models/official/utils/misc/model_helpers.py

# Copyright 2018 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.
# ==============================================================================
"""Miscellaneous functions that can be called by models."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numbers

from absl import logging
import tensorflow as tf

from tensorflow.python.util import nest
# pylint:disable=logging-format-interpolation


def past_stop_threshold(stop_threshold, eval_metric):
  """Return a boolean representing whether a model should be stopped.

  Args:
    stop_threshold: float, the threshold above which a model should stop
      training.
    eval_metric: float, the current value of the relevant metric to check.

  Returns:
    True if training should stop, False otherwise.

  Raises:
    ValueError: if either stop_threshold or eval_metric is not a number
  """
  if stop_threshold is None:
    return False

  if not isinstance(stop_threshold, numbers.Number):
    raise ValueError("Threshold for checking stop conditions must be a number.")
  if not isinstance(eval_metric, numbers.Number):
    raise ValueError("Eval metric being checked against stop conditions "
                     "must be a number.")

  if eval_metric >= stop_threshold:
    logging.info("Stop threshold of {} was passed with metric value {}.".format(
        stop_threshold, eval_metric))
    return True

  return False


def generate_synthetic_data(
    input_shape, input_value=0, input_dtype=None, label_shape=None,
    label_value=0, label_dtype=None):
  """Create a repeating dataset with constant values.

  Args:
    input_shape: a tf.TensorShape object or nested tf.TensorShapes. The shape of
      the input data.
    input_value: Value of each input element.
    input_dtype: Input dtype. If None, will be inferred by the input value.
    label_shape: a tf.TensorShape object or nested tf.TensorShapes. The shape of
      the label data.
    label_value: Value of each input element.
    label_dtype: Input dtype. If None, will be inferred by the target value.

  Returns:
    Dataset of tensors or tuples of tensors (if label_shape is set).
  """
  # TODO(kathywu): Replace with SyntheticDataset once it is in contrib.
  element = input_element = nest.map_structure(
      lambda s: tf.constant(input_value, input_dtype, s), input_shape)

  if label_shape:
    label_element = nest.map_structure(
        lambda s: tf.constant(label_value, label_dtype, s), label_shape)
    element = (input_element, label_element)

  return tf.data.Dataset.from_tensors(element).repeat()


def apply_clean(flags_obj):
  if flags_obj.clean and tf.io.gfile.exists(flags_obj.model_dir):
    logging.info("--clean flag set. Removing existing model dir:"
                 " {}".format(flags_obj.model_dir))
    tf.io.gfile.rmtree(flags_obj.model_dir)