122 lines
4.4 KiB
Java
122 lines
4.4 KiB
Java
/*
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* ******************************************************************************
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* *
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* *
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* * This program and the accompanying materials are made available under the
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* * terms of the Apache License, Version 2.0 which is available at
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* * https://www.apache.org/licenses/LICENSE-2.0.
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* *
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* * See the NOTICE file distributed with this work for additional
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* * information regarding copyright ownership.
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* * Unless required by applicable law or agreed to in writing, software
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* * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* * License for the specific language governing permissions and limitations
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* * under the License.
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* *
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* * SPDX-License-Identifier: Apache-2.0
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* *****************************************************************************
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*/
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package org.deeplearning4j.datasets.iterator;
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import lombok.extern.slf4j.Slf4j;
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import org.deeplearning4j.BaseDL4JTest;
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import org.deeplearning4j.datasets.iterator.impl.EmnistDataSetIterator;
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import org.junit.jupiter.api.Test;
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import org.nd4j.linalg.api.buffer.DataType;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.dataset.DataSet;
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import org.nd4j.linalg.factory.Nd4j;
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import static org.junit.jupiter.api.Assertions.*;
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@Slf4j
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public class TestEmnistDataSetIterator extends BaseDL4JTest {
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@Override
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public DataType getDataType(){
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return DataType.FLOAT;
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}
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@Test
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public void testEmnistDataSetIterator() throws Exception {
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int batchSize = 128;
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EmnistDataSetIterator.Set[] sets;
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if(isIntegrationTests()){
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sets = EmnistDataSetIterator.Set.values();
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} else {
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sets = new EmnistDataSetIterator.Set[]{EmnistDataSetIterator.Set.MNIST, EmnistDataSetIterator.Set.LETTERS};
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}
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for (EmnistDataSetIterator.Set s : sets) {
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boolean isBalanced = EmnistDataSetIterator.isBalanced(s);
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int numLabels = EmnistDataSetIterator.numLabels(s);
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INDArray labelCounts = null;
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for (boolean train : new boolean[] {true, false}) {
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if (isBalanced && train) {
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labelCounts = Nd4j.create(numLabels);
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} else {
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labelCounts = null;
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}
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log.info("Starting test: {}, {}", s, (train ? "train" : "test"));
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EmnistDataSetIterator iter = new EmnistDataSetIterator(s, batchSize, train, 12345);
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assertTrue(iter.asyncSupported());
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assertTrue(iter.resetSupported());
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int expNumExamples;
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if (train) {
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expNumExamples = EmnistDataSetIterator.numExamplesTrain(s);
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} else {
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expNumExamples = EmnistDataSetIterator.numExamplesTest(s);
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}
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assertEquals(numLabels, iter.getLabels().size());
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assertEquals(numLabels, iter.getLabelsArrays().length);
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char[] labelArr = iter.getLabelsArrays();
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for (char c : labelArr) {
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boolean isExpected = (c >= '0' && c <= '9') || (c >= 'A' && c <= 'Z') || (c >= 'a' && c <= 'z');
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assertTrue(isExpected);
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}
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int totalCount = 0;
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while (iter.hasNext()) {
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DataSet ds = iter.next();
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assertNotNull(ds.getFeatures());
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assertNotNull(ds.getLabels());
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assertEquals(ds.getFeatures().size(0), ds.getLabels().size(0));
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totalCount += ds.getFeatures().size(0);
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assertEquals(784, ds.getFeatures().size(1));
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assertEquals(numLabels, ds.getLabels().size(1));
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if (isBalanced && train) {
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labelCounts.addi(ds.getLabels().sum(0));
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}
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}
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assertEquals(expNumExamples, totalCount);
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if (isBalanced && train) {
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int min = labelCounts.minNumber().intValue();
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int max = labelCounts.maxNumber().intValue();
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int exp = expNumExamples / numLabels;
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assertTrue(min > 0);
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assertEquals(exp, min);
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assertEquals(exp, max);
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}
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}
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}
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}
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}
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