B ٻd@sdZddlmZddlmZddlmZddlmZddlmZ ddl m Z dZ d Z e d ed e d ed e dddZe dddZGdddejZdS)z*Cardinality analysis of `Dataset` objects.) dataset_ops)dtypes)ops)gen_dataset_ops)gen_experimental_dataset_ops) tf_exportz&data.experimental.INFINITE_CARDINALITYINFINITEz%data.experimental.UNKNOWN_CARDINALITYUNKNOWNzdata.experimental.cardinalitycCs t|jS)aReturns the cardinality of `dataset`, if known. The operation returns the cardinality of `dataset`. The operation may return `tf.data.experimental.INFINITE_CARDINALITY` if `dataset` contains an infinite number of elements or `tf.data.experimental.UNKNOWN_CARDINALITY` if the analysis fails to determine the number of elements in `dataset` (e.g. when the dataset source is a file). >>> dataset = tf.data.Dataset.range(42) >>> print(tf.data.experimental.cardinality(dataset).numpy()) 42 >>> dataset = dataset.repeat() >>> cardinality = tf.data.experimental.cardinality(dataset) >>> print((cardinality == tf.data.experimental.INFINITE_CARDINALITY).numpy()) True >>> dataset = dataset.filter(lambda x: True) >>> cardinality = tf.data.experimental.cardinality(dataset) >>> print((cardinality == tf.data.experimental.UNKNOWN_CARDINALITY).numpy()) True Args: dataset: A `tf.data.Dataset` for which to determine cardinality. Returns: A scalar `tf.int64` `Tensor` representing the cardinality of `dataset`. If the cardinality is infinite or unknown, the operation returns the named constant `INFINITE_CARDINALITY` and `UNKNOWN_CARDINALITY` respectively. )rZdataset_cardinality_variant_tensor)datasetre/var/www/html/venv/lib/python3.7/site-packages/tensorflow/python/data/experimental/ops/cardinality.py cardinality!srz$data.experimental.assert_cardinalitycsfdd}|S)aTAsserts the cardinality of the input dataset. NOTE: The following assumes that "examples.tfrecord" contains 42 records. >>> dataset = tf.data.TFRecordDataset("examples.tfrecord") >>> cardinality = tf.data.experimental.cardinality(dataset) >>> print((cardinality == tf.data.experimental.UNKNOWN_CARDINALITY).numpy()) True >>> dataset = dataset.apply(tf.data.experimental.assert_cardinality(42)) >>> print(tf.data.experimental.cardinality(dataset).numpy()) 42 Args: expected_cardinality: The expected cardinality of the input dataset. Returns: A `Dataset` transformation function, which can be passed to `tf.data.Dataset.apply`. Raises: FailedPreconditionError: The assertion is checked at runtime (when iterating the dataset) and an error is raised if the actual and expected cardinality differ. cs t|S)N)_AssertCardinalityDataset)r )expected_cardinalityrr _apply_fn]sz%assert_cardinality.._apply_fnr)rrr)rrassert_cardinalityCs rcs eZdZdZfddZZS)rz5A `Dataset` that assert the cardinality of its input.csH||_tj|tjdd|_tj|jj|jf|j }t t | ||dS)Nr)Zdtypename) Z_input_datasetrZconvert_to_tensorrZint64Z_expected_cardinalityged_opsZassert_cardinality_datasetr Z_flat_structuresuperr__init__)selfZ input_datasetrZvariant_tensor) __class__rrrfs z"_AssertCardinalityDataset.__init__)__name__ __module__ __qualname____doc__r __classcell__rr)rrrcsrN)rZtensorflow.python.data.opsrZtensorflow.python.frameworkrrZtensorflow.python.opsrrrZ tensorflow.python.util.tf_exportrr r Zexport_constantrrrZUnaryUnchangedStructureDatasetrrrrrs      "