data.py

from torch.utils.data import Dataset
from torchvision.datasets.vision import VisionDataset
import warnings
from PIL import Image
import os
import os.path
import numpy as np
import torch
import codecs
import torch.utils.data as data
import torch
from torchvision.datasets.mnist import *

class MNIST(VisionDataset):
    """`MNIST <http://yann.lecun.com/exdb/mnist/>`_ Dataset.

    Args:
        root (string): Root directory of dataset where ``MNIST/processed/training.pt``
            and  ``MNIST/processed/test.pt`` exist.
        train (bool, optional): If True, creates dataset from ``training.pt``,
            otherwise from ``test.pt``.
        download (bool, optional): If true, downloads the dataset from the internet and
            puts it in root directory. If dataset is already downloaded, it is not
            downloaded again.
        transform (callable, optional): A function/transform that  takes in an PIL image
            and returns a transformed version. E.g, ``transforms.RandomCrop``
        target_transform (callable, optional): A function/transform that takes in the
            target and transforms it.
    """

    resources = [
        ("http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz", "f68b3c2dcbeaaa9fbdd348bbdeb94873"),
        ("http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz", "d53e105ee54ea40749a09fcbcd1e9432"),
        ("http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz", "9fb629c4189551a2d022fa330f9573f3"),
        ("http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz", "ec29112dd5afa0611ce80d1b7f02629c")
    ]

    training_file = 'training.pt'
    test_file = 'test.pt'
    classes = ['0 - zero', '1 - one', '2 - two', '3 - three', '4 - four',
               '5 - five', '6 - six', '7 - seven', '8 - eight', '9 - nine']

    @property
    def train_labels(self):
        warnings.warn("train_labels has been renamed targets")
        return self.targets

    @property
    def test_labels(self):
        warnings.warn("test_labels has been renamed targets")
        return self.targets

    @property
    def train_data(self):
        warnings.warn("train_data has been renamed data")
        return self.data

    @property
    def test_data(self):
        warnings.warn("test_data has been renamed data")
        return self.data

    def __init__(self, root, train=True, transform=None, target_transform=None,
                 download=False):
        super(MNIST, self).__init__(root, transform=transform,
                                    target_transform=target_transform)
        self.train = train  # training set or test set

        if download:
            self.download()

        if not self._check_exists():
            raise RuntimeError('Dataset not found.' +
                               ' You can use download=True to download it')

        if self.train:
            data_file = self.training_file
            self.data, self.targets = torch.load(os.path.join(self.processed_folder, data_file))
        else:
            data_file = self.test_file
            self.data, self.targets = torch.load(os.path.join(self.processed_folder, data_file)) 
            indices = [3, 2, 1, 18, 6, 8, 11, 17, 61, 16] # Manually picked support set
            self.support_set = np.stack([self.transform(Image.fromarray(self.data[x].numpy(), mode='L')) for x in indices])   

    def __getitem__(self, index):
        image1_index = np.random.randint(0, len(self.data))
        image1 = self.data[image1_index]
        target1 = int(self.targets[image1_index])

        image2 = self.data[index]
        target2 = int(self.targets[index])

        target = 1.0 if target1 == target2 else 0.0

        # doing this so that it is consistent with all other datasets
        # to return a PIL Image
        image1 = Image.fromarray(image1.numpy(), mode='L')
        image2 = Image.fromarray(image2.numpy(), mode='L')

        if self.transform is not None:
            image1 = self.transform(image1)
            image2 = self.transform(image2)
            
        return image1, image2, target, target1

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

    @property
    def raw_folder(self):
        return os.path.join(self.root, self.__class__.__name__, 'raw')

    @property
    def processed_folder(self):
        return os.path.join(self.root, self.__class__.__name__, 'processed')

    @property
    def class_to_idx(self):
        return {_class: i for i, _class in enumerate(self.classes)}

    def _check_exists(self):
        return (os.path.exists(os.path.join(self.processed_folder,
                                            self.training_file)) and
                os.path.exists(os.path.join(self.processed_folder,
                                            self.test_file)))

    def download(self):
        """Download the MNIST data if it doesn't exist in processed_folder already."""

        if self._check_exists():
            return

        makedir_exist_ok(self.raw_folder)
        makedir_exist_ok(self.processed_folder)

        # download files
        for url, md5 in self.resources:
            filename = url.rpartition('/')[2]
            download_and_extract_archive(url, download_root=self.raw_folder, filename=filename, md5=md5)

        # process and save as torch files
        print('Processing...')

        training_set = (
            read_image_file(os.path.join(self.raw_folder, 'train-images-idx3-ubyte')),
            read_label_file(os.path.join(self.raw_folder, 'train-labels-idx1-ubyte'))
        )
        test_set = (
            read_image_file(os.path.join(self.raw_folder, 't10k-images-idx3-ubyte')),
            read_label_file(os.path.join(self.raw_folder, 't10k-labels-idx1-ubyte'))
        )
        with open(os.path.join(self.processed_folder, self.training_file), 'wb') as f:
            torch.save(training_set, f)
        with open(os.path.join(self.processed_folder, self.test_file), 'wb') as f:
            torch.save(test_set, f)

        print('Done!')

    def extra_repr(self):
        return "Split: {}".format("Train" if self.train is True else "Test")