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[JAVA] Code style check added (#1984)

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This commit is contained in:
Anna Likholat 2020-09-09 17:49:23 +03:00 committed by GitHub
parent 40fd1858a2
commit 53c03db307
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GPG Key ID: 4AEE18F83AFDEB23
31 changed files with 491 additions and 401 deletions
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33
.github/workflows/code_style.yml vendored
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@ -31,10 +31,37 @@ jobs:
if: failure()
run: |
ngraph/maint/apply-code-format.sh
git diff >code_style_diff.patch
git diff >ngraph_code_style_diff.patch
- uses: actions/upload-artifact@v2
if: failure()
with:
name: code_style_diff
path: code_style_diff.patch
name: ngraph_code_style_diff
path: ngraph_code_style_diff.patch
Java:
runs-on: ubuntu-18.04
steps:
- uses: actions/checkout@v2
- uses: actions/setup-java@v1
with:
java-version: '11'
- name: Install dependencies
run: |
wget -nc https://github.com/google/google-java-format/releases/download/google-java-format-1.9/google-java-format-1.9-all-deps.jar
- name: Check code style
run: |
java -jar google-java-format-1.9-all-deps.jar --set-exit-if-changed -a -i $(find . -type f -name "*.java")
- name: Create code style diff
if: failure()
run: |
git diff >java_code_style_diff.patch
- uses: actions/upload-artifact@v2
if: failure()
with:
name: java_code_style_diff
path: java_code_style_diff.patch

4
inference-engine/ie_bridges/java/org/intel/openvino/Blob.java
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@ -11,7 +11,7 @@ public class Blob extends IEWrapper {
}
public Blob(TensorDesc tensorDesc, byte[] data) {
super(BlobByte(tensorDesc.getNativeObjAddr(), data)) ;
super(BlobByte(tensorDesc.getNativeObjAddr(), data));
}
public Blob(TensorDesc tensorDesc, float[] data) {
@ -22,7 +22,7 @@ public class Blob extends IEWrapper {
super(BlobCArray(tensorDesc.nativeObj, cArray));
}
public TensorDesc getTensorDesc(){
public TensorDesc getTensorDesc() {
return new TensorDesc(GetTensorDesc(nativeObj));
}

10
inference-engine/ie_bridges/java/org/intel/openvino/CNNNetwork.java
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@ -8,11 +8,11 @@ public class CNNNetwork extends IEWrapper {
super(addr);
}
public String getName(){
public String getName() {
return getName(nativeObj);
}
public int getBatchSize(){
public int getBatchSize() {
return getBatchSize(nativeObj);
}
@ -20,7 +20,7 @@ public class CNNNetwork extends IEWrapper {
return GetOutputsInfo(nativeObj);
}
public Map<String, InputInfo> getInputsInfo(){
public Map<String, InputInfo> getInputsInfo() {
return GetInputsInfo(nativeObj);
}
@ -28,7 +28,7 @@ public class CNNNetwork extends IEWrapper {
reshape(nativeObj, inputShapes);
}
public Map<String, int[]> getInputShapes(){
public Map<String, int[]> getInputShapes() {
return getInputShapes(nativeObj);
}
@ -46,7 +46,7 @@ public class CNNNetwork extends IEWrapper {
private static native int getBatchSize(long addr);
private static native Map<String, InputInfo> GetInputsInfo(long addr);
private static native Map<String, Data> GetOutputsInfo(long addr);
private static native void reshape(long addr, Map<String, int[]> inputShapes);

2
inference-engine/ie_bridges/java/org/intel/openvino/Data.java
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@ -1,6 +1,6 @@
package org.intel.openvino;
public class Data extends IEWrapper{
public class Data extends IEWrapper {
protected Data(long addr) {
super(addr);

12
inference-engine/ie_bridges/java/org/intel/openvino/IECore.java
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@ -25,8 +25,10 @@ public class IECore extends IEWrapper {
return new ExecutableNetwork(LoadNetwork(nativeObj, net.getNativeObjAddr(), device));
}
public ExecutableNetwork LoadNetwork(CNNNetwork net, final String device, final Map<String, String> config) {
return new ExecutableNetwork(LoadNetwork1(nativeObj, net.getNativeObjAddr(), device, config));
public ExecutableNetwork LoadNetwork(
CNNNetwork net, final String device, final Map<String, String> config) {
long network = LoadNetwork1(nativeObj, net.getNativeObjAddr(), device, config);
return new ExecutableNetwork(network);
}
public void RegisterPlugin(String pluginName, String deviceName) {
@ -64,11 +66,13 @@ public class IECore extends IEWrapper {
/*----------------------------------- native methods -----------------------------------*/
private static native long ReadNetwork(long core, final String modelFileName);
private static native long ReadNetwork1(long core, final String modelPath, final String weightPath);
private static native long ReadNetwork1(
long core, final String modelPath, final String weightPath);
private static native long LoadNetwork(long core, long net, final String device);
private static native long LoadNetwork1(long core, long net, final String device, final Map<String, String> config);
private static native long LoadNetwork1(
long core, long net, final String device, final Map<String, String> config);
private static native void RegisterPlugin(long core, String pluginName, String deviceName);

2
inference-engine/ie_bridges/java/org/intel/openvino/IEWrapper.java
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@ -3,7 +3,7 @@ package org.intel.openvino;
public class IEWrapper {
protected final long nativeObj;
protected IEWrapper(long addr){
protected IEWrapper(long addr) {
nativeObj = addr;
}

4
inference-engine/ie_bridges/java/org/intel/openvino/InferRequest.java
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@ -28,13 +28,13 @@ public class InferRequest extends IEWrapper {
return StatusCode.valueOf(Wait(nativeObj, waitMode.getValue()));
}
public void SetCompletionCallback(Runnable runnable){
public void SetCompletionCallback(Runnable runnable) {
SetCompletionCallback(nativeObj, runnable);
}
public Map<String, InferenceEngineProfileInfo> GetPerformanceCounts() {
return GetPerformanceCounts(nativeObj);
}
}
/*----------------------------------- native methods -----------------------------------*/
private static native void Infer(long addr);

22
inference-engine/ie_bridges/java/org/intel/openvino/InferenceEngineProfileInfo.java
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@ -5,27 +5,27 @@ import java.util.Map;
public class InferenceEngineProfileInfo {
public enum LayerStatus {
NOT_RUN(0),
OPTIMIZED_OUT(1),
NOT_RUN(0),
OPTIMIZED_OUT(1),
EXECUTED(2);
private int value;
private static Map<Integer, LayerStatus> map = new HashMap<Integer, LayerStatus>();
static {
for (LayerStatus layerStatus : LayerStatus.values()) {
map.put(layerStatus.value, layerStatus);
}
}
LayerStatus(int value) {
this.value = value;
}
int getValue() {
return value;
}
static LayerStatus valueOf(int value) {
return map.get(value);
}
@ -38,7 +38,13 @@ public class InferenceEngineProfileInfo {
public String layerType;
public int executionIndex;
public InferenceEngineProfileInfo(LayerStatus status, long realTimeUSec, long cpuUSec, String execType, String layerType, int executionIndex) {
public InferenceEngineProfileInfo(
LayerStatus status,
long realTimeUSec,
long cpuUSec,
String execType,
String layerType,
int executionIndex) {
this.status = status;
this.realTimeUSec = realTimeUSec;
this.cpuUSec = cpuUSec;

8
inference-engine/ie_bridges/java/org/intel/openvino/InputInfo.java
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@ -1,6 +1,6 @@
package org.intel.openvino;
public class InputInfo extends IEWrapper{
public class InputInfo extends IEWrapper {
public InputInfo(long addr) {
super(addr);
@ -14,7 +14,7 @@ public class InputInfo extends IEWrapper{
SetLayout(nativeObj, layout.getValue());
}
public Layout getLayout(){
public Layout getLayout() {
return Layout.valueOf(getLayout(nativeObj));
}
@ -22,11 +22,11 @@ public class InputInfo extends IEWrapper{
SetPrecision(nativeObj, precision.getValue());
}
public Precision getPrecision(){
public Precision getPrecision() {
return Precision.valueOf(getPrecision(nativeObj));
}
public TensorDesc getTensorDesc(){
public TensorDesc getTensorDesc() {
return new TensorDesc(GetTensorDesc(nativeObj));
}

2
inference-engine/ie_bridges/java/org/intel/openvino/Parameter.java
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@ -20,4 +20,4 @@ public class Parameter extends IEWrapper {
@Override
protected native void delete(long nativeObj);
}
}

2
inference-engine/ie_bridges/java/org/intel/openvino/PreProcessInfo.java
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@ -1,6 +1,6 @@
package org.intel.openvino;
public class PreProcessInfo extends IEWrapper{
public class PreProcessInfo extends IEWrapper {
public PreProcessInfo(long addr) {
super(addr);

23
inference-engine/ie_bridges/java/org/intel/openvino/Precision.java
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@ -5,17 +5,17 @@ import java.util.Map;
public enum Precision {
UNSPECIFIED(255),
MIXED(0),
FP32(10),
FP16(11),
Q78(20),
I16(30),
U8(40),
I8(50),
U16(60),
I32(70),
I64(72),
BIN(71),
MIXED(0),
FP32(10),
FP16(11),
Q78(20),
I16(30),
U8(40),
I8(50),
U16(60),
I32(70),
I64(72),
BIN(71),
CUSTOM(80);
private int value;
@ -39,4 +39,3 @@ public enum Precision {
return map.get(value);
}
}

4
inference-engine/ie_bridges/java/org/intel/openvino/ResizeAlgorithm.java
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@ -1,7 +1,9 @@
package org.intel.openvino;
public enum ResizeAlgorithm {
NO_RESIZE(0), RESIZE_BILINEAR(1), RESIZE_AREA(2);
NO_RESIZE(0),
RESIZE_BILINEAR(1),
RESIZE_AREA(2);
private int value;

17
inference-engine/ie_bridges/java/org/intel/openvino/StatusCode.java
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@ -1,12 +1,21 @@
package org.intel.openvino;
import java.util.Map;
import java.util.HashMap;
import java.util.Map;
public enum StatusCode {
OK(0), GENERAL_ERROR(-1), NOT_IMPLEMENTED(-2), NETWORK_NOT_LOADED(-3),
PARAMETER_MISMATCH(-4), NOT_FOUND(-5), OUT_OF_BOUNDS(-6), UNEXPECTED(-7),
REQUEST_BUSY(-8), RESULT_NOT_READY(-9), NOT_ALLOCATED(-10), INFER_NOT_STARTED(-11),
OK(0),
GENERAL_ERROR(-1),
NOT_IMPLEMENTED(-2),
NETWORK_NOT_LOADED(-3),
PARAMETER_MISMATCH(-4),
NOT_FOUND(-5),
OUT_OF_BOUNDS(-6),
UNEXPECTED(-7),
REQUEST_BUSY(-8),
RESULT_NOT_READY(-9),
NOT_ALLOCATED(-10),
INFER_NOT_STARTED(-11),
NETWORK_NOT_READ(-12);
private int value;

10
inference-engine/ie_bridges/java/org/intel/openvino/TensorDesc.java
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@ -1,11 +1,9 @@
package org.intel.openvino;
import java.util.concurrent.BlockingDeque;
public class TensorDesc extends IEWrapper {
public TensorDesc(long addr){
super(addr);
public TensorDesc(long addr) {
super(addr);
}
public TensorDesc(Precision precision, int[] dims, Layout layout) {
@ -16,11 +14,11 @@ public class TensorDesc extends IEWrapper {
return GetDims(nativeObj);
}
public Layout getLayout(){
public Layout getLayout() {
return Layout.valueOf(getLayout(nativeObj));
}
public Precision getPrecision(){
public Precision getPrecision() {
return Precision.valueOf(getPrecision(nativeObj));
}

5
inference-engine/ie_bridges/java/org/intel/openvino/WaitMode.java
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@ -1,7 +1,8 @@
package org.intel.openvino;
public enum WaitMode {
RESULT_READY(-1), STATUS_ONLY(0);
RESULT_READY(-1),
STATUS_ONLY(0);
private int value;
@ -12,4 +13,4 @@ public enum WaitMode {
public int getValue() {
return value;
}
}
}

8
inference-engine/ie_bridges/java/samples/ArgumentParser.java
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@ -1,5 +1,5 @@
import java.util.Map;
import java.util.HashMap;
import java.util.Map;
public class ArgumentParser {
private Map<String, String> input;
@ -25,8 +25,8 @@ public class ArgumentParser {
}
public void parseArgs(String[] args) {
try{
for(int i = 0; i < args.length; i++) {
try {
for (int i = 0; i < args.length; i++) {
String arg = args[i];
if (arg.equals("--help") | arg.equals("-h")) {
printHelp();
@ -40,7 +40,7 @@ public class ArgumentParser {
}
}
}
} catch(ArrayIndexOutOfBoundsException e) {
} catch (ArrayIndexOutOfBoundsException e) {
System.out.println("Error: Incorrect number of arguments");
System.exit(0);
}

5
inference-engine/ie_bridges/java/samples/README.md
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@ -61,10 +61,7 @@ https://download.01.org/opencv/2019/open_model_zoo/R1/models_bin/face-detection-
## Build and run
Build and run steps are similar to ```benchmark_app```, but you need to add OpenCV path.
### Build
Add an environment variable with OpenCV installation or build path:
Build and run steps are similar to ```benchmark_app```, but you need to add an environment variable with OpenCV installation or build path before building:
```bash
export OpenCV_DIR=/path/to/opencv/
```

27
inference-engine/ie_bridges/java/samples/benchmark_app/InferReqWrap.java
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@ -1,21 +1,22 @@
import java.util.Map;
import org.intel.openvino.*;
import java.util.Map;
public class InferReqWrap {
public InferReqWrap(ExecutableNetwork net, int id, InferRequestsQueue irQueue) {
request = net.CreateInferRequest();
public InferReqWrap(ExecutableNetwork net, int id, InferRequestsQueue irQueue) {
request = net.CreateInferRequest();
this.id = id;
this.irQueue = irQueue;
request.SetCompletionCallback(new Runnable() {
request.SetCompletionCallback(
new Runnable() {
@Override
public void run() {
endTime = System.nanoTime();
irQueue.putIdleRequest(id, getExecutionTimeInMilliseconds());
}
});
@Override
public void run() {
endTime = System.nanoTime();
irQueue.putIdleRequest(id, getExecutionTimeInMilliseconds());
}
});
}
void startAsync() {
@ -43,9 +44,9 @@ public class InferReqWrap {
}
double getExecutionTimeInMilliseconds() {
return (double)(endTime - startTime) * 1e-6;
return (double) (endTime - startTime) * 1e-6;
}
InferRequest request;
private InferRequestsQueue irQueue;
private long startTime;

24
inference-engine/ie_bridges/java/samples/benchmark_app/InferRequestsQueue.java
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@ -1,9 +1,9 @@
import org.intel.openvino.*;
import java.util.Vector;
import java.util.concurrent.BlockingQueue;
import java.util.concurrent.LinkedBlockingQueue;
import org.intel.openvino.*;
public class InferRequestsQueue {
public InferRequestsQueue(ExecutableNetwork net, int nireq) {
for (int id = 0; id < nireq; id++) {
@ -12,17 +12,17 @@ public class InferRequestsQueue {
}
resetTimes();
}
void resetTimes() {
startTime = Long.MAX_VALUE;
endTime = Long.MIN_VALUE;
latencies.clear();
}
double getDurationInMilliseconds() {
return (double)(endTime - startTime) * 1e-6;
return (double) (endTime - startTime) * 1e-6;
}
void putIdleRequest(int id, double latency) {
latencies.add(latency);
idleIds.add(id);
@ -32,7 +32,7 @@ public class InferRequestsQueue {
foo.notify();
}
}
InferReqWrap getIdleRequest() {
try {
InferReqWrap request = requests.get(idleIds.take());
@ -43,11 +43,11 @@ public class InferRequestsQueue {
}
return null;
}
void waitAll() {
synchronized (foo) {
try {
while(idleIds.size() != requests.size()) {
while (idleIds.size() != requests.size()) {
foo.wait();
}
} catch (InterruptedException e) {
@ -55,16 +55,16 @@ public class InferRequestsQueue {
}
}
}
Vector<Double> getLatencies() {
return latencies;
}
Vector<InferReqWrap> requests = new Vector<InferReqWrap>();
private BlockingQueue<Integer> idleIds = new LinkedBlockingQueue<Integer>();
private long startTime;
private long endTime;
Vector<Double> latencies = new Vector<Double>();
Vector<Double> latencies = new Vector<Double>();
Object foo = new Object();
}

198
inference-engine/ie_bridges/java/samples/benchmark_app/Main.java
View File

@ -1,28 +1,26 @@
import java.util.Map;
import java.util.Vector;
import javax.management.RuntimeErrorException;
import java.util.Random;
import java.util.HashMap;
import java.util.LinkedList;
import java.util.ArrayList;
import java.util.Arrays;
import org.intel.openvino.*;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;
import java.util.Random;
import java.util.Vector;
public class Main {
static boolean adjustShapesBatch(Map<String, int[]> shapes, int batchSize, Map<String, InputInfo> inputInfo) {
static boolean adjustShapesBatch(
Map<String, int[]> shapes, int batchSize, Map<String, InputInfo> inputInfo) {
boolean updated = false;
for (Map.Entry<String, InputInfo> entry : inputInfo.entrySet()) {
Layout layout = entry.getValue().getTensorDesc().getLayout();
int batchIndex = -1;
if ((layout == Layout.NCHW) || (layout == Layout.NCDHW) ||
(layout == Layout.NHWC) || (layout == Layout.NDHWC) ||
(layout == Layout.NC)) {
if ((layout == Layout.NCHW)
|| (layout == Layout.NCDHW)
|| (layout == Layout.NHWC)
|| (layout == Layout.NDHWC)
|| (layout == Layout.NC)) {
batchIndex = 0;
} else if (layout == Layout.CN) {
batchIndex = 1;
@ -35,28 +33,42 @@ public class Main {
return updated;
}
static String setThroughputStreams(IECore core, Map<String, String> device_config, String device, int nstreams, boolean isAsync) {
static String setThroughputStreams(
IECore core,
Map<String, String> device_config,
String device,
int nstreams,
boolean isAsync) {
String key = device + "_THROUGHPUT_STREAMS";
if (nstreams > 0) {
device_config.put(key, Integer.toString(nstreams));
} else if (!device_config.containsKey(key) && isAsync) {
System.err.println("[ WARNING ] -nstreams default value is determined automatically for " + device + " device. " +
"Although the automatic selection usually provides a reasonable performance," +
"but it still may be non-optimal for some cases, for more information look at README.");
System.err.println(
"[ WARNING ] -nstreams default value is determined automatically for "
+ device
+ " device. Although the automatic selection usually provides a"
+ " reasonable performance,but it still may be non-optimal for some"
+ " cases, for more information look at README.");
device_config.put(key, device + "_THROUGHPUT_AUTO");
}
return device_config.get(key);
};
}
static void fillBlobs(Vector<InferReqWrap> requests, Map<String, InputInfo> inputsInfo) {
for (Map.Entry<String, InputInfo> entry : inputsInfo.entrySet()) {
String inputName = entry.getKey();
TensorDesc tDesc = entry.getValue().getTensorDesc();
System.err.print("[ INFO ] Network input '" + inputName + "' precision " + tDesc.getPrecision()
+ ", dimensions (" + tDesc.getLayout() + "): ");
for (int dim : tDesc.getDims())
System.err.print(dim + " ");
System.err.print(
"[ INFO ] Network input '"
+ inputName
+ "' precision "
+ tDesc.getPrecision()
+ ", dimensions ("
+ tDesc.getLayout()
+ "): ");
for (int dim : tDesc.getDims()) System.err.print(dim + " ");
System.err.println();
}
@ -66,7 +78,7 @@ public class Main {
String inputName = entry.getKey();
TensorDesc tDesc = entry.getValue().getTensorDesc();
request.SetBlob(inputName, blobRandomByte(tDesc));
}
}
}
}
@ -74,7 +86,7 @@ public class Main {
int dims[] = tDesc.getDims();
int size = 1;
for(int i = 0; i < dims.length; i++) {
for (int i = 0; i < dims.length; i++) {
size *= dims[i];
}
@ -87,53 +99,59 @@ public class Main {
static double getMedianValue(Vector<Double> vec) {
Object[] objArr = vec.toArray();
Double[] arr = Arrays.copyOf(objArr, objArr.length, Double[].class);
Double[] arr = Arrays.copyOf(objArr, objArr.length, Double[].class);
Arrays.sort(arr);
if (arr.length % 2 == 0)
return ((double)arr[arr.length / 2] + (double)arr[arr.length / 2 - 1]) / 2;
else
return (double)arr[arr.length / 2];
return ((double) arr[arr.length / 2] + (double) arr[arr.length / 2 - 1]) / 2;
else return (double) arr[arr.length / 2];
}
static boolean getApiBoolean(String api) throws RuntimeException {
if(api.equals("sync"))
return false;
else if(api.equals("async"))
return true;
if (api.equals("sync")) return false;
else if (api.equals("async")) return true;
else throw new RuntimeException("Incorrect argument: '-api'");
}
static int step = 0;
static void nextStep(String stepInfo) {
static void nextStep(String stepInfo) {
step += 1;
System.out.println("[Step " + step + "/11] " + stepInfo);
}
static int deviceDefaultDeviceDurationInSeconds(String device) {
final Map<String, Integer> deviceDefaultDurationInSeconds = new HashMap<String, Integer>() {{
put("CPU", 60 );
put("GPU", 60 );
put("VPU", 60 );
put("MYRIAD", 60 );
put("HDDL", 60 );
put("FPGA", 120);
put("UNKNOWN", 120);
}};
final Map<String, Integer> deviceDefaultDurationInSeconds =
new HashMap<String, Integer>() {
{
put("CPU", 60);
put("GPU", 60);
put("VPU", 60);
put("MYRIAD", 60);
put("HDDL", 60);
put("FPGA", 120);
put("UNKNOWN", 120);
}
};
Integer duration = deviceDefaultDurationInSeconds.get(device);
if (duration == null) {
duration = deviceDefaultDurationInSeconds.get("UNKNOWN");
System.err.println("[ WARNING ] Default duration " + duration + " seconds for unknown device '" + device + "' is used");
System.err.println(
"[ WARNING ] Default duration "
+ duration
+ " seconds for unknown device '"
+ device
+ "' is used");
}
return duration;
}
static long getTotalMsTime(long startTimeMilliSec) {
return (System.currentTimeMillis() - startTimeMilliSec);
};
}
static long getDurationInMilliseconds(int seconds) {
return seconds * 1000L;
@ -147,7 +165,7 @@ public class Main {
System.exit(1);
}
// ----------------- 1. Parsing and validating input arguments ---------------------------------------------
// ----------------- 1. Parsing and validating input arguments -----------------
nextStep("Parsing and validating input arguments");
ArgumentParser parser = new ArgumentParser("This is benchmarking application");
@ -169,39 +187,38 @@ public class Main {
int batchSize = parser.getInteger("-b", 0);
int nthreads = parser.getInteger("-nthreads", 0);
int nstreams = parser.getInteger("-nstreams", 0);
int timeLimit = parser.getInteger("-t",0);
int timeLimit = parser.getInteger("-t", 0);
String api = parser.get("-api", "async");
boolean isAsync;
try{
try {
isAsync = getApiBoolean(api);
} catch(RuntimeException e) {
} catch (RuntimeException e) {
System.out.println(e.getMessage());
return;
}
if(xmlPath == null) {
if (xmlPath == null) {
System.out.println("Error: Missed argument: -m");
return;
}
// ----------------- 2. Loading the Inference Engine --------------------------------------------------------
// ----------------- 2. Loading the Inference Engine --------------------------
nextStep("Loading the Inference Engine");
IECore core = new IECore();
// ----------------- 3. Setting device configuration --------------------------------------------------------
// ----------------- 3. Setting device configuration --------------------------
nextStep("Setting device configuration");
Map<String, String> device_config = new HashMap<>();
if (device.equals("CPU")) { // CPU supports few special performance-oriented keys
if (device.equals("CPU")) { // CPU supports few special performance-oriented keys
// limit threading for CPU portion of inference
if (nthreads > 0)
device_config.put("CPU_THREADS_NUM", Integer.toString(nthreads));
if (nthreads > 0) device_config.put("CPU_THREADS_NUM", Integer.toString(nthreads));
if (!device_config.containsKey("CPU_BIND_THREAD")) {
device_config.put("CPU_BIND_THREAD", "YES");
device_config.put("CPU_BIND_THREAD", "YES");
}
// for CPU execution, more throughput-oriented execution via streams
@ -212,15 +229,14 @@ public class Main {
} else if (device.equals("MYRIAD")) {
device_config.put("LOG_LEVEL", "LOG_WARNING");
} else if (device.equals("GNA")) {
device_config.put("GNA_PRECISION", "I16");
device_config.put("GNA_PRECISION", "I16");
if (nthreads > 0)
device_config.put("GNA_LIB_N_THREADS", Integer.toString(nthreads));
if (nthreads > 0) device_config.put("GNA_LIB_N_THREADS", Integer.toString(nthreads));
}
core.SetConfig(device_config, device);
// ----------------- 4. Reading the Intermediate Representation network -------------------------------------
// ----------- 4. Reading the Intermediate Representation network -------------
nextStep("Reading the Intermediate Representation network");
long startTime = System.currentTimeMillis();
@ -233,14 +249,14 @@ public class Main {
String inputName = new ArrayList<String>(inputsInfo.keySet()).get(0);
InputInfo inputInfo = inputsInfo.get(inputName);
// ----------------- 5. Resizing network to match image sizes and given batch -------------------------------
// ----- 5. Resizing network to match image sizes and given batch --------------
nextStep("Resizing network to match image sizes and given batch");
int inputBatchSize = batchSize;
batchSize = net.getBatchSize();
Map<String, int[]> shapes = net.getInputShapes();
if ((inputBatchSize != 0) && (batchSize != inputBatchSize)) {
adjustShapesBatch(shapes, batchSize, inputsInfo);
@ -252,15 +268,19 @@ public class Main {
System.err.println("[ INFO ] Reshape network took " + durationMs + " ms");
}
System.err.println((inputBatchSize != 0 ? "[ INFO ] Network batch size was changed to: " : "[ INFO ] Network batch size: ") + batchSize);
System.err.println(
(inputBatchSize != 0
? "[ INFO ] Network batch size was changed to: "
: "[ INFO ] Network batch size: ")
+ batchSize);
// ----------------- 6. Configuring input -------------------------------------------------------------------
// ----------------- 6. Configuring input -------------------------------------
nextStep("Configuring input");
inputInfo.getPreProcess().setResizeAlgorithm(ResizeAlgorithm.RESIZE_BILINEAR);
inputInfo.setPrecision(Precision.U8);
// ----------------- 7. Loading the model to the device -----------------------------------------------------
// ----------------- 7. Loading the model to the device -----------------------
nextStep("Loading the model to the device");
startTime = System.currentTimeMillis();
@ -269,11 +289,12 @@ public class Main {
System.err.println("[ INFO ] Load network took " + durationMs + " ms");
// ----------------- 8. Setting optimal runtime parameters --------------------------------------------------
// ---------------- 8. Setting optimal runtime parameters ---------------------
nextStep("Setting optimal runtime parameters");
// Update number of streams
nstreams = Integer.parseInt(core.GetConfig(device, device + "_THROUGHPUT_STREAMS").asString());
String nStr = core.GetConfig(device, device + "_THROUGHPUT_STREAMS").asString();
nstreams = Integer.parseInt(nStr);
// Number of requests
if (nireq == 0) {
@ -289,8 +310,12 @@ public class Main {
int temp = niter;
niter = ((niter + nireq - 1) / nireq) * nireq;
if (temp != niter) {
System.err.println("[ INFO ] Number of iterations was aligned by request number from " +
temp + " to " + niter + " using number of requests " + nireq);
System.err.println(
"[ INFO ] Number of iterations was aligned by request number from "
+ " to "
+ niter
+ " using number of requests "
+ nireq);
}
}
@ -304,14 +329,14 @@ public class Main {
durationSeconds = deviceDefaultDeviceDurationInSeconds(device);
}
durationMs = getDurationInMilliseconds(durationSeconds);
// ----------------- 9. Creating infer requests and filling input blobs -------------------------------------
// ---------- 9. Creating infer requests and filling input blobs ---------------
nextStep("Creating infer requests and filling input blobs");
InferRequestsQueue inferRequestsQueue = new InferRequestsQueue(executableNetwork, nireq);
fillBlobs(inferRequestsQueue.requests, inputsInfo);
// ----------------- 10. Measuring performance --------------------------------------------------------------
// ---------- 10. Measuring performance ----------------------------------------
String ss = "Start inference " + api + "ronously";
if (isAsync) {
if (!ss.isEmpty()) {
@ -352,18 +377,19 @@ public class Main {
startTime = System.currentTimeMillis();
long execTime = getTotalMsTime(startTime);
while ((niter != 0 && iteration < niter) ||
(durationMs != 0L && execTime < durationMs) ||
(isAsync && iteration % nireq != 0)) {
while ((niter != 0 && iteration < niter)
|| (durationMs != 0L && execTime < durationMs)
|| (isAsync && iteration % nireq != 0)) {
inferRequest = inferRequestsQueue.getIdleRequest();
if (isAsync) {
// As the inference request is currently idle, the wait() adds no additional overhead
//(and should return immediately).
// As the inference request is currently idle, the wait() adds no additional
// overhead (and should return immediately).
// The primary reason for calling the method is exception checking/re-throwing.
// Callback, that governs the actual execution can handle errors as well,
// but as it uses just error codes it has no details like ‘what()’ method of `std::exception`
// but as it uses just error codes it has no details like ‘what()’ method of
// `std::exception`.
// So, rechecking for any exceptions here.
inferRequest._wait();
inferRequest.startAsync();
@ -380,10 +406,12 @@ public class Main {
double latency = getMedianValue(inferRequestsQueue.getLatencies());
double totalDuration = inferRequestsQueue.getDurationInMilliseconds();
double fps = (!isAsync) ? batchSize * 1000.0 / latency :
batchSize * 1000.0 * iteration / totalDuration;
double fps =
(!isAsync)
? batchSize * 1000.0 / latency
: batchSize * 1000.0 * iteration / totalDuration;
// ----------------- 11. Dumping statistics report ----------------------------------------------------------
// ------------ 11. Dumping statistics report ----------------------------------
nextStep("Dumping statistics report");
System.out.println("Count: " + iteration + " iterations");

51
inference-engine/ie_bridges/java/samples/face_detection_java_sample/Main.java
View File

@ -1,20 +1,19 @@
import org.opencv.core.*;
import org.opencv.imgcodecs.*;
import org.opencv.highgui.HighGui;
import org.opencv.imgproc.Imgproc;
import org.intel.openvino.*;
import org.opencv.core.*;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.*;
import org.opencv.imgproc.Imgproc;
import java.util.Map;
import java.util.Set;
import java.util.ArrayList;
import java.util.Map;
/*
This is face detection java sample.
Upon the start-up the sample application reads command line parameters and loads a network
and an image to the Inference Engine device. When inference is done, the application will show
the image with detected objects enclosed in rectangles in new window.It also outputs the
confidence value and the coordinates of the rectangle to the standard output stream.
Upon the start-up the sample application reads command line parameters and loads a network
and an image to the Inference Engine device. When inference is done, the application will show
the image with detected objects enclosed in rectangles in new window.It also outputs the
confidence value and the coordinates of the rectangle to the standard output stream.
To get the list of command line parameters run the application with `--help` paramether.
*/
@ -42,24 +41,25 @@ public class Main {
String imgPath = parser.get("-i", null);
String xmlPath = parser.get("-m", null);
if(imgPath == null) {
if (imgPath == null) {
System.out.println("Error: Missed argument: -i");
return;
}
if(xmlPath == null) {
if (xmlPath == null) {
System.out.println("Error: Missed argument: -m");
return;
}
Mat image = Imgcodecs.imread(imgPath);
int[] dimsArr = {1, image.channels(), image.height(), image.width()};
TensorDesc tDesc = new TensorDesc(Precision.U8, dimsArr, Layout.NHWC);
// The source image is also used at the end of the program to display the detection results,
// therefore the Mat object won't be destroyed by Garbage Collector while the network is running.
// The source image is also used at the end of the program to display the detection results,
// therefore the Mat object won't be destroyed by Garbage Collector while the network is
// running.
Blob imgBlob = new Blob(tDesc, image.dataAddr());
IECore core = new IECore();
CNNNetwork net = core.ReadNetwork(xmlPath);
@ -77,7 +77,7 @@ public class Main {
ExecutableNetwork executableNetwork = core.LoadNetwork(net, "CPU");
InferRequest inferRequest = executableNetwork.CreateInferRequest();
inferRequest.SetBlob(inputName, imgBlob);
inferRequest.SetBlob(inputName, imgBlob);
inferRequest.Infer();
Blob output = inferRequest.GetBlob(outputName);
@ -89,27 +89,28 @@ public class Main {
for (int curProposal = 0; curProposal < maxProposalCount; curProposal++) {
int image_id = (int) detection[curProposal * 7];
if (image_id < 0)
break;
if (image_id < 0) break;
float confidence = detection[curProposal * 7 + 2];
// Drawing only objects with >70% probability
if (confidence < THRESHOLD)
continue;
if (confidence < THRESHOLD) continue;
int label = (int) (detection[curProposal * 7 + 1]);
int xmin = (int) (detection[curProposal * 7 + 3] * image.cols());
int ymin = (int) (detection[curProposal * 7 + 4] * image.rows());
int xmax = (int) (detection[curProposal * 7 + 5] * image.cols());
int ymax = (int) (detection[curProposal * 7 + 6] * image.rows());
System.out.println("[" + curProposal + "," + label + "] element, prob = " + confidence + " (" + xmin
+ "," + ymin + ")-(" + xmax + "," + ymax + ")");
String result = "[" + curProposal + "," + label + "] element, prob = " + confidence;
result += " (" + xmin + "," + ymin + ")-(" + xmax + "," + ymax + ")";
System.out.println(result);
System.out.println(" - WILL BE PRINTED!");
// Draw rectangle around detected object.
Imgproc.rectangle(image, new Point(xmin, ymin), new Point(xmax, ymax), new Scalar(0, 255, 0));
Imgproc.rectangle(
image, new Point(xmin, ymin), new Point(xmax, ymax), new Scalar(0, 255, 0));
}
HighGui.namedWindow("Detection", HighGui.WINDOW_AUTOSIZE);

238
inference-engine/ie_bridges/java/samples/face_detection_sample_async/Main.java
View File

@ -1,32 +1,30 @@
import org.intel.openvino.*;
import org.opencv.core.*;
import org.opencv.imgcodecs.*;
import org.opencv.videoio.*;
import org.opencv.imgproc.Imgproc;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.*;
import org.opencv.imgproc.Imgproc;
import org.opencv.videoio.*;
import java.util.ArrayList;
import java.util.LinkedList;
import java.util.Map;
import java.util.Queue;
import java.util.Vector;
import java.util.concurrent.BlockingQueue;
import java.util.concurrent.LinkedBlockingQueue;
import java.util.concurrent.TimeUnit;
import java.util.Map;
import java.util.Queue;
import java.util.ArrayList;
import java.util.HashMap;
import org.intel.openvino.*;
/*
This is async face detection java sample.
Upon the start-up the sample application reads command line parameters and loads a network
and an images to the Inference Engine device. When inference is done, the application
Upon the start-up the sample application reads command line parameters and loads a network
and an images to the Inference Engine device. When inference is done, the application
shows the video with detected objects enclosed in rectangles in new window.
To get the list of command line parameters run the application with `--help` paramether.
*/
public class Main {
public static Blob imageToBlob(Mat image) {
int[] dimsArr = {1, image.channels(), image.height(), image.width()};
TensorDesc tDesc = new TensorDesc(Precision.U8, dimsArr, Layout.NHWC);
@ -41,9 +39,8 @@ public class Main {
while (!startedRequestsIds.isEmpty()) {
int requestId = startedRequestsIds.peek();
InferRequest inferRequest = inferRequests.get(requestId);
if (inferRequest.Wait(wait) != StatusCode.OK)
return;
if (inferRequest.Wait(wait) != StatusCode.OK) return;
if (size == 0 && res == null) {
size = inferRequest.GetBlob(outputName).size();
@ -86,11 +83,11 @@ public class Main {
String device = parser.get("-d", "CPU");
int inferRequestsSize = parser.getInteger("-nireq", 2);
if(imgsPath == null ) {
if (imgsPath == null) {
System.out.println("Error: Missed argument: -i");
return;
}
if(xmlPath == null) {
if (xmlPath == null) {
System.out.println("Error: Missed argument: -m");
return;
}
@ -99,137 +96,145 @@ public class Main {
BlockingQueue<Mat> framesQueue = new LinkedBlockingQueue<Mat>();
Thread captureThread = new Thread(new Runnable() {
@Override
public void run() {
VideoCapture cam = new VideoCapture();
cam.open(imgsPath);
Mat frame = new Mat();
Runnable capture =
new Runnable() {
@Override
public void run() {
VideoCapture cam = new VideoCapture();
cam.open(imgsPath);
Mat frame = new Mat();
while (cam.read(frame)) {
framesCounter++;
framesQueue.add(frame.clone());
}
}
});
Thread inferThread = new Thread(new Runnable() {
@Override
public void run() {
try {
IECore core = new IECore();
CNNNetwork net = core.ReadNetwork(xmlPath);
Map<String, InputInfo> inputsInfo = net.getInputsInfo();
String inputName = new ArrayList<String>(inputsInfo.keySet()).get(0);
InputInfo inputInfo = inputsInfo.get(inputName);
inputInfo.getPreProcess().setResizeAlgorithm(ResizeAlgorithm.RESIZE_BILINEAR);
inputInfo.setLayout(Layout.NHWC);
inputInfo.setPrecision(Precision.U8);
outputName = new ArrayList<String>(net.getOutputsInfo().keySet()).get(0);
ExecutableNetwork executableNetwork = core.LoadNetwork(net, device);
asyncInferIsFree = new Vector<Boolean>(inferRequestsSize);
for (int i = 0; i < inferRequestsSize; i++) {
inferRequests.add(executableNetwork.CreateInferRequest());
asyncInferIsFree.add(true);
}
boolean isRunning = true;
while (captureThread.isAlive() || !framesQueue.isEmpty()) {
processInferRequets(WaitMode.STATUS_ONLY);
for (int i = 0; i < inferRequestsSize; i++) {
if (!asyncInferIsFree.get(i))
continue;
Mat frame = framesQueue.poll(0, TimeUnit.SECONDS);
if (frame == null)
break;
InferRequest request = inferRequests.get(i);
asyncInferIsFree.setElementAt(false, i);
processedFramesQueue.add(frame); // predictionsQueue is used in rendering
// The source frame is kept in processedFramesQueue,
// so the frame will be removed by java Garbage Collector only after completion of inference,
// and we can create Blob object using Mat object data address.
Blob imgBlob = imageToBlob(frame);
request.SetBlob(inputName, imgBlob);
startedRequestsIds.add(i);
request.StartAsync();
while (cam.read(frame)) {
framesCounter++;
framesQueue.add(frame.clone());
}
}
processInferRequets(WaitMode.RESULT_READY);
} catch (InterruptedException e) {
e.printStackTrace();
for (Thread t : Thread.getAllStackTraces().keySet())
if (t.getState()==Thread.State.RUNNABLE)
t.interrupt();
}
}
});
};
Thread captureThread = new Thread(capture);
Runnable infer =
new Runnable() {
@Override
public void run() {
try {
IECore core = new IECore();
CNNNetwork net = core.ReadNetwork(xmlPath);
Map<String, InputInfo> inputsInfo = net.getInputsInfo();
String inputName = new ArrayList<String>(inputsInfo.keySet()).get(0);
InputInfo inputInfo = inputsInfo.get(inputName);
inputInfo
.getPreProcess()
.setResizeAlgorithm(ResizeAlgorithm.RESIZE_BILINEAR);
inputInfo.setLayout(Layout.NHWC);
inputInfo.setPrecision(Precision.U8);
outputName =
new ArrayList<String>(net.getOutputsInfo().keySet()).get(0);
ExecutableNetwork execNetwork = core.LoadNetwork(net, device);
asyncInferIsFree = new Vector<Boolean>(inferRequestsSize);
for (int i = 0; i < inferRequestsSize; i++) {
inferRequests.add(execNetwork.CreateInferRequest());
asyncInferIsFree.add(true);
}
boolean isRunning = true;
while (captureThread.isAlive() || !framesQueue.isEmpty()) {
processInferRequets(WaitMode.STATUS_ONLY);
for (int i = 0; i < inferRequestsSize; i++) {
if (!asyncInferIsFree.get(i)) continue;
Mat frame = framesQueue.poll(0, TimeUnit.SECONDS);
if (frame == null) break;
InferRequest request = inferRequests.get(i);
asyncInferIsFree.setElementAt(false, i);
// processedFramesQueue is used in rendering
processedFramesQueue.add(frame);
// The source frame is kept in processedFramesQueue,
// so the frame will be removed by java Garbage
// Collector only after completion of inference,
// and we can create Blob object using Mat object data address.
Blob imgBlob = imageToBlob(frame);
request.SetBlob(inputName, imgBlob);
startedRequestsIds.add(i);
request.StartAsync();
}
}
processInferRequets(WaitMode.RESULT_READY);
} catch (InterruptedException e) {
e.printStackTrace();
for (Thread t : Thread.getAllStackTraces().keySet())
if (t.getState() == Thread.State.RUNNABLE) t.interrupt();
}
}
};
Thread inferThread = new Thread(infer);
captureThread.start();
inferThread.start();
TickMeter tm = new TickMeter();
TickMeter tm = new TickMeter();
Scalar color = new Scalar(0, 255, 0);
try {
while (inferThread.isAlive() || !detectionOutput.isEmpty()) {
float[] detection = detectionOutput.poll(waitingTime, TimeUnit.SECONDS);
if (detection == null)
continue;
Mat img = processedFramesQueue.poll(waitingTime, TimeUnit.SECONDS);
float[] detection = detectionOutput.poll(waitingTime, TimeUnit.SECONDS);
if (detection == null) continue;
Mat img = processedFramesQueue.poll(waitingTime, TimeUnit.SECONDS);
int maxProposalCount = detection.length / 7;
for (int curProposal = 0; curProposal < maxProposalCount; curProposal++) {
int imageId = (int) detection[curProposal * 7];
if (imageId < 0)
break;
if (imageId < 0) break;
float confidence = detection[curProposal * 7 + 2];
// Drawing only objects with >70% probability
if (confidence < CONFIDENCE_THRESHOLD)
continue;
if (confidence < CONFIDENCE_THRESHOLD) continue;
int label = (int) (detection[curProposal * 7 + 1]);
int xmin = (int) (detection[curProposal * 7 + 3] * img.cols());
int ymin = (int) (detection[curProposal * 7 + 4] * img.rows());
int xmax = (int) (detection[curProposal * 7 + 5] * img.cols());
int ymax = (int) (detection[curProposal * 7 + 6] * img.rows());
// Draw rectangle around detected object.
Imgproc.rectangle(img, new Point(xmin, ymin), new Point(xmax, ymax), new Scalar(0, 255, 0), 2);
Point lt = new Point(xmin, ymin);
Point br = new Point(xmax, ymax);
Imgproc.rectangle(img, lt, br, color, 2);
}
if (resultCounter == warmupNum) {
if (resultCounter == warmupNum) {
tm.start();
} else if (resultCounter > warmupNum) {
tm.stop();
double worksFps = ((double)(resultCounter - warmupNum)) / tm.getTimeSec();
double readFps = ((double)(framesCounter - warmupNum)) / tm.getTimeSec();
double worksFps = ((double) (resultCounter - warmupNum)) / tm.getTimeSec();
double readFps = ((double) (framesCounter - warmupNum)) / tm.getTimeSec();
tm.start();
Imgproc.putText(img, "Reading fps: " + String.format("%.3f", readFps), new Point(10, 50), 0 , 0.7, new Scalar(0, 255, 0), 1);
Imgproc.putText(img, "Inference fps: " + String.format("%.3f", worksFps), new Point(10, 80), 0 , 0.7, new Scalar(0, 255, 0), 1);
String label = "Reading fps: " + String.format("%.3f", readFps);
String label1 = "Inference fps: " + String.format("%.3f", worksFps);
Imgproc.putText(img, label, new Point(10, 50), 0, 0.7, color, 1);
Imgproc.putText(img, label1, new Point(10, 80), 0, 0.7, color, 1);
}
HighGui.imshow("Detection", img);
}
captureThread.join();
inferThread.join();
@ -239,8 +244,7 @@ public class Main {
} catch (InterruptedException e) {
e.printStackTrace();
for (Thread t : Thread.getAllStackTraces().keySet())
if (t.getState()==Thread.State.RUNNABLE)
t.interrupt();
if (t.getState() == Thread.State.RUNNABLE) t.interrupt();
}
}

5
inference-engine/ie_bridges/java/tests/BlobTests.java
View File

@ -1,10 +1,9 @@
import org.intel.openvino.*;
import org.junit.Assert;
import org.junit.Test;
import org.intel.openvino.*;
public class BlobTests extends IETest {
@Test
public void testGetBlob() {
int[] dimsArr = {1, 3, 200, 200};

9
inference-engine/ie_bridges/java/tests/CNNNetworkTests.java
View File

@ -1,12 +1,12 @@
import static org.junit.Assert.*;
import org.intel.openvino.*;
import org.junit.Test;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.Map;
import org.intel.openvino.*;
public class CNNNetworkTests extends IETest {
IECore core = new IECore();
@ -37,13 +37,12 @@ public class CNNNetworkTests extends IETest {
public void testAddOutput() {
CNNNetwork net = core.ReadNetwork(modelXml);
Map<String, Data> output = net.getOutputsInfo();
assertEquals("Input size", 1, output.size());
net.addOutput("19/WithoutBiases");
output = net.getOutputsInfo();
assertEquals("Input size", 2, output.size());
}
}

12
inference-engine/ie_bridges/java/tests/IECoreTests.java
View File

@ -1,14 +1,14 @@
import static org.junit.Assert.*;
import org.junit.Test;
import org.intel.openvino.*;
import org.junit.Test;
import java.util.Map;
import java.util.HashMap;
import java.util.Map;
public class IECoreTests extends IETest {
IECore core = new IECore();
@Test
public void testReadNetwork() {
CNNNetwork net = core.ReadNetwork(modelXml, modelBin);
@ -57,7 +57,7 @@ public class IECoreTests extends IETest {
Map<String, String> testMap = new HashMap<String, String>();
//When specifying key values as raw strings, omit the KEY_ prefix
// When specifying key values as raw strings, omit the KEY_ prefix
testMap.put("CPU_BIND_THREAD", "YES");
testMap.put("CPU_THREADS_NUM", "1");
@ -75,6 +75,8 @@ public class IECoreTests extends IETest {
} catch (Exception e) {
exceptionMessage = e.getMessage();
}
assertTrue(exceptionMessage.contains("Device with \"DEVISE\" name is not registered in the InferenceEngine"));
assertTrue(
exceptionMessage.contains(
"Device with \"DEVISE\" name is not registered in the InferenceEngine"));
}
}

45
inference-engine/ie_bridges/java/tests/IETest.java
View File

@ -1,15 +1,11 @@
import org.junit.Assert;
import org.junit.Before;
import org.junit.Rule;
import org.intel.openvino.*;
import org.junit.Ignore;
import org.junit.runner.Description;
import org.junit.Rule;
import org.junit.rules.TestWatcher;
import org.junit.runner.Description;
import java.nio.file.Paths;
import org.intel.openvino.*;
@Ignore
public class IETest {
String modelXml;
@ -23,20 +19,33 @@ public class IETest {
System.err.println("Failed to load Inference Engine library\n" + e);
System.exit(1);
}
modelXml = Paths.get(System.getenv("MODELS_PATH"), "models", "test_model", "test_model_fp32.xml").toString();
modelBin = Paths.get(System.getenv("MODELS_PATH"), "models", "test_model", "test_model_fp32.bin").toString();
modelXml =
Paths.get(
System.getenv("MODELS_PATH"),
"models",
"test_model",
"test_model_fp32.xml")
.toString();
modelBin =
Paths.get(
System.getenv("MODELS_PATH"),
"models",
"test_model",
"test_model_fp32.bin")
.toString();
}
@Rule
public TestWatcher watchman = new TestWatcher() {
@Override
protected void succeeded(Description description) {
System.out.println(description + " - OK");
}
public TestWatcher watchman =
new TestWatcher() {
@Override
protected void succeeded(Description description) {
System.out.println(description + " - OK");
}
@Override
protected void failed(Throwable e, Description description) {
System.out.println(description + " - FAILED");
}
};
@Override
protected void failed(Throwable e, Description description) {
System.out.println(description + " - FAILED");
}
};
}

43
inference-engine/ie_bridges/java/tests/InferRequestTests.java
View File

@ -1,13 +1,12 @@
import static org.junit.Assert.*;
import org.junit.Test;
import org.junit.Before;
import java.util.Map;
import java.util.Vector;
import java.util.ArrayList;
import org.intel.openvino.*;
import org.intel.openvino.InferenceEngineProfileInfo.LayerStatus;
import org.junit.Before;
import org.junit.Test;
import java.util.ArrayList;
import java.util.Map;
import java.util.Vector;
public class InferRequestTests extends IETest {
IECore core;
@ -59,13 +58,14 @@ public class InferRequestTests extends IETest {
ArrayList<String> resKeySet = new ArrayList<String>(res.keySet());
for (int i = 0; i < res.size(); i++) {
String key = resKeySet.get(i);
String key = resKeySet.get(i);
InferenceEngineProfileInfo resVal = res.get(key);
assertEquals(key + " execType", key, layer_name.elementAt(i));
assertEquals(key + " executionIndex", i, resVal.executionIndex);
assertTrue(resVal.status == InferenceEngineProfileInfo.LayerStatus.EXECUTED
|| resVal.status == InferenceEngineProfileInfo.LayerStatus.NOT_RUN);
assertTrue(
resVal.status == InferenceEngineProfileInfo.LayerStatus.EXECUTED
|| resVal.status == InferenceEngineProfileInfo.LayerStatus.NOT_RUN);
}
}
@ -79,20 +79,21 @@ public class InferRequestTests extends IETest {
@Test
public void testSetCompletionCallback() {
inferRequest.SetCompletionCallback(new Runnable() {
inferRequest.SetCompletionCallback(
new Runnable() {
@Override
public void run() {
completionCallback = true;
}
});
@Override
public void run() {
completionCallback = true;
}
});
for(int i = 0; i < 5; i++) {
inferRequest.Wait(WaitMode.RESULT_READY);
for (int i = 0; i < 5; i++) {
inferRequest.Wait(WaitMode.RESULT_READY);
inferRequest.StartAsync();
}
inferRequest.Wait(WaitMode.RESULT_READY);
}
inferRequest.Wait(WaitMode.RESULT_READY);
inferRequest.StartAsync();
StatusCode statusCode = inferRequest.Wait(WaitMode.RESULT_READY);

7
inference-engine/ie_bridges/java/tests/InputInfoTests.java
View File

@ -1,11 +1,11 @@
import static org.junit.Assert.*;
import org.intel.openvino.*;
import org.junit.Test;
import java.util.ArrayList;
import java.util.Map;
import org.intel.openvino.*;
public class InputInfoTests extends IETest {
IECore core = new IECore();
@ -26,12 +26,11 @@ public class InputInfoTests extends IETest {
public void testSetPrecision() {
CNNNetwork net = core.ReadNetwork(modelXml);
Map<String, InputInfo> inputsInfo = net.getInputsInfo();
String inputName = new ArrayList<String>(inputsInfo.keySet()).get(0);
InputInfo inputInfo = inputsInfo.get(inputName);
inputInfo.setPrecision(Precision.U8);
assertEquals("setPrecision", Precision.U8, inputInfo.getPrecision());
}
}

5
inference-engine/ie_bridges/java/tests/OpenVinoTestRunner.java
View File

@ -1,3 +1,4 @@
import org.intel.openvino.*;
import org.junit.runner.JUnitCore;
import org.junit.runner.Result;
import org.junit.runner.notification.Failure;
@ -11,9 +12,9 @@ public class OpenVinoTestRunner {
IETest.device = parser.get("-d", "CPU");
Result result = JUnitCore.runClasses(TestsSuite.class);
for (Failure failure : result.getFailures()) {
System.out.println(failure.toString());
System.out.println(failure.toString());
}
}
}

55
inference-engine/ie_bridges/java/tests/TestsSuite.java
View File

@ -1,40 +1,38 @@
import org.junit.runner.RunWith;
import org.junit.runners.AllTests;
import junit.framework.TestSuite;
import java.util.List;
import java.util.ArrayList;
import java.util.zip.*;
import java.nio.file.FileSystems;
import java.nio.file.Path;
import java.nio.file.Paths;
import org.intel.openvino.*;
import org.junit.runner.RunWith;
import org.junit.runners.AllTests;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.lang.Class;
import java.net.*;
import org.intel.openvino.*;
import java.util.ArrayList;
import java.util.List;
import java.util.zip.*;
@RunWith(AllTests.class)
public class TestsSuite extends IETest{
public class TestsSuite extends IETest {
public static TestSuite suite() {
TestSuite suite = new TestSuite();
try {
//get openvino_test.jar path
String dir = new File(TestsSuite.class.getProtectionDomain().getCodeSource().getLocation().toURI()).getPath().toString();
// get openvino_test.jar path
String dir =
new File(
TestsSuite.class
.getProtectionDomain()
.getCodeSource()
.getLocation()
.toURI())
.getPath()
.toString();
List<Class<?>> results = findClasses(dir);
for (Class<?> cl : results) {
if (cl.getName() == "ArgumentParser")
continue;
if (cl.getName() == "ArgumentParser") continue;
suite.addTest(new junit.framework.JUnit4TestAdapter(cl));
}
} catch (ClassNotFoundException e) {
@ -51,14 +49,19 @@ public class TestsSuite extends IETest{
ZipInputStream zip = new ZipInputStream(new FileInputStream(directory));
for (ZipEntry entry = zip.getNextEntry(); entry != null; entry = zip.getNextEntry()) {
String name = entry.getName().toString();
if (name.endsWith(".class") && !name.contains("$") && !name.contains("/")
&& !name.equals("TestsSuite.class") && !name.equals("OpenVinoTestRunner.class") && !name.equals("IETest.class")) {
classes.add(Class.forName(name.substring(0, name.length() - ".class".length())));
if (name.endsWith(".class")
&& !name.contains("$")
&& !name.contains("/")
&& !name.equals("TestsSuite.class")
&& !name.equals("OpenVinoTestRunner.class")
&& !name.equals("IETest.class")) {
classes.add(
Class.forName(name.substring(0, name.length() - ".class".length())));
}
}
} catch(FileNotFoundException e) {
} catch (FileNotFoundException e) {
System.out.println("FileNotFoundException: " + e.getMessage());
} catch(IOException e) {
} catch (IOException e) {
System.out.println("IOException: " + e.getMessage());
}
return classes;