6514c213c3
* Ubuntu 22.04 support * Try to fix setuptools * Try to fix arm * Try to add more packages * Test 2 * test 3 * Turn dependnecies download off * Fix * Fix * Fix * Fix * Fix * test * Fix * restore eveything * Try to restore * Restore install_openvino_dependencies.sh * clean-up raspbian * New if conditions * Removed excess dependencies * COsmetic chnages * Removed autools * Removed libgkt-2 * Added HDLDL libs * Test * Removed some dependnecies * Better fixes * Removed some dependencies * Fixed compilation * Removed all extra
113 lines
2.9 KiB
C++
113 lines
2.9 KiB
C++
// Copyright (C) 2020 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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// ! [ov:include]
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#include <openvino/openvino.hpp>
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// ! [ov:include]
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int main() {
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ov::Core core;
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std::shared_ptr<ov::Model> model = core.read_model("model.xml");
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ov::CompiledModel compiled_model;
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//! [create_infer_request]
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auto infer_request = compiled_model.create_infer_request();
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//! [create_infer_request]
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//! [sync_infer]
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infer_request.infer();
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//! [sync_infer]
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//! [async_infer]
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infer_request.start_async();
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//! [async_infer]
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//! [wait]
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infer_request.wait();
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//! [wait]
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//! [wait_for]
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infer_request.wait_for(std::chrono::milliseconds(10));
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//! [wait_for]
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//! [set_callback]
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infer_request.set_callback([&](std::exception_ptr ex_ptr) {
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if (!ex_ptr) {
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// all done. Output data can be processed.
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// You can fill the input data and run inference one more time:
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infer_request.start_async();
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} else {
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// Something wrong, you can analyze exception_ptr
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}
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});
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//! [set_callback]
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//! [cancel]
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infer_request.cancel();
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//! [cancel]
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{
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//! [get_set_one_tensor]
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auto input_tensor = infer_request.get_input_tensor();
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auto output_tensor = infer_request.get_output_tensor();
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//! [get_set_one_tensor]
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}
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{
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//! [get_set_index_tensor]
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auto input_tensor = infer_request.get_input_tensor(0);
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auto output_tensor = infer_request.get_output_tensor(1);
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//! [get_set_index_tensor]
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}
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//! [get_set_tensor]
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auto tensor1 = infer_request.get_tensor("tensor_name1");
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ov::Tensor tensor2;
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infer_request.set_tensor("tensor_name2", tensor2);
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//! [get_set_tensor]
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{
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//! [get_set_tensor_by_port]
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auto input_port = model->input(0);
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auto output_port = model->output("tensor_name");
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ov::Tensor input_tensor;
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infer_request.set_tensor(input_port, input_tensor);
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auto output_tensor = infer_request.get_tensor(output_port);
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//! [get_set_tensor_by_port]
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}
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auto infer_request1 = compiled_model.create_infer_request();
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auto infer_request2 = compiled_model.create_infer_request();
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//! [cascade_models]
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auto output = infer_request1.get_output_tensor(0);
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infer_request2.set_input_tensor(0, output);
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//! [cascade_models]
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//! [roi_tensor]
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/** input_tensor points to input of a previous network and
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cropROI contains coordinates of output bounding box **/
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ov::Tensor input_tensor(ov::element::f32, ov::Shape({1, 3, 20, 20}));
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ov::Coordinate begin({0, 0, 0, 0});
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ov::Coordinate end({1, 2, 3, 3});
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//...
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/** roi_tensor uses shared memory of input_tensor and describes cropROI
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according to its coordinates **/
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ov::Tensor roi_tensor(input_tensor, begin, end);
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infer_request2.set_tensor("input_name", roi_tensor);
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//! [roi_tensor]
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{
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//! [remote_tensor]
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ov::RemoteContext context = core.get_default_context("GPU");
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auto input_port = compiled_model.input("tensor_name");
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ov::RemoteTensor remote_tensor = context.create_tensor(input_port.get_element_type(), input_port.get_shape());
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infer_request.set_tensor(input_port, remote_tensor);
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//! [remote_tensor]
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}
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return 0;
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}
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