iter, INFERENCE_TENSOR_DATA_TYPE_FLOAT32, \
{ "/opt/usr/images/image_classification.bin" }, 224,\
224, 3, { "input_2" }, { "dense_3/Softmax" }, \
- { "/usr/share/capi-media-vision/models/IC/tflite/ic_tflite_model.tflite" }, \
+ { "/opt/usr/home/owner/media/Others/mv_test/res/inference/models/ic_tflite_model.tflite" }, \
{ 3 }
-#define PARAM_TYPE_TFLITE_IC_Q_INFER(backend, device, iter) \
- backend, device, TEST_MODEL_IMAGE_CLASSIFICATION, \
- iter, INFERENCE_TENSOR_DATA_TYPE_UINT8, \
- { "/opt/usr/images/image_classification_q.bin" }, \
- 224, 224, 3, { "input" }, \
- { "MobilenetV1/Predictions/Reshape_1" }, \
- { "/usr/share/capi-media-vision/models/IC_Q/tflite/ic_tflite_q_model.tflite" }, \
+#define PARAM_TYPE_TFLITE_IC_Q_INFER(backend, device, iter) \
+ backend, device, TEST_MODEL_IMAGE_CLASSIFICATION, \
+ iter, INFERENCE_TENSOR_DATA_TYPE_UINT8, \
+ { "/opt/usr/images/image_classification_q.bin" }, \
+ 224, 224, 3, { "input" }, \
+ { "MobilenetV1/Predictions/Reshape_1" }, \
+ { "/opt/usr/home/owner/media/Others/mv_test/open_model_zoo/models/IC/tflite/quant_mobilenet_v1_224x224.tflite" }, \
{ 955 }
#define PARAM_TYPE_TFLITE_OD_INFER(backend, device, iter) \
"TFLite_Detection_PostProcess:1", \
"TFLite_Detection_PostProcess:2", \
"TFLite_Detection_PostProcess:3" }, \
- { "/usr/share/capi-media-vision/models/OD/tflite/od_tflite_model.tflite" }, \
+ { "/opt/usr/home/owner/media/Others/mv_test/res/inference/models/od_tflite_model.tflite" }, \
{ 451, 474, 714, 969 }
#define PARAM_TYPE_TFLITE_FD_INFER(backend, device, iter) \
"TFLite_Detection_PostProcess:1", \
"TFLite_Detection_PostProcess:2", \
"TFLite_Detection_PostProcess:3" }, \
- { "/usr/share/capi-media-vision/models/FD/tflite/fd_tflite_model1.tflite" }, \
+ { "/opt/usr/home/owner/media/Others/mv_test/res/inference/models/fd_tflite_model1.tflite" }, \
{ 727, 225, 960, 555 }
#define PARAM_TYPE_TFLITE_PE_INFER(backend, device, iter) \
{ "/opt/usr/images/pose_estimation.bin" }, \
192, 192, 3, { "image" }, \
{ "Convolutional_Pose_Machine/stage_5_out" }, \
- { "/usr/share/capi-media-vision/models/PE/tflite/ped_tflite_model.tflite" }, \
+ { "/opt/usr/home/owner/media/Others/mv_test/open_model_zoo/models/pose-estimation/tflite/pld_cpm_192x192.tflite" }, \
{ 382, 351, 320, 257, 226, 414, 414, 445, 351, 351, \
351, 382, 382, 382, 76, 146, 170, 193, 216, 146, \
123, 99, 287, 381, 451, 287, 381, 475 }
-#define PARAM_TYPE_TFLITE_AICHG_1_INFER(backend, device, iter) \
- backend, device, TEST_MODEL_AIC_HAND_GESTURE_1, \
- iter, INFERENCE_TENSOR_DATA_TYPE_FLOAT32, \
- { "/opt/usr/images/hand.bin" }, 224, 224, 3, \
- { "input" }, \
- { "mobilenetv2/boundingbox", "mobilenetv2/heatmap" }, \
- { "/usr/share/capi-media-vision/models/PE_1/tflite/posenet1_lite_224.tflite" }, \
- { 0 }
-
-#define PARAM_TYPE_TFLITE_AICHG_2_INFER(backend, device, iter) \
- backend, device, TEST_MODEL_AIC_HAND_GESTURE_2, \
- iter, INFERENCE_TENSOR_DATA_TYPE_FLOAT32, \
- { "/opt/usr/images/hand.bin" }, 56, 56, 21, \
- { "input" }, \
- { "mobilenetv2/coord_refine", "mobilenetv2/gesture" }, \
- { "/usr/share/capi-media-vision/models/PE_2/tflite/posenet2_lite_224.tflite" }, \
- { 55, 39, 51, 40, 50, 42, 61, 43, 71, 39, \
- 78, 36, 82, 42, 82, 44, 83, 45, 35, 37, \
- 61, 36, 59, 36, 52, 39, 35, 32, 40, 34, \
- 62, 39, 70, 40, 58, 41, 34, 42, 34, 41, \
- 38, 38, 12 }
-
-
-// Macros for tflite model based cltuner test cases.
-// parameter order : CLTuner active flag, CLTuner update flag, CLTuner tuning level,
-// backend name, target device, test iteration count
-#define PARAM_TYPE_TFLITE_IC_CLTUNER(active, update, mode, backend, device, iter) \
- active, update, mode, \
- PARAM_TYPE_TFLITE_IC_INFER(backend, device, iter)
-
-#define PARAM_TYPE_TFLITE_IC_Q_CLTUNER(active, update, mode, backend, device, iter) \
- active, update, mode, \
- PARAM_TYPE_TFLITE_IC_Q_INFER(backend, device, iter)
-
-#define PARAM_TYPE_TFLITE_OD_CLTUNER(active, update, mode, backend, device, iter) \
- active, update, mode, \
- PARAM_TYPE_TFLITE_OD_INFER(backend, device, iter)
-
-#define PARAM_TYPE_TFLITE_FD_CLTUNER(active, update, mode, backend, device, iter) \
- active, update, mode, \
- PARAM_TYPE_TFLITE_FD_INFER(backend, device, iter)
-
-#define PARAM_TYPE_TFLITE_PE_CLTUNER(active, update, mode, backend, device, iter) \
- active, update, mode, \
- PARAM_TYPE_TFLITE_PE_INFER(backend, device, iter)
-
using namespace testing;
typedef std::tuple<std::string, int, int, int, int, std::vector<std::string>,
std::vector<int> >
ParamType_Infer;
-
-typedef std::tuple<bool, bool, inference_engine_cltuner_mode_e, std::string, int,
- int, int, int, std::vector<std::string>,
- int, int, int, std::vector<std::string>,
- std::vector<std::string>, std::vector<std::string>,
- std::vector<int> >
- ParamType_CLTuner;
-
-class InferenceEngineTfliteCLTunerTest : public testing::TestWithParam<ParamType_CLTuner>
-{};
class InferenceEngineTfliteTest : public testing::TestWithParam<ParamType_Infer>
{};
class InferenceEngineCaffeTest : public testing::TestWithParam<ParamType_Infer>
models.clear();
}
-TEST_P(InferenceEngineTfliteCLTunerTest, Inference)
-{
- bool active;
- bool update;
- inference_engine_cltuner_mode_e tuning_mode;
- std::string backend_name;
- int target_devices;
- int test_type;
- int iteration;
- int tensor_type;
- std::vector<std::string> image_paths;
- size_t height;
- size_t width;
- size_t ch;
- std::vector<std::string> input_layers;
- std::vector<std::string> output_layers;
- std::vector<std::string> model_paths;
- std::vector<int> answers;
-
- std::tie(active, update, tuning_mode, backend_name, target_devices, test_type,
- iteration, tensor_type, image_paths, height, width, ch, input_layers,
- output_layers, model_paths, answers) = GetParam();
-
- if (iteration < 1) {
- iteration = 1;
- }
-
- MachineCapacity *Cap = GetMachineCapacity();
- if (Cap == NULL) {
- std::cout << "Failed to get machine capacity" << std::endl;
- return;
- }
-
- // If current machine doesn't support inference engine then skip this test.
- if (Cap->available == false) {
- return;
- }
-
- // If current machine doesn't support OpenCL then skip the inference on GPU.
- if (target_devices == INFERENCE_TARGET_GPU && Cap->has_gpu == false) {
- return;
- }
-
- std::string test_name = GetModelString(test_type);
- ASSERT_NE(test_name, "");
-
- std::cout << test_name << " inference test : backend = " << backend_name
- << ", target device = " << Target_Formats[target_devices]
- << ", CLTuning mode = " << tuning_mode << std::endl;
-
- int backend_type = -1;
-
- // If backend name is "one" then change it to "mlapi"
- // and set backend_type to INFERENCE_BACKEND_ONE.
- if (backend_name.compare("one") == 0) {
- backend_name = "mlapi";
- backend_type = INFERENCE_BACKEND_ONE;
- }
-
- inference_engine_config config = { .backend_name = backend_name,
- .backend_type = backend_type,
- .target_devices = target_devices };
-
- auto engine = std::make_unique<InferenceEngineCommon>();
- ASSERT_TRUE(engine);
-
- int ret = engine->EnableProfiler(true);
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- if (backend_type == INFERENCE_BACKEND_ONE)
- backend_name = "one";
-
- ret = engine->DumpProfileToFile("profile_data_" + backend_name +
- "_" + Target_Formats[target_devices] +
- "_tflite_model.txt");
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- ret = engine->LoadConfigFile();
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- ret = engine->BindBackend(&config);
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- inference_engine_capacity capacity;
- ret = engine->GetBackendCapacity(&capacity);
- EXPECT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- if (capacity.cltuner_supported) {
- inference_engine_cltuner cltuner;
- cltuner.active = active;
- cltuner.update = update;
- cltuner.tuning_mode = tuning_mode;
-
- ret = engine->SetCLTuner(&cltuner);
- EXPECT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
- }
-
- ret = engine->SetTargetDevices(target_devices);
- EXPECT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- std::vector<std::string> models;
- int model_type = GetModelInfo(model_paths, models);
- ASSERT_NE(model_type, -1);
-
- inference_engine_layer_property input_property;
-
- inference_engine_tensor_info input_tensor_info = {
- { 1, ch, height, width },
- INFERENCE_TENSOR_SHAPE_NCHW,
- static_cast<inference_tensor_data_type_e>(tensor_type),
- static_cast<size_t>(1 * ch * height * width),
- 0.0f, 0, INFERENCE_TENSOR_QUANTIZATION_NONE
- };
-
- for (auto& input : input_layers) {
- input_property.layers.insert(std::make_pair(input, input_tensor_info));
- }
-
- ret = engine->SetInputLayerProperty(input_property);
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- inference_engine_layer_property output_property;
-
- inference_engine_tensor_info output_tensor_info = {
- std::vector<size_t>{1},
- INFERENCE_TENSOR_SHAPE_NCHW,
- INFERENCE_TENSOR_DATA_TYPE_FLOAT32,
- 1,
- 0.0f,
- 0,
- INFERENCE_TENSOR_QUANTIZATION_NONE
- };
-
- for (auto& layer : output_layers) {
- output_property.layers.insert(std::make_pair(layer, output_tensor_info));
- }
-
- ret = engine->SetOutputLayerProperty(output_property);
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- ret = engine->Load(models, (inference_model_format_e) model_type);
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- IETensorBuffer inputs, outputs;
- ret = PrepareTensorBuffers(engine.get(), inputs, outputs);
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- // Copy input image tensor data from a given file to input tensor buffer.
- ASSERT_EQ(image_paths.size(), inputs.size());
- int imageIndex = 0;
- for (auto& input : inputs) {
- CopyFileToMemory(image_paths[imageIndex++].c_str(), input.second, input.second.size);
- }
-
- for (int repeat = 0; repeat < iteration; ++repeat) {
- ret = engine->Run(inputs, outputs);
- EXPECT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
- }
-
- tensor_t result;
- FillOutputResult(engine.get(), outputs, result);
-
- switch (test_type) {
- case TEST_MODEL_IMAGE_CLASSIFICATION:
- ret = VerifyImageClassificationResults(result, answers[0]);
- EXPECT_EQ(ret, 1);
- break;
- case TEST_MODEL_OBJECT_DETECTION:
- // 1072 : fixed height size of dumped image, 1608 : fixed width size of dumped image.
- ret = VerifyObjectDetectionResults(result, answers, 1072, 1608);
- EXPECT_EQ(ret, 1);
- break;
- case TEST_MODEL_FACE_DETECTION:
- // 1152 : fixed height size of dumped image, 1536 : fixed width size of dumped image.
- ret = VerifyObjectDetectionResults(result, answers, 1152, 1536);
- EXPECT_EQ(ret, 1);
- break;
- case TEST_MODEL_FACIAL_LANDMARK_DETECTION:
- // TODO.
- break;
- case TEST_MODEL_POSE_ESTIMATION:
- // 563 : fixed height size of dumped image, 750 : fixed width size of dumped image.
- ret = VerifyPoseEstimationResults(result, answers, 563, 750);
- EXPECT_EQ(ret, 1);
- break;
- case TEST_MODEL_AIC_HAND_GESTURE_1:
- ret = VerifyAICHandGesture1Results(outputs);
- EXPECT_EQ(ret, 1);
- break;
- case TEST_MODEL_AIC_HAND_GESTURE_2:
- ret = VerifyAICHandGesture2Results(outputs, answers);
- EXPECT_EQ(ret, 1);
- break;
- }
-
- CleanupTensorBuffers(inputs, outputs);
-
- engine->UnbindBackend();
- models.clear();
-}
-
-
-TEST_P(InferenceEngineCaffeTest, Inference)
-{
- std::string backend_name;
- int target_devices;
- int test_type;
- int iteration;
- int tensor_type;
- std::vector<std::string> image_paths;
- size_t height;
- size_t width;
- size_t ch;
- std::vector<std::string> input_layers;
- std::vector<std::string> output_layers;
- std::vector<std::string> model_paths;
- std::vector<int> answers;
-
- std::tie(backend_name, target_devices, test_type, iteration, tensor_type,
- image_paths, height, width, ch, input_layers, output_layers,
- model_paths, answers) = GetParam();
-
- if (iteration < 1) {
- iteration = 1;
- }
-
- MachineCapacity *Cap = GetMachineCapacity();
- if (Cap == NULL) {
- std::cout << "Failed to get machine capacity" << std::endl;
- return;
- }
-
- // If current machine doesn't support inference engine then skip this test.
- if (Cap->available == false) {
- return;
- }
-
- // If current machine doesn't support OpenCL then skip the inference on GPU.
- if (target_devices == INFERENCE_TARGET_GPU && Cap->has_gpu == false) {
- return;
- }
-
- std::string test_name = GetModelString(test_type);
- ASSERT_NE(test_name, "");
-
- std::cout << test_name << " inference test : backend = " << backend_name
- << ", target device = " << Target_Formats[target_devices]
- << std::endl;
- inference_engine_config config = { .backend_name = backend_name,
- .backend_type = -1,
- .target_devices = target_devices };
-
- auto engine = std::make_unique<InferenceEngineCommon>();
- ASSERT_TRUE(engine);
-
- int ret = engine->EnableProfiler(true);
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- ret = engine->DumpProfileToFile("profile_data_" + backend_name +
- "_caffe_model.txt");
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- ret = engine->LoadConfigFile();
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- ret = engine->BindBackend(&config);
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- inference_engine_capacity capacity;
- ret = engine->GetBackendCapacity(&capacity);
- EXPECT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- ret = engine->SetTargetDevices(target_devices);
- EXPECT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- std::vector<std::string> models;
- int model_type = GetModelInfo(model_paths, models);
- ASSERT_NE(model_type, -1);
-
- inference_engine_layer_property input_property;
-
- inference_engine_tensor_info input_tensor_info = {
- { 1, ch, height, width },
- INFERENCE_TENSOR_SHAPE_NCHW,
- (inference_tensor_data_type_e) tensor_type,
- (size_t)(1 * ch * height * width),
- 0.0f, 0, INFERENCE_TENSOR_QUANTIZATION_NONE
- };
-
- for (auto& layer : input_layers) {
- input_property.layers.insert(std::make_pair(layer, input_tensor_info));
- }
-
- ret = engine->SetInputLayerProperty(input_property);
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- inference_engine_layer_property output_property;
-
- inference_engine_tensor_info output_tensor_info = {
- std::vector<size_t>{1},
- INFERENCE_TENSOR_SHAPE_NCHW,
- INFERENCE_TENSOR_DATA_TYPE_FLOAT32,
- 1,
- 0.0f,
- 0,
- INFERENCE_TENSOR_QUANTIZATION_NONE
- };
-
- for (auto& layer : output_layers) {
- output_property.layers.insert(std::make_pair(layer, output_tensor_info));
- }
-
- ret = engine->SetOutputLayerProperty(output_property);
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- ret = engine->Load(models, (inference_model_format_e) model_type);
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- IETensorBuffer inputs, outputs;
- ret = PrepareTensorBuffers(engine.get(), inputs, outputs);
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- // Copy input image tensor data from a given file to input tensor buffer.
- ASSERT_EQ(image_paths.size(), inputs.size());
- int imageIndex = 0;
- for (auto& input : inputs) {
- CopyFileToMemory(image_paths[imageIndex++].c_str(), input.second, input.second.size);
- }
-
- for (int repeat = 0; repeat < iteration; ++repeat) {
- ret = engine->Run(inputs, outputs);
- EXPECT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
- }
-
- tensor_t result;
- FillOutputResult(engine.get(), outputs, result);
-
- switch (test_type) {
- case TEST_MODEL_IMAGE_CLASSIFICATION:
- ret = VerifyImageClassificationResults(result, answers[0]);
- EXPECT_EQ(ret, 1);
- break;
- case TEST_MODEL_OBJECT_DETECTION:
- // 1024 : fixed height size of dumped image, 636 : fixed width size of dumped image.
- ret = VerifyObjectDetectionResults(result, answers, 636, 1024);
- EXPECT_EQ(ret, 1);
- break;
- case TEST_MODEL_FACE_DETECTION:
- // 1152 : fixed height size of dumped image, 1536 : fixed width size of dumped image.
- ret = VerifyObjectDetectionResults(result, answers, 1152, 1536);
- EXPECT_EQ(ret, 1);
- break;
- case TEST_MODEL_FACIAL_LANDMARK_DETECTION:
- // 128 : fixed height size of dumped image, 128 : fixed width size of dumped image.
- ret = VerifyFacialLandmarkDetectionResults(result, answers, 128, 128);
- EXPECT_EQ(ret, 1);
- break;
- case TEST_MODEL_POSE_ESTIMATION:
- // 563 : fixed height size of dumped image, 750 : fixed width size of dumped image.
- ret = VerifyPoseEstimationResults(result, answers, 563, 750);
- EXPECT_EQ(ret, 1);
- break;
- }
-
- CleanupTensorBuffers(inputs, outputs);
-
- engine->UnbindBackend();
- models.clear();
-}
-
-TEST_P(InferenceEngineDldtTest, Inference)
-{
- std::string backend_name;
- int target_devices;
- int test_type;
- int iteration;
- int tensor_type;
- std::vector<std::string> image_paths;
- size_t height;
- size_t width;
- size_t ch;
- std::vector<std::string> input_layers;
- std::vector<std::string> output_layers;
- std::vector<std::string> model_paths;
- std::vector<int> answers;
-
- std::tie(backend_name, target_devices, test_type, iteration, tensor_type,
- image_paths, height, width, ch, input_layers, output_layers,
- model_paths, answers) = GetParam();
-
- if (iteration < 1) {
- iteration = 1;
- }
-
- MachineCapacity *Cap = GetMachineCapacity();
- if (Cap == NULL) {
- std::cout << "Failed to get machine capacity" << std::endl;
- return;
- }
-
- // If current machine doesn't support inference engine then skip this test.
- if (Cap->available == false) {
- return;
- }
-
- // If current machine doesn't support OpenCL then skip the inference on GPU.
- if (target_devices == INFERENCE_TARGET_GPU && Cap->has_gpu == false) {
- return;
- }
-
- std::string test_name = GetModelString(test_type);
- ASSERT_NE(test_name, "");
-
- std::cout << test_name << " inference test : backend = " << backend_name
- << ", target device = " << Target_Formats[target_devices]
- << std::endl;
- inference_engine_config config = { .backend_name = backend_name,
- .backend_type = -1,
- .target_devices = target_devices };
-
- auto engine = std::make_unique<InferenceEngineCommon>();
- ASSERT_TRUE(engine);
-
- int ret = engine->EnableProfiler(true);
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- ret = engine->DumpProfileToFile("profile_data_" + backend_name +
- "_dldt_model.txt");
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- ret = engine->LoadConfigFile();
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- ret = engine->BindBackend(&config);
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- inference_engine_capacity capacity;
- ret = engine->GetBackendCapacity(&capacity);
- EXPECT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- ret = engine->SetTargetDevices(target_devices);
- EXPECT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- std::vector<std::string> models;
- int model_type = GetModelInfo(model_paths, models);
- ASSERT_NE(model_type, -1);
-
- inference_engine_layer_property input_property;
-
- inference_engine_tensor_info input_tensor_info = {
- { 1, ch, height, width },
- INFERENCE_TENSOR_SHAPE_NCHW,
- (inference_tensor_data_type_e) tensor_type,
- (size_t)(1 * ch * height * width),
- 0.0f, 0, INFERENCE_TENSOR_QUANTIZATION_NONE
- };
-
- for (auto& layer : input_layers) {
- input_property.layers.insert(std::make_pair(layer, input_tensor_info));
- }
-
- ret = engine->SetInputLayerProperty(input_property);
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- inference_engine_layer_property output_property;
-
- inference_engine_tensor_info output_tensor_info = {
- { 1, ch, height, width },
- INFERENCE_TENSOR_SHAPE_NCHW,
- (inference_tensor_data_type_e) tensor_type,
- (size_t)(1 * ch * height * width),
- 0.0f, 0, INFERENCE_TENSOR_QUANTIZATION_NONE
- };
-
- for (auto& layer : output_layers) {
- output_property.layers.insert(std::make_pair(layer, output_tensor_info));
- }
-
- ret = engine->SetOutputLayerProperty(output_property);
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- ret = engine->Load(models, (inference_model_format_e) model_type);
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- IETensorBuffer inputs, outputs;
- ret = PrepareTensorBuffers(engine.get(), inputs, outputs);
- ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-
- // Copy input image tensor data from a given file to input tensor buffer.
- ASSERT_EQ(image_paths.size(), inputs.size());
- int imageIndex = 0;
- for (auto& input : inputs) {
- CopyFileToMemory(image_paths[imageIndex++].c_str(), input.second, input.second.size);
- }
-
- for (int repeat = 0; repeat < iteration; ++repeat) {
- ret = engine->Run(inputs, outputs);
- EXPECT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
- }
-
- tensor_t result;
- FillOutputResult(engine.get(), outputs, result);
-
- switch (test_type) {
- case TEST_MODEL_IMAGE_CLASSIFICATION:
- ret = VerifyImageClassificationResults(result, answers[0]);
- EXPECT_EQ(ret, 1);
- break;
- case TEST_MODEL_OBJECT_DETECTION:
- // 1024 : fixed height size of dumped image, 636 : fixed width size of dumped image.
- ret = VerifyObjectDetectionResults(result, answers, 636, 1024);
- EXPECT_EQ(ret, 1);
- break;
- case TEST_MODEL_FACE_DETECTION:
- // 1152 : fixed height size of dumped image, 1536 : fixed width size of dumped image.
- ret = VerifyObjectDetectionResults(result, answers, 1152, 1536);
- EXPECT_EQ(ret, 1);
- break;
- case TEST_MODEL_FACIAL_LANDMARK_DETECTION:
- // 128 : fixed height size of dumped image, 128 : fixed width size of dumped image.
- ret = VerifyFacialLandmarkDetectionResults(result, answers, 128, 128);
- EXPECT_EQ(ret, 1);
- break;
- case TEST_MODEL_POSE_ESTIMATION:
- // 563 : fixed height size of dumped image, 750 : fixed width size of dumped image.
- ret = VerifyPoseEstimationResults(result, answers, 563, 750);
- EXPECT_EQ(ret, 1);
- break;
- }
-
- CleanupTensorBuffers(inputs, outputs);
-
- engine->UnbindBackend();
- models.clear();
-}
-
INSTANTIATE_TEST_CASE_P(
Opensource, InferenceEngineTfliteTest,
testing::Values(
- // parameter order : backend name, target device, test iteration count.
- // mobilenet based image classification test
- // ARMNN.
- ParamType_Infer(
- PARAM_TYPE_TFLITE_IC_INFER("armnn", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
- // quantized mobilenet based image classification test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_IC_Q_INFER("armnn", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
- // object detection test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_OD_INFER("armnn", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
- // face detection test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_FD_INFER("armnn", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
- // pose estimation test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_PE_INFER("armnn", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
- // Hand gesture model 1 from AIC
- ParamType_Infer(
- PARAM_TYPE_TFLITE_AICHG_1_INFER("armnn", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
- // Hand gesture model 2 from AIC
- ParamType_Infer(
- PARAM_TYPE_TFLITE_AICHG_2_INFER("armnn", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
-
- // mobilenet based image classification test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_IC_INFER("armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- // quantized mobilenet based image classification test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_IC_Q_INFER("armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- // object detection test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_OD_INFER("armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- // face detection test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_FD_INFER("armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- // pose estimation test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_PE_INFER("armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- // Hand gesture model 1 from AIC
- ParamType_Infer(
- PARAM_TYPE_TFLITE_AICHG_1_INFER("armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- // Hand gesture model 2 from AIC
- ParamType_Infer(
- PARAM_TYPE_TFLITE_AICHG_2_INFER("armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
-
- /*********************************************************************************/
// parameter order : backend name, target device, test iteration count.
// mobilenet based image classification test
// TFLITE.
// pose estimation test
ParamType_Infer(
PARAM_TYPE_TFLITE_PE_INFER("tflite", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
- // Hand gesture model 1 from AIC
- ParamType_Infer(
- PARAM_TYPE_TFLITE_AICHG_1_INFER("tflite", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
- // Hand gesture model 2 from AIC
- ParamType_Infer(
- PARAM_TYPE_TFLITE_AICHG_2_INFER("tflite", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
-
// mobilenet based image classification test
ParamType_Infer(
PARAM_TYPE_TFLITE_IC_INFER("tflite", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
PARAM_TYPE_TFLITE_FD_INFER("tflite", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
// pose estimation test
ParamType_Infer(
- PARAM_TYPE_TFLITE_PE_INFER("tflite", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- // Hand gesture model 1 from AIC
- ParamType_Infer(
- PARAM_TYPE_TFLITE_AICHG_1_INFER("tflite", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- // Hand gesture model 2 from AIC
- ParamType_Infer(
- PARAM_TYPE_TFLITE_AICHG_2_INFER("tflite", INFERENCE_TARGET_GPU, INFERENCE_ITERATION))
+ PARAM_TYPE_TFLITE_PE_INFER("tflite", INFERENCE_TARGET_GPU, INFERENCE_ITERATION))
/* TODO */
));
-INSTANTIATE_TEST_CASE_P(
- Inhouse, InferenceEngineTfliteTest,
- testing::Values(
- /*********************************************************************************/
- // parameter order : backend name, target device, test iteration count.
- // mobilenet based image classification test
- // ONE via MLAPI.
- ParamType_Infer(
- PARAM_TYPE_TFLITE_IC_INFER("one", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
- // quantized mobilenet based image classification test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_IC_Q_INFER("one", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
- // object detection test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_OD_INFER("one", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
- // face detection test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_FD_INFER("one", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
- // pose estimation test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_PE_INFER("one", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
- // Hand gesture model 1 from AIC
- ParamType_Infer(
- PARAM_TYPE_TFLITE_AICHG_1_INFER("one", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
- // Hand gesture model 2 from AIC
- ParamType_Infer(
- PARAM_TYPE_TFLITE_AICHG_2_INFER("one", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
-
- // mobilenet based image classification test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_IC_INFER("one", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- // quantized mobilenet based image classification test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_IC_Q_INFER("one", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- // object detection test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_OD_INFER("one", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- // face detection test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_FD_INFER("one", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- // pose estimation test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_PE_INFER("one", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- // Hand gesture model 1 from AIC
- ParamType_Infer(
- PARAM_TYPE_TFLITE_AICHG_1_INFER("one", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- // Hand gesture model 2 from AIC
- ParamType_Infer(
- PARAM_TYPE_TFLITE_AICHG_2_INFER("one", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
-
- /*********************************************************************************/
- // parameter order : backend name, target device, test iteration count.
- // mobilenet based image classification test
- // TFLITE via MLAPI.
- ParamType_Infer(
- PARAM_TYPE_TFLITE_IC_INFER("one", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
- // quantized mobilenet based image classification test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_IC_Q_INFER("one", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
- // object detection test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_OD_INFER("one", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
- // face detection test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_FD_INFER("one", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
- // pose estimation test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_PE_INFER("one", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
- // Hand gesture model 1 from AIC
- ParamType_Infer(
- PARAM_TYPE_TFLITE_AICHG_1_INFER("one", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
- // Hand gesture model 2 from AIC
- ParamType_Infer(
- PARAM_TYPE_TFLITE_AICHG_2_INFER("one", INFERENCE_TARGET_CPU, INFERENCE_ITERATION)),
-
- // mobilenet based image classification test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_IC_INFER("one", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- // quantized mobilenet based image classification test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_IC_Q_INFER("one", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- // object detection test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_OD_INFER("one", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- // face detection test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_FD_INFER("one", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- // pose estimation test
- ParamType_Infer(
- PARAM_TYPE_TFLITE_PE_INFER("one", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- // Hand gesture model 1 from AIC
- ParamType_Infer(
- PARAM_TYPE_TFLITE_AICHG_1_INFER("one", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- // Hand gesture model 2 from AIC
- ParamType_Infer(
- PARAM_TYPE_TFLITE_AICHG_2_INFER("one", INFERENCE_TARGET_GPU, INFERENCE_ITERATION))
- /* TODO */
- ));
-
-INSTANTIATE_TEST_CASE_P(
- Opensource, InferenceEngineTfliteCLTunerTest,
- testing::Values(
- // parameter order : CLTuner active flag, CLTuner update flag, CLTuner tuning level or mode, backend name, target device, test iteration count.
- // mobilenet based image classification test
- // ARMNN.
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_IC_CLTUNER(true, true, INFERENCE_ENGINE_CLTUNER_RAPID, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_IC_CLTUNER(true, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_IC_CLTUNER(true, true, INFERENCE_ENGINE_CLTUNER_NORMAL, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_IC_CLTUNER(true, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_IC_CLTUNER(true, true, INFERENCE_ENGINE_CLTUNER_EXHAUSTIVE, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_IC_CLTUNER(true, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
-
- // quantized mobilenet based image classification test
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_IC_Q_CLTUNER(true, true, INFERENCE_ENGINE_CLTUNER_RAPID, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_IC_Q_CLTUNER(true, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_IC_Q_CLTUNER(true, true, INFERENCE_ENGINE_CLTUNER_NORMAL, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_IC_Q_CLTUNER(true, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_IC_Q_CLTUNER(true, true, INFERENCE_ENGINE_CLTUNER_EXHAUSTIVE, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_IC_Q_CLTUNER(true, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
-
- // object detection test
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_OD_CLTUNER(true, true, INFERENCE_ENGINE_CLTUNER_RAPID, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_OD_CLTUNER(true, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_OD_CLTUNER(true, true, INFERENCE_ENGINE_CLTUNER_NORMAL, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_OD_CLTUNER(true, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_OD_CLTUNER(true, true, INFERENCE_ENGINE_CLTUNER_EXHAUSTIVE, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_OD_CLTUNER(true, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
-
- // face detection test
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_FD_CLTUNER(true, true, INFERENCE_ENGINE_CLTUNER_RAPID, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_FD_CLTUNER(true, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_FD_CLTUNER(true, true, INFERENCE_ENGINE_CLTUNER_NORMAL, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_FD_CLTUNER(true, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_FD_CLTUNER(true, true, INFERENCE_ENGINE_CLTUNER_EXHAUSTIVE, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_FD_CLTUNER(true, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
-
- // pose estimation test
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_PE_CLTUNER(true, true, INFERENCE_ENGINE_CLTUNER_RAPID, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_PE_CLTUNER(true, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_PE_CLTUNER(true, true, INFERENCE_ENGINE_CLTUNER_NORMAL, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_PE_CLTUNER(true, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_PE_CLTUNER(true, true, INFERENCE_ENGINE_CLTUNER_EXHAUSTIVE, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_PE_CLTUNER(true, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
-
- // CLTuner file generation.
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_IC_CLTUNER(true, true, INFERENCE_ENGINE_CLTUNER_EXHAUSTIVE, "armnn", INFERENCE_TARGET_GPU, 1)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_IC_Q_CLTUNER(true, true, INFERENCE_ENGINE_CLTUNER_EXHAUSTIVE, "armnn", INFERENCE_TARGET_GPU, 1)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_OD_CLTUNER(true, true, INFERENCE_ENGINE_CLTUNER_EXHAUSTIVE, "armnn", INFERENCE_TARGET_GPU, 1)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_FD_CLTUNER(true, true, INFERENCE_ENGINE_CLTUNER_EXHAUSTIVE, "armnn", INFERENCE_TARGET_GPU, 1)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_PE_CLTUNER(true, true, INFERENCE_ENGINE_CLTUNER_EXHAUSTIVE, "armnn", INFERENCE_TARGET_GPU, 1)),
-
- // Measure inference performance without CLTuner.
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_IC_CLTUNER(false, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_IC_Q_CLTUNER(false, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_OD_CLTUNER(false, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_FD_CLTUNER(false, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_PE_CLTUNER(false, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
-
- // Measure inference performance with CLTuner.
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_IC_CLTUNER(true, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_IC_Q_CLTUNER(true, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_OD_CLTUNER(true, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_FD_CLTUNER(true, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION)),
- ParamType_CLTuner(
- PARAM_TYPE_TFLITE_PE_CLTUNER(true, false, INFERENCE_ENGINE_CLTUNER_READ, "armnn", INFERENCE_TARGET_GPU, INFERENCE_ITERATION))
- /* TODO */
- ));
-
-INSTANTIATE_TEST_CASE_P(
- Opensource, InferenceEngineCaffeTest,
- testing::Values(
- // parameter order : backend_name, target_devices, test_type, iteration, tensor_type, image_paths, height, width, ch, input_layers, output_layers, model_paths, answers
- // OPENCV
- // squeezenet based image classification test
- ParamType_Infer(
- "opencv", INFERENCE_TARGET_CPU,
- TEST_MODEL_IMAGE_CLASSIFICATION, INFERENCE_ITERATION,
- INFERENCE_TENSOR_DATA_TYPE_FLOAT32,
- { "/opt/usr/images/image_classification_caffe.bin" },
- 227, 227, 3, { "data" }, { "prob" },
- { "/usr/share/capi-media-vision/models/IC/caffe/ic_caffe_model_squeezenet.caffemodel",
- "/usr/share/capi-media-vision/models/IC/caffe/ic_caffe_model_squeezenet.prototxt" },
- { 281 }),
- ParamType_Infer(
- "opencv", INFERENCE_TARGET_GPU,
- TEST_MODEL_IMAGE_CLASSIFICATION, INFERENCE_ITERATION,
- INFERENCE_TENSOR_DATA_TYPE_FLOAT32,
- { "/opt/usr/images/image_classification_caffe.bin" },
- 227, 227, 3, { "data" }, { "prob" },
- { "/usr/share/capi-media-vision/models/IC/caffe/ic_caffe_model_squeezenet.caffemodel",
- "/usr/share/capi-media-vision/models/IC/caffe/ic_caffe_model_squeezenet.prototxt" },
- { 281 }),
-
- // mobilenet-ssd based object detection test
- ParamType_Infer(
- "opencv", INFERENCE_TARGET_CPU, TEST_MODEL_OBJECT_DETECTION,
- INFERENCE_ITERATION, INFERENCE_TENSOR_DATA_TYPE_FLOAT32,
- { "/opt/usr/images/object_detection_caffe.bin" }, 300,
- 300, 3, { "data" }, { "detection_out" },
- { "/usr/share/capi-media-vision/models/OD/caffe/od_caffe_model_mobilenetv1ssd.caffemodel",
- "/usr/share/capi-media-vision/models/OD/caffe/od_caffe_model_mobilenetv1ssd.prototxt" },
- { 15, 19, 335, 557 }),
- ParamType_Infer(
- "opencv", INFERENCE_TARGET_GPU, TEST_MODEL_OBJECT_DETECTION,
- INFERENCE_ITERATION, INFERENCE_TENSOR_DATA_TYPE_FLOAT32,
- { "/opt/usr/images/object_detection_caffe.bin" }, 300,
- 300, 3, { "data" }, { "detection_out" },
- { "/usr/share/capi-media-vision/models/OD/caffe/od_caffe_model_mobilenetv1ssd.caffemodel",
- "/usr/share/capi-media-vision/models/OD/caffe/od_caffe_model_mobilenetv1ssd.prototxt" },
- { 15, 19, 335, 557 }),
-
- // mobilenet-ssd based object detection test
- ParamType_Infer(
- "opencv", INFERENCE_TARGET_CPU, TEST_MODEL_FACE_DETECTION, INFERENCE_ITERATION,
- INFERENCE_TENSOR_DATA_TYPE_FLOAT32,
- { "/opt/usr/images/face_detection_caffe.bin" }, 300,
- 300, 3, { "data" }, { "detection_out" },
- { "/usr/share/capi-media-vision/models/FD/caffe/fd_caffe_model_resnet10ssd.caffemodel",
- "/usr/share/capi-media-vision/models/FD/caffe/fd_caffe_model_resnet10ssd.prototxt" },
- { 733, 233, 965, 539 }),
- ParamType_Infer(
- "opencv", INFERENCE_TARGET_GPU, TEST_MODEL_FACE_DETECTION, INFERENCE_ITERATION,
- INFERENCE_TENSOR_DATA_TYPE_FLOAT32,
- { "/opt/usr/images/face_detection_caffe.bin" }, 300,
- 300, 3, { "data" }, { "detection_out" },
- { "/usr/share/capi-media-vision/models/FD/caffe/fd_caffe_model_resnet10ssd.caffemodel",
- "/usr/share/capi-media-vision/models/FD/caffe/fd_caffe_model_resnet10ssd.prototxt" },
- { 733, 233, 965, 539 }),
-
- // tweakcnn based facial landmark detection test
- ParamType_Infer(
- "opencv", INFERENCE_TARGET_CPU,
- TEST_MODEL_FACIAL_LANDMARK_DETECTION, INFERENCE_ITERATION,
- INFERENCE_TENSOR_DATA_TYPE_FLOAT32,
- { "/opt/usr/images/faciallandmark_detection_caffe.bin" },
- 128, 128, 3, { "data" }, { "Sigmoid_fc2" },
- { "/usr/share/capi-media-vision/models/FLD/caffe/fld_caffe_model_tweak.caffemodel",
- "/usr/share/capi-media-vision/models/FLD/caffe/fld_caffe_model_tweak.prototxt" },
- { 53, 45, 85, 46, 66, 64, 54, 78, 82, 79 }),
- ParamType_Infer(
- "opencv", INFERENCE_TARGET_GPU,
- TEST_MODEL_FACIAL_LANDMARK_DETECTION, INFERENCE_ITERATION,
- INFERENCE_TENSOR_DATA_TYPE_FLOAT32,
- { "/opt/usr/images/faciallandmark_detection_caffe.bin" },
- 128, 128, 3, { "data" }, { "Sigmoid_fc2" },
- { "/usr/share/capi-media-vision/models/FLD/caffe/fld_caffe_model_tweak.caffemodel",
- "/usr/share/capi-media-vision/models/FLD/caffe/fld_caffe_model_tweak.prototxt" },
- { 53, 45, 85, 46, 66, 64, 54, 78, 82, 79 })
- /* TODO */
- ));
-
-INSTANTIATE_TEST_CASE_P(
- Opensource, InferenceEngineDldtTest,
- testing::Values(
- // DLDT
- ParamType_Infer(
- "dldt", INFERENCE_TARGET_CUSTOM,
- TEST_MODEL_IMAGE_CLASSIFICATION, INFERENCE_ITERATION,
- INFERENCE_TENSOR_DATA_TYPE_FLOAT32,
- { "/opt/usr/images/dldt_banana_classification.bin" },
- 224, 224, 3, { "data" }, { "prob" },
- { "/usr/share/capi-media-vision/models/IC/dldt/googlenet-v1.xml",
- "/usr/share/capi-media-vision/models/IC/dldt/googlenet-v1.bin" },
- { 954 })));
-
int main(int argc, char **argv)
{
InitGoogleTest(&argc, argv);
projects / platform / core / multimedia / inference-engine-interface.git / commitdiff