Copied and pasted RegressionTest100b4.java to RegressionTest100b6.jav… (#215)
* Copied and pasted RegressionTest100b4.java to RegressionTest100b6.java with renamed b4->b6 * assertEquals > assertTrue for half dtype Signed-off-by: atuzhykov <andrewtuzhukov@gmail.com>master
Andrii T
GitHub
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/*
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* Copyright (c) 2015-2019 Skymind, Inc.
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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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* 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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package org.deeplearning4j.regressiontest;
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import org.deeplearning4j.BaseDL4JTest;
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import org.deeplearning4j.TestUtils;
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import org.deeplearning4j.nn.conf.BackpropType;
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import org.deeplearning4j.nn.conf.ConvolutionMode;
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import org.deeplearning4j.nn.conf.graph.LayerVertex;
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import org.deeplearning4j.nn.conf.layers.*;
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import org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D;
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import org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional;
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import org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn;
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import org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder;
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import org.deeplearning4j.nn.graph.ComputationGraph;
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import org.deeplearning4j.nn.graph.vertex.impl.MergeVertex;
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import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
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import org.deeplearning4j.nn.weights.WeightInitXavier;
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import org.deeplearning4j.regressiontest.customlayer100a.CustomLayer;
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import org.junit.Test;
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import org.nd4j.linalg.activations.impl.*;
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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.factory.Nd4j;
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import org.nd4j.linalg.learning.config.Adam;
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import org.nd4j.linalg.learning.config.RmsProp;
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import org.nd4j.linalg.learning.regularization.L2Regularization;
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import org.nd4j.linalg.lossfunctions.impl.LossMAE;
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import org.nd4j.linalg.lossfunctions.impl.LossMCXENT;
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import org.nd4j.resources.Resources;
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import java.io.DataInputStream;
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import java.io.File;
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import java.io.FileInputStream;
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import static org.junit.Assert.*;
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public class RegressionTest100b6 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 testCustomLayer() throws Exception {
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for (DataType dtype : new DataType[]{DataType.DOUBLE, DataType.FLOAT, DataType.HALF}) {
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String dtypeName = dtype.toString().toLowerCase();
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File f = Resources.asFile("regression_testing/100b6/CustomLayerExample_100b6_" + dtypeName + ".bin");
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MultiLayerNetwork.load(f, true);
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MultiLayerNetwork net = MultiLayerNetwork.load(f, true);
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// net = net.clone();
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DenseLayer l0 = (DenseLayer) net.getLayer(0).conf().getLayer();
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assertEquals(new ActivationTanH(), l0.getActivationFn());
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assertEquals(new L2Regularization(0.03), TestUtils.getL2Reg(l0));
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assertEquals(new RmsProp(0.95), l0.getIUpdater());
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CustomLayer l1 = (CustomLayer) net.getLayer(1).conf().getLayer();
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assertEquals(new ActivationTanH(), l1.getActivationFn());
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assertEquals(new ActivationSigmoid(), l1.getSecondActivationFunction());
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assertEquals(new RmsProp(0.95), l1.getIUpdater());
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INDArray outExp;
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File f2 = Resources
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.asFile("regression_testing/100b6/CustomLayerExample_Output_100b6_" + dtypeName + ".bin");
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try (DataInputStream dis = new DataInputStream(new FileInputStream(f2))) {
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outExp = Nd4j.read(dis);
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}
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INDArray in;
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File f3 = Resources.asFile("regression_testing/100b6/CustomLayerExample_Input_100b6_" + dtypeName + ".bin");
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try (DataInputStream dis = new DataInputStream(new FileInputStream(f3))) {
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in = Nd4j.read(dis);
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}
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assertEquals(dtype, in.dataType());
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assertEquals(dtype, outExp.dataType());
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assertEquals(dtype, net.params().dataType());
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assertEquals(dtype, net.getFlattenedGradients().dataType());
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assertEquals(dtype, net.getUpdater().getStateViewArray().dataType());
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//System.out.println(Arrays.toString(net.params().data().asFloat()));
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INDArray outAct = net.output(in);
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assertEquals(dtype, outAct.dataType());
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assertEquals(dtype, net.getLayerWiseConfigurations().getDataType());
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assertEquals(dtype, net.params().dataType());
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boolean eq = outExp.equalsWithEps(outAct, 0.01);
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assertTrue(outExp + " vs " + outAct, eq); }
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}
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@Test
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public void testLSTM() throws Exception {
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File f = Resources.asFile("regression_testing/100b6/GravesLSTMCharModelingExample_100b6.bin");
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MultiLayerNetwork net = MultiLayerNetwork.load(f, true);
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LSTM l0 = (LSTM) net.getLayer(0).conf().getLayer();
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assertEquals(new ActivationTanH(), l0.getActivationFn());
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assertEquals(200, l0.getNOut());
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assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
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assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0));
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assertEquals(new Adam(0.005), l0.getIUpdater());
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LSTM l1 = (LSTM) net.getLayer(1).conf().getLayer();
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assertEquals(new ActivationTanH(), l1.getActivationFn());
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assertEquals(200, l1.getNOut());
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assertEquals(new WeightInitXavier(), l1.getWeightInitFn());
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assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l1));
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assertEquals(new Adam(0.005), l1.getIUpdater());
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RnnOutputLayer l2 = (RnnOutputLayer) net.getLayer(2).conf().getLayer();
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assertEquals(new ActivationSoftmax(), l2.getActivationFn());
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assertEquals(77, l2.getNOut());
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assertEquals(new WeightInitXavier(), l2.getWeightInitFn());
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assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l2));
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assertEquals(new Adam(0.005), l2.getIUpdater());
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assertEquals(BackpropType.TruncatedBPTT, net.getLayerWiseConfigurations().getBackpropType());
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assertEquals(50, net.getLayerWiseConfigurations().getTbpttBackLength());
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assertEquals(50, net.getLayerWiseConfigurations().getTbpttFwdLength());
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INDArray outExp;
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File f2 = Resources.asFile("regression_testing/100b6/GravesLSTMCharModelingExample_Output_100b6.bin");
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try (DataInputStream dis = new DataInputStream(new FileInputStream(f2))) {
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outExp = Nd4j.read(dis);
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}
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INDArray in;
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File f3 = Resources.asFile("regression_testing/100b6/GravesLSTMCharModelingExample_Input_100b6.bin");
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try (DataInputStream dis = new DataInputStream(new FileInputStream(f3))) {
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in = Nd4j.read(dis);
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}
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INDArray outAct = net.output(in);
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assertEquals(outExp, outAct);
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}
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@Test
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public void testVae() throws Exception {
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File f = Resources.asFile("regression_testing/100b6/VaeMNISTAnomaly_100b6.bin");
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MultiLayerNetwork net = MultiLayerNetwork.load(f, true);
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VariationalAutoencoder l0 = (VariationalAutoencoder) net.getLayer(0).conf().getLayer();
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assertEquals(new ActivationLReLU(), l0.getActivationFn());
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assertEquals(32, l0.getNOut());
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assertArrayEquals(new int[]{256, 256}, l0.getEncoderLayerSizes());
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assertArrayEquals(new int[]{256, 256}, l0.getDecoderLayerSizes());
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assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
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assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0));
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assertEquals(new Adam(1e-3), l0.getIUpdater());
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INDArray outExp;
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File f2 = Resources.asFile("regression_testing/100b6/VaeMNISTAnomaly_Output_100b6.bin");
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try (DataInputStream dis = new DataInputStream(new FileInputStream(f2))) {
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outExp = Nd4j.read(dis);
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}
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INDArray in;
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File f3 = Resources.asFile("regression_testing/100b6/VaeMNISTAnomaly_Input_100b6.bin");
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try (DataInputStream dis = new DataInputStream(new FileInputStream(f3))) {
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in = Nd4j.read(dis);
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}
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INDArray outAct = net.output(in);
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assertEquals(outExp, outAct);
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}
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@Test
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public void testYoloHouseNumber() throws Exception {
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File f = Resources.asFile("regression_testing/100b6/HouseNumberDetection_100b6.bin");
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ComputationGraph net = ComputationGraph.load(f, true);
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int nBoxes = 5;
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int nClasses = 10;
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ConvolutionLayer cl = (ConvolutionLayer) ((LayerVertex) net.getConfiguration().getVertices()
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.get("convolution2d_9")).getLayerConf().getLayer();
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assertEquals(nBoxes * (5 + nClasses), cl.getNOut());
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assertEquals(new ActivationIdentity(), cl.getActivationFn());
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assertEquals(ConvolutionMode.Same, cl.getConvolutionMode());
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assertEquals(new WeightInitXavier(), cl.getWeightInitFn());
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assertArrayEquals(new int[]{1, 1}, cl.getKernelSize());
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INDArray outExp;
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File f2 = Resources.asFile("regression_testing/100b6/HouseNumberDetection_Output_100b6.bin");
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try (DataInputStream dis = new DataInputStream(new FileInputStream(f2))) {
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outExp = Nd4j.read(dis);
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}
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INDArray in;
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File f3 = Resources.asFile("regression_testing/100b6/HouseNumberDetection_Input_100b6.bin");
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try (DataInputStream dis = new DataInputStream(new FileInputStream(f3))) {
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in = Nd4j.read(dis);
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}
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INDArray outAct = net.outputSingle(in);
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boolean eq = outExp.equalsWithEps(outAct.castTo(outExp.dataType()), 1e-3);
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assertTrue(eq);
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}
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@Test
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public void testSyntheticCNN() throws Exception {
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File f = Resources.asFile("regression_testing/100b6/SyntheticCNN_100b6.bin");
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MultiLayerNetwork net = MultiLayerNetwork.load(f, true);
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ConvolutionLayer l0 = (ConvolutionLayer) net.getLayer(0).conf().getLayer();
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assertEquals(new ActivationReLU(), l0.getActivationFn());
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assertEquals(4, l0.getNOut());
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assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
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assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0));
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assertEquals(new Adam(0.005), l0.getIUpdater());
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assertArrayEquals(new int[]{3, 3}, l0.getKernelSize());
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assertArrayEquals(new int[]{2, 1}, l0.getStride());
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assertArrayEquals(new int[]{1, 1}, l0.getDilation());
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assertArrayEquals(new int[]{0, 0}, l0.getPadding());
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SeparableConvolution2D l1 = (SeparableConvolution2D) net.getLayer(1).conf().getLayer();
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assertEquals(new ActivationReLU(), l1.getActivationFn());
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assertEquals(8, l1.getNOut());
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assertEquals(new WeightInitXavier(), l1.getWeightInitFn());
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assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l1));
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assertEquals(new Adam(0.005), l1.getIUpdater());
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assertArrayEquals(new int[]{3, 3}, l1.getKernelSize());
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assertArrayEquals(new int[]{1, 1}, l1.getStride());
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assertArrayEquals(new int[]{1, 1}, l1.getDilation());
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assertArrayEquals(new int[]{0, 0}, l1.getPadding());
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assertEquals(ConvolutionMode.Same, l1.getConvolutionMode());
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assertEquals(1, l1.getDepthMultiplier());
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SubsamplingLayer l2 = (SubsamplingLayer) net.getLayer(2).conf().getLayer();
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assertArrayEquals(new int[]{3, 3}, l2.getKernelSize());
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assertArrayEquals(new int[]{2, 2}, l2.getStride());
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assertArrayEquals(new int[]{1, 1}, l2.getDilation());
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assertArrayEquals(new int[]{0, 0}, l2.getPadding());
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assertEquals(PoolingType.MAX, l2.getPoolingType());
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ZeroPaddingLayer l3 = (ZeroPaddingLayer) net.getLayer(3).conf().getLayer();
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assertArrayEquals(new int[]{4, 4, 4, 4}, l3.getPadding());
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Upsampling2D l4 = (Upsampling2D) net.getLayer(4).conf().getLayer();
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assertArrayEquals(new int[]{3, 3}, l4.getSize());
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DepthwiseConvolution2D l5 = (DepthwiseConvolution2D) net.getLayer(5).conf().getLayer();
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assertEquals(new ActivationReLU(), l5.getActivationFn());
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assertEquals(16, l5.getNOut());
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assertEquals(new WeightInitXavier(), l5.getWeightInitFn());
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assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l5));
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assertEquals(new Adam(0.005), l5.getIUpdater());
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assertArrayEquals(new int[]{3, 3}, l5.getKernelSize());
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assertArrayEquals(new int[]{1, 1}, l5.getStride());
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assertArrayEquals(new int[]{1, 1}, l5.getDilation());
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assertArrayEquals(new int[]{0, 0}, l5.getPadding());
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assertEquals(2, l5.getDepthMultiplier());
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SubsamplingLayer l6 = (SubsamplingLayer) net.getLayer(6).conf().getLayer();
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assertArrayEquals(new int[]{2, 2}, l6.getKernelSize());
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assertArrayEquals(new int[]{2, 2}, l6.getStride());
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assertArrayEquals(new int[]{1, 1}, l6.getDilation());
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assertArrayEquals(new int[]{0, 0}, l6.getPadding());
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assertEquals(PoolingType.MAX, l6.getPoolingType());
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Cropping2D l7 = (Cropping2D) net.getLayer(7).conf().getLayer();
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assertArrayEquals(new int[]{3, 3, 2, 2}, l7.getCropping());
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ConvolutionLayer l8 = (ConvolutionLayer) net.getLayer(8).conf().getLayer();
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assertEquals(4, l8.getNOut());
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assertEquals(new WeightInitXavier(), l8.getWeightInitFn());
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assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l8));
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assertEquals(new Adam(0.005), l8.getIUpdater());
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assertArrayEquals(new int[]{4, 4}, l8.getKernelSize());
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assertArrayEquals(new int[]{1, 1}, l8.getStride());
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assertArrayEquals(new int[]{1, 1}, l8.getDilation());
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assertArrayEquals(new int[]{0, 0}, l8.getPadding());
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CnnLossLayer l9 = (CnnLossLayer) net.getLayer(9).conf().getLayer();
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assertEquals(new WeightInitXavier(), l9.getWeightInitFn());
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assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l9));
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assertEquals(new Adam(0.005), l9.getIUpdater());
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assertEquals(new LossMAE(), l9.getLossFn());
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INDArray outExp;
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File f2 = Resources.asFile("regression_testing/100b6/SyntheticCNN_Output_100b6.bin");
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try (DataInputStream dis = new DataInputStream(new FileInputStream(f2))) {
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outExp = Nd4j.read(dis);
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}
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INDArray in;
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File f3 = Resources.asFile("regression_testing/100b6/SyntheticCNN_Input_100b6.bin");
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try (DataInputStream dis = new DataInputStream(new FileInputStream(f3))) {
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in = Nd4j.read(dis);
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}
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INDArray outAct = net.output(in);
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|
|
||||||
|
//19 layers - CPU vs. GPU difference accumulates notably, but appears to be correct
|
||||||
|
if(Nd4j.getBackend().getClass().getName().toLowerCase().contains("native")){
|
||||||
|
assertEquals(outExp, outAct);
|
||||||
|
} else {
|
||||||
|
boolean eq = outExp.equalsWithEps(outAct, 0.1);
|
||||||
|
assertTrue(eq);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
@Test
|
||||||
|
public void testSyntheticBidirectionalRNNGraph() throws Exception {
|
||||||
|
|
||||||
|
File f = Resources.asFile("regression_testing/100b6/SyntheticBidirectionalRNNGraph_100b6.bin");
|
||||||
|
ComputationGraph net = ComputationGraph.load(f, true);
|
||||||
|
|
||||||
|
Bidirectional l0 = (Bidirectional) net.getLayer("rnn1").conf().getLayer();
|
||||||
|
|
||||||
|
LSTM l1 = (LSTM) l0.getFwd();
|
||||||
|
assertEquals(16, l1.getNOut());
|
||||||
|
assertEquals(new ActivationReLU(), l1.getActivationFn());
|
||||||
|
assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l1));
|
||||||
|
|
||||||
|
LSTM l2 = (LSTM) l0.getBwd();
|
||||||
|
assertEquals(16, l2.getNOut());
|
||||||
|
assertEquals(new ActivationReLU(), l2.getActivationFn());
|
||||||
|
assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l2));
|
||||||
|
|
||||||
|
Bidirectional l3 = (Bidirectional) net.getLayer("rnn2").conf().getLayer();
|
||||||
|
|
||||||
|
SimpleRnn l4 = (SimpleRnn) l3.getFwd();
|
||||||
|
assertEquals(16, l4.getNOut());
|
||||||
|
assertEquals(new ActivationReLU(), l4.getActivationFn());
|
||||||
|
assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l4));
|
||||||
|
|
||||||
|
SimpleRnn l5 = (SimpleRnn) l3.getBwd();
|
||||||
|
assertEquals(16, l5.getNOut());
|
||||||
|
assertEquals(new ActivationReLU(), l5.getActivationFn());
|
||||||
|
assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l5));
|
||||||
|
|
||||||
|
MergeVertex mv = (MergeVertex) net.getVertex("concat");
|
||||||
|
|
||||||
|
GlobalPoolingLayer gpl = (GlobalPoolingLayer) net.getLayer("pooling").conf().getLayer();
|
||||||
|
assertEquals(PoolingType.MAX, gpl.getPoolingType());
|
||||||
|
assertArrayEquals(new int[]{2}, gpl.getPoolingDimensions());
|
||||||
|
assertTrue(gpl.isCollapseDimensions());
|
||||||
|
|
||||||
|
OutputLayer outl = (OutputLayer) net.getLayer("out").conf().getLayer();
|
||||||
|
assertEquals(3, outl.getNOut());
|
||||||
|
assertEquals(new LossMCXENT(), outl.getLossFn());
|
||||||
|
|
||||||
|
INDArray outExp;
|
||||||
|
File f2 = Resources.asFile("regression_testing/100b6/SyntheticBidirectionalRNNGraph_Output_100b6.bin");
|
||||||
|
try (DataInputStream dis = new DataInputStream(new FileInputStream(f2))) {
|
||||||
|
outExp = Nd4j.read(dis);
|
||||||
|
}
|
||||||
|
|
||||||
|
INDArray in;
|
||||||
|
File f3 = Resources.asFile("regression_testing/100b6/SyntheticBidirectionalRNNGraph_Input_100b6.bin");
|
||||||
|
try (DataInputStream dis = new DataInputStream(new FileInputStream(f3))) {
|
||||||
|
in = Nd4j.read(dis);
|
||||||
|
}
|
||||||
|
|
||||||
|
INDArray outAct = net.output(in)[0];
|
||||||
|
|
||||||
|
assertEquals(outExp, outAct);
|
||||||
|
}
|
||||||
|
}
|
||||||
Loading…
Reference in New Issue