generic_utils.py

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Python utilities required by Keras."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import binascii
import codecs
import marshal
import os
import re
import sys
import time
import types as python_types

import numpy as np
import six

from tensorflow.python.util import nest
from tensorflow.python.util import tf_decorator
from tensorflow.python.util import tf_inspect
from tensorflow.python.util.tf_export import tf_export

_GLOBAL_CUSTOM_OBJECTS = {}


@tf_export('keras.utils.CustomObjectScope')
class CustomObjectScope(object):
  """Provides a scope that changes to `_GLOBAL_CUSTOM_OBJECTS` cannot escape.

  Code within a `with` statement will be able to access custom objects
  by name. Changes to global custom objects persist
  within the enclosing `with` statement. At end of the `with` statement,
  global custom objects are reverted to state
  at beginning of the `with` statement.

  Example:

  Consider a custom object `MyObject` (e.g. a class):

  ```python
      with CustomObjectScope({'MyObject':MyObject}):
          layer = Dense(..., kernel_regularizer='MyObject')
          # save, load, etc. will recognize custom object by name
  ```
  """

  def __init__(self, *args):
    self.custom_objects = args
    self.backup = None

  def __enter__(self):
    self.backup = _GLOBAL_CUSTOM_OBJECTS.copy()
    for objects in self.custom_objects:
      _GLOBAL_CUSTOM_OBJECTS.update(objects)
    return self

  def __exit__(self, *args, **kwargs):
    _GLOBAL_CUSTOM_OBJECTS.clear()
    _GLOBAL_CUSTOM_OBJECTS.update(self.backup)


@tf_export('keras.utils.custom_object_scope')
def custom_object_scope(*args):
  """Provides a scope that changes to `_GLOBAL_CUSTOM_OBJECTS` cannot escape.

  Convenience wrapper for `CustomObjectScope`.
  Code within a `with` statement will be able to access custom objects
  by name. Changes to global custom objects persist
  within the enclosing `with` statement. At end of the `with` statement,
  global custom objects are reverted to state
  at beginning of the `with` statement.

  Example:

  Consider a custom object `MyObject`

  ```python
      with custom_object_scope({'MyObject':MyObject}):
          layer = Dense(..., kernel_regularizer='MyObject')
          # save, load, etc. will recognize custom object by name
  ```

  Arguments:
      *args: Variable length list of dictionaries of name,
          class pairs to add to custom objects.

  Returns:
      Object of type `CustomObjectScope`.
  """
  return CustomObjectScope(*args)


@tf_export('keras.utils.get_custom_objects')
def get_custom_objects():
  """Retrieves a live reference to the global dictionary of custom objects.

  Updating and clearing custom objects using `custom_object_scope`
  is preferred, but `get_custom_objects` can
  be used to directly access `_GLOBAL_CUSTOM_OBJECTS`.

  Example:

  ```python
      get_custom_objects().clear()
      get_custom_objects()['MyObject'] = MyObject
  ```

  Returns:
      Global dictionary of names to classes (`_GLOBAL_CUSTOM_OBJECTS`).
  """
  return _GLOBAL_CUSTOM_OBJECTS


def serialize_keras_class_and_config(cls_name, cls_config):
  """Returns the serialization of the class with the given config."""
  return {'class_name': cls_name, 'config': cls_config}


@tf_export('keras.utils.serialize_keras_object')
def serialize_keras_object(instance):
  _, instance = tf_decorator.unwrap(instance)
  if instance is None:
    return None
  if hasattr(instance, 'get_config'):
    return serialize_keras_class_and_config(instance.__class__.__name__,
                                            instance.get_config())
  if hasattr(instance, '__name__'):
    return instance.__name__
  else:
    raise ValueError('Cannot serialize', instance)


def class_and_config_for_serialized_keras_object(
    config,
    module_objects=None,
    custom_objects=None,
    printable_module_name='object'):
  """Returns the class name and config for a serialized keras object."""
  if (not isinstance(config, dict) or 'class_name' not in config or
      'config' not in config):
    raise ValueError('Improper config format: ' + str(config))

  class_name = config['class_name']
  if custom_objects and class_name in custom_objects:
    cls = custom_objects[class_name]
  elif class_name in _GLOBAL_CUSTOM_OBJECTS:
    cls = _GLOBAL_CUSTOM_OBJECTS[class_name]
  else:
    module_objects = module_objects or {}
    cls = module_objects.get(class_name)
    if cls is None:
      raise ValueError('Unknown ' + printable_module_name + ': ' + class_name)
  return (cls, config['config'])


@tf_export('keras.utils.deserialize_keras_object')
def deserialize_keras_object(identifier,
                             module_objects=None,
                             custom_objects=None,
                             printable_module_name='object'):
  if identifier is None:
    return None
  if isinstance(identifier, dict):
    # In this case we are dealing with a Keras config dictionary.
    config = identifier
    (cls, cls_config) = class_and_config_for_serialized_keras_object(
        config, module_objects, custom_objects, printable_module_name)

    if hasattr(cls, 'from_config'):
      arg_spec = tf_inspect.getfullargspec(cls.from_config)
      custom_objects = custom_objects or {}

      if 'custom_objects' in arg_spec.args:
        return cls.from_config(
            cls_config,
            custom_objects=dict(
                list(_GLOBAL_CUSTOM_OBJECTS.items()) +
                list(custom_objects.items())))
      with CustomObjectScope(custom_objects):
        return cls.from_config(cls_config)
    else:
      # Then `cls` may be a function returning a class.
      # in this case by convention `config` holds
      # the kwargs of the function.
      custom_objects = custom_objects or {}
      with CustomObjectScope(custom_objects):
        return cls(**cls_config)
  elif isinstance(identifier, six.string_types):
    function_name = identifier
    if custom_objects and function_name in custom_objects:
      fn = custom_objects.get(function_name)
    elif function_name in _GLOBAL_CUSTOM_OBJECTS:
      fn = _GLOBAL_CUSTOM_OBJECTS[function_name]
    else:
      fn = module_objects.get(function_name)
      if fn is None:
        raise ValueError('Unknown ' + printable_module_name + ':' +
                         function_name)
    return fn
  else:
    raise ValueError('Could not interpret serialized ' + printable_module_name +
                     ': ' + identifier)


def func_dump(func):
  """Serializes a user defined function.

  Arguments:
      func: the function to serialize.

  Returns:
      A tuple `(code, defaults, closure)`.
  """
  if os.name == 'nt':
    raw_code = marshal.dumps(func.__code__).replace(b'\\', b'/')
    code = codecs.encode(raw_code, 'base64').decode('ascii')
  else:
    raw_code = marshal.dumps(func.__code__)
    code = codecs.encode(raw_code, 'base64').decode('ascii')
  defaults = func.__defaults__
  if func.__closure__:
    closure = tuple(c.cell_contents for c in func.__closure__)
  else:
    closure = None
  return code, defaults, closure


def func_load(code, defaults=None, closure=None, globs=None):
  """Deserializes a user defined function.

  Arguments:
      code: bytecode of the function.
      defaults: defaults of the function.
      closure: closure of the function.
      globs: dictionary of global objects.

  Returns:
      A function object.
  """
  if isinstance(code, (tuple, list)):  # unpack previous dump
    code, defaults, closure = code
    if isinstance(defaults, list):
      defaults = tuple(defaults)

  def ensure_value_to_cell(value):
    """Ensures that a value is converted to a python cell object.

    Arguments:
        value: Any value that needs to be casted to the cell type

    Returns:
        A value wrapped as a cell object (see function "func_load")
    """
    def dummy_fn():
      # pylint: disable=pointless-statement
      value  # just access it so it gets captured in .__closure__

    cell_value = dummy_fn.__closure__[0]
    if not isinstance(value, type(cell_value)):
      return cell_value
    else:
      return value

  if closure is not None:
    closure = tuple(ensure_value_to_cell(_) for _ in closure)
  try:
    raw_code = codecs.decode(code.encode('ascii'), 'base64')
  except (UnicodeEncodeError, binascii.Error):
    raw_code = code.encode('raw_unicode_escape')
  code = marshal.loads(raw_code)
  if globs is None:
    globs = globals()
  return python_types.FunctionType(
      code, globs, name=code.co_name, argdefs=defaults, closure=closure)


def has_arg(fn, name, accept_all=False):
  """Checks if a callable accepts a given keyword argument.

  Arguments:
      fn: Callable to inspect.
      name: Check if `fn` can be called with `name` as a keyword argument.
      accept_all: What to return if there is no parameter called `name`
                  but the function accepts a `**kwargs` argument.

  Returns:
      bool, whether `fn` accepts a `name` keyword argument.
  """
  arg_spec = tf_inspect.getfullargspec(fn)
  if accept_all and arg_spec.varkw is not None:
    return True
  return name in arg_spec.args


@tf_export('keras.utils.Progbar')
class Progbar(object):
  """Displays a progress bar.

  Arguments:
      target: Total number of steps expected, None if unknown.
      width: Progress bar width on screen.
      verbose: Verbosity mode, 0 (silent), 1 (verbose), 2 (semi-verbose)
      stateful_metrics: Iterable of string names of metrics that
          should *not* be averaged over time. Metrics in this list
          will be displayed as-is. All others will be averaged
          by the progbar before display.
      interval: Minimum visual progress update interval (in seconds).
      unit_name: Display name for step counts (usually "step" or "sample").
  """

  def __init__(self, target, width=30, verbose=1, interval=0.05,
               stateful_metrics=None, unit_name='step'):
    self.target = target
    self.width = width
    self.verbose = verbose
    self.interval = interval
    self.unit_name = unit_name
    if stateful_metrics:
      self.stateful_metrics = set(stateful_metrics)
    else:
      self.stateful_metrics = set()

    self._dynamic_display = ((hasattr(sys.stdout, 'isatty') and
                              sys.stdout.isatty()) or
                             'ipykernel' in sys.modules or
                             'posix' in sys.modules)
    self._total_width = 0
    self._seen_so_far = 0
    # We use a dict + list to avoid garbage collection
    # issues found in OrderedDict
    self._values = {}
    self._values_order = []
    self._start = time.time()
    self._last_update = 0

  def update(self, current, values=None):
    """Updates the progress bar.

    Arguments:
        current: Index of current step.
        values: List of tuples:
            `(name, value_for_last_step)`.
            If `name` is in `stateful_metrics`,
            `value_for_last_step` will be displayed as-is.
            Else, an average of the metric over time will be displayed.
    """
    values = values or []
    for k, v in values:
      if k not in self._values_order:
        self._values_order.append(k)
      if k not in self.stateful_metrics:
        if k not in self._values:
          self._values[k] = [v * (current - self._seen_so_far),
                             current - self._seen_so_far]
        else:
          self._values[k][0] += v * (current - self._seen_so_far)
          self._values[k][1] += (current - self._seen_so_far)
      else:
        # Stateful metrics output a numeric value. This representation
        # means "take an average from a single value" but keeps the
        # numeric formatting.
        self._values[k] = [v, 1]
    self._seen_so_far = current

    now = time.time()
    info = ' - %.0fs' % (now - self._start)
    if self.verbose == 1:
      if (now - self._last_update < self.interval and
          self.target is not None and current < self.target):
        return

      prev_total_width = self._total_width
      if self._dynamic_display:
        sys.stdout.write('\b' * prev_total_width)
        sys.stdout.write('\r')
      else:
        sys.stdout.write('\n')

      if self.target is not None:
        numdigits = int(np.floor(np.log10(self.target))) + 1
        barstr = '%%%dd/%d [' % (numdigits, self.target)
        bar = barstr % current
        prog = float(current) / self.target
        prog_width = int(self.width * prog)
        if prog_width > 0:
          bar += ('=' * (prog_width - 1))
          if current < self.target:
            bar += '>'
          else:
            bar += '='
        bar += ('.' * (self.width - prog_width))
        bar += ']'
      else:
        bar = '%7d/Unknown' % current

      self._total_width = len(bar)
      sys.stdout.write(bar)

      if current:
        time_per_unit = (now - self._start) / current
      else:
        time_per_unit = 0
      if self.target is not None and current < self.target:
        eta = time_per_unit * (self.target - current)
        if eta > 3600:
          eta_format = '%d:%02d:%02d' % (eta // 3600,
                                         (eta % 3600) // 60,
                                         eta % 60)
        elif eta > 60:
          eta_format = '%d:%02d' % (eta // 60, eta % 60)
        else:
          eta_format = '%ds' % eta

        info = ' - ETA: %s' % eta_format
      else:
        if time_per_unit >= 1 or time_per_unit == 0:
          info += ' %.0fs/%s' % (time_per_unit, self.unit_name)
        elif time_per_unit >= 1e-3:
          info += ' %.0fms/%s' % (time_per_unit * 1e3, self.unit_name)
        else:
          info += ' %.0fus/%s' % (time_per_unit * 1e6, self.unit_name)

      for k in self._values_order:
        info += ' - %s:' % k
        if isinstance(self._values[k], list):
          avg = np.mean(self._values[k][0] / max(1, self._values[k][1]))
          if abs(avg) > 1e-3:
            info += ' %.4f' % avg
          else:
            info += ' %.4e' % avg
        else:
          info += ' %s' % self._values[k]

      self._total_width += len(info)
      if prev_total_width > self._total_width:
        info += (' ' * (prev_total_width - self._total_width))

      if self.target is not None and current >= self.target:
        info += '\n'

      sys.stdout.write(info)
      sys.stdout.flush()

    elif self.verbose == 2:
      if self.target is None or current >= self.target:
        for k in self._values_order:
          info += ' - %s:' % k
          avg = np.mean(self._values[k][0] / max(1, self._values[k][1]))
          if avg > 1e-3:
            info += ' %.4f' % avg
          else:
            info += ' %.4e' % avg
        info += '\n'

        sys.stdout.write(info)
        sys.stdout.flush()

    self._last_update = now

  def add(self, n, values=None):
    self.update(self._seen_so_far + n, values)


def make_batches(size, batch_size):
  """Returns a list of batch indices (tuples of indices).

  Arguments:
      size: Integer, total size of the data to slice into batches.
      batch_size: Integer, batch size.

  Returns:
      A list of tuples of array indices.
  """
  num_batches = int(np.ceil(size / float(batch_size)))
  return [(i * batch_size, min(size, (i + 1) * batch_size))
          for i in range(0, num_batches)]


def slice_arrays(arrays, start=None, stop=None):
  """Slice an array or list of arrays.

  This takes an array-like, or a list of
  array-likes, and outputs:
      - arrays[start:stop] if `arrays` is an array-like
      - [x[start:stop] for x in arrays] if `arrays` is a list

  Can also work on list/array of indices: `slice_arrays(x, indices)`

  Arguments:
      arrays: Single array or list of arrays.
      start: can be an integer index (start index)
          or a list/array of indices
      stop: integer (stop index); should be None if
          `start` was a list.

  Returns:
      A slice of the array(s).

  Raises:
      ValueError: If the value of start is a list and stop is not None.
  """
  if arrays is None:
    return [None]
  if isinstance(start, list) and stop is not None:
    raise ValueError('The stop argument has to be None if the value of start '
                     'is a list.')
  elif isinstance(arrays, list):
    if hasattr(start, '__len__'):
      # hdf5 datasets only support list objects as indices
      if hasattr(start, 'shape'):
        start = start.tolist()
      return [None if x is None else x[start] for x in arrays]
    else:
      return [None if x is None else x[start:stop] for x in arrays]
  else:
    if hasattr(start, '__len__'):
      if hasattr(start, 'shape'):
        start = start.tolist()
      return arrays[start]
    elif hasattr(start, '__getitem__'):
      return arrays[start:stop]
    else:
      return [None]


def to_list(x):
  """Normalizes a list/tensor into a list.

  If a tensor is passed, we return
  a list of size 1 containing the tensor.

  Arguments:
      x: target object to be normalized.

  Returns:
      A list.
  """
  if isinstance(x, list):
    return x
  return [x]


def object_list_uid(object_list):
  """Creates a single string from object ids."""
  object_list = nest.flatten(object_list)
  return ', '.join([str(abs(id(x))) for x in object_list])


def to_snake_case(name):
  intermediate = re.sub('(.)([A-Z][a-z0-9]+)', r'\1_\2', name)
  insecure = re.sub('([a-z])([A-Z])', r'\1_\2', intermediate).lower()
  # If the class is private the name starts with "_" which is not secure
  # for creating scopes. We prefix the name with "private" in this case.
  if insecure[0] != '_':
    return insecure
  return 'private' + insecure


def is_all_none(iterable_or_element):
  if not isinstance(iterable_or_element, (list, tuple)):
    iterable = [iterable_or_element]
  else:
    iterable = iterable_or_element
  # We cannot use Python's `any` because the iterable may return Tensors.
  for element in iterable:
    if element is not None:
      return False
  return True