jsimsa and tensorflower-gardener [tf.data] Internal refactoring of C++ classes and APIs.
- replacing `OpKernelContext` with newly introduced `DatasetContext` in `DatasetBase` constructor to make it possible to instantiate `DatasetBase` in places where an instance of `OpKernelContext` is not available

- replacing `dataset::MakeIteratorContext(OpKernelContext* ctx)` factory with `IteratorContext(OpKernelContext *ctx)` constructor.

- folding `GraphDatasetBase` into `DataseBase` and removing the default implementation of `AsGraphDefInternal`, making it the responsibility of the derived class to implement it to encourage/hint developers to provide serialization logic

PiperOrigin-RevId: 208560010
Latest commit 83f1458 Aug 14, 2018

README.md

tf.contrib.data API

NOTE: The tf.contrib.data module has been deprecated. Use tf.data instead. We are continuing to support existing code using the tf.contrib.data APIs in the current version of TensorFlow, but will eventually remove support. The tf.data APIs are subject to backwards compatibility guarantees.

Porting your code to tf.data

The tf.contrib.data.Dataset class has been renamed to tf.data.Dataset, and the tf.contrib.data.Iterator class has been renamed to tf.data.Iterator. Most code can be ported by removing .contrib from the names of the classes. However, there are some small differences, which are outlined below.

The arguments accepted by the Dataset.map() transformation have changed:

  • dataset.map(..., num_threads=T) is now dataset.map(num_parallel_calls=T).
  • dataset.map(..., output_buffer_size=B) is now dataset.map(...).prefetch(B).

Some transformations have been removed from tf.data.Dataset, and you must instead apply them using Dataset.apply() transformation. The full list of changes is as follows:

  • dataset.dense_to_sparse_batch(...) is now dataset.apply(tf.contrib.data.dense_to_sparse_batch(...).
  • dataset.enumerate(...) is now dataset.apply(tf.contrib.data.enumerate_dataset(...)).
  • dataset.group_by_window(...) is now dataset.apply(tf.contrib.data.group_by_window(...)).
  • dataset.ignore_errors() is now dataset.apply(tf.contrib.data.ignore_errors()).
  • dataset.unbatch() is now dataset.apply(tf.contrib.data.unbatch()).

The Dataset.make_dataset_resource() and Iterator.dispose_op() methods have been removed from the API. Please open a GitHub issue if you have a need for either of these.