Porting to TF2

Fixes #75 (closed)

bob/learn/tensorflow/script/cache_dataset.py

-#!/usr/bin/env python
-"""Trains networks using Tensorflow estimators.
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import logging
-
-import click
-import tensorflow as tf
-
-from bob.extension.scripts.click_helper import ConfigCommand
-from bob.extension.scripts.click_helper import ResourceOption
-from bob.extension.scripts.click_helper import log_parameters
-from bob.extension.scripts.click_helper import verbosity_option
-
-from ..utils import is_argument_available
-
-logger = logging.getLogger(__name__)
-
-
-@click.command(entry_point_group="bob.learn.tensorflow.config", cls=ConfigCommand)
-@click.option(
-    "--input-fn",
-    "-i",
-    required=True,
-    cls=ResourceOption,
-    entry_point_group="bob.learn.tensorflow.input_fn",
-    help="The ``input_fn`` that will return the features and labels. "
-    "You should call the dataset.cache(...) yourself in the input "
-    "function. If the ``input_fn`` accepts a ``cache_only`` argument, "
-    "it will be given as True.",
-)
-@click.option(
-    "--mode",
-    cls=ResourceOption,
-    default=tf.estimator.ModeKeys.TRAIN,
-    show_default=True,
-    type=click.Choice(
-        (
-            tf.estimator.ModeKeys.TRAIN,
-            tf.estimator.ModeKeys.EVAL,
-            tf.estimator.ModeKeys.PREDICT,
-        )
-    ),
-    help="mode value to be given to the input_fn.",
-)
-@verbosity_option(cls=ResourceOption)
-def cache_dataset(input_fn, mode, **kwargs):
-    """Trains networks using Tensorflow estimators."""
-    log_parameters(logger)
-
-    kwargs = {}
-    if is_argument_available("cache_only", input_fn):
-        kwargs["cache_only"] = True
-        logger.info("cache_only as True will be passed to input_fn.")
-
-    # call the input function manually
-    with tf.compat.v1.Session() as sess:
-        data = input_fn(mode, **kwargs)
-        if isinstance(data, tf.data.Dataset):
-            iterator = tf.compat.v1.data.make_initializable_iterator(data)
-            data = iterator.get_next()
-            sess.run(iterator.initializer)
-        sess.run(tf.compat.v1.initializers.global_variables())
-        try:
-            while True:
-                sess.run(data)
-        except tf.errors.OutOfRangeError:
-            click.echo("Finished reading the dataset.")
-            return