如何为多输出回归问题编写 Pytorch Dataset 类和 DataLoader？

原文标题 ：How to write Pytorch Dataset class and DataLoader for a multi-output regression problem?

我正在使用基于事件的数据（包括时间戳、x 和 y 坐标以及极性）来解决神经形态问题。但是，我的问题是，当我尝试预测多个输出时，如何创建 Dataset 和正确的 DataLoader？具体来说，我试图预测速度矢量的 x 分量、y 分量和 z 分量。这是我当前的自定义数据集类的样子：

import os
import ast
import torch
import tonic
import torchvision
import numpy as np 
import pandas as pd
import tonic.transforms as transforms
from torch.utils.data import DataLoader

class SyntheticRecording(tonic.Dataset):
    """
        Synthetic event camera recordings dataset.
    """
    def __init__(self, csv_file):
        super(SyntheticRecording, self).__init__()
        self.csv_file = csv_file
        df = pd.read_csv(self.csv_file, index_col = False)
        self.events = df['Events'] # Select only last column of dataframe
        self.target = df[['Vel_x', 'Vel_y', 'Vel_z']] # Select every column except last column of dataframe
        assert(self.target.shape[0] == len(self.events))
        self.sensor_size = (1920, 1080, 2)
    
    """
        Retrieve the index i to get the ith sample from the dataset. Apply the appropriate transformations.
    """
    def __getitem__(self, index):
      list_ = ast.literal_eval(self.events[index])
      t = []
      x = []
      y = []
      p = []
      for e in list_:
        t.append(e[0] * 1e6) # Convert to microseconds
        x.append(e[1])
        y.append(e[2])
        p.append(e[3])
      events = tonic.io.make_structured_array(x, y, t, p) # Ordering is xytp now
      
      # Denoise removes isolated, one-off events
      frame_transform = transforms.Compose([transforms.Denoise(filter_time = 10000), 
                                            transforms.ToFrame(sensor_size = self.sensor_size, 
                                                         time_window = 1000)
                                            ])
      transformed_frames = frame_transform(events)
      vel_x = np.array(self.target.loc[index][0]).astype('float')
      vel_y = np.array(self.target.loc[index][1]).astype('float')
      vel_z = np.array(self.target.loc[index][2]).astype('float')
      
      sample = {'frames': transformed_frames,
                'vel_x': vel_x,
                'vel_y': vel_y,
                'vel_z': vel_z}

      return sample

这就是我尝试创建 DataLoader 的方式：

batch_size = 16
trainloader = DataLoader(sr, batch_size = batch_size, collate_fn = tonic.collation.PadTensors(), shuffle = True, drop_last = True)

每当我尝试遍历帧和目标值（3 个值）时，都会收到以下错误：

for frames, targets in trainloader:
  print(frames.shape)
  print(targets.shape)

frames, targets = next(iter(trainloader))

结果：ValueError: too many values to unpack (expected 2)

创建可以处理多预测回归问题的 DataLoader 的正确方法是什么？

编辑：

我使用它作为我的资源来尝试处理多输出部分：https://medium.com/jdsc-tech-blog/multioutput-cnn-in-pytorch-c5f702d4915f