How to make dataset and dataloader from wavelet transformed image(npy.file)?

Hello teachers.
I’m a starter for PyTorch and i’m thinking of building up CNN model for fault detection of vibration signals.

Before making pytorch dataset and dataloader, i created wavelet transformed image from signal.

  • type : numpy array
  • dimension : (100,50,50), 100 images with 50x50
  • value : around -0.01~0.01

after loading npy.file with np.load function,
I don’t know how to make my Dataset Class for Dataloader,
because it is not regular image.

Please help me :frowning:

Have a good day!

You don’t have to use regular images in your Dataset so your custom numpy arrays should be fine.
Here is a small dummy example:

class MyDataset(Dataset):
    def __init__(self, data): = torch.from_numpy(data).float()
    def __getitem__(self, index):
        x =[index]
        return x
    def __len__(self):
        return len(

I’m not sure, what your targets are, so you might want to pass them additionally to the Dataset or calculate them in __getitem__.

1 Like

Hi Ptrblck,
I really appreciate with your answer but there is still a problem.

Error says : object of type ‘type’ has no len()

  • The thing that i wanna do with this is, inputting images with 50x50 size and
    building Convolutional AutoEncoder model with unsupervised learning for anomaly detection.
  • I’m wondering i need to resize (100,30,30) into (100,1,30,30)… because input dimension is comprised of (batchsize, channel, height, width) in every the PyTorch Tutorials.

Thanks again!!

You want to pass an instance of the dataset to the dataloader constructor, not the type.

Best regards


Thank you very much, tom,

but im afraid that i still don’t know how to modify the code for passing an instance for this case…



i solved with dataset=MyDataset()

Thank you very much :slight_smile: