Pytorch双图像输入：带数据加载器的断管

Question

我正在用pytorch在python中开发一个神经网络，以便对图像对进行分类。因此，我想返回2张图像和地面真实情况作为输出，但是每当尝试使用数据加载器时，都会收到错误消息“ Broken pipe”。

我希望它作为分类器工作，所以我以this link (pytorch classifier CIFAR10) 为例

这是我的代码：

###Defining class
class continuousImgDataset(Dataset):
  def __init__(self, tabDataset, transform=None):
        """
        Args:
            tabDataset : Contains the imported data like : Item 1 = [im1,im2,ground-truth]

            transform (callable, optional): Optional transform to be applied
                on a sample.

        """
        self.tabPairImg = tabDataset
        self.transform = transform

    def __len__(self):
        return len(self.tabPairImg)

    def __getitem__(self, idx):
        img1 = self.tabPairImg[idx][0]
        img2 = self.tabPairImg[idx][1]
        label = self.tabPairImg[idx][2]
        if self.transform:
            img1 = self.transform(img1)
            img2 = self.transform(img2)
        return img1, img2,label

transform = transforms.Compose(
                   [transforms.Scale((32,32)),
                    transforms.ToTensor(),
                    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])


###Make the training and testing dataset

custom_dataset = continuousImgDataset(pairImg,transform)
train_dataset = continuousImgDataset(pairImg[: int(len(custom_dataset) * .90)],transform)
test_dataset =  continuousImgDataset(pairImg[int(len(custom_dataset) * .90) : int(len(custom_dataset))],transform)

print(len(custom_dataset.tabPairImg)) #Output :22620
print(len(train_dataset.tabPairImg)) #Output : 20358
print(len(test_dataset.tabPairImg))  #Output : 2262


### Loaders

trainloader = torch.utils.data.DataLoader(train_dataset, batch_size=4,
                                          shuffle=True, num_workers=2)

testloader = torch.utils.data.DataLoader(test_dataset, batch_size=4,
                                         shuffle=False, num_workers=2)

classes = ('joint','disjoint')


### Image show and Error

"""From the official website but a little modified to have 2 imgs"""
def imshow(img):
    img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))
    plt.show()

# get some random training images
dataiter = iter(trainloader)              #ERROR HERE : Broken pipe
images1,images2, labels = dataiter.next()

# show images
imshow(torchvision.utils.make_grid(images1))
imshow(torchvision.utils.make_grid(images2))
# print labels
print(' '.join('%5s' % classes[labels[j]] for j in range(2)))

我可能不完全了解数据加载器要对其进行迭代的期望。任何帮助，将不胜感激:)

编辑：这是完整的错误：

BrokenPipeError                           Traceback (most recent call last)
<ipython-input-58-314e85cc8fbb> in <module>
      7 
      8 # get some random training images
----> 9 dataiter = iter(trainloader)
     10 images1,images2, labels = dataiter.next()
     11 

C:\ProgramData\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py in __iter__(self)
    191 
    192     def __iter__(self):
--> 193         return _DataLoaderIter(self)
    194 
    195     def __len__(self):

C:\ProgramData\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py in __init__(self, loader)
    467                 #     before it starts, and __del__ tries to join but will get:
    468                 #     AssertionError: can only join a started process.
--> 469                 w.start()
    470                 self.index_queues.append(index_queue)
    471                 self.workers.append(w)

C:\ProgramData\Anaconda3\lib\multiprocessing\process.py in start(self)
    110                'daemonic processes are not allowed to have children'
    111         _cleanup()
--> 112         self._popen = self._Popen(self)
    113         self._sentinel = self._popen.sentinel
    114         # Avoid a refcycle if the target function holds an indirect

C:\ProgramData\Anaconda3\lib\multiprocessing\context.py in _Popen(process_obj)
    221     @staticmethod
    222     def _Popen(process_obj):
--> 223         return _default_context.get_context().Process._Popen(process_obj)
    224 
    225 class DefaultContext(BaseContext):

C:\ProgramData\Anaconda3\lib\multiprocessing\context.py in _Popen(process_obj)
    320         def _Popen(process_obj):
    321             from .popen_spawn_win32 import Popen
--> 322             return Popen(process_obj)
    323 
    324     class SpawnContext(BaseContext):

C:\ProgramData\Anaconda3\lib\multiprocessing\popen_spawn_win32.py in __init__(self, process_obj)
     87             try:
     88                 reduction.dump(prep_data, to_child)
---> 89                 reduction.dump(process_obj, to_child)
     90             finally:
     91                 set_spawning_popen(None)

C:\ProgramData\Anaconda3\lib\multiprocessing\reduction.py in dump(obj, file, protocol)
     58 def dump(obj, file, protocol=None):
     59     '''Replacement for pickle.dump() using ForkingPickler.'''
---> 60     ForkingPickler(file, protocol).dump(obj)
     61 
     62 #

BrokenPipeError: [Errno 32] Broken pipe