code: def PermutedMNIST(
      n_experiences: int,
      *,
      return_task_id=False,
      seed: Optional[int] = None,
      train_transform: Optional[Any] = _default_mnist_train_transform,
      eval_transform: Optional[Any] = _default_mnist_eval_transform,
      dataset_root: Optional[Union[str, Path]] = None,
  ) -> NCScenario:
      """
      Creates a Permuted MNIST benchmark.
  
      If the dataset is not present in the computer, this method will
      automatically download and store it.
  
      Random pixel permutations are used to permute the MNIST images in
      ``n_experiences`` different manners. This means that each experience is
      composed of all the original 10 MNIST classes, but the pixel in the images
      are permuted in a different way.
  
      The benchmark instance returned by this method will have two fields,
      `train_stream` and `test_stream`, which can be iterated to obtain
      training and test :class:`Experience`. Each Experience contains the
      `dataset` and the associated task label.
  
      A progressive task label, starting from 0, is applied to each experience.
  
      The benchmark API is quite simple and is uniform across all benchmark
      generators. It is recommended to check the tutorial of the "benchmark" API,
      which contains usage examples ranging from "basic" to "advanced".
  
      :param return_task_id: if True, a progressive task id is returned for every
          experience. If False, all experiences will have a task ID of 0.
      :param n_experiences: The number of experiences (tasks) in the current
          benchmark. It indicates how many different permutations of the MNIST
          dataset have to be created.
          The value of this parameter should be a divisor of 10.
      :param seed: A valid int used to initialize the random number generator.
          Can be None.
      :param train_transform: The transformation to apply to the training data
          before the random permutation, e.g. a random crop, a normalization or a
          concatenation of different transformations (see torchvision.transform
          documentation for a comprehensive list of possible transformations).
          If no transformation is passed, the default train transformation
          will be used.
      :param eval_transform: The transformation to apply to the test data
          before the random permutation, e.g. a random crop, a normalization or a
          concatenation of different transformations (see torchvision.transform
          documentation for a comprehensive list of possible transformations).
          If no transformation is passed, the default test transformation
          will be used.
      :param dataset_root: The root path of the dataset. Defaults to None, which
          means that the default location for 'mnist' will be used.
  
      :returns: A properly initialized :class:`NCScenario` instance.
      """
  
      list_train_dataset = []
      list_test_dataset = []
      rng_permute = np.random.RandomState(seed)
  
      mnist_train, mnist_test = get_mnist_dataset(dataset_root)
  
      # for every incremental experience
      for _ in range(n_experiences):
          # choose a random permutation of the pixels in the image
          idx_permute = torch.from_numpy(
              rng_permute.permutation(784)
          ).type(torch.int64)
  
          permutation = PixelsPermutation(idx_permute)
  
          # Freeze the permutation
          permuted_train = make_avalanche_dataset(
              _make_taskaware_classification_dataset(mnist_train),
              frozen_transform_groups=DefaultTransformGroups(
                  (permutation, None)
              ),
          )
  
          permuted_test = make_avalanche_dataset(
              _make_taskaware_classification_dataset(mnist_test),
              frozen_transform_groups=DefaultTransformGroups(
                  (permutation, None)
              ),
          )
  
          list_train_dataset.append(permuted_train)
          list_test_dataset.append(permuted_test)
  
      return nc_benchmark(
          list_train_dataset,
          list_test_dataset,
          n_experiences=len(list_train_dataset),
          task_labels=return_task_id,
          shuffle=False,
          class_ids_from_zero_in_each_exp=True,
          one_dataset_per_exp=True,
          train_transform=train_transform,
          eval_transform=eval_transform,
      )
  
file_path: avalanche/avalanche/benchmarks/classic/cmnist.py
imports: from pathlib import Path
  from typing import Optional, Sequence, Union, Any
  import torch
  from PIL.Image import Image
  from torch import Tensor
  from torchvision.transforms import ToTensor, ToPILImage, Compose, Normalize, RandomRotation
  import numpy
  from avalanche.benchmarks import NCScenario, nc_benchmark
  from avalanche.benchmarks.classic.classic_benchmarks_utils import check_vision_benchmark
  from avalanche.benchmarks.datasets.external_datasets.mnist import get_mnist_dataset
  from avalanche.benchmarks.utils import _make_taskaware_classification_dataset, DefaultTransformGroups
  from avalanche.benchmarks.utils.data import make_avalanche_dataset
label_desc: Use image node_id 2 for calling a global function with example usage: PermutedMNIST(n_experiences) and returns: nc_benchmark