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Fix warnings preventing Onnx build (#13447)

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Juan Villamizar 2022-10-26 09:53:55 -05:00 committed by GitHub
parent 8fbdc6cc46
commit 48b2ec944c
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GPG key ID: 4AEE18F83AFDEB23
7 changed files with 22 additions and 23 deletions
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10
onnxruntime/test/contrib_ops/bias_dropout_op_test.cc
View file

@ -136,14 +136,14 @@ void RunBiasDropoutTest(const bool use_mask, const std::vector<int64_t>& input_s
}
auto output_verifier = [&](const std::vector<OrtValue>& fetches, const std::string& provider_type) {
ASSERT_GE(fetches.size(), 1);
ASSERT_GE(fetches.size(), 1u);
const auto& output_tensor = FetchTensor(fetches[0]);
auto output_span = output_tensor.DataAsSpan<float>();
const auto num_dropped_values = std::count(output_span.begin(), output_span.end(), residual_value);
if (ratio == 1.0f) {
ASSERT_EQ(num_dropped_values, static_cast<size_t>(output_span.size())) << "provider: " << provider_type;
ASSERT_EQ(static_cast<unsigned int>(num_dropped_values), static_cast<size_t>(output_span.size())) << "provider: " << provider_type;
} else {
ASSERT_NEAR(static_cast<float>(num_dropped_values) / static_cast<size_t>(output_span.size()),
training_mode == TrainingTrue ? ratio : 0.0f, 0.1f)
@ -159,7 +159,7 @@ void RunBiasDropoutTest(const bool use_mask, const std::vector<int64_t>& input_s
}
if (use_mask) {
ASSERT_GE(fetches.size(), 2);
ASSERT_GE(fetches.size(), 2u);
const auto& mask_tensor = FetchTensor(fetches[1]);
auto mask_span = mask_tensor.DataAsSpan<bool>();
ASSERT_EQ(mask_span.size(), output_span.size()) << "provider: " << provider_type;
@ -186,11 +186,11 @@ void RunBiasDropoutTest(const bool use_mask, const std::vector<int64_t>& input_s
t.Run(OpTester::ExpectResult::kExpectSuccess, "", {}, nullptr, &t_eps);
std::vector<OrtValue> dropout_outputs = t.GetFetches();
ASSERT_GE(dropout_outputs.size(), 1);
ASSERT_GE(dropout_outputs.size(), 1u);
const float* output_values = FetchTensor(dropout_outputs[0]).Data<float>();
t_bitmask.AddOutput<float>("output", input_shape, output_values, input_size);
if (use_mask) {
ASSERT_GE(dropout_outputs.size(), 2);
ASSERT_GE(dropout_outputs.size(), 2u);
const bool* mask_values = FetchTensor(dropout_outputs[1]).Data<bool>();
std::vector<BitmaskElementType> bitmask_values = MasksToBitmasks(input_size, mask_values);
t_bitmask.AddOutput<BitmaskElementType>("mask", {static_cast<int64_t>(bitmask_values.size())}, bitmask_values);

2
onnxruntime/test/contrib_ops/bitmask_dropout_op_test.cc
View file

@ -129,7 +129,7 @@ void RunTestForTraining(const std::vector<int64_t>& input_dims) {
dropout.Run(OpTester::ExpectResult::kExpectSuccess, "", {}, nullptr, &dropout_eps);
std::vector<OrtValue> dropout_outputs = dropout.GetFetches();
ASSERT_EQ(dropout_outputs.size(), 2);
ASSERT_EQ(dropout_outputs.size(), 2u);
const T* output_values = FetchTensor(dropout_outputs[0]).Data<T>();
const bool* mask_values = FetchTensor(dropout_outputs[1]).Data<bool>();
std::vector<BitmaskElementType> bitmask_values = MasksToBitmasks(input_size, mask_values);

4
onnxruntime/test/framework/execution_provider_test.cc
View file

@ -39,7 +39,7 @@ TEST(ExecutionProviderTest, MetadefIdGeneratorUsingModelPath) {
HashValue model_hash;
int id = ep.GetId(viewer, model_hash);
ASSERT_EQ(id, 0);
ASSERT_NE(model_hash, 0);
ASSERT_NE(model_hash, 0u);
for (int i = 1; i < 4; ++i) {
HashValue cur_model_hash;
@ -70,7 +70,7 @@ TEST(ExecutionProviderTest, MetadefIdGeneratorUsingModelHashing) {
HashValue model_hash;
int id = ep.GetId(viewer, model_hash);
ASSERT_EQ(id, 0);
ASSERT_NE(model_hash, 0);
ASSERT_NE(model_hash, 0u);
// now load the model from bytes and check the hash differs
std::ifstream model_file_stream(model_path, std::ios::in | std::ios::binary);

2
onnxruntime/test/framework/inference_session_test.cc
View file

@ -353,7 +353,7 @@ void RunModelWithBindingMatMul(InferenceSession& session_object,
#if defined(USE_CUDA) || defined(USE_ROCM)
// in this case we need to copy the tensor from cuda to cpu
vector<OrtValue>& outputs = io_binding->GetOutputs();
ASSERT_EQ(1, outputs.size());
ASSERT_EQ(1u, outputs.size());
auto& rtensor = outputs.front().Get<Tensor>();
auto element_type = rtensor.DataType();
auto& shape = rtensor.Shape();

20
onnxruntime/test/framework/random_test.cc
View file

@ -32,26 +32,26 @@ TEST(RandomTest, PhiloxGeneratorTest) {
PhiloxGenerator generator(17);
auto seeds = generator.NextPhiloxSeeds(1);
ASSERT_EQ(seeds.first, 17);
ASSERT_EQ(seeds.second, 0);
ASSERT_EQ(seeds.first, 17u);
ASSERT_EQ(seeds.second, 0u);
seeds = generator.NextPhiloxSeeds(10);
ASSERT_EQ(seeds.first, 17);
ASSERT_EQ(seeds.second, 1);
ASSERT_EQ(seeds.first, 17u);
ASSERT_EQ(seeds.second, 1u);
seeds = generator.NextPhiloxSeeds(0);
ASSERT_EQ(seeds.first, 17);
ASSERT_EQ(seeds.second, 11);
ASSERT_EQ(seeds.first, 17u);
ASSERT_EQ(seeds.second, 11u);
seeds = generator.NextPhiloxSeeds(1);
ASSERT_EQ(seeds.first, 17);
ASSERT_EQ(seeds.second, 11);
ASSERT_EQ(seeds.first, 17u);
ASSERT_EQ(seeds.second, 11u);
generator.SetSeed(17);
seeds = generator.NextPhiloxSeeds(1);
ASSERT_EQ(seeds.first, 17);
ASSERT_EQ(seeds.second, 0);
ASSERT_EQ(seeds.first, 17u);
ASSERT_EQ(seeds.second, 0u);
}
} // namespace test

3
onnxruntime/test/framework/session_state_test.cc
View file

@ -100,7 +100,6 @@ TEST_P(SessionStateAddGetKernelTest, AddGetKernelTest) {
INSTANTIATE_TEST_SUITE_P(SessionStateTests, SessionStateAddGetKernelTest, testing::Values(0, 1));
namespace {
class TestParam {
public:
int ir_version;
@ -108,7 +107,7 @@ class TestParam {
int thread_count;
};
TestParam param_list[] = {{3, true, 0}, {4, true, 0}, {3, false, 0}, {4, false, 0}, {3, true, 1}, {4, true, 1}, {3, false, 1}, {4, false, 1}};
} // namespace
class SessionStateTestP : public testing::TestWithParam<TestParam> {};
// Test that we separate out constant and non-constant initializers correctly
TEST_P(SessionStateTestP, TestInitializerProcessing) {

4
onnxruntime/test/providers/cpu/generator/random_test.cc
View file

@ -381,7 +381,7 @@ void RunRandomNormalGpuTest(const std::vector<int64_t> dims, const float mean, c
auto output_verifier = [&](const std::vector<OrtValue>& fetches, const std::string& provider_type) {
// Only one output, and mean of output values are near attribute mean.
ASSERT_EQ(fetches.size(), 1);
ASSERT_EQ(fetches.size(), 1u);
const auto& output_tensor = FetchTensor(fetches[0]);
if (output_dtype == TensorProto_DataType::TensorProto_DataType_FLOAT) {
auto output_span = output_tensor.DataAsSpan<float>();
@ -474,7 +474,7 @@ void RunRandomUniformGpuTest(const std::vector<int64_t> dims, const float low, c
auto output_verifier = [&](const std::vector<OrtValue>& fetches, const std::string& provider_type) {
// Only one output. Each value in output tensoer is between low and high.
// Mean of output values are near attribute mean of low and high.
ASSERT_EQ(fetches.size(), 1);
ASSERT_EQ(fetches.size(), 1u);
const auto& output_tensor = FetchTensor(fetches[0]);
if (output_dtype == TensorProto_DataType::TensorProto_DataType_FLOAT) {
auto output_span = output_tensor.DataAsSpan<float>();