original_docstring: 
      Creates a CL scenario for CORe50.
  
      If the dataset is not present in the computer, this method will
      automatically download and store it.
  
      This generator can be used to obtain the NI, NC, NIC and NICv2-* scenarios.
  
      The scenario 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.
  
      The task label "0" will be assigned to each experience.
  
      The scenario API is quite simple and is uniform across all scenario
      generators. It is recommended to check the tutorial of the "benchmark" API,
      which contains usage examples ranging from "basic" to "advanced".
  
      :param root: Path indicating where to store the dataset and related
          metadata. By default they will be stored in
          "~/.avalanche/datasets/core50/data/".
      :param scenario: CORe50 main scenario. It can be chosen between 'ni', 'nc',
          'nic', 'nicv2_79', 'nicv2_196' or 'nicv2_391.'
      :param run: number of run for the scenario. Each run defines a different
          ordering. Must be a number between 0 and 9.
      :param train_transform: The transformation to apply to the training data,
          e.g. a random crop, a normalization or a concatenation of different
          transformations (see torchvision.transform documentation for a
          comprehensive list of possible transformations). Defaults to None.
      :param eval_transform: The transformation to apply to the test data,
          e.g. a random crop, a normalization or a concatenation of different
          transformations (see torchvision.transform documentation for a
          comprehensive list of possible transformations). Defaults to None.
  
      :returns: a properly initialized :class:`GenericCLScenario` instance.
      
docstring: Creates a CL scenario for CORe50.
  If the dataset is not present in the computer, this method will
  automatically download and store it.
  
  This generator can be used to obtain the NI, NC, NIC and NICv2-* scenarios.
  
  
  
  The task label "0" will be assigned to each experience.
  
  The scenario API is quite simple and is uniform across all scenario
  generators.
docstring_tokens: Creates a CL scenario for CORe50 . If the dataset is not present in the computer this method will automatically download and store it . This generator can be used to obtain the NI NC NIC and NICv2 - * scenarios . The task label " 0 " will be assigned to each experience . The scenario API is quite simple and is uniform across all scenario generators .
code: def CORe50(root=expanduser("~") + "/.avalanche/data/core50/",
             scenario="nicv2_391",
             run=0,
             train_transform=None,
             eval_transform=None):
      """
      Creates a CL scenario for CORe50.
  
      If the dataset is not present in the computer, this method will
      automatically download and store it.
  
      This generator can be used to obtain the NI, NC, NIC and NICv2-* scenarios.
  
      The scenario 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.
  
      The task label "0" will be assigned to each experience.
  
      The scenario API is quite simple and is uniform across all scenario
      generators. It is recommended to check the tutorial of the "benchmark" API,
      which contains usage examples ranging from "basic" to "advanced".
  
      :param root: Path indicating where to store the dataset and related
          metadata. By default they will be stored in
          "~/.avalanche/datasets/core50/data/".
      :param scenario: CORe50 main scenario. It can be chosen between 'ni', 'nc',
          'nic', 'nicv2_79', 'nicv2_196' or 'nicv2_391.'
      :param run: number of run for the scenario. Each run defines a different
          ordering. Must be a number between 0 and 9.
      :param train_transform: The transformation to apply to the training data,
          e.g. a random crop, a normalization or a concatenation of different
          transformations (see torchvision.transform documentation for a
          comprehensive list of possible transformations). Defaults to None.
      :param eval_transform: The transformation to apply to the test data,
          e.g. a random crop, a normalization or a concatenation of different
          transformations (see torchvision.transform documentation for a
          comprehensive list of possible transformations). Defaults to None.
  
      :returns: a properly initialized :class:`GenericCLScenario` instance.
      """
  
      assert (0 <= run <= 9), "Pre-defined run of CORe50 are only 10. Indicate " \
                              "a number between 0 and 9."
      assert (scenario in nbatch.keys()), "The selected scenario is note " \
                                          "recognized: it should be 'ni', 'nc'," \
                                          "'nic', 'nicv2_79', 'nicv2_196' or " \
                                          "'nicv2_391'."
      if root is None:
          core_data = CORE50_DATA()
      else:
          core_data = CORE50_DATA(root)
  
      root = core_data.data_folder
      root_img = root + "core50_128x128/"
  
      filelists_bp = scen2dirs[scenario] + "run" + str(run) + "/"
      train_failists_paths = []
      for i in range(nbatch[scenario]):
          train_failists_paths.append(
              root + filelists_bp + "train_batch_" +
              str(i).zfill(2) + "_filelist.txt")
  
      scenario_obj = create_generic_benchmark_from_filelists(
          root_img, train_failists_paths,
          [root + filelists_bp + "test_filelist.txt"],
          task_labels=[0 for _ in range(nbatch[scenario])],
          complete_test_set_only=True,
          train_transform=train_transform,
          eval_transform=eval_transform)
  
      return scenario_obj
code_tokens: def CORe50 ( root = expanduser ( "~" ) + "/.avalanche/data/core50/" , scenario = "nicv2_391" , run = 0 , train_transform = None , eval_transform = None ) : assert ( 0 <= run <= 9 ) , "Pre-defined run of CORe50 are only 10. Indicate " "a number between 0 and 9." assert ( scenario in nbatch . keys ( ) ) , "The selected scenario is note " "recognized: it should be 'ni', 'nc'," "'nic', 'nicv2_79', 'nicv2_196' or " "'nicv2_391'." if root is None : core_data = CORE50_DATA ( ) else : core_data = CORE50_DATA ( root ) root = core_data . data_folder root_img = root + "core50_128x128/" filelists_bp = scen2dirs [ scenario ] + "run" + str ( run ) + "/" train_failists_paths = [ ] for i in range ( nbatch [ scenario ] ) : train_failists_paths . append ( root + filelists_bp + "train_batch_" + str ( i ) . zfill ( 2 ) + "_filelist.txt" ) scenario_obj = create_generic_benchmark_from_filelists ( root_img , train_failists_paths , [ root + filelists_bp + "test_filelist.txt" ] , task_labels = [ 0 for _ in range ( nbatch [ scenario ] ) ] , complete_test_set_only = True , train_transform = train_transform , eval_transform = eval_transform ) return scenario_obj
short_docstring: Creates a CL scenario for CORe50.
short_docstring_tokens: Creates a CL scenario for CORe50 .
comment: """
      Creates a CL scenario for CORe50.
  
      If the dataset is not present in the computer, this method will
      automatically download and store it.
  
      This generator can be used to obtain the NI, NC, NIC and NICv2-* scenarios.
  
      The scenario 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.
  
      The task label "0" will be assigned to each experience.
  
      The scenario API is quite simple and is uniform across all scenario
      generators. It is recommended to check the tutorial of the "benchmark" API,
      which contains usage examples ranging from "basic" to "advanced".
  
      :param root: Path indicating where to store the dataset and related
          metadata. By default they will be stored in
          "~/.avalanche/datasets/core50/data/".
      :param scenario: CORe50 main scenario. It can be chosen between 'ni', 'nc',
          'nic', 'nicv2_79', 'nicv2_196' or 'nicv2_391.'
      :param run: number of run for the scenario. Each run defines a different
          ordering. Must be a number between 0 and 9.
      :param train_transform: The transformation to apply to the training data,
          e.g. a random crop, a normalization or a concatenation of different
          transformations (see torchvision.transform documentation for a
          comprehensive list of possible transformations). Defaults to None.
      :param eval_transform: The transformation to apply to the test data,
          e.g. a random crop, a normalization or a concatenation of different
          transformations (see torchvision.transform documentation for a
          comprehensive list of possible transformations). Defaults to None.
  
      :returns: a properly initialized :class:`GenericCLScenario` instance.
      """