Hi everyone,
I am trying to load a 3D dataset using both the Dataset class and the DataLoader. My image data is an ndarray in int16 and loading it works using just the Dataset but breaks when using the DataLoader with the following error:

RuntimeError: can’t convert a given np.ndarray to a tensor - it has an invalid type. The only supported types are: double, float, int64, int32, and uint8.

Now I am wondering why the Dataset accepts loading from a numpy int16 ndarray but the DataLoader does not.
My ToTensor class looks like this:
class ToTensor(object):
    """Convert ndarrays in sample to Tensors."""

    def __call__(self, sample):
        image, label = sample['image'], sample['label']

        # Expand with channel axis
        # numpy image: H x W x Z
        # torch image: C x H x W x Z
        #print(image.dtype)
        image = torch.from_numpy(image).unsqueeze(0)
        label = torch.ByteTensor(label)

        print(image.size(), label.size())
        return {'image': image,
                'label': label}


ppmi_dataset = PPMIDataset(excel_file=‘PPMI_main_table.xlsx’,
root_dir=FOLDER, transform=ToTensor())
print(len(ppmi_dataset))
a = ppmi_dataset[0][‘image’].numpy()
print(a.dtype)
plt.imshow(a.squeeze()[:,:,45], cmap=‘gray’)
plt.show()

This works correctly and gives as an output (together with the image):

606
torch.Size([1, 105, 127, 105]) torch.Size([1])
int16

Using it with the DataLoader
dataloader = DataLoader(ppmi_dataset, batch_size=2, shuffle=True)

for i, batch in enumerate(dataloader):
    if i == 2:
        plt.figure()
        for idx in range(1):
            plt.subplot(1, 2, 1 + idx)
            plt.imshow(batch[idx]['image'].squeeze().numpy()[:,:,45])
        plt.show()
        break

This results in the error from above. Converting the ndarray to int32 in the ToTensor class works in both cases. Is this some intended feature that I don’t see?