const std::string& get_name() const { return m_graph_proto->name(); }
OutputVector make_ng_nodes(const Node& onnx_node) const;
const GraphCache& get_graph_cache() const;
+ const OpsetImports& get_opset_imports() const;
protected:
Graph(const ONNX_NAMESPACE::GraphProto& proto,
{
namespace onnx_import
{
+ /// \brief Type of container which stores opset version and domain in ONNX format
+ using OpsetImports =
+ ::google::protobuf::RepeatedPtrField<ONNX_NAMESPACE::OperatorSetIdProto>;
+
std::string get_node_domain(const ONNX_NAMESPACE::NodeProto& node_proto);
std::int64_t get_opset_version(const ONNX_NAMESPACE::ModelProto& model_proto,
const std::string& get_producer_name() const { return m_model_proto->producer_name(); }
const ONNX_NAMESPACE::GraphProto& get_graph() const { return m_model_proto->graph(); }
std::int64_t get_model_version() const { return m_model_proto->model_version(); }
+ const OpsetImports& get_opset_imports() const;
const std::string& get_producer_version() const
{
return m_model_proto->producer_version();
ONNX_NAMESPACE::ModelProto model_proto;
const auto& graph = m_attribute_proto->g();
*(model_proto.mutable_graph()) = graph;
- // We're creating here a model with unset `opset_import` field. This shouldn't
- // be a problem, since we add ONNX opset as a default available opset. Moreover
- // if we encounter a node absent in current available opsets we will try
- // to add it's domain to available opsets.
+
+ // set opset version and domain from the parent graph
+ *model_proto.mutable_opset_import() = parent_graph.get_opset_imports();
+
Model model{model_proto};
return Subgraph{graph, model, parent_graph};
}
as_node_vector(ng_inputs));
}
+ const OpsetImports& Graph::get_opset_imports() const
+ {
+ return m_model->get_opset_imports();
+ }
+
Subgraph::Subgraph(const ONNX_NAMESPACE::GraphProto& proto,
Model& model,
const Graph& parent_graph)
}
}
+ const OpsetImports& Model::get_opset_imports() const
+ {
+ return m_model_proto->opset_import();
+ }
+
} // namespace onnx_import
} // namespace ngraph
"While validating node 'v1::<name> (sizes[0]:i64{4},"
"Convert_29306[0]:f32{4}) -> (dynamic?)' with friendly_name '<name>':"
"Argument element types are inconsistent.")
-xfail_issue_41813 = xfail_test(reason="RuntimeError: While validating ONNX node"
- " '<Node(Loop): generic_loop_Loop__69>'"
- "While validating ONNX node '<Node(Resize): Resize__143>':"
- "vector::_M_range_check: __n (which is 2) >= this->size() (which is 2")
+xfail_issue_42297 = xfail_test(reason="RuntimeError: While validating ONNX node '<Node(Conv): Conv__7398>':"
+ "Check 'data.get_partial_shape().rank().is_static()'"
+ " failed at ngraph/frontend/onnx_import/src/op/conv.cpp:102:"
+ "The input data tensor's rank has to be known (static)")
xfail_issue_41814 = xfail_test(reason="RuntimeError: While validating ONNX node '<Node(Loop):"
" generic_loop_Loop__121>':"
"While validating ONNX node '<Node(TopK):"
from tests import (
xfail_issue_38701,
- xfail_issue_41813,
+ xfail_issue_42297,
xfail_issue_41815,
xfail_issue_41814,
xfail_issue_36533,
import_xfail_list = [
# ONNX Model Zoo
(xfail_issue_38701, "test_onnx_model_zoo_text_machine_comprehension_bidirectional_attention_flow_model_bidaf_9_bidaf_bidaf_cpu"),
- (xfail_issue_41813, "test_onnx_model_zoo_vision_object_detection_segmentation_ssd_mobilenetv1_model_ssd_mobilenet_v1_10_ssd_mobilenet_v1_ssd_mobilenet_v1_cpu"),
+ (xfail_issue_42297, "test_onnx_model_zoo_vision_object_detection_segmentation_ssd_mobilenetv1_model_ssd_mobilenet_v1_10_ssd_mobilenet_v1_ssd_mobilenet_v1_cpu"),
(xfail_issue_38726, "test_onnx_model_zoo_text_machine_comprehension_t5_model_t5_decoder_with_lm_head_12_t5_decoder_with_lm_head_cpu"),
# Model MSFT
- (xfail_issue_41813, "test_MSFT_opset10_mlperf_ssd_mobilenet_300_ssd_mobilenet_v1_coco_2018_01_28_cpu"),
+ (xfail_issue_42297, "test_MSFT_opset10_mlperf_ssd_mobilenet_300_ssd_mobilenet_v1_coco_2018_01_28_cpu"),
(xfail_issue_41814, "test_MSFT_opset10_mlperf_ssd_resnet34_1200_ssd_resnet34_mAP_20.2_cpu"),
(xfail_issue_37957, "test_MSFT_opset10_mask_rcnn_keras_mask_rcnn_keras_cpu"),
(xfail_issue_36465, "test_MSFT_opset9_LSTM_Seq_lens_unpacked_model_cpu"),
--- /dev/null
+ir_version: 6
+producer_name: "nGraph ONNX Importer"
+graph {
+ name: "basic loop"
+ node {
+ input: "trip_count"
+ input: ""
+ input: "a_init"
+ output: "a_final"
+ output: "a_values"
+ op_type: "Loop"
+ attribute {
+ name: "body"
+ g {
+ node {
+ input: "a_in"
+ input: "b"
+ output: "current_a"
+ name: "loop_body_mul"
+ op_type: "Mul"
+ }
+ node {
+ input: "cond_in"
+ output: "cond_out"
+ name: "cond_identity"
+ op_type: "Identity"
+ }
+ node {
+ input: "current_a"
+ output: "a_out"
+ name: "output_accumulator"
+ op_type: "Identity"
+ }
+ name: "simple add"
+ initializer {
+ dims: 1
+ dims: 2
+ data_type: 1
+ float_data: 1
+ float_data: 1
+ name: "b"
+ }
+ input {
+ name: "i"
+ type {
+ tensor_type {
+ elem_type: 7
+ shape {
+ }
+ }
+ }
+ }
+ input {
+ name: "cond_in"
+ type {
+ tensor_type {
+ elem_type: 9
+ shape {
+ }
+ }
+ }
+ }
+ input {
+ name: "a_in"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ dim {
+ dim_value: 1
+ }
+ dim {
+ dim_value: 2
+ }
+ }
+ }
+ }
+ }
+ output {
+ name: "cond_out"
+ type {
+ tensor_type {
+ elem_type: 9
+ shape {
+ }
+ }
+ }
+ }
+ output {
+ name: "current_a"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ }
+ }
+ }
+ }
+ output {
+ name: "a_out"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ }
+ }
+ }
+ }
+ }
+ type: GRAPH
+ }
+ }
+ initializer {
+ dims: 1
+ data_type: 7
+ int64_data: 3
+ name: "trip_count"
+ }
+ input {
+ name: "a_init"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ dim {
+ dim_value: 1
+ }
+ dim {
+ dim_value: 2
+ }
+ }
+ }
+ }
+ }
+ output {
+ name: "a_final"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ }
+ }
+ }
+ }
+ output {
+ name: "a_values"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ }
+ }
+ }
+ }
+}
+opset_import {
+ version: 1
+}
#include "gtest/gtest.h"
#include "ngraph/file_util.hpp"
+#include "ngraph/type.hpp"
#include "ngraph/type/element_type.hpp"
#include "onnx_import/default_opset.hpp"
#include "onnx_import/onnx.hpp"
test_case.run();
}
+NGRAPH_TEST(${BACKEND_NAME}, onnx_controlflow_loop_the_proper_opset_in_subgraph)
+{
+ const auto function = onnx_import::import_onnx_model(
+ file_util::path_join(SERIALIZED_ZOO, "onnx/loop/loop_2d_mul_opset1.prototxt"));
+
+ const auto parent_ops = function->get_ops();
+ const auto loop_node_it =
+ std::find_if(parent_ops.begin(), parent_ops.end(), [](const std::shared_ptr<Node>& op) {
+ return std::string{op->get_type_name()} == "Loop";
+ });
+ const auto body_ops =
+ ngraph::as_type_ptr<ngraph::opset5::Loop>(*loop_node_it)->get_function()->get_ops();
+ const auto body_mul_node_it =
+ std::find_if(body_ops.begin(), body_ops.end(), [](const std::shared_ptr<Node>& op) {
+ return std::string{op->get_type_name()} == "Multiply";
+ });
+ const auto body_mul_node = ngraph::as_type_ptr<ngraph::opset5::Multiply>(*body_mul_node_it);
+ EXPECT_TRUE(body_mul_node);
+ EXPECT_EQ(
+ body_mul_node->get_autob().m_type,
+ ngraph::op::AutoBroadcastType::NONE); // legacy Mul from ONNX opset1 has NONE broadcasting
+}
+
// ~~~~~~~~STATIC/DYNAMIC/CONSTANT INPUTS TESTS:~~~~~~~~
NGRAPH_TEST(${BACKEND_NAME}, onnx_controlflow_loop_scalars)
projects / platform / upstream / dldt.git / commitdiff