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Disable oneDNN cache cleanup
1 parent 83e312f commit 409731b

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+85
-66
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2 files changed

+85
-66
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‎samples/cpp/hello_classification/main.cpp

+82-63
Original file line numberDiff line numberDiff line change
@@ -22,98 +22,117 @@
2222
* @brief Main with support Unicode paths, wide strings
2323
*/
2424
int tmain(int argc, tchar* argv[]) {
25-
try {
25+
{
2626
// -------- Get OpenVINO runtime version --------
2727
slog::info << ov::get_openvino_version() << slog::endl;
2828

2929
// -------- Parsing and validation of input arguments --------
30-
if (argc != 4) {
31-
slog::info << "Usage : " << argv[0] << " <path_to_model> <path_to_image> <device_name>" << slog::endl;
32-
return EXIT_FAILURE;
30+
if (argc != 3) {
31+
slog::info << "Usage : " << argv[0] << " <path_to_model> <device_name>" << slog::endl;
32+
return EXIT_FAILURE;
3333
}
34-
35-
const std::string args = TSTRING2STRING(argv[0]);
34+
//
35+
//const std::string args = TSTRING2STRING(argv[0]);
3636
const std::string model_path = TSTRING2STRING(argv[1]);
37-
const std::string image_path = TSTRING2STRING(argv[2]);
38-
const std::string device_name = TSTRING2STRING(argv[3]);
37+
// const std::string image_path = TSTRING2STRING(argv[2]);
38+
const std::string device_name = TSTRING2STRING(argv[2]);
3939

4040
// -------- Step 1. Initialize OpenVINO Runtime Core --------
4141
ov::Core core;
4242

43+
// std::vector<std::string> availableDevices = core.get_available_devices();
44+
// for (auto&& device : availableDevices) {
45+
// slog::info << device << slog::endl;
46+
47+
// // Query supported properties and print all of them
48+
// slog::info << "\tSUPPORTED_PROPERTIES: " << slog::endl;
49+
// auto supported_properties = core.get_property(device, ov::supported_properties);
50+
// for (auto&& property : supported_properties) {
51+
// if (property != ov::supported_properties.name()) {
52+
// slog::info << "\t\t" << (property.is_mutable() ? "Mutable: " : "Immutable: ") << property << " : "
53+
// << slog::flush;
54+
55+
// }
56+
// }
57+
58+
// slog::info << slog::endl;
59+
// }
60+
//core.set_property(ov::cache_dir("C:\\Intel\\XeSDK"));
4361
// -------- Step 2. Read a model --------
44-
slog::info << "Loading model files: " << model_path << slog::endl;
62+
//slog::info << "Loading model files: " << model_path << slog::endl;
4563
std::shared_ptr<ov::Model> model = core.read_model(model_path);
46-
printInputAndOutputsInfo(*model);
64+
//printInputAndOutputsInfo(*model);
4765

4866
OPENVINO_ASSERT(model->inputs().size() == 1, "Sample supports models with 1 input only");
49-
OPENVINO_ASSERT(model->outputs().size() == 1, "Sample supports models with 1 output only");
67+
//OPENVINO_ASSERT(model->outputs().size() == 1, "Sample supports models with 1 output only");
5068

5169
// -------- Step 3. Set up input
5270

5371
// Read input image to a tensor and set it to an infer request
5472
// without resize and layout conversions
55-
FormatReader::ReaderPtr reader(image_path.c_str());
56-
if (reader.get() == nullptr) {
57-
std::stringstream ss;
58-
ss << "Image " + image_path + " cannot be read!";
59-
throw std::logic_error(ss.str());
60-
}
61-
62-
ov::element::Type input_type = ov::element::u8;
63-
ov::Shape input_shape = {1, reader->height(), reader->width(), 3};
64-
std::shared_ptr<unsigned char> input_data = reader->getData();
65-
66-
// just wrap image data by ov::Tensor without allocating of new memory
67-
ov::Tensor input_tensor = ov::Tensor(input_type, input_shape, input_data.get());
68-
69-
const ov::Layout tensor_layout{"NHWC"};
70-
71-
// -------- Step 4. Configure preprocessing --------
72-
73-
ov::preprocess::PrePostProcessor ppp(model);
74-
75-
// 1) Set input tensor information:
76-
// - input() provides information about a single model input
77-
// - reuse precision and shape from already available `input_tensor`
78-
// - layout of data is 'NHWC'
79-
ppp.input().tensor().set_shape(input_shape).set_element_type(input_type).set_layout(tensor_layout);
80-
// 2) Adding explicit preprocessing steps:
81-
// - convert layout to 'NCHW' (from 'NHWC' specified above at tensor layout)
82-
// - apply linear resize from tensor spatial dims to model spatial dims
83-
ppp.input().preprocess().resize(ov::preprocess::ResizeAlgorithm::RESIZE_LINEAR);
84-
// 4) Here we suppose model has 'NCHW' layout for input
85-
ppp.input().model().set_layout("NCHW");
86-
// 5) Set output tensor information:
87-
// - precision of tensor is supposed to be 'f32'
88-
ppp.output().tensor().set_element_type(ov::element::f32);
89-
90-
// 6) Apply preprocessing modifying the original 'model'
91-
model = ppp.build();
73+
//FormatReader::ReaderPtr reader(image_path.c_str());
74+
//if (reader.get() == nullptr) {
75+
// std::stringstream ss;
76+
// ss << "Image " + image_path + " cannot be read!";
77+
// throw std::logic_error(ss.str());
78+
//}
79+
80+
//ov::element::Type input_type = ov::element::u8;
81+
//ov::Shape input_shape = {1, reader->height(), reader->width(), 3};
82+
//std::shared_ptr<unsigned char> input_data = reader->getData();
83+
//
84+
//// just wrap image data by ov::Tensor without allocating of new memory
85+
//ov::Tensor input_tensor = ov::Tensor(input_type, input_shape, input_data.get());
86+
//
87+
//const ov::Layout tensor_layout{"NHWC"};
88+
//
89+
//// -------- Step 4. Configure preprocessing --------
90+
//
91+
//ov::preprocess::PrePostProcessor ppp(model);
92+
//
93+
//// 1) Set input tensor information:
94+
//// - input() provides information about a single model input
95+
//// - reuse precision and shape from already available `input_tensor`
96+
//// - layout of data is 'NHWC'
97+
//ppp.input().tensor().set_shape(input_shape).set_element_type(input_type).set_layout(tensor_layout);
98+
//// 2) Adding explicit preprocessing steps:
99+
//// - convert layout to 'NCHW' (from 'NHWC' specified above at tensor layout)
100+
//// - apply linear resize from tensor spatial dims to model spatial dims
101+
//ppp.input().preprocess().resize(ov::preprocess::ResizeAlgorithm::RESIZE_LINEAR);
102+
//// 4) Here we suppose model has 'NCHW' layout for input
103+
//ppp.input().model().set_layout("NCHW");
104+
//// 5) Set output tensor information:
105+
//// - precision of tensor is supposed to be 'f32'
106+
//ppp.output().tensor().set_element_type(ov::element::f32);
107+
//
108+
//// 6) Apply preprocessing modifying the original 'model'
109+
//model = ppp.build();
92110

93111
// -------- Step 5. Loading a model to the device --------
94112
ov::CompiledModel compiled_model = core.compile_model(model, device_name);
95113

96114
// -------- Step 6. Create an infer request --------
97115
ov::InferRequest infer_request = compiled_model.create_infer_request();
116+
98117
// -----------------------------------------------------------------------------------------------------
99118

100119
// -------- Step 7. Prepare input --------
101-
infer_request.set_input_tensor(input_tensor);
102-
103-
// -------- Step 8. Do inference synchronously --------
104-
infer_request.infer();
105-
106-
// -------- Step 9. Process output
107-
const ov::Tensor& output_tensor = infer_request.get_output_tensor();
108-
109-
// Print classification results
110-
ClassificationResult classification_result(output_tensor, {image_path});
111-
classification_result.show();
120+
//infer_request.set_input_tensor(input_tensor);
121+
//
122+
//// -------- Step 8. Do inference synchronously --------
123+
//infer_request.infer();
124+
//
125+
//// -------- Step 9. Process output
126+
//const ov::Tensor& output_tensor = infer_request.get_output_tensor();
127+
//
128+
//// Print classification results
129+
//ClassificationResult classification_result(output_tensor, {image_path});
130+
//classification_result.show();
112131
// -----------------------------------------------------------------------------------------------------
113-
} catch (const std::exception& ex) {
114-
std::cerr << ex.what() << std::endl;
115-
return EXIT_FAILURE;
132+
//} catch (const std::exception& ex) {
133+
// std::cerr << ex.what() << std::endl;
134+
// return EXIT_FAILURE;
116135
}
117136

118137
return EXIT_SUCCESS;
119-
}
138+
}

‎src/plugins/intel_gpu/src/plugin/plugin.cpp

+3-3
Original file line numberDiff line numberDiff line change
@@ -165,13 +165,13 @@ Plugin::~Plugin() {
165165
// To prevent hanging during oneDNN's primitive cache desctruction,
166166
// trigger earlier cache cleanup by setting its capacity to 0.
167167
// Related ticket: 106154.
168-
dnnl::set_primitive_cache_capacity(0);
168+
// dnnl::set_primitive_cache_capacity(0);
169169

170170
// In case of multiple ov::Core instances (and multiple GPU plugins) we need to restore original
171171
// cache capacity to prevent working with zero-capacity cache in other GPU Plugin instances, since
172172
// cache is shared between all of GPU Plugin instances and cache clean up affects all of them.
173-
const int default_cache_capacity = 1024;
174-
dnnl::set_primitive_cache_capacity(default_cache_capacity);
173+
// const int default_cache_capacity = 1024;
174+
// dnnl::set_primitive_cache_capacity(default_cache_capacity);
175175
#endif
176176
}
177177

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