[ONNX Importer] Resolved problem with incorrect opset version set in subgraph (#2959)
This commit is contained in:
Mateusz Bencer
GitHub
parent
0a71ada75e
commit
0baad0696c
@ -46,6 +46,7 @@ namespace ngraph
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const std::string& get_name() const { return m_graph_proto->name(); }
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OutputVector make_ng_nodes(const Node& onnx_node) const;
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const GraphCache& get_graph_cache() const;
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const OpsetImports& get_opset_imports() const;
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protected:
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Graph(const ONNX_NAMESPACE::GraphProto& proto,
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@ -27,6 +27,10 @@ namespace ngraph
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{
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namespace onnx_import
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{
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/// \brief Type of container which stores opset version and domain in ONNX format
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using OpsetImports =
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::google::protobuf::RepeatedPtrField<ONNX_NAMESPACE::OperatorSetIdProto>;
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std::string get_node_domain(const ONNX_NAMESPACE::NodeProto& node_proto);
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std::int64_t get_opset_version(const ONNX_NAMESPACE::ModelProto& model_proto,
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@ -47,6 +51,7 @@ namespace ngraph
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const std::string& get_producer_name() const { return m_model_proto->producer_name(); }
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const ONNX_NAMESPACE::GraphProto& get_graph() const { return m_model_proto->graph(); }
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std::int64_t get_model_version() const { return m_model_proto->model_version(); }
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const OpsetImports& get_opset_imports() const;
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const std::string& get_producer_version() const
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{
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return m_model_proto->producer_version();
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@ -42,10 +42,10 @@ namespace ngraph
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ONNX_NAMESPACE::ModelProto model_proto;
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const auto& graph = m_attribute_proto->g();
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*(model_proto.mutable_graph()) = graph;
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// We're creating here a model with unset `opset_import` field. This shouldn't
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// be a problem, since we add ONNX opset as a default available opset. Moreover
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// if we encounter a node absent in current available opsets we will try
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// to add it's domain to available opsets.
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// set opset version and domain from the parent graph
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*model_proto.mutable_opset_import() = parent_graph.get_opset_imports();
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Model model{model_proto};
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return Subgraph{graph, model, parent_graph};
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}
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@ -296,6 +296,11 @@ namespace ngraph
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as_node_vector(ng_inputs));
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}
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const OpsetImports& Graph::get_opset_imports() const
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{
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return m_model->get_opset_imports();
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}
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Subgraph::Subgraph(const ONNX_NAMESPACE::GraphProto& proto,
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Model& model,
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const Graph& parent_graph)
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@ -110,6 +110,11 @@ namespace ngraph
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}
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}
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const OpsetImports& Model::get_opset_imports() const
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{
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return m_model_proto->opset_import();
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}
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} // namespace onnx_import
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} // namespace ngraph
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@ -137,10 +137,10 @@ xfail_issue_38714 = xfail_test(reason="RuntimeError: While validating ONNX node
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"While validating node 'v1::<name> (sizes[0]:i64{4},"
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"Convert_29306[0]:f32{4}) -> (dynamic?)' with friendly_name '<name>':"
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"Argument element types are inconsistent.")
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xfail_issue_41813 = xfail_test(reason="RuntimeError: While validating ONNX node"
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" '<Node(Loop): generic_loop_Loop__69>'"
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"While validating ONNX node '<Node(Resize): Resize__143>':"
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"vector::_M_range_check: __n (which is 2) >= this->size() (which is 2")
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xfail_issue_42297 = xfail_test(reason="RuntimeError: While validating ONNX node '<Node(Conv): Conv__7398>':"
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"Check 'data.get_partial_shape().rank().is_static()'"
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" failed at ngraph/frontend/onnx_import/src/op/conv.cpp:102:"
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"The input data tensor's rank has to be known (static)")
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xfail_issue_41814 = xfail_test(reason="RuntimeError: While validating ONNX node '<Node(Loop):"
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" generic_loop_Loop__121>':"
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"While validating ONNX node '<Node(TopK):"
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@ -25,7 +25,7 @@ from tests.test_onnx.utils.model_importer import ModelImportRunner
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from tests import (
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xfail_issue_38701,
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xfail_issue_41813,
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xfail_issue_42297,
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xfail_issue_41815,
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xfail_issue_41814,
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xfail_issue_36533,
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@ -116,11 +116,11 @@ if len(zoo_models) > 0:
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import_xfail_list = [
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# ONNX Model Zoo
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(xfail_issue_38701, "test_onnx_model_zoo_text_machine_comprehension_bidirectional_attention_flow_model_bidaf_9_bidaf_bidaf_cpu"),
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(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"),
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(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"),
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(xfail_issue_38726, "test_onnx_model_zoo_text_machine_comprehension_t5_model_t5_decoder_with_lm_head_12_t5_decoder_with_lm_head_cpu"),
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# Model MSFT
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(xfail_issue_41813, "test_MSFT_opset10_mlperf_ssd_mobilenet_300_ssd_mobilenet_v1_coco_2018_01_28_cpu"),
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(xfail_issue_42297, "test_MSFT_opset10_mlperf_ssd_mobilenet_300_ssd_mobilenet_v1_coco_2018_01_28_cpu"),
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(xfail_issue_41814, "test_MSFT_opset10_mlperf_ssd_resnet34_1200_ssd_resnet34_mAP_20.2_cpu"),
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(xfail_issue_37957, "test_MSFT_opset10_mask_rcnn_keras_mask_rcnn_keras_cpu"),
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(xfail_issue_36465, "test_MSFT_opset9_LSTM_Seq_lens_unpacked_model_cpu"),
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158
ngraph/test/models/onnx/loop/loop_2d_mul_opset1.prototxt
Normal file
158
ngraph/test/models/onnx/loop/loop_2d_mul_opset1.prototxt
Normal file
@ -0,0 +1,158 @@
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ir_version: 6
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producer_name: "nGraph ONNX Importer"
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graph {
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name: "basic loop"
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node {
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input: "trip_count"
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input: ""
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input: "a_init"
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output: "a_final"
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output: "a_values"
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op_type: "Loop"
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attribute {
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name: "body"
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g {
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node {
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input: "a_in"
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input: "b"
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output: "current_a"
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name: "loop_body_mul"
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op_type: "Mul"
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}
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node {
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input: "cond_in"
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output: "cond_out"
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name: "cond_identity"
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op_type: "Identity"
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}
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node {
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input: "current_a"
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output: "a_out"
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name: "output_accumulator"
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op_type: "Identity"
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}
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name: "simple add"
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initializer {
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dims: 1
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dims: 2
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data_type: 1
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float_data: 1
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float_data: 1
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name: "b"
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}
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input {
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name: "i"
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type {
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tensor_type {
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elem_type: 7
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shape {
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}
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}
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}
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}
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input {
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name: "cond_in"
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type {
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tensor_type {
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elem_type: 9
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shape {
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}
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}
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}
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}
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input {
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name: "a_in"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim {
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dim_value: 1
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}
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dim {
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dim_value: 2
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}
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}
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}
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}
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}
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output {
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name: "cond_out"
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type {
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tensor_type {
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elem_type: 9
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shape {
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}
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}
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}
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}
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output {
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name: "current_a"
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type {
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tensor_type {
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elem_type: 1
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shape {
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}
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}
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}
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}
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output {
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name: "a_out"
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type {
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tensor_type {
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elem_type: 1
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shape {
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}
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}
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}
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}
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}
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type: GRAPH
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}
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}
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initializer {
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dims: 1
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data_type: 7
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int64_data: 3
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name: "trip_count"
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}
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input {
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name: "a_init"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim {
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dim_value: 1
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}
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dim {
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dim_value: 2
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}
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}
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}
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}
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}
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output {
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name: "a_final"
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type {
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tensor_type {
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elem_type: 1
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shape {
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}
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}
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}
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}
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output {
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name: "a_values"
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type {
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tensor_type {
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elem_type: 1
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shape {
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}
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}
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}
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}
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}
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opset_import {
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version: 1
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}
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@ -16,6 +16,7 @@
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#include "gtest/gtest.h"
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#include "ngraph/file_util.hpp"
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#include "ngraph/type.hpp"
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#include "ngraph/type/element_type.hpp"
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#include "onnx_import/default_opset.hpp"
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#include "onnx_import/onnx.hpp"
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@ -219,6 +220,29 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_controlflow_loop_add_value_the_same_node_from_
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test_case.run();
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}
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NGRAPH_TEST(${BACKEND_NAME}, onnx_controlflow_loop_the_proper_opset_in_subgraph)
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{
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const auto function = onnx_import::import_onnx_model(
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file_util::path_join(SERIALIZED_ZOO, "onnx/loop/loop_2d_mul_opset1.prototxt"));
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const auto parent_ops = function->get_ops();
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const auto loop_node_it =
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std::find_if(parent_ops.begin(), parent_ops.end(), [](const std::shared_ptr<Node>& op) {
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return std::string{op->get_type_name()} == "Loop";
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});
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const auto body_ops =
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ngraph::as_type_ptr<ngraph::opset5::Loop>(*loop_node_it)->get_function()->get_ops();
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const auto body_mul_node_it =
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std::find_if(body_ops.begin(), body_ops.end(), [](const std::shared_ptr<Node>& op) {
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return std::string{op->get_type_name()} == "Multiply";
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});
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const auto body_mul_node = ngraph::as_type_ptr<ngraph::opset5::Multiply>(*body_mul_node_it);
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EXPECT_TRUE(body_mul_node);
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EXPECT_EQ(
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body_mul_node->get_autob().m_type,
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ngraph::op::AutoBroadcastType::NONE); // legacy Mul from ONNX opset1 has NONE broadcasting
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}
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// ~~~~~~~~STATIC/DYNAMIC/CONSTANT INPUTS TESTS:~~~~~~~~
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NGRAPH_TEST(${BACKEND_NAME}, onnx_controlflow_loop_scalars)
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