def main():
    # Connect with the gRPC server
    server_address = "127.0.0.1:50051"
    request_timeout = 5.0
    channel = grpc.insecure_channel(server_address)
    stub = predict_pb2.PredictionServiceStub(channel)

    # Make request data
    request = predict_pb2.PredictRequest()
    image = Image.open('../mnist_jpgs/4/pic_test1010.png')
    array = np.array(image)/(255*1.0)
    samples_features =  array.reshape([-1,784])

    # samples_features = np.array(
    #     [[10, 10, 10, 8, 6, 1, 8, 9, 1], [10, 10, 10, 8, 6, 1, 8, 9, 1]])
    samples_keys = np.array([1])
    # Convert numpy to TensorProto
    request.inputs["features"].CopyFrom(tensor_util.make_tensor_proto(
        samples_features))
    request.inputs["key"].CopyFrom(tensor_util.make_tensor_proto(samples_keys))

    # Invoke gRPC request
    response = stub.Predict(request, request_timeout)

    # Convert TensorProto to numpy
    result = {}
    for k, v in response.outputs.items():
        result[k] = tensor_util.MakeNdarray(v)
    print(result)

  def testQuantizedTypes(self):
    # Test with array.
    data = [(21,), (22,), (23,)]

    t = tensor_util.make_tensor_proto(data, dtype=tf.qint32)
    self.assertProtoEquals("""
      dtype: DT_QINT32
      tensor_shape { dim { size: 3 } }
      tensor_content: "\025\000\000\000\026\000\000\000\027\000\000\000"
      """, t)
    a = tensor_util.MakeNdarray(t)
    self.assertEquals(tf.qint32.as_numpy_dtype, a.dtype)
    self.assertAllEqual(np.array(data, dtype=a.dtype), a)

    t = tensor_util.make_tensor_proto(data, dtype=tf.quint8)
    self.assertProtoEquals("""
      dtype: DT_QUINT8
      tensor_shape { dim { size: 3 } }
      tensor_content: "\025\026\027"
      """, t)
    a = tensor_util.MakeNdarray(t)
    self.assertEquals(tf.quint8.as_numpy_dtype, a.dtype)
    self.assertAllEqual(np.array(data, dtype=a.dtype), a)

    t = tensor_util.make_tensor_proto(data, dtype=tf.qint8)
    self.assertProtoEquals("""
      dtype: DT_QINT8
      tensor_shape { dim { size: 3 } }
      tensor_content: "\025\026\027"
      """, t)
    a = tensor_util.MakeNdarray(t)
    self.assertEquals(tf.qint8.as_numpy_dtype, a.dtype)
    self.assertAllEqual(np.array(data, dtype=a.dtype), a)

 def testTensorShapeVerification(self):
   array = np.array([[1], [2]])
   correct_shape = (2, 1)
   incorrect_shape = (1, 2)
   tensor_util.make_tensor_proto(array, shape=correct_shape, verify_shape=True)
   with self.assertRaises(TypeError):
     tensor_util.make_tensor_proto(
         array, shape=incorrect_shape, verify_shape=True)

  def testTransformGraph(self):
    input_graph_def = graph_pb2.GraphDef()

    const_op1 = input_graph_def.node.add()
    const_op1.op = "Const"
    const_op1.name = "const_op1"
    const_op1.attr["dtype"].CopyFrom(attr_value_pb2.AttrValue(
        type=dtypes.float32.as_datatype_enum))
    const_op1.attr["value"].CopyFrom(
        attr_value_pb2.AttrValue(tensor=tensor_util.make_tensor_proto(
            [1, 2], dtypes.float32, [1, 2])))

    const_op2 = input_graph_def.node.add()
    const_op2.op = "Const"
    const_op2.name = "const_op2"
    const_op2.attr["dtype"].CopyFrom(attr_value_pb2.AttrValue(
        type=dtypes.float32.as_datatype_enum))
    const_op2.attr["value"].CopyFrom(
        attr_value_pb2.AttrValue(tensor=tensor_util.make_tensor_proto(
            [3, 4], dtypes.float32, [1, 2])))

    # Create an add that has two constants as inputs.
    add_op = input_graph_def.node.add()
    add_op.op = "Add"
    add_op.attr["T"].CopyFrom(attr_value_pb2.AttrValue(
        type=dtypes.float32.as_datatype_enum))
    add_op.name = "add_op"
    add_op.input.extend(["const_op1", "const_op2"])

    # Create a relu that reads from the add.
    relu_op = input_graph_def.node.add()
    relu_op.op = "Relu"
    relu_op.attr["T"].CopyFrom(attr_value_pb2.AttrValue(
        type=dtypes.float32.as_datatype_enum))
    relu_op.name = "relu_op"
    relu_op.input.extend(["add_op"])

    # We're specifying that add_op is the final output, and so the relu isn't
    # needed.
    input_names = []
    output_names = ["add_op"]
    transforms = ["strip_unused_nodes"]
    transformed_graph_def = TransformGraph(input_graph_def, input_names,
                                           output_names, transforms)

    # We expect that the relu is no longer present after running the transform.
    for node in transformed_graph_def.node:
      self.assertNotEqual("Relu", node.op)

def convert_variables_to_constants(sess, input_graph_def, output_node_names):
    variable_names = []
    variable_dict_names = []
    for node in input_graph_def.node:
        if node.op == "Assign":
            variable_name = node.input[0]
            variable_dict_names.append(variable_name)
            variable_names.append(variable_name + ":0")
    returned_variables = sess.run(variable_names)
    found_variables = dict(zip(variable_dict_names, returned_variables))
    print("Frozen %d variables." % len(returned_variables))

    inference_graph = extract_sub_graph(input_graph_def, output_node_names)

    output_graph_def = graph_pb2.GraphDef()
    how_many_converted = 0
    for input_node in inference_graph.node:
        output_node = graph_pb2.NodeDef()
        if input_node.name in found_variables:
            output_node.op = "Const"
            output_node.name = input_node.name
            dtype = input_node.attr["dtype"]
            data = found_variables[input_node.name]
            output_node.attr["dtype"].CopyFrom(dtype)
            output_node.attr["value"].CopyFrom(attr_value_pb2.AttrValue(
                tensor=tensor_util.make_tensor_proto(data, dtype=dtype.type, shape=data.shape)))
            how_many_converted += 1
        else:
            output_node.CopyFrom(input_node)
        output_graph_def.node.extend([output_node])
    print("Converted %d variables to const ops." % how_many_converted)
    return output_graph_def

 def testLowRankSupported(self):
   t = tensor_util.make_tensor_proto(np.array(7))
   self.assertProtoEquals("""
     dtype: DT_INT64
     tensor_shape {}
     int64_val: 7
     """, t)

  def testNoOutputs(self):
    with session_lib.Session() as sess:
      # Build a function with a single Const node, whose output is ignored.
      fdef = function_pb2.FunctionDef()
      fdef.signature.name = "KernelWithNoOutputs"
      node = node_def_pb2.NodeDef()
      node.op = "Const"
      node.name = "ignored"
      node.attr["dtype"].type = dtypes.int32.as_datatype_enum
      tensor = tensor_util.make_tensor_proto([0], dtype=dtypes.int32, shape=[])
      node.attr["value"].tensor.CopyFrom(tensor)
      fdef.node_def.extend([node])

      # Check that calling the result as a compiled kernel doesn't crash.
      @function.Defun(compiled=True)
      def KernelWithNoOutputs():
        return constant_op.constant(100)

      # Hack to override the definition.  By accessing .definition, we
      # force the _DefinedFunction initialized internally. Then, we
      # replace it's internal FunctionDef proto. We do this hack here
      # because one typically can't construct KernelWithNoOutputs
      # function via Defun decorator directly.
      _ = KernelWithNoOutputs.definition
      foo = KernelWithNoOutputs
      foo._definition = fdef
      call = KernelWithNoOutputs()
      sess.run(call, {})

 def testStringWithImplicitRepeat(self):
   t = tensor_util.make_tensor_proto(["f", "g"], shape=[3, 4])
   a = tensor_util.MakeNdarray(t)
   self.assertAllEqual(
       np.array([[b"f", b"g", b"g", b"g"], [b"g", b"g", b"g", b"g"],
                 [b"g", b"g", b"g", b"g"]],
                dtype=np.object), a)

 def testFloatSizesLessValues(self):
   t = tensor_util.make_tensor_proto(10.0, shape=[1, 3])
   self.assertProtoEquals("""
     dtype: DT_FLOAT
     tensor_shape { dim { size: 1 } dim { size: 3 } }
     float_val: 10.0
     """, t)

 def testComplexWithImplicitRepeat(self):
   t = tensor_util.make_tensor_proto((1+1j), shape=[3, 4],
                                     dtype=tf.complex64)
   a = tensor_util.MakeNdarray(t)
   self.assertAllClose(np.array([[(1+1j), (1+1j), (1+1j), (1+1j)],
                                 [(1+1j), (1+1j), (1+1j), (1+1j)],
                                 [(1+1j), (1+1j), (1+1j), (1+1j)]],
                                dtype=np.complex64), a)

 def testFloatTypesWithImplicitRepeat(self):
   for dtype, nptype in [
       (tf.float32, np.float32), (tf.float64, np.float64)]:
     t = tensor_util.make_tensor_proto([10.0], shape=[3, 4], dtype=dtype)
     a = tensor_util.MakeNdarray(t)
     self.assertAllClose(np.array([[10.0, 10.0, 10.0, 10.0],
                                   [10.0, 10.0, 10.0, 10.0],
                                   [10.0, 10.0, 10.0, 10.0]], dtype=nptype), a)

def set_attr_tensor(node, key, value, dtype, shape=None):
  try:
    node.attr[key].CopyFrom(tf.AttrValue(
        tensor=tensor_util.make_tensor_proto(value,
                                             dtype=dtype,
                                             shape=shape)))
  except KeyError:
    pass

 def testShapeEquals(self):
     t = tensor_util.make_tensor_proto([10, 20, 30, 40], shape=[2, 2])
     self.assertTrue(tensor_util.ShapeEquals(t, [2, 2]))
     self.assertTrue(tensor_util.ShapeEquals(t, (2, 2)))
     self.assertTrue(tensor_util.ShapeEquals(t, tensor_util.MakeTensorShapeProto([2, 2])))
     self.assertFalse(tensor_util.ShapeEquals(t, [5, 3]))
     self.assertFalse(tensor_util.ShapeEquals(t, [1, 4]))
     self.assertFalse(tensor_util.ShapeEquals(t, [4]))

 def testLongNpArray(self):
   t = tensor_util.make_tensor_proto(np.array([10, 20, 30]))
   self.assertProtoEquals("""
     dtype: DT_INT64
     tensor_shape { dim { size: 3 } }
     tensor_content: "\\n\000\000\000\000\000\000\000\024\000\000\000\000\000\000\000\036\000\000\000\000\000\000\000"
     """, t)
   a = tensor_util.MakeNdarray(t)
   self.assertEquals(np.int64, a.dtype)

 def testIntNDefaultType(self):
   t = tensor_util.make_tensor_proto([10, 20, 30, 40], shape=[2, 2])
   self.assertProtoEquals("""
     dtype: DT_INT32
     tensor_shape { dim { size: 2 } dim { size: 2 } }
     tensor_content: "\\n\000\000\000\024\000\000\000\036\000\000\000(\000\000\000"
     """, t)
   a = tensor_util.MakeNdarray(t)
   self.assertEquals(np.int32, a.dtype)
   self.assertAllClose(np.array([[10, 20], [30, 40]], dtype=np.int32), a)

 def testComplexWithImplicitRepeat(self):
   for dtype, np_dtype in [(tf.complex64, np.complex64),
                           (tf.complex128, np.complex128)]:
     t = tensor_util.make_tensor_proto((1+1j), shape=[3, 4],
                                       dtype=dtype)
     a = tensor_util.MakeNdarray(t)
     self.assertAllClose(np.array([[(1+1j), (1+1j), (1+1j), (1+1j)],
                                   [(1+1j), (1+1j), (1+1j), (1+1j)],
                                   [(1+1j), (1+1j), (1+1j), (1+1j)]],
                                  dtype=np_dtype), a)

 def testString(self):
   t = tensor_util.make_tensor_proto("foo")
   self.assertProtoEquals("""
     dtype: DT_STRING
     tensor_shape {}
     string_val: "foo"
     """, t)
   a = tensor_util.MakeNdarray(t)
   self.assertEquals(np.object, a.dtype)
   self.assertEquals([b"foo"], a)

  def testUnsupportedDTypes(self):
    with self.assertRaises(TypeError):
      tensor_util.make_tensor_proto(np.array([1]), 0)
    with self.assertRaises(TypeError):
      tensor_util.make_tensor_proto(3, dtype=dtypes.qint8)
    with self.assertRaises(TypeError):
      tensor_util.make_tensor_proto([3], dtype=dtypes.qint8)

    # Validate the helpful error message when trying to convert an
    # unconvertible list as strings.
    with self.assertRaisesRegexp(TypeError, "Failed to convert object"):
      tensor_util.make_tensor_proto([tensor_shape.Dimension(1)])

 def testFloat(self):
   t = tensor_util.make_tensor_proto(10.0)
   self.assertProtoEquals("""
     dtype: DT_FLOAT
     tensor_shape {}
     float_val: 10.0
     """, t)
   a = tensor_util.MakeNdarray(t)
   self.assertEquals(np.float32, a.dtype)
   self.assertAllClose(np.array(10.0, dtype=np.float32), a)

 def testLong(self):
   t = tensor_util.make_tensor_proto(10, dtype=dtypes.int64)
   self.assertProtoEquals("""
     dtype: DT_INT64
     tensor_shape {}
     int64_val: 10
     """, t)
   a = tensor_util.MakeNdarray(t)
   self.assertEquals(np.int64, a.dtype)
   self.assertAllClose(np.array(10, dtype=np.int64), a)