def _restore_op(self, iterator_resource):
iterator_state_variant = parsing_ops.parse_tensor(
io_ops.read_file(self._iterator_checkpoint_prefix_local()),
dtypes.variant)
restore_op = gen_dataset_ops.deserialize_iterator(iterator_resource,
iterator_state_variant)
return restore_op
def testInvalidInput(self):
with self.test_session():
serialized = array_ops.placeholder(dtypes.string)
tensor = parsing_ops.parse_tensor(serialized, dtypes.uint16)
with self.assertRaisesOpError(
"Could not parse `serialized` as TensorProto: 'bogus'"):
tensor.eval(feed_dict={serialized: "bogus"})
with self.assertRaisesOpError(
r"Expected `serialized` to be a scalar, got shape: \[1\]"):
tensor.eval(feed_dict={serialized: ["bogus"]})
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 = array_ops.placeholder(dtypes.string)
tensor = parsing_ops.parse_tensor(serialized, dtypes.uint16)
with self.assertRaisesOpError(
r"Type mismatch between parsed tensor \(uint8\) and dtype "
r"\(uint16\)"):
tensor.eval(feed_dict={serialized: tensor_proto.SerializeToString()})
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 = array_ops.placeholder(dtypes.string)
tensor = parsing_ops.parse_tensor(serialized, dtypes.uint8)
result = tensor.eval(
feed_dict={serialized: tensor_proto.SerializeToString()})
self.assertAllEqual(expected, result)
def testInvalidInput(self):
with self.test_session():
serialized = array_ops.placeholder(dtypes.string)
tensor = parsing_ops.parse_tensor(serialized, dtypes.uint16)
with self.assertRaisesOpError(
"Could not parse `serialized` as TensorProto: 'bogus'"):
tensor.eval(feed_dict={serialized: "bogus"})
with self.assertRaisesOpError(
r"Expected `serialized` to be a scalar, got shape: \[1\]"):
tensor.eval(feed_dict={serialized: ["bogus"]})
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 = array_ops.placeholder(dtypes.string)
tensor = parsing_ops.parse_tensor(serialized, dtypes.uint16)
with self.assertRaisesOpError(
r"Type mismatch between parsed tensor \(uint8\) and dtype "
r"\(uint16\)"):
tensor.eval(feed_dict={serialized: tensor_proto.SerializeToString()})
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 = array_ops.placeholder(dtypes.string)
tensor = parsing_ops.parse_tensor(serialized, dtypes.uint8)
result = tensor.eval(
feed_dict={serialized: tensor_proto.SerializeToString()})
self.assertAllEqual(expected, result)
def _restore_op(iterator_resource):
iterator_state_variant = parsing_ops.parse_tensor(
io_ops.read_file(_path()), dtypes.variant)
restore_op = gen_dataset_ops.deserialize_iterator(iterator_resource,
iterator_state_variant)
return restore_op
def _restore_op(iterator_resource):
iterator_state_variant = parsing_ops.parse_tensor(
io_ops.read_file(_path()), dtypes.variant)
restore_op = gen_dataset_ops.deserialize_iterator(iterator_resource,
iterator_state_variant)
return restore_op
