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[Android] class for ml-service
Add new class and interface for ml-service api. Signed-off-by: Jaeyun Jung <[email protected]>
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‎java/android/nnstreamer/src/androidTest/assets/README.txt

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@@ -3,6 +3,9 @@ If you want to do unit tests, put the following files in this directory.
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$ tree .
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.
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└── nnstreamer
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├── config
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│   ├── config_pipeline_imgclf.conf
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│   └── config_single_imgclf.conf
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├── pytorch_data
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│   ├── mobilenetv2-quant_core-nnapi.pt
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│   └── orange_float.raw

‎java/android/nnstreamer/src/androidTest/java/org/nnsuite/nnstreamer/APITestCommon.java

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@@ -119,6 +119,20 @@ public static Context getContext() {
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return InstrumentationRegistry.getTargetContext();
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}
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/**
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* Gets the path string of configurations.
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*/
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public static String getConfigPath() {
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String root = getRootDirectory();
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File config = new File(root + "/nnstreamer/config");
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if (!config.exists()) {
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fail();
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}
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return config.getAbsolutePath();
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}
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/**
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* Gets the File object of tensorflow-lite model.
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* Note that, to invoke model in the storage, the permission READ_EXTERNAL_STORAGE is required.

‎java/android/nnstreamer/src/androidTest/java/org/nnsuite/nnstreamer/APITestMLService.java

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package org.nnsuite.nnstreamer;
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import android.support.test.runner.AndroidJUnit4;
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import org.junit.Before;
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import org.junit.Test;
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import org.junit.runner.RunWith;
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import java.nio.ByteBuffer;
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import static org.junit.Assert.*;
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/**
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* Testcases for MLService.
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*/
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@RunWith(AndroidJUnit4.class)
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public class APITestMLService {
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private int mReceived = 0;
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private boolean mInvalidState = false;
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private boolean mIsPipeline = false;
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/**
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* The event callback for image classification model.
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*/
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private MLService.NewEventCallback mEventCb = new MLService.NewEventCallback() {
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@Override
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public void onNewDataReceived(String name, TensorsData data) {
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if (mIsPipeline) {
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if (name == null || !name.equals("result_clf")) {
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mInvalidState = true;
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return;
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}
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}
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if (data == null || data.getTensorsCount() != 1) {
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mInvalidState = true;
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return;
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}
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ByteBuffer buffer = data.getTensorData(0);
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int labelIndex = APITestCommon.getMaxScore(buffer);
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/* check label index (orange) */
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if (labelIndex != 951) {
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mInvalidState = true;
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}
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mReceived++;
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}
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};
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@Before
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public void setUp() {
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APITestCommon.initNNStreamer();
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}
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@Test
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public void testNullConfig_n() {
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try {
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new MLService(null, mEventCb);
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fail();
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} catch (Exception e) {
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/* expected */
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}
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}
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@Test
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public void testEmptyConfig_n() {
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try {
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new MLService("", mEventCb);
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fail();
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} catch (Exception e) {
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/* expected */
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}
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}
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@Test
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public void testInvalidConfig_n() {
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try {
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String config = APITestCommon.getConfigPath() + "/config_invalid.conf";
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new MLService(config, mEventCb);
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fail();
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} catch (Exception e) {
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/* expected */
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}
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}
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@Test
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public void testNullCallback_n() {
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try {
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String config = APITestCommon.getConfigPath() + "/config_single_imgclf.conf";
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new MLService(config, null);
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fail();
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} catch (Exception e) {
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/* expected */
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}
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}
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@Test
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public void testInputNullData_n() {
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try {
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String config = APITestCommon.getConfigPath() + "/config_pipeline_imgclf.conf";
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MLService service = new MLService(config, mEventCb);
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service.start();
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service.inputData("input_img", null);
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fail();
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} catch (Exception e) {
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/* expected */
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}
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}
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@Test
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public void testInputInvalidNode_n() {
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try {
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String config = APITestCommon.getConfigPath() + "/config_pipeline_imgclf.conf";
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MLService service = new MLService(config, mEventCb);
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service.start();
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service.inputData("invalid_node", APITestCommon.readRawImageData());
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fail();
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} catch (Exception e) {
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/* expected */
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}
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}
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@Test
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public void testGetInputInfo() {
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try {
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String config = APITestCommon.getConfigPath() + "/config_pipeline_imgclf.conf";
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MLService service = new MLService(config, mEventCb);
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TensorsInfo info = service.getInputInformation("input_img");
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assertEquals(1, info.getTensorsCount());
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assertEquals(NNStreamer.TensorType.UINT8, info.getTensorType(0));
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assertArrayEquals(new int[]{3,224,224,1}, info.getTensorDimension(0));
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service.close();
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} catch (Exception e) {
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fail();
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}
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}
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@Test
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public void testGetInputInfoInvalidNode_n() {
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try {
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String config = APITestCommon.getConfigPath() + "/config_pipeline_imgclf.conf";
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MLService service = new MLService(config, mEventCb);
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service.getInputInformation("invalid_node");
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fail();
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} catch (Exception e) {
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/* expected */
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}
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}
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@Test
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public void testGetOutputInfo() {
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try {
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String config = APITestCommon.getConfigPath() + "/config_pipeline_imgclf.conf";
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MLService service = new MLService(config, mEventCb);
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TensorsInfo info = service.getOutputInformation("result_clf");
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assertEquals(1, info.getTensorsCount());
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assertEquals(NNStreamer.TensorType.UINT8, info.getTensorType(0));
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assertArrayEquals(new int[]{1001,1}, info.getTensorDimension(0));
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service.close();
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} catch (Exception e) {
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fail();
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}
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}
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@Test
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public void testGetOutputInfoInvalidNode_n() {
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try {
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String config = APITestCommon.getConfigPath() + "/config_pipeline_imgclf.conf";
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MLService service = new MLService(config, mEventCb);
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service.getOutputInformation("invalid_node");
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fail();
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} catch (Exception e) {
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/* expected */
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}
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}
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@Test
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public void testSetInfoNullName_n() {
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try {
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String config = APITestCommon.getConfigPath() + "/config_pipeline_imgclf.conf";
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MLService service = new MLService(config, mEventCb);
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service.setInformation(null, "test_value");
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fail();
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} catch (Exception e) {
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/* expected */
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}
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}
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@Test
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public void testSetInfoEmptyName_n() {
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try {
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String config = APITestCommon.getConfigPath() + "/config_pipeline_imgclf.conf";
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MLService service = new MLService(config, mEventCb);
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service.setInformation("", "test_value");
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fail();
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} catch (Exception e) {
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/* expected */
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}
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}
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@Test
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public void testSetInfoNullValue_n() {
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try {
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String config = APITestCommon.getConfigPath() + "/config_pipeline_imgclf.conf";
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MLService service = new MLService(config, mEventCb);
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service.setInformation("test_info", null);
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fail();
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} catch (Exception e) {
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/* expected */
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}
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}
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@Test
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public void testSetInfoEmptyValue_n() {
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try {
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String config = APITestCommon.getConfigPath() + "/config_pipeline_imgclf.conf";
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MLService service = new MLService(config, mEventCb);
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service.setInformation("test_info", "");
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fail();
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} catch (Exception e) {
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/* expected */
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}
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}
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@Test
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public void testGetInfoNullName_n() {
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try {
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String config = APITestCommon.getConfigPath() + "/config_pipeline_imgclf.conf";
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MLService service = new MLService(config, mEventCb);
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service.getInformation(null);
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fail();
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} catch (Exception e) {
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/* expected */
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}
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}
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@Test
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public void testGetInfoEmptyName_n() {
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try {
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String config = APITestCommon.getConfigPath() + "/config_pipeline_imgclf.conf";
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MLService service = new MLService(config, mEventCb);
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service.getInformation("");
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fail();
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} catch (Exception e) {
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/* expected */
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}
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}
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@Test
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public void testGetInfoInvalidName_n() {
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try {
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String config = APITestCommon.getConfigPath() + "/config_pipeline_imgclf.conf";
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MLService service = new MLService(config, mEventCb);
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service.getInformation("invalid_name");
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fail();
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} catch (Exception e) {
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/* expected */
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}
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}
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@Test
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public void testGetInfo() {
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try {
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String config = APITestCommon.getConfigPath() + "/config_single_imgclf.conf";
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MLService service = new MLService(config, mEventCb);
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service.setInformation("test_info", "test_value");
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assertEquals("0.5", service.getInformation("threshold"));
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assertEquals("test_value", service.getInformation("test_info"));
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service.close();
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} catch (Exception e) {
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fail();
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}
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}
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/**
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* Runs image classification with configuration.
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*/
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private void runImageClassification(String config, boolean isPipeline) {
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mIsPipeline = isPipeline;
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try {
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MLService service = new MLService(config, mEventCb);
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service.start();
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/* push input buffer */
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TensorsData input = APITestCommon.readRawImageData();
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for (int i = 0; i < 5; i++) {
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service.inputData("input_img", input);
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Thread.sleep(100);
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}
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/* sleep 200 to invoke */
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Thread.sleep(200);
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/* check received data from output node */
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assertFalse(mInvalidState);
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assertTrue(mReceived > 0);
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service.close();
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} catch (Exception e) {
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fail();
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}
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}
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@Test
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public void testRunPipeline() {
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String config = APITestCommon.getConfigPath() + "/config_pipeline_imgclf.conf";
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runImageClassification(config, true);
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}
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@Test
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public void testRunSingleShot() {
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String config = APITestCommon.getConfigPath() + "/config_single_imgclf.conf";
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runImageClassification(config, false);
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}
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}

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