def test_deprecated_illegal_args(self): instructions = "This is how you update..." with self.assertRaisesRegexp(ValueError, "date"): deprecation.deprecated(None, instructions) with self.assertRaisesRegexp(ValueError, "date"): deprecation.deprecated("", instructions) with self.assertRaisesRegexp(ValueError, "YYYY-MM-DD"): deprecation.deprecated("07-04-2016", instructions) date = "2016-07-04" with self.assertRaisesRegexp(ValueError, "instructions"): deprecation.deprecated(date, None) with self.assertRaisesRegexp(ValueError, "instructions"): deprecation.deprecated(date, "")
def test_deprecated_namedtuple(self, mock_warning): date = "2016-07-04" instructions = "This is how you update..." mytuple = deprecation.deprecated(date, instructions, warn_once=True)( collections.namedtuple("my_tuple", ["field1", "field2"])) mytuple(1, 2) self.assertEqual(1, mock_warning.call_count) mytuple(3, 4) self.assertEqual(1, mock_warning.call_count) self.assertIn("IS DEPRECATED", mytuple.__doc__)
from tensorflow.python.training import monitored_session from tensorflow.python.training import queue_runner from tensorflow.python.training import saver as tf_saver from tensorflow.python.training import session_manager as session_manager_lib from tensorflow.python.training import summary_io from tensorflow.python.training import supervisor as tf_supervisor from tensorflow.python.util.deprecation import deprecated # Singleton for SummaryWriter per logdir folder. _SUMMARY_WRITERS = {} # Lock protecting _SUMMARY_WRITERS _summary_writer_lock = threading.Lock() _graph_action_deprecation = deprecated( '2017-02-15', 'graph_actions.py will be deleted. Use tf.train.* utilities instead. ' 'You can use learn/estimators/estimator.py as an example.') @_graph_action_deprecation def clear_summary_writers(): """Clear cached summary writers. Currently only used for unit tests.""" return summary_io.SummaryWriterCache.clear() def get_summary_writer(logdir): """Returns single SummaryWriter per logdir in current run. Args: logdir: str, folder to write summaries.
m = np.max(a.shape[-2:].as_list()) else: m = tf.reduce_max(tf.shape(a)[-2:]) eps = np.finfo(dtype_util.as_numpy_dtype(a.dtype)).eps tol = (eps * tf.cast(m, a.dtype) * tf.reduce_max(s, axis=-1, keepdims=True)) return tf.reduce_sum(tf.cast(s > tol, tf.int32), axis=-1) try: matrix_rank = tf.linalg.matrix_rank except AttributeError: pass matrix_rank = deprecation.deprecated( '2019-10-01', 'tfp.math.matrix_rank is deprecated. Use tf.linalg.matrix_rank instead', warn_once=True)(matrix_rank) def cholesky_concat(chol, cols, name=None): """Concatenates `chol @ chol.T` with additional rows and columns. This operation is conceptually identical to: ```python def cholesky_concat_slow(chol, cols): # cols shaped (n + m) x m = z x m mat = tf.matmul(chol, chol, adjoint_b=True) # batch of n x n # Concat columns. mat = tf.concat([mat, cols[..., :tf.shape(mat)[-2], :]], axis=-1) # n x z # Concat rows. mat = tf.concat([mat, tf.linalg.matrix_transpose(cols)], axis=-2) # z x z return tf.linalg.cholesky(mat)
# See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """Functions for computing statistics of samples.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow_probability.python import stats from tensorflow.python.util import deprecation __all__ = [ "auto_correlation", "percentile", ] auto_correlation_deprecator = deprecation.deprecated( "2018-10-01", "auto_correlation is moved to the `stats` namespace. Access it via: " "`tfp.stats.auto_correlation`.", warn_once=True) auto_correlation = auto_correlation_deprecator(stats.auto_correlation) percentile_deprecator = deprecation.deprecated( "2018-10-01", "percentile is moved to the `stats` namespace. Access it via: " "`tfp.stats.percentile`.", warn_once=True) percentile = percentile_deprecator(stats.percentile)
scale = tf.convert_to_tensor(value=scale, name="softplus_scale", dtype=dtype) super(_InverseGammaWithSoftplusConcentrationScale, self).__init__( concentration=tf.nn.softplus(concentration, name="softplus_concentration"), scale=tf.nn.softplus(scale, name="softplus_scale"), validate_args=validate_args, allow_nan_stats=allow_nan_stats, name=name) self._parameters = parameters _rate_deprecator = deprecation.deprecated( "2019-06-05", "InverseGammaWithSoftplusConcentrationRate is deprecated, use " "InverseGamma(concentration=tf.nn.softplus(concentration), " "scale=tf.nn.softplus(scale)) instead.", warn_once=True) # pylint: disable=invalid-name InverseGammaWithSoftplusConcentrationRate = _rate_deprecator( _InverseGammaWithSoftplusConcentrationScale) _scale_deprecator = deprecation.deprecated( "2019-06-05", "InverseGammaWithSoftplusConcentrationScale is deprecated, use " "InverseGamma(concentration=tf.nn.softplus(concentration), " "scale=tf.nn.softplus(scale)) instead.", warn_once=True) InverseGammaWithSoftplusConcentrationScale = _scale_deprecator( _InverseGammaWithSoftplusConcentrationScale)
from tensorflow_probability.python.math.root_search import secant_root from tensorflow_probability.python.math.scan_associative import scan_associative from tensorflow_probability.python.math.sparse import dense_to_sparse from tensorflow_probability.python.math.special import erfcinv from tensorflow_probability.python.math.special import lambertw from tensorflow_probability.python.math.special import lambertw_winitzki_approx from tensorflow_probability.python.math.special import lbeta from tensorflow_probability.python.math.special import log_gamma_correction from tensorflow_probability.python.math.special import log_gamma_difference from tensorflow_probability.python.math.special import round_exponential_bump_function from tensorflow_probability.python.random import rademacher as random_rademacher from tensorflow_probability.python.random import rayleigh as random_rayleigh from tensorflow.python.util import deprecation # pylint: disable=g-direct-tensorflow-import random_rademacher = deprecation.deprecated( '2020-09-20', 'Use tfp.random.rademacher')(random_rademacher) random_rayleigh = deprecation.deprecated( '2020-09-20', 'Use tfp.random.rayleigh')(random_rayleigh) _allowed_symbols = [ 'round_exponential_bump_function', 'batch_interp_regular_1d_grid', 'batch_interp_regular_nd_grid', 'bessel_iv_ratio', 'bessel_ive', 'bessel_kve', 'cholesky_concat', 'cholesky_update', 'clip_by_value_preserve_gradient', 'custom_gradient', 'dense_to_sparse',
from tensorflow_probability.python.internal.reparameterization import FULLY_REPARAMETERIZED from tensorflow_probability.python.internal.reparameterization import NOT_REPARAMETERIZED from tensorflow_probability.python.internal.reparameterization import ReparameterizationType # Deprecated: from tensorflow_probability.python.experimental.substrates.numpy.math.generic import reduce_weighted_logsumexp as _reduce_weighted_logsumexp from tensorflow_probability.python.experimental.substrates.numpy.math.generic import softplus_inverse as _softplus_inverse from tensorflow_probability.python.experimental.substrates.numpy.math.linalg import fill_triangular as _fill_triangular from tensorflow_probability.python.experimental.substrates.numpy.math.linalg import fill_triangular_inverse as _fill_triangular_inverse from tensorflow_probability.python.experimental.substrates.numpy.util.seed_stream import SeedStream as _SeedStream # Module management: from tensorflow_probability.python.experimental.substrates.numpy.distributions.kullback_leibler import augment_kl_xent_docs from tensorflow.python.util import deprecation # pylint: disable=g-direct-tensorflow-import _deprecated = deprecation.deprecated('2019-10-01', 'This function has moved to `tfp.math`.') fill_triangular = _deprecated(_fill_triangular) fill_triangular_inverse = _deprecated(_fill_triangular_inverse) softplus_inverse = _deprecated(_softplus_inverse) reduce_weighted_logsumexp = _deprecated(_reduce_weighted_logsumexp) class SeedStream(_SeedStream): __init__ = deprecation.deprecated( '2019-10-01', 'SeedStream has moved to `tfp.util.SeedStream`.')(_SeedStream.__init__)
class SeedStream(_SeedStream): __init__ = deprecation.deprecated( '2019-10-01', 'SeedStream has moved to `tfp.util.SeedStream`.')(_SeedStream.__init__)
Deprecated: please see the new location of this module at `tfx.types.artifact` and `tfx.types.artifact_utils`. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from typing import Dict, List, Text from tensorflow.python.util import deprecation # pylint: disable=g-direct-tensorflow-import from tfx.types import artifact_utils from tfx.types.artifact import Artifact TfxType = deprecation.deprecated( # pylint: disable=invalid-name None, 'tfx.utils.types.TfxType has been renamed to tfx.types.Artifact as of ' 'TFX 0.14.0.')(Artifact) TfxArtifact = deprecation.deprecated( # pylint: disable=invalid-name None, 'tfx.utils.types.TfxArtifact has been renamed to tfx.types.Artifact as of ' 'TFX 0.14.0.')(Artifact) @deprecation.deprecated( None, 'tfx.utils.types.parse_tfx_type_dict has been renamed to ' 'tfx.types.artifact_utils.parse_artifact_dict as of TFX 0.14.0.') def parse_tfx_type_dict(json_str: Text) -> Dict[Text, List[Artifact]]: return artifact_utils.parse_artifact_dict(json_str)
name="InverseGammaWithSoftplusConcentrationScale"): if rate is not None: scale = rate parameters = dict(locals()) with tf.name_scope(name, values=[concentration, scale]) as name: dtype = dtype_util.common_dtype([concentration, scale]) concentration = tf.convert_to_tensor(value=concentration, name="softplus_concentration", dtype=dtype) scale = tf.convert_to_tensor(value=scale, name="softplus_scale", dtype=dtype) super(InverseGammaWithSoftplusConcentrationScale, self).__init__( concentration=tf.nn.softplus(concentration, name="softplus_concentration"), scale=tf.nn.softplus(scale, name="softplus_scale"), validate_args=validate_args, allow_nan_stats=allow_nan_stats, name=name) self._parameters = parameters _rate_deprecator = deprecation.deprecated( "2019-05-08", "InverseGammaWithSoftplusConcentrationRate is deprecated, use " "InverseGammaWithSoftplusConcentrationScale instead.", warn_once=True) # pylint: disable=invalid-name InverseGammaWithSoftplusConcentrationRate = _rate_deprecator( InverseGammaWithSoftplusConcentrationScale)
from tensorflow.python.training import coordinator from tensorflow.python.training import queue_runner from tensorflow.python.training import saver as tf_saver from tensorflow.python.training import session_manager as session_manager_lib from tensorflow.python.training import summary_io from tensorflow.python.training import supervisor as tf_supervisor from tensorflow.python.util.deprecation import deprecated # Singleton for SummaryWriter per logdir folder. _SUMMARY_WRITERS = {} # Lock protecting _SUMMARY_WRITERS _summary_writer_lock = threading.Lock() _graph_action_deprecation = deprecated( '2017-02-15', 'graph_actions.py will be deleted. Use tf.train.* utilities instead. ' 'You can use learn/estimators/estimator.py as an example.') @_graph_action_deprecation def clear_summary_writers(): """Clear cached summary writers. Currently only used for unit tests.""" return summary_io.SummaryWriterCache.clear() @deprecated(None, 'Use `SummaryWriterCache.get` directly.') def get_summary_writer(logdir): """Returns single SummaryWriter per logdir in current run. Args: logdir: str, folder to write summaries.
'pipeline_config_path', '', 'Path to a pipeline_pb2.TrainEvalPipelineConfig config ' 'file. If provided, other configs are ignored') flags.DEFINE_string('eval_config_path', '', 'Path to an eval_pb2.EvalConfig config file.') flags.DEFINE_string('input_config_path', '', 'Path to an input_reader_pb2.InputReader config file.') flags.DEFINE_string('model_config_path', '', 'Path to a model_pb2.DetectionModel config file.') flags.DEFINE_boolean( 'run_once', False, 'Option to only run a single pass of ' 'evaluation. Overrides the `max_evals` parameter in the ' 'provided config.') FLAGS = flags.FLAGS deprecated(None, 'Use object_detection/model_main.py.') def main(unused_argv): assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.' assert FLAGS.eval_dir, '`eval_dir` is missing.' tf.gfile.MakeDirs(FLAGS.eval_dir) if FLAGS.pipeline_config_path: configs = config_util.get_configs_from_pipeline_file( FLAGS.pipeline_config_path) tf.gfile.Copy(FLAGS.pipeline_config_path, os.path.join(FLAGS.eval_dir, 'pipeline.config'), overwrite=True) else: configs = config_util.get_configs_from_multiple_files( model_config_path=FLAGS.model_config_path,
from tensorflow.python.ops import gen_sparse_ops from tensorflow.python.ops import gen_spectral_ops from tensorflow.python.platform import tf_logging as logging # go/tf-wildcard-import # pylint: disable=wildcard-import from tensorflow.python.ops.gen_math_ops import * # pylint: enable=wildcard-import from tensorflow.python.util import compat from tensorflow.python.util import deprecation from tensorflow.python.util import nest from tensorflow.python.util.tf_export import tf_export # Aliases for some automatically-generated names. linspace = gen_math_ops.lin_space arg_max = deprecation.deprecated(None, "Use `argmax` instead")(arg_max) # pylint: disable=used-before-assignment arg_min = deprecation.deprecated(None, "Use `argmin` instead")(arg_min) # pylint: disable=used-before-assignment tf_export("arg_max")(arg_max) tf_export("arg_min")(arg_min) # This is set by resource_variable_ops.py. It is included in this way since # there is a circular dependency between math_ops and resource_variable_ops _resource_variable_type = None def _set_doc(doc): def _decorator(func): func.__doc__ = doc return func return _decorator