Removed deprecated parser tests (#17151)
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Ryszard Jezierski
GitHub
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commit
561bf6d478
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// 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 "parser_tests.hpp"
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ir_test_params ir_test_cases[] = {
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ir_test_params("GNA", "FP32", negative_conv_kernel_x_case),
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ir_test_params("GNA", "FP32", negative_conv_kernel_y_case),
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ir_test_params("GNA", "FP32", negative_conv_stride_x_case),
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ir_test_params("GNA", "FP32", negative_conv_weights_case),
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ir_test_params("GNA", "FP32", negative_conv_biases_case),
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ir_test_params("GNA", "FP32", negative_fc_out_size_case),
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ir_test_params("GNA", "FP32", negative_fc_weights_case),
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ir_test_params("GNA", "FP32", negative_fc_biases_case),
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ir_test_params("GNA", "FP32", negative_deconv_kernel_x_case),
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ir_test_params("GNA", "FP32", negative_deconv_kernel_y_case),
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ir_test_params("GNA", "FP32", negative_deconv_stride_x_case),
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ir_test_params("GNA", "FP32", negative_deconv_weights_case),
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ir_test_params("GNA", "FP32", negative_deconv_biases_case),
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ir_test_params("GNA", "FP32", negative_pool_kernel_x_case),
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ir_test_params("GNA", "FP32", negative_pool_kernel_y_case),
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ir_test_params("GNA", "FP32", negative_pool_stride_x_case),
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ir_test_params("GNA", "FP32", incorrect_pool_type_case),
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ir_test_params("GNA", "FP32", negative_norm_local_size_case),
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ir_test_params("GNA", "FP32", negative_norm_k_case)
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};
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INSTANTIATE_TEST_SUITE_P(FunctionalTest_smoke, IncorrectIRTests,
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::testing::ValuesIn(ir_test_cases),
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getTestName);
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@ -1,174 +0,0 @@
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// 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 <gtest/gtest.h>
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#include <tests_common.hpp>
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#include <utility>
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#include <cctype>
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#include <ie_core.hpp>
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#include "xml_net_builder.hpp"
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struct layer_params {
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layer_params(std::string type, std::vector<size_t> in, std::vector<size_t> out,
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int weights, int biases, std::map<std::string, std::string> params)
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: type(std::move(type)), in(std::move(in)), out(std::move(out)), params(std::move(params)),
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weights(weights), biases(biases) {}
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std::string type;
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std::vector<size_t> in;
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std::vector<size_t> out;
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int weights;
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int biases;
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std::map<std::string, std::string> params;
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};
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struct ir_test_params : layer_params {
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ir_test_params(std::string name, std::string precision, layer_params param)
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: layer_params(param), device_name(name), precision(std::move(precision)) {}
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std::string device_name;
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std::string precision;
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};
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std::map<std::string, std::vector<std::string>> smokeTests{};
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std::string getTestName(testing::TestParamInfo<ir_test_params> obj) {
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std::string name = obj.param.device_name + "__" + obj.param.precision + "__" + obj.param.type + "__";
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bool isSmoke{ false };
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if (smokeTests.find(obj.param.device_name) == smokeTests.end()) {
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smokeTests.insert(std::make_pair(obj.param.device_name, std::vector<std::string>{ obj.param.type }));
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isSmoke = true;
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}
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else {
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auto& typeVector = smokeTests.at(obj.param.device_name);
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bool flag = (std::find(typeVector.begin(), typeVector.end(), obj.param.type) == typeVector.end());
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if (flag) {
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typeVector.push_back(obj.param.type);
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isSmoke = true;
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}
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}
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if (isSmoke)
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name = obj.param.device_name + "__" + obj.param.precision + "__" + obj.param.type + "__";
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for (size_t i = 0; i < obj.param.in.size(); i++) {
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if (i)
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name += "_";
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name += std::to_string(obj.param.in[i]);
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}
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name += "__";
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for (size_t i = 0; i < obj.param.out.size(); i++) {
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if (i)
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name += "_";
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name += std::to_string(obj.param.out[i]);
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}
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name += "__";
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if (obj.param.weights < 0)
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name += "n";
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name += std::to_string(abs(obj.param.weights));
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name += "__";
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if (obj.param.biases < 0)
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name += "n";
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name += std::to_string(abs(obj.param.biases));
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name += "__";
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std::string param;
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for (const auto& it : obj.param.params) {
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if (!param.empty())
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name += "__";
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std::string key = it.first;
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std::string value = it.second;
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for (char &i : key) {
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if (!isalnum(i))
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i = '_';
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}
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for (char &i : value) {
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if (!isalnum(i))
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i = '_';
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}
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name += key + "___" + value;
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}
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name += param;
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return name;
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}
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class IncorrectIRTests: public TestsCommon,
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public testing::WithParamInterface<ir_test_params> {
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protected:
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InferenceEngine::TBlob<uint8_t>::Ptr GetNetworkWeights(const layer_params &p) {
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size_t weigtsSize = (abs(p.weights) + abs(p.biases))*sizeof(float);
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if (weigtsSize == 0)
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return nullptr;
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InferenceEngine::TBlob<uint8_t> *weights = new InferenceEngine::TBlob<uint8_t>({
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InferenceEngine::Precision::U8, { weigtsSize }, InferenceEngine::Layout::C});
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weights->allocate();
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fill_data(weights->buffer().as<float*>(),
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weights->size() / sizeof(float));
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InferenceEngine::TBlob<uint8_t>::Ptr weights_ptr = InferenceEngine::TBlob<uint8_t>::Ptr(weights);
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return weights_ptr;
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}
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};
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TEST_F(IncorrectIRTests, smoke_loadIRWithIncorrectInput) {
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std::map<std::string, std::string> params = {{"negative_slope", "0"}};
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std::string model = CommonTestUtils::V2NetBuilder::buildNetworkWithOneInput("ReLU_WithInput_Only", {1, 3, 4, 4}, "FP32")
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.addLayer("ReLU", "FP32", ¶ms, {{{1, 3, 2, 2}}, {{1, 3, 4, 4}}})
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.finish(false);
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InferenceEngine::Core ie;
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ASSERT_THROW(ie.ReadNetwork(model, InferenceEngine::Blob::CPtr()),
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InferenceEngine::Exception);
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}
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TEST_P(IncorrectIRTests, loadIncorrectLayer) {
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auto param = GetParam();
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std::string model = CommonTestUtils::V2NetBuilder::buildNetworkWithOneInput(param.type + "_Only", param.in, param.precision)
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.addLayer(param.type, param.precision, ¶m.params, {{param.in}, {param.out}}, param.weights, param.biases)
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.finish(false);
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try {
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InferenceEngine::Core ie;
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auto network = ie.ReadNetwork(model, GetNetworkWeights(param));
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auto exec = ie.LoadNetwork(network, param.device_name);
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} catch(...) {
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return;
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}
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FAIL() << "Topology was loaded successfully.";
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}
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// Convolution
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#define negative_conv_kernel_x_case layer_params("Convolution", {1, 3, 224, 224}, {1, 64, 112, 112}, 64*3*7*7, 64, {{"kernel-x", "-7"}, {"kernel-y", "7"}, {"stride-x", "2"}, {"stride-y", "2"}, {"pad-x", "2"}, {"pad-y", "2"}, {"dilation-x", "0"}, {"dilation-y", "0"}, {"output", "64"}, {"group", "1"}})
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#define negative_conv_kernel_y_case layer_params("Convolution", {1, 3, 224, 224}, {1, 64, 112, 112}, 64*3*7*7, 64, {{"kernel-x", "7"}, {"kernel-y", "-7"}, {"stride-x", "2"}, {"stride-y", "2"}, {"pad-x", "2"}, {"pad-y", "2"}, {"dilation-x", "0"}, {"dilation-y", "0"}, {"output", "64"}, {"group", "1"}})
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#define negative_conv_stride_x_case layer_params("Convolution", {1, 3, 224, 224}, {1, 64, 112, 112}, 64*3*7*7, 64, {{"kernel-x", "7"}, {"kernel-y", "7"}, {"stride-x", "-2"}, {"stride-y", "2"}, {"pad-x", "2"}, {"pad-y", "2"}, {"dilation-x", "0"}, {"dilation-y", "0"}, {"output", "64"}, {"group", "1"}})
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#define negative_conv_weights_case layer_params("Convolution", {1, 3, 224, 224}, {1, 64, 112, 112}, -64*3*7*7, 64, {{"kernel-x", "7"}, {"kernel-y", "7"}, {"stride-x", "2"}, {"stride-y", "2"}, {"pad-x", "2"}, {"pad-y", "2"}, {"dilation-x", "0"}, {"dilation-y", "0"}, {"output", "64"}, {"group", "1"}})
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#define negative_conv_biases_case layer_params("Convolution", {1, 3, 224, 224}, {1, 64, 112, 112}, 64*3*7*7, -64, {{"kernel-x", "7"}, {"kernel-y", "7"}, {"stride-x", "2"}, {"stride-y", "2"}, {"pad-x", "2"}, {"pad-y", "2"}, {"dilation-x", "0"}, {"dilation-y", "0"}, {"output", "64"}, {"group", "1"}})
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// Fully connected
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#define negative_fc_out_size_case layer_params("InnerProduct", {1, 3, 224, 224}, {1, 64, 112, 112}, 224*224*3*1000, 1000, {{"out-size", "-1000"}})
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#define negative_fc_weights_case layer_params("InnerProduct", {1, 3, 224, 224}, {1, 64, 112, 112}, -224*224*3*1000, 1000, {{"out-size", "1000"}})
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#define negative_fc_biases_case layer_params("InnerProduct", {1, 3, 224, 224}, {1, 64, 112, 112}, 224*224*3*1000, -1000, {{"out-size", "1000"}})
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// Deconvolution
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#define negative_deconv_kernel_x_case layer_params("Deconvolution", {1, 64, 224, 224}, {1, 3, 112, 112}, 64*3*7*7, 64, {{"kernel-x", "-7"}, {"kernel-y", "7"}, {"stride-x", "2"}, {"stride-y", "2"}, {"pad-x", "2"}, {"pad-y", "2"}, {"dilation-x", "0"}, {"dilation-y", "0"}, {"output", "64"}, {"group", "1"}})
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#define negative_deconv_kernel_y_case layer_params("Deconvolution", {1, 64, 224, 224}, {1, 3, 112, 112}, 64*3*7*7, 64, {{"kernel-x", "7"}, {"kernel-y", "-7"}, {"stride-x", "2"}, {"stride-y", "2"}, {"pad-x", "2"}, {"pad-y", "2"}, {"dilation-x", "0"}, {"dilation-y", "0"}, {"output", "64"}, {"group", "1"}})
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#define negative_deconv_stride_x_case layer_params("Deconvolution", {1, 64, 224, 224}, {1, 3, 112, 112}, 64*3*7*7, 64, {{"kernel-x", "7"}, {"kernel-y", "7"}, {"stride-x", "-2"}, {"stride-y", "2"}, {"pad-x", "2"}, {"pad-y", "2"}, {"dilation-x", "0"}, {"dilation-y", "0"}, {"output", "64"}, {"group", "1"}})
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#define negative_deconv_weights_case layer_params("Deconvolution", {1, 64, 224, 224}, {1, 3, 112, 112}, -64*3*7*7, 64, {{"kernel-x", "7"}, {"kernel-y", "7"}, {"stride-x", "2"}, {"stride-y", "2"}, {"pad-x", "2"}, {"pad-y", "2"}, {"dilation-x", "0"}, {"dilation-y", "0"}, {"output", "64"}, {"group", "1"}})
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#define negative_deconv_biases_case layer_params("Deconvolution", {1, 64, 224, 224}, {1, 3, 112, 112}, 64*3*7*7, -64, {{"kernel-x", "7"}, {"kernel-y", "7"}, {"stride-x", "2"}, {"stride-y", "2"}, {"pad-x", "2"}, {"pad-y", "2"}, {"dilation-x", "0"}, {"dilation-y", "0"}, {"output", "64"}, {"group", "1"}})
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// Pooling
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#define negative_pool_kernel_x_case layer_params("Pooling", {1, 3, 224, 224}, {1, 3, 112, 112}, 0, 0, {{"kernel-x", "-2"}, {"kernel-y", "2"}, {"stride-x", "2"}, {"stride-y", "2"}, {"pad-x", "0"}, {"pad-y", "0"}, {"rounding-type", "ceil"}, {"pool-method", "max"}})
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#define negative_pool_kernel_y_case layer_params("Pooling", {1, 3, 224, 224}, {1, 3, 112, 112}, 0, 0, {{"kernel-x", "2"}, {"kernel-y", "-2"}, {"stride-x", "2"}, {"stride-y", "2"}, {"pad-x", "0"}, {"pad-y", "0"}, {"rounding-type", "ceil"}, {"pool-method", "max"}})
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#define negative_pool_stride_x_case layer_params("Pooling", {1, 3, 224, 224}, {1, 3, 112, 112}, 0, 0, {{"kernel-x", "2"}, {"kernel-y", "2"}, {"stride-x", "-2"}, {"stride-y", "2"}, {"pad-x", "0"}, {"pad-y", "0"}, {"rounding-type", "ceil"}, {"pool-method", "max"}})
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#define incorrect_pool_type_case layer_params("Pooling", {1, 3, 224, 224}, {1, 3, 112, 112}, 0, 0, {{"kernel-x", "2"}, {"kernel-y", "2"}, {"stride-x", "2"}, {"stride-y", "2"}, {"pad-x", "0"}, {"pad-y", "0"}, {"rounding-type", "ceil"}, {"pool-method", "unknown"}})
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// Norm
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#define negative_norm_local_size_case layer_params("Norm", {1, 3, 224, 224}, {1, 3, 224, 224}, 0, 0, {{"alpha", "9.9999997e-05"}, {"beta", "0.75"}, {"local_size", "-5"}, {"region", "across"}, {"k", "1"}})
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#define negative_norm_k_case layer_params("Norm", {1, 3, 224, 224}, {1, 3, 224, 224}, 0, 0, {{"alpha", "9.9999997e-05"}, {"beta", "0.75"}, {"local_size", "5"}, {"region", "across"}, {"k", "-2"}})
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// TODO: Add Concat and split tests
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