Python utils.read_datasets() Examples

The following are 9 code examples of utils.read_datasets(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module utils , or try the search function .
Example #1
Source File: run_lfads.py    From DOTA_models with Apache License 2.0 5 votes vote down vote up 
def load_datasets(data_dir, data_filename_stem):
  """Load the datasets from a specified directory.

  Example files look like
    >data_dir/my_dataset_first_day
    >data_dir/my_dataset_second_day

  If my_dataset (filename) stem is in the directory, the read routine will try
  and load it.  The datasets dictionary will then look like
  dataset['first_day'] -> (first day data dictionary)
  dataset['second_day'] -> (first day data dictionary)

  Args:
    data_dir: The directory from which to load the datasets.
    data_filename_stem: The stem of the filename for the datasets.

  Returns:
    datasets: a dataset dictionary, with one name->data dictionary pair for
    each dataset file.
  """
  print("Reading data from ", data_dir)
  datasets = utils.read_datasets(data_dir, data_filename_stem)
  for k, data_dict in datasets.items():
    datasets[k] = clean_data_dict(data_dict)

    train_total_size = len(data_dict['train_data'])
    if train_total_size == 0:
      print("Did not load training set.")
    else:
      print("Found training set with number examples: ", train_total_size)

    valid_total_size = len(data_dict['valid_data'])
    if valid_total_size == 0:
      print("Did not load validation set.")
    else:
      print("Found validation set with number examples: ", valid_total_size)

  return datasets
Example #2
Source File: run_lfads.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def load_datasets(data_dir, data_filename_stem):
  """Load the datasets from a specified directory.

  Example files look like
    >data_dir/my_dataset_first_day
    >data_dir/my_dataset_second_day

  If my_dataset (filename) stem is in the directory, the read routine will try
  and load it.  The datasets dictionary will then look like
  dataset['first_day'] -> (first day data dictionary)
  dataset['second_day'] -> (first day data dictionary)

  Args:
    data_dir: The directory from which to load the datasets.
    data_filename_stem: The stem of the filename for the datasets.

  Returns:
    datasets: a dataset dictionary, with one name->data dictionary pair for
    each dataset file.
  """
  print("Reading data from ", data_dir)
  datasets = utils.read_datasets(data_dir, data_filename_stem)
  for k, data_dict in datasets.items():
    datasets[k] = clean_data_dict(data_dict)

    train_total_size = len(data_dict['train_data'])
    if train_total_size == 0:
      print("Did not load training set.")
    else:
      print("Found training set with number examples: ", train_total_size)

    valid_total_size = len(data_dict['valid_data'])
    if valid_total_size == 0:
      print("Did not load validation set.")
    else:
      print("Found validation set with number examples: ", valid_total_size)

  return datasets
Example #3
Source File: run_lfads.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def load_datasets(data_dir, data_filename_stem):
  """Load the datasets from a specified directory.

  Example files look like
    >data_dir/my_dataset_first_day
    >data_dir/my_dataset_second_day

  If my_dataset (filename) stem is in the directory, the read routine will try
  and load it.  The datasets dictionary will then look like
  dataset['first_day'] -> (first day data dictionary)
  dataset['second_day'] -> (first day data dictionary)

  Args:
    data_dir: The directory from which to load the datasets.
    data_filename_stem: The stem of the filename for the datasets.

  Returns:
    datasets: a dataset dictionary, with one name->data dictionary pair for
    each dataset file.
  """
  print("Reading data from ", data_dir)
  datasets = utils.read_datasets(data_dir, data_filename_stem)
  for k, data_dict in datasets.items():
    datasets[k] = clean_data_dict(data_dict)

    train_total_size = len(data_dict['train_data'])
    if train_total_size == 0:
      print("Did not load training set.")
    else:
      print("Found training set with number examples: ", train_total_size)

    valid_total_size = len(data_dict['valid_data'])
    if valid_total_size == 0:
      print("Did not load validation set.")
    else:
      print("Found validation set with number examples: ", valid_total_size)

  return datasets
Example #4
Source File: run_lfads.py    From hands-detection with MIT License 5 votes vote down vote up 
def load_datasets(data_dir, data_filename_stem):
  """Load the datasets from a specified directory.

  Example files look like
    >data_dir/my_dataset_first_day
    >data_dir/my_dataset_second_day

  If my_dataset (filename) stem is in the directory, the read routine will try
  and load it.  The datasets dictionary will then look like
  dataset['first_day'] -> (first day data dictionary)
  dataset['second_day'] -> (first day data dictionary)

  Args:
    data_dir: The directory from which to load the datasets.
    data_filename_stem: The stem of the filename for the datasets.

  Returns:
    datasets: a dataset dictionary, with one name->data dictionary pair for
    each dataset file.
  """
  print("Reading data from ", data_dir)
  datasets = utils.read_datasets(data_dir, data_filename_stem)
  for k, data_dict in datasets.items():
    datasets[k] = clean_data_dict(data_dict)

    train_total_size = len(data_dict['train_data'])
    if train_total_size == 0:
      print("Did not load training set.")
    else:
      print("Found training set with number examples: ", train_total_size)

    valid_total_size = len(data_dict['valid_data'])
    if valid_total_size == 0:
      print("Did not load validation set.")
    else:
      print("Found validation set with number examples: ", valid_total_size)

  return datasets
Example #5
Source File: run_lfads.py    From object_detection_kitti with Apache License 2.0 5 votes vote down vote up 
def load_datasets(data_dir, data_filename_stem):
  """Load the datasets from a specified directory.

  Example files look like
    >data_dir/my_dataset_first_day
    >data_dir/my_dataset_second_day

  If my_dataset (filename) stem is in the directory, the read routine will try
  and load it.  The datasets dictionary will then look like
  dataset['first_day'] -> (first day data dictionary)
  dataset['second_day'] -> (first day data dictionary)

  Args:
    data_dir: The directory from which to load the datasets.
    data_filename_stem: The stem of the filename for the datasets.

  Returns:
    datasets: a dataset dictionary, with one name->data dictionary pair for
    each dataset file.
  """
  print("Reading data from ", data_dir)
  datasets = utils.read_datasets(data_dir, data_filename_stem)
  for k, data_dict in datasets.items():
    datasets[k] = clean_data_dict(data_dict)

    train_total_size = len(data_dict['train_data'])
    if train_total_size == 0:
      print("Did not load training set.")
    else:
      print("Found training set with number examples: ", train_total_size)

    valid_total_size = len(data_dict['valid_data'])
    if valid_total_size == 0:
      print("Did not load validation set.")
    else:
      print("Found validation set with number examples: ", valid_total_size)

  return datasets
Example #6
Source File: run_lfads.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def load_datasets(data_dir, data_filename_stem):
  """Load the datasets from a specified directory.

  Example files look like
    >data_dir/my_dataset_first_day
    >data_dir/my_dataset_second_day

  If my_dataset (filename) stem is in the directory, the read routine will try
  and load it.  The datasets dictionary will then look like
  dataset['first_day'] -> (first day data dictionary)
  dataset['second_day'] -> (first day data dictionary)

  Args:
    data_dir: The directory from which to load the datasets.
    data_filename_stem: The stem of the filename for the datasets.

  Returns:
    datasets: a dataset dictionary, with one name->data dictionary pair for
    each dataset file.
  """
  print("Reading data from ", data_dir)
  datasets = utils.read_datasets(data_dir, data_filename_stem)
  for k, data_dict in datasets.items():
    datasets[k] = clean_data_dict(data_dict)

    train_total_size = len(data_dict['train_data'])
    if train_total_size == 0:
      print("Did not load training set.")
    else:
      print("Found training set with number examples: ", train_total_size)

    valid_total_size = len(data_dict['valid_data'])
    if valid_total_size == 0:
      print("Did not load validation set.")
    else:
      print("Found validation set with number examples: ", valid_total_size)

  return datasets
Example #7
Source File: run_lfads.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def load_datasets(data_dir, data_filename_stem):
  """Load the datasets from a specified directory.

  Example files look like
    >data_dir/my_dataset_first_day
    >data_dir/my_dataset_second_day

  If my_dataset (filename) stem is in the directory, the read routine will try
  and load it.  The datasets dictionary will then look like
  dataset['first_day'] -> (first day data dictionary)
  dataset['second_day'] -> (first day data dictionary)

  Args:
    data_dir: The directory from which to load the datasets.
    data_filename_stem: The stem of the filename for the datasets.

  Returns:
    datasets: a dataset dictionary, with one name->data dictionary pair for
    each dataset file.
  """
  print("Reading data from ", data_dir)
  datasets = utils.read_datasets(data_dir, data_filename_stem)
  for k, data_dict in datasets.items():
    datasets[k] = clean_data_dict(data_dict)

    train_total_size = len(data_dict['train_data'])
    if train_total_size == 0:
      print("Did not load training set.")
    else:
      print("Found training set with number examples: ", train_total_size)

    valid_total_size = len(data_dict['valid_data'])
    if valid_total_size == 0:
      print("Did not load validation set.")
    else:
      print("Found validation set with number examples: ", valid_total_size)

  return datasets
Example #8
Source File: run_lfads.py    From models with Apache License 2.0 5 votes vote down vote up 
def load_datasets(data_dir, data_filename_stem):
  """Load the datasets from a specified directory.

  Example files look like
    >data_dir/my_dataset_first_day
    >data_dir/my_dataset_second_day

  If my_dataset (filename) stem is in the directory, the read routine will try
  and load it.  The datasets dictionary will then look like
  dataset['first_day'] -> (first day data dictionary)
  dataset['second_day'] -> (first day data dictionary)

  Args:
    data_dir: The directory from which to load the datasets.
    data_filename_stem: The stem of the filename for the datasets.

  Returns:
    datasets: a dataset dictionary, with one name->data dictionary pair for
    each dataset file.
  """
  print("Reading data from ", data_dir)
  datasets = utils.read_datasets(data_dir, data_filename_stem)
  for k, data_dict in datasets.items():
    datasets[k] = clean_data_dict(data_dict)

    train_total_size = len(data_dict['train_data'])
    if train_total_size == 0:
      print("Did not load training set.")
    else:
      print("Found training set with number examples: ", train_total_size)

    valid_total_size = len(data_dict['valid_data'])
    if valid_total_size == 0:
      print("Did not load validation set.")
    else:
      print("Found validation set with number examples: ", valid_total_size)

  return datasets
Example #9
Source File: run_lfads.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def load_datasets(data_dir, data_filename_stem):
  """Load the datasets from a specified directory.

  Example files look like
    >data_dir/my_dataset_first_day
    >data_dir/my_dataset_second_day

  If my_dataset (filename) stem is in the directory, the read routine will try
  and load it.  The datasets dictionary will then look like
  dataset['first_day'] -> (first day data dictionary)
  dataset['second_day'] -> (first day data dictionary)

  Args:
    data_dir: The directory from which to load the datasets.
    data_filename_stem: The stem of the filename for the datasets.

  Returns:
    datasets: a dataset dictionary, with one name->data dictionary pair for
    each dataset file.
  """
  print("Reading data from ", data_dir)
  datasets = utils.read_datasets(data_dir, data_filename_stem)
  for k, data_dict in datasets.items():
    datasets[k] = clean_data_dict(data_dict)

    train_total_size = len(data_dict['train_data'])
    if train_total_size == 0:
      print("Did not load training set.")
    else:
      print("Found training set with number examples: ", train_total_size)

    valid_total_size = len(data_dict['valid_data'])
    if valid_total_size == 0:
      print("Did not load validation set.")
    else:
      print("Found validation set with number examples: ", valid_total_size)

  return datasets