datasets/datasets.py [163:184]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        super().__init__(
            batch_size,
            n_rotations,
            n_x_translations,
            n_y_translations,
            scaling_factors,
            seed,
            pairs,
        )
        self.n_pixels = self.X_orig_train[0].shape[1]
        self.n_channels = 1

    def get_original(self):
        """Returns original training and test images"""
        mnist_train, mnist_val, mnist_test = self.download_mnist()
        # normalize MNIST so values are between [0, 1]
        x_train = mnist_train.data.unsqueeze(1) / 255.0
        x_val = mnist_val.data.unsqueeze(1) / 255.0
        x_test = mnist_test.data.unsqueeze(1) / 255.0
        return x_train, x_val, x_test

    @staticmethod
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



datasets/datasets.py [282:303]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        super().__init__(
            batch_size,
            n_rotations,
            n_x_translations,
            n_y_translations,
            scaling_factors,
            seed,
            pairs,
        )
        self.n_pixels = self.X_orig_train[0].shape[1]
        self.n_channels = 1

    def get_original(self):
        """Returns original training and test images"""
        mnist_train, mnist_val, mnist_test = self.download_mnist()
        # normalize MNIST so values are between [0, 1]
        x_train = mnist_train.data.unsqueeze(1) / 255.0
        x_val = mnist_val.data.unsqueeze(1) / 255.0
        x_test = mnist_test.data.unsqueeze(1) / 255.0
        return x_train, x_val, x_test

    @staticmethod
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -