How to use DataLoader for PyTorch on iPython Console of Spyder
I check this Tutorial and can't figure out a way to actually use my DataLoader to train a ANN.
When iterating over my DataLoader a cmd prompt pops up and immediately closes itself, afterwards nothing happens. My original data are both 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)


I tried many times and had a glimpse at the commandprompt which says something like

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: 0
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


Here is how I do that:

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)


then you can simple add to the loader: 

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


Also, X, y are just numpy arrays. 

And for the training you can access your data with a for loop: 

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

