1283 lines
44 KiB
C++
1283 lines
44 KiB
C++
// Copyright (C) 2018-2023 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#include <cpp/ie_cnn_network.h>
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#include <gtest/gtest.h>
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#include <algorithm>
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#include <fstream>
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#include <ie_core.hpp>
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#include <map>
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#include <memory>
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#include <ngraph/function.hpp>
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#include <ngraph/graph_util.hpp>
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#include <ngraph/op/constant.hpp>
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#include <ngraph/op/interpolate.hpp>
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#include <ngraph/op/op.hpp>
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#include <ngraph/op/parameter.hpp>
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#include <ngraph/op/relu.hpp>
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#include <ngraph/op/result.hpp>
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#include <ngraph/opsets/opset.hpp>
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#include <sstream>
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#include <string>
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#include <vector>
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#include "common_test_utils/common_utils.hpp"
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#include "common_test_utils/data_utils.hpp"
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#include "common_test_utils/file_utils.hpp"
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#include "common_test_utils/test_common.hpp"
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#include "ie_common.h"
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#include "openvino/core/partial_shape.hpp"
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#include "openvino/core/shape.hpp"
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using namespace testing;
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using namespace InferenceEngine;
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using NGraphReshapeTests = ov::test::TestsCommon;
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TEST_F(NGraphReshapeTests, getBatchSize) {
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std::shared_ptr<ngraph::Function> ngraph;
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{
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ngraph::PartialShape shape({1, 3, 22, 22});
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ngraph::element::Type type(ngraph::element::Type_t::f32);
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auto param = std::make_shared<ngraph::op::Parameter>(type, shape);
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auto relu = std::make_shared<ngraph::op::Relu>(param);
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auto result = std::make_shared<ngraph::op::Result>(relu);
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ngraph::ParameterVector params = {param};
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ngraph::ResultVector results = {result};
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ngraph = std::make_shared<ngraph::Function>(results, params);
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}
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CNNNetwork cnnNetwork(ngraph);
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ASSERT_EQ(1, cnnNetwork.getBatchSize());
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}
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TEST_F(NGraphReshapeTests, ReshapedDynamicShapeLayout) {
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std::shared_ptr<ngraph::Function> ngraph;
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{
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ngraph::PartialShape shape({-1, 3, 22, 22});
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ngraph::element::Type type(ngraph::element::Type_t::f32);
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auto param = std::make_shared<ngraph::op::Parameter>(type, shape);
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param->set_friendly_name("A");
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auto relu = std::make_shared<ngraph::op::Relu>(param);
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ngraph::ParameterVector params = {param};
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ngraph = std::make_shared<ngraph::Function>(relu, params);
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}
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CNNNetwork cnnNetwork(ngraph);
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ASSERT_EQ(Layout::NCHW, cnnNetwork.getInputsInfo()["A"]->getLayout());
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ASSERT_EQ(cnnNetwork.getInputsInfo()["A"]->getInputData()->getDims(), (SizeVector{0, 3, 22, 22}));
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ICNNNetwork::InputShapes new_shape;
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new_shape["A"] = {1, 3, 22, 22};
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cnnNetwork.reshape(new_shape);
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ASSERT_EQ(Layout::NCHW, cnnNetwork.getInputsInfo()["A"]->getLayout());
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ASSERT_EQ(cnnNetwork.getInputsInfo()["A"]->getInputData()->getDims(), (SizeVector{1, 3, 22, 22}));
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}
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TEST_F(NGraphReshapeTests, CNNReshapeSpatialReLU) {
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std::shared_ptr<const ngraph::Function> ngraph;
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{
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ngraph::PartialShape shape({1, 3, 22, 22});
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ngraph::element::Type type(ngraph::element::Type_t::f32);
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auto param = std::make_shared<ngraph::op::Parameter>(type, shape);
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param->set_friendly_name("data");
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auto relu = std::make_shared<ngraph::op::Relu>(param);
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auto result = std::make_shared<ngraph::op::Result>(relu);
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ngraph::ParameterVector params = {param};
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ngraph::ResultVector results = {result};
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ngraph = std::make_shared<const ngraph::Function>(results, params);
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}
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ASSERT_EQ(ngraph->get_parameters()[0]->get_shape(), ngraph::Shape({1, 3, 22, 22}));
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ASSERT_EQ(ngraph->get_results()[0]->get_shape(), ngraph::Shape({1, 3, 22, 22}));
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CNNNetwork cnnNetwork(ngraph::clone_function(*ngraph));
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std::map<std::string, SizeVector> shapes;
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shapes["data"] = {1, 3, 25, 25};
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ASSERT_NO_THROW(cnnNetwork.reshape(shapes));
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auto changedFunction = cnnNetwork.getFunction();
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ASSERT_NE(nullptr, changedFunction);
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ASSERT_EQ(changedFunction->get_parameters()[0]->get_shape(), ngraph::Shape({1, 3, 25, 25}));
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ASSERT_EQ(changedFunction->get_results()[0]->get_shape(), ngraph::Shape({1, 3, 25, 25}));
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ASSERT_EQ(ngraph->get_parameters()[0]->get_shape(), ngraph::Shape({1, 3, 22, 22}));
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ASSERT_EQ(ngraph->get_results()[0]->get_shape(), ngraph::Shape({1, 3, 22, 22}));
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ASSERT_EQ(Layout::NCHW, cnnNetwork.getInputsInfo()["data"]->getLayout());
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ASSERT_EQ(cnnNetwork.getInputsInfo()["data"]->getInputData()->getDims(), (SizeVector{1, 3, 25, 25}));
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}
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TEST_F(NGraphReshapeTests, CNNReshapeSpatialReLUWithoutCloneFunction) {
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std::shared_ptr<ngraph::Function> ngraph;
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{
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ngraph::PartialShape shape({1, 3, 22, 22});
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ngraph::element::Type type(ngraph::element::Type_t::f32);
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auto param = std::make_shared<ngraph::op::Parameter>(type, shape);
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param->set_friendly_name("data");
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auto relu = std::make_shared<ngraph::op::Relu>(param);
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auto result = std::make_shared<ngraph::op::Result>(relu);
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ngraph::ParameterVector params = {param};
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ngraph::ResultVector results = {result};
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ngraph = std::make_shared<ngraph::Function>(results, params);
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}
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ASSERT_EQ(ngraph->get_parameters()[0]->get_shape(), ngraph::Shape({1, 3, 22, 22}));
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ASSERT_EQ(ngraph->get_results()[0]->get_shape(), ngraph::Shape({1, 3, 22, 22}));
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CNNNetwork cnnNetwork(ngraph);
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std::map<std::string, SizeVector> shapes;
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shapes["data"] = {1, 3, 25, 25};
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ASSERT_NO_THROW(cnnNetwork.reshape(shapes));
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auto changedFunction = cnnNetwork.getFunction();
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ASSERT_NE(nullptr, changedFunction);
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ASSERT_EQ(changedFunction->get_parameters()[0]->get_shape(), ngraph::Shape({1, 3, 25, 25}));
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ASSERT_EQ(changedFunction->get_results()[0]->get_shape(), ngraph::Shape({1, 3, 25, 25}));
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ASSERT_EQ(ngraph->get_parameters()[0]->get_shape(), ngraph::Shape({1, 3, 25, 25}));
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ASSERT_EQ(ngraph->get_results()[0]->get_shape(), ngraph::Shape({1, 3, 25, 25}));
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ASSERT_EQ(Layout::NCHW, cnnNetwork.getInputsInfo()["data"]->getLayout());
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ASSERT_EQ(cnnNetwork.getInputsInfo()["data"]->getInputData()->getDims(), (SizeVector{1, 3, 25, 25}));
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}
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class CustomTestOp : public ngraph::op::Op {
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public:
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OPENVINO_OP("CustomTestLayer", "test_extension");
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CustomTestOp() = default;
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CustomTestOp(const ngraph::Output<ngraph::Node>& arg, bool test1, int64_t test2)
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: Op({arg}),
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test1(test1),
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test2(test2) {
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constructor_validate_and_infer_types();
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}
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void validate_and_infer_types() override {
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auto input_pshape = get_input_partial_shape(0);
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if (input_pshape.is_static()) {
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auto input_shape = input_pshape.to_shape();
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ngraph::Shape output_shape(input_shape);
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for (size_t i = 0; i < input_shape.size(); ++i) {
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output_shape[i] = input_shape[i] * test2 + (test1 ? 0 : 1);
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}
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set_output_type(0, get_input_element_type(0), ngraph::PartialShape(output_shape));
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} else {
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set_output_type(0, get_input_element_type(0), ngraph::PartialShape::dynamic());
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}
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}
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std::shared_ptr<ngraph::Node> clone_with_new_inputs(const ngraph::OutputVector& new_args) const override {
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if (new_args.size() != 1) {
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OPENVINO_THROW("Incorrect number of new arguments");
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}
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return std::make_shared<CustomTestOp>(new_args.at(0), test1, test2);
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}
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bool visit_attributes(ngraph::AttributeVisitor& visitor) override {
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visitor.on_attribute("test1", test1);
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visitor.on_attribute("test2", test2);
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return true;
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}
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private:
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bool test1;
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int64_t test2;
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};
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class TestInPlaceExtension : public InferenceEngine::IExtension {
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public:
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void GetVersion(const InferenceEngine::Version*& versionInfo) const noexcept override {}
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void Unload() noexcept override {}
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std::map<std::string, ngraph::OpSet> getOpSets() override {
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static std::map<std::string, ngraph::OpSet> opsets;
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if (opsets.empty()) {
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ngraph::OpSet opset;
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opset.insert<CustomTestOp>();
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opsets[CustomTestOp::get_type_info_static().version_id] = opset;
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}
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return opsets;
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}
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private:
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};
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#if defined(ENABLE_OV_IR_FRONTEND)
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TEST_F(NGraphReshapeTests, ReshapeNewIRWithNewExtension1) {
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std::string model = R"V0G0N(
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<net name="Activation" version="10">
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<layers>
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<layer name="in1" type="Parameter" id="0" version="opset1">
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<data shape="1,3,22,22" element_type="f32"/>
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<output>
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<port id="0" precision="FP32">
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<dim>1</dim>
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<dim>3</dim>
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<dim>22</dim>
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<dim>22</dim>
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</port>
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</output>
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</layer>
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<layer name="activation" id="1" type="CustomTestLayer" version="test_extension">
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<data test1="true" test2="2"/>
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<input>
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<port id="1" precision="FP32">
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<dim>1</dim>
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<dim>3</dim>
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<dim>22</dim>
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<dim>22</dim>
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</port>
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</input>
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<output>
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<port id="2" precision="FP32">
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<dim>1</dim>
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<dim>3</dim>
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<dim>22</dim>
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<dim>22</dim>
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</port>
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</output>
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</layer>
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<layer name="output" type="Result" id="2" version="opset1">
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<input>
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<port id="0" precision="FP32">
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<dim>1</dim>
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<dim>3</dim>
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<dim>22</dim>
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<dim>22</dim>
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</port>
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</input>
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</layer>
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</layers>
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<edges>
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<edge from-layer="0" from-port="0" to-layer="1" to-port="1"/>
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<edge from-layer="1" from-port="2" to-layer="2" to-port="0"/>
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</edges>
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</net>
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)V0G0N";
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InferenceEngine::Core ie;
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ie.AddExtension(std::make_shared<TestInPlaceExtension>());
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Blob::Ptr weights;
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SizeVector refBeforeReshape = {1, 3, 22, 22};
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SizeVector refAfterReshape = {4, 6, 44, 44};
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auto network = ie.ReadNetwork(model, weights);
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InferenceEngine::ICNNNetwork::InputShapes newShapes;
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newShapes["in1"] = {2, 3, 22, 22};
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ASSERT_NO_THROW(network.reshape(newShapes));
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auto output = network.getOutputsInfo();
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SizeVector outDims = output["activation"]->getTensorDesc().getDims();
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ASSERT_EQ(outDims, refAfterReshape);
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}
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TEST_F(NGraphReshapeTests, ReshapeNewIRWithNewExtension2) {
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std::string model = R"V0G0N(
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<net name="Activation" version="10">
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<layers>
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<layer name="in1" type="Parameter" id="0" version="opset1">
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<data shape="1,3,22,22" element_type="f32"/>
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<output>
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<port id="0" precision="FP32">
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<dim>1</dim>
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<dim>3</dim>
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<dim>22</dim>
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<dim>22</dim>
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</port>
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</output>
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</layer>
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<layer name="activation" id="1" type="CustomTestLayer" version="test_extension">
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<data test1="0" test2="3"/>
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<input>
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<port id="1" precision="FP32">
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<dim>1</dim>
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<dim>3</dim>
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<dim>22</dim>
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<dim>22</dim>
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</port>
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</input>
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<output>
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<port id="2" precision="FP32">
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<dim>1</dim>
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<dim>3</dim>
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<dim>22</dim>
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<dim>22</dim>
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</port>
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</output>
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</layer>
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<layer name="output" type="Result" id="2" version="opset1">
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<input>
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<port id="0" precision="FP32">
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<dim>1</dim>
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<dim>3</dim>
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<dim>22</dim>
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<dim>22</dim>
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</port>
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</input>
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</layer>
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</layers>
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<edges>
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<edge from-layer="0" from-port="0" to-layer="1" to-port="1"/>
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<edge from-layer="1" from-port="2" to-layer="2" to-port="0"/>
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</edges>
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</net>
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)V0G0N";
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InferenceEngine::Core ie;
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ie.AddExtension(std::make_shared<TestInPlaceExtension>());
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Blob::Ptr weights;
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SizeVector refBeforeReshape = {1, 3, 22, 22};
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SizeVector refAfterReshape = {7, 10, 67, 67};
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auto network = ie.ReadNetwork(model, weights);
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InferenceEngine::ICNNNetwork::InputShapes newShapes;
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newShapes["in1"] = {2, 3, 22, 22};
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ASSERT_NO_THROW(network.reshape(newShapes));
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auto output = network.getOutputsInfo();
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SizeVector outDims = output["activation"]->getTensorDesc().getDims();
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ASSERT_EQ(outDims, refAfterReshape);
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}
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#endif // defined(ENABLE_OV_IR_FRONTEND)
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class BadExtension : public InferenceEngine::IExtension {
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public:
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BadExtension() {}
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void GetVersion(const InferenceEngine::Version*& versionInfo) const noexcept override{};
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void Unload() noexcept override{};
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std::map<std::string, ngraph::OpSet> getOpSets() override {
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static std::map<std::string, ngraph::OpSet> opsets;
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if (opsets.empty()) {
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ngraph::OpSet opset;
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opset.insert<CustomTestOp>();
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opsets["opset1"] = opset;
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}
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return opsets;
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}
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};
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TEST_F(NGraphReshapeTests, LoadBadNewExtension) {
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InferenceEngine::Core ie;
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ASSERT_THROW(ie.AddExtension(std::make_shared<BadExtension>()), InferenceEngine::Exception);
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}
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TEST_F(NGraphReshapeTests, TestInterpParameters) {
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auto inp = std::make_shared<ngraph::op::Parameter>(ngraph::element::f32, ngraph::Shape{2, 3, 4, 5});
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inp->set_friendly_name("test");
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ngraph::op::v0::InterpolateAttrs attrs;
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attrs.pads_begin.push_back(0);
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attrs.pads_end.push_back(0);
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attrs.axes = ngraph::AxisSet{2, 3};
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attrs.align_corners = false;
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attrs.mode = "nearest";
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attrs.antialias = false;
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std::vector<int64_t> shape = {8, 10};
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auto out_shape = std::make_shared<ngraph::op::v0::Constant>(ngraph::element::i64, ngraph::Shape{2}, shape);
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auto interp = std::make_shared<ngraph::op::v0::Interpolate>(inp, out_shape, attrs);
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auto output = std::make_shared<ngraph::op::Result>(interp);
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auto ngraph_function =
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std::make_shared<ngraph::Function>(ngraph::ResultVector{output}, ngraph::ParameterVector{inp});
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CNNNetwork cnn(ngraph_function);
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std::map<std::string, InferenceEngine::SizeVector> inShape;
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inShape["test"] = {1, 3, 4, 5};
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cnn.reshape(inShape);
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}
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#ifdef ENABLE_OV_IR_FRONTEND
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TEST_F(NGraphReshapeTests, ReshapeWithDefaultGenericOps) {
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// the RNNCEll was initially marked as "experimental" operation but later was added to opset
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// the test checks that IR reader properly instantiate the "experimental" RNNCell as "opset6" RNNCell
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std::string model = R"V0G0N(
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<net name="Activation" version="10">
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<layers>
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<layer name="in1" type="Parameter" id="0" version="opset1">
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<data shape="1,16" element_type="f32"/>
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<output>
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<port id="0" precision="FP32">
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<dim>1</dim>
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<dim>16</dim>
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</port>
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</output>
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</layer>
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<layer name="in2" type="Parameter" id="1" version="opset1">
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<data shape="1,128" element_type="f32"/>
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<output>
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<port id="0" precision="FP32">
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<dim>1</dim>
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<dim>128</dim>
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</port>
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</output>
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</layer>
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<layer name="in3" type="Parameter" id="2" version="opset1">
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<data shape="128,16" element_type="f32"/>
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<output>
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<port id="0" precision="FP32">
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<dim>128</dim>
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<dim>16</dim>
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</port>
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</output>
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</layer>
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<layer name="in4" type="Parameter" id="3" version="opset1">
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<data shape="128,128" element_type="f32"/>
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<output>
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<port id="0" precision="FP32">
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<dim>128</dim>
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<dim>128</dim>
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</port>
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</output>
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</layer>
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<layer name="in5" type="Parameter" id="4" version="opset1">
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<data shape="128" element_type="f32"/>
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<output>
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<port id="0" precision="FP32">
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<dim>128</dim>
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</port>
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</output>
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</layer>
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<layer id="5" name="77/RNNCell" type="RNNCell" version="experimental">
|
|
<data hidden_size="128" linear_before_reset="1"/>
|
|
<input>
|
|
<port id="0">
|
|
<dim>1</dim>
|
|
<dim>16</dim>
|
|
</port>
|
|
<port id="1">
|
|
<dim>1</dim>
|
|
<dim>128</dim>
|
|
</port>
|
|
<port id="2">
|
|
<dim>128</dim>
|
|
<dim>16</dim>
|
|
</port>
|
|
<port id="3">
|
|
<dim>128</dim>
|
|
<dim>128</dim>
|
|
</port>
|
|
<port id="4">
|
|
<dim>128</dim>
|
|
</port>
|
|
</input>
|
|
<output>
|
|
<port id="5" precision="FP32">
|
|
<dim>1</dim>
|
|
<dim>128</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="output" type="Result" id="6" version="opset1">
|
|
<input>
|
|
<port id="0" precision="FP32">
|
|
<dim>1</dim>
|
|
<dim>128</dim>
|
|
</port>
|
|
</input>
|
|
</layer>
|
|
</layers>
|
|
<edges>
|
|
<edge from-layer="0" from-port="0" to-layer="5" to-port="0"/>
|
|
<edge from-layer="1" from-port="0" to-layer="5" to-port="1"/>
|
|
<edge from-layer="2" from-port="0" to-layer="5" to-port="2"/>
|
|
<edge from-layer="3" from-port="0" to-layer="5" to-port="3"/>
|
|
<edge from-layer="4" from-port="0" to-layer="5" to-port="4"/>
|
|
<edge from-layer="5" from-port="5" to-layer="6" to-port="0"/>
|
|
</edges>
|
|
</net>
|
|
)V0G0N";
|
|
InferenceEngine::Core ie;
|
|
Blob::Ptr weights;
|
|
|
|
auto network = ie.ReadNetwork(model, weights);
|
|
InferenceEngine::ICNNNetwork::InputShapes newShapes;
|
|
newShapes["in1"] = {2, 16};
|
|
newShapes["in2"] = {2, 128};
|
|
|
|
ASSERT_NO_THROW(network.reshape(newShapes));
|
|
}
|
|
|
|
TEST_F(NGraphReshapeTests, ReshapeEDDetectionOutput) {
|
|
std::string model = R"V0G0N(
|
|
<net name="ExperimentalDetectronDetectionOutput" version="10">
|
|
<layers>
|
|
<layer name="in0" type="Parameter" id="0" version="opset1">
|
|
<data shape="1000,4" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>1000</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="in1" type="Parameter" id="1" version="opset1">
|
|
<data shape="1000,324" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>1000</dim>
|
|
<dim>324</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="in2" type="Parameter" id="2" version="opset1">
|
|
<data shape="1000,81" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>1000</dim>
|
|
<dim>81</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="in3" type="Parameter" id="3" version="opset1">
|
|
<data shape="1,3" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>1</dim>
|
|
<dim>3</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer id="4" name="DO" type="ExperimentalDetectronDetectionOutput" version="experimental">
|
|
<data class_agnostic_box_regression="0" deltas_weights="10.0,10.0,5.0,5.0" max_delta_log_wh="4.135166645050049" max_detections_per_image="100" nms_threshold="0.5" num_classes="81" post_nms_count="2000" score_threshold="0.05000000074505806"/>
|
|
<input>
|
|
<port id="0">
|
|
<dim>1000</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
<port id="1">
|
|
<dim>1000</dim>
|
|
<dim>324</dim>
|
|
</port>
|
|
<port id="2">
|
|
<dim>1000</dim>
|
|
<dim>81</dim>
|
|
</port>
|
|
<port id="3">
|
|
<dim>1</dim>
|
|
<dim>3</dim>
|
|
</port>
|
|
</input>
|
|
<output>
|
|
<port id="4" precision="FP32">
|
|
<dim>100</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
<port id="5" precision="I32">
|
|
<dim>100</dim>
|
|
</port>
|
|
<port id="6" precision="FP32">
|
|
<dim>100</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="out_0" type="Result" id="5" version="opset1">
|
|
<input>
|
|
<port id="0" precision="FP32">
|
|
<dim>100</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
</input>
|
|
</layer>
|
|
<layer name="out_1" type="Result" id="6" version="opset1">
|
|
<input>
|
|
<port id="0" precision="FP32">
|
|
<dim>100</dim>
|
|
</port>
|
|
</input>
|
|
</layer>
|
|
<layer name="out_2" type="Result" id="7" version="opset1">
|
|
<input>
|
|
<port id="0" precision="FP32">
|
|
<dim>100</dim>
|
|
</port>
|
|
</input>
|
|
</layer>
|
|
</layers>
|
|
<edges>
|
|
<edge from-layer="0" from-port="0" to-layer="4" to-port="0"/>
|
|
<edge from-layer="1" from-port="0" to-layer="4" to-port="1"/>
|
|
<edge from-layer="2" from-port="0" to-layer="4" to-port="2"/>
|
|
<edge from-layer="3" from-port="0" to-layer="4" to-port="3"/>
|
|
<edge from-layer="4" from-port="4" to-layer="5" to-port="0"/>
|
|
<edge from-layer="4" from-port="5" to-layer="6" to-port="0"/>
|
|
<edge from-layer="4" from-port="6" to-layer="7" to-port="0"/>
|
|
</edges>
|
|
</net>
|
|
)V0G0N";
|
|
InferenceEngine::Core ie;
|
|
Blob::Ptr weights;
|
|
auto network = ie.ReadNetwork(model, weights);
|
|
InferenceEngine::ICNNNetwork::InputShapes newShapes;
|
|
newShapes["in0"] = {2000, 4};
|
|
newShapes["in1"] = {2000, 324};
|
|
newShapes["in2"] = {2000, 81};
|
|
|
|
ASSERT_NO_THROW(network.reshape(newShapes));
|
|
}
|
|
|
|
TEST_F(NGraphReshapeTests, ReshapeEDPriorGridGenerator) {
|
|
std::string model = R"V0G0N(
|
|
<net name="PriorGridGenerator" version="10">
|
|
<layers>
|
|
<layer name="in0" type="Parameter" id="0" version="opset1">
|
|
<data shape="3,4" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>3</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="in1" type="Parameter" id="1" version="opset1">
|
|
<data shape="1,256,200,336" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>1</dim>
|
|
<dim>256</dim>
|
|
<dim>200</dim>
|
|
<dim>336</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="in2" type="Parameter" id="2" version="opset1">
|
|
<data shape="1,3,800,1344" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>1000</dim>
|
|
<dim>81</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer id="3" name="1117" type="ExperimentalDetectronPriorGridGenerator" version="experimental">
|
|
<data flatten="1" h="0" stride_x="4.0" stride_y="4.0" w="0"/>
|
|
<input>
|
|
<port id="0">
|
|
<dim>3</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
<port id="1">
|
|
<dim>1</dim>
|
|
<dim>256</dim>
|
|
<dim>200</dim>
|
|
<dim>336</dim>
|
|
</port>
|
|
<port id="2">
|
|
<dim>1</dim>
|
|
<dim>3</dim>
|
|
<dim>800</dim>
|
|
<dim>1344</dim>
|
|
</port>
|
|
</input>
|
|
<output>
|
|
<port id="3" precision="FP32">
|
|
<dim>201600</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="out_0" type="Result" id="4" version="opset1">
|
|
<input>
|
|
<port id="0" precision="FP32">
|
|
<dim>201600</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
</input>
|
|
</layer>
|
|
</layers>
|
|
<edges>
|
|
<edge from-layer="0" from-port="0" to-layer="3" to-port="0"/>
|
|
<edge from-layer="1" from-port="0" to-layer="3" to-port="1"/>
|
|
<edge from-layer="2" from-port="0" to-layer="3" to-port="2"/>
|
|
<edge from-layer="3" from-port="3" to-layer="4" to-port="0"/>
|
|
</edges>
|
|
</net>
|
|
)V0G0N";
|
|
InferenceEngine::Core ie;
|
|
Blob::Ptr weights;
|
|
auto network = ie.ReadNetwork(model, weights);
|
|
InferenceEngine::ICNNNetwork::InputShapes newShapes;
|
|
newShapes["in1"] = {2, 256, 200, 336};
|
|
newShapes["in2"] = {2, 3, 800, 1344};
|
|
ASSERT_NO_THROW(network.reshape(newShapes));
|
|
}
|
|
|
|
TEST_F(NGraphReshapeTests, ReshapeEDGenerateProposalsSingleImage) {
|
|
std::string model = R"V0G0N(
|
|
<net name="GenerateProposalsSingleImage" version="10">
|
|
<layers>
|
|
<layer name="in0" type="Parameter" id="0" version="opset1">
|
|
<data shape="3" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>3</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="in1" type="Parameter" id="1" version="opset1">
|
|
<data shape="201600,4" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>201600</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="in2" type="Parameter" id="2" version="opset1">
|
|
<data shape="12,200,336" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>12</dim>
|
|
<dim>200</dim>
|
|
<dim>336</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="in3" type="Parameter" id="3" version="opset1">
|
|
<data shape="3,200,336" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>3</dim>
|
|
<dim>200</dim>
|
|
<dim>336</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer id="4" name="1133" type="ExperimentalDetectronGenerateProposalsSingleImage" version="experimental">
|
|
<data min_size="0.0" nms_threshold="0.699999988079071" post_nms_count="1000" pre_nms_count="1000"/>
|
|
<input>
|
|
<port id="0">
|
|
<dim>3</dim>
|
|
</port>
|
|
<port id="1">
|
|
<dim>201600</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
<port id="2">
|
|
<dim>12</dim>
|
|
<dim>200</dim>
|
|
<dim>336</dim>
|
|
</port>
|
|
<port id="3">
|
|
<dim>3</dim>
|
|
<dim>200</dim>
|
|
<dim>336</dim>
|
|
</port>
|
|
</input>
|
|
<output>
|
|
<port id="4" precision="FP32">
|
|
<dim>1000</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
<port id="5" precision="FP32">
|
|
<dim>1000</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="out_0" type="Result" id="5" version="opset1">
|
|
<input>
|
|
<port id="0" precision="FP32">
|
|
<dim>1000</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
</input>
|
|
</layer>
|
|
<layer name="out_1" type="Result" id="6" version="opset1">
|
|
<input>
|
|
<port id="0" precision="FP32">
|
|
<dim>1000</dim>
|
|
</port>
|
|
</input>
|
|
</layer>
|
|
</layers>
|
|
<edges>
|
|
<edge from-layer="0" from-port="0" to-layer="4" to-port="0"/>
|
|
<edge from-layer="1" from-port="0" to-layer="4" to-port="1"/>
|
|
<edge from-layer="2" from-port="0" to-layer="4" to-port="2"/>
|
|
<edge from-layer="3" from-port="0" to-layer="4" to-port="3"/>
|
|
<edge from-layer="4" from-port="4" to-layer="5" to-port="0"/>
|
|
<edge from-layer="4" from-port="5" to-layer="6" to-port="0"/>
|
|
</edges>
|
|
</net>
|
|
)V0G0N";
|
|
InferenceEngine::Core ie;
|
|
Blob::Ptr weights;
|
|
auto network = ie.ReadNetwork(model, weights);
|
|
InferenceEngine::ICNNNetwork::InputShapes newShapes;
|
|
newShapes["in2"] = {12, 200, 300};
|
|
newShapes["in3"] = {2, 200, 300};
|
|
ASSERT_NO_THROW(network.reshape(newShapes));
|
|
}
|
|
|
|
TEST_F(NGraphReshapeTests, ReshapeEDGenerateProposalsSingleImage_opset6) {
|
|
std::string model = R"V0G0N(
|
|
<net name="GenerateProposalsSingleImage" version="10">
|
|
<layers>
|
|
<layer name="in0" type="Parameter" id="0" version="opset1">
|
|
<data shape="3" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>3</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="in1" type="Parameter" id="1" version="opset1">
|
|
<data shape="201600,4" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>201600</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="in2" type="Parameter" id="2" version="opset1">
|
|
<data shape="12,200,336" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>12</dim>
|
|
<dim>200</dim>
|
|
<dim>336</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="in3" type="Parameter" id="3" version="opset1">
|
|
<data shape="3,200,336" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>3</dim>
|
|
<dim>200</dim>
|
|
<dim>336</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer id="4" name="1133" type="ExperimentalDetectronGenerateProposalsSingleImage" version="opset6">
|
|
<data min_size="0.0" nms_threshold="0.699999988079071" post_nms_count="1000" pre_nms_count="1000"/>
|
|
<input>
|
|
<port id="0">
|
|
<dim>3</dim>
|
|
</port>
|
|
<port id="1">
|
|
<dim>201600</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
<port id="2">
|
|
<dim>12</dim>
|
|
<dim>200</dim>
|
|
<dim>336</dim>
|
|
</port>
|
|
<port id="3">
|
|
<dim>3</dim>
|
|
<dim>200</dim>
|
|
<dim>336</dim>
|
|
</port>
|
|
</input>
|
|
<output>
|
|
<port id="4" precision="FP32">
|
|
<dim>1000</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
<port id="5" precision="FP32">
|
|
<dim>1000</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="out_0" type="Result" id="5" version="opset1">
|
|
<input>
|
|
<port id="0" precision="FP32">
|
|
<dim>1000</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
</input>
|
|
</layer>
|
|
<layer name="out_1" type="Result" id="6" version="opset1">
|
|
<input>
|
|
<port id="0" precision="FP32">
|
|
<dim>1000</dim>
|
|
</port>
|
|
</input>
|
|
</layer>
|
|
</layers>
|
|
<edges>
|
|
<edge from-layer="0" from-port="0" to-layer="4" to-port="0"/>
|
|
<edge from-layer="1" from-port="0" to-layer="4" to-port="1"/>
|
|
<edge from-layer="2" from-port="0" to-layer="4" to-port="2"/>
|
|
<edge from-layer="3" from-port="0" to-layer="4" to-port="3"/>
|
|
<edge from-layer="4" from-port="4" to-layer="5" to-port="0"/>
|
|
<edge from-layer="4" from-port="5" to-layer="6" to-port="0"/>
|
|
</edges>
|
|
</net>
|
|
)V0G0N";
|
|
InferenceEngine::Core ie;
|
|
Blob::Ptr weights;
|
|
auto network = ie.ReadNetwork(model, weights);
|
|
InferenceEngine::ICNNNetwork::InputShapes newShapes;
|
|
newShapes["in2"] = {12, 200, 300};
|
|
newShapes["in3"] = {2, 200, 300};
|
|
ASSERT_NO_THROW(network.reshape(newShapes));
|
|
}
|
|
|
|
TEST_F(NGraphReshapeTests, ReshapeGenerateProposals) {
|
|
std::string model = R"V0G0N(
|
|
<net name="GenerateProposals" version="10">
|
|
<layers>
|
|
<layer name="in0" type="Parameter" id="0" version="opset1">
|
|
<data shape="8,3" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>8</dim>
|
|
<dim>3</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="in1" type="Parameter" id="1" version="opset1">
|
|
<data shape="50,84,3,4" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>50</dim>
|
|
<dim>84</dim>
|
|
<dim>3</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="in2" type="Parameter" id="2" version="opset1">
|
|
<data shape="8,12,50,84" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>8</dim>
|
|
<dim>12</dim>
|
|
<dim>50</dim>
|
|
<dim>84</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="in3" type="Parameter" id="3" version="opset1">
|
|
<data shape="8,3,50,84" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>8</dim>
|
|
<dim>3</dim>
|
|
<dim>50</dim>
|
|
<dim>84</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer id="4" name="1133" type="GenerateProposals" version="opset9">
|
|
<data min_size="0.0" nms_threshold="0.699999988079071" post_nms_count="1000" pre_nms_count="1000" roi_num_type="i32"/>
|
|
<input>
|
|
<port id="0">
|
|
<dim>8</dim>
|
|
<dim>3</dim>
|
|
</port>
|
|
<port id="1">
|
|
<dim>50</dim>
|
|
<dim>84</dim>
|
|
<dim>3</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
<port id="2">
|
|
<dim>8</dim>
|
|
<dim>12</dim>
|
|
<dim>50</dim>
|
|
<dim>84</dim>
|
|
</port>
|
|
<port id="3">
|
|
<dim>8</dim>
|
|
<dim>3</dim>
|
|
<dim>50</dim>
|
|
<dim>84</dim>
|
|
</port>
|
|
</input>
|
|
<output>
|
|
<port id="4" precision="FP32">
|
|
<dim>-1</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
<port id="5" precision="FP32">
|
|
<dim>-1</dim>
|
|
</port>
|
|
<port id="6" precision="I32">
|
|
<dim>8</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="out_0" type="Result" id="5" version="opset1">
|
|
<input>
|
|
<port id="0" precision="FP32">
|
|
<dim>-1</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
</input>
|
|
</layer>
|
|
<layer name="out_1" type="Result" id="6" version="opset1">
|
|
<input>
|
|
<port id="0" precision="FP32">
|
|
<dim>-1</dim>
|
|
</port>
|
|
</input>
|
|
</layer>
|
|
<layer name="out_2" type="Result" id="7" version="opset1">
|
|
<input>
|
|
<port id="0" precision="I32">
|
|
<dim>8</dim>
|
|
</port>
|
|
</input>
|
|
</layer>
|
|
</layers>
|
|
<edges>
|
|
<edge from-layer="0" from-port="0" to-layer="4" to-port="0"/>
|
|
<edge from-layer="1" from-port="0" to-layer="4" to-port="1"/>
|
|
<edge from-layer="2" from-port="0" to-layer="4" to-port="2"/>
|
|
<edge from-layer="3" from-port="0" to-layer="4" to-port="3"/>
|
|
<edge from-layer="4" from-port="4" to-layer="5" to-port="0"/>
|
|
<edge from-layer="4" from-port="5" to-layer="6" to-port="0"/>
|
|
<edge from-layer="4" from-port="6" to-layer="7" to-port="0"/>
|
|
</edges>
|
|
</net>
|
|
)V0G0N";
|
|
InferenceEngine::Core ie;
|
|
Blob::Ptr weights;
|
|
auto network = ie.ReadNetwork(model, weights);
|
|
InferenceEngine::ICNNNetwork::InputShapes newShapes;
|
|
newShapes["in1"] = {100, 100, 4, 4};
|
|
newShapes["in2"] = {8, 16, 100, 100};
|
|
newShapes["in3"] = {8, 4, 100, 100};
|
|
ASSERT_NO_THROW(network.reshape(newShapes));
|
|
|
|
InferenceEngine::ICNNNetwork::InputShapes newShapes2;
|
|
newShapes2["in0"] = {2, 4};
|
|
newShapes2["in1"] = {100, 100, 4, 4};
|
|
newShapes2["in2"] = {2, 16, 100, 100};
|
|
newShapes2["in3"] = {2, 4, 100, 100};
|
|
ASSERT_NO_THROW(network.reshape(newShapes2));
|
|
}
|
|
|
|
TEST_F(NGraphReshapeTests, ReshapeEDROIFeatureExtractor) {
|
|
std::string model = R"V0G0N(
|
|
<net name="ExperimentalDetectronROIFeatureExtractor" version="10">
|
|
<layers>
|
|
<layer name="in0" type="Parameter" id="0" version="opset1">
|
|
<data shape="1000,4" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>1000</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="in1" type="Parameter" id="1" version="opset1">
|
|
<data shape="1,256,200,336" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>1</dim>
|
|
<dim>256</dim>
|
|
<dim>200</dim>
|
|
<dim>336</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer id="2" name="1190" type="ExperimentalDetectronROIFeatureExtractor" version="experimental">
|
|
<data aligned="0" output_size="7" pyramid_scales="4" sampling_ratio="2"/>
|
|
<input>
|
|
<port id="0">
|
|
<dim>1000</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
<port id="1">
|
|
<dim>1</dim>
|
|
<dim>256</dim>
|
|
<dim>200</dim>
|
|
<dim>336</dim>
|
|
</port>
|
|
</input>
|
|
<output>
|
|
<port id="2" precision="FP32">
|
|
<dim>1000</dim>
|
|
<dim>256</dim>
|
|
<dim>7</dim>
|
|
<dim>7</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="out_0" type="Result" id="3" version="opset1">
|
|
<input>
|
|
<port id="0" precision="FP32">
|
|
<dim>1000</dim>
|
|
<dim>256</dim>
|
|
<dim>7</dim>
|
|
<dim>7</dim>
|
|
</port>
|
|
</input>
|
|
</layer>
|
|
</layers>
|
|
<edges>
|
|
<edge from-layer="0" from-port="0" to-layer="2" to-port="0"/>
|
|
<edge from-layer="1" from-port="0" to-layer="2" to-port="1"/>
|
|
<edge from-layer="2" from-port="2" to-layer="3" to-port="0"/>
|
|
</edges>
|
|
</net>
|
|
)V0G0N";
|
|
InferenceEngine::Core ie;
|
|
Blob::Ptr weights;
|
|
auto network = ie.ReadNetwork(model, weights);
|
|
InferenceEngine::ICNNNetwork::InputShapes newShapes;
|
|
newShapes["in0"] = {1256, 4};
|
|
newShapes["in1"] = {1, 256, 7, 7};
|
|
ASSERT_NO_THROW(network.reshape(newShapes));
|
|
}
|
|
|
|
TEST_F(NGraphReshapeTests, ReshapeEDROIFeatureExtractorOpset6) {
|
|
std::string model = R"V0G0N(
|
|
<net name="ExperimentalDetectronROIFeatureExtractor" version="10">
|
|
<layers>
|
|
<layer name="in0" type="Parameter" id="0" version="opset1">
|
|
<data shape="1000,4" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>1000</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="in1" type="Parameter" id="1" version="opset1">
|
|
<data shape="1,256,200,336" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>1</dim>
|
|
<dim>256</dim>
|
|
<dim>200</dim>
|
|
<dim>336</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer id="2" name="1190" type="ExperimentalDetectronROIFeatureExtractor" version="opset6">
|
|
<data aligned="0" output_size="7" pyramid_scales="4" sampling_ratio="2"/>
|
|
<input>
|
|
<port id="0">
|
|
<dim>1000</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
<port id="1">
|
|
<dim>1</dim>
|
|
<dim>256</dim>
|
|
<dim>200</dim>
|
|
<dim>336</dim>
|
|
</port>
|
|
</input>
|
|
<output>
|
|
<port id="2" precision="FP32">
|
|
<dim>1000</dim>
|
|
<dim>256</dim>
|
|
<dim>7</dim>
|
|
<dim>7</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="out_0" type="Result" id="3" version="opset1">
|
|
<input>
|
|
<port id="0" precision="FP32">
|
|
<dim>1000</dim>
|
|
<dim>256</dim>
|
|
<dim>7</dim>
|
|
<dim>7</dim>
|
|
</port>
|
|
</input>
|
|
</layer>
|
|
</layers>
|
|
<edges>
|
|
<edge from-layer="0" from-port="0" to-layer="2" to-port="0"/>
|
|
<edge from-layer="1" from-port="0" to-layer="2" to-port="1"/>
|
|
<edge from-layer="2" from-port="2" to-layer="3" to-port="0"/>
|
|
</edges>
|
|
</net>
|
|
)V0G0N";
|
|
InferenceEngine::Core ie;
|
|
Blob::Ptr weights;
|
|
auto network = ie.ReadNetwork(model, weights);
|
|
InferenceEngine::ICNNNetwork::InputShapes newShapes;
|
|
newShapes["in0"] = {1256, 4};
|
|
newShapes["in1"] = {1, 256, 7, 7};
|
|
ASSERT_NO_THROW(network.reshape(newShapes));
|
|
}
|
|
|
|
TEST_F(NGraphReshapeTests, ReshapeEDTopKROIs) {
|
|
std::string model = R"V0G0N(
|
|
<net name="ExperimentalDetectronTopKROIs" version="10">
|
|
<layers>
|
|
<layer name="in0" type="Parameter" id="0" version="opset1">
|
|
<data shape="5000,4" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>5000</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="in1" type="Parameter" id="1" version="opset1">
|
|
<data shape="5000" element_type="f32"/>
|
|
<output>
|
|
<port id="0" precision="FP32">
|
|
<dim>5000</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer id="2" name="1189" type="ExperimentalDetectronTopKROIs" version="experimental">
|
|
<data max_rois="1000"/>
|
|
<input>
|
|
<port id="0">
|
|
<dim>5000</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
<port id="1">
|
|
<dim>5000</dim>
|
|
</port>
|
|
</input>
|
|
<output>
|
|
<port id="2" precision="FP32">
|
|
<dim>1000</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
<layer name="out_0" type="Result" id="3" version="opset1">
|
|
<input>
|
|
<port id="0" precision="FP32">
|
|
<dim>1000</dim>
|
|
<dim>4</dim>
|
|
</port>
|
|
</input>
|
|
</layer>
|
|
</layers>
|
|
<edges>
|
|
<edge from-layer="0" from-port="0" to-layer="2" to-port="0"/>
|
|
<edge from-layer="1" from-port="0" to-layer="2" to-port="1"/>
|
|
<edge from-layer="2" from-port="2" to-layer="3" to-port="0"/>
|
|
</edges>
|
|
</net>
|
|
)V0G0N";
|
|
InferenceEngine::Core ie;
|
|
Blob::Ptr weights;
|
|
auto network = ie.ReadNetwork(model, weights);
|
|
InferenceEngine::ICNNNetwork::InputShapes newShapes;
|
|
newShapes["in0"] = {10000, 4};
|
|
newShapes["in1"] = {10000};
|
|
ASSERT_NO_THROW(network.reshape(newShapes));
|
|
}
|
|
#endif
|