Python check.IsTrue方法代碼示例

# 需要導入模塊: from syntaxnet.util import check [as 別名]
# 或者: from syntaxnet.util.check import IsTrue [as 別名]
def testCheckIsTrue(self):
    check.IsTrue(1 == 1.0, 'foo')
    check.IsTrue(True, 'foo')
    check.IsTrue([0], 'foo')
    check.IsTrue({'x': 1}, 'foo')
    check.IsTrue(not 0, 'foo')
    check.IsTrue(not None, 'foo')
    with self.assertRaisesRegexp(ValueError, 'bar'):
      check.IsTrue(False, 'bar')
    with self.assertRaisesRegexp(ValueError, 'bar'):
      check.IsTrue(None, 'bar')
    with self.assertRaisesRegexp(ValueError, 'bar'):
      check.IsTrue(0, 'bar')
    with self.assertRaisesRegexp(ValueError, 'bar'):
      check.IsTrue([], 'bar')
    with self.assertRaisesRegexp(ValueError, 'bar'):
      check.IsTrue({}, 'bar')
    with self.assertRaisesRegexp(RuntimeError, 'baz'):
      check.IsTrue('', 'baz', RuntimeError) 

# 需要導入模塊: from syntaxnet.util import check [as 別名]
# 或者: from syntaxnet.util.check import IsTrue [as 別名]
def _validate_embedded_fixed_features(comp):
  """Checks that the embedded fixed features of |comp| are set up properly."""
  for feature in comp.spec.fixed_feature:
    check.Gt(feature.embedding_dim, 0,
             'Embeddings requested for non-embedded feature: %s' % feature)
    if feature.is_constant:
      check.IsTrue(feature.HasField('pretrained_embedding_matrix'),
                   'Constant embeddings must be pretrained: %s' % feature) 

# 需要導入模塊: from syntaxnet.util import check [as 別名]
# 或者: from syntaxnet.util.check import IsTrue [as 別名]
def _validate_embedded_fixed_features(comp):
  """Checks that the embedded fixed features of |comp| are set up properly."""
  for feature in comp.spec.fixed_feature:
    check.Gt(feature.embedding_dim, 0,
             'Embeddings requested for non-embedded feature: %s' % feature)
    if feature.is_constant:
      check.IsTrue(
          feature.HasField('pretrained_embedding_matrix'),
          'Constant embeddings must be pretrained: %s' % feature) 

# 需要導入模塊: from syntaxnet.util import check [as 別名]
# 或者: from syntaxnet.util.check import IsTrue [as 別名]
def fill_from_resources(self, resource_path, tf_master=''):
    """Fills in feature sizes and vocabularies using SyntaxNet lexicon.

    Must be called before the spec is ready to be used to build TensorFlow
    graphs. Requires a SyntaxNet lexicon built at the resource_path. Using the
    lexicon, this will call the SyntaxNet custom ops to return the number of
    features and vocabulary sizes based on the FML specifications and the
    lexicons. It will also compute the number of actions of the transition
    system.

    This will often CHECK-fail if the spec doesn't correspond to a valid
    transition system or feature setup.

    Args:
      resource_path: Path to the lexicon.
      tf_master: TensorFlow master executor (string, defaults to '' to use the
        local instance).
    """
    check.IsTrue(
        self.spec.transition_system.registered_name,
        'Set a transition system before calling fill_from_resources().')

    context = lexicon.create_lexicon_context(resource_path)
    for key, value in self.spec.transition_system.parameters.iteritems():
      context.parameter.add(name=key, value=value)

    context.parameter.add(
        name='brain_parser_embedding_dims',
        value=';'.join(
            [str(x.embedding_dim) for x in self.spec.fixed_feature]))
    context.parameter.add(
        name='brain_parser_features',
        value=';'.join([x.fml for x in self.spec.fixed_feature]))
    context.parameter.add(
        name='brain_parser_predicate_maps',
        value=';'.join(['' for x in self.spec.fixed_feature]))
    context.parameter.add(
        name='brain_parser_embedding_names',
        value=';'.join([x.name for x in self.spec.fixed_feature]))
    context.parameter.add(
        name='brain_parser_transition_system',
        value=self.spec.transition_system.registered_name)

    # Propagate information from SyntaxNet C++ backends into the DRAGNN
    # self.spec.
    with tf.Session(tf_master) as sess:
      feature_sizes, domain_sizes, _, num_actions = sess.run(
          gen_parser_ops.feature_size(task_context_str=str(context)))
      self.spec.num_actions = int(num_actions)
      for i in xrange(len(feature_sizes)):
        self.spec.fixed_feature[i].size = int(feature_sizes[i])
        self.spec.fixed_feature[i].vocabulary_size = int(domain_sizes[i])

    for i in xrange(len(self.spec.linked_feature)):
      self.spec.linked_feature[i].size = len(
          self.spec.linked_feature[i].fml.split(' '))

    for resource in context.input:
      self.spec.resource.add(name=resource.name).part.add(
          file_pattern=resource.part[0].file_pattern)