IEMOCAP数据集加载（Pytorch）代码

import torch
from torch.utils.data import Dataset
from torch.nn.utils.rnn import pad_sequence
import pickle
import pandas as pd


class IEMOCAPDataset(Dataset):

    def __init__(self, path, train=True):
        self.videoIDs, self.videoSpeakers, self.videoLabels, self.videoText, \
        self.videoAudio, self.videoVisual, self.videoSentence, self.trainVid, \
        self.testVid = pickle.load(open(path, 'rb'), encoding='latin1')
        '''
        label index mapping = {'happy':0, 'sad':1, 'neutral':2, 'angry':3, 'excioted':4, 'frustrated':5}
        '''
        self.keys = [x for x in (self.trainVid if train else self.testVid)]

        self.len = len(self.keys)

    def __getitem__(self, index):
        vid = self.keys[index]
        return torch.FloatTensor(self.videoText[vid]), \
               torch.FloatTensor(self.videoVisual[vid]), \
               torch.FloatTensor(self.videoAudio[vid]), \
               torch.FloatTensor([[1, 0] if x == 'M' else [0, 1] for x in \
                                  self.videoSpeakers[vid]]), \
               torch.FloatTensor([1] * len(self.videoLabels[vid])), \
               torch.LongTensor(self.videoLabels[vid]), \
               vid

    def __len__(self):
        return self.len

    def collate_fn(self, data): #将输入的数据进行长度补齐并堆叠
        dat = pd.DataFrame(data)  # 生成一个表格
        return [pad_sequence(dat[i]) if i < 4 else pad_sequence(dat[i], True) if i < 6 else dat[i].tolist() for i in
                dat]