Python tensor_signature.tensors_compatible函数代码示例

  def testTensorSignatureCompatible(self):
    placeholder_a = tf.placeholder(name='test',
                                   shape=[None, 100],
                                   dtype=tf.int32)
    placeholder_b = tf.placeholder(name='another',
                                   shape=[256, 100],
                                   dtype=tf.int32)
    placeholder_c = tf.placeholder(name='mismatch',
                                   shape=[256, 100],
                                   dtype=tf.float32)
    signatures = tensor_signature.create_signatures(placeholder_a)
    self.assertTrue(tensor_signature.tensors_compatible(placeholder_a,
                                                        signatures))
    self.assertTrue(tensor_signature.tensors_compatible(placeholder_b,
                                                        signatures))
    self.assertFalse(tensor_signature.tensors_compatible(placeholder_c,
                                                         signatures))

    inputs = {'a': placeholder_a}
    signatures = tensor_signature.create_signatures(inputs)
    self.assertTrue(tensor_signature.tensors_compatible(inputs, signatures))
    self.assertFalse(tensor_signature.tensors_compatible(placeholder_a,
                                                         signatures))
    self.assertFalse(tensor_signature.tensors_compatible(placeholder_b,
                                                         signatures))
    self.assertFalse(tensor_signature.tensors_compatible(
        {'b': placeholder_b}, signatures))
    self.assertTrue(tensor_signature.tensors_compatible(
        {'a': placeholder_b,
         'c': placeholder_c}, signatures))
    self.assertFalse(tensor_signature.tensors_compatible(
        {'a': placeholder_c}, signatures))

 def _check_inputs(self, features, targets):
   if self._features_info is not None:
     if not tensor_signature.tensors_compatible(features, self._features_info):
       raise ValueError('Features are incompatible with given information. '
                        'Given features: %s, required signatures: %s.' %
                        (str(features), str(self._features_info)))
   else:
     self._features_info = tensor_signature.create_signatures(features)
   if self._targets_info is not None:
     if not tensor_signature.tensors_compatible(targets, self._targets_info):
       raise ValueError('Targets are incompatible with given information. '
                        'Given targets: %s, required signatures: %s.' %
                        (str(targets), str(self._targets_info)))
   else:
     self._targets_info = tensor_signature.create_signatures(targets)

 def testTensorSignatureExampleParserSingle(self):
   examples = tf.placeholder(name='example', shape=[None], dtype=tf.string)
   placeholder_a = tf.placeholder(name='test',
                                  shape=[None, 100],
                                  dtype=tf.int32)
   signatures = tensor_signature.create_signatures(placeholder_a)
   result = tensor_signature.create_example_parser_from_signatures(
       signatures, examples)
   self.assertTrue(tensor_signature.tensors_compatible(result, signatures))
   new_signatures = tensor_signature.create_signatures(result)
   self.assertTrue(new_signatures.is_compatible_with(signatures))

  def testTensorSignaturePlaceholders(self):
    placeholder_a = array_ops.placeholder(
        name='test', shape=[None, 100], dtype=dtypes.int32)
    signatures = tensor_signature.create_signatures(placeholder_a)
    placeholder_out = tensor_signature.create_placeholders_from_signatures(
        signatures)
    self.assertEqual(placeholder_out.dtype, placeholder_a.dtype)
    self.assertTrue(placeholder_out.get_shape().is_compatible_with(
        placeholder_a.get_shape()))
    self.assertTrue(
        tensor_signature.tensors_compatible(placeholder_out, signatures))

    inputs = {'a': placeholder_a}
    signatures = tensor_signature.create_signatures(inputs)
    placeholders_out = tensor_signature.create_placeholders_from_signatures(
        signatures)
    self.assertEqual(placeholders_out['a'].dtype, placeholder_a.dtype)
    self.assertTrue(placeholders_out['a'].get_shape().is_compatible_with(
        placeholder_a.get_shape()))
    self.assertTrue(
        tensor_signature.tensors_compatible(placeholders_out, signatures))

  def testUnknownShape(self):
    placeholder_unk = array_ops.placeholder(
        name='unk', shape=None, dtype=dtypes.string)
    placeholder_a = array_ops.placeholder(
        name='a', shape=[None], dtype=dtypes.string)
    placeholder_b = array_ops.placeholder(
        name='b', shape=[128, 2], dtype=dtypes.string)
    placeholder_c = array_ops.placeholder(
        name='c', shape=[128, 2], dtype=dtypes.int32)
    unk_signature = tensor_signature.create_signatures(placeholder_unk)
    # Tensors of same dtype match unk shape signature.
    self.assertTrue(
        tensor_signature.tensors_compatible(placeholder_unk, unk_signature))
    self.assertTrue(
        tensor_signature.tensors_compatible(placeholder_a, unk_signature))
    self.assertTrue(
        tensor_signature.tensors_compatible(placeholder_b, unk_signature))
    self.assertFalse(
        tensor_signature.tensors_compatible(placeholder_c, unk_signature))

    string_signature = tensor_signature.create_signatures(placeholder_a)
    int_signature = tensor_signature.create_signatures(placeholder_c)
    # Unk shape Tensor matche signatures same dtype.
    self.assertTrue(
        tensor_signature.tensors_compatible(placeholder_unk, string_signature))
    self.assertFalse(
        tensor_signature.tensors_compatible(placeholder_unk, int_signature))

 def _check_inputs(self, features, labels):
   if self._features_info is not None:
     logging.debug('Given features: %s, required signatures: %s.',
                   str(features), str(self._features_info))
     if not tensor_signature.tensors_compatible(features, self._features_info):
       raise ValueError('Features are incompatible with given information. '
                        'Given features: %s, required signatures: %s.' %
                        (str(features), str(self._features_info)))
   else:
     self._features_info = tensor_signature.create_signatures(features)
     logging.debug('Setting feature info to %s.', str(self._features_info))
   if labels is not None:
     if self._labels_info is not None:
       logging.debug('Given labels: %s, required signatures: %s.',
                     str(labels), str(self._labels_info))
       if not tensor_signature.tensors_compatible(labels, self._labels_info):
         raise ValueError('Labels are incompatible with given information. '
                          'Given labels: %s, required signatures: %s.' %
                          (str(labels), str(self._labels_info)))
     else:
       self._labels_info = tensor_signature.create_signatures(labels)
       logging.debug('Setting labels info to %s', str(self._labels_info))

 def _check_inputs(self, features, targets):
     if self._features_info is not None:
         logging.warning("Given features: %s, required signatures: %s.", str(features), str(self._features_info))
         if not tensor_signature.tensors_compatible(features, self._features_info):
             raise ValueError(
                 "Features are incompatible with given information. "
                 "Given features: %s, required signatures: %s." % (str(features), str(self._features_info))
             )
     else:
         self._features_info = tensor_signature.create_signatures(features)
         logging.warning("Setting feature info to %s", str(self._features_info))
     if targets is not None:
         if self._targets_info is not None:
             logging.warning("Given targets: %s, required signatures: %s.", str(targets), str(self._targets_info))
             if not tensor_signature.tensors_compatible(targets, self._targets_info):
                 raise ValueError(
                     "Targets are incompatible with given information. "
                     "Given targets: %s, required signatures: %s." % (str(targets), str(self._targets_info))
                 )
         else:
             self._targets_info = tensor_signature.create_signatures(targets)
             logging.warning("Setting targets info to %s", str(self._targets_info))

 def testTensorSignatureExampleParserDict(self):
   examples = array_ops.placeholder(
       name='example', shape=[None], dtype=dtypes.string)
   placeholder_a = array_ops.placeholder(
       name='test', shape=[None, 100], dtype=dtypes.int32)
   placeholder_b = array_ops.placeholder(
       name='bb', shape=[None, 100], dtype=dtypes.float64)
   inputs = {'a': placeholder_a, 'b': placeholder_b}
   signatures = tensor_signature.create_signatures(inputs)
   result = tensor_signature.create_example_parser_from_signatures(signatures,
                                                                   examples)
   self.assertTrue(tensor_signature.tensors_compatible(result, signatures))
   new_signatures = tensor_signature.create_signatures(result)
   self.assertTrue(new_signatures['a'].is_compatible_with(signatures['a']))
   self.assertTrue(new_signatures['b'].is_compatible_with(signatures['b']))

  def _set_infer_mode_feature_signature(self, features):
    for mode in list(self._features_info.keys()):
      if tensor_signature.tensors_compatible(features, self._features_info[mode]):
        self._features_info[model_fn_lib.ModeKeys.INFER] = self._features_info[mode]
        self._labels_info[model_fn_lib.ModeKeys.INFER] = self._labels_info[mode]
        break

    if model_fn_lib.ModeKeys.INFER not in self._features_info:
      logging.warning('Features for mode %s are incompatible with neither train mode nor eval mode.'
                      ' Given features: %s' % (model_fn_lib.ModeKeys.INFER, str(features)))
      for mode in list(self._features_info.keys()):
        logging.warning('Whereas %s mode signatures: %s' % (mode, str(self._features_info[mode])))
      self._check_inputs(features, None, model_fn_lib.ModeKeys.INFER)
      if model_fn_lib.ModeKeys.TRAIN in self._labels_info:
        logging.warning('Setting labels info for mode infer equal to that of labels info for train mode')
        self._labels_info[model_fn_lib.ModeKeys.INFER] = self._labels_info[model_fn_lib.ModeKeys.TRAIN]
      else:
        self._labels_info[model_fn_lib.ModeKeys.INFER] = {}

 def testSparseTensorCompatible(self):
   t = tf.SparseTensor(
       indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4])
   signatures = tensor_signature.create_signatures(t)
   self.assertTrue(tensor_signature.tensors_compatible(t, signatures))