cherrypick from 2021.2 release branch: added draft version of doc for networks with state (PR #3372) (#4072)
* added draft version of doc for networks with state (#3372)
* added draft version of doc for networks with state
* update code snippet
* update example
* review fixes
* fixed png
* review fixes
* image fix
* minor fix
* LowLatency documentation
* apply review comments
* Apply suggestions from code review
Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com>
* review fixes
Co-authored-by: Ivan Tikhonov <ivan.tikhonov@intel.com>
Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com>
(cherry picked from commit 99c746f2c3)
* take latest changes from 2021.2
This commit is contained in:
Svetlana Dolinina
GitHub
parent
192e66e013
commit
5c4e862257
3
docs/IE_DG/img/applying_low_latency.png
Executable file
3
docs/IE_DG/img/applying_low_latency.png
Executable file
@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:f4f6e9d35869fa2c414e58914aaec1607eb7d4768b69c0cbcce5d5fa3ceddba3
|
||||||
|
size 56444
|
||||||
3
docs/IE_DG/img/low_latency_limitation_1.png
Executable file
3
docs/IE_DG/img/low_latency_limitation_1.png
Executable file
@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:28f4e7ee50785e9c571725942e67c899d08e87af3802f6bea4721c64bfdb2bac
|
||||||
|
size 21722
|
||||||
3
docs/IE_DG/img/low_latency_limitation_2.png
Executable file
3
docs/IE_DG/img/low_latency_limitation_2.png
Executable file
@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:0923af3acfb69dd0b88a5edf097e60c2655828b643d8e328561b13b0196c0850
|
||||||
|
size 47997
|
||||||
3
docs/IE_DG/img/state_network_example.png
Normal file
3
docs/IE_DG/img/state_network_example.png
Normal file
@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:9976341ca931f3ab4e4fbccea26844b738adb27b091149a4c6231eda841ab867
|
||||||
|
size 144541
|
||||||
275
docs/IE_DG/network_state_intro.md
Normal file
275
docs/IE_DG/network_state_intro.md
Normal file
@ -0,0 +1,275 @@
|
|||||||
|
Introduction to OpenVINO state API {#openvino_docs_IE_DG_network_state_intro}
|
||||||
|
==============================
|
||||||
|
|
||||||
|
This section describes how to work with stateful networks in OpenVINO toolkit, specifically:
|
||||||
|
* How stateful networks are represented in IR and nGraph
|
||||||
|
* How operations with state can be done
|
||||||
|
|
||||||
|
The section additionally provides small examples of stateful network and code to infer it.
|
||||||
|
|
||||||
|
## What is a stateful network
|
||||||
|
|
||||||
|
Several use cases require processing of data sequences. When length of a sequence is known and small enough,
|
||||||
|
we can process it with RNN like networks that contain a cycle inside. But in some cases, like online speech recognition of time series
|
||||||
|
forecasting, length of data sequence is unknown. Then data can be divided in small portions and processed step-by-step. But dependency
|
||||||
|
between data portions should be addressed. For that, networks save some data between inferences - state. When one dependent sequence is over,
|
||||||
|
state should be reset to initial value and new sequence can be started.
|
||||||
|
|
||||||
|
Several frameworks have special API for states in networks. For example, Keras have special option for RNNs `stateful` that turns on saving state
|
||||||
|
between inferences. Kaldi contains special specifier `Offset` to define time offset in a network.
|
||||||
|
|
||||||
|
OpenVINO also contains special API to simplify work with networks with states. State is automatically saved between inferences,
|
||||||
|
and there is a way to reset state when needed. You can also read state or set it to some new value between inferences.
|
||||||
|
|
||||||
|
## OpenVINO state representation
|
||||||
|
|
||||||
|
OpenVINO contains special abstraction variable to represent state in a network. There are two operations to work with state:
|
||||||
|
* `Assign` to save value in state
|
||||||
|
* `ReadValue` to read value saved on previous iteration
|
||||||
|
|
||||||
|
You can find more details on these operations in [ReadValue specification](../ops/infrastructure/ReadValue_3.md) and
|
||||||
|
[Assign specification](../ops/infrastructure/Assign_3.md).
|
||||||
|
|
||||||
|
## Examples of representation of a network with states
|
||||||
|
|
||||||
|
To get a model with states ready for inference, you can convert a model from another framework to IR with Model Optimizer or create an nGraph function
|
||||||
|
(details can be found in [Build nGraph Function section](../nGraph_DG/build_function.md)).
|
||||||
|
Let's represent the following graph in both forms:
|
||||||
|
![state_network_example]
|
||||||
|
|
||||||
|
### Example of IR with state
|
||||||
|
|
||||||
|
The `bin` file for this graph should contain float 0 in binary form. Content of `xml` is the following.
|
||||||
|
|
||||||
|
```xml
|
||||||
|
<?xml version="1.0" ?>
|
||||||
|
<net name="summator" version="10">
|
||||||
|
<layers>
|
||||||
|
<layer id="0" name="init_value" type="Const" version="opset5">
|
||||||
|
<data element_type="f32" offset="0" shape="1,1" size="4"/>
|
||||||
|
<output>
|
||||||
|
<port id="1" precision="FP32">
|
||||||
|
<dim>1</dim>
|
||||||
|
<dim>1</dim>
|
||||||
|
</port>
|
||||||
|
</output>
|
||||||
|
</layer>
|
||||||
|
<layer id="1" name="read" type="ReadValue" version="opset5">
|
||||||
|
<data variable_id="id"/>
|
||||||
|
<input>
|
||||||
|
<port id="0">
|
||||||
|
<dim>1</dim>
|
||||||
|
<dim>1</dim>
|
||||||
|
</port>
|
||||||
|
</input>
|
||||||
|
<output>
|
||||||
|
<port id="1" precision="FP32">
|
||||||
|
<dim>1</dim>
|
||||||
|
<dim>1</dim>
|
||||||
|
</port>
|
||||||
|
</output>
|
||||||
|
</layer>
|
||||||
|
<layer id="2" name="input" type="Parameter" version="opset5">
|
||||||
|
<data element_type="f32" shape="1,1"/>
|
||||||
|
<output>
|
||||||
|
<port id="0" precision="FP32">
|
||||||
|
<dim>1</dim>
|
||||||
|
<dim>1</dim>
|
||||||
|
</port>
|
||||||
|
</output>
|
||||||
|
</layer>
|
||||||
|
<layer id="3" name="add_sum" type="Add" version="opset5">
|
||||||
|
<input>
|
||||||
|
<port id="0">
|
||||||
|
<dim>1</dim>
|
||||||
|
<dim>1</dim>
|
||||||
|
</port>
|
||||||
|
<port id="1">
|
||||||
|
<dim>1</dim>
|
||||||
|
<dim>1</dim>
|
||||||
|
</port>
|
||||||
|
</input>
|
||||||
|
<output>
|
||||||
|
<port id="2" precision="FP32">
|
||||||
|
<dim>1</dim>
|
||||||
|
<dim>1</dim>
|
||||||
|
</port>
|
||||||
|
</output>
|
||||||
|
</layer>
|
||||||
|
<layer id="4" name="save" type="Assign" version="opset5">
|
||||||
|
<data variable_id="id"/>
|
||||||
|
<input>
|
||||||
|
<port id="0">
|
||||||
|
<dim>1</dim>
|
||||||
|
<dim>1</dim>
|
||||||
|
</port>
|
||||||
|
</input>
|
||||||
|
</layer>
|
||||||
|
<layer id="10" name="add" type="Add" version="opset5">
|
||||||
|
<data axis="1"/>
|
||||||
|
<input>
|
||||||
|
<port id="0">
|
||||||
|
<dim>1</dim>
|
||||||
|
<dim>1</dim>
|
||||||
|
</port>
|
||||||
|
<port id="1">
|
||||||
|
<dim>1</dim>
|
||||||
|
<dim>1</dim>
|
||||||
|
</port>
|
||||||
|
</input>
|
||||||
|
<output>
|
||||||
|
<port id="2" precision="FP32">
|
||||||
|
<dim>1</dim>
|
||||||
|
<dim>1</dim>
|
||||||
|
</port>
|
||||||
|
</output>
|
||||||
|
</layer>
|
||||||
|
<layer id="5" name="output/sink_port_0" type="Result" version="opset5">
|
||||||
|
<input>
|
||||||
|
<port id="0">
|
||||||
|
<dim>1</dim>
|
||||||
|
<dim>1</dim>
|
||||||
|
</port>
|
||||||
|
</input>
|
||||||
|
</layer>
|
||||||
|
</layers>
|
||||||
|
<edges>
|
||||||
|
<edge from-layer="0" from-port="1" to-layer="1" to-port="0"/>
|
||||||
|
<edge from-layer="2" from-port="0" to-layer="3" to-port="1"/>
|
||||||
|
<edge from-layer="1" from-port="1" to-layer="3" to-port="0"/>
|
||||||
|
<edge from-layer="3" from-port="2" to-layer="4" to-port="0"/>
|
||||||
|
<edge from-layer="3" from-port="2" to-layer="10" to-port="0"/>
|
||||||
|
<edge from-layer="1" from-port="1" to-layer="10" to-port="1"/>
|
||||||
|
<edge from-layer="10" from-port="2" to-layer="5" to-port="0"/>
|
||||||
|
</edges>
|
||||||
|
<meta_data>
|
||||||
|
<MO_version value="unknown version"/>
|
||||||
|
<cli_parameters>
|
||||||
|
</cli_parameters>
|
||||||
|
</meta_data>
|
||||||
|
</net>
|
||||||
|
```
|
||||||
|
|
||||||
|
### Example of creating model nGraph API
|
||||||
|
|
||||||
|
```cpp
|
||||||
|
auto arg = make_shared<op::Parameter>(element::f32, Shape{1, 1});
|
||||||
|
auto init_const = op::Constant::create(element::f32, Shape{1, 1}, {0});
|
||||||
|
auto read = make_shared<op::ReadValue>(init_const, "v0");
|
||||||
|
std::vector<shared_ptr<Node>> args = {arg, read};
|
||||||
|
auto add = make_shared<op::Add>(arg, read);
|
||||||
|
auto assign = make_shared<op::Assign>(add, "v0");
|
||||||
|
auto add2 = make_shared<op::Add>(add, read);
|
||||||
|
auto res = make_shared<op::Result>(add2);
|
||||||
|
|
||||||
|
auto f = make_shared<Function>(ResultVector({res}), ParameterVector({arg}), SinkVector({assign}));
|
||||||
|
```
|
||||||
|
|
||||||
|
In this example, `SinkVector` is used to create `ngraph::Function`. For network with states, except inputs and outputs, `Assign` nodes should also point to `Function`
|
||||||
|
to avoid deleting it during graph transformations. You can do it with the constructor, as shown in the example, or with the special method `add_sinks(const SinkVector& sinks)`. Also you can delete
|
||||||
|
sink from `ngraph::Function` after deleting the node from graph with the `delete_sink()` method.
|
||||||
|
|
||||||
|
## OpenVINO state API
|
||||||
|
|
||||||
|
Inference Engine has the `InferRequest::QueryState` method to get the list of states from a network and `IVariableState` interface to operate with states. Below you can find brief description of methods and the workable example of how to use this interface.
|
||||||
|
is below and next section contains small workable example how this interface can be used.
|
||||||
|
|
||||||
|
* `std::string GetName() const`
|
||||||
|
returns name(variable_id) of according Variable
|
||||||
|
* `void Reset()`
|
||||||
|
reset state to default value
|
||||||
|
* `void SetState(Blob::Ptr newState)`
|
||||||
|
set new value for state
|
||||||
|
* `Blob::CPtr GetState() const`
|
||||||
|
returns current value of state
|
||||||
|
|
||||||
|
## Example of stateful network inference
|
||||||
|
|
||||||
|
Let's take an IR from the previous section example. The example below demonstrates inference of two independent sequences of data. State should be reset between these sequences.
|
||||||
|
|
||||||
|
One infer request and one thread
|
||||||
|
will be used in this example. Using several threads is possible if you have several independent sequences. Then each sequence can be processed in its own infer
|
||||||
|
request. Inference of one sequence in several infer requests is not recommended. In one infer request state will be saved automatically between inferences, but
|
||||||
|
if the first step is done in one infer request and the second in another, state should be set in new infer request manually (using `IVariableState::SetState` method).
|
||||||
|
|
||||||
|
@snippet openvino/docs/snippets/InferenceEngine_network_with_state_infer.cpp part1
|
||||||
|
|
||||||
|
You can find more powerful examples demonstrating how to work with networks with states in speech sample and demo.
|
||||||
|
Decsriptions can be found in [Samples Overview](./Samples_Overview.md)
|
||||||
|
|
||||||
|
[state_network_example]: ./img/state_network_example.png
|
||||||
|
|
||||||
|
|
||||||
|
## LowLatency transformation
|
||||||
|
|
||||||
|
If the original framework does not have a special API for working with states, after importing the model, OpenVINO representation will not contain Assign/ReadValue layers. For example, if the original ONNX model contains RNN operations, IR will contain TensorIterator operations and the values will be obtained only after the execution of whole TensorIterator primitive, intermediate values from each iteration will not be available. To be able to work with these intermediate values of each iteration and receive them with a low latency after each infer request, a special LowLatency transformation was introduced.
|
||||||
|
|
||||||
|
LowLatency transformation changes the structure of the network containing [TensorIterator](../ops/infrastructure/TensorIterator_1.md) by adding the ability to work with state, inserting Assign/ReadValue layers as it is shown in the picture below.
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
### Steps to apply LowLatency transformation
|
||||||
|
|
||||||
|
1. Get CNNNetwork. Any way is acceptable:
|
||||||
|
|
||||||
|
* [from IR or ONNX model](Integrate_with_customer_application_new_API.md#integration-steps)
|
||||||
|
* [from nGraph Function](../nGraph_DG/build_function.md)
|
||||||
|
|
||||||
|
2. [Reshape](ShapeInference) CNNNetwork network if necessary
|
||||||
|
**Necessary case:** the sequence_lengths dimention of input > 1, it means TensorIterator layer will have number_iterations > 1. We should reshape the inputs of the network to set sequence_dimension exactly to 1.
|
||||||
|
```cpp
|
||||||
|
|
||||||
|
// Network before reshape: Parameter (name: X, shape: [2 (sequence_lengths), 1, 16]) -> TensorIterator (num_iteration = 2, axis = 0) -> ...
|
||||||
|
|
||||||
|
cnnNetwork.reshape({"X" : {1, 1, 16});
|
||||||
|
|
||||||
|
// Network after reshape: Parameter (name: X, shape: [1 (sequence_lengths), 1, 16]) -> TensorIterator (num_iteration = 1, axis = 0) -> ...
|
||||||
|
|
||||||
|
```
|
||||||
|
|
||||||
|
3. Apply LowLatency transformation
|
||||||
|
```cpp
|
||||||
|
#include "ie_transformations.hpp"
|
||||||
|
|
||||||
|
...
|
||||||
|
|
||||||
|
InferenceEngine::LowLatency(cnnNetwork);
|
||||||
|
```
|
||||||
|
**State naming rule:** a name of state is a concatenation of names: original TensorIterator operation, Parameter of the body, and additional suffix "variable_" + id (0-base indexing, new indexing for each TensorIterator), for example:
|
||||||
|
```
|
||||||
|
tensor_iterator_name = "TI_name"
|
||||||
|
body_parameter_name = "param_name"
|
||||||
|
|
||||||
|
state_name = "TI_name/param_name/variable_0"
|
||||||
|
```
|
||||||
|
4. [Use state API](#openvino-state-api)
|
||||||
|
|
||||||
|
|
||||||
|
### Known limitations
|
||||||
|
1. Parameters are directly connected to States (ReadValues).
|
||||||
|
|
||||||
|
Removing Parameters from `ngraph::Function` is not possible.
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
**Current solution:** replace Parameter with Constant (freeze) with the value [0, 0, 0 … 0] via [ModelOptimizer CLI](../MO_DG/prepare_model/convert_model/Converting_Model_General.md) `--input` or `--freeze_placeholder_with_value`.
|
||||||
|
|
||||||
|
2. Non-reshapable network.
|
||||||
|
|
||||||
|
Value of shapes is hard-coded somewhere in the network.
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
**Current solution:** trim non-reshapable layers via [ModelOptimizer CLI](../MO_DG/prepare_model/convert_model/Converting_Model_General.md) `--input`, `--output` or via nGraph.
|
||||||
|
|
||||||
|
```cpp
|
||||||
|
// nGraph example:
|
||||||
|
auto func = cnnNetwork.getFunction();
|
||||||
|
auto new_const = std::make_shared<ngraph::opset5::Constant>(); // type, shape, value
|
||||||
|
for (const auto& node : func->get_ops()) {
|
||||||
|
if (node->get_friendly_name() == "name_of_non_reshapable_const") {
|
||||||
|
auto bad_const = std::dynamic_pointer_cast<ngraph::opset5::Constant>(node);
|
||||||
|
ngraph::replace_node(bad_const, new_const); // replace constant
|
||||||
|
}
|
||||||
|
}
|
||||||
|
```
|
||||||
@ -286,6 +286,7 @@ limitations under the License.
|
|||||||
<tab type="usergroup" title="Utilities to Validate Your Converted Model" url="@ref openvino_inference_engine_tools_cross_check_tool_README">
|
<tab type="usergroup" title="Utilities to Validate Your Converted Model" url="@ref openvino_inference_engine_tools_cross_check_tool_README">
|
||||||
<tab type="user" title="Using Cross Check Tool for Per-Layer Comparison Between Plugins" url="@ref openvino_inference_engine_tools_cross_check_tool_README"/>
|
<tab type="user" title="Using Cross Check Tool for Per-Layer Comparison Between Plugins" url="@ref openvino_inference_engine_tools_cross_check_tool_README"/>
|
||||||
</tab>
|
</tab>
|
||||||
|
<tab type="user" title="Introduction to OpenVINO state API" url="@ref openvino_docs_IE_DG_network_state_intro"/>
|
||||||
<tab type="usergroup" title="Supported Devices" url="@ref openvino_docs_IE_DG_supported_plugins_Supported_Devices">
|
<tab type="usergroup" title="Supported Devices" url="@ref openvino_docs_IE_DG_supported_plugins_Supported_Devices">
|
||||||
<tab type="usergroup" title="GPU Plugin" url="@ref openvino_docs_IE_DG_supported_plugins_CL_DNN">
|
<tab type="usergroup" title="GPU Plugin" url="@ref openvino_docs_IE_DG_supported_plugins_CL_DNN">
|
||||||
<tab type="user" title="RemoteBlob API of GPU Plugin" url="@ref openvino_docs_IE_DG_supported_plugins_GPU_RemoteBlob_API"/>
|
<tab type="user" title="RemoteBlob API of GPU Plugin" url="@ref openvino_docs_IE_DG_supported_plugins_GPU_RemoteBlob_API"/>
|
||||||
|
|||||||
109
docs/snippets/InferenceEngine_network_with_state_infer.cpp
Normal file
109
docs/snippets/InferenceEngine_network_with_state_infer.cpp
Normal file
@ -0,0 +1,109 @@
|
|||||||
|
// Copyright (C) 2018-2020 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#include <iostream>
|
||||||
|
#include <inference_engine.hpp>
|
||||||
|
|
||||||
|
using namespace InferenceEngine;
|
||||||
|
|
||||||
|
int main(int argc, char *argv[]) {
|
||||||
|
try {
|
||||||
|
// --------------------------- 1. Load inference engine -------------------------------------
|
||||||
|
std::cout << "Loading Inference Engine" << std::endl;
|
||||||
|
Core ie;
|
||||||
|
|
||||||
|
// 2. Read a model in OpenVINO Intermediate Representation (.xml and .bin files) or ONNX (.onnx file) format
|
||||||
|
std::cout << "Loading network files" << std::endl;
|
||||||
|
CNNNetwork network;
|
||||||
|
network = ie.ReadNetwork(std::string("c:\\work\\git\\github_dldt3\\openvino\\model-optimizer\\summator.xml"));
|
||||||
|
network.setBatchSize(1);
|
||||||
|
|
||||||
|
// 3. Load network to CPU
|
||||||
|
ExecutableNetwork executableNet = ie.LoadNetwork(network, "CPU");
|
||||||
|
// 4. Create Infer Request
|
||||||
|
InferRequest inferRequest = executableNet.CreateInferRequest();
|
||||||
|
|
||||||
|
// 5. Prepare inputs
|
||||||
|
ConstInputsDataMap cInputInfo = executableNet.GetInputsInfo();
|
||||||
|
std::vector<Blob::Ptr> ptrInputBlobs;
|
||||||
|
for (const auto& input : cInputInfo) {
|
||||||
|
ptrInputBlobs.push_back(inferRequest.GetBlob(input.first));
|
||||||
|
}
|
||||||
|
InputsDataMap inputInfo;
|
||||||
|
inputInfo = network.getInputsInfo();
|
||||||
|
for (auto &item : inputInfo) {
|
||||||
|
Precision inputPrecision = Precision::FP32;
|
||||||
|
item.second->setPrecision(inputPrecision);
|
||||||
|
}
|
||||||
|
|
||||||
|
// 6. Prepare outputs
|
||||||
|
std::vector<Blob::Ptr> ptrOutputBlobs;
|
||||||
|
ConstOutputsDataMap cOutputInfo = executableNet.GetOutputsInfo();
|
||||||
|
for (const auto& output : cOutputInfo) {
|
||||||
|
ptrOutputBlobs.push_back(inferRequest.GetBlob(output.first));
|
||||||
|
}
|
||||||
|
|
||||||
|
// 7. Initialize memory state before starting
|
||||||
|
for (auto &&state : inferRequest.QueryState()) {
|
||||||
|
state.Reset();
|
||||||
|
}
|
||||||
|
|
||||||
|
//! [part1]
|
||||||
|
// input data
|
||||||
|
std::vector<float> data = { 1,2,3,4,5,6};
|
||||||
|
// infer the first utterance
|
||||||
|
for (size_t next_input = 0; next_input < data.size()/2; next_input++) {
|
||||||
|
MemoryBlob::Ptr minput = as<MemoryBlob>(ptrInputBlobs[0]);
|
||||||
|
auto minputHolder = minput->wmap();
|
||||||
|
|
||||||
|
std::memcpy(minputHolder.as<void *>(),
|
||||||
|
&data[next_input],
|
||||||
|
sizeof(float));
|
||||||
|
|
||||||
|
inferRequest.Infer();
|
||||||
|
// check states
|
||||||
|
auto states = inferRequest.QueryState();
|
||||||
|
auto mstate = as<MemoryBlob>(states[0].GetState());
|
||||||
|
auto state_buf = mstate->rmap();
|
||||||
|
float * state =state_buf.as<float*>();
|
||||||
|
std::cout << state[0] << "\n";
|
||||||
|
}
|
||||||
|
|
||||||
|
// resetting state between utterances
|
||||||
|
std::cout<<"Reset state\n";
|
||||||
|
for (auto &&state : inferRequest.QueryState()) {
|
||||||
|
state.Reset();
|
||||||
|
}
|
||||||
|
|
||||||
|
// infer the second utterance
|
||||||
|
for (size_t next_input = data.size()/2; next_input < data.size(); next_input++) {
|
||||||
|
MemoryBlob::Ptr minput = as<MemoryBlob>(ptrInputBlobs[0]);
|
||||||
|
auto minputHolder = minput->wmap();
|
||||||
|
|
||||||
|
std::memcpy(minputHolder.as<void *>(),
|
||||||
|
&data[next_input],
|
||||||
|
sizeof(float));
|
||||||
|
|
||||||
|
inferRequest.Infer();
|
||||||
|
// check states
|
||||||
|
auto states = inferRequest.QueryState();
|
||||||
|
auto mstate = as<MemoryBlob>(states[0].GetState());
|
||||||
|
auto state_buf = mstate->rmap();
|
||||||
|
float * state =state_buf.as<float*>();
|
||||||
|
std::cout << state[0] << "\n";
|
||||||
|
}
|
||||||
|
//! [part1]
|
||||||
|
}
|
||||||
|
catch (const std::exception &error) {
|
||||||
|
std::cerr << error.what() << std::endl;
|
||||||
|
return 1;
|
||||||
|
}
|
||||||
|
catch (...) {
|
||||||
|
std::cerr << "Unknown/internal exception happened" << std::endl;
|
||||||
|
return 1;
|
||||||
|
}
|
||||||
|
|
||||||
|
std::cerr << "Execution successful" << std::endl;
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
Loading…
Reference in New Issue
Block a user