils.data import Dataset, DataLoader
from torch.nn.utils.rnn import pack_sequence
import random
class MELD_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
temp_speakerList = []
context = []
context_speaker = []
self.speakerNum = []
# 'anger', 'disgust', 'fear', 'joy', 'neutral', 'sadness', 'surprise'
emodict = {'anger': "anger", 'disgust': "disgust", 'fear': "fear", 'joy': "joy", 'neutral': "neutral", 'sadness': "sad", 'surprise': 'surprise'}
self.sentidict = {'positive': ["joy"], 'negative': ["anger", "disgust", "fear", "sadness"], 'neutral': ["neutral", "surprise"]}
self.emoSet = set()
self.sentiSet = set()
for i, data in enumerate(dataset):
if i < 2:
continue
if data == '\n' and len(self.dialogs) > 0:
self.speakerNum.append(len(temp_speakerList))
temp_speakerList = []
context = []
context_speaker = []
continue
speaker, utt, emo, senti = data.strip().split('\t')
context.append(utt)
if speaker not in temp_speakerList:
temp_speakerList.append(speaker)
speakerCLS = temp_speakerList.index(speaker)
context_speaker.append(speakerCLS)
self.dialogs.append([context_speaker[:], context[:], emodict[emo], senti])
self.emoSet.add(emodict[emo])
self.sentiSet.add(senti)
self.emoList = sorted(self.emoSet)
self.sentiList = sorted(self.sentiSet)
if dataclass == 'emotion':
self.labelList = self.emoList
else:
self.labelList = self.sentiList
self.speakerNum.append(len(temp_speakerList))
def __len__(self):
return len(self.dialogs)
def __getitem__(self, idx):
return self.dialogs[idx], self.labelList, self.sentidict
class Emory_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
"""sentiment"""
# 'Joyful', 'Mad', 'Neutral', 'Peaceful', 'Powerful', 'Sad', 'Scared'
pos = ['Joyful', 'Peaceful', 'Powerful']
neg = ['Mad', 'Sad', 'Scared']
neu = ['Neutral']
emodict = {'Joyful': "joy", 'Mad': "mad", 'Peaceful': "peaceful", 'Powerful': "powerful", 'Neutral': "neutral", 'Sad': "sad", 'Scared': 'scared'}
self.sentidict = {'positive': pos, 'negative': neg, 'neutral': neu}
temp_speakerList = []
context = []
context_speaker = []
self.speakerNum = []
self.emoSet = set()
self.sentiSet = set()
for i, data in enumerate(dataset):
if data == '\n' and len(self.dialogs) > 0:
self.speakerNum.append(len(temp_speakerList))
temp_speakerList = []
context = []
context_speaker = []
continue
speaker, utt, emo = data.strip().split('\t')
context.append(utt)
if emo in pos:
senti = "positive"
elif emo in neg:
senti = "negative"
elif emo in neu:
senti = "neutral"
else:
print('ERROR emotion&sentiment')
if speaker not in temp_speakerList:
temp_speakerList.append(speaker)
speakerCLS = temp_speakerList.index(speaker)
context_speaker.append(speakerCLS)
self.dialogs.append([context_speaker[:], context[:], emodict[emo], senti])
self.emoSet.add(emodict[emo])
self.sentiSet.add(senti)
self.emoList = sorted(self.emoSet)
self.sentiList = sorted(self.sentiSet)
if dataclass == 'emotion':
self.labelList = self.emoList
else:
self.labelList = self.sentiList
self.speakerNum.append(len(temp_speakerList))
def __len__(self):
return len(self.dialogs)
def __getitem__(self, idx):
return self.dialogs[idx], self.labelList, self.sentidict
class IEMOCAP_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
temp_speakerList = []
context = []
context_speaker = []
self.speakerNum = []
pos = ['ang', 'exc', 'hap']
neg = ['fru', 'sad']
neu = ['neu']
emodict = {'ang': "angry", 'exc': "excited", 'fru': "frustrated", 'hap': "happy", 'neu': "neutral", 'sad': "sad"}
self.sentidict = {'positive': pos, 'negative': neg, 'neutral': neu}
# use: 'hap', 'sad', 'neu', 'ang', 'exc', 'fru'
# discard: disgust, fear, other, surprise, xxx
self.emoSet = set()
self.sentiSet = set()
for i, data in enumerate(dataset):
if data == '\n' and len(self.dialogs) > 0:
self.speakerNum.append(len(temp_speakerList))
temp_speakerList = []
context = []
context_speaker = []
continue
speaker = data.strip().split('\t')[0]
utt = ' '.join(data.strip().split('\t')[1:-1])
emo = data.strip().split('\t')[-1]
context.append(utt)
if emo in pos:
senti = "positive"
elif emo in neg:
senti = "negative"
elif emo in neu:
senti = "neutral"
else:
print('ERROR emotion&sentiment')
if speaker not in temp_speakerList:
temp_speakerList.append(speaker)
speakerCLS = temp_speakerList.index(speaker)
context_speaker.append(speakerCLS)
self.dialogs.append([context_speaker[:], context[:], emodict[emo], senti])
self.emoSet.add(emodict[emo])
self.emoList = sorted(self.emoSet)
self.sentiList = sorted(self.sentiSet)
if dataclass == 'emotion':
self.labelList = self.emoList
else:
self.labelList = self.sentiList
self.speakerNum.append(len(temp_speakerList))
def __len__(self):
return len(self.dialogs)
def __getitem__(self, idx):
return self.dialogs[idx], self.labelList, self.sentidict
class DD_loader(Dataset):
def __init__(self, txt_file, dataclass):
self.dialogs = []
f = open(txt_file, 'r')
dataset = f.readlines()
f.close()
temp_speakerList = []
context = []
context_speaker = []
self.speakerNum = []
self.emoSet = set()
self.sentiSet = set()
# {'anger', 'disgust', 'fear', 'happiness', 'neutral', 'sadness', 'surprise'}
pos = ['happiness']
neg = ['anger', 'disgust', 'fear', 'sadness']
neu = ['neutral', 'surprise']
emodict = {'anger': "anger", 'disgust': "disgust", 'fear': "fear", 'happiness': "happy", 'neutral': "neutral", 'sadness': "sad", 'surprise': "surprise"}
self.sentidict = {'positive': pos, 'negative': neg, 'neutral': neu}
for i, data in enumerate(dataset):
if data == '\n' and len(self.dialogs) > 0:
self.speakerNum.append(len(temp_speakerList))
temp_speakerList = []
context = []
context_speaker = []
continue
speaker = data.strip().split('\t')[0]
utt = ' '.join(data.strip().split('\t')[1:-1])
emo = data.strip().split('\t')[-1]
if emo in pos:
senti = "positive"
elif emo in neg:
senti = "negative"
elif emo in neu:
senti = "neutral"
else:
print('ERROR emotion&sentiment')
context.append(utt)
if speaker not in temp_speakerList:
temp_speakerList.append(speaker)
speakerCLS = temp_speakerList.index(speaker)
context_speaker.append(speakerCLS)
self.dialogs.append([context_speaker[:], context[:], emodict[emo], senti])
self.emoSet.add(emodict[emo])
self.emoList = sorted(self.emoSet)
self.sentiList = sorted(self.sentiSet)
if dataclass == 'emotion':
self.labelList = self.emoList
else:
self.labelList = self.sentiList
self.speakerNum.append(len(temp_speakerList))
def __len__(self):
return len(self.dialogs)
def __getitem__(self, idx):
return self.dialogs[idx], self.labelList, self.sentidict