python - 如何在Spyder的iPython控制台上将Data Loader用于PyTorch

我检查了this Tutorial，却想不出一种实际使用DataLoader训练ANN的方法。
遍历我的DataLoader时，会弹出一个cmd提示，并立即将其自身关闭，此后没有任何反应。我的原始数据都是np.arrays。

import torch
from torch.utils import data
import numpy as np

class Dataset(data.Dataset):
  'Characterizes a dataset for PyTorch'
  def __init__(self, datax, labels):
        'Initialization'
        self.labels = torch.tensor(labels)
        self.datax = torch.tensor(datax)
        self.len = len(datax)

  def __len__(self):
        'Denotes the total number of samples'
        return self.len

  def __getitem__(self, index):
        'Generates one sample of data'
        # Load data and get label
        X = self.datax[index]
        y = self.labels[index]
        return X, y

params = {'batch_size': 64,
          'shuffle': True,
          'num_workers': 1}
training_set = Dataset(datax=X, labels=labels)
training_generator = data.DataLoader(training_set, **params)

for x in training_generator:
    print(1)

我尝试了很多次，对命令提示符一目了然，

OMP: Info #212: KMP_AFFINITY: decoding x2APIC ids.
OMP: Info #210: KMP_AFFINITY: Affinity capable, using global cpuid leaf 11 info
OMP: Info #154: KMP_AFFINITY: Initial OS proc set respected: 0𔂭
OMP: Info #156: KMP_AFFINITY: 4 available OS procs
OMP: Info #157: KMP_AFFINITY: Uniform topology
OMP: Info #179: KMP_AFFINITY: 1 packages x 2 cores/pkg x 2 threads/core (2 total cores)
OMP: Info #214: KMP_AFFINITY: OS proc to physical thread map:
OMP: Info #171: KMP_AFFINITY: OS proc 0 maps to package 0 core 0 thread 0
OMP: Info #171: KMP_AFFINITY: OS proc 1 maps to package 0 core 0 thread 1
OMP: Info #171: KMP_AFFINITY: OS proc 2 maps to package 0 core 1 thread 0
OMP: Info #171: KMP_AFFINITY: OS proc 3 maps to package 0 core 1 thread 1
OMP: Info #250: KMP_AFFINITY: pid 10264 tid 2388 thread 0 bound to OS proc set 0
OMP: Info #250: KMP_AFFINITY: pid 10264 tid 3288 thread 1 bound to OS proc set 2

最佳答案

这是我的方法：

class myDataset(Dataset):
    '''
    a dataset for PyTorch
    '''
    def __init__(self, X, y):
        self.X = X
        self.y = y
    def __getitem__(self, index):
        return self.X[index], self.y[index]
    def __len__(self):
        return len(self.X)

然后您可以简单地添加到加载器中：

full_dataset = myDataset(X,y)
train_loader = DataLoader(full_dataset, batch_size=batch_size)

另外，X，y只是numpy数组。

对于培训，您可以使用for循环访问数据：

for data, target in train_loader:
        if train_on_gpu:
            data, target = data.double().cuda(), target.double().cuda()