Python tensor_pb2.TensorProto方法代碼示例

# 需要導入模塊: from tensorflow.core.framework import tensor_pb2 [as 別名]
# 或者: from tensorflow.core.framework.tensor_pb2 import TensorProto [as 別名]
def ShapeEquals(tensor_proto, shape):
  """Returns True if "tensor_proto" has the given "shape".

  Args:
    tensor_proto: A TensorProto.
    shape: A tensor shape, expressed as a TensorShape, list, or tuple.

  Returns:
    True if "tensor_proto" has the given "shape", otherwise False.

  Raises:
    TypeError: If "tensor_proto" is not a TensorProto, or shape is not a
      TensorShape, list, or tuple.
  """
  if not isinstance(tensor_proto, tensor_pb2.TensorProto):
    raise TypeError("tensor_proto is not a tensor_pb2.TensorProto object")
  if isinstance(shape, tensor_shape_pb2.TensorShapeProto):
    shape = [d.size for d in shape.dim]
  elif not isinstance(shape, (list, tuple)):
    raise TypeError("shape is not a list or tuple")
  tensor_shape_list = [d.size for d in tensor_proto.tensor_shape.dim]
  return all(x == y for x, y in zip(tensor_shape_list, shape)) 

# 需要導入模塊: from tensorflow.core.framework import tensor_pb2 [as 別名]
# 或者: from tensorflow.core.framework.tensor_pb2 import TensorProto [as 別名]
def _prepare_output_as_AppendArrayToTensorProto(
        inference_output,
        model_available_outputs):
    response = predict_pb2.PredictResponse()
    for response_output_name, model_output_name in \
            model_available_outputs.items():
        if model_output_name in inference_output:
            dtype = dtypes.as_dtype(inference_output[model_output_name].dtype)
            output_tensor = tensor_pb2.TensorProto(
                dtype=dtype.as_datatype_enum,
                tensor_shape=tensor_shape.as_shape(
                    inference_output[model_output_name].shape).as_proto())
            result = inference_output[model_output_name].flatten()
            tensor_util._NP_TO_APPEND_FN[dtype.as_numpy_dtype](output_tensor,
                                                               result)
            response.outputs[response_output_name].CopyFrom(output_tensor)
    return response 

# 需要導入模塊: from tensorflow.core.framework import tensor_pb2 [as 別名]
# 或者: from tensorflow.core.framework.tensor_pb2 import TensorProto [as 別名]
def _MakeTensor(v, arg_name):
  """Ensure v is a TensorProto."""
  if isinstance(v, tensor_pb2.TensorProto):
    return v
  raise TypeError(
      "Don't know how to convert %s to a TensorProto for argument '%s'" %
      (repr(v), arg_name)) 

# 需要導入模塊: from tensorflow.core.framework import tensor_pb2 [as 別名]
# 或者: from tensorflow.core.framework.tensor_pb2 import TensorProto [as 別名]
def _create_const_node(self, name, value):
        tensor_content = value.tobytes()
        dt = tf.as_dtype(value.dtype).as_datatype_enum
        tensor_shape = TensorShapeProto(
            dim=[TensorShapeProto.Dim(size=s) for s in value.shape])
        tensor_proto = TensorProto(tensor_content=tensor_content,
                                   tensor_shape=tensor_shape,
                                   dtype=dt)
        node = tf.compat.v1.NodeDef(name=name, op='Const',
                                    attr={'value': tf.compat.v1.AttrValue(tensor=tensor_proto),
                                          'dtype': tf.compat.v1.AttrValue(type=dt)})
        return node 

# 需要導入模塊: from tensorflow.core.framework import tensor_pb2 [as 別名]
# 或者: from tensorflow.core.framework.tensor_pb2 import TensorProto [as 別名]
def make_const_node(data: Tensor, name: str = None) -> NodeDef:
    """
    Create a TF graph node containing a constant value.
    The resulting node is equivalent to using `tf.constant` on the
    default graph.

    Args:
        data: Numpy-array containing the data, shape, and datatype
        name: Optional name of the node

    Returns:
        Graph node for adding to a TF Graph instance
    """
    dtype = as_dtype(data.dtype).as_datatype_enum
    tensor_content = data.tobytes()
    tensor_dim = [TensorShapeProto.Dim(size=size) for size in data.shape]
    tensor_shape = TensorShapeProto(dim=tensor_dim)
    tensor_proto = TensorProto(tensor_content=tensor_content,
                               tensor_shape=tensor_shape,
                               dtype=dtype)
    node_def = NodeDef(op='Const', name=name or 'Const',
                       attr={
                           'value': AttrValue(tensor=tensor_proto),
                           'dtype': AttrValue(type=dtype)
                        })
    return node_def 

# 需要導入模塊: from tensorflow.core.framework import tensor_pb2 [as 別名]
# 或者: from tensorflow.core.framework.tensor_pb2 import TensorProto [as 別名]
def make_tensor(v, arg_name):
  """Ensure v is a TensorProto."""
  if isinstance(v, tensor_pb2.TensorProto):
    return v
  elif isinstance(v, six.string_types):
    pb = tensor_pb2.TensorProto()
    text_format.Merge(v, pb)
    return pb
  raise TypeError(
      "Don't know how to convert %s to a TensorProto for argument '%s'." %
      (repr(v), arg_name))