Python tensorflow.python.framework.tensor_util.make_tensor_proto() Examples

The following are 30 code examples of tensorflow.python.framework.tensor_util.make_tensor_proto(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.framework.tensor_util , or try the search function .
Example #1
Source File: _graph_cvt.py    From keras-onnx with MIT License 6 votes vote down vote up 
def _populate_const_op(output_node, node_name, dtype, data, data_shape):
  """Creates a Const op.

  Args:
    output_node: TensorFlow NodeDef.
    node_name: str node name.
    dtype: AttrValue with a populated .type field.
    data: numpy data value.
    data_shape: Tuple of integers containing data shape.
  """
  output_node.op = "Const"
  output_node.name = node_name
  output_node.attr["dtype"].CopyFrom(dtype)
  tensor = tensor_util.make_tensor_proto(
      data, dtype=dtype.type, shape=data_shape)
  output_node.attr["value"].tensor.CopyFrom(tensor)
Example #2
Source File: tensor_util_test.py    From deep_image_model with Apache License 2.0 6 votes vote down vote up 
def testIntTypes(self):
    for dtype, nptype in [
        (tf.int32, np.int32),
        (tf.uint8, np.uint8),
        (tf.uint16, np.uint16),
        (tf.int16, np.int16),
        (tf.int8, np.int8)]:
      # Test with array.
      t = tensor_util.make_tensor_proto([10, 20, 30], dtype=dtype)
      self.assertEquals(dtype, t.dtype)
      self.assertProtoEquals("dim { size: 3 }", t.tensor_shape)
      a = tensor_util.MakeNdarray(t)
      self.assertEquals(nptype, a.dtype)
      self.assertAllClose(np.array([10, 20, 30], dtype=nptype), a)
      # Test with ndarray.
      t = tensor_util.make_tensor_proto(np.array([10, 20, 30], dtype=nptype))
      self.assertEquals(dtype, t.dtype)
      self.assertProtoEquals("dim { size: 3 }", t.tensor_shape)
      a = tensor_util.MakeNdarray(t)
      self.assertEquals(nptype, a.dtype)
      self.assertAllClose(np.array([10, 20, 30], dtype=nptype), a)
Example #3
Source File: tensor_util_test.py    From deep_image_model with Apache License 2.0 6 votes vote down vote up 
def testComplex64N(self):
    t = tensor_util.make_tensor_proto([(1+2j), (3+4j), (5+6j)], shape=[1, 3],
                                      dtype=tf.complex64)
    self.assertProtoEquals("""
      dtype: DT_COMPLEX64
      tensor_shape { dim { size: 1 } dim { size: 3 } }
      scomplex_val: 1
      scomplex_val: 2
      scomplex_val: 3
      scomplex_val: 4
      scomplex_val: 5
      scomplex_val: 6
      """, t)
    a = tensor_util.MakeNdarray(t)
    self.assertEquals(np.complex64, a.dtype)
    self.assertAllEqual(np.array([[(1+2j), (3+4j), (5+6j)]]), a)
Example #4
Source File: tensor_util_test.py    From deep_image_model with Apache License 2.0 6 votes vote down vote up 
def testComplex64NpArray(self):
    t = tensor_util.make_tensor_proto(
        np.array([[(1+2j), (3+4j)], [(5+6j), (7+8j)]]), dtype=tf.complex64)
    # scomplex_val are real_0, imag_0, real_1, imag_1, ...
    self.assertProtoEquals("""
      dtype: DT_COMPLEX64
      tensor_shape { dim { size: 2 } dim { size: 2 } }
      scomplex_val: 1
      scomplex_val: 2
      scomplex_val: 3
      scomplex_val: 4
      scomplex_val: 5
      scomplex_val: 6
      scomplex_val: 7
      scomplex_val: 8
      """, t)
    a = tensor_util.MakeNdarray(t)
    self.assertEquals(np.complex64, a.dtype)
    self.assertAllEqual(np.array([[(1+2j), (3+4j)], [(5+6j), (7+8j)]]), a)
Example #5
Source File: tensor_util_test.py    From deep_image_model with Apache License 2.0 6 votes vote down vote up 
def testHalf(self):
    t = tensor_util.make_tensor_proto(np.array([10.0, 20.0], dtype=np.float16))
    self.assertProtoEquals("""
      dtype: DT_HALF
      tensor_shape {
        dim {
          size: 2
        }
      }
      half_val: 18688
      half_val: 19712
      """, t)

    a = tensor_util.MakeNdarray(t)
    self.assertEquals(np.float16, a.dtype)
    self.assertAllClose(np.array([10.0, 20.0], dtype=np.float16), a)
Example #6
Source File: tensor_util_test.py    From deep_image_model with Apache License 2.0 6 votes vote down vote up 
def testComplex128NpArray(self):
    t = tensor_util.make_tensor_proto(
        np.array([[(1+2j), (3+4j)], [(5+6j), (7+8j)]]), dtype=tf.complex128)
    # scomplex_val are real_0, imag_0, real_1, imag_1, ...
    self.assertProtoEquals("""
      dtype: DT_COMPLEX128
      tensor_shape { dim { size: 2 } dim { size: 2 } }
      dcomplex_val: 1
      dcomplex_val: 2
      dcomplex_val: 3
      dcomplex_val: 4
      dcomplex_val: 5
      dcomplex_val: 6
      dcomplex_val: 7
      dcomplex_val: 8
      """, t)
    a = tensor_util.MakeNdarray(t)
    self.assertEquals(np.complex128, a.dtype)
    self.assertAllEqual(np.array([[(1+2j), (3+4j)], [(5+6j), (7+8j)]]), a)
Example #7
Source File: tensor_util_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def testShapeTooLarge(self):
    with self.assertRaises(ValueError):
      tensor_util.make_tensor_proto(np.array([1, 2]), shape=[1])
Example #8
Source File: tensor_util_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def testStringN(self):
    t = tensor_util.make_tensor_proto([b"foo", b"bar", b"baz"], shape=[1, 3])
    self.assertProtoEquals("""
      dtype: DT_STRING
      tensor_shape { dim { size: 1 } dim { size: 3 } }
      string_val: "foo"
      string_val: "bar"
      string_val: "baz"
      """, t)
    a = tensor_util.MakeNdarray(t)
    self.assertEquals(np.object, a.dtype)
    self.assertAllEqual(np.array([[b"foo", b"bar", b"baz"]]), a)
Example #9
Source File: tensor_util_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def testStringNpArray(self):
    t = tensor_util.make_tensor_proto(np.array([[b"a", b"ab"],
                                                [b"abc", b"abcd"]]))
    self.assertProtoEquals("""
      dtype: DT_STRING
      tensor_shape { dim { size: 2 } dim { size: 2 } }
      string_val: "a"
      string_val: "ab"
      string_val: "abc"
      string_val: "abcd"
      """, t)
    a = tensor_util.MakeNdarray(t)
    self.assertEquals(np.object, a.dtype)
    self.assertAllEqual(np.array([[b"a", b"ab"], [b"abc", b"abcd"]]), a)
Example #10
Source File: tensor_util_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def testStringNestedTuple(self):
    t = tensor_util.make_tensor_proto(((b"a", b"ab"), (b"abc", b"abcd")))
    self.assertProtoEquals("""
      dtype: DT_STRING
      tensor_shape { dim { size: 2 } dim { size: 2 } }
      string_val: "a"
      string_val: "ab"
      string_val: "abc"
      string_val: "abcd"
      """, t)
    a = tensor_util.MakeNdarray(t)
    self.assertEquals(np.object, a.dtype)
    self.assertAllEqual(np.array(((b"a", b"ab"), (b"abc", b"abcd"))), a)
Example #11
Source File: tensor_util_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def testComplex64(self):
    t = tensor_util.make_tensor_proto((1+2j), dtype=tf.complex64)
    self.assertProtoEquals("""
      dtype: DT_COMPLEX64
      tensor_shape {}
      scomplex_val: 1
      scomplex_val: 2
      """, t)
    a = tensor_util.MakeNdarray(t)
    self.assertEquals(np.complex64, a.dtype)
    self.assertAllEqual(np.array(1 + 2j), a)
Example #12
Source File: tensor_util_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def testComplex128(self):
    t = tensor_util.make_tensor_proto((1+2j), dtype=tf.complex128)
    self.assertProtoEquals("""
      dtype: DT_COMPLEX128
      tensor_shape {}
      dcomplex_val: 1
      dcomplex_val: 2
      """, t)
    a = tensor_util.MakeNdarray(t)
    self.assertEquals(np.complex128, a.dtype)
    self.assertAllEqual(np.array(1 + 2j), a)
Example #13
Source File: tensor_util_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
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)
Example #14
Source File: tensor_util_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def testUnsupportedDType(self):
    with self.assertRaises(TypeError):
      tensor_util.make_tensor_proto(np.array([1]), 0)
Example #15
Source File: optimize_for_inference_test.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def set_attr_tensor(self, node, key, value, dtype, shape=None):
    node.attr[key].CopyFrom(
        attr_value_pb2.AttrValue(tensor=tensor_util.make_tensor_proto(
            value, dtype=dtype, shape=shape)))
Example #16
Source File: tensor_util_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def testLowRankSupported(self):
    t = tensor_util.make_tensor_proto(np.array(7))
    self.assertProtoEquals("""
      dtype: DT_INT64
      tensor_shape {}
      int64_val: 7
      """, t)
Example #17
Source File: graph_util_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def set_attr_tensor(self, node, key, value, dtype, shape=None):
    node.attr[key].CopyFrom(tf.AttrValue(
        tensor=tensor_util.make_tensor_proto(value,
                                             dtype=dtype,
                                             shape=shape)))
Example #18
Source File: quantize_graph.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
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
Example #19
Source File: quantize_graph.py    From tensorflow-for-poets-2 with Apache License 2.0 5 votes vote down vote up 
def set_attr_tensor(node, key, value, dtype, shape=None):
  try:
    node.attr[key].CopyFrom(
        attr_value_pb2.AttrValue(tensor=tensor_util.make_tensor_proto(
            value, dtype=dtype, shape=shape)))
  except KeyError:
    pass
Example #20
Source File: quantize_graph.py    From MobileNet with Apache License 2.0 5 votes vote down vote up 
def set_attr_tensor(node, key, value, dtype, shape=None):
  try:
    node.attr[key].CopyFrom(
        attr_value_pb2.AttrValue(tensor=tensor_util.make_tensor_proto(
            value, dtype=dtype, shape=shape)))
  except KeyError:
    pass
Example #21
Source File: quantize_graph.py    From sketch-to-react-native with MIT License 5 votes vote down vote up 
def set_attr_tensor(node, key, value, dtype, shape=None):
  try:
    node.attr[key].CopyFrom(
        attr_value_pb2.AttrValue(tensor=tensor_util.make_tensor_proto(
            value, dtype=dtype, shape=shape)))
  except KeyError:
    pass
Example #22
Source File: quantize_graph.py    From pokemon-mini with Apache License 2.0 5 votes vote down vote up 
def set_attr_tensor(node, key, value, dtype, shape=None):
  try:
    node.attr[key].CopyFrom(
        attr_value_pb2.AttrValue(tensor=tensor_util.make_tensor_proto(
            value, dtype=dtype, shape=shape)))
  except KeyError:
    pass
Example #23
Source File: quantize_graph.py    From AudioNet with MIT License 5 votes vote down vote up 
def set_attr_tensor(node, key, value, dtype, shape=None):
  try:
    node.attr[key].CopyFrom(
        attr_value_pb2.AttrValue(tensor=tensor_util.make_tensor_proto(
            value, dtype=dtype, shape=shape)))
  except KeyError:
    pass
Example #24
Source File: generic_predict_client.py    From cloud-ml-sdk with Apache License 2.0 5 votes vote down vote up 
def predict(server, model, data, timeout=10.0):
  """Request generic gRPC server with specified data.
 
  Args:
    server: The address of server. Example: "localhost:9000".
    model: The name of the model. Example: "mnist".
    data: The json data to request. Example: {"keys_dtype": "int32", "keys": [[1], [2]]}.

  Returns:
    The predict result in dictionary format. Example: {"keys": [1, 2]}.
  """
  host, port = server.split(":")
  channel = implementations.insecure_channel(host, int(port))
  stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)

  request = predict_pb2.PredictRequest()
  request.model_spec.name = model
  for k, v in data.items():
    if k.endswith("_dtype") == False:
      numpy_data = np.array(v)
      dtype = data[k + "_dtype"]
      request.inputs[k].CopyFrom(tensor_util.make_tensor_proto(numpy_data,
                                                               dtype=dtype))

  result = stub.Predict(request, timeout)
  result_dict = {}
  for k, v in result.outputs.items():
    result_dict[k] = get_tensor_values(v)
  return result_dict
Example #25
Source File: optimize_for_inference_test.py    From keras-lambda with MIT License 5 votes vote down vote up 
def set_attr_tensor(self, node, key, value, dtype, shape=None):
    node.attr[key].CopyFrom(
        attr_value_pb2.AttrValue(tensor=tensor_util.make_tensor_proto(
            value, dtype=dtype, shape=shape)))
Example #26
Source File: tensor_util_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def testFloatSizes2(self):
    t = tensor_util.make_tensor_proto([10.0, 20.0, 30.0], shape=[3, 1])
    self.assertProtoEquals("""
      dtype: DT_FLOAT
      tensor_shape { dim { size: 3 } dim { size: 1 } }
      tensor_content: "\000\000 A\000\000\240A\000\000\360A"
      """, t)
    a = tensor_util.MakeNdarray(t)
    self.assertEquals(np.float32, a.dtype)
    self.assertAllClose(np.array([[10.0], [20.0], [30.0]], dtype=np.float32),
                        a)
Example #27
Source File: parsing_ops_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def testToFloat32(self):
    with self.test_session():
      expected = np.random.rand(3, 4, 5).astype(np.float32)
      tensor_proto = tensor_util.make_tensor_proto(expected)

      serialized = tf.placeholder(tf.string)
      tensor = tf.parse_tensor(serialized, tf.float32)

      result = tensor.eval(
          feed_dict={serialized: tensor_proto.SerializeToString()})

      self.assertAllEqual(expected, result)
Example #28
Source File: parsing_ops_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def testToUint8(self):
    with self.test_session():
      expected = np.random.rand(3, 4, 5).astype(np.uint8)
      tensor_proto = tensor_util.make_tensor_proto(expected)

      serialized = tf.placeholder(tf.string)
      tensor = tf.parse_tensor(serialized, tf.uint8)

      result = tensor.eval(
          feed_dict={serialized: tensor_proto.SerializeToString()})

      self.assertAllEqual(expected, result)
Example #29
Source File: parsing_ops_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def testTypeMismatch(self):
    with self.test_session():
      expected = np.random.rand(3, 4, 5).astype(np.uint8)
      tensor_proto = tensor_util.make_tensor_proto(expected)

      serialized = tf.placeholder(tf.string)
      tensor = tf.parse_tensor(serialized, tf.uint16)

      with self.assertRaisesOpError(
          r"Type mismatch between parsed tensor \(uint8\) and dtype "
          r"\(uint16\)"):
        tensor.eval(feed_dict={serialized: tensor_proto.SerializeToString()})
Example #30
Source File: optimize_for_inference_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def set_attr_tensor(self, node, key, value, dtype, shape=None):
    node.attr[key].CopyFrom(tf.AttrValue(
        tensor=tensor_util.make_tensor_proto(value,
                                             dtype=dtype,
                                             shape=shape)))