pytutorial/pytorch/interfacing_data/README.md

Interfacing data

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The goal

We need to handle our data and make it accessible for PyTorch.

Questions to David Rotermund

There are options to interface your data.

torch.utils.data.TensorDataset

CLASS torch.utils.data.TensorDataset(*tensors)

Dataset wrapping tensors.

Each sample will be retrieved by indexing tensors along the first dimension.

*tensors : (Tensor) – tensors that have the same size of the first dimension.

torch.utils.data.Dataset

In the case we might not be able to load the fully dataset into memory, the torch.utils.data.Dataset is very helpful.

CLASS torch.utils.data.Dataset(*args, **kwds)

An abstract class representing a Dataset.

All datasets that represent a map from keys to data samples should subclass it. All subclasses should overwrite __getitem__(), supporting fetching a data sample for a given key. Subclasses could also optionally overwrite __len__(), which is expected to return the size of the dataset by many Sampler implementations and the default options of DataLoader. Subclasses could also optionally implement __getitems__(), for speedup batched samples loading. This method accepts list of indices of samples of batch and returns list of samples.

We need to create a new class which is derived from torch.utils.data.Dataset. We can do what every we want in this class as long as we service the functions

• __len__() : gives us the number of pattern in the dataset
• __getitem__(index) : gives us the information about ONE pattern at position index in the data set. In the following example, I return the image as 3d torch.Tensor and the corresponding class for that pattern (for which I use int).

We have a lot of freedom for our own design. e.g.:

• The argument train:bool of the contructor was introduced by me.
• The __getitem__(index) doesn't need to give back the data for that pattern in exactly this way (means: order of variables, types of variables, number of variables).

We assume that the data is in the four following files:

• train_pattern_storage.npy
• train_label_storage.npy
• test_pattern_storage.npy
• test_label_storage.npy

import numpy as np
import torch


class MyDataset(torch.utils.data.Dataset):

    # Initialize
    def __init__(self, train: bool = False) -> None:
        super(MyDataset, self).__init__()

        if train is True:
            self.pattern_storage: np.ndarray = np.load("train_pattern_storage.npy")
            self.label_storage: np.ndarray = np.load("train_label_storage.npy")
        else:
            self.pattern_storage = np.load("test_pattern_storage.npy")
            self.label_storage = np.load("test_label_storage.npy")

        self.pattern_storage = self.pattern_storage.astype(np.float32)
        self.pattern_storage /= np.max(self.pattern_storage)

        # How many pattern are there?
        self.number_of_pattern: int = self.label_storage.shape[0]

    def __len__(self) -> int:
        return self.number_of_pattern

    # Get one pattern at position index
    def __getitem__(self, index: int) -> tuple[torch.Tensor, int]:

        image = torch.tensor(self.pattern_storage[index, np.newaxis, :, :])
        target = int(self.label_storage[index])

        return image, target


if __name__ == "__main__":
    pass