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Fix rnn parameterized tests

master
agibsonccc 2021-03-18 15:49:27 +09:00
parent 7bd1c5cbaa
commit d1989b8529
35 changed files with 421 additions and 308 deletions
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199
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/datavec/RecordReaderDataSetiteratorTest.java
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@ -20,6 +20,9 @@
package org.deeplearning4j.datasets.datavec;
import org.junit.jupiter.params.ParameterizedTest;
import org.junit.jupiter.params.provider.MethodSource;
import org.nd4j.linalg.factory.Nd4jBackend;
import org.nd4j.shade.guava.io.Files;
import lombok.extern.slf4j.Slf4j;
import org.apache.commons.io.FileUtils;
@ -72,6 +75,7 @@ import static org.junit.jupiter.api.Assertions.assertThrows;
@Slf4j
@DisplayName("Record Reader Data Setiterator Test")
@Disabled
class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
@Override
@ -82,9 +86,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
@TempDir
public Path temporaryFolder;
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Record Reader")
void testRecordReader() throws Exception {
void testRecordReader(Nd4jBackend nd4jBackend) throws Exception {
RecordReader recordReader = new CSVRecordReader();
FileSplit csv = new FileSplit(Resources.asFile("csv-example.csv"));
recordReader.initialize(csv);
@ -93,9 +98,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
assertEquals(34, next.numExamples());
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Record Reader Max Batch Limit")
void testRecordReaderMaxBatchLimit() throws Exception {
void testRecordReaderMaxBatchLimit(Nd4jBackend backend) throws Exception {
RecordReader recordReader = new CSVRecordReader();
FileSplit csv = new FileSplit(Resources.asFile("csv-example.csv"));
recordReader.initialize(csv);
@ -108,9 +114,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
assertEquals(false, iter.hasNext());
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Record Reader Multi Regression")
void testRecordReaderMultiRegression() throws Exception {
void testRecordReaderMultiRegression(Nd4jBackend backend) throws Exception {
for (boolean builder : new boolean[] { false, true }) {
RecordReader csv = new CSVRecordReader();
csv.initialize(new FileSplit(Resources.asFile("iris.txt")));
@ -138,9 +145,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
}
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Sequence Record Reader")
void testSequenceRecordReader() throws Exception {
void testSequenceRecordReader(Nd4jBackend backend) throws Exception {
File rootDir = temporaryFolder.toFile();
// need to manually extract
for (int i = 0; i < 3; i++) {
@ -217,9 +225,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
assertEquals(dsList.get(2).getLabels(), expL2);
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Sequence Record Reader Meta")
void testSequenceRecordReaderMeta() throws Exception {
void testSequenceRecordReaderMeta(Nd4jBackend backend) throws Exception {
File rootDir = temporaryFolder.toFile();
// need to manually extract
for (int i = 0; i < 3; i++) {
@ -244,9 +253,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
}
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Sequence Record Reader Regression")
void testSequenceRecordReaderRegression() throws Exception {
void testSequenceRecordReaderRegression(Nd4jBackend backend) throws Exception {
// need to manually extract
File rootDir = temporaryFolder.toFile();
for (int i = 0; i < 3; i++) {
@ -296,9 +306,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
assertEquals(3, count);
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Sequence Record Reader Multi Regression")
void testSequenceRecordReaderMultiRegression() throws Exception {
void testSequenceRecordReaderMultiRegression(Nd4jBackend backend) throws Exception {
File rootDir = temporaryFolder.toFile();
// need to manually extract
for (int i = 0; i < 3; i++) {
@ -351,9 +362,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
assertEquals(3, count);
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Sequence Record Reader Reset")
void testSequenceRecordReaderReset() throws Exception {
void testSequenceRecordReaderReset(Nd4jBackend backend) throws Exception {
File rootDir = temporaryFolder.toFile();
// need to manually extract
for (int i = 0; i < 3; i++) {
@ -385,9 +397,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
}
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test CSV Loading Regression")
void testCSVLoadingRegression() throws Exception {
void testCSVLoadingRegression(Nd4jBackend backend) throws Exception {
int nLines = 30;
int nFeatures = 5;
int miniBatchSize = 10;
@ -447,9 +460,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
return new Pair<>(dArr, temp);
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Variable Length Sequence")
void testVariableLengthSequence() throws Exception {
void testVariableLengthSequence(Nd4jBackend backend) throws Exception {
File rootDir = temporaryFolder.toFile();
// need to manually extract
for (int i = 0; i < 3; i++) {
@ -582,9 +596,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
}
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Sequence Record Reader Single Reader")
void testSequenceRecordReaderSingleReader() throws Exception {
void testSequenceRecordReaderSingleReader(Nd4jBackend backend) throws Exception {
File rootDir = temporaryFolder.toFile();
// need to manually extract
for (int i = 0; i < 3; i++) {
@ -680,9 +695,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
assertEquals(1, iteratorRegression.totalOutcomes());
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Sequence Record Reader Single Reader With Empty Sequence Throws")
void testSequenceRecordReaderSingleReaderWithEmptySequenceThrows() {
void testSequenceRecordReaderSingleReaderWithEmptySequenceThrows(Nd4jBackend backend) {
assertThrows(ZeroLengthSequenceException.class, () -> {
SequenceRecordReader reader = new CSVSequenceRecordReader(1, ",");
reader.initialize(new FileSplit(Resources.asFile("empty.txt")));
@ -690,9 +706,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
});
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Sequence Record Reader Two Readers With Empty Feature Sequence Throws")
void testSequenceRecordReaderTwoReadersWithEmptyFeatureSequenceThrows() {
void testSequenceRecordReaderTwoReadersWithEmptyFeatureSequenceThrows(Nd4jBackend backend) {
assertThrows(ZeroLengthSequenceException.class, () -> {
SequenceRecordReader featureReader = new CSVSequenceRecordReader(1, ",");
SequenceRecordReader labelReader = new CSVSequenceRecordReader(1, ",");
@ -702,9 +719,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
});
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Sequence Record Reader Two Readers With Empty Label Sequence Throws")
void testSequenceRecordReaderTwoReadersWithEmptyLabelSequenceThrows() {
void testSequenceRecordReaderTwoReadersWithEmptyLabelSequenceThrows(Nd4jBackend backend) {
assertThrows(ZeroLengthSequenceException.class, () -> {
SequenceRecordReader featureReader = new CSVSequenceRecordReader(1, ",");
SequenceRecordReader labelReader = new CSVSequenceRecordReader(1, ",");
@ -715,9 +733,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
});
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Sequence Record Reader Single Reader Meta Data")
void testSequenceRecordReaderSingleReaderMetaData() throws Exception {
void testSequenceRecordReaderSingleReaderMetaData(Nd4jBackend backend) throws Exception {
File rootDir = temporaryFolder.toFile();
// need to manually extract
for (int i = 0; i < 3; i++) {
@ -744,9 +763,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
}
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Seq RRDSI Array Writable One Reader")
void testSeqRRDSIArrayWritableOneReader() {
void testSeqRRDSIArrayWritableOneReader(Nd4jBackend backend) {
List<List<Writable>> sequence1 = new ArrayList<>();
sequence1.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 1, 2, 3 }, new long[] { 1, 3 })), new IntWritable(0)));
sequence1.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 4, 5, 6 }, new long[] { 1, 3 })), new IntWritable(1)));
@ -767,16 +787,17 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
assertEquals(expLabels, ds.getLabels());
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Seq RRDSI Array Writable One Reader Regression")
void testSeqRRDSIArrayWritableOneReaderRegression() {
void testSeqRRDSIArrayWritableOneReaderRegression(Nd4jBackend backend) {
// Regression, where the output is an array writable
List<List<Writable>> sequence1 = new ArrayList<>();
sequence1.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 1, 2, 3 }, new long[] { 1, 3 })), new NDArrayWritable(Nd4j.create(new double[] { 100, 200, 300 }, new long[] { 1, 3 }))));
sequence1.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 4, 5, 6 }, new long[] { 1, 3 })), new NDArrayWritable(Nd4j.create(new double[] { 400, 500, 600 }, new long[] { 1, 3 }))));
sequence1.add(Arrays.asList(new NDArrayWritable(Nd4j.create(new double[] { 1, 2, 3 }, new long[] { 1, 3 })), new NDArrayWritable(Nd4j.create(new double[] { 100, 200, 300 }, new long[] { 1, 3 }))));
sequence1.add(Arrays.asList(new NDArrayWritable(Nd4j.create(new double[] { 4, 5, 6 }, new long[] { 1, 3 })), new NDArrayWritable(Nd4j.create(new double[] { 400, 500, 600 }, new long[] { 1, 3 }))));
List<List<Writable>> sequence2 = new ArrayList<>();
sequence2.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 7, 8, 9 }, new long[] { 1, 3 })), new NDArrayWritable(Nd4j.create(new double[] { 700, 800, 900 }, new long[] { 1, 3 }))));
sequence2.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 10, 11, 12 }, new long[] { 1, 3 })), new NDArrayWritable(Nd4j.create(new double[] { 1000, 1100, 1200 }, new long[] { 1, 3 }))));
sequence2.add(Arrays.asList(new NDArrayWritable(Nd4j.create(new double[] { 7, 8, 9 }, new long[] { 1, 3 })), new NDArrayWritable(Nd4j.create(new double[] { 700, 800, 900 }, new long[] { 1, 3 }))));
sequence2.add(Arrays.asList(new NDArrayWritable(Nd4j.create(new double[] { 10, 11, 12 }, new long[] { 1, 3 })), new NDArrayWritable(Nd4j.create(new double[] { 1000, 1100, 1200 }, new long[] { 1, 3 }))));
SequenceRecordReader rr = new CollectionSequenceRecordReader(Arrays.asList(sequence1, sequence2));
SequenceRecordReaderDataSetIterator iter = new SequenceRecordReaderDataSetIterator(rr, 2, -1, 1, true);
DataSet ds = iter.next();
@ -791,16 +812,17 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
assertEquals(expLabels, ds.getLabels());
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Seq RRDSI Multiple Array Writables One Reader")
void testSeqRRDSIMultipleArrayWritablesOneReader() {
void testSeqRRDSIMultipleArrayWritablesOneReader(Nd4jBackend backend) {
// Input with multiple array writables:
List<List<Writable>> sequence1 = new ArrayList<>();
sequence1.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 1, 2, 3 }, new long[] { 1, 3 })), new NDArrayWritable(Nd4j.create(new double[] { 100, 200, 300 }, new long[] { 1, 3 })), new IntWritable(0)));
sequence1.add(Arrays.asList(new NDArrayWritable(Nd4j.create(new double[] { 1, 2, 3 }, new long[] { 1, 3 })), new NDArrayWritable(Nd4j.create(new double[] { 100, 200, 300 }, new long[] { 1, 3 })), new IntWritable(0)));
sequence1.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 4, 5, 6 }, new long[] { 1, 3 })), new NDArrayWritable(Nd4j.create(new double[] { 400, 500, 600 }, new long[] { 1, 3 })), new IntWritable(1)));
List<List<Writable>> sequence2 = new ArrayList<>();
sequence2.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 7, 8, 9 }, new long[] { 1, 3 })), new NDArrayWritable(Nd4j.create(new double[] { 700, 800, 900 }, new long[] { 1, 3 })), new IntWritable(2)));
sequence2.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 10, 11, 12 }, new long[] { 1, 3 })), new NDArrayWritable(Nd4j.create(new double[] { 1000, 1100, 1200 }, new long[] { 1, 3 })), new IntWritable(3)));
sequence2.add(Arrays.asList(new NDArrayWritable(Nd4j.create(new double[] { 7, 8, 9 }, new long[] { 1, 3 })), new NDArrayWritable(Nd4j.create(new double[] { 700, 800, 900 }, new long[] { 1, 3 })), new IntWritable(2)));
sequence2.add(Arrays.asList(new NDArrayWritable(Nd4j.create(new double[] { 10, 11, 12 }, new long[] { 1, 3 })), new NDArrayWritable(Nd4j.create(new double[] { 1000, 1100, 1200 }, new long[] { 1, 3 })), new IntWritable(3)));
SequenceRecordReader rr = new CollectionSequenceRecordReader(Arrays.asList(sequence1, sequence2));
SequenceRecordReaderDataSetIterator iter = new SequenceRecordReaderDataSetIterator(rr, 2, 4, 2, false);
DataSet ds = iter.next();
@ -815,22 +837,23 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
assertEquals(expLabels, ds.getLabels());
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Seq RRDSI Array Writable Two Readers")
void testSeqRRDSIArrayWritableTwoReaders() {
void testSeqRRDSIArrayWritableTwoReaders(Nd4jBackend backend) {
List<List<Writable>> sequence1 = new ArrayList<>();
sequence1.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 1, 2, 3 }, new long[] { 1, 3 })), new IntWritable(100)));
sequence1.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 4, 5, 6 }, new long[] { 1, 3 })), new IntWritable(200)));
sequence1.add(Arrays.asList(new NDArrayWritable(Nd4j.create(new double[] { 1, 2, 3 }, new long[] { 1, 3 })), new IntWritable(100)));
sequence1.add(Arrays.asList(new NDArrayWritable(Nd4j.create(new double[] { 4, 5, 6 }, new long[] { 1, 3 })), new IntWritable(200)));
List<List<Writable>> sequence2 = new ArrayList<>();
sequence2.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 7, 8, 9 }, new long[] { 1, 3 })), new IntWritable(300)));
sequence2.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 10, 11, 12 }, new long[] { 1, 3 })), new IntWritable(400)));
sequence2.add(Arrays.asList(new NDArrayWritable(Nd4j.create(new double[] { 7, 8, 9 }, new long[] { 1, 3 })), new IntWritable(300)));
sequence2.add(Arrays.asList(new NDArrayWritable(Nd4j.create(new double[] { 10, 11, 12 }, new long[] { 1, 3 })), new IntWritable(400)));
SequenceRecordReader rrFeatures = new CollectionSequenceRecordReader(Arrays.asList(sequence1, sequence2));
List<List<Writable>> sequence1L = new ArrayList<>();
sequence1L.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 100, 200, 300 }, new long[] { 1, 3 })), new IntWritable(101)));
sequence1L.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 400, 500, 600 }, new long[] { 1, 3 })), new IntWritable(201)));
sequence1L.add(Arrays.asList(new NDArrayWritable(Nd4j.create(new double[] { 100, 200, 300 }, new long[] { 1, 3 })), new IntWritable(101)));
sequence1L.add(Arrays.asList(new NDArrayWritable(Nd4j.create(new double[] { 400, 500, 600 }, new long[] { 1, 3 })), new IntWritable(201)));
List<List<Writable>> sequence2L = new ArrayList<>();
sequence2L.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 700, 800, 900 }, new long[] { 1, 3 })), new IntWritable(301)));
sequence2L.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 1000, 1100, 1200 }, new long[] { 1, 3 })), new IntWritable(401)));
sequence2L.add(Arrays.asList(new NDArrayWritable(Nd4j.create(new double[] { 700, 800, 900 }, new long[] { 1, 3 })), new IntWritable(301)));
sequence2L.add(Arrays.asList(new NDArrayWritable(Nd4j.create(new double[] { 1000, 1100, 1200 }, new long[] { 1, 3 })), new IntWritable(401)));
SequenceRecordReader rrLabels = new CollectionSequenceRecordReader(Arrays.asList(sequence1L, sequence2L));
SequenceRecordReaderDataSetIterator iter = new SequenceRecordReaderDataSetIterator(rrFeatures, rrLabels, 2, -1, true);
// 2 examples, 4 values per time step, 2 time steps
@ -845,7 +868,8 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
assertEquals(expLabels, ds.getLabels());
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Record Reader Meta Data")
void testRecordReaderMetaData() throws Exception {
RecordReader csv = new CSVRecordReader();
@ -878,9 +902,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
}
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test RRDS Iwith Async")
void testRRDSIwithAsync() throws Exception {
void testRRDSIwithAsync(Nd4jBackend backend) throws Exception {
RecordReader csv = new CSVRecordReader();
csv.initialize(new FileSplit(Resources.asFile("iris.txt")));
int batchSize = 10;
@ -893,9 +918,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
}
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Record Reader Data Set Iterator ND Array Writable Labels")
void testRecordReaderDataSetIteratorNDArrayWritableLabels() {
void testRecordReaderDataSetIteratorNDArrayWritableLabels(Nd4jBackend backend) {
Collection<Collection<Writable>> data = new ArrayList<>();
data.add(Arrays.<Writable>asList(new DoubleWritable(0), new DoubleWritable(1), new NDArrayWritable(Nd4j.create(new double[] { 1.1, 2.1, 3.1 }, new long[] { 1, 3 }))));
data.add(Arrays.<Writable>asList(new DoubleWritable(2), new DoubleWritable(3), new NDArrayWritable(Nd4j.create(new double[] { 4.1, 5.1, 6.1 }, new long[] { 1, 3 }))));
@ -925,10 +951,11 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
assertEquals(expLabels, ds2.getLabels());
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@Disabled
@DisplayName("Special RR Test 4")
void specialRRTest4() throws Exception {
void specialRRTest4(Nd4jBackend backend) throws Exception {
RecordReader rr = new SpecialImageRecordReader(25000, 10, 3, 224, 224);
RecordReaderDataSetIterator rrdsi = new RecordReaderDataSetIterator(rr, 128);
int cnt = 0;
@ -1026,9 +1053,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
}
*/
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Record Reader Data Set Iterator Concat")
void testRecordReaderDataSetIteratorConcat() {
void testRecordReaderDataSetIteratorConcat(Nd4jBackend backend) {
// [DoubleWritable, DoubleWritable, NDArrayWritable([1,10]), IntWritable] -> concatenate to a [1,13] feature vector automatically.
List<Writable> l = Arrays.<Writable>asList(new DoubleWritable(1), new NDArrayWritable(Nd4j.create(new double[] { 2, 3, 4 })), new DoubleWritable(5), new NDArrayWritable(Nd4j.create(new double[] { 6, 7, 8 })), new IntWritable(9), new IntWritable(1));
RecordReader rr = new CollectionRecordReader(Collections.singletonList(l));
@ -1040,9 +1068,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
assertEquals(expL, ds.getLabels());
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Record Reader Data Set Iterator Concat 2")
void testRecordReaderDataSetIteratorConcat2() {
void testRecordReaderDataSetIteratorConcat2(Nd4jBackend backend) {
List<Writable> l = new ArrayList<>();
l.add(new IntWritable(0));
l.add(new NDArrayWritable(Nd4j.arange(1, 9)));
@ -1054,11 +1083,12 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
assertEquals(expF, ds.getFeatures());
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Record Reader Data Set Iterator Disjoint Features")
void testRecordReaderDataSetIteratorDisjointFeatures() {
void testRecordReaderDataSetIteratorDisjointFeatures(Nd4jBackend backend) {
// Idea: input vector is like [f,f,f,f,l,l,f,f] or similar - i.e., label writables aren't start/end
List<Writable> l = Arrays.<Writable>asList(new DoubleWritable(1), new NDArrayWritable(Nd4j.create(new float[] { 2, 3, 4 }, new long[] { 1, 3 })), new DoubleWritable(5), new NDArrayWritable(Nd4j.create(new float[] { 6, 7, 8 }, new long[] { 1, 3 })));
List<Writable> l = Arrays.asList(new DoubleWritable(1), new NDArrayWritable(Nd4j.create(new float[] { 2, 3, 4 }, new long[] { 1, 3 })), new DoubleWritable(5), new NDArrayWritable(Nd4j.create(new float[] { 6, 7, 8 }, new long[] { 1, 3 })));
INDArray expF = Nd4j.create(new float[] { 1, 6, 7, 8 }, new long[] { 1, 4 });
INDArray expL = Nd4j.create(new float[] { 2, 3, 4, 5 }, new long[] { 1, 4 });
RecordReader rr = new CollectionRecordReader(Collections.singletonList(l));
@ -1068,9 +1098,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
assertEquals(expL, ds.getLabels());
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Normalizer Prefetch Reset")
void testNormalizerPrefetchReset() throws Exception {
void testNormalizerPrefetchReset(Nd4jBackend backend) throws Exception {
// Check NPE fix for: https://github.com/eclipse/deeplearning4j/issues/4214
RecordReader csv = new CSVRecordReader();
csv.initialize(new FileSplit(Resources.asFile("iris.txt")));
@ -1087,9 +1118,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
iter.next();
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Reading From Stream")
void testReadingFromStream() throws Exception {
void testReadingFromStream(Nd4jBackend backend) throws Exception {
for (boolean b : new boolean[] { false, true }) {
int batchSize = 1;
int labelIndex = 4;
@ -1121,9 +1153,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
}
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Images RRDSI")
void testImagesRRDSI() throws Exception {
void testImagesRRDSI(Nd4jBackend backend) throws Exception {
File parentDir = temporaryFolder.toFile();
parentDir.deleteOnExit();
String str1 = FilenameUtils.concat(parentDir.getAbsolutePath(), "Zico/");
@ -1150,16 +1183,17 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
assertArrayEquals(new long[] { 2, 2 }, ds.getLabels().shape());
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Seq RRDSI No Labels")
void testSeqRRDSINoLabels() {
void testSeqRRDSINoLabels(Nd4jBackend backend) {
List<List<Writable>> sequence1 = new ArrayList<>();
sequence1.add(Arrays.asList((Writable) new DoubleWritable(1), new DoubleWritable(2)));
sequence1.add(Arrays.asList((Writable) new DoubleWritable(3), new DoubleWritable(4)));
sequence1.add(Arrays.asList((Writable) new DoubleWritable(5), new DoubleWritable(6)));
sequence1.add(Arrays.asList(new DoubleWritable(1), new DoubleWritable(2)));
sequence1.add(Arrays.asList(new DoubleWritable(3), new DoubleWritable(4)));
sequence1.add(Arrays.asList(new DoubleWritable(5), new DoubleWritable(6)));
List<List<Writable>> sequence2 = new ArrayList<>();
sequence2.add(Arrays.asList((Writable) new DoubleWritable(10), new DoubleWritable(20)));
sequence2.add(Arrays.asList((Writable) new DoubleWritable(30), new DoubleWritable(40)));
sequence2.add(Arrays.asList(new DoubleWritable(10), new DoubleWritable(20)));
sequence2.add(Arrays.asList(new DoubleWritable(30), new DoubleWritable(40)));
SequenceRecordReader rrFeatures = new CollectionSequenceRecordReader(Arrays.asList(sequence1, sequence2));
SequenceRecordReaderDataSetIterator iter = new SequenceRecordReaderDataSetIterator(rrFeatures, 2, -1, -1);
DataSet ds = iter.next();
@ -1167,9 +1201,10 @@ class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
assertNull(ds.getLabels());
}
@Test
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
@DisplayName("Test Collect Meta Data")
void testCollectMetaData() {
void testCollectMetaData(Nd4jBackend backend) {
RecordReaderDataSetIterator trainIter = new RecordReaderDataSetIterator.Builder(new CollectionRecordReader(Collections.<List<Writable>>emptyList()), 1).collectMetaData(true).build();
assertTrue(trainIter.isCollectMetaData());
trainIter.setCollectMetaData(false);

2
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/iterator/TestFileIterators.java
View File

@ -24,6 +24,7 @@ import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.datasets.iterator.file.FileDataSetIterator;
import org.deeplearning4j.datasets.iterator.file.FileMultiDataSetIterator;
import org.junit.jupiter.api.Disabled;
import org.junit.jupiter.api.Test;
import org.junit.jupiter.api.io.TempDir;
@ -40,6 +41,7 @@ import java.util.*;
import static org.junit.jupiter.api.Assertions.assertArrayEquals;
import static org.junit.jupiter.api.Assertions.assertEquals;
@Disabled
public class TestFileIterators extends BaseDL4JTest {

85
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/CNNGradientCheckTest.java
View File

@ -41,14 +41,19 @@ import org.junit.jupiter.api.Test;
import org.junit.jupiter.params.ParameterizedTest;
import org.junit.jupiter.params.provider.Arguments;
import org.junit.jupiter.params.provider.MethodSource;
import org.nd4j.linalg.BaseNd4jTestWithBackends;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.factory.Nd4jBackend;
import org.nd4j.linalg.learning.config.NoOp;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.stream.Stream;
import static org.deeplearning4j.nn.conf.ConvolutionMode.Same;
@ -56,6 +61,7 @@ import static org.deeplearning4j.nn.conf.ConvolutionMode.Truncate;
import static org.junit.jupiter.api.Assertions.*;
import org.junit.jupiter.api.DisplayName;
import org.junit.jupiter.api.extension.ExtendWith;
import org.nd4j.shade.guava.collect.Lists;
@DisplayName("Cnn Gradient Check Test")
class CNNGradientCheckTest extends BaseDL4JTest {
@ -77,7 +83,13 @@ class CNNGradientCheckTest extends BaseDL4JTest {
public static Stream<Arguments> params() {
return Arrays.asList(CNN2DFormat.values()).stream().map(Arguments::of);
List<Arguments> args = new ArrayList<>();
for(Nd4jBackend nd4jBackend : BaseNd4jTestWithBackends.BACKENDS) {
for(CNN2DFormat format : CNN2DFormat.values()) {
args.add(Arguments.of(format,nd4jBackend));
}
}
return args.stream();
}
@Override
@ -85,11 +97,10 @@ class CNNGradientCheckTest extends BaseDL4JTest {
return 999990000L;
}
@Test
@DisplayName("Test Gradient CNNMLN")
@ParameterizedTest
@MethodSource("#params")
public void testGradientCNNMLN(CNN2DFormat format) {
@MethodSource("org.deeplearning4j.gradientcheck.CNNGradientCheckTest#params")
public void testGradientCNNMLN(CNN2DFormat format,Nd4jBackend backend) {
if (// Only test NCHW due to flat input format...
format != CNN2DFormat.NCHW)
return;
@ -144,9 +155,10 @@ class CNNGradientCheckTest extends BaseDL4JTest {
}
}
@Test
@ParameterizedTest
@MethodSource("org.deeplearning4j.gradientcheck.CNNGradientCheckTest#params")
@DisplayName("Test Gradient CNNL 1 L 2 MLN")
void testGradientCNNL1L2MLN(CNN2DFormat format) {
void testGradientCNNL1L2MLN(CNN2DFormat format,Nd4jBackend backend) {
if (// Only test NCHW due to flat input format...
format != CNN2DFormat.NCHW)
return;
@ -207,9 +219,10 @@ class CNNGradientCheckTest extends BaseDL4JTest {
}
@Disabled
@Test
@ParameterizedTest
@MethodSource("org.deeplearning4j.gradientcheck.CNNGradientCheckTest#params")
@DisplayName("Test Cnn With Space To Depth")
void testCnnWithSpaceToDepth() {
void testCnnWithSpaceToDepth(CNN2DFormat format,Nd4jBackend backend) {
Nd4j.getRandom().setSeed(12345);
int nOut = 4;
int minibatchSize = 2;
@ -246,8 +259,8 @@ class CNNGradientCheckTest extends BaseDL4JTest {
@Test
@DisplayName("Test Cnn With Space To Batch")
@ParameterizedTest
@MethodSource("#params")
public void testCnnWithSpaceToBatch(CNN2DFormat format) {
@MethodSource("org.deeplearning4j.gradientcheck.CNNGradientCheckTest#params")
public void testCnnWithSpaceToBatch(CNN2DFormat format,Nd4jBackend backend) {
Nd4j.getRandom().setSeed(12345);
int nOut = 4;
int[] minibatchSizes = { 2, 4 };
@ -292,8 +305,8 @@ class CNNGradientCheckTest extends BaseDL4JTest {
@Test
@DisplayName("Test Cnn With Upsampling")
@ParameterizedTest
@MethodSource("#params")
void testCnnWithUpsampling(CNN2DFormat format) {
@MethodSource("org.deeplearning4j.gradientcheck.CNNGradientCheckTest#params")
void testCnnWithUpsampling(CNN2DFormat format,Nd4jBackend backend) {
Nd4j.getRandom().setSeed(12345);
int nOut = 4;
int[] minibatchSizes = { 1, 3 };
@ -328,8 +341,8 @@ class CNNGradientCheckTest extends BaseDL4JTest {
@Test
@DisplayName("Test Cnn With Subsampling")
@ParameterizedTest
@MethodSource("#params")
void testCnnWithSubsampling(CNN2DFormat format) {
@MethodSource("org.deeplearning4j.gradientcheck.CNNGradientCheckTest#params")
void testCnnWithSubsampling(CNN2DFormat format,Nd4jBackend backend) {
Nd4j.getRandom().setSeed(12345);
int nOut = 4;
int[] minibatchSizes = { 1, 3 };
@ -372,8 +385,8 @@ class CNNGradientCheckTest extends BaseDL4JTest {
@Test
@DisplayName("Test Cnn With Subsampling V 2")
@ParameterizedTest
@MethodSource("#params")
void testCnnWithSubsamplingV2(CNN2DFormat format) {
@MethodSource("org.deeplearning4j.gradientcheck.CNNGradientCheckTest#params")
void testCnnWithSubsamplingV2(CNN2DFormat format,Nd4jBackend backend) {
Nd4j.getRandom().setSeed(12345);
int nOut = 4;
int[] minibatchSizes = { 1, 3 };
@ -412,8 +425,8 @@ class CNNGradientCheckTest extends BaseDL4JTest {
@Test
@DisplayName("Test Cnn Locally Connected 2 D")
@ParameterizedTest
@MethodSource("#params")
void testCnnLocallyConnected2D(CNN2DFormat format) {
@MethodSource("org.deeplearning4j.gradientcheck.CNNGradientCheckTest#params")
void testCnnLocallyConnected2D(CNN2DFormat format,Nd4jBackend backend) {
int nOut = 3;
int width = 5;
int height = 5;
@ -444,8 +457,8 @@ class CNNGradientCheckTest extends BaseDL4JTest {
@Test
@DisplayName("Test Cnn Multi Layer")
@ParameterizedTest
@MethodSource("#params")
void testCnnMultiLayer(CNN2DFormat format) {
@MethodSource("org.deeplearning4j.gradientcheck.CNNGradientCheckTest#params")
void testCnnMultiLayer(CNN2DFormat format,Nd4jBackend backend) {
int nOut = 2;
int[] minibatchSizes = { 1, 2, 5 };
int width = 5;
@ -486,8 +499,8 @@ class CNNGradientCheckTest extends BaseDL4JTest {
@Test
@DisplayName("Test Cnn Same Padding Mode")
@ParameterizedTest
@MethodSource("#params")
void testCnnSamePaddingMode(CNN2DFormat format) {
@MethodSource("org.deeplearning4j.gradientcheck.CNNGradientCheckTest#params")
void testCnnSamePaddingMode(CNN2DFormat format,Nd4jBackend backend) {
int nOut = 2;
int[] minibatchSizes = { 1, 3, 3, 2, 1, 2 };
// Same padding mode: insensitive to exact input size...
@ -522,8 +535,8 @@ class CNNGradientCheckTest extends BaseDL4JTest {
@Test
@DisplayName("Test Cnn Same Padding Mode Strided")
@ParameterizedTest
@MethodSource("#params")
void testCnnSamePaddingModeStrided(CNN2DFormat format) {
@MethodSource("org.deeplearning4j.gradientcheck.CNNGradientCheckTest#params")
void testCnnSamePaddingModeStrided(CNN2DFormat format,Nd4jBackend backend) {
int nOut = 2;
int[] minibatchSizes = { 1, 3 };
int width = 16;
@ -567,8 +580,8 @@ class CNNGradientCheckTest extends BaseDL4JTest {
@Test
@DisplayName("Test Cnn Zero Padding Layer")
@ParameterizedTest
@MethodSource("#params")
void testCnnZeroPaddingLayer(CNN2DFormat format) {
@MethodSource("org.deeplearning4j.gradientcheck.CNNGradientCheckTest#params")
void testCnnZeroPaddingLayer(CNN2DFormat format,Nd4jBackend backend) {
Nd4j.getRandom().setSeed(12345);
int nOut = 4;
int width = 6;
@ -615,8 +628,8 @@ class CNNGradientCheckTest extends BaseDL4JTest {
@Test
@DisplayName("Test Deconvolution 2 D")
@ParameterizedTest
@MethodSource("#params")
void testDeconvolution2D(CNN2DFormat format) {
@MethodSource("org.deeplearning4j.gradientcheck.CNNGradientCheckTest#params")
void testDeconvolution2D(CNN2DFormat format,Nd4jBackend backend) {
int nOut = 2;
int[] minibatchSizes = new int[] { 1, 3, 3, 1, 3 };
int[] kernelSizes = new int[] { 1, 1, 1, 3, 3 };
@ -662,8 +675,8 @@ class CNNGradientCheckTest extends BaseDL4JTest {
@Test
@DisplayName("Test Separable Conv 2 D")
@ParameterizedTest
@MethodSource("#params")
void testSeparableConv2D(CNN2DFormat format) {
@MethodSource("org.deeplearning4j.gradientcheck.CNNGradientCheckTest#params")
void testSeparableConv2D(CNN2DFormat format,Nd4jBackend backend) {
int nOut = 2;
int width = 6;
int height = 6;
@ -709,8 +722,8 @@ class CNNGradientCheckTest extends BaseDL4JTest {
@Test
@DisplayName("Test Cnn Dilated")
@ParameterizedTest
@MethodSource("#params")
void testCnnDilated(CNN2DFormat format) {
@MethodSource("org.deeplearning4j.gradientcheck.CNNGradientCheckTest#params")
void testCnnDilated(CNN2DFormat format,Nd4jBackend backend) {
int nOut = 2;
int minibatchSize = 2;
int width = 8;
@ -761,8 +774,8 @@ class CNNGradientCheckTest extends BaseDL4JTest {
@Test
@DisplayName("Test Cropping 2 D Layer")
@ParameterizedTest
@MethodSource("#params")
void testCropping2DLayer(CNN2DFormat format) {
@MethodSource("org.deeplearning4j.gradientcheck.CNNGradientCheckTest#params")
void testCropping2DLayer(CNN2DFormat format,Nd4jBackend backend) {
Nd4j.getRandom().setSeed(12345);
int nOut = 2;
int width = 12;
@ -807,8 +820,8 @@ class CNNGradientCheckTest extends BaseDL4JTest {
@Test
@DisplayName("Test Depthwise Conv 2 D")
@ParameterizedTest
@MethodSource("#params")
void testDepthwiseConv2D(CNN2DFormat format) {
@MethodSource("org.deeplearning4j.gradientcheck.CNNGradientCheckTest#params")
void testDepthwiseConv2D(CNN2DFormat format,Nd4jBackend backendt) {
int nIn = 3;
int depthMultiplier = 2;
int nOut = nIn * depthMultiplier;

22
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/YoloGradientCheckTests.java
View File

@ -43,6 +43,7 @@ import org.junit.jupiter.api.io.TempDir;
import org.junit.jupiter.params.ParameterizedTest;
import org.junit.jupiter.params.provider.Arguments;
import org.junit.jupiter.params.provider.MethodSource;
import org.nd4j.linalg.BaseNd4jTestWithBackends;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
@ -51,13 +52,16 @@ import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.common.io.ClassPathResource;
import org.nd4j.linalg.factory.Nd4jBackend;
import org.nd4j.linalg.learning.config.NoOp;
import java.io.File;
import java.io.FileOutputStream;
import java.io.InputStream;
import java.nio.file.Path;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.stream.Stream;
import static org.junit.jupiter.api.Assertions.assertArrayEquals;
@ -70,8 +74,16 @@ public class YoloGradientCheckTests extends BaseDL4JTest {
}
@TempDir Path testDir;
public static Stream<Arguments> params() {
return Arrays.asList(CNN2DFormat.values()).stream().map(Arguments::of);
List<Arguments> args = new ArrayList<>();
for(Nd4jBackend nd4jBackend : BaseNd4jTestWithBackends.BACKENDS) {
for(CNN2DFormat format : CNN2DFormat.values()) {
args.add(Arguments.of(format,nd4jBackend));
}
}
return args.stream();
}
@Override
@ -80,8 +92,8 @@ public class YoloGradientCheckTests extends BaseDL4JTest {
}
@ParameterizedTest
@MethodSource("#params")
public void testYoloOutputLayer(CNN2DFormat format) {
@MethodSource("org.deeplearning4j.gradientcheckYoloGradientCheckTests.#params")
public void testYoloOutputLayer(CNN2DFormat format,Nd4jBackend backend) {
int depthIn = 2;
int c = 3;
int b = 3;
@ -180,8 +192,8 @@ public class YoloGradientCheckTests extends BaseDL4JTest {
@ParameterizedTest
@MethodSource("#params")
public void yoloGradientCheckRealData(@TempDir Path testDir,CNN2DFormat format) throws Exception {
@MethodSource("org.deeplearning4j.gradientcheckYoloGradientCheckTests#params")
public void yoloGradientCheckRealData(CNN2DFormat format,Nd4jBackend backend) throws Exception {
Nd4j.getRandom().setSeed(12345);
InputStream is1 = new ClassPathResource("yolo/VOC_TwoImage/JPEGImages/2007_009346.jpg").getInputStream();
InputStream is2 = new ClassPathResource("yolo/VOC_TwoImage/Annotations/2007_009346.xml").getInputStream();

8
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/graph/TestComputationGraphNetwork.java
View File

@ -1779,7 +1779,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
@Test
public void testCompGraphDropoutOutputLayers(){
//https://github.com/deeplearning4j/deeplearning4j/issues/6326
//https://github.com/eclipse/deeplearning4j/issues/6326
ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()
.dropOut(0.8)
.graphBuilder()
@ -1817,7 +1817,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
@Test
public void testCompGraphDropoutOutputLayers2() {
//https://github.com/deeplearning4j/deeplearning4j/issues/6326
//https://github.com/eclipse/deeplearning4j/issues/6326
ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()
.dropOut(0.8)
.graphBuilder()
@ -1976,7 +1976,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
@Test
public void testVerticesAndMasking7027(){
//https://github.com/deeplearning4j/deeplearning4j/issues/7027
//https://github.com/eclipse/deeplearning4j/issues/7027
int inputSize = 300;
int hiddenSize = 100;
int dataSize = 10;
@ -2017,7 +2017,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
@Test
public void testCompGraphUpdaterBlocks(){
//Check that setting learning rate results in correct rearrangement of updater state within updater blocks
//https://github.com/deeplearning4j/deeplearning4j/issues/6809#issuecomment-463892644
//https://github.com/eclipse/deeplearning4j/issues/6809#issuecomment-463892644
double lr = 1e-3;
ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()

90
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/convolution/ConvDataFormatTests.java
View File

@ -43,11 +43,13 @@ import org.junit.jupiter.api.Test;
import org.junit.jupiter.params.ParameterizedTest;
import org.junit.jupiter.params.provider.Arguments;
import org.junit.jupiter.params.provider.MethodSource;
import org.nd4j.linalg.BaseNd4jTestWithBackends;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.common.primitives.Pair;
import org.nd4j.linalg.factory.Nd4jBackend;
import java.util.ArrayList;
import java.util.Arrays;
@ -61,19 +63,25 @@ import static org.junit.jupiter.api.Assertions.assertTrue;
public class ConvDataFormatTests extends BaseDL4JTest {
public static Stream<Arguments> params(){
return Arrays.asList(new DataType[]{DataType.FLOAT, DataType.DOUBLE}).stream().map(Arguments::of);
public static Stream<Arguments> params() {
List<Arguments> args = new ArrayList<>();
for(Nd4jBackend nd4jBackend : BaseNd4jTestWithBackends.BACKENDS) {
for(DataType dataType : Arrays.asList(new DataType[]{DataType.FLOAT, DataType.DOUBLE})) {
args.add(Arguments.of(dataType,nd4jBackend));
}
}
return args.stream();
}
@Override
public long getTimeoutMilliseconds() {
return 999999999L;
}
@Test
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.convolution.ConvDataFormatTests#params")
@ParameterizedTest
public void testConv2d(DataType dataType) {
public void testConv2d(DataType dataType,Nd4jBackend backend) {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Truncate, ConvolutionMode.Same}) {
@ -105,10 +113,9 @@ public class ConvDataFormatTests extends BaseDL4JTest {
}
}
@Test
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.convolution.ConvDataFormatTests#params")
@ParameterizedTest
public void testSubsampling2d(DataType dataType) {
public void testSubsampling2d(DataType dataType,Nd4jBackend backend) {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Truncate, ConvolutionMode.Same}) {
@ -140,10 +147,9 @@ public class ConvDataFormatTests extends BaseDL4JTest {
}
}
@Test
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.convolution.ConvDataFormatTests#params")
@ParameterizedTest
public void testDepthwiseConv2d(DataType dataType) {
public void testDepthwiseConv2d(DataType dataType,Nd4jBackend backend) {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Truncate, ConvolutionMode.Same}) {
@ -175,10 +181,9 @@ public class ConvDataFormatTests extends BaseDL4JTest {
}
}
@Test
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.convolution.ConvDataFormatTests#params")
@ParameterizedTest
public void testSeparableConv2d(DataType dataType) {
public void testSeparableConv2d(DataType dataType,Nd4jBackend backend) {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Truncate, ConvolutionMode.Same}) {
@ -210,10 +215,9 @@ public class ConvDataFormatTests extends BaseDL4JTest {
}
}
@Test
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.convolution.ConvDataFormatTests#params")
@ParameterizedTest
public void testDeconv2d(DataType dataType) {
public void testDeconv2d(DataType dataType,Nd4jBackend backend) {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Truncate, ConvolutionMode.Same}) {
@ -245,10 +249,9 @@ public class ConvDataFormatTests extends BaseDL4JTest {
}
}
@Test
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.convolution.ConvDataFormatTests#params")
@ParameterizedTest
public void testLRN(DataType dataType) {
public void testLRN(DataType dataType,Nd4jBackend backend) {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Truncate, ConvolutionMode.Same}) {
@ -280,10 +283,9 @@ public class ConvDataFormatTests extends BaseDL4JTest {
}
}
@Test
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.convolution.ConvDataFormatTests#params")
@ParameterizedTest
public void testZeroPaddingLayer(DataType dataType) {
public void testZeroPaddingLayer(DataType dataType,Nd4jBackend backend) {
try {
for (boolean helpers : new boolean[]{false, true}) {
Nd4j.getRandom().setSeed(12345);
@ -313,10 +315,9 @@ public class ConvDataFormatTests extends BaseDL4JTest {
}
}
@Test
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.convolution.ConvDataFormatTests#params")
@ParameterizedTest
public void testCropping2DLayer(DataType dataType) {
public void testCropping2DLayer(DataType dataType,Nd4jBackend backend) {
try {
for (boolean helpers : new boolean[]{false, true}) {
Nd4j.getRandom().setSeed(12345);
@ -346,10 +347,9 @@ public class ConvDataFormatTests extends BaseDL4JTest {
}
}
@Test
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.convolution.ConvDataFormatTests#params")
@ParameterizedTest
public void testUpsampling2d(DataType dataType) {
public void testUpsampling2d(DataType dataType,Nd4jBackend backend) {
try {
for (boolean helpers : new boolean[]{false, true}) {
Nd4j.getRandom().setSeed(12345);
@ -379,10 +379,9 @@ public class ConvDataFormatTests extends BaseDL4JTest {
}
}
@Test
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.convolution.ConvDataFormatTests#params")
@ParameterizedTest
public void testBatchNormNet(DataType dataType) {
public void testBatchNormNet(DataType dataType,Nd4jBackend backend) {
try {
for(boolean useLogStd : new boolean[]{true, false}) {
for (boolean helpers : new boolean[]{false, true}) {
@ -414,10 +413,9 @@ public class ConvDataFormatTests extends BaseDL4JTest {
}
}
@Test
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.convolution.ConvDataFormatTests#params")
@ParameterizedTest
public void testCnnLossLayer(DataType dataType) {
public void testCnnLossLayer(DataType dataType,Nd4jBackend backend) {
try {
for (boolean helpers : new boolean[]{false, true}) {
Nd4j.getRandom().setSeed(12345);
@ -452,10 +450,9 @@ public class ConvDataFormatTests extends BaseDL4JTest {
}
}
@Test
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.convolution.ConvDataFormatTests#params")
@ParameterizedTest
public void testSpaceToDepthNet(DataType dataType) {
public void testSpaceToDepthNet(DataType dataType,Nd4jBackend backend) {
try {
for (boolean helpers : new boolean[]{false, true}) {
Nd4j.getRandom().setSeed(12345);
@ -485,10 +482,9 @@ public class ConvDataFormatTests extends BaseDL4JTest {
}
}
@Test
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.convolution.ConvDataFormatTests#params")
@ParameterizedTest
public void testSpaceToBatchNet(DataType dataType) {
public void testSpaceToBatchNet(DataType dataType,Nd4jBackend backend) {
try {
for (boolean helpers : new boolean[]{false, true}) {
Nd4j.getRandom().setSeed(12345);
@ -518,10 +514,9 @@ public class ConvDataFormatTests extends BaseDL4JTest {
}
}
@Test
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.convolution.ConvDataFormatTests#params")
@ParameterizedTest
public void testLocallyConnected(DataType dataType) {
public void testLocallyConnected(DataType dataType,Nd4jBackend backend) {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Truncate, ConvolutionMode.Same}) {
@ -554,10 +549,9 @@ public class ConvDataFormatTests extends BaseDL4JTest {
}
@Test
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.convolution.ConvDataFormatTests#params")
@ParameterizedTest
public void testGlobalPooling(DataType dataType) {
public void testGlobalPooling(DataType dataType,Nd4jBackend backend) {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (PoolingType pt : PoolingType.values()) {
@ -1014,7 +1008,7 @@ public class ConvDataFormatTests extends BaseDL4JTest {
@Test
public void testWrongFormatIn(){
public void testWrongFormatIn() {
for(CNN2DFormat df : CNN2DFormat.values()) {
for(int i = 0; i < 4; i++) {

49
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/recurrent/BidirectionalTest.java
View File

@ -50,6 +50,7 @@ import org.junit.jupiter.params.ParameterizedTest;
import org.junit.jupiter.params.provider.Arguments;
import org.junit.jupiter.params.provider.MethodSource;
import org.nd4j.enums.RnnDataFormat;
import org.nd4j.linalg.BaseNd4jTestWithBackends;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
@ -57,6 +58,7 @@ import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.MultiDataSet;
import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.factory.Nd4jBackend;
import org.nd4j.linalg.learning.config.Adam;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.nd4j.common.primitives.Pair;
@ -64,7 +66,9 @@ import org.deeplearning4j.nn.workspace.ArrayType;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import java.io.ByteArrayInputStream;
import java.io.ByteArrayOutputStream;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.stream.Stream;
import static org.deeplearning4j.nn.conf.RNNFormat.NCW;
@ -79,14 +83,20 @@ class BidirectionalTest extends BaseDL4JTest {
public static Stream<Arguments> params() {
return Arrays.asList(RNNFormat.values()).stream().map(Arguments::of);
List<Arguments> args = new ArrayList<>();
for(Nd4jBackend nd4jBackend : BaseNd4jTestWithBackends.BACKENDS) {
for(RNNFormat rnnFormat : RNNFormat.values()) {
args.add(Arguments.of(rnnFormat,nd4jBackend));
}
}
return args.stream();
}
@Test
@DisplayName("Compare Implementations")
@ParameterizedTest
@MethodSource("#params")
void compareImplementations(RNNFormat rnnDataFormat) {
@MethodSource("org.deeplearning4j.nn.layers.recurrent.BidirectionalTest#params")
void compareImplementations(RNNFormat rnnDataFormat,Nd4jBackend backend) {
for (WorkspaceMode wsm : WorkspaceMode.values()) {
log.info("*** Starting workspace mode: " + wsm);
// Bidirectional(GravesLSTM) and GravesBidirectionalLSTM should be equivalent, given equivalent params
@ -151,8 +161,8 @@ class BidirectionalTest extends BaseDL4JTest {
@DisplayName("Compare Implementations Comp Graph")
@ParameterizedTest
@MethodSource("#params")
void compareImplementationsCompGraph(RNNFormat rnnFormat) {
@MethodSource("org.deeplearning4j.nn.layers.recurrent.BidirectionalTest#params")
void compareImplementationsCompGraph(RNNFormat rnnFormat,Nd4jBackend backend) {
// for(WorkspaceMode wsm : WorkspaceMode.values()) {
for (WorkspaceMode wsm : new WorkspaceMode[] { WorkspaceMode.NONE, WorkspaceMode.ENABLED }) {
log.info("*** Starting workspace mode: " + wsm);
@ -206,11 +216,10 @@ class BidirectionalTest extends BaseDL4JTest {
}
}
@Test
@DisplayName("Test Serialization")
@ParameterizedTest
@MethodSource("#params")
void testSerialization(RNNFormat rnnDataFormat) throws Exception {
@MethodSource("org.deeplearning4j.nn.layers.recurrent.BidirectionalTest#params")
void testSerialization(RNNFormat rnnDataFormat,Nd4jBackend backend) throws Exception {
for (WorkspaceMode wsm : WorkspaceMode.values()) {
log.info("*** Starting workspace mode: " + wsm);
Nd4j.getRandom().setSeed(12345);
@ -245,11 +254,10 @@ class BidirectionalTest extends BaseDL4JTest {
}
}
@Test
@DisplayName("Test Serialization Comp Graph")
@ParameterizedTest
@MethodSource("#params")
void testSerializationCompGraph(RNNFormat rnnDataFormat) throws Exception {
@MethodSource("org.deeplearning4j.nn.layers.recurrent.BidirectionalTest#params")
void testSerializationCompGraph(RNNFormat rnnDataFormat,Nd4jBackend backend) throws Exception {
for (WorkspaceMode wsm : WorkspaceMode.values()) {
log.info("*** Starting workspace mode: " + wsm);
Nd4j.getRandom().setSeed(12345);
@ -282,11 +290,10 @@ class BidirectionalTest extends BaseDL4JTest {
}
}
@Test
@DisplayName("Test Simple Bidirectional")
@ParameterizedTest
@MethodSource("#params")
public void testSimpleBidirectional(RNNFormat rnnDataFormat) {
@MethodSource("org.deeplearning4j.nn.layers.recurrent.BidirectionalTest#params")
public void testSimpleBidirectional(RNNFormat rnnDataFormat,Nd4jBackend backend) {
for (WorkspaceMode wsm : WorkspaceMode.values()) {
log.info("*** Starting workspace mode: " + wsm);
Nd4j.getRandom().setSeed(12345);
@ -369,11 +376,10 @@ class BidirectionalTest extends BaseDL4JTest {
}
}
@Test
@DisplayName("Test Simple Bidirectional Comp Graph")
@ParameterizedTest
@MethodSource("#params")
void testSimpleBidirectionalCompGraph(RNNFormat rnnDataFormat) {
@MethodSource("org.deeplearning4j.nn.layers.recurrent.BidirectionalTest#params")
void testSimpleBidirectionalCompGraph(RNNFormat rnnDataFormat,Nd4jBackend backend) {
for (WorkspaceMode wsm : WorkspaceMode.values()) {
log.info("*** Starting workspace mode: " + wsm);
Nd4j.getRandom().setSeed(12345);
@ -462,10 +468,11 @@ class BidirectionalTest extends BaseDL4JTest {
}
}
@Test
@DisplayName("Test Issue 5472")
void testIssue5472() {
// https://github.com/deeplearning4j/deeplearning4j/issues/5472
@MethodSource("org.deeplearning4j.nn.layers.recurrent.BidirectionalTest#params")
@ParameterizedTest
void testIssue5472(RNNFormat rnnDataFormat,Nd4jBackend backend) {
// https://github.com/eclipse/deeplearning4j/issues/5472
int in = 2;
int out = 2;
ComputationGraphConfiguration.GraphBuilder builder = new NeuralNetConfiguration.Builder().updater(new Adam(0.01)).activation(Activation.RELU).graphBuilder().addInputs("IN").setInputTypes(InputType.recurrent(in)).addLayer("AUTOENCODER", new VariationalAutoencoder.Builder().encoderLayerSizes(64).decoderLayerSizes(64).nOut(7).pzxActivationFunction(Activation.IDENTITY).reconstructionDistribution(new BernoulliReconstructionDistribution(Activation.SIGMOID.getActivationFunction())).build(), "IN").addLayer("RNN", new Bidirectional(Bidirectional.Mode.ADD, new GravesLSTM.Builder().nOut(128).build()), "AUTOENCODER").addLayer("OUT", new RnnOutputLayer.Builder().nOut(out).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "RNN").setOutputs("OUT");

52
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/recurrent/GravesBidirectionalLSTMTest.java
View File

@ -39,15 +39,19 @@ import org.junit.jupiter.params.ParameterizedTest;
import org.junit.jupiter.params.provider.Arguments;
import org.junit.jupiter.params.provider.MethodSource;
import org.nd4j.common.primitives.Pair;
import org.nd4j.linalg.BaseNd4jTestWithBackends;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.activations.impl.ActivationSigmoid;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.factory.Nd4jBackend;
import org.nd4j.linalg.learning.config.AdaGrad;
import org.nd4j.linalg.learning.config.NoOp;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.stream.Stream;
import static org.junit.jupiter.api.Assertions.*;
@ -59,16 +63,20 @@ class GravesBidirectionalLSTMTest extends BaseDL4JTest {
public static Stream<Arguments> params(){
return Arrays.asList(RNNFormat.values()).stream().map(Arguments::of);
public static Stream<Arguments> params() {
List<Arguments> args = new ArrayList<>();
for(Nd4jBackend nd4jBackend : BaseNd4jTestWithBackends.BACKENDS) {
for(RNNFormat rnnFormat : RNNFormat.values()) {
args.add(Arguments.of(rnnFormat,nd4jBackend));
}
}
return args.stream();
}
@Test
@DisplayName("Test Bidirectional LSTM Graves Forward Basic")
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTMTest#params")
@ParameterizedTest
void testBidirectionalLSTMGravesForwardBasic(RNNFormat rnnDataFormat) {
void testBidirectionalLSTMGravesForwardBasic(RNNFormat rnnDataFormat,Nd4jBackend backend) {
// Very basic test of forward prop. of LSTM layer with a time series.
// Essentially make sure it doesn't throw any exceptions, and provides output in the correct shape.
int nIn = 13;
@ -108,11 +116,10 @@ class GravesBidirectionalLSTMTest extends BaseDL4JTest {
}
}
@Test
@DisplayName("Test Bidirectional LSTM Graves Backward Basic")
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTMTest#params")
@ParameterizedTest
void testBidirectionalLSTMGravesBackwardBasic(RNNFormat rnnDataFormat) {
void testBidirectionalLSTMGravesBackwardBasic(RNNFormat rnnDataFormat,Nd4jBackend backend) {
// Very basic test of backprop for mini-batch + time series
// Essentially make sure it doesn't throw any exceptions, and provides output in the correct shape.
testGravesBackwardBasicHelper(rnnDataFormat,13, 3, 17, 10, 7);
@ -168,9 +175,10 @@ class GravesBidirectionalLSTMTest extends BaseDL4JTest {
}
}
@Test
@DisplayName("Test Graves Bidirectional LSTM Forward Pass Helper")
void testGravesBidirectionalLSTMForwardPassHelper() throws Exception {
@ParameterizedTest
@MethodSource("org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTMTest#params")
void testGravesBidirectionalLSTMForwardPassHelper(RNNFormat rnnDataFormat,Nd4jBackend backend) throws Exception {
// GravesBidirectionalLSTM.activateHelper() has different behaviour (due to optimizations) when forBackprop==true vs false
// But should otherwise provide identical activations
Nd4j.getRandom().setSeed(12345);
@ -204,11 +212,10 @@ class GravesBidirectionalLSTMTest extends BaseDL4JTest {
}
}
@Test
@DisplayName("Test Get Set Parmas")
@MethodSource("#params")
@DisplayName("Test Get Set Params")
@MethodSource("org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTMTest#params")
@ParameterizedTest
void testGetSetParmas(RNNFormat rnnDataFormat) {
void testGetSetParmas(RNNFormat rnnDataFormat,Nd4jBackend backend) {
final int nIn = 2;
final int layerSize = 3;
final int miniBatchSize = 2;
@ -226,11 +233,10 @@ class GravesBidirectionalLSTMTest extends BaseDL4JTest {
assertArrayEquals(act2.data().asDouble(), act1.data().asDouble(), 1e-8);
}
@Test
@DisplayName("Test Simple Forwards And Backwards Activation")
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTMTest#params")
@ParameterizedTest
void testSimpleForwardsAndBackwardsActivation(RNNFormat rnnDataFormat) {
void testSimpleForwardsAndBackwardsActivation(RNNFormat rnnDataFormat,Nd4jBackend backend) {
final int nIn = 2;
final int layerSize = 3;
final int miniBatchSize = 1;
@ -336,9 +342,10 @@ class GravesBidirectionalLSTMTest extends BaseDL4JTest {
assertArrayEquals(backEpsilon.dup().data().asDouble(), refEpsilon.dup().data().asDouble(), 1e-6);
}
@Test
@MethodSource("org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTMTest#params")
@DisplayName("Test Serialization")
void testSerialization() {
@ParameterizedTest
void testSerialization(RNNFormat rnnDataFormat,Nd4jBackend backend) {
final MultiLayerConfiguration conf1 = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(new AdaGrad(0.1)).l2(0.001).seed(12345).list().layer(0, new org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder().activation(Activation.TANH).nIn(2).nOut(2).dist(new UniformDistribution(-0.05, 0.05)).build()).layer(1, new org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder().activation(Activation.TANH).nIn(2).nOut(2).dist(new UniformDistribution(-0.05, 0.05)).build()).layer(2, new org.deeplearning4j.nn.conf.layers.RnnOutputLayer.Builder().activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).nIn(2).nOut(2).build()).build();
final String json1 = conf1.toJson();
final MultiLayerConfiguration conf2 = MultiLayerConfiguration.fromJson(json1);
@ -346,11 +353,10 @@ class GravesBidirectionalLSTMTest extends BaseDL4JTest {
assertEquals(json1, json2);
}
@Test
@DisplayName("Test Gate Activation Fns Sanity Check")
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTMTest#params")
@ParameterizedTest
void testGateActivationFnsSanityCheck(RNNFormat rnnDataFormat) {
void testGateActivationFnsSanityCheck(RNNFormat rnnDataFormat,Nd4jBackend backend) {
for (String gateAfn : new String[] { "sigmoid", "hardsigmoid" }) {
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).seed(12345).list().layer(0, new org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder().gateActivationFunction(gateAfn).activation(Activation.TANH).nIn(2).nOut(2).dataFormat(rnnDataFormat).build()).layer(1, new org.deeplearning4j.nn.conf.layers.RnnOutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(2).nOut(2).dataFormat(rnnDataFormat).activation(Activation.TANH).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);

22
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/recurrent/MaskZeroLayerTest.java
View File

@ -34,12 +34,17 @@ import org.junit.jupiter.api.Test;
import org.junit.jupiter.params.ParameterizedTest;
import org.junit.jupiter.params.provider.Arguments;
import org.junit.jupiter.params.provider.MethodSource;
import org.nd4j.linalg.BaseNd4jTestWithBackends;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.factory.Nd4jBackend;
import org.nd4j.linalg.indexing.NDArrayIndex;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.stream.Stream;
import static org.junit.jupiter.api.Assertions.assertEquals;
@ -51,13 +56,20 @@ class MaskZeroLayerTest extends BaseDL4JTest {
public static Stream<Arguments> params() {
return Arrays.asList(RNNFormat.values()).stream().map(Arguments::of);
List<Arguments> args = new ArrayList<>();
for(Nd4jBackend nd4jBackend : BaseNd4jTestWithBackends.BACKENDS) {
for(RNNFormat rnnFormat : RNNFormat.values()) {
args.add(Arguments.of(rnnFormat,nd4jBackend));
}
}
return args.stream();
}
@DisplayName("Activate")
@ParameterizedTest
@MethodSource("#params")
void activate(RNNFormat rnnDataFormat) {
@MethodSource("org.deeplearning4j.nn.layers.recurrent.MaskZeroLayerTest#params")
void activate(RNNFormat rnnDataFormat,Nd4jBackend backend) {
// GIVEN two examples where some of the timesteps are zero.
INDArray ex1 = Nd4j.create(new double[][] { new double[] { 0, 3, 5 }, new double[] { 0, 0, 2 } });
INDArray ex2 = Nd4j.create(new double[][] { new double[] { 0, 0, 2 }, new double[] { 0, 0, 2 } });
@ -96,8 +108,8 @@ class MaskZeroLayerTest extends BaseDL4JTest {
@DisplayName("Test Serialization")
@ParameterizedTest
@MethodSource("#params")
void testSerialization(RNNFormat rnnDataFormat) {
@MethodSource("org.deeplearning4j.nn.layers.recurrent.MaskZeroLayerTest#params")
void testSerialization(RNNFormat rnnDataFormat,Nd4jBackend backend) {
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().list().layer(new org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer.Builder().setMaskValue(0.0).setUnderlying(new LSTM.Builder().nIn(4).nOut(5).dataFormat(rnnDataFormat).build()).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();

66
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/recurrent/RnnDataFormatTests.java
View File

@ -44,11 +44,13 @@ import org.junit.jupiter.api.Test;
import org.junit.jupiter.params.ParameterizedTest;
import org.junit.jupiter.params.provider.Arguments;
import org.junit.jupiter.params.provider.MethodSource;
import org.nd4j.linalg.BaseNd4jTestWithBackends;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.common.primitives.Pair;
import org.nd4j.linalg.factory.Nd4jBackend;
import java.util.ArrayList;
import java.util.List;
@ -66,42 +68,42 @@ public class RnnDataFormatTests extends BaseDL4JTest {
for (boolean helpers: new boolean[]{true, false})
for (boolean lastTimeStep: new boolean[]{true, false})
for (boolean maskZero: new boolean[]{true, false})
ret.add(new Object[]{helpers, lastTimeStep, maskZero});
for(Nd4jBackend backend : BaseNd4jTestWithBackends.BACKENDS)
ret.add(new Object[]{helpers, lastTimeStep, maskZero,backend});
return ret.stream().map(Arguments::of);
}
@Test
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.recurrent.RnnDataFormatTests#params")
@ParameterizedTest
public void testSimpleRnn(boolean helpers,
boolean lastTimeStep,
boolean maskZeros
) {
boolean lastTimeStep,
boolean maskZeros,
Nd4jBackend backend) {
try {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = "Helpers: " + helpers + ", lastTimeStep: " + lastTimeStep + ", maskZeros: " + maskZeros;
System.out.println(" --- " + msg + " ---");
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = "Helpers: " + helpers + ", lastTimeStep: " + lastTimeStep + ", maskZeros: " + maskZeros;
System.out.println(" --- " + msg + " ---");
INDArray inNCW = Nd4j.rand(DataType.FLOAT, 2, 3, 12);
INDArray inNCW = Nd4j.rand(DataType.FLOAT, 2, 3, 12);
INDArray labelsNWC = (lastTimeStep) ?TestUtils.randomOneHot(2, 10): TestUtils.randomOneHot(2 * 12, 10).reshape(2, 12, 10);
INDArray labelsNWC = (lastTimeStep) ?TestUtils.randomOneHot(2, 10): TestUtils.randomOneHot(2 * 12, 10).reshape(2, 12, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getSimpleRnnNet(RNNFormat.NCW, true, lastTimeStep, maskZeros))
.net2(getSimpleRnnNet(RNNFormat.NCW, false, lastTimeStep, maskZeros))
.net3(getSimpleRnnNet(RNNFormat.NWC, true, lastTimeStep, maskZeros))
.net4(getSimpleRnnNet(RNNFormat.NWC, false, lastTimeStep, maskZeros))
.inNCW(inNCW)
.labelsNCW((lastTimeStep)? labelsNWC: labelsNWC.permute(0, 2, 1))
.labelsNWC(labelsNWC)
.testLayerIdx(1)
.build();
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getSimpleRnnNet(RNNFormat.NCW, true, lastTimeStep, maskZeros))
.net2(getSimpleRnnNet(RNNFormat.NCW, false, lastTimeStep, maskZeros))
.net3(getSimpleRnnNet(RNNFormat.NWC, true, lastTimeStep, maskZeros))
.net4(getSimpleRnnNet(RNNFormat.NWC, false, lastTimeStep, maskZeros))
.inNCW(inNCW)
.labelsNCW((lastTimeStep)? labelsNWC: labelsNWC.permute(0, 2, 1))
.labelsNWC(labelsNWC)
.testLayerIdx(1)
.build();
TestCase.testHelper(tc);
TestCase.testHelper(tc);
} finally {
@ -110,10 +112,10 @@ public class RnnDataFormatTests extends BaseDL4JTest {
}
@ParameterizedTest
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.recurrent.RnnDataFormatTests#params")
public void testLSTM(boolean helpers,
boolean lastTimeStep,
boolean maskZeros) {
boolean maskZeros,Nd4jBackend backend) {
try {
Nd4j.getRandom().setSeed(12345);
@ -146,12 +148,11 @@ public class RnnDataFormatTests extends BaseDL4JTest {
}
@Test
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.recurrent.RnnDataFormatTests#params")
@ParameterizedTest
public void testGraveLSTM(boolean helpers,
boolean lastTimeStep,
boolean maskZeros) {
boolean maskZeros,Nd4jBackend backend) {
try {
Nd4j.getRandom().setSeed(12345);
@ -184,12 +185,11 @@ public class RnnDataFormatTests extends BaseDL4JTest {
}
@Test
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.recurrent.RnnDataFormatTests#params")
@ParameterizedTest
public void testGraveBiLSTM(boolean helpers,
boolean lastTimeStep,
boolean maskZeros) {
boolean maskZeros,Nd4jBackend backend) {
try {
Nd4j.getRandom().setSeed(12345);
@ -276,7 +276,7 @@ public class RnnDataFormatTests extends BaseDL4JTest {
.layer(layer)
.layer(
(lastTimeStep)?new OutputLayer.Builder().activation(Activation.SOFTMAX).nOut(10).build():
new RnnOutputLayer.Builder().activation(Activation.SOFTMAX).nOut(10).dataFormat(format).build()
new RnnOutputLayer.Builder().activation(Activation.SOFTMAX).nOut(10).dataFormat(format).build()
)
.setInputType(InputType.recurrent(3, 12, format));

25
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/recurrent/TestRnnLayers.java
View File

@ -41,14 +41,17 @@ import org.junit.jupiter.params.ParameterizedTest;
import org.junit.jupiter.params.provider.Arguments;
import org.junit.jupiter.params.provider.MethodSource;
import org.nd4j.enums.RnnDataFormat;
import org.nd4j.linalg.BaseNd4jTestWithBackends;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.factory.Nd4jBackend;
import org.nd4j.linalg.learning.config.NoOp;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.nd4j.common.primitives.Pair;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.Random;
@ -61,13 +64,19 @@ import static org.junit.jupiter.api.Assertions.assertTrue;
public class TestRnnLayers extends BaseDL4JTest {
public static Stream<Arguments> params(){
return Arrays.asList(RNNFormat.values()).stream().map(Arguments::of);
public static Stream<Arguments> params() {
List<Arguments> args = new ArrayList<>();
for(Nd4jBackend nd4jBackend : BaseNd4jTestWithBackends.BACKENDS) {
for(RNNFormat rnnFormat : RNNFormat.values()) {
args.add(Arguments.of(rnnFormat,nd4jBackend));
}
}
return args.stream();
}
@ParameterizedTest
@MethodSource("#params")
public void testTimeStepIs3Dimensional(RNNFormat rnnDataFormat) {
@MethodSource("org.deeplearning4j.nn.layers.recurrent.TestRnnLayers#params")
public void testTimeStepIs3Dimensional(RNNFormat rnnDataFormat,Nd4jBackend backend) {
int nIn = 12;
int nOut = 3;
@ -117,8 +126,8 @@ public class TestRnnLayers extends BaseDL4JTest {
}
@ParameterizedTest
@MethodSource("#params")
public void testDropoutRecurrentLayers(RNNFormat rnnDataFormat){
@MethodSource("org.deeplearning4j.nn.layers.recurrent.TestRnnLayers#params")
public void testDropoutRecurrentLayers(RNNFormat rnnDataFormat,Nd4jBackend backend) {
Nd4j.getRandom().setSeed(12345);
String[] layerTypes = new String[]{"graves", "lstm", "simple"};
@ -216,8 +225,8 @@ public class TestRnnLayers extends BaseDL4JTest {
}
@ParameterizedTest
@MethodSource("#params")
public void testMismatchedInputLabelLength(RNNFormat rnnDataFormat){
@MethodSource("org.deeplearning4j.nn.layers.recurrent.TestRnnLayers#params")
public void testMismatchedInputLabelLength(RNNFormat rnnDataFormat,Nd4jBackend backend){
for( int i = 0; i < 2; i++) {

20
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/recurrent/TestSimpleRnn.java
View File

@ -33,14 +33,18 @@ import org.junit.jupiter.api.Test;
import org.junit.jupiter.params.ParameterizedTest;
import org.junit.jupiter.params.provider.Arguments;
import org.junit.jupiter.params.provider.MethodSource;
import org.nd4j.linalg.BaseNd4jTestWithBackends;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.factory.Nd4jBackend;
import org.nd4j.linalg.learning.config.NoOp;
import org.nd4j.linalg.ops.transforms.Transforms;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.stream.Stream;
import static org.junit.jupiter.api.Assertions.assertEquals;
@ -52,12 +56,18 @@ public class TestSimpleRnn extends BaseDL4JTest {
public static Stream<Arguments> params() {
return Arrays.asList(RNNFormat.values()).stream().map(Arguments::of);
List<Arguments> args = new ArrayList<>();
for(Nd4jBackend nd4jBackend : BaseNd4jTestWithBackends.BACKENDS) {
for(RNNFormat rnnFormat : RNNFormat.values()) {
args.add(Arguments.of(rnnFormat,nd4jBackend));
}
}
return args.stream();
}
@ParameterizedTest
@MethodSource("#params")
public void testSimpleRnn(RNNFormat rnnDataFormat) {
@MethodSource("org.deeplearning4j.nn.layers.recurrent.TestRnnLayers#params")
public void testSimpleRnn(RNNFormat rnnDataFormat, Nd4jBackend backend) {
Nd4j.getRandom().setSeed(12345);
int m = 3;
@ -126,8 +136,8 @@ public class TestSimpleRnn extends BaseDL4JTest {
}
@ParameterizedTest
@MethodSource("#params")
public void testBiasInit(RNNFormat rnnDataFormat) {
@MethodSource("org.deeplearning4j.nn.layers.recurrent.TestRnnLayers#params")
public void testBiasInit(RNNFormat rnnDataFormat,Nd4jBackend backend) {
Nd4j.getRandom().setSeed(12345);
int nIn = 5;
int layerSize = 6;

23
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/recurrent/TestTimeDistributed.java
View File

@ -41,15 +41,19 @@ import org.junit.jupiter.api.Test;
import org.junit.jupiter.params.ParameterizedTest;
import org.junit.jupiter.params.provider.Arguments;
import org.junit.jupiter.params.provider.MethodSource;
import org.nd4j.linalg.BaseNd4jTestWithBackends;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.factory.Nd4jBackend;
import org.nd4j.linalg.learning.config.Adam;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.stream.Stream;
import static org.junit.jupiter.api.Assertions.assertEquals;
@ -57,13 +61,19 @@ import static org.junit.jupiter.api.Assertions.assertEquals;
public class TestTimeDistributed extends BaseDL4JTest {
public static Stream<Arguments> params(){
return Arrays.asList(RNNFormat.values()).stream().map(Arguments::of);
public static Stream<Arguments> params() {
List<Arguments> args = new ArrayList<>();
for(Nd4jBackend nd4jBackend : BaseNd4jTestWithBackends.BACKENDS) {
for(RNNFormat rnnFormat : RNNFormat.values()) {
args.add(Arguments.of(rnnFormat,nd4jBackend));
}
}
return args.stream();
}
@ParameterizedTest
@MethodSource("#params")
public void testTimeDistributed(RNNFormat rnnDataFormat){
@MethodSource("org.deeplearning4j.nn.layers.recurrent.TestTimeDistributed#params")
public void testTimeDistributed(RNNFormat rnnDataFormat,Nd4jBackend backend){
for(WorkspaceMode wsm : new WorkspaceMode[]{WorkspaceMode.ENABLED, WorkspaceMode.NONE}) {
MultiLayerConfiguration conf1 = new NeuralNetConfiguration.Builder()
@ -133,10 +143,9 @@ public class TestTimeDistributed extends BaseDL4JTest {
}
@Test
@MethodSource("#params")
@MethodSource("org.deeplearning4j.nn.layers.recurrent.TestTimeDistributed#params")
@ParameterizedTest
public void testTimeDistributedDense(RNNFormat rnnDataFormat){
public void testTimeDistributedDense(RNNFormat rnnDataFormat,Nd4jBackend backend) {
for( int rnnType = 0; rnnType < 3; rnnType++ ) {
for( int ffType = 0; ffType < 3; ffType++ ) {

2
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/misc/TestMemoryReports.java
View File

@ -261,7 +261,7 @@ public class TestMemoryReports extends BaseDL4JTest {
@Test
public void testPreprocessors() throws Exception {
//https://github.com/deeplearning4j/deeplearning4j/issues/4223
//https://github.com/eclipse/deeplearning4j/issues/4223
File f = new ClassPathResource("4223/CompGraphConfig.json").getTempFileFromArchive();
String s = FileUtils.readFileToString(f, Charset.defaultCharset());

4
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/misc/WorkspaceTests.java
View File

@ -88,7 +88,7 @@ public class WorkspaceTests extends BaseDL4JTest {
@Test
public void testWorkspaceIndependence() {
//https://github.com/deeplearning4j/deeplearning4j/issues/4337
//https://github.com/eclipse/deeplearning4j/issues/4337
int depthIn = 2;
int depthOut = 2;
int nOut = 2;
@ -143,7 +143,7 @@ public class WorkspaceTests extends BaseDL4JTest {
@Test
public void testWithPreprocessorsCG() {
//https://github.com/deeplearning4j/deeplearning4j/issues/4347
//https://github.com/eclipse/deeplearning4j/issues/4347
//Cause for the above issue was layerVertex.setInput() applying the preprocessor, with the result
// not being detached properly from the workspace...

2
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/mkldnn/ValidateMKLDNN.java
View File

@ -195,7 +195,7 @@ public class ValidateMKLDNN extends BaseDL4JTest {
}
}
@Test @Disabled //https://github.com/deeplearning4j/deeplearning4j/issues/7272
@Test @Disabled //https://github.com/eclipse/deeplearning4j/issues/7272
public void validateLRN() {
//Only run test if using nd4j-native backend

2
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/multilayer/MultiLayerTest.java
View File

@ -938,7 +938,7 @@ public class MultiLayerTest extends BaseDL4JTest {
@DisplayName("Test MLN Updater Blocks")
void testMLNUpdaterBlocks() {
// Check that setting learning rate results in correct rearrangement of updater state within updater blocks
// https://github.com/deeplearning4j/deeplearning4j/issues/6809#issuecomment-463892644
// https://github.com/eclipse/deeplearning4j/issues/6809#issuecomment-463892644
double lr = 1e-3;
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).weightInit(WeightInit.XAVIER).updater(new Adam(lr)).list().layer(new DenseLayer.Builder().nIn(5).nOut(3).build()).layer(new DenseLayer.Builder().nIn(3).nOut(2).build()).layer(new OutputLayer.Builder(LossFunctions.LossFunction.XENT).nIn(2).nOut(1).activation(Activation.SIGMOID).build()).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);

2
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/transferlearning/TransferLearningCompGraphTest.java
View File

@ -181,7 +181,7 @@ class TransferLearningCompGraphTest extends BaseDL4JTest {
@Test
@DisplayName("Test Object Overrides")
void testObjectOverrides() {
// https://github.com/deeplearning4j/deeplearning4j/issues/4368
// https://github.com/eclipse/deeplearning4j/issues/4368
ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().dropOut(0.5).weightNoise(new DropConnect(0.5)).l2(0.5).constrainWeights(new UnitNormConstraint()).graphBuilder().addInputs("in").addLayer("layer", new DenseLayer.Builder().nIn(10).nOut(10).build(), "in").setOutputs("layer").build();
ComputationGraph orig = new ComputationGraph(conf);
orig.init();

2
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/transferlearning/TransferLearningMLNTest.java
View File

@ -317,7 +317,7 @@ class TransferLearningMLNTest extends BaseDL4JTest {
@Test
@DisplayName("Test Object Overrides")
void testObjectOverrides() {
// https://github.com/deeplearning4j/deeplearning4j/issues/4368
// https://github.com/eclipse/deeplearning4j/issues/4368
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().dropOut(0.5).weightNoise(new DropConnect(0.5)).l2(0.5).constrainWeights(new UnitNormConstraint()).list().layer(new DenseLayer.Builder().nIn(10).nOut(10).build()).build();
MultiLayerNetwork orig = new MultiLayerNetwork(conf);
orig.init();

2
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/regressiontest/RegressionTest100a.java
View File

@ -200,7 +200,7 @@ public class RegressionTest100a extends BaseDL4JTest {
//Minor bug in 1.0.0-beta and earlier: not adding epsilon value to forward pass for batch norm
//Which means: the record output doesn't have this. To account for this, we'll manually set eps to 0.0 here
//https://github.com/deeplearning4j/deeplearning4j/issues/5836#issuecomment-405526228
//https://github.com/eclipse/deeplearning4j/issues/5836#issuecomment-405526228
for(Layer l : net.getLayers()){
if(l.conf().getLayer() instanceof BatchNormalization){
BatchNormalization bn = (BatchNormalization) l.conf().getLayer();

2
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/regressiontest/customlayer100a/CustomLayer.java
View File

@ -97,7 +97,7 @@ public class CustomLayer extends FeedForwardLayer {
//In this case, we can use the DefaultParamInitializer, which is the same one used for DenseLayer
//For more complex layers, you may need to implement a custom parameter initializer
//See the various parameter initializers here:
//https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j-core/src/main/java/org/deeplearning4j/nn/params
//https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j-core/src/main/java/org/deeplearning4j/nn/params
return DefaultParamInitializer.getInstance();
}

2
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/util/CrashReportingUtilTest.java
View File

@ -36,6 +36,7 @@ import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.junit.jupiter.api.AfterEach;
import org.junit.jupiter.api.Disabled;
import org.junit.jupiter.api.Test;
import org.junit.jupiter.api.io.TempDir;
import org.nd4j.linalg.activations.Activation;
@ -73,6 +74,7 @@ class CrashReportingUtilTest extends BaseDL4JTest {
@Test
@DisplayName("Test")
@Disabled
void test() throws Exception {
File dir = testDir.toFile();
CrashReportingUtil.crashDumpOutputDirectory(dir);

2
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/util/ModelSerializerTest.java
View File

@ -33,6 +33,7 @@ import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.junit.jupiter.api.Disabled;
import org.junit.jupiter.api.Test;
import org.junit.jupiter.api.io.TempDir;
import org.nd4j.linalg.activations.Activation;
@ -57,6 +58,7 @@ import java.nio.file.Path;
import org.junit.jupiter.api.extension.ExtendWith;
@DisplayName("Model Serializer Test")
@Disabled
class ModelSerializerTest extends BaseDL4JTest {
@TempDir

4
deeplearning4j/deeplearning4j-zoo/src/test/java/org/deeplearning4j/zoo/TestInstantiation.java
View File

@ -281,7 +281,7 @@ public class TestInstantiation extends BaseDL4JTest {
@Test
public void testYolo4635() throws Exception {
ignoreIfCuda();
//https://github.com/deeplearning4j/deeplearning4j/issues/4635
//https://github.com/eclipse/deeplearning4j/issues/4635
int nClasses = 10;
TinyYOLO model = TinyYOLO.builder().numClasses(nClasses).build();
@ -292,7 +292,7 @@ public class TestInstantiation extends BaseDL4JTest {
@Test
public void testTransferLearning() throws Exception {
ignoreIfCuda();
//https://github.com/deeplearning4j/deeplearning4j/issues/7193
//https://github.com/eclipse/deeplearning4j/issues/7193
ComputationGraph cg = (ComputationGraph) ResNet50.builder().build().initPretrained();

2
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/random/impl/DropOut.java
View File

@ -36,7 +36,7 @@ public class DropOut extends BaseRandomOp {
public DropOut(SameDiff sameDiff, SDVariable input, double p) {
super(sameDiff, input);
this.p = p;
//https://github.com/deeplearning4j/deeplearning4j/issues/5650
//https://github.com/eclipse/deeplearning4j/issues/5650
throw new UnsupportedOperationException("Dropout SameDiff support disabled pending backprop support");
}

2
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/versioncheck/VersionCheck.java
View File

@ -250,7 +250,7 @@ public class VersionCheck {
}
} catch (NoClassDefFoundError e){
//Should only happen on Android 7.0 or earlier - silently ignore
//https://github.com/deeplearning4j/deeplearning4j/issues/6609
//https://github.com/eclipse/deeplearning4j/issues/6609
} catch (Throwable e){
//log and skip
log.debug("Error finding/loading version check resources", e);

2
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/LossOpValidation.java
View File

@ -383,7 +383,7 @@ public class LossOpValidation extends BaseOpValidation {
.build();
Nd4j.getExecutioner().exec(op);
INDArray exp = Nd4j.scalar(0.6); //https://github.com/deeplearning4j/deeplearning4j/issues/6532
INDArray exp = Nd4j.scalar(0.6); //https://github.com/eclipse/deeplearning4j/issues/6532
assertEquals(exp, out);
}

14
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/MiscOpValidation.java
View File

@ -141,7 +141,7 @@ public class MiscOpValidation extends BaseOpValidation {
bcOp = new FloorModOp(sd, in3, in2).outputVariable();
name = "floormod";
if(OpValidationSuite.IGNORE_FAILING){
//https://github.com/deeplearning4j/deeplearning4j/issues/5976
//https://github.com/eclipse/deeplearning4j/issues/5976
continue;
}
break;
@ -232,7 +232,7 @@ public class MiscOpValidation extends BaseOpValidation {
bcOp = new FloorModOp(sd, in3, in2).outputVariable();
name = "floormod";
if(OpValidationSuite.IGNORE_FAILING){
//https://github.com/deeplearning4j/deeplearning4j/issues/5976
//https://github.com/eclipse/deeplearning4j/issues/5976
continue;
}
break;
@ -334,7 +334,7 @@ public class MiscOpValidation extends BaseOpValidation {
bcOp = new FloorModOp(sd, in3, in2).outputVariable();
name = "floormod";
if(OpValidationSuite.IGNORE_FAILING){
//https://github.com/deeplearning4j/deeplearning4j/issues/5976
//https://github.com/eclipse/deeplearning4j/issues/5976
continue;
}
break;
@ -717,7 +717,7 @@ public class MiscOpValidation extends BaseOpValidation {
for (char bOrder : new char[]{'c', 'f'}) {
for (boolean transposeA : new boolean[]{false, true}) {
for (boolean transposeB : new boolean[]{false, true}) {
for (boolean transposeResult : new boolean[]{false, true}) { //https://github.com/deeplearning4j/deeplearning4j/issues/5648
for (boolean transposeResult : new boolean[]{false, true}) { //https://github.com/eclipse/deeplearning4j/issues/5648
Nd4j.getRandom().setSeed(12345);
INDArray aArr = Nd4j.rand(DataType.DOUBLE, t(transposeA, aShape)).dup(aOrder);
@ -761,7 +761,7 @@ public class MiscOpValidation extends BaseOpValidation {
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
public void testBatchMmulBasic(Nd4jBackend backend) {
OpValidationSuite.ignoreFailing(); //https://github.com/deeplearning4j/deeplearning4j/issues/6873
OpValidationSuite.ignoreFailing(); //https://github.com/eclipse/deeplearning4j/issues/6873
int M = 5;
int N = 3;
int K = 4;
@ -1188,7 +1188,7 @@ public class MiscOpValidation extends BaseOpValidation {
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
public void testOneHotOp(){
//https://www.tensorflow.org/api_docs/python/tf/one_hot
//https://github.com/deeplearning4j/deeplearning4j/blob/master/libnd4j/include/ops/declarable/generic/parity_ops/onehot.cpp
//https://github.com/eclipse/deeplearning4j/blob/master/libnd4j/include/ops/declarable/generic/parity_ops/onehot.cpp
for( int axis=-1; axis<=0; axis++ ) {
String err = OpValidation.validate(new OpTestCase(new OneHot(Nd4j.create(new double[]{0, 1, 2}),
@ -1244,7 +1244,7 @@ public class MiscOpValidation extends BaseOpValidation {
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
public void testOneHot3(Nd4jBackend backend) {
//https://github.com/deeplearning4j/deeplearning4j/issues/6872
//https://github.com/eclipse/deeplearning4j/issues/6872
//https://www.tensorflow.org/api_docs/python/tf/one_hot
//indices = [[0, 2], [1, -1]]

2
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/RandomOpValidation.java
View File

@ -227,7 +227,7 @@ public class RandomOpValidation extends BaseOpValidation {
break;
case 4:
if(OpValidationSuite.IGNORE_FAILING){
//https://github.com/deeplearning4j/deeplearning4j/issues/6036
//https://github.com/eclipse/deeplearning4j/issues/6036
continue;
}
name = "truncatednormal";

2
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/ReductionOpValidation.java
View File

@ -721,7 +721,7 @@ public class ReductionOpValidation extends BaseOpValidation {
break;
case 6:
if (OpValidationSuite.IGNORE_FAILING) {
//https://github.com/deeplearning4j/deeplearning4j/issues/6069
//https://github.com/eclipse/deeplearning4j/issues/6069
continue;
}
name = "dot";

6
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/ShapeOpValidation.java
View File

@ -126,7 +126,7 @@ public class ShapeOpValidation extends BaseOpValidation {
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
public void testReshapeGradient(Nd4jBackend backend) {
//https://github.com/deeplearning4j/deeplearning4j/issues/6873
//https://github.com/eclipse/deeplearning4j/issues/6873
int[] origShape = new int[]{3, 4, 5};
@ -1305,7 +1305,7 @@ public class ShapeOpValidation extends BaseOpValidation {
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
public void testSegmentOps(){
OpValidationSuite.ignoreFailing();
//https://github.com/deeplearning4j/deeplearning4j/issues/6952
//https://github.com/eclipse/deeplearning4j/issues/6952
INDArray s = Nd4j.create(new double[]{0,0,0,1,2,2,3,3}, new long[]{8}).castTo(DataType.INT);
INDArray d = Nd4j.create(new double[]{5,1,7,2,3,4,1,3}, new long[]{8});
int numSegments = 4;
@ -1910,7 +1910,7 @@ public class ShapeOpValidation extends BaseOpValidation {
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
public void testDistancesExec(){
//https://github.com/deeplearning4j/deeplearning4j/issues/7001
//https://github.com/eclipse/deeplearning4j/issues/7001
for(String s : new String[]{"euclidean", "manhattan", "cosinesim", "cosinedist", "jaccard"}) {
log.info("Starting: {}", s);
INDArray defaultTestCase = Nd4j.create(4, 4);

2
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/TransformOpValidation.java
View File

@ -1745,7 +1745,7 @@ public class TransformOpValidation extends BaseOpValidation {
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
public void testZeta(Nd4jBackend backend) {
OpValidationSuite.ignoreFailing(); //https://github.com/deeplearning4j/deeplearning4j/issues/6182
OpValidationSuite.ignoreFailing(); //https://github.com/eclipse/deeplearning4j/issues/6182
INDArray x = Nd4j.rand(3, 4).addi(1.0);
INDArray q = Nd4j.rand(3, 4);

2
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/Nd4jTestsC.java
View File

@ -7429,7 +7429,7 @@ public class Nd4jTestsC extends BaseNd4jTestWithBackends {
@ParameterizedTest
@MethodSource("org.nd4j.linalg.BaseNd4jTestWithBackends#configs")
public void testGet(){
//https://github.com/deeplearning4j/deeplearning4j/issues/6133
//https://github.com/eclipse/deeplearning4j/issues/6133
INDArray m = Nd4j.linspace(0,99,100, DataType.DOUBLE).reshape('c', 10,10);
INDArray exp = Nd4j.create(new double[]{5, 15, 25, 35, 45, 55, 65, 75, 85, 95}, new int[]{10});
INDArray col = m.getColumn(5);

2
nd4j/nd4j-common-tests/src/main/java/org/nd4j/linalg/BaseNd4jTestWithBackends.java
View File

@ -40,7 +40,7 @@ import java.util.stream.Stream;
@Slf4j
public abstract class BaseNd4jTestWithBackends extends BaseND4JTest {
private static List<Nd4jBackend> BACKENDS = new ArrayList<>();
public static List<Nd4jBackend> BACKENDS = new ArrayList<>();
static {
List<String> backendsToRun = Nd4jTestSuite.backendsToRun();

2
nd4j/nd4j-common/src/test/java/org/nd4j/common/io/ClassPathResourceTest.java
View File

@ -36,7 +36,7 @@ public class ClassPathResourceTest {
@Test
public void testDirExtractingIntelliJ(@TempDir Path testDir) throws Exception {
//https://github.com/deeplearning4j/deeplearning4j/issues/6483
//https://github.com/eclipse/deeplearning4j/issues/6483
ClassPathResource cpr = new ClassPathResource("somedir");