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test_inference.cc
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test_inference.cc
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// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
#include "core/session/onnxruntime_cxx_api.h"
#include "providers.h"
#include <memory>
#include <vector>
#include <iostream>
#include <atomic>
#include <gtest/gtest.h>
#include "test_allocator.h"
#include "test_fixture.h"
using namespace onnxruntime;
void RunSession(OrtAllocator* env, OrtSession* session_object,
const std::vector<size_t>& dims_x,
const std::vector<float>& values_x,
const std::vector<int64_t>& dims_y,
const std::vector<float>& values_y,
OrtValue* output_tensor) {
std::unique_ptr<OrtValue, decltype(&OrtReleaseValue)> value_x(nullptr, OrtReleaseValue);
std::vector<OrtValue*> inputs(1);
inputs[0] = OrtCreateTensorAsOrtValue(env, dims_x, ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT);
value_x.reset(inputs[0]);
void* raw_data;
ORT_THROW_ON_ERROR(OrtGetTensorMutableData(inputs[0], &raw_data));
memcpy(raw_data, values_x.data(), values_x.size() * sizeof(values_x[0]));
std::vector<const char*> input_names{"X"};
const char* output_names[] = {"Y"};
bool is_output_allocated_by_ort = output_tensor == nullptr;
OrtValue* old_output_ptr = output_tensor;
ORT_THROW_ON_ERROR(OrtRun(session_object, NULL, input_names.data(), inputs.data(), inputs.size(), output_names, 1, &output_tensor));
ASSERT_NE(output_tensor, nullptr);
if (!is_output_allocated_by_ort)
ASSERT_EQ(output_tensor, old_output_ptr);
std::unique_ptr<OrtTensorTypeAndShapeInfo> shape_info;
{
OrtTensorTypeAndShapeInfo* shape_info_ptr;
ORT_THROW_ON_ERROR(OrtGetTensorShapeAndType(output_tensor, &shape_info_ptr));
shape_info.reset(shape_info_ptr);
}
size_t rtensor_dims = OrtGetNumOfDimensions(shape_info.get());
std::vector<int64_t> shape_array(rtensor_dims);
OrtGetDimensions(shape_info.get(), shape_array.data(), shape_array.size());
ASSERT_EQ(shape_array, dims_y);
size_t total_len = 1;
for (size_t i = 0; i != rtensor_dims; ++i) {
total_len *= shape_array[i];
}
ASSERT_EQ(values_y.size(), total_len);
float* f;
ORT_THROW_ON_ERROR(OrtGetTensorMutableData(output_tensor, (void**)&f));
for (size_t i = 0; i != total_len; ++i) {
ASSERT_EQ(values_y[i], f[i]);
}
if (is_output_allocated_by_ort) OrtReleaseValue(output_tensor);
}
template <typename T>
void TestInference(OrtEnv* env, T model_uri,
const std::vector<size_t>& dims_x,
const std::vector<float>& values_x,
const std::vector<int64_t>& expected_dims_y,
const std::vector<float>& expected_values_y,
int provider_type, bool custom_op) {
SessionOptionsWrapper sf(env);
if (provider_type == 1) {
#ifdef USE_CUDA
ORT_THROW_ON_ERROR(OrtSessionOptionsAppendExecutionProvider_CUDA(sf, 0));
std::cout << "Running simple inference with cuda provider" << std::endl;
#else
return;
#endif
} else if (provider_type == 2) {
#ifdef USE_MKLDNN
ORT_THROW_ON_ERROR(OrtSessionOptionsAppendExecutionProvider_Mkldnn(sf, 1));
std::cout << "Running simple inference with mkldnn provider" << std::endl;
#else
return;
#endif
} else if (provider_type == 3) {
#ifdef USE_NUPHAR
ORT_THROW_ON_ERROR(OrtSessionOptionsAppendExecutionProvider_Nuphar(sf, 0, ""));
std::cout << "Running simple inference with nuphar provider" << std::endl;
#else
return;
#endif
} else {
std::cout << "Running simple inference with default provider" << std::endl;
}
if (custom_op) {
sf.AppendCustomOpLibPath("libonnxruntime_custom_op_shared_lib_test.so");
}
std::unique_ptr<OrtSession, decltype(&OrtReleaseSession)>
inference_session(sf.OrtCreateSession(model_uri), OrtReleaseSession);
std::unique_ptr<MockedOrtAllocator> default_allocator(std::make_unique<MockedOrtAllocator>());
// Now run
//without preallocated output tensor
RunSession(default_allocator.get(),
inference_session.get(),
dims_x,
values_x,
expected_dims_y,
expected_values_y,
nullptr);
//with preallocated output tensor
std::unique_ptr<OrtValue, decltype(&OrtReleaseValue)> value_y(nullptr, OrtReleaseValue);
{
std::vector<OrtValue*> allocated_outputs(1);
std::vector<size_t> dims_y(expected_dims_y.size());
for (size_t i = 0; i != expected_dims_y.size(); ++i) {
dims_y[i] = static_cast<size_t>(expected_dims_y[i]);
}
allocated_outputs[0] =
OrtCreateTensorAsOrtValue(default_allocator.get(), dims_y, ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT);
value_y.reset(allocated_outputs[0]);
}
//test it twice
for (int i = 0; i != 2; ++i)
RunSession(default_allocator.get(),
inference_session.get(),
dims_x,
values_x,
expected_dims_y,
expected_values_y,
value_y.get());
}
static constexpr PATH_TYPE MODEL_URI = TSTR("testdata/mul_1.pb");
static constexpr PATH_TYPE CUSTOM_OP_MODEL_URI = TSTR("testdata/foo_1.pb");
class CApiTestWithProvider : public CApiTest,
public ::testing::WithParamInterface<int> {
};
// Tests that the Foo::Bar() method does Abc.
TEST_P(CApiTestWithProvider, simple) {
// simple inference test
// prepare inputs
std::vector<size_t> dims_x = {3, 2};
std::vector<float> values_x = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f};
// prepare expected inputs and outputs
std::vector<int64_t> expected_dims_y = {3, 2};
std::vector<float> expected_values_y = {1.0f, 4.0f, 9.0f, 16.0f, 25.0f, 36.0f};
TestInference<PATH_TYPE>(env, MODEL_URI, dims_x, values_x, expected_dims_y, expected_values_y, GetParam(), false);
}
INSTANTIATE_TEST_CASE_P(CApiTestWithProviders,
CApiTestWithProvider,
::testing::Values(0, 1, 2, 3, 4));
#ifndef _WIN32
//doesn't work, failed in type comparison
TEST_F(CApiTest, DISABLED_custom_op) {
std::cout << "Running custom op inference" << std::endl;
std::vector<size_t> dims_x = {3, 2};
std::vector<float> values_x = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f};
// prepare expected inputs and outputs
std::vector<int64_t> expected_dims_y = {3, 2};
std::vector<float> expected_values_y = {2.0f, 4.0f, 6.0f, 8.0f, 10.0f, 12.0f};
TestInference<PATH_TYPE>(env, CUSTOM_OP_MODEL_URI, dims_x, values_x, expected_dims_y, expected_values_y, false, true);
}
#endif
#ifdef ORT_RUN_EXTERNAL_ONNX_TESTS
TEST_F(CApiTest, create_session_without_session_option) {
constexpr PATH_TYPE model_uri = TSTR("../models/opset8/test_squeezenet/model.onnx");
OrtSession* ret;
ORT_THROW_ON_ERROR(::OrtCreateSession(env, model_uri, nullptr, &ret));
ASSERT_NE(nullptr, ret);
OrtReleaseSession(ret);
}
#endif
TEST_F(CApiTest, create_tensor) {
const char* s[] = {"abc", "kmp"};
size_t expected_len = 2;
std::unique_ptr<MockedOrtAllocator> default_allocator(std::make_unique<MockedOrtAllocator>());
{
std::unique_ptr<OrtValue, decltype(&OrtReleaseValue)> tensor(
OrtCreateTensorAsOrtValue(default_allocator.get(), {expected_len}, ONNX_TENSOR_ELEMENT_DATA_TYPE_STRING), OrtReleaseValue);
ORT_THROW_ON_ERROR(OrtFillStringTensor(tensor.get(), s, expected_len));
std::unique_ptr<OrtTensorTypeAndShapeInfo> shape_info;
{
OrtTensorTypeAndShapeInfo* shape_info_ptr;
ORT_THROW_ON_ERROR(OrtGetTensorShapeAndType(tensor.get(), &shape_info_ptr));
shape_info.reset(shape_info_ptr);
}
size_t len = static_cast<size_t>(OrtGetTensorShapeElementCount(shape_info.get()));
ASSERT_EQ(len, expected_len);
std::vector<int64_t> shape_array(len);
size_t data_len;
ORT_THROW_ON_ERROR(OrtGetStringTensorDataLength(tensor.get(), &data_len));
std::string result(data_len, '\0');
std::vector<size_t> offsets(len);
ORT_THROW_ON_ERROR(OrtGetStringTensorContent(tensor.get(), (void*)result.data(), data_len, offsets.data(), offsets.size()));
}
}
TEST_F(CApiTest, create_tensor_with_data) {
float values[] = {3.0f, 1.0f, 2.f, 0.f};
constexpr size_t values_length = sizeof(values) / sizeof(values[0]);
OrtAllocatorInfo* info;
ORT_THROW_ON_ERROR(OrtCreateAllocatorInfo("Cpu", OrtDeviceAllocator, 0, OrtMemTypeDefault, &info));
std::vector<size_t> dims = {4};
std::unique_ptr<OrtValue, decltype(&OrtReleaseValue)> tensor(
OrtCreateTensorWithDataAsOrtValue(info, values, values_length * sizeof(float), dims, ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT), OrtReleaseValue);
OrtReleaseAllocatorInfo(info);
void* new_pointer;
ORT_THROW_ON_ERROR(OrtGetTensorMutableData(tensor.get(), &new_pointer));
ASSERT_EQ(new_pointer, values);
struct OrtTypeInfo* type_info;
ORT_THROW_ON_ERROR(OrtGetTypeInfo(tensor.get(), &type_info));
const struct OrtTensorTypeAndShapeInfo* tensor_info = OrtCastTypeInfoToTensorInfo(type_info);
ASSERT_NE(tensor_info, nullptr);
ASSERT_EQ(1, OrtGetNumOfDimensions(tensor_info));
OrtReleaseTypeInfo(type_info);
}
int main(int argc, char** argv) {
::testing::InitGoogleTest(&argc, argv);
return RUN_ALL_TESTS();
}