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Support non-sequence tensor fed through as a python list (#2782)

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* Support list feeds in Python
This commit is contained in:
Hariharan Seshadri 2020-01-20 05:15:10 +05:30 committed by Scott McKay
parent f9f25ec047
commit b21576eeb0
2 changed files with 58 additions and 7 deletions
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48
onnxruntime/python/onnxruntime_pybind_mlvalue.cc
View file

@ -203,8 +203,9 @@ std::unique_ptr<Tensor> CreateTensor(AllocatorPtr alloc, const std::string& name
return p_tensor;
}
void CreateSequenceOfTensors(AllocatorPtr alloc, const std::string& name_input,
const InputDefList* input_def_list, PyObject* pylist_obj, OrtValue* p_mlvalue) {
bool CheckIfInputIsSequenceType(const std::string& name_input,
const InputDefList* input_def_list,
/*out*/ onnx::TypeProto& type_proto) {
// get sequence type from the model
const auto& def_list = *input_def_list;
auto ret_it = std::find_if(std::begin(def_list), std::end(def_list),
@ -212,9 +213,21 @@ void CreateSequenceOfTensors(AllocatorPtr alloc, const std::string& name_input,
if (ret_it == std::end(def_list)) {
throw std::runtime_error("Failed to find input with name: " + name_input + " in the model input def list");
}
const auto* type_proto = (*ret_it)->TypeAsProto();
if (!type_proto || !(type_proto->has_sequence_type())) {
throw std::runtime_error("Either type_proto was null or it was not of sequence type");
const auto* temp = (*ret_it)->TypeAsProto();
if (!temp) {
throw std::runtime_error("Corresponding type_proto is null");
} else {
type_proto = *temp;
}
return type_proto.has_sequence_type();
}
void CreateSequenceOfTensors(AllocatorPtr alloc, const std::string& name_input,
const InputDefList* input_def_list, PyObject* pylist_obj, OrtValue* p_mlvalue) {
onnx::TypeProto type_proto;
if (!CheckIfInputIsSequenceType(name_input, input_def_list, type_proto)) {
throw std::runtime_error("Input is not of sequence type");
}
// populate the seq
@ -232,9 +245,9 @@ void CreateSequenceOfTensors(AllocatorPtr alloc, const std::string& name_input,
}
}
// set the seq type
// set the seq type
MLDataType seq_dtype = OrtTypeInfo::ElementTypeFromProto(
static_cast<ONNX_NAMESPACE::TensorProto_DataType>(type_proto->sequence_type().elem_type().tensor_type().elem_type()));
static_cast<ONNX_NAMESPACE::TensorProto_DataType>(type_proto.sequence_type().elem_type().tensor_type().elem_type()));
auto p_seq_tensors = onnxruntime::make_unique<TensorSeq>(seq_dtype);
p_seq_tensors->SetElements(std::move(tensors));
auto ml_tensor_sequence = DataTypeImpl::GetType<TensorSeq>();
@ -460,9 +473,30 @@ void CreateGenericIterableMLValue(PyObject* iterator, AllocatorPtr alloc, const
void CreateGenericMLValue(const onnxruntime::InputDefList* input_def_list, AllocatorPtr alloc, const std::string& name_input,
py::object& value, OrtValue* p_mlvalue) {
onnx::TypeProto type_proto;
if (PyObjectCheck_Array(value.ptr())) {
// The most frequent case: input comes as an array.
PyArrayObject* arr = reinterpret_cast<PyArrayObject*>(value.ptr());
CreateTensorMLValue(alloc, name_input, arr, p_mlvalue);
} else if (PyList_Check(value.ptr()) &&
!CheckIfInputIsSequenceType(name_input, input_def_list, type_proto)) {
// This is not a sequence tensor. This is just a regular tensor fed through as a list.
if (!type_proto.tensor_type().has_elem_type()) {
std::runtime_error("The graph is missing type information needed to construct the ORT tensor");
}
MLDataType dtype = OrtTypeInfo::ElementTypeFromProto(
static_cast<ONNX_NAMESPACE::TensorProto_DataType>(type_proto.tensor_type().elem_type()));
int numpy_dtype = OnnxRuntimeTensorToNumpyType(dtype);
PyArrayObject* arr = reinterpret_cast<PyArrayObject*>(
PyArray_FromAny(value.ptr(), PyArray_DescrFromType(numpy_dtype), 0, 0, 0, nullptr));
if (!arr) {
throw std::runtime_error("Could not create tensor from given input list");
}
CreateTensorMLValue(alloc, name_input, arr, p_mlvalue);
} else if (PyList_Check(value.ptr())) {
auto* seq_tensors = reinterpret_cast<PyObject*>(value.ptr());

17
onnxruntime/test/python/onnxruntime_test_python.py
View file

@ -162,6 +162,23 @@ class TestInferenceSession(unittest.TestCase):
t1.join()
t2.join()
def testListAsInput(self):
sess = onnxrt.InferenceSession(self.get_name("mul_1.onnx"))
x = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=np.float32)
input_name = sess.get_inputs()[0].name
res = sess.run([], {input_name: x.tolist()})
output_expected = np.array(
[[1.0, 4.0], [9.0, 16.0], [25.0, 36.0]], dtype=np.float32)
np.testing.assert_allclose(
output_expected, res[0], rtol=1e-05, atol=1e-08)
def testStringListAsInput(self):
sess = onnxrt.InferenceSession(self.get_name("identity_string.onnx"))
x = np.array(['this', 'is', 'identity', 'test'], dtype=np.str).reshape((2, 2))
x_name = sess.get_inputs()[0].name
res = sess.run([], {x_name: x.tolist()})
np.testing.assert_equal(x, res[0])
def testRunDevice(self):
device = onnxrt.get_device()
self.assertTrue('CPU' in device or 'GPU' in device)