Hello,
i have a problem with iterating my data. I am also roughly aware of why this is happening, but I have no solution for it.
My task is to pass a dataset of hyperspectral images through a Convolution Auto Encoder. Due to the fact that I am not interested in the local information I want to create a 1D Convolutional Auto encoder. My problem now is to load the latencies in the necessary form.
For this purpose a data set exists in the form.
[18000x91] = [Pixel, Channel]
Now I transform this dataset into the original form of the dataset.
45x91x400] = [Images, channel, pixels in the images].
The code for loading the data is shown below.

class SpectralDataset(Dataset):
    def __init__(self):
        xy = np.loadtxt(Dataset_Path, delimiter=",", dtype=np.float32)
        self.len = xy.shape[0]
        # pylint: disable=E1101 # Fehler in VSC muss hier hinzugefügt werden
        x = torch.from_numpy(xy[:, 2:])
        x = torch.reshape(x, (45, 400, 91))
        self.x = x.permute(0, 2, 1)
        # pylint: enable=E1101 # Fehler in VSC muss hier hinzugefügt werden

    def __getitem__(self, index):
        return self.x[index]

    def __len__(self):
        return self.len
dataset = SpectralDataset()

Since I want to do batch training with shuffled data sets, I also use the Dataloader function of Pytorch. But now it gives me index values, which are far beyond the range of [0 45] .
The Dataloader uses index values from the original dataset, i.e. [0 18000] .
How can you avoid this problem?