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Bump linter versions (#18341)

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Bump linter versions and run format.
This commit is contained in:
Justin Chu 2023-11-08 13:04:40 -08:00 committed by GitHub
parent 812532592e
commit c250540722
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GPG key ID: 4AEE18F83AFDEB23
32 changed files with 102 additions and 78 deletions
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2
include/onnxruntime/core/platform/EigenNonBlockingThreadPool.h
View file

@ -278,7 +278,7 @@ class ThreadPoolProfiler {
int num_threads_;
#ifdef _MSC_VER
#pragma warning(push)
// C4324: structure was padded due to alignment specifier
// C4324: structure was padded due to alignment specifier
#pragma warning(disable : 4324)
#endif // _MSC_VER
struct ORT_ALIGN_TO_AVOID_FALSE_SHARING ChildThreadStat {

4
onnxruntime/python/backend/backend.py
View file

@ -63,7 +63,7 @@ class OnnxRuntimeBackend(Backend):
error_message = (
"Skipping this test as only released onnx opsets are supported."
"To run this test set env variable ALLOW_RELEASED_ONNX_OPSET_ONLY to 0."
" Got Domain '{}' version '{}'.".format(domain, opset.version)
f" Got Domain '{domain}' version '{opset.version}'."
)
return False, error_message
except AttributeError:
@ -74,7 +74,7 @@ class OnnxRuntimeBackend(Backend):
error_message = (
"Skipping this test as only released onnx opsets are supported."
"To run this test set env variable ALLOW_RELEASED_ONNX_OPSET_ONLY to 0."
" Got Domain '{}' version '{}'.".format(domain, opset.version)
f" Got Domain '{domain}' version '{opset.version}'."
)
return False, error_message
return True, ""

2
onnxruntime/python/tools/transformers/models/stable_diffusion/diffusion_models.py
View file

@ -463,7 +463,7 @@ class CLIP(BaseModel):
assert self.clip_skip >= 0 and self.clip_skip < hidden_layers
node_output_name = "/text_model/encoder/layers.{}/Add_1_output_0".format(hidden_layers - 1 - self.clip_skip)
node_output_name = f"/text_model/encoder/layers.{hidden_layers - 1 - self.clip_skip}/Add_1_output_0"
# search the name in outputs of all node
found = False

4
onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc
View file

@ -1238,7 +1238,7 @@ TEST(MathOpTest, Sum_8_Test1) {
// This test runs fine on CPU Plugin
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider, kOpenVINOExecutionProvider});
#else
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); // TensorRT: Expected output shape [{3,3,3}] did not match run output shape [{3,1,1}] for sum
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); // TensorRT: Expected output shape [{3,3,3}] did not match run output shape [{3,1,1}] for sum
#endif
}
@ -1264,7 +1264,7 @@ TEST(MathOpTest, Sum_8_Test1_double) {
// This test runs fine on CPU Plugin
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider, kOpenVINOExecutionProvider});
#else
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); // TensorRT: Expected output shape [{3,3,3}] did not match run output shape [{3,1,1}] for sum
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); // TensorRT: Expected output shape [{3,3,3}] did not match run output shape [{3,1,1}] for sum
#endif
}
TEST(MathOpTest, Sum_8_Test2) {

4
onnxruntime/test/providers/cpu/reduction/reduction_ops_test.cc
View file

@ -1086,7 +1086,7 @@ TEST(ReductionOpTest, ReduceMax_int32) {
#if defined(OPENVINO_CONFIG_GPU_FP32) || defined(OPENVINO_CONFIG_GPU_FP16)
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider, kOpenVINOExecutionProvider}); // OpenVINO: Disabled temporarily
#else
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); // TensorRT: axis must be 0
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); // TensorRT: axis must be 0
#endif
}
@ -1107,7 +1107,7 @@ TEST(ReductionOpTest, ReduceMax_int64) {
#if defined(OPENVINO_CONFIG_GPU_FP32) || defined(OPENVINO_CONFIG_GPU_FP16)
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider, kOpenVINOExecutionProvider}); // OpenVINO: Disabled temporarily
#else
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); // TensorRT: axis must be 0
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); // TensorRT: axis must be 0
#endif
}

9
onnxruntime/test/providers/nnapi/nnapi_basic_test.cc
View file

@ -556,10 +556,11 @@ TEST(NnapiExecutionProviderTest, ActivationOutsideOfPartition) {
constexpr auto* model_file_name = ORT_TSTR("testdata/mnist.basic.ort");
// stop NNAPI partitioning at Relu so NNAPI EP only takes first Conv
const auto nnapi_partitioning_stop_ops = "Relu";
TestModelLoad(model_file_name, std::make_unique<NnapiExecutionProvider>(0, nnapi_partitioning_stop_ops),
// expect one NNAPI partition
[](const Graph& graph) { ASSERT_EQ(CountAssignedNodes(graph, kNnapiExecutionProvider), 1)
<< "Exactly one node should have been taken by the NNAPI EP"; });
TestModelLoad(
model_file_name, std::make_unique<NnapiExecutionProvider>(0, nnapi_partitioning_stop_ops),
// expect one NNAPI partition
[](const Graph& graph) { ASSERT_EQ(CountAssignedNodes(graph, kNnapiExecutionProvider), 1)
<< "Exactly one node should have been taken by the NNAPI EP"; });
}
} // namespace test

4
orttraining/orttraining/python/training/ortmodule/_fallback.py
View file

@ -175,9 +175,9 @@ class _FallbackManager:
# This warning will not be raised again if retry is not enabled
self._logger.warning(
"Fallback to PyTorch due to exception {} was triggered. "
f"Fallback to PyTorch due to exception {exception_type} was triggered. "
"Report this issue with a minimal repro at https://www.github.com/microsoft/onnxruntime. "
"See details below:\n\n{}".format(exception_type, exception_string)
f"See details below:\n\n{exception_string}"
)
self._raised_fallback_exception = True

8
requirements-lintrunner.txt
View file

@ -1,9 +1,9 @@
# This file is auto updated by dependabot
lintrunner-adapters>=0.8.0
lintrunner-adapters>=0.11.0
# RUFF
ruff==0.0.292
ruff==0.1.4
# BLACK-ISORT
black==23.7.0
black==23.10.1
isort==5.12.0
# CLANGFORMAT
clang-format==16.0.6
clang-format==17.0.4

10
tools/ci_build/build.py
View file

@ -1637,9 +1637,7 @@ def run_android_tests(args, source_dir, build_dir, config, cwd):
# GCOV_PREFIX specifies the root directory
# for creating the runtime code coverage files.
if args.code_coverage:
adb_shell(
"cd {0} && GCOV_PREFIX={0} GCOV_PREFIX_STRIP={1} {2}".format(device_dir, cwd.count(os.sep) + 1, cmd)
)
adb_shell(f"cd {device_dir} && GCOV_PREFIX={device_dir} GCOV_PREFIX_STRIP={cwd.count(os.sep) + 1} {cmd}")
else:
adb_shell(f"cd {device_dir} && {cmd}")
@ -1689,7 +1687,7 @@ def run_android_tests(args, source_dir, build_dir, config, cwd):
)
if args.use_nnapi:
run_adb_shell("{0}/onnx_test_runner -e nnapi {0}/test".format(device_dir))
run_adb_shell(f"{device_dir}/onnx_test_runner -e nnapi {device_dir}/test")
else:
run_adb_shell(f"{device_dir}/onnx_test_runner {device_dir}/test")
@ -1702,9 +1700,9 @@ def run_android_tests(args, source_dir, build_dir, config, cwd):
adb_push("onnxruntime_customopregistration_test", device_dir, cwd=cwd)
adb_shell(f"chmod +x {device_dir}/onnxruntime_shared_lib_test")
adb_shell(f"chmod +x {device_dir}/onnxruntime_customopregistration_test")
run_adb_shell("LD_LIBRARY_PATH=$LD_LIBRARY_PATH:{0} {0}/onnxruntime_shared_lib_test".format(device_dir))
run_adb_shell(f"LD_LIBRARY_PATH=$LD_LIBRARY_PATH:{device_dir} {device_dir}/onnxruntime_shared_lib_test")
run_adb_shell(
"LD_LIBRARY_PATH=$LD_LIBRARY_PATH:{0} {0}/onnxruntime_customopregistration_test".format(device_dir)
f"LD_LIBRARY_PATH=$LD_LIBRARY_PATH:{device_dir} {device_dir}/onnxruntime_customopregistration_test"
)

2
tools/python/find_optimizer_opset_version_updates_required.py
View file

@ -54,7 +54,7 @@ def get_call_args_from_file(filename: str, function_or_declaration: str) -> typi
# TODO: handle automatically by merging lines
log.error(
"Call/Declaration is split over multiple lines. Please check manually."
"File:{} Line:{}".format(filename, line_num)
f"File:{filename} Line:{line_num}"
)
continue

3
winml/lib/Api.Image/inc/ImageConversionHelpers.h
View file

@ -52,5 +52,6 @@ bool VideoFramesHaveSameDevice(const wm::IVideoFrame& video_frame_1, const wm::I
wgdx::Direct3D11::IDirect3DDevice GetDeviceFromDirect3DSurface(const wgdx::Direct3D11::IDirect3DSurface& d3dSurface);
constexpr std::array<DXGI_FORMAT, 3> supportedWinMLFormats = {
DXGI_FORMAT_R8G8B8A8_UNORM, DXGI_FORMAT_B8G8R8A8_UNORM, DXGI_FORMAT_B8G8R8X8_UNORM};
DXGI_FORMAT_R8G8B8A8_UNORM, DXGI_FORMAT_B8G8R8A8_UNORM, DXGI_FORMAT_B8G8R8X8_UNORM
};
} // namespace _winml::Imaging

5
winml/lib/Api.Image/inc/TensorToVideoFrameConverter.h
View file

@ -40,10 +40,7 @@ class TensorToVideoFrameConverter : public ImageConverter {
private:
GUID _d3d11TextureGUID = {
0x14bf1054,
0x6ce7,
0x4c00,
{0xa1, 0x32, 0xb0, 0xf2, 0x11, 0x5D, 0xE0, 0x7f}
0x14bf1054, 0x6ce7, 0x4c00, {0xa1, 0x32, 0xb0, 0xf2, 0x11, 0x5D, 0xE0, 0x7f}
}; // {14BF1054-6CE7-4C00-A132-B0F2115DE07F}
GUID _handleGUID = {
0x700148fc, 0xc0cb, 0x4a7e, {0xa7, 0xc0, 0xe7, 0x43, 0xc1, 0x9, 0x9d, 0x62}

5
winml/lib/Api.Image/inc/VideoFrameToTensorConverter.h
View file

@ -50,10 +50,7 @@ class VideoFrameToTensorConverter : public ImageConverter {
private:
GUID d3d11_texture_GUID_ = {
0x485e4bb3,
0x3fe8,
0x497b,
{0x85, 0x9e, 0xc7, 0x5, 0x18, 0xdb, 0x11, 0x2a}
0x485e4bb3, 0x3fe8, 0x497b, {0x85, 0x9e, 0xc7, 0x5, 0x18, 0xdb, 0x11, 0x2a}
}; // {485E4BB3-3FE8-497B-859E-C70518DB112A}
GUID handle_GUID_ = {
0xce43264e, 0x41f7, 0x4882, {0x9e, 0x20, 0xfa, 0xa5, 0x1e, 0x37, 0x64, 0xfc}

6
winml/lib/Api.Ort/OnnxruntimeModel.cpp
View file

@ -81,7 +81,8 @@ HRESULT ModelInfo::RuntimeClassInitialize(_In_ OnnxruntimeEngineFactory* engine_
winml_adapter_api->ModelGetInputCount,
winml_adapter_api->ModelGetInputName,
winml_adapter_api->ModelGetInputDescription,
winml_adapter_api->ModelGetInputTypeInfo};
winml_adapter_api->ModelGetInputTypeInfo
};
// Create inputs
std::vector<OnnxruntimeValueInfoWrapper> inputs;
@ -93,7 +94,8 @@ HRESULT ModelInfo::RuntimeClassInitialize(_In_ OnnxruntimeEngineFactory* engine_
winml_adapter_api->ModelGetOutputCount,
winml_adapter_api->ModelGetOutputName,
winml_adapter_api->ModelGetOutputDescription,
winml_adapter_api->ModelGetOutputTypeInfo};
winml_adapter_api->ModelGetOutputTypeInfo
};
std::vector<OnnxruntimeValueInfoWrapper> outputs;
RETURN_IF_FAILED(CreateFeatureDescriptors(engine_factory, &output_helpers, ort_model, outputs));

3
winml/lib/Api/impl/TensorBase.h
View file

@ -217,7 +217,8 @@ struct TensorBase : TBase {
}
D3D12_HEAP_PROPERTIES heapProperties = {
D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0};
D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0
};
D3D12_RESOURCE_DESC resourceDesc = {
D3D12_RESOURCE_DIMENSION_BUFFER,
0,

6
winml/test/adapter/AdapterDmlEpTest.cpp
View file

@ -116,7 +116,8 @@ std::array<float, tensor_size> tensor_values = {};
winrt::com_ptr<ID3D12Resource> CreateD3D12Resource(ID3D12Device& device) {
constexpr uint64_t buffer_size = tensor_size * sizeof(float);
constexpr D3D12_HEAP_PROPERTIES heap_properties = {
D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0};
D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0
};
constexpr D3D12_RESOURCE_DESC resource_desc = {
D3D12_RESOURCE_DIMENSION_BUFFER,
0,
@ -365,6 +366,7 @@ const AdapterDmlEpTestApi& getapi() {
DmlCopyTensor,
CreateCustomRegistry,
ValueGetDeviceId,
SessionGetInputRequiredDeviceId};
SessionGetInputRequiredDeviceId
};
return api;
}

3
winml/test/adapter/AdapterSessionTest.cpp
View file

@ -368,7 +368,8 @@ const AdapterSessionTestAPI& getapi() {
Profiling,
CopyInputAcrossDevices,
CopyInputAcrossDevices_DML,
GetNumberOfIntraOpThreads};
GetNumberOfIntraOpThreads
};
if (SkipGpuTests()) {
api.AppendExecutionProvider_DML = SkipTest;

9
winml/test/api/LearningModelAPITest.cpp
View file

@ -247,9 +247,11 @@ static void CheckLearningModelPixelRange() {
// Normalized_0_1 and image output
L"Add_ImageNet1920WithImageMetadataBgr8_SRGB_0_1.onnx",
// Normalized_1_1 and image output
L"Add_ImageNet1920WithImageMetadataBgr8_SRGB_1_1.onnx"};
L"Add_ImageNet1920WithImageMetadataBgr8_SRGB_1_1.onnx"
};
std::vector<LearningModelPixelRange> pixelRanges = {
LearningModelPixelRange::ZeroTo255, LearningModelPixelRange::ZeroToOne, LearningModelPixelRange::MinusOneToOne};
LearningModelPixelRange::ZeroTo255, LearningModelPixelRange::ZeroToOne, LearningModelPixelRange::MinusOneToOne
};
for (uint32_t model_i = 0; model_i < modelPaths.size(); model_i++) {
LearningModel learningModel = nullptr;
WINML_EXPECT_NO_THROW(APITest::LoadModel(modelPaths[model_i], learningModel));
@ -329,7 +331,8 @@ const LearningModelApiTestsApi& getapi() {
CloseModelCheckEval,
CloseModelNoNewSessions,
CheckMetadataCaseInsensitive,
CreateCorruptModel};
CreateCorruptModel
};
if (RuntimeParameterExists(L"noVideoFrameTests")) {
api.CloseModelCheckEval = SkipTest;

3
winml/test/api/LearningModelBindingAPITest.cpp
View file

@ -669,7 +669,8 @@ const LearningModelBindingAPITestsApi& getapi() {
VerifyOutputAfterEvaluateAsyncCalledTwice,
VerifyOutputAfterImageBindCalledTwice,
SequenceLengthTensorFloat,
SequenceConstructTensorString};
SequenceConstructTensorString
};
if (SkipGpuTests()) {
api.GpuSqueezeNet = SkipTest;

9
winml/test/api/LearningModelSessionAPITest.cpp
View file

@ -793,7 +793,8 @@ static void STFT(
auto n_dfts = static_cast<size_t>(1 + floor((signal_size - dft_size) / hop_size));
auto input_shape = std::vector<int64_t>{1, INT64(signal_size)};
auto output_shape = std::vector<int64_t>{
INT64(batch_size), INT64(n_dfts), is_onesided ? ((INT64(dft_size) >> 1) + 1) : INT64(dft_size), 2};
INT64(batch_size), INT64(n_dfts), is_onesided ? ((INT64(dft_size) >> 1) + 1) : INT64(dft_size), 2
};
auto dft_length = TensorInt64Bit::CreateFromArray({}, {INT64(dft_size)});
auto model =
@ -1372,7 +1373,8 @@ static void ModelBuilding_GridSample_Internal(LearningModelDeviceKind kind) {
5.0000f,
5.0000f,
10.0000f,
10.0000f};
10.0000f
};
input_dims = {1, 1, 3, 2};
grid_dims = {1, 2, 4, 2};
@ -2312,7 +2314,8 @@ const LearningModelSessionAPITestsApi& getapi() {
ModelBuilding_STFT,
ModelBuilding_MelSpectrogramOnThreeToneSignal,
ModelBuilding_MelWeightMatrix,
SetName};
SetName
};
if (SkipGpuTests()) {
api.CreateSessionDeviceDirectX = SkipTest;

3
winml/test/api/RawApiHelpers.cpp
View file

@ -38,7 +38,8 @@ void RunOnDevice(ml::learning_model& model, ml::learning_model_device& device, I
auto channel_buffers_pointers = std::vector<float*>{
&input_data.at(0),
&input_data.at(0) + channel_buffers_sizes[0],
&input_data.at(0) + channel_buffers_sizes[0] + +channel_buffers_sizes[1]};
&input_data.at(0) + channel_buffers_sizes[0] + +channel_buffers_sizes[1]
};
WINML_EXPECT_HRESULT_SUCCEEDED(binding->bind_as_references<float>(
input_name,

3
winml/test/api/RawApiTestsGpu.cpp
View file

@ -165,7 +165,8 @@ const RawApiTestsGpuApi& getapi() {
CreateDirectXMinPowerDevice,
Evaluate,
EvaluateNoInputCopy,
EvaluateManyBuffers};
EvaluateManyBuffers
};
if (SkipGpuTests()) {
api.CreateDirectXDevice = SkipTest;

12
winml/test/concurrency/ConcurrencyTests.cpp
View file

@ -141,7 +141,8 @@ void EvalAsyncDifferentBindings() {
std::vector<EvaluationUnit> evaluation_units(num_units, EvaluationUnit());
std::vector<ImageFeatureValue> ifvs = {
FileHelpers::LoadImageFeatureValue(L"kitten_224.png"), FileHelpers::LoadImageFeatureValue(L"fish.png")};
FileHelpers::LoadImageFeatureValue(L"kitten_224.png"), FileHelpers::LoadImageFeatureValue(L"fish.png")
};
// same session, different binding
auto model = LearningModel::LoadFromFilePath(FileHelpers::GetModulePath() + L"model.onnx");
@ -191,7 +192,8 @@ void MultiThreadMultiSessionOnDevice(const LearningModelDevice& device) {
auto path = FileHelpers::GetModulePath() + L"model.onnx";
auto model = LearningModel::LoadFromFilePath(path);
std::vector<ImageFeatureValue> ivfs = {
FileHelpers::LoadImageFeatureValue(L"kitten_224.png"), FileHelpers::LoadImageFeatureValue(L"fish.png")};
FileHelpers::LoadImageFeatureValue(L"kitten_224.png"), FileHelpers::LoadImageFeatureValue(L"fish.png")
};
std::vector<int> max_indices = {
281, // tabby, tabby cat
0 // tench, Tinca tinca
@ -257,7 +259,8 @@ void MultiThreadSingleSessionOnDevice(const LearningModelDevice& device) {
LearningModelSession model_session = nullptr;
WINML_EXPECT_NO_THROW(model_session = LearningModelSession(model, device));
std::vector<ImageFeatureValue> ivfs = {
FileHelpers::LoadImageFeatureValue(L"kitten_224.png"), FileHelpers::LoadImageFeatureValue(L"fish.png")};
FileHelpers::LoadImageFeatureValue(L"kitten_224.png"), FileHelpers::LoadImageFeatureValue(L"fish.png")
};
std::vector<int> max_indices = {
281, // tabby, tabby cat
0 // tench, Tinca tinca
@ -322,7 +325,8 @@ const ConcurrencyTestsApi& getapi() {
MultiThreadSingleSessionGpu,
EvalAsyncDifferentModels,
EvalAsyncDifferentSessions,
EvalAsyncDifferentBindings};
EvalAsyncDifferentBindings
};
if (SkipGpuTests()) {
api.MultiThreadMultiSessionGpu = SkipTest;

3
winml/test/image/imageTestHelper.cpp
View file

@ -148,7 +148,8 @@ TensorFloat LoadInputImageFromGPU(SoftwareBitmap softwareBitmap, const std::wstr
// 3 is number of channels we use. R G B without alpha.
UINT64 bufferbytesize = 3 * sizeof(float) * softwareBitmap.PixelWidth() * softwareBitmap.PixelHeight();
D3D12_HEAP_PROPERTIES heapProperties = {
D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0};
D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0
};
D3D12_RESOURCE_DESC resourceDesc = {
D3D12_RESOURCE_DIMENSION_BUFFER,
0,

3
winml/test/image/imagetests.cpp
View file

@ -939,7 +939,8 @@ TEST_F(ImageTests, ImageBindingAsGPUTensor) {
UINT64 buffer_byte_size =
static_cast<uint64_t>(software_bitmap.PixelWidth()) * software_bitmap.PixelHeight() * 3 * sizeof(float);
D3D12_HEAP_PROPERTIES heap_properties = {
D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0};
D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0
};
D3D12_RESOURCE_DESC resource_desc = {
D3D12_RESOURCE_DIMENSION_BUFFER,
0,

3
winml/test/model/model_tests.cpp
View file

@ -232,7 +232,8 @@ static std::vector<ITestCase*> GetAllTestCases() {
ORT_TSTR("tf_resnet_v2_152"),
ORT_TSTR("vgg19"),
ORT_TSTR("yolov3"),
ORT_TSTR("zfnet512")};
ORT_TSTR("zfnet512")
};
allDisabledTests.insert(std::begin(x86DisabledTests), std::end(x86DisabledTests));
#endif
// Bad onnx test output caused by previously wrong SAME_UPPER/SAME_LOWER for ConvTranspose

10
winml/test/model/skip_model_tests.h
View file

@ -161,10 +161,8 @@ std::unordered_map<std::string, std::pair<std::string, std::string>> disabledGpu
test name -> absolute difference sampleTolerance
*/
std::unordered_map<std::string, double> sampleTolerancePerTests({
{"fp16_inception_v1_opset7_GPU",0.005 },
{"fp16_inception_v1_opset8_GPU", 0.005},
{ "candy_opset9_GPU",
0.00150000 }, // Intel(R) UHD Graphics 630 (29.20.100.9020) AP machine has inaccurate GPU results for FNS Candy opset 9 https://microsoft.visualstudio.com/OS/_workitems/edit/30696168/
{ "fp16_tiny_yolov2_opset8_GPU",
0.109000 }, // Intel(R) UHD Graphics 630 (29.20.100.9020) AP machine has inaccurate GPU results for FNS Candy opset 9 https://microsoft.visualstudio.com/OS/_workitems/edit/30696168/
{"fp16_inception_v1_opset7_GPU", 0.005},
{"fp16_inception_v1_opset8_GPU", 0.005},
{ "candy_opset9_GPU", 0.00150000}, // Intel(R) UHD Graphics 630 (29.20.100.9020) AP machine has inaccurate GPU results for FNS Candy opset 9 https://microsoft.visualstudio.com/OS/_workitems/edit/30696168/
{ "fp16_tiny_yolov2_opset8_GPU", 0.109000}, // Intel(R) UHD Graphics 630 (29.20.100.9020) AP machine has inaccurate GPU results for FNS Candy opset 9 https://microsoft.visualstudio.com/OS/_workitems/edit/30696168/
});

3
winml/test/scenario/cppwinrt/CustomNullOp.h
View file

@ -69,7 +69,8 @@ struct NullOperatorFactory : winrt::implements<NullOperatorFactory, IMLOperatorK
std::vector<MLOperatorEdgeDescription> allowedEdges{
CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Double),
CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float),
CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float16)};
CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float16)
};
typeConstraint.allowedTypes = allowedEdges.data();
typeConstraint.allowedTypeCount = static_cast<uint32_t>(allowedEdges.size());

15
winml/test/scenario/cppwinrt/CustomOps.cpp
View file

@ -305,7 +305,8 @@ static void CustomKernelWithBuiltInSchema() {
// Register the kernel
MLOperatorEdgeDescription floatTensorType = {
MLOperatorEdgeType::Tensor, static_cast<uint64_t>(MLOperatorTensorDataType::Float)};
MLOperatorEdgeType::Tensor, static_cast<uint64_t>(MLOperatorTensorDataType::Float)
};
MLOperatorEdgeTypeConstrant constraint = {"T", &floatTensorType, 1};
@ -318,7 +319,8 @@ static void CustomKernelWithBuiltInSchema() {
1,
nullptr,
0,
MLOperatorKernelOptions::AllowDynamicInputShapes};
MLOperatorKernelOptions::AllowDynamicInputShapes
};
Microsoft::WRL::ComPtr<MLOperatorKernelFactory> factory =
wil::MakeOrThrow<MLOperatorKernelFactory>(CreateABIFooKernel<false>);
@ -614,7 +616,8 @@ static void CustomKernelWithCustomSchema() {
MLOperatorEdgeTypeConstrant kernelConstraint = {"T1", &floatTensorEdgeDesc, 1};
MLOperatorKernelDescription kernelDesc = {
"", "Foo", 7, MLOperatorExecutionType::Cpu, &kernelConstraint, testCases[caseIndex].useTypeLabel ? 1u : 0u};
"", "Foo", 7, MLOperatorExecutionType::Cpu, &kernelConstraint, testCases[caseIndex].useTypeLabel ? 1u : 0u
};
if (!testCases[caseIndex].attributeDefaultsInSchema) {
kernelDesc.defaultAttributes = defaultAttributes;
@ -693,10 +696,8 @@ static void CustomKernelWithCustomSchema() {
const CustomOpsTestsApi& getapi() {
static CustomOpsTestsApi api = {
CustomOpsScenarioTestsClassSetup,
CustomOperatorFusion,
CustomKernelWithBuiltInSchema,
CustomKernelWithCustomSchema};
CustomOpsScenarioTestsClassSetup, CustomOperatorFusion, CustomKernelWithBuiltInSchema, CustomKernelWithCustomSchema
};
if (SkipGpuTests()) {
api.CustomOperatorFusion = SkipTest;

12
winml/test/scenario/cppwinrt/NoisyReluCpu.h
View file

@ -157,7 +157,8 @@ struct NoisyReluOperatorFactory : winrt::implements<NoisyReluOperatorFactory, IM
std::vector<MLOperatorEdgeDescription> allowedEdges{
CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Double),
CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float),
CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float16)};
CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float16)
};
typeConstraint.allowedTypes = allowedEdges.data();
typeConstraint.allowedTypeCount = static_cast<uint32_t>(allowedEdges.size());
@ -194,7 +195,8 @@ struct NoisyReluOperatorFactory : winrt::implements<NoisyReluOperatorFactory, IM
noisyReluVarianceAttributeValue.floats = defaultVariance;
std::vector<MLOperatorAttributeNameValue> attributeDefaultValues{
noisyReluMeanAttributeValue, noisyReluVarianceAttributeValue};
noisyReluMeanAttributeValue, noisyReluVarianceAttributeValue
};
noisyReluSchema.defaultAttributes = attributeDefaultValues.data();
noisyReluSchema.defaultAttributeCount = static_cast<uint32_t>(attributeDefaultValues.size());
@ -216,7 +218,8 @@ struct NoisyReluOperatorFactory : winrt::implements<NoisyReluOperatorFactory, IM
std::vector<MLOperatorEdgeDescription> allowedEdges{
CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Double),
CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float),
CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float16)};
CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float16)
};
typeConstraint.allowedTypes = allowedEdges.data();
typeConstraint.allowedTypeCount = static_cast<uint32_t>(allowedEdges.size());
@ -239,7 +242,8 @@ struct NoisyReluOperatorFactory : winrt::implements<NoisyReluOperatorFactory, IM
noisyReluVarianceAttributeValue.floats = defaultVariance;
std::vector<MLOperatorAttributeNameValue> attributeDefaultValues{
noisyReluMeanAttributeValue, noisyReluVarianceAttributeValue};
noisyReluMeanAttributeValue, noisyReluVarianceAttributeValue
};
kernelDescription.defaultAttributes = attributeDefaultValues.data();
kernelDescription.defaultAttributeCount = static_cast<uint32_t>(attributeDefaultValues.size());
kernelDescription.options = MLOperatorKernelOptions::None;

3
winml/test/scenario/cppwinrt/ReluCpu.h
View file

@ -114,7 +114,8 @@ struct ReluOperatorFactory : winrt::implements<ReluOperatorFactory, IMLOperatorK
std::vector<MLOperatorEdgeDescription> allowedEdges{
CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Double),
CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float),
CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float16)};
CreateEdgeDescriptor(MLOperatorEdgeType::Tensor, MLOperatorTensorDataType::Float16)
};
typeConstraint.allowedTypes = allowedEdges.data();
typeConstraint.allowedTypeCount = static_cast<uint32_t>(allowedEdges.size());

9
winml/test/scenario/cppwinrt/scenariotestscppwinrt.cpp
View file

@ -510,7 +510,8 @@ static void Scenario9LoadBindEvalInputTensorGPU() {
UINT64 bufferbytesize = 720 * 720 * 3 * sizeof(float);
D3D12_HEAP_PROPERTIES heapProperties = {
D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0};
D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0
};
D3D12_RESOURCE_DESC resourceDesc = {
D3D12_RESOURCE_DIMENSION_BUFFER,
0,
@ -983,7 +984,8 @@ static void Scenario22ImageBindingAsGPUTensor() {
// 3 is number of channels we use. R G B without alpha.
UINT64 bufferbytesize = 3 * sizeof(float) * softwareBitmap.PixelWidth() * softwareBitmap.PixelHeight();
D3D12_HEAP_PROPERTIES heapProperties = {
D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0};
D3D12_HEAP_TYPE_DEFAULT, D3D12_CPU_PAGE_PROPERTY_UNKNOWN, D3D12_MEMORY_POOL_UNKNOWN, 0, 0
};
D3D12_RESOURCE_DESC resourceDesc = {
D3D12_RESOURCE_DIMENSION_BUFFER,
0,
@ -1085,7 +1087,8 @@ static void Scenario23NominalPixelRange() {
std::vector<std::wstring> modelPaths = {// Normalized_0_1 and image output
modulePath + L"Add_ImageNet1920WithImageMetadataBgr8_SRGB_0_1.onnx",
// Normalized_1_1 and image output
modulePath + L"Add_ImageNet1920WithImageMetadataBgr8_SRGB_1_1.onnx"};
modulePath + L"Add_ImageNet1920WithImageMetadataBgr8_SRGB_1_1.onnx"
};
for (uint32_t model_i = 0; model_i < modelPaths.size(); model_i++) {
// load model and create session