[IE][VPU]: Support dynamic data in Broadcast DTS (#3548)
Ticket - #-44546 Changes: * Support dynamic data as broadcast input in Broadcast DTS * Update DTS tests to support both dynamic and static inputs * Update inference tests: a) Refactor tests to have only one testing class - NonZero_Broadcast b) Make DSR_TestsCommon base class to reuse createInputSubgraphWithDSR and inputs generating utils. c) Add possibility to add additional results in DSR_TestsCommon, because NonZero doesn't support cases when both its outputs are unused, so we need to add at least one of them to function results.
This commit is contained in:
Andrew Bakalin
GitHub
parent
b6f311b463
commit
578ea2fc3c
@ -45,23 +45,19 @@ void dynamicToStaticShapeBroadcast(std::shared_ptr<ngraph::Node> target) {
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std::shared_ptr<ngraph::Node> dsr;
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if (broadcast->get_broadcast_spec() == ngraph::op::BroadcastType::BIDIRECTIONAL) {
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const auto inputShape = broadcast->get_input_shape(0);
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const auto dataDSR = ngraph::as_type_ptr<ngraph::vpu::op::DynamicShapeResolver>(broadcast->input_value(0).get_node_shared_ptr());
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const auto shapeElementType = dataDSR ? dataDSR->get_input_element_type(1) : broadcast->get_input_element_type(1);
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const auto dataShape = dataDSR ? dataDSR->input_value(1) : shapeToConstant(shapeElementType, broadcast->get_input_shape(0));
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const auto targetShape = broadcast->input_value(1).get_node_shared_ptr();
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const auto shapeType = targetShape->get_element_type();
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const auto inputShapeDimsCount = inputShape.size();
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const auto dataShapeDimsCount = ngraph::shape_size(dataShape.get_shape());
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const auto targetShapeDimsCount = ngraph::shape_size(broadcast->get_input_partial_shape(1).get_shape());
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const auto inputShapeConst = std::make_shared<ngraph::opset5::Constant>(
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shapeType,
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ngraph::Shape{static_cast<size_t>(inputShapeDimsCount)},
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inputShape);
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const auto minRank = std::min(inputShapeDimsCount, targetShapeDimsCount);
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const auto maxRank = std::max(inputShapeDimsCount, targetShapeDimsCount);
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const auto minRankNode = minRank == inputShapeDimsCount ? inputShapeConst : targetShape;
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const auto maxRankNode = minRank == inputShapeDimsCount ? targetShape : inputShapeConst;
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const auto minRank = std::min(dataShapeDimsCount, targetShapeDimsCount);
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const auto maxRank = std::max(dataShapeDimsCount, targetShapeDimsCount);
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const auto minRankNode = minRank == dataShapeDimsCount ? dataShape : targetShape;
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const auto maxRankNode = minRank == dataShapeDimsCount ? targetShape : dataShape;
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ngraph::NodeVector dims;
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@ -69,19 +65,19 @@ void dynamicToStaticShapeBroadcast(std::shared_ptr<ngraph::Node> target) {
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dims.push_back(
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std::make_shared<ngraph::opset5::Gather>(
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maxRankNode,
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ngraph::opset5::Constant::create(shapeType, ngraph::Shape{1}, {i}),
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ngraph::opset5::Constant::create(shapeType, ngraph::Shape{1}, {0})));
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ngraph::opset5::Constant::create(shapeElementType, ngraph::Shape{1}, {i}),
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ngraph::opset5::Constant::create(shapeElementType, ngraph::Shape{1}, {0})));
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}
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for (int i = 0; i < minRank; i++) {
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const auto minRankDim = std::make_shared<ngraph::opset5::Gather>(
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minRankNode,
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ngraph::opset5::Constant::create(shapeType, ngraph::Shape{1}, {i}),
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ngraph::opset5::Constant::create(shapeType, ngraph::Shape{1}, {0}));
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ngraph::opset5::Constant::create(shapeElementType, ngraph::Shape{1}, {i}),
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ngraph::opset5::Constant::create(shapeElementType, ngraph::Shape{1}, {0}));
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const auto maxRankDim = std::make_shared<ngraph::opset5::Gather>(
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maxRankNode,
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ngraph::opset5::Constant::create(shapeType, ngraph::Shape{1}, {maxRank - minRank + i}),
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ngraph::opset5::Constant::create(shapeType, ngraph::Shape{1}, {0}));
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ngraph::opset5::Constant::create(shapeElementType, ngraph::Shape{1}, {maxRank - minRank + i}),
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ngraph::opset5::Constant::create(shapeElementType, ngraph::Shape{1}, {0}));
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dims.push_back(std::make_shared<ngraph::opset5::Maximum>(minRankDim, maxRankDim));
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}
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@ -6,6 +6,7 @@
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#include "vpu/ngraph/transformations/dynamic_to_static_shape.hpp"
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#include "vpu/ngraph/operations/static_shape_broadcast.hpp"
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#include "vpu/ngraph/operations/dynamic_shape_resolver.hpp"
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#include "vpu/ngraph/utilities.hpp"
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#include <ngraph_functions/utils/ngraph_helpers.hpp>
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#include <ngraph/function.hpp>
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@ -26,12 +27,22 @@ using TensorType = ngraph::element::Type;
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using TensorShape = ngraph::PartialShape;
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using AxesMapping = std::vector<size_t>;
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enum class BroadcastInputType {
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DYNAMIC,
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STATIC
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};
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struct BroadcastShapes {
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TensorShape srcShape;
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TensorShape targetShape;
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AxesMapping axesMapping;
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};
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using BroadcastTestParams = std::tuple<TensorType, BroadcastShapes>;
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using BroadcastTestParams = std::tuple<
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TensorType,
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TensorType,
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BroadcastShapes,
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BroadcastInputType>;
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class DynamicToStaticShapeBroadcastExplicitTests
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: public CommonTestUtils::TestsCommon,
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@ -40,37 +51,52 @@ public:
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void SetUp() override {
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const auto& parameters = GetParam();
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const auto& tensorType = std::get<0>(parameters);
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const auto& tensorShape = std::get<1>(parameters).srcShape;
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const auto& targetShape = std::get<1>(parameters).targetShape;
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const auto& axesMapping = std::get<1>(parameters).axesMapping;
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const auto& shapeType = std::get<1>(parameters);
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const auto& tensorShape = std::get<2>(parameters).srcShape;
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const auto& targetShape = std::get<2>(parameters).targetShape;
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const auto& axesMapping = std::get<2>(parameters).axesMapping;
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const auto& broadcastInputType = std::get<3>(parameters);
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ngraph::helpers::CompareFunctions(
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*transform(tensorType, tensorShape, targetShape, axesMapping),
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*reference(tensorType, tensorShape, targetShape, axesMapping));
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*transform(tensorType, shapeType, tensorShape, targetShape, axesMapping, broadcastInputType),
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*reference(tensorType, shapeType, tensorShape, targetShape, axesMapping, broadcastInputType));
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}
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protected:
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std::shared_ptr<const ngraph::Function> transform(
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const TensorType& tensorType,
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const TensorType& shapeType,
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const TensorShape& tensorShape,
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const TensorShape& targetShape,
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const AxesMapping& axesMapping) const {
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const AxesMapping& axesMapping,
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BroadcastInputType broadcastInputType) const {
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const auto tensorParam = std::make_shared<ngraph::opset3::Parameter>(tensorType, tensorShape);
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const auto tensorWithTargetShapeParam = std::make_shared<ngraph::opset3::Parameter>(tensorType, targetShape);
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const auto shapeOfNode = std::make_shared<ngraph::opset3::ShapeOf>(tensorWithTargetShapeParam);
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const auto shapeOfNode = std::make_shared<ngraph::opset3::ShapeOf>(tensorWithTargetShapeParam, shapeType);
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shapeOfNode->set_is_foldable(false);
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ngraph::ParameterVector params{tensorParam, tensorWithTargetShapeParam};
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std::shared_ptr<ngraph::Node> broadcastInput = tensorParam;
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if (broadcastInputType == BroadcastInputType::DYNAMIC) {
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const auto shapeParam = std::make_shared<ngraph::opset5::Parameter>(
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shapeType,
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ngraph::Shape{static_cast<size_t>(tensorShape.rank().get_length())});
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params.push_back(shapeParam);
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broadcastInput = std::make_shared<ngraph::vpu::op::DynamicShapeResolver>(tensorParam, shapeParam);
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}
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const auto axesMappingConstant = std::make_shared<ngraph::opset3::Constant>(
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ngraph::element::u64,
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ngraph::Shape{axesMapping.size()},
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axesMapping);
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const auto broadcast = std::make_shared<ngraph::opset3::Broadcast>(tensorParam, shapeOfNode, axesMappingConstant);
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const auto broadcast = std::make_shared<ngraph::opset3::Broadcast>(broadcastInput, shapeOfNode, axesMappingConstant);
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auto function = std::make_shared<ngraph::Function>(
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ngraph::NodeVector{broadcast},
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ngraph::ParameterVector{tensorParam, tensorWithTargetShapeParam},
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params,
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"Actual");
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// We need to set broadcast output shape to make its rank static.
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@ -87,24 +113,37 @@ protected:
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std::shared_ptr<const ngraph::Function> reference(
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const TensorType& tensorType,
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const TensorType& shapeType,
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const TensorShape& tensorShape,
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const TensorShape& targetShape,
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const AxesMapping& axesMapping) const {
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const AxesMapping& axesMapping,
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BroadcastInputType broadcastInputType) const {
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const auto tensorParam = std::make_shared<ngraph::opset3::Parameter>(tensorType, tensorShape);
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const auto tensorWithTargetShapeParam = std::make_shared<ngraph::opset3::Parameter>(tensorType, targetShape);
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const auto shapeOf = std::make_shared<ngraph::opset3::ShapeOf>(tensorWithTargetShapeParam);
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const auto shapeOf = std::make_shared<ngraph::opset3::ShapeOf>(tensorWithTargetShapeParam, shapeType);
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ngraph::ParameterVector params{tensorParam, tensorWithTargetShapeParam};
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std::shared_ptr<ngraph::Node> broadcastInput = tensorParam;
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if (broadcastInputType == BroadcastInputType::DYNAMIC) {
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const auto shapeParam = std::make_shared<ngraph::opset5::Parameter>(
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shapeType,
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ngraph::Shape{static_cast<size_t>(tensorShape.rank().get_length())});
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params.push_back(shapeParam);
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broadcastInput = std::make_shared<ngraph::vpu::op::DynamicShapeResolver>(tensorParam, shapeParam);
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}
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const auto axesMappingConstant = std::make_shared<ngraph::opset3::Constant>(
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ngraph::element::u64,
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ngraph::element::i64,
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ngraph::Shape{axesMapping.size()},
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axesMapping);
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const auto staticShapeBroadcast = std::make_shared<ngraph::vpu::op::StaticShapeBroadcast>(tensorParam, shapeOf, axesMappingConstant);
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const auto staticShapeBroadcast = std::make_shared<ngraph::vpu::op::StaticShapeBroadcast>(broadcastInput, shapeOf, axesMappingConstant);
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const auto dsrOut = std::make_shared<ngraph::vpu::op::DynamicShapeResolver>(staticShapeBroadcast, shapeOf);
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return std::make_shared<ngraph::Function>(
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ngraph::NodeVector{dsrOut},
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ngraph::ParameterVector{tensorParam, tensorWithTargetShapeParam},
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params,
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"Expected");
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}
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};
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@ -119,9 +158,15 @@ INSTANTIATE_TEST_CASE_P(smoke_NGraph, DynamicToStaticShapeBroadcastExplicitTests
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ngraph::element::i32,
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ngraph::element::i64,
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ngraph::element::u8),
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testing::Values(
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ngraph::element::i32,
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ngraph::element::i64),
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testing::Values(
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BroadcastShapes{TensorShape{16}, TensorShape{1, 16, 50, 50}, AxesMapping{1}},
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BroadcastShapes{TensorShape{50, 50}, TensorShape{1, 50, 50, 16}, AxesMapping{1, 2}})
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BroadcastShapes{TensorShape{50, 50}, TensorShape{1, 50, 50, 16}, AxesMapping{1, 2}}),
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testing::Values(
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BroadcastInputType::DYNAMIC,
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BroadcastInputType::STATIC)
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));
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@ -131,30 +176,45 @@ public:
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void SetUp() override {
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const auto& parameters = GetParam();
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const auto& tensorType = std::get<0>(parameters);
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const auto& tensorShape = std::get<1>(parameters).srcShape;
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const auto& targetShape = std::get<1>(parameters).targetShape;
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const auto& shapeType = std::get<1>(parameters);
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const auto& tensorShape = std::get<2>(parameters).srcShape;
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const auto& targetShape = std::get<2>(parameters).targetShape;
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const auto& broadcastInputType = std::get<3>(parameters);
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ngraph::helpers::CompareFunctions(
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*transform(tensorType, tensorShape, targetShape),
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*reference(tensorType, tensorShape, targetShape));
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*transform(tensorType, shapeType, tensorShape, targetShape, broadcastInputType),
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*reference(tensorType, shapeType, tensorShape, targetShape, broadcastInputType));
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}
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protected:
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std::shared_ptr<const ngraph::Function> transform(
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const TensorType& tensorType,
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const TensorType& shapeType,
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const TensorShape& tensorShape,
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const TensorShape& targetShape) const {
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const TensorShape& targetShape,
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BroadcastInputType broadcastInputType) const {
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const auto tensorParam = std::make_shared<ngraph::opset5::Parameter>(tensorType, tensorShape);
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const auto tensorWithTargetShapeParam = std::make_shared<ngraph::opset5::Parameter>(tensorType, targetShape);
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const auto tensorWithTargetShapeParam = std::make_shared<ngraph::opset5::Parameter>(shapeType, targetShape);
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const auto shapeOfNode = std::make_shared<ngraph::opset5::ShapeOf>(tensorWithTargetShapeParam);
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const auto shapeOfNode = std::make_shared<ngraph::opset5::ShapeOf>(tensorWithTargetShapeParam, shapeType);
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shapeOfNode->set_is_foldable(false);
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const auto broadcast = std::make_shared<ngraph::opset5::Broadcast>(tensorParam, shapeOfNode, ngraph::op::BroadcastType::BIDIRECTIONAL);
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ngraph::ParameterVector params{tensorParam, tensorWithTargetShapeParam};
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std::shared_ptr<ngraph::Node> broadcastInput = tensorParam;
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if (broadcastInputType == BroadcastInputType::DYNAMIC) {
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const auto shapeParam = std::make_shared<ngraph::opset5::Parameter>(
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shapeType,
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ngraph::Shape{static_cast<size_t>(tensorShape.rank().get_length())});
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params.push_back(shapeParam);
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broadcastInput = std::make_shared<ngraph::vpu::op::DynamicShapeResolver>(tensorParam, shapeParam);
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}
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const auto broadcast = std::make_shared<ngraph::opset5::Broadcast>(broadcastInput, shapeOfNode, ngraph::op::BroadcastType::BIDIRECTIONAL);
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auto function = std::make_shared<ngraph::Function>(
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ngraph::NodeVector{broadcast},
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ngraph::ParameterVector{tensorParam, tensorWithTargetShapeParam},
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params,
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"Actual");
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const auto transformations = vpu::Transformations{{ngraph::opset5::Broadcast::type_info, vpu::dynamicToStaticShapeBroadcast}};
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@ -164,29 +224,41 @@ protected:
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std::shared_ptr<const ngraph::Function> reference(
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const TensorType& tensorType,
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const TensorType& shapeType,
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const TensorShape& tensorShape,
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const TensorShape& targetShape) const {
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const TensorShape& targetShape,
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BroadcastInputType broadcastInputType) const {
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const auto tensorParam = std::make_shared<ngraph::opset5::Parameter>(tensorType, tensorShape);
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const auto tensorWithTargetShapeParam = std::make_shared<ngraph::opset5::Parameter>(tensorType, targetShape);
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std::shared_ptr<ngraph::Node> shapeOf = std::make_shared<ngraph::opset5::ShapeOf>(tensorWithTargetShapeParam);
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const auto tensorWithTargetShapeParam = std::make_shared<ngraph::opset5::Parameter>(shapeType, targetShape);
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std::shared_ptr<ngraph::Node> shapeOf = std::make_shared<ngraph::opset5::ShapeOf>(tensorWithTargetShapeParam, shapeType);
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ngraph::ParameterVector params{tensorParam, tensorWithTargetShapeParam};
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std::shared_ptr<ngraph::Node> broadcastInput = tensorParam;
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if (broadcastInputType == BroadcastInputType::DYNAMIC) {
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const auto shapeParam = std::make_shared<ngraph::opset5::Parameter>(
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shapeType,
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ngraph::Shape{static_cast<size_t>(tensorShape.rank().get_length())});
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params.push_back(shapeParam);
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broadcastInput = std::make_shared<ngraph::vpu::op::DynamicShapeResolver>(tensorParam, shapeParam);
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}
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const auto staticShapeBroadcast = std::make_shared<ngraph::vpu::op::StaticShapeBroadcast>(
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tensorParam,
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broadcastInput,
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shapeOf,
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ngraph::op::BroadcastType::BIDIRECTIONAL);
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const auto tensorShapeDimsCount = tensorShape.rank().get_length();
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const auto targetShapeDimsCount = targetShape.rank().get_length();
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std::shared_ptr<ngraph::Node> tensorShapeConst = std::make_shared<ngraph::opset5::Constant>(
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ngraph::element::i64,
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ngraph::Shape{static_cast<size_t>(tensorShapeDimsCount)},
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tensorShape.get_shape());
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const auto tensorShapeNode = broadcastInputType == BroadcastInputType::DYNAMIC ?
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staticShapeBroadcast->input_value(0).get_node_shared_ptr()->input_value(1) :
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vpu::shapeToConstant(shapeType, tensorShape.get_shape());
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const auto maxRankNode = tensorShapeDimsCount > targetShapeDimsCount ? tensorShapeConst : shapeOf;
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const auto minRankNode = maxRankNode == tensorShapeConst ? shapeOf : tensorShapeConst;
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const auto maxRank = maxRankNode == tensorShapeConst ? tensorShapeDimsCount : targetShapeDimsCount;
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const auto minRank = minRankNode == tensorShapeConst ? tensorShapeDimsCount : targetShapeDimsCount;
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const auto maxRankNode = tensorShapeDimsCount > targetShapeDimsCount ? tensorShapeNode : shapeOf;
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const auto minRankNode = maxRankNode == tensorShapeNode ? shapeOf : tensorShapeNode;
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const auto maxRank = maxRankNode == tensorShapeNode ? tensorShapeDimsCount : targetShapeDimsCount;
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const auto minRank = minRankNode == tensorShapeNode ? tensorShapeDimsCount : targetShapeDimsCount;
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ngraph::NodeVector dims;
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@ -216,7 +288,7 @@ protected:
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staticShapeBroadcast->output(0), outShape);
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return std::make_shared<ngraph::Function>(
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ngraph::NodeVector{dsrOut},
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ngraph::ParameterVector{tensorParam, tensorWithTargetShapeParam},
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params,
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"Expected");
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}
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};
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@ -231,14 +303,19 @@ INSTANTIATE_TEST_CASE_P(smoke_NGraph, DynamicToStaticShapeBroadcastBidirectional
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ngraph::element::i32,
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ngraph::element::i64,
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ngraph::element::u8),
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testing::Values(
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ngraph::element::i32,
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ngraph::element::i64),
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testing::Values(
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BroadcastShapes{TensorShape{1, 1, 4}, TensorShape{300, 2, 4}, {}},
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BroadcastShapes{TensorShape{15, 1}, TensorShape{2, 16, 15, 14}, {}},
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BroadcastShapes{TensorShape{2, 16, 15, 14}, TensorShape{15, 14}, {}},
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BroadcastShapes{TensorShape{2, 16, 15, 14}, TensorShape{16, 1, 1}, {}},
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BroadcastShapes{TensorShape{2, 16, 15, 14}, TensorShape{16, 1, 14}, {}},
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BroadcastShapes{TensorShape{16, 15, 1}, TensorShape{2, 1, 15, 14}, {}})
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BroadcastShapes{TensorShape{16, 15, 1}, TensorShape{2, 1, 15, 14}, {}}),
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testing::Values(
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BroadcastInputType::DYNAMIC,
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BroadcastInputType::STATIC)
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));
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} // namespace
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@ -29,6 +29,7 @@ class DSR_TestsCommon : virtual public LayerTestsUtils::LayerTestsCommon {
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protected:
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std::unordered_map<std::string, DataShape> m_shapes;
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ngraph::ParameterVector m_parameterVector;
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ngraph::ResultVector m_additionalResults;
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std::shared_ptr<ngraph::opset3::Parameter> createParameter(
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const ngraph::element::Type& element_type,
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@ -69,6 +70,7 @@ protected:
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for (const auto& output : testedOp->outputs()) {
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results.emplace_back(std::make_shared<ngraph::opset3::Result>(output));
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}
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results.insert(results.end(), m_additionalResults.begin(), m_additionalResults.end());
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function = std::make_shared<ngraph::Function>(
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results,
|
||||
|
||||
@ -15,7 +15,7 @@ using TensorType = ngraph::element::Type;
|
||||
using TensorShape = ngraph::Shape;
|
||||
|
||||
struct BroadcastInputParams {
|
||||
TensorShape inputShape;
|
||||
DataShapeWithUpperBound inputShape;
|
||||
DataShapeWithUpperBound targetShape;
|
||||
InferenceEngine::SizeVector axesMapping;
|
||||
};
|
||||
@ -24,8 +24,8 @@ using BroadcastTestParams = std::tuple<
|
||||
BroadcastInputParams, TensorType, LayerTestsUtils::TargetDevice>;
|
||||
|
||||
|
||||
class NonZero_BroadcastBidirectional : public testing::WithParamInterface<BroadcastTestParams>,
|
||||
virtual public LayerTestsUtils::LayerTestsCommon {
|
||||
class NonZero_Broadcast : public testing::WithParamInterface<BroadcastTestParams>,
|
||||
public DSR_TestsCommon {
|
||||
protected:
|
||||
size_t getDynamicAxis(const DataShape& shapeA, const DataShape& shapeB) const {
|
||||
size_t res = 0;
|
||||
@ -35,9 +35,7 @@ protected:
|
||||
return res;
|
||||
}
|
||||
|
||||
void prepareBroadcastInputs() {
|
||||
SetRefMode(LayerTestsUtils::RefMode::CONSTANT_FOLDING);
|
||||
|
||||
std::shared_ptr<ngraph::Node> createTestedOp() override {
|
||||
const auto& parameters = GetParam();
|
||||
const auto& broadcastParams = std::get<0>(parameters);
|
||||
const auto& tensorType = std::get<1>(parameters);
|
||||
@ -48,54 +46,56 @@ protected:
|
||||
|
||||
const auto dynamicAxis = getDynamicAxis(upperBoundShape, realShape);
|
||||
|
||||
m_param = std::make_shared<ngraph::opset5::Parameter>(tensorType, TensorShape{upperBoundShape[dynamicAxis]});
|
||||
m_nonZero = std::make_shared<ngraph::opset5::NonZero>(m_param);
|
||||
const auto shapeOfNonZero = std::make_shared<ngraph::opset5::ShapeOf>(m_nonZero);
|
||||
const auto& nonZeroParam = createParameter(tensorType, TensorShape{upperBoundShape[dynamicAxis]});
|
||||
const auto& nonZero = std::make_shared<ngraph::opset5::NonZero>(nonZeroParam, ngraph::element::i32);
|
||||
m_additionalResults.push_back(std::make_shared<ngraph::opset3::Result>(nonZero->output(0)));
|
||||
const auto shapeOfNonZero = std::make_shared<ngraph::opset5::ShapeOf>(nonZero, ngraph::element::i32);
|
||||
const auto numNonZeros = std::make_shared<ngraph::opset5::Gather>(
|
||||
shapeOfNonZero,
|
||||
ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1}),
|
||||
ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0}));
|
||||
|
||||
m_broadcastTargetShape = numNonZeros;
|
||||
std::shared_ptr<ngraph::Node> broadcastTargetShape = numNonZeros;
|
||||
|
||||
if (dynamicAxis > 0) {
|
||||
m_broadcastTargetShape = std::make_shared<ngraph::opset5::Concat>(
|
||||
broadcastTargetShape = std::make_shared<ngraph::opset5::Concat>(
|
||||
ngraph::NodeVector{
|
||||
ngraph::opset5::Constant::create(
|
||||
ngraph::element::i64,
|
||||
ngraph::element::i32,
|
||||
ngraph::Shape{dynamicAxis},
|
||||
std::vector<size_t>{upperBoundShape.begin(), upperBoundShape.begin() + dynamicAxis}),
|
||||
m_broadcastTargetShape},
|
||||
broadcastTargetShape},
|
||||
0);
|
||||
}
|
||||
|
||||
if (dynamicAxis < upperBoundShape.size() - 1) {
|
||||
m_broadcastTargetShape = std::make_shared<ngraph::opset5::Concat>(
|
||||
broadcastTargetShape = std::make_shared<ngraph::opset5::Concat>(
|
||||
ngraph::NodeVector{
|
||||
m_broadcastTargetShape,
|
||||
broadcastTargetShape,
|
||||
ngraph::opset5::Constant::create(
|
||||
ngraph::element::i64,
|
||||
ngraph::element::i32,
|
||||
ngraph::Shape{upperBoundShape.size() - dynamicAxis - 1},
|
||||
std::vector<size_t>{upperBoundShape.begin() + dynamicAxis + 1, upperBoundShape.end()})},
|
||||
0);
|
||||
}
|
||||
|
||||
m_broadcastInput = ngraph::builder::makeConstant(tensorType, ngraph::Shape{broadcastParams.inputShape}, std::vector<int64_t>{}, true);
|
||||
const auto& broadcastInput = broadcastParams.inputShape.upperBoundShape.size() ?
|
||||
createInputSubgraphWithDSR(tensorType, broadcastParams.inputShape) :
|
||||
ngraph::builder::makeConstant(tensorType, ngraph::Shape{broadcastParams.inputShape.shape}, std::vector<int64_t>{}, true);
|
||||
|
||||
if (broadcastParams.axesMapping.size() != 0) {
|
||||
const auto& axesMapping = std::get<0>(GetParam()).axesMapping;
|
||||
const auto axesMappingConst = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{axesMapping.size()}, axesMapping);
|
||||
|
||||
return std::make_shared<ngraph::opset5::Broadcast>(broadcastInput, broadcastTargetShape, axesMappingConst);
|
||||
}
|
||||
|
||||
void SetUp() override {
|
||||
prepareBroadcastInputs();
|
||||
|
||||
const auto broadcast = std::make_shared<ngraph::opset5::Broadcast>(m_broadcastInput, m_broadcastTargetShape, ngraph::op::BroadcastType::BIDIRECTIONAL);
|
||||
|
||||
function = std::make_shared<ngraph::Function>(
|
||||
ngraph::NodeVector{broadcast, m_nonZero},
|
||||
ngraph::ParameterVector{m_param},
|
||||
"NonZero-Broadcast");
|
||||
return std::make_shared<ngraph::opset5::Broadcast>(broadcastInput, broadcastTargetShape, ngraph::op::BroadcastType::BIDIRECTIONAL);
|
||||
}
|
||||
|
||||
InferenceEngine::Blob::Ptr GenerateInput(const InferenceEngine::InputInfo& info) const override {
|
||||
// We emulate dynamic shape through the number of non-zeros in NonZero input tensor
|
||||
if (info.name() == m_parameterVector.front()->get_friendly_name()) {
|
||||
// We emulate dynamic target shape through the number of non-zeros in NonZero input tensor
|
||||
const auto &broadcastParams = std::get<0>(GetParam());
|
||||
const auto numNonZeros = broadcastParams.targetShape.shape[getDynamicAxis(
|
||||
broadcastParams.targetShape.upperBoundShape,
|
||||
@ -115,66 +115,30 @@ protected:
|
||||
return blob;
|
||||
}
|
||||
|
||||
protected:
|
||||
std::shared_ptr<ngraph::Node> m_broadcastInput;
|
||||
std::shared_ptr<ngraph::Node> m_broadcastTargetShape;
|
||||
std::shared_ptr<ngraph::opset5::NonZero> m_nonZero;
|
||||
std::shared_ptr<ngraph::opset5::Parameter> m_param;
|
||||
return DSR_TestsCommon::GenerateInput(info);
|
||||
}
|
||||
};
|
||||
|
||||
TEST_P(NonZero_BroadcastBidirectional, CompareWithReference) {
|
||||
TEST_P(NonZero_Broadcast, CompareWithReference) {
|
||||
Run();
|
||||
}
|
||||
|
||||
std::vector<BroadcastInputParams> broadcastBidirectionalTestParams = {
|
||||
{ {1, 1, 4}, DataShapeWithUpperBound{ {200, 2, 4}, {300, 2, 4} }, {} },
|
||||
{ {15, 14}, DataShapeWithUpperBound{ {2, 16, 1, 14}, {2, 16, 15, 14} }, {} },
|
||||
{ {15, 1}, DataShapeWithUpperBound{ {1, 16, 15, 14}, {2, 16, 15, 14} }, {} },
|
||||
{ {2, 16, 15, 14}, DataShapeWithUpperBound{ {1, 15, 14}, {16, 15, 14} }, {} },
|
||||
{ {2, 16, 15, 14}, DataShapeWithUpperBound{ {16, 1, 1}, {16, 1, 14}}, {} },
|
||||
{ {16, 15, 1}, DataShapeWithUpperBound{ {2, 1, 15, 14}, {2, 16, 15, 14} }, {} },
|
||||
std::vector<BroadcastInputParams> broadcastTestParams = {
|
||||
{ DataShapeWithUpperBound{ {1, 1, 4}, {} }, DataShapeWithUpperBound{ {200, 2, 4}, {300, 2, 4} }, {} },
|
||||
{ DataShapeWithUpperBound{ {15, 14}, {} }, DataShapeWithUpperBound{ {2, 16, 1, 14}, {2, 16, 15, 14} }, {} },
|
||||
{ DataShapeWithUpperBound{ {15, 1}, {} }, DataShapeWithUpperBound{ {1, 16, 15, 14}, {2, 16, 15, 14} }, {} },
|
||||
{ DataShapeWithUpperBound{ {2, 16, 15, 14}, {} }, DataShapeWithUpperBound{ {1, 15, 14}, {16, 15, 14} }, {} },
|
||||
{ DataShapeWithUpperBound{ {2, 16, 15, 14}, {} }, DataShapeWithUpperBound{ {16, 1, 1}, {16, 1, 14}}, {} },
|
||||
{ DataShapeWithUpperBound{ {16, 15, 1}, {} }, DataShapeWithUpperBound{ {2, 1, 15, 14}, {2, 16, 15, 14} }, {} },
|
||||
{ DataShapeWithUpperBound{ {142, 1, 1, 64}, {300, 1, 1, 64} }, DataShapeWithUpperBound { {142, 3, 64, 64}, {300, 3, 64, 64} }, {} },
|
||||
{ DataShapeWithUpperBound{ {1}, {} }, DataShapeWithUpperBound{ {1, 800}, {1, 1000} }, {0} },
|
||||
{ DataShapeWithUpperBound{ {4}, {} }, DataShapeWithUpperBound{ {100, 4}, {1000, 4} }, {1} },
|
||||
{ DataShapeWithUpperBound{ {128, 256}, {} }, DataShapeWithUpperBound{ {1, 128, 256}, {3, 128, 256} }, {1, 2} },
|
||||
};
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(smoke_DynamicBroadcast, NonZero_BroadcastBidirectional,
|
||||
INSTANTIATE_TEST_CASE_P(smoke_DynamicBroadcast, NonZero_Broadcast,
|
||||
::testing::Combine(
|
||||
::testing::ValuesIn(broadcastBidirectionalTestParams),
|
||||
::testing::ValuesIn(broadcastTestParams),
|
||||
::testing::Values(ngraph::element::f16, ngraph::element::f32, ngraph::element::i32),
|
||||
::testing::Values(CommonTestUtils::DEVICE_MYRIAD)));
|
||||
|
||||
using BroadcastExplicitTestParams = std::tuple<
|
||||
BroadcastTestParams, TensorShape, TensorType, LayerTestsUtils::TargetDevice>;
|
||||
|
||||
class NonZero_BroadcastExplicit : public NonZero_BroadcastBidirectional {
|
||||
protected:
|
||||
void SetUp() override {
|
||||
prepareBroadcastInputs();
|
||||
|
||||
const auto& axesMapping = std::get<0>(GetParam()).axesMapping;
|
||||
const auto axesMappingConst = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{axesMapping.size()}, axesMapping);
|
||||
|
||||
const auto broadcast = std::make_shared<ngraph::opset5::Broadcast>(m_broadcastInput, m_broadcastTargetShape, axesMappingConst);
|
||||
|
||||
function = std::make_shared<ngraph::Function>(
|
||||
ngraph::NodeVector{broadcast, m_nonZero},
|
||||
ngraph::ParameterVector{m_param},
|
||||
"NonZero-Broadcast");
|
||||
}
|
||||
};
|
||||
|
||||
TEST_P(NonZero_BroadcastExplicit, CompareWithReference) {
|
||||
Run();
|
||||
}
|
||||
|
||||
std::vector<BroadcastInputParams> broadcastExplicitTestParams = {
|
||||
{ {1}, DataShapeWithUpperBound{ {1, 800}, {1, 1000} }, {0} },
|
||||
{ {4}, DataShapeWithUpperBound{ {100, 4}, {1000, 4} }, {1} },
|
||||
{ {128, 256}, DataShapeWithUpperBound{ {1, 128, 256}, {3, 128, 256} }, {1, 2} },
|
||||
};
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(smoke_DynamicBroadcast, NonZero_BroadcastExplicit,
|
||||
::testing::Combine(
|
||||
::testing::ValuesIn(broadcastExplicitTestParams),
|
||||
::testing::Values(ngraph::element::f16, ngraph::element::f32, ngraph::element::i32),
|
||||
::testing::Values(CommonTestUtils::DEVICE_MYRIAD)));
|
||||
|
||||
} // namespace
|
||||
|
||||
Loading…
Reference in New Issue
Block a user