From 56df3962e3e6389c557ee3fd39a853e885b2a319 Mon Sep 17 00:00:00 2001 From: Maksim Derbasov Date: Fri, 1 Apr 2022 16:10:51 +0300 Subject: [PATCH] Fix for warnings spotted by clang compiler (#11384) --- .../tests/fusions/convolution_fusion_test.cpp | 2 +- .../module_tests/test_module_fusing_reorder.cpp | 5 ----- .../include/behavior/ov_plugin/core_integration.hpp | 4 ++-- .../common/layers/myriad_layers_convolution_test.cpp | 2 +- .../layers/myriad_layers_detection_output_test.cpp | 2 +- .../vpu/common/layers/myriad_layers_eltwise_test.hpp | 6 +++--- .../myriad_layers_exp_detectionoutput_test.hpp | 12 ++++++------ .../myriad_layers_exp_generateproposals_test.hpp | 12 ++++++------ .../layers/myriad_layers_exp_topkrois_test.hpp | 8 ++++---- .../vpu/common/layers/myriad_layers_nonzero_test.hpp | 2 +- .../vpu/vpu_base/vpu_layer_tests_utils.cpp | 6 +++--- 11 files changed, 28 insertions(+), 33 deletions(-) diff --git a/src/plugins/intel_gpu/tests/fusions/convolution_fusion_test.cpp b/src/plugins/intel_gpu/tests/fusions/convolution_fusion_test.cpp index d8c5bfd2e03..a3f05bded1a 100644 --- a/src/plugins/intel_gpu/tests/fusions/convolution_fusion_test.cpp +++ b/src/plugins/intel_gpu/tests/fusions/convolution_fusion_test.cpp @@ -2963,7 +2963,7 @@ TEST_P(conv_int8_activation_eltwise_quantize_onednn, bsv32_fsv32) { input_layout("input", get_input_layout(p)), data("weights", get_mem(get_weights_layout(p), -1, 1)), data("bias", get_mem(get_bias_layout(p))), - data("eltwise_data", get_mem(eltwise_layout, -0.5, 0.5)), + data("eltwise_data", get_mem(eltwise_layout, -1, 1)), data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)), data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)), data("out_lo", get_mem(get_single_element_layout(p), -127)), diff --git a/src/plugins/intel_gpu/tests/module_tests/test_module_fusing_reorder.cpp b/src/plugins/intel_gpu/tests/module_tests/test_module_fusing_reorder.cpp index 8bf0b2e99df..81699618c00 100644 --- a/src/plugins/intel_gpu/tests/module_tests/test_module_fusing_reorder.cpp +++ b/src/plugins/intel_gpu/tests/module_tests/test_module_fusing_reorder.cpp @@ -66,7 +66,6 @@ TEST(test_can_fuse_reorder, reorder_for_mixed_type_convolution_fsv32_onednn) auto& node = node_ptr->as(); auto& input = node.input(); for (auto usr : node_ptr->get_users()) { - auto temp = usr->get_output_layout(); EXPECT_EQ(false, lo.can_fuse_reorder(input, *usr, node.input().get_output_layout().format, usr->get_output_layout().format)); } } @@ -108,7 +107,6 @@ TEST(test_can_fuse_reorder, reorder_for_mixed_type_convolution_fsv32_cldnn) auto& node = node_ptr->as(); auto& input = node.input(); for (auto usr : node_ptr->get_users()) { - auto temp = usr->get_output_layout(); EXPECT_EQ(true, lo.can_fuse_reorder(input, *usr, node.input().get_output_layout().format, usr->get_output_layout().format)); } } @@ -186,7 +184,6 @@ TEST_P(test_fused_reorder_deep_depth, no_removal_for_deep_depth_conv) auto& node = node_ptr->as(); auto& input = node.input(); for (auto usr : node_ptr->get_users()) { - auto temp = usr->get_output_layout(); EXPECT_EQ(p.expected_result, lo.can_fuse_reorder(input, *usr, node.input().get_output_layout().format, usr->get_output_layout().format)); } } @@ -237,7 +234,6 @@ TEST_P(test_can_fuse_reorder_cldnn, reorder_for_firstconv_cldnn) auto& node = node_ptr->as(); auto& input = node.input(); for (auto usr : node_ptr->get_users()) { - auto temp = usr->get_output_layout(); EXPECT_EQ(p.expected_result, lo.can_fuse_reorder(input, *usr, node.input().get_output_layout().format, usr->get_output_layout().format)); } } @@ -285,7 +281,6 @@ TEST_P(test_can_fuse_reorder_onednn, reorder_for_firstconv_onednn) auto& node = node_ptr->as(); auto& input = node.input(); for (auto usr : node_ptr->get_users()) { - auto temp = usr->get_output_layout(); EXPECT_EQ(p.expected_result, lo.can_fuse_reorder(input, *usr, node.input().get_output_layout().format, usr->get_output_layout().format)); } } diff --git a/src/tests/functional/plugin/shared/include/behavior/ov_plugin/core_integration.hpp b/src/tests/functional/plugin/shared/include/behavior/ov_plugin/core_integration.hpp index 62d0cc42dd0..45bb35855ed 100644 --- a/src/tests/functional/plugin/shared/include/behavior/ov_plugin/core_integration.hpp +++ b/src/tests/functional/plugin/shared/include/behavior/ov_plugin/core_integration.hpp @@ -652,7 +652,7 @@ TEST_P(OVClassGetMetricTest_OPTIMIZATION_CAPABILITIES, GetMetricAndPrintNoThrow) TEST_P(OVClassGetMetricTest_MAX_BATCH_SIZE, GetMetricAndPrintNoThrow) { ov::Core ie; - uint32_t max_batch_size; + uint32_t max_batch_size = 0; ASSERT_NO_THROW(max_batch_size = ie.get_property(deviceName, ov::max_batch_size)); @@ -680,7 +680,7 @@ TEST_P(OVClassGetMetricTest_DEVICE_TYPE, GetMetricAndPrintNoThrow) { TEST_P(OVClassGetMetricTest_RANGE_FOR_ASYNC_INFER_REQUESTS, GetMetricAndPrintNoThrow) { ov::Core ie = createCoreWithTemplate(); - unsigned int start, end, step; + unsigned int start{0}, end{0}, step{0}; ASSERT_NO_THROW(std::tie(start, end, step) = ie.get_property(deviceName, ov::range_for_async_infer_requests)); diff --git a/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution_test.cpp b/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution_test.cpp index a2de6431058..0d5de68fb6a 100644 --- a/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution_test.cpp +++ b/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution_test.cpp @@ -887,7 +887,7 @@ TEST_F(myriadLayersTests_nightly, SmallConv_CorruptInputBug) { { ie_fp16 *dst = input->buffer().as(); for (int i = 0; i < input->size(); ++i) { - float val = static_cast(std::rand()) / RAND_MAX; + float val = static_cast(std::rand()) / static_cast(RAND_MAX); dst[i] = PrecisionUtils::f32tof16(val); } } diff --git a/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_detection_output_test.cpp b/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_detection_output_test.cpp index 6c8b4e1d60e..50a9c7b93f1 100644 --- a/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_detection_output_test.cpp +++ b/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_detection_output_test.cpp @@ -860,7 +860,7 @@ public: gen_confidence.resize(NUM_CONF); for (size_t i = 0; i < NUM_CONF; ++i) { - gen_confidence[i] = static_cast(std::rand()) / RAND_MAX; + gen_confidence[i] = static_cast(std::rand()) / static_cast(RAND_MAX); } InferenceEngine::Core ie; diff --git a/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_eltwise_test.hpp b/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_eltwise_test.hpp index be89204814a..f36aaa4d848 100644 --- a/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_eltwise_test.hpp +++ b/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_eltwise_test.hpp @@ -140,7 +140,7 @@ static const std::map s_kernels = { }; void genRandomDataPow(Blob::Ptr blob) { - float scale = 2.0f / RAND_MAX; + float scale = 2.0f / float(RAND_MAX); /* fill by random data in the range (-1, 1)*/ auto * blobRawDataFp16 = blob->buffer().as(); size_t count = blob->size(); @@ -160,7 +160,7 @@ void genRandomDataLogic(Blob::Ptr blob) { size_t count = blob->size(); const auto TrueVal = PrecisionUtils::f32tof16(1.f); const auto FalseVal = PrecisionUtils::f32tof16(0.f); - float scale = 1.0f / RAND_MAX; + float scale = 1.0f / float(RAND_MAX); for (size_t indx = 0; indx < count; ++indx) { float val = rand() * scale; blobRawDataFp16[indx] = val <.5f ? FalseVal : TrueVal; @@ -315,7 +315,7 @@ protected: std::vector coeff; for (int i = 0; i < count; i++) - coeff.push_back(withCoefs ? ((float)rand() / RAND_MAX) * 2.0f : 1.0f); + coeff.push_back(withCoefs ? (float(rand()) / float(RAND_MAX)) * 2.0f : 1.0f); if (withCoefs) { _params["coeff"] = std::to_string(coeff[0]); for (int i = 1; i < count; i++) diff --git a/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_detectionoutput_test.hpp b/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_detectionoutput_test.hpp index 3cfec80b2b1..8769bd8ba15 100644 --- a/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_detectionoutput_test.hpp +++ b/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_detectionoutput_test.hpp @@ -39,8 +39,8 @@ static void generateData(Blob::Ptr inputBoxesBlob, // boxes generator auto genXY = [](int min, int max, int minSize, int maxSize) { - int a = min + maxSize * (float(std::rand()) / RAND_MAX); - int b = min + maxSize * (float(std::rand()) / RAND_MAX); + int a = min + maxSize * (float(std::rand()) / float(RAND_MAX)); + int b = min + maxSize * (float(std::rand()) / float(RAND_MAX)); if (b < a) std::swap(a, b); if (b - a < minSize) @@ -81,14 +81,14 @@ static void generateData(Blob::Ptr inputBoxesBlob, { for (int class_idx = 0; class_idx < numClasses; ++class_idx) { - float dx = 0.5*layerParams.deltas_weights[0] + layerParams.deltas_weights[0] * (float(std::rand()) / RAND_MAX); - float dy = 0.5*layerParams.deltas_weights[1] + layerParams.deltas_weights[1] * (float(std::rand()) / RAND_MAX); + float dx = 0.5*layerParams.deltas_weights[0] + layerParams.deltas_weights[0] * (float(std::rand()) / float(RAND_MAX)); + float dy = 0.5*layerParams.deltas_weights[1] + layerParams.deltas_weights[1] * (float(std::rand()) / float(RAND_MAX)); const float minD = 0.95; const float maxD = 1.10; - float d_log_w = std::log(layerParams.deltas_weights[2] * (minD + (maxD - minD) * (float(std::rand()) / RAND_MAX))); - float d_log_h = std::log(layerParams.deltas_weights[3] * (minD + (maxD - minD) * (float(std::rand()) / RAND_MAX))); + float d_log_w = std::log(layerParams.deltas_weights[2] * (minD + (maxD - minD) * (float(std::rand()) / float(RAND_MAX)))); + float d_log_h = std::log(layerParams.deltas_weights[3] * (minD + (maxD - minD) * (float(std::rand()) / float(RAND_MAX)))); ie_fp16* ideltas = &inputDeltas[(roi_idx * numClasses + class_idx) * 4]; diff --git a/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_generateproposals_test.hpp b/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_generateproposals_test.hpp index 90ae644c5f7..a4f91e88b78 100644 --- a/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_generateproposals_test.hpp +++ b/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_generateproposals_test.hpp @@ -49,8 +49,8 @@ static void genInputs(InferenceEngine::BlobMap inputMap, // boxes generator auto genXY = [](int min, int max, int maxSize) { - int a = min + maxSize * (static_cast(rand()) / RAND_MAX); - int b = a + maxSize * (static_cast(rand()) / RAND_MAX) + 1; + int a = min + maxSize * (static_cast(rand()) / static_cast(RAND_MAX)); + int b = a + maxSize * (static_cast(rand()) / static_cast(RAND_MAX)) + 1; if (b > max) { const int d = b - max; @@ -84,13 +84,13 @@ static void genInputs(InferenceEngine::BlobMap inputMap, for (int h = 0; h < iScoresDims[1]; ++h) { for (int w = 0; w < iScoresDims[0]; ++w) { const float maxDelta = 16.0f; - float dx = maxDelta * (static_cast(std::rand()) / RAND_MAX); - float dy = maxDelta * (static_cast(std::rand()) / RAND_MAX); + float dx = maxDelta * (static_cast(std::rand()) / static_cast(RAND_MAX)); + float dy = maxDelta * (static_cast(std::rand()) / static_cast(RAND_MAX)); const float maxlogDelta = 1000.f / 128; const float minlogDelta = 0.65; - float d_log_w = std::log(minlogDelta + (maxlogDelta - minlogDelta) * (static_cast(std::rand()) / RAND_MAX)); - float d_log_h = std::log(minlogDelta + (maxlogDelta - minlogDelta) * (static_cast(std::rand()) / RAND_MAX)); + float d_log_w = std::log(minlogDelta + (maxlogDelta - minlogDelta) * (static_cast(std::rand()) / static_cast(RAND_MAX))); + float d_log_h = std::log(minlogDelta + (maxlogDelta - minlogDelta) * (static_cast(std::rand()) / static_cast(RAND_MAX))); ie_fp16* ideltas = &inputDeltas[idx * step_hw * 4]; diff --git a/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_topkrois_test.hpp b/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_topkrois_test.hpp index 290fbfdb7a0..dcc4ae1d038 100644 --- a/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_topkrois_test.hpp +++ b/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_topkrois_test.hpp @@ -35,8 +35,8 @@ static void genInputs(InferenceEngine::BlobMap inputMap) { // boxes generator auto genXY = [](int min, int max, int maxSize) { - int a = min + maxSize * (float(rand()) / RAND_MAX); - int b = a + maxSize * (float(rand()) / RAND_MAX) + 1; + int a = min + maxSize * (float(rand()) / float(RAND_MAX)); + int b = a + maxSize * (float(rand()) / float(RAND_MAX)) + 1; if (b > max) { const int d = b - max; @@ -50,8 +50,8 @@ static void genInputs(InferenceEngine::BlobMap inputMap) { { const int minS = 200; const int maxS = 880; - const int W = minS + maxS * (float(rand()) / RAND_MAX); - const int H = minS + maxS * (float(rand()) / RAND_MAX); + const int W = minS + maxS * (float(rand()) / float(RAND_MAX)); + const int H = minS + maxS * (float(rand()) / float(RAND_MAX)); const int X0 = 0, X1 = W, SX = (X1 - X0 + 1) * 3 / 5; const int Y0 = 0, Y1 = H, SY = (Y1 - Y0 + 1) * 3 / 5; diff --git a/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_nonzero_test.hpp b/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_nonzero_test.hpp index f6fb2e09c2c..3746193a65e 100644 --- a/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_nonzero_test.hpp +++ b/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_nonzero_test.hpp @@ -22,7 +22,7 @@ protected: const auto getRandomValue = [&generator]() { // Each third value will be a zero for test NonZero functionality - return generator() % 3 ? float(generator()) / generator.max() * 255.f : 0.f; + return generator() % 3 ? float(generator()) / float(generator.max()) * 255.f : 0.f; }; size_t count = blob->size(); diff --git a/src/tests_deprecated/functional/vpu/vpu_base/vpu_layer_tests_utils.cpp b/src/tests_deprecated/functional/vpu/vpu_base/vpu_layer_tests_utils.cpp index 62daabaec17..e2527da1a1c 100644 --- a/src/tests_deprecated/functional/vpu/vpu_base/vpu_layer_tests_utils.cpp +++ b/src/tests_deprecated/functional/vpu/vpu_base/vpu_layer_tests_utils.cpp @@ -195,7 +195,7 @@ void zeroWeightsRange(uint16_t* ptr, size_t weightsSize) { void defaultWeightsRange(uint16_t* ptr, size_t weightsSize) { ASSERT_NE(ptr, nullptr); - float scale = 2.0f / RAND_MAX; + float scale = 2.0f / float(RAND_MAX); for (size_t count = 0 ; count < weightsSize; ++count) { float val = rand(); val = val * scale - 1.0f; @@ -205,7 +205,7 @@ void defaultWeightsRange(uint16_t* ptr, size_t weightsSize) { void smallWeightsRange(uint16_t* ptr, size_t weightsSize) { ASSERT_NE(ptr, nullptr); - float scale = 2.0f / RAND_MAX; + float scale = 2.0f / float(RAND_MAX); for (size_t count = 0 ; count < weightsSize; ++count) { float val = rand(); val = (val * scale - 1.0f) / 512; @@ -265,7 +265,7 @@ bool fromBinaryFile(std::string input_binary, InferenceEngine::Blob::Ptr blob) { WeightsBlob* GenWeights(size_t sz, float min_val, float max_val) { // TODO: pass seed as parameter - float scale = (max_val - min_val) / RAND_MAX; + float scale = (max_val - min_val) / float(RAND_MAX); WeightsBlob *weights = new WeightsBlob({InferenceEngine::Precision::U8, {(sz) * sizeof(uint16_t)}, InferenceEngine::C}); weights->allocate(); uint16_t *inputBlobRawDataFp16 = weights->data().as();