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[GNA] Fix bug with broadcasting constant layer with fq layer (openvin…
…otoolkit#5766) * fix bug with broadcasting constant layer with fq layer * BroadcastConstWithFakeQuantizePass is removed; BroadcastConstPass is moved up in pass list * constLayer->outData.front()->setDims is moved to conditions * prevLayer->outData.front()->setLayout(nextLayer->outData.front()->getLayout()); is added
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inference-engine/tests/functional/plugin/gna/pass_tests/broadcast_const_with_fq.cpp
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| // Copyright (C) 2018-2021 Intel Corporation | ||
| // SPDX-License-Identifier: Apache-2.0 | ||
| // | ||
| // | ||
| #include <vector> | ||
| #include <tuple> | ||
| #include <string> | ||
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| #include <ie_core.hpp> | ||
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| #include "common_test_utils/common_utils.hpp" | ||
| #include "functional_test_utils/plugin_cache.hpp" | ||
| #include "shared_test_classes/base/layer_test_utils.hpp" | ||
| #include "functional_test_utils/blob_utils.hpp" | ||
| #include "ngraph_functions/utils/ngraph_helpers.hpp" | ||
| #include "ngraph_functions/builders.hpp" | ||
| #include "ngraph_functions/pass/convert_prc.hpp" | ||
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| using BroadcastConstWithFqParamsTuple = typename std::tuple< | ||
| InferenceEngine::Precision, // Network Precision | ||
| std::vector<size_t>, // Input shapes for Params Layer | ||
| std::vector<size_t>, // Input shapes for Constant Layer | ||
| size_t, // Quantization level | ||
| std::map<std::string, std::string>, // Configuration | ||
| std::string>; // Device name | ||
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| namespace LayerTestsDefinitions { | ||
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| class BroadcastConstWithFq : public testing::WithParamInterface<BroadcastConstWithFqParamsTuple>, | ||
| public LayerTestsUtils::LayerTestsCommon { | ||
| public: | ||
| static std::string getTestCaseName(testing::TestParamInfo<BroadcastConstWithFqParamsTuple> obj) { | ||
| InferenceEngine::Precision netPrecision; | ||
| std::vector<size_t> inputShape1; | ||
| std::vector<size_t> inputShape2; | ||
| size_t level{0}; | ||
| std::map<std::string, std::string> configuration; | ||
| std::string targetDevice; | ||
| std::tie(netPrecision, inputShape1, inputShape2, level, configuration, targetDevice) = obj.param; | ||
| std::ostringstream result; | ||
| result << "netPRC=" << netPrecision.name() << "_"; | ||
| result << "targetDevice=" << targetDevice << "_"; | ||
| for (auto const& configItem : configuration) { | ||
| result << "configItem=" << configItem.first << "_" << configItem.second << "_"; | ||
| } | ||
| result << "inputShape1=" << CommonTestUtils::vec2str(inputShape1) << "_"; | ||
| result << "inputShape2=" << CommonTestUtils::vec2str(inputShape2) << "_"; | ||
| result << "level=" << level; | ||
| return result.str(); | ||
| } | ||
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| protected: | ||
| void SetUp() override { | ||
| size_t level{0}; | ||
| InferenceEngine::Precision netPrecision; | ||
| std::vector<size_t> inputShape1; | ||
| std::vector<size_t> inputShape2; | ||
| std::tie(netPrecision, inputShape1, inputShape2, level, configuration, targetDevice) = this->GetParam(); | ||
| auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision); | ||
| auto params = ngraph::builder::makeParams(ngPrc, {inputShape1}); | ||
| auto fakeQuantize1 = ngraph::builder::makeFakeQuantize(params[0], ngPrc, level, {}, {-0.5}, {0.5}, {-0.5}, {0.5}); | ||
| auto constant = ngraph::builder::makeConstant<float>(ngPrc, inputShape2, {}, true); | ||
| auto fakeQuantize2 = ngraph::builder::makeFakeQuantize(constant, ngPrc, level, {}, {-0.5}, {0.5}, {-0.5}, {0.5}); | ||
| auto add = std::make_shared<ngraph::opset1::Add>(fakeQuantize1, fakeQuantize2); | ||
| ngraph::ResultVector results{ std::make_shared<ngraph::opset1::Result>(add)}; | ||
| function = std::make_shared<ngraph::Function>(results, params, "BroadcastConstWithFq"); | ||
| } | ||
| }; | ||
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| TEST_P(BroadcastConstWithFq, CompareWithRefImpl) { | ||
| Run(); | ||
| }; | ||
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| std::vector<std::vector<size_t>> inputShapes1 = { {1, 1, 21, 160} }; | ||
| std::vector<std::vector<size_t>> inputShapes2 = { {1, 1, 1, 160} }; | ||
| const std::vector<size_t> level = { 65535 }; | ||
| const std::vector<InferenceEngine::Precision> netPrecisions = {InferenceEngine::Precision::FP32, InferenceEngine::Precision::FP16}; | ||
| const std::vector<std::map<std::string, std::string>> configs = { | ||
| { {"GNA_DEVICE_MODE", "GNA_SW_FP32"} }, | ||
| { {"GNA_DEVICE_MODE", "GNA_SW_EXACT"} } | ||
| }; | ||
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| INSTANTIATE_TEST_CASE_P(smoke_broadcast_const_with_fq, BroadcastConstWithFq, | ||
| ::testing::Combine( | ||
| ::testing::ValuesIn(netPrecisions), | ||
| ::testing::ValuesIn(inputShapes1), | ||
| ::testing::ValuesIn(inputShapes2), | ||
| ::testing::ValuesIn(level), | ||
| ::testing::ValuesIn(configs), | ||
| ::testing::Values(CommonTestUtils::DEVICE_GNA)), | ||
| BroadcastConstWithFq::getTestCaseName); | ||
| } // namespace LayerTestsDefinitions |