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The following commit(s) were added to refs/heads/main by this push:
new e15a618e OPENNLP-1618 - AbstractDL does not release Ort Resources
e15a618e is described below
commit e15a618ec9ee524c91bb8e9068ed901b927f177e
Author: Richard Zowalla <[email protected]>
AuthorDate: Tue Oct 8 11:26:58 2024 +0200
OPENNLP-1618 - AbstractDL does not release Ort Resources
---
.../src/main/java/opennlp/dl/AbstractDL.java | 18 +-
.../dl/doccat/DocumentCategorizerDLEval.java | 273 +++++++++++----------
.../opennlp/dl/namefinder/NameFinderDLEval.java | 117 +++++----
.../opennlp/dl/vectors/SentenceVectorsDLEval.java | 17 +-
4 files changed, 228 insertions(+), 197 deletions(-)
diff --git a/opennlp-dl/src/main/java/opennlp/dl/AbstractDL.java
b/opennlp-dl/src/main/java/opennlp/dl/AbstractDL.java
index 7d891bea..b1b0bf67 100644
--- a/opennlp-dl/src/main/java/opennlp/dl/AbstractDL.java
+++ b/opennlp-dl/src/main/java/opennlp/dl/AbstractDL.java
@@ -34,7 +34,7 @@ import opennlp.tools.tokenize.Tokenizer;
/**
* Base class for OpenNLP deep-learning classes using ONNX Runtime.
*/
-public abstract class AbstractDL {
+public abstract class AbstractDL implements AutoCloseable {
public static final String INPUT_IDS = "input_ids";
public static final String ATTENTION_MASK = "attention_mask";
@@ -50,7 +50,6 @@ public abstract class AbstractDL {
*
* @param vocabFile The vocabulary file.
* @return A map of vocabulary words to integer IDs.
- *
* @throws IOException Thrown if the vocabulary file cannot be opened or
read.
*/
public Map<String, Integer> loadVocab(final File vocabFile) throws
IOException {
@@ -66,4 +65,19 @@ public abstract class AbstractDL {
return vocab;
}
+ /**
+ * Closes this resource, relinquishing any underlying resources.
+ *
+ * @throws Exception If it failed to close.
+ */
+ @Override
+ public void close() throws Exception {
+ if (session != null) {
+ session.close();
+ }
+ if (env != null) {
+ env.close();
+ }
+ }
+
}
diff --git
a/opennlp-dl/src/test/java/opennlp/dl/doccat/DocumentCategorizerDLEval.java
b/opennlp-dl/src/test/java/opennlp/dl/doccat/DocumentCategorizerDLEval.java
index 506b0bc5..6f86e8f9 100644
--- a/opennlp-dl/src/test/java/opennlp/dl/doccat/DocumentCategorizerDLEval.java
+++ b/opennlp-dl/src/test/java/opennlp/dl/doccat/DocumentCategorizerDLEval.java
@@ -18,7 +18,6 @@
package opennlp.dl.doccat;
import java.io.File;
-import java.io.IOException;
import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
@@ -26,7 +25,6 @@ import java.util.Iterator;
import java.util.Map;
import java.util.Set;
-import ai.onnxruntime.OrtException;
import org.junit.jupiter.api.Assertions;
import org.junit.jupiter.api.Disabled;
import org.junit.jupiter.api.Test;
@@ -42,97 +40,99 @@ public class DocumentCategorizerDLEval extends
AbstractDLTest {
private static final Logger logger =
LoggerFactory.getLogger(DocumentCategorizerDLEval.class);
final String text = "We try hard to identify the sources and licenses of all
media such as text," +
- " images or sounds used in our encyclopedia articles. Still, we
cannot guarantee that all " +
- "media are used or marked correctly: for example, if an image
description page states " +
- "that an image was in the public domain, you should still check
yourself whether that claim " +
- "appears correct and decide for yourself whether your use of the
image would be fine under " +
- "the laws applicable to you. Wikipedia is primarily subject to U.S.
law; re-users outside " +
- "the U.S. should be aware that they are subject to the laws of their
country, which almost " +
- "certainly are different. Images published under the GFDL or one of
the Creative Commons " +
- "Licenses are unlikely to pose problems, as these are specific
licenses with precise terms " +
- "worldwide. Public domain images may need to be re-evaluated by a
re-user because it depends " +
- "on each country's copyright laws what is in the public domain
there. There is no guarantee " +
- "that something in the public domain in the U.S. was also in the
public domain in your country.";
+ " images or sounds used in our encyclopedia articles. Still, we cannot
guarantee that all " +
+ "media are used or marked correctly: for example, if an image
description page states " +
+ "that an image was in the public domain, you should still check yourself
whether that claim " +
+ "appears correct and decide for yourself whether your use of the image
would be fine under " +
+ "the laws applicable to you. Wikipedia is primarily subject to U.S. law;
re-users outside " +
+ "the U.S. should be aware that they are subject to the laws of their
country, which almost " +
+ "certainly are different. Images published under the GFDL or one of the
Creative Commons " +
+ "Licenses are unlikely to pose problems, as these are specific licenses
with precise terms " +
+ "worldwide. Public domain images may need to be re-evaluated by a
re-user because it depends " +
+ "on each country's copyright laws what is in the public domain there.
There is no guarantee " +
+ "that something in the public domain in the U.S. was also in the public
domain in your country.";
@Test
- public void categorize() throws IOException, OrtException {
+ public void categorize() throws Exception {
final File model = new File(getOpennlpDataDir(),
"onnx/doccat/nlptown_bert-base-multilingual-uncased-sentiment.onnx");
final File vocab = new File(getOpennlpDataDir(),
"onnx/doccat/nlptown_bert-base-multilingual-uncased-sentiment.vocab");
- final DocumentCategorizerDL documentCategorizerDL =
- new DocumentCategorizerDL(model, vocab, getCategories(),
- new AverageClassificationScoringStrategy(),
- new InferenceOptions());
+ try (final DocumentCategorizerDL documentCategorizerDL =
+ new DocumentCategorizerDL(model, vocab, getCategories(),
+ new AverageClassificationScoringStrategy(),
+ new InferenceOptions())) {
- final double[] result = documentCategorizerDL.categorize(new
String[]{text});
+ final double[] result = documentCategorizerDL.categorize(new String[]
{text});
- // Sort the result for easier comparison.
- final double[] sortedResult = Arrays.stream(result)
- .boxed()
-
.sorted(Collections.reverseOrder()).mapToDouble(Double::doubleValue).toArray();
+ // Sort the result for easier comparison.
+ final double[] sortedResult = Arrays.stream(result)
+ .boxed()
+
.sorted(Collections.reverseOrder()).mapToDouble(Double::doubleValue).toArray();
- final double[] expected = new double[]
- {0.3391093313694,
- 0.2611352801322937,
- 0.24420668184757233,
- 0.11939861625432968,
- 0.03615010157227516};
+ final double[] expected = new double[]
+ {0.3391093313694,
+ 0.2611352801322937,
+ 0.24420668184757233,
+ 0.11939861625432968,
+ 0.03615010157227516};
- logger.debug("Actual: {}", Arrays.toString(sortedResult));
- logger.debug("Expected: {}", Arrays.toString(expected));
+ logger.debug("Actual: {}", Arrays.toString(sortedResult));
+ logger.debug("Expected: {}", Arrays.toString(expected));
- Assertions.assertArrayEquals(expected, sortedResult, 0.000001);
- Assertions.assertEquals(5, result.length);
+ Assertions.assertArrayEquals(expected, sortedResult, 0.000001);
+ Assertions.assertEquals(5, result.length);
- final String category = documentCategorizerDL.getBestCategory(result);
- Assertions.assertEquals("bad", category);
+ final String category = documentCategorizerDL.getBestCategory(result);
+ Assertions.assertEquals("bad", category);
+ }
}
@Test
- public void categorizeWithAutomaticLabels() throws IOException, OrtException
{
+ public void categorizeWithAutomaticLabels() throws Exception {
final File model = new File(getOpennlpDataDir(),
-
"onnx/doccat/nlptown_bert-base-multilingual-uncased-sentiment.onnx");
+ "onnx/doccat/nlptown_bert-base-multilingual-uncased-sentiment.onnx");
final File vocab = new File(getOpennlpDataDir(),
-
"onnx/doccat/nlptown_bert-base-multilingual-uncased-sentiment.vocab");
+ "onnx/doccat/nlptown_bert-base-multilingual-uncased-sentiment.vocab");
final File config = new File(getOpennlpDataDir(),
-
"onnx/doccat/nlptown_bert-base-multilingual-uncased-sentiment.json");
+ "onnx/doccat/nlptown_bert-base-multilingual-uncased-sentiment.json");
- final DocumentCategorizerDL documentCategorizerDL =
- new DocumentCategorizerDL(model, vocab, config,
- new AverageClassificationScoringStrategy(),
- new InferenceOptions());
+ try (final DocumentCategorizerDL documentCategorizerDL =
+ new DocumentCategorizerDL(model, vocab, config,
+ new AverageClassificationScoringStrategy(),
+ new InferenceOptions())) {
- final double[] result = documentCategorizerDL.categorize(new
String[]{text});
+ final double[] result = documentCategorizerDL.categorize(new String[]
{text});
- // Sort the result for easier comparison.
- final double[] sortedResult = Arrays.stream(result)
- .boxed()
-
.sorted(Collections.reverseOrder()).mapToDouble(Double::doubleValue).toArray();
+ // Sort the result for easier comparison.
+ final double[] sortedResult = Arrays.stream(result)
+ .boxed()
+
.sorted(Collections.reverseOrder()).mapToDouble(Double::doubleValue).toArray();
- final double[] expected = new double[]
- {0.3391093313694,
- 0.2611352801322937,
- 0.24420668184757233,
- 0.11939861625432968,
- 0.03615010157227516};
+ final double[] expected = new double[]
+ {0.3391093313694,
+ 0.2611352801322937,
+ 0.24420668184757233,
+ 0.11939861625432968,
+ 0.03615010157227516};
- logger.debug("Actual: {}", Arrays.toString(sortedResult));
- logger.debug("Expected: {}", Arrays.toString(expected));
+ logger.debug("Actual: {}", Arrays.toString(sortedResult));
+ logger.debug("Expected: {}", Arrays.toString(expected));
- Assertions.assertArrayEquals(expected, sortedResult, 0.000001);
- Assertions.assertEquals(5, result.length);
+ Assertions.assertArrayEquals(expected, sortedResult, 0.000001);
+ Assertions.assertEquals(5, result.length);
- final String category = documentCategorizerDL.getBestCategory(result);
- Assertions.assertEquals("2 stars", category);
+ final String category = documentCategorizerDL.getBestCategory(result);
+ Assertions.assertEquals("2 stars", category);
+ }
}
- @Disabled("This test will should only be run if a GPU device is present.")
+ @Disabled("This test should only be run if a GPU device is present.")
@Test
public void categorizeWithGpu() throws Exception {
@@ -145,26 +145,27 @@ public class DocumentCategorizerDLEval extends
AbstractDLTest {
inferenceOptions.setGpu(true);
inferenceOptions.setGpuDeviceId(0);
- final DocumentCategorizerDL documentCategorizerDL =
- new DocumentCategorizerDL(model, vocab, getCategories(),
- new AverageClassificationScoringStrategy(),
- new InferenceOptions());
+ try (final DocumentCategorizerDL documentCategorizerDL =
+ new DocumentCategorizerDL(model, vocab, getCategories(),
+ new AverageClassificationScoringStrategy(),
+ new InferenceOptions())) {
- final double[] result = documentCategorizerDL.categorize(new String[]{"I
am happy"});
- logger.debug(Arrays.toString(result));
+ final double[] result = documentCategorizerDL.categorize(new String[]
{"I am happy"});
+ logger.debug(Arrays.toString(result));
- final double[] expected = new double[]
- {0.007819971069693565,
- 0.006593209225684404,
- 0.04995147883892059,
- 0.3003573715686798,
- 0.6352779865264893};
+ final double[] expected = new double[]
+ {0.007819971069693565,
+ 0.006593209225684404,
+ 0.04995147883892059,
+ 0.3003573715686798,
+ 0.6352779865264893};
- Assertions.assertArrayEquals(expected, result, 0.000001);
- Assertions.assertEquals(5, result.length);
+ Assertions.assertArrayEquals(expected, result, 0.000001);
+ Assertions.assertEquals(5, result.length);
- final String category = documentCategorizerDL.getBestCategory(result);
- Assertions.assertEquals("very good", category);
+ final String category = documentCategorizerDL.getBestCategory(result);
+ Assertions.assertEquals("very good", category);
+ }
}
@@ -183,21 +184,21 @@ public class DocumentCategorizerDLEval extends
AbstractDLTest {
categories.put(0, "negative");
categories.put(1, "positive");
- final DocumentCategorizerDL documentCategorizerDL =
- new DocumentCategorizerDL(model, vocab, categories,
- new AverageClassificationScoringStrategy(),
- inferenceOptions);
-
- final double[] result = documentCategorizerDL.categorize(new String[]{"I
am angry"});
+ try (final DocumentCategorizerDL documentCategorizerDL =
+ new DocumentCategorizerDL(model, vocab, categories,
+ new AverageClassificationScoringStrategy(),
+ inferenceOptions)) {
- final double[] expected = new double[]{0.8851314783096313,
0.11486853659152985};
+ final double[] result = documentCategorizerDL.categorize(new String[]
{"I am angry"});
- Assertions.assertArrayEquals(expected, result, 0.000001);
- Assertions.assertEquals(2, result.length);
+ final double[] expected = new double[] {0.8851314783096313,
0.11486853659152985};
- final String category = documentCategorizerDL.getBestCategory(result);
- Assertions.assertEquals("negative", category);
+ Assertions.assertArrayEquals(expected, result, 0.000001);
+ Assertions.assertEquals(2, result.length);
+ final String category = documentCategorizerDL.getBestCategory(result);
+ Assertions.assertEquals("negative", category);
+ }
}
@Test
@@ -208,85 +209,89 @@ public class DocumentCategorizerDLEval extends
AbstractDLTest {
final File vocab = new File(getOpennlpDataDir(),
"onnx/doccat/nlptown_bert-base-multilingual-uncased-sentiment.vocab");
- final DocumentCategorizerDL documentCategorizerDL =
- new DocumentCategorizerDL(model, vocab, getCategories(),
- new AverageClassificationScoringStrategy(),
- new InferenceOptions());
+ try (final DocumentCategorizerDL documentCategorizerDL =
+ new DocumentCategorizerDL(model, vocab, getCategories(),
+ new AverageClassificationScoringStrategy(),
+ new InferenceOptions())) {
- final Map<String, Double> result = documentCategorizerDL.scoreMap(new
String[]{"I am happy"});
+ final Map<String, Double> result = documentCategorizerDL.scoreMap(new
String[] {"I am happy"});
- Assertions.assertEquals(0.6352779865264893, result.get("very good"),
0.000001);
- Assertions.assertEquals(0.3003573715686798, result.get("good"), 0.000001);
- Assertions.assertEquals(0.04995147883892059, result.get("neutral"),
0.000001);
- Assertions.assertEquals(0.006593209225684404, result.get("bad"), 0.000001);
- Assertions.assertEquals(0.007819971069693565, result.get("very bad"),
0.000001);
+ Assertions.assertEquals(0.6352779865264893, result.get("very good"),
0.000001);
+ Assertions.assertEquals(0.3003573715686798, result.get("good"),
0.000001);
+ Assertions.assertEquals(0.04995147883892059, result.get("neutral"),
0.000001);
+ Assertions.assertEquals(0.006593209225684404, result.get("bad"),
0.000001);
+ Assertions.assertEquals(0.007819971069693565, result.get("very bad"),
0.000001);
+ }
}
@Test
- public void sortedScoreMap() throws IOException, OrtException {
+ public void sortedScoreMap() throws Exception {
final File model = new File(getOpennlpDataDir(),
"onnx/doccat/nlptown_bert-base-multilingual-uncased-sentiment.onnx");
final File vocab = new File(getOpennlpDataDir(),
"onnx/doccat/nlptown_bert-base-multilingual-uncased-sentiment.vocab");
- final DocumentCategorizerDL documentCategorizerDL =
- new DocumentCategorizerDL(model, vocab, getCategories(),
- new AverageClassificationScoringStrategy(),
- new InferenceOptions());
+ try (final DocumentCategorizerDL documentCategorizerDL =
+ new DocumentCategorizerDL(model, vocab, getCategories(),
+ new AverageClassificationScoringStrategy(),
+ new InferenceOptions())) {
- final Map<Double, Set<String>> result =
documentCategorizerDL.sortedScoreMap(new String[]{"I am happy"});
+ final Map<Double, Set<String>> result =
+ documentCategorizerDL.sortedScoreMap(new String[] {"I am happy"});
- Assertions.assertNotNull(result, "Result must not be NULL.");
- Assertions.assertEquals(5, result.size());
+ Assertions.assertNotNull(result, "Result must not be NULL.");
+ Assertions.assertEquals(5, result.size());
- final Iterator<Map.Entry<Double,Set<String>>> it =
result.entrySet().iterator();
+ final Iterator<Map.Entry<Double, Set<String>>> it =
result.entrySet().iterator();
- // we assume a sorted map here, so lets check in sorted order (lower
values first).
- Map.Entry<Double, Set<String>> e = it.next();
- Assertions.assertEquals(0.006593209225684404, e.getKey(), 0.000001);
- Assertions.assertEquals(e.getValue().size(), 1);
+ // we assume a sorted map here, so lets check in sorted order (lower
values first).
+ Map.Entry<Double, Set<String>> e = it.next();
+ Assertions.assertEquals(0.006593209225684404, e.getKey(), 0.000001);
+ Assertions.assertEquals(e.getValue().size(), 1);
- e = it.next();
- Assertions.assertEquals(0.007819971069693565, e.getKey(), 0.000001);
- Assertions.assertEquals(e.getValue().size(), 1);
+ e = it.next();
+ Assertions.assertEquals(0.007819971069693565, e.getKey(), 0.000001);
+ Assertions.assertEquals(e.getValue().size(), 1);
- e = it.next();
- Assertions.assertEquals(0.04995147883892059, e.getKey(), 0.000001);
- Assertions.assertEquals(e.getValue().size(), 1);
+ e = it.next();
+ Assertions.assertEquals(0.04995147883892059, e.getKey(), 0.000001);
+ Assertions.assertEquals(e.getValue().size(), 1);
- e = it.next();
- Assertions.assertEquals(0.3003573715686798, e.getKey(), 0.000001);
- Assertions.assertEquals(e.getValue().size(), 1);
+ e = it.next();
+ Assertions.assertEquals(0.3003573715686798, e.getKey(), 0.000001);
+ Assertions.assertEquals(e.getValue().size(), 1);
- e = it.next();
- Assertions.assertEquals(0.6352779865264893, e.getKey(), 0.000001);
- Assertions.assertEquals(e.getValue().size(), 1);
+ e = it.next();
+ Assertions.assertEquals(0.6352779865264893, e.getKey(), 0.000001);
+ Assertions.assertEquals(e.getValue().size(), 1);
+ }
}
@Test
- public void doccat() throws IOException, OrtException {
+ public void doccat() throws Exception {
final File model = new File(getOpennlpDataDir(),
"onnx/doccat/nlptown_bert-base-multilingual-uncased-sentiment.onnx");
final File vocab = new File(getOpennlpDataDir(),
"onnx/doccat/nlptown_bert-base-multilingual-uncased-sentiment.vocab");
- final DocumentCategorizerDL documentCategorizerDL =
- new DocumentCategorizerDL(model, vocab, getCategories(),
- new AverageClassificationScoringStrategy(),
- new InferenceOptions());
+ try (final DocumentCategorizerDL documentCategorizerDL =
+ new DocumentCategorizerDL(model, vocab, getCategories(),
+ new AverageClassificationScoringStrategy(),
+ new InferenceOptions())) {
- final int index = documentCategorizerDL.getIndex("bad");
- Assertions.assertEquals(1, index);
+ final int index = documentCategorizerDL.getIndex("bad");
+ Assertions.assertEquals(1, index);
- final String category = documentCategorizerDL.getCategory(3);
- Assertions.assertEquals("good", category);
+ final String category = documentCategorizerDL.getCategory(3);
+ Assertions.assertEquals("good", category);
- final int number = documentCategorizerDL.getNumberOfCategories();
- Assertions.assertEquals(5, number);
+ final int number = documentCategorizerDL.getNumberOfCategories();
+ Assertions.assertEquals(5, number);
+ }
}
diff --git
a/opennlp-dl/src/test/java/opennlp/dl/namefinder/NameFinderDLEval.java
b/opennlp-dl/src/test/java/opennlp/dl/namefinder/NameFinderDLEval.java
index 513765dc..f8febbd0 100644
--- a/opennlp-dl/src/test/java/opennlp/dl/namefinder/NameFinderDLEval.java
+++ b/opennlp-dl/src/test/java/opennlp/dl/namefinder/NameFinderDLEval.java
@@ -36,7 +36,7 @@ import opennlp.tools.util.Span;
public class NameFinderDLEval extends AbstractDLTest {
private static final Logger logger =
LoggerFactory.getLogger(NameFinderDLEval.class);
- private final SentenceDetector sentenceDetector ;
+ private final SentenceDetector sentenceDetector;
public NameFinderDLEval() throws IOException {
this.sentenceDetector = new SentenceDetectorME("en");
@@ -54,18 +54,21 @@ public class NameFinderDLEval extends AbstractDLTest {
final String[] tokens = new String[]
{"George", "Washington", "was", "president", "of", "the", "United",
"States", "."};
- final NameFinderDL nameFinderDL = new NameFinderDL(model, vocab,
getIds2Labels(), sentenceDetector);
- final Span[] spans = nameFinderDL.find(tokens);
+ try (final NameFinderDL nameFinderDL = new NameFinderDL(model, vocab,
getIds2Labels(),
+ sentenceDetector)) {
+ final Span[] spans = nameFinderDL.find(tokens);
- for (Span span : spans) {
- logger.debug(span.toString());
- }
+ for (Span span : spans) {
+ logger.debug(span.toString());
+ }
- Assertions.assertEquals(1, spans.length);
- Assertions.assertEquals(0, spans[0].getStart());
- Assertions.assertEquals(17, spans[0].getEnd());
- Assertions.assertEquals(8.251646041870117, spans[0].getProb(), 0.00001);
- Assertions.assertEquals("George Washington",
spans[0].getCoveredText(String.join(" ", tokens)));
+ Assertions.assertEquals(1, spans.length);
+ Assertions.assertEquals(0, spans[0].getStart());
+ Assertions.assertEquals(17, spans[0].getEnd());
+ Assertions.assertEquals(8.251646041870117, spans[0].getProb(), 0.00001);
+ Assertions.assertEquals("George Washington",
+ spans[0].getCoveredText(String.join(" ", tokens)));
+ }
}
@@ -78,19 +81,20 @@ public class NameFinderDLEval extends AbstractDLTest {
final File model = new File(getOpennlpDataDir(),
"onnx/namefinder/model.onnx");
final File vocab = new File(getOpennlpDataDir(),
"onnx/namefinder/vocab.txt");
- final String[] tokens = new String[]{"His", "name", "was", "George",
"Washington"};
+ final String[] tokens = new String[] {"His", "name", "was", "George",
"Washington"};
- final NameFinderDL nameFinderDL = new NameFinderDL(model, vocab,
getIds2Labels(), sentenceDetector);
- final Span[] spans = nameFinderDL.find(tokens);
-
- for (Span span : spans) {
- logger.debug(span.toString());
- }
+ try (final NameFinderDL nameFinderDL = new NameFinderDL(model, vocab,
getIds2Labels(),
+ sentenceDetector)) {
+ final Span[] spans = nameFinderDL.find(tokens);
- Assertions.assertEquals(1, spans.length);
- Assertions.assertEquals(13, spans[0].getStart());
- Assertions.assertEquals(30, spans[0].getEnd());
+ for (Span span : spans) {
+ logger.debug(span.toString());
+ }
+ Assertions.assertEquals(1, spans.length);
+ Assertions.assertEquals(13, spans[0].getStart());
+ Assertions.assertEquals(30, spans[0].getEnd());
+ }
}
@Test
@@ -102,19 +106,20 @@ public class NameFinderDLEval extends AbstractDLTest {
final File model = new File(getOpennlpDataDir(),
"onnx/namefinder/model.onnx");
final File vocab = new File(getOpennlpDataDir(),
"onnx/namefinder/vocab.txt");
- final String[] tokens = new String[]{"His", "name", "was", "George"};
-
- final NameFinderDL nameFinderDL = new NameFinderDL(model, vocab,
getIds2Labels(), sentenceDetector);
- final Span[] spans = nameFinderDL.find(tokens);
+ final String[] tokens = new String[] {"His", "name", "was", "George"};
- for (Span span : spans) {
- logger.debug(span.toString());
- }
+ try (final NameFinderDL nameFinderDL = new NameFinderDL(model, vocab,
getIds2Labels(),
+ sentenceDetector)) {
+ final Span[] spans = nameFinderDL.find(tokens);
- Assertions.assertEquals(1, spans.length);
- Assertions.assertEquals(13, spans[0].getStart());
- Assertions.assertEquals(19, spans[0].getEnd());
+ for (Span span : spans) {
+ logger.debug(span.toString());
+ }
+ Assertions.assertEquals(1, spans.length);
+ Assertions.assertEquals(13, spans[0].getStart());
+ Assertions.assertEquals(19, spans[0].getEnd());
+ }
}
@Test
@@ -126,13 +131,14 @@ public class NameFinderDLEval extends AbstractDLTest {
final File model = new File(getOpennlpDataDir(),
"onnx/namefinder/model.onnx");
final File vocab = new File(getOpennlpDataDir(),
"onnx/namefinder/vocab.txt");
- final String[] tokens = new String[]{};
-
- final NameFinderDL nameFinderDL = new NameFinderDL(model, vocab,
getIds2Labels(), sentenceDetector);
- final Span[] spans = nameFinderDL.find(tokens);
+ final String[] tokens = new String[] {};
- Assertions.assertEquals(0, spans.length);
+ try (final NameFinderDL nameFinderDL = new NameFinderDL(model, vocab,
getIds2Labels(),
+ sentenceDetector)) {
+ final Span[] spans = nameFinderDL.find(tokens);
+ Assertions.assertEquals(0, spans.length);
+ }
}
@Test
@@ -144,12 +150,14 @@ public class NameFinderDLEval extends AbstractDLTest {
final File model = new File(getOpennlpDataDir(),
"onnx/namefinder/model.onnx");
final File vocab = new File(getOpennlpDataDir(),
"onnx/namefinder/vocab.txt");
- final String[] tokens = new String[]{"I", "went", "to", "the", "park"};
+ final String[] tokens = new String[] {"I", "went", "to", "the", "park"};
- final NameFinderDL nameFinderDL = new NameFinderDL(model, vocab,
getIds2Labels(), sentenceDetector);
- final Span[] spans = nameFinderDL.find(tokens);
+ try (final NameFinderDL nameFinderDL = new NameFinderDL(model, vocab,
getIds2Labels(),
+ sentenceDetector)) {
+ final Span[] spans = nameFinderDL.find(tokens);
- Assertions.assertEquals(0, spans.length);
+ Assertions.assertEquals(0, spans.length);
+ }
}
@@ -162,21 +170,24 @@ public class NameFinderDLEval extends AbstractDLTest {
final File model = new File(getOpennlpDataDir(),
"onnx/namefinder/model.onnx");
final File vocab = new File(getOpennlpDataDir(),
"onnx/namefinder/vocab.txt");
- final String[] tokens = new String[]{"George", "Washington", "and",
"Abraham", "Lincoln",
+ final String[] tokens = new String[] {"George", "Washington", "and",
"Abraham", "Lincoln",
"were", "presidents"};
- final NameFinderDL nameFinderDL = new NameFinderDL(model, vocab,
getIds2Labels(), sentenceDetector);
- final Span[] spans = nameFinderDL.find(tokens);
+ try (final NameFinderDL nameFinderDL = new NameFinderDL(model, vocab,
getIds2Labels(),
+ sentenceDetector)) {
+ final Span[] spans = nameFinderDL.find(tokens);
- for (Span span : spans) {
- logger.debug(span.toString());
- }
+ for (Span span : spans) {
+ logger.debug(span.toString());
+ }
+
+ Assertions.assertEquals(2, spans.length);
+ Assertions.assertEquals(0, spans[0].getStart());
+ Assertions.assertEquals(17, spans[0].getEnd());
+ Assertions.assertEquals(22, spans[1].getStart());
+ Assertions.assertEquals(37, spans[1].getEnd());
- Assertions.assertEquals(2, spans.length);
- Assertions.assertEquals(0, spans[0].getStart());
- Assertions.assertEquals(17, spans[0].getEnd());
- Assertions.assertEquals(22, spans[1].getStart());
- Assertions.assertEquals(37, spans[1].getEnd());
+ }
}
@@ -190,7 +201,9 @@ public class NameFinderDLEval extends AbstractDLTest {
final File model = new File("invalid.onnx");
final File vocab = new File("vocab.txt");
- new NameFinderDL(model, vocab, getIds2Labels(), sentenceDetector);
+ try (final NameFinderDL nameFinderDL = new NameFinderDL(model, vocab,
getIds2Labels(),
+ sentenceDetector)) {
+ }
});
}
diff --git
a/opennlp-dl/src/test/java/opennlp/dl/vectors/SentenceVectorsDLEval.java
b/opennlp-dl/src/test/java/opennlp/dl/vectors/SentenceVectorsDLEval.java
index f63fa3f4..b7c92e1c 100644
--- a/opennlp-dl/src/test/java/opennlp/dl/vectors/SentenceVectorsDLEval.java
+++ b/opennlp-dl/src/test/java/opennlp/dl/vectors/SentenceVectorsDLEval.java
@@ -18,9 +18,7 @@
package opennlp.dl.vectors;
import java.io.File;
-import java.io.IOException;
-import ai.onnxruntime.OrtException;
import org.junit.jupiter.api.Assertions;
import org.junit.jupiter.api.Test;
@@ -29,21 +27,22 @@ import opennlp.dl.AbstractDLTest;
public class SentenceVectorsDLEval extends AbstractDLTest {
@Test
- public void generateVectorsTest() throws IOException, OrtException {
+ public void generateVectorsTest() throws Exception {
final File MODEL_FILE_NAME = new File(getOpennlpDataDir(),
"onnx/sentence-transformers/model.onnx");
final File VOCAB_FILE_NAME = new File(getOpennlpDataDir(),
"onnx/sentence-transformers/vocab.txt");
final String sentence = "george washington was president";
- final SentenceVectorsDL sv = new SentenceVectorsDL(MODEL_FILE_NAME,
VOCAB_FILE_NAME);
+ try (final SentenceVectorsDL sv = new SentenceVectorsDL(MODEL_FILE_NAME,
VOCAB_FILE_NAME)) {
- final float[] vectors = sv.getVectors(sentence);
+ final float[] vectors = sv.getVectors(sentence);
- Assertions.assertEquals(vectors[0], 0.39994872, 0.00001);
- Assertions.assertEquals(vectors[1], -0.055101186, 0.00001);
- Assertions.assertEquals(vectors[2], 0.2817594, 0.00001);
- Assertions.assertEquals(vectors.length, 384);
+ Assertions.assertEquals(vectors[0], 0.39994872, 0.00001);
+ Assertions.assertEquals(vectors[1], -0.055101186, 0.00001);
+ Assertions.assertEquals(vectors[2], 0.2817594, 0.00001);
+ Assertions.assertEquals(vectors.length, 384);
+ }
}
