First steps

"""

Cell Counting Dataset.

Loads the cell counting dataset from

http://www.robots.ox.ac.uk/~vgg/research/counting/index_org.html. Labels aren't

available for this dataset, so only raw images are provided.

"""

def load_cell_counting(split=None):

  """Load Cell Counting dataset.

  Loads the cell counting dataset from http://www.robots.ox.ac.uk/~vgg/research/counting/index_org.html.

  """

  data_dir = deepchem.utils.get_data_dir()

  # No tasks since no labels provided.

  cell_counting_tasks = []

  if reload:

    save_dir = os.path.join(data_dir, "cell_counting/" + featurizer + "/" + str(split))

    loaded, all_dataset, transformers = deepchem.utils.save.load_dataset_from_disk(

        save_dir)

    if loaded:

      return cell_counting_tasks, all_dataset, transformers

  dataset_file = os.path.join(data_dir, "cells.zip")

  if not os.path.exists(dataset_file):

    deepchem.utils.download_url(

        'http://www.robots.ox.ac.uk/~vgg/research/counting/cells.zip'

    )

  loader = deepchem.data.ImageLoader(

      tasks=cell_counting_tasks)

  dataset = loader.featurize(dataset_file, shard_size=8192)

  transformers = []

  if split == None:

    return tox21_tasks, (dataset, None, None), transformers

  splitters = {

      'index': deepchem.splits.IndexSplitter(),

      'random': deepchem.splits.RandomSplitter(),

  }

  if split not in splitters:

    raise ValueError("Only index and random splits supported.")

  train, valid, test = splitter.train_valid_test_split(dataset)

  all_dataset = (train, valid, test)

  if reload:

    deepchem.utils.save.save_dataset_to_disk(save_dir, train, valid, test,

                                              transformers)

  return cell_counting_tasks, all_dataset, transformers