From c34790f9320844e6119970ef36afac769ed89d36 Mon Sep 17 00:00:00 2001 From: Andrii T <39699084+atuzhykov@users.noreply.github.com> Date: Fri, 14 Feb 2020 02:53:35 +0200 Subject: [PATCH] =?UTF-8?q?Copied=20and=20pasted=20RegressionTest100b4.jav?= =?UTF-8?q?a=20to=20RegressionTest100b6.jav=E2=80=A6=20(#215)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Copied and pasted RegressionTest100b4.java to RegressionTest100b6.java with renamed b4->b6 * assertEquals > assertTrue for half dtype Signed-off-by: atuzhykov --- .../regressiontest/RegressionTest100b6.java | 390 ++++++++++++++++++ 1 file changed, 390 insertions(+) create mode 100644 deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/regressiontest/RegressionTest100b6.java diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/regressiontest/RegressionTest100b6.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/regressiontest/RegressionTest100b6.java new file mode 100644 index 000000000..637f5860f --- /dev/null +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/regressiontest/RegressionTest100b6.java @@ -0,0 +1,390 @@ +/* + * Copyright (c) 2015-2019 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + */ + +package org.deeplearning4j.regressiontest; + +import org.deeplearning4j.BaseDL4JTest; +import org.deeplearning4j.TestUtils; +import org.deeplearning4j.nn.conf.BackpropType; +import org.deeplearning4j.nn.conf.ConvolutionMode; +import org.deeplearning4j.nn.conf.graph.LayerVertex; +import org.deeplearning4j.nn.conf.layers.*; +import org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D; +import org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional; +import org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn; +import org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder; +import org.deeplearning4j.nn.graph.ComputationGraph; +import org.deeplearning4j.nn.graph.vertex.impl.MergeVertex; +import org.deeplearning4j.nn.multilayer.MultiLayerNetwork; +import org.deeplearning4j.nn.weights.WeightInitXavier; +import org.deeplearning4j.regressiontest.customlayer100a.CustomLayer; +import org.junit.Test; +import org.nd4j.linalg.activations.impl.*; +import org.nd4j.linalg.api.buffer.DataType; +import org.nd4j.linalg.api.ndarray.INDArray; +import org.nd4j.linalg.factory.Nd4j; +import org.nd4j.linalg.learning.config.Adam; +import org.nd4j.linalg.learning.config.RmsProp; +import org.nd4j.linalg.learning.regularization.L2Regularization; +import org.nd4j.linalg.lossfunctions.impl.LossMAE; +import org.nd4j.linalg.lossfunctions.impl.LossMCXENT; +import org.nd4j.resources.Resources; + +import java.io.DataInputStream; +import java.io.File; +import java.io.FileInputStream; + +import static org.junit.Assert.*; + +public class RegressionTest100b6 extends BaseDL4JTest { + + @Override + public DataType getDataType() { + return DataType.FLOAT; + } + + @Test + public void testCustomLayer() throws Exception { + + for (DataType dtype : new DataType[]{DataType.DOUBLE, DataType.FLOAT, DataType.HALF}) { + + String dtypeName = dtype.toString().toLowerCase(); + + File f = Resources.asFile("regression_testing/100b6/CustomLayerExample_100b6_" + dtypeName + ".bin"); + MultiLayerNetwork.load(f, true); + + MultiLayerNetwork net = MultiLayerNetwork.load(f, true); +// net = net.clone(); + + DenseLayer l0 = (DenseLayer) net.getLayer(0).conf().getLayer(); + assertEquals(new ActivationTanH(), l0.getActivationFn()); + assertEquals(new L2Regularization(0.03), TestUtils.getL2Reg(l0)); + assertEquals(new RmsProp(0.95), l0.getIUpdater()); + + CustomLayer l1 = (CustomLayer) net.getLayer(1).conf().getLayer(); + assertEquals(new ActivationTanH(), l1.getActivationFn()); + assertEquals(new ActivationSigmoid(), l1.getSecondActivationFunction()); + assertEquals(new RmsProp(0.95), l1.getIUpdater()); + + INDArray outExp; + File f2 = Resources + .asFile("regression_testing/100b6/CustomLayerExample_Output_100b6_" + dtypeName + ".bin"); + try (DataInputStream dis = new DataInputStream(new FileInputStream(f2))) { + outExp = Nd4j.read(dis); + } + + INDArray in; + File f3 = Resources.asFile("regression_testing/100b6/CustomLayerExample_Input_100b6_" + dtypeName + ".bin"); + try (DataInputStream dis = new DataInputStream(new FileInputStream(f3))) { + in = Nd4j.read(dis); + } + + assertEquals(dtype, in.dataType()); + assertEquals(dtype, outExp.dataType()); + assertEquals(dtype, net.params().dataType()); + assertEquals(dtype, net.getFlattenedGradients().dataType()); + assertEquals(dtype, net.getUpdater().getStateViewArray().dataType()); + + //System.out.println(Arrays.toString(net.params().data().asFloat())); + + INDArray outAct = net.output(in); + assertEquals(dtype, outAct.dataType()); + + assertEquals(dtype, net.getLayerWiseConfigurations().getDataType()); + assertEquals(dtype, net.params().dataType()); + boolean eq = outExp.equalsWithEps(outAct, 0.01); + assertTrue(outExp + " vs " + outAct, eq); } + } + + + @Test + public void testLSTM() throws Exception { + + File f = Resources.asFile("regression_testing/100b6/GravesLSTMCharModelingExample_100b6.bin"); + MultiLayerNetwork net = MultiLayerNetwork.load(f, true); + + LSTM l0 = (LSTM) net.getLayer(0).conf().getLayer(); + assertEquals(new ActivationTanH(), l0.getActivationFn()); + assertEquals(200, l0.getNOut()); + assertEquals(new WeightInitXavier(), l0.getWeightInitFn()); + assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0)); + assertEquals(new Adam(0.005), l0.getIUpdater()); + + LSTM l1 = (LSTM) net.getLayer(1).conf().getLayer(); + assertEquals(new ActivationTanH(), l1.getActivationFn()); + assertEquals(200, l1.getNOut()); + assertEquals(new WeightInitXavier(), l1.getWeightInitFn()); + assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l1)); + assertEquals(new Adam(0.005), l1.getIUpdater()); + + RnnOutputLayer l2 = (RnnOutputLayer) net.getLayer(2).conf().getLayer(); + assertEquals(new ActivationSoftmax(), l2.getActivationFn()); + assertEquals(77, l2.getNOut()); + assertEquals(new WeightInitXavier(), l2.getWeightInitFn()); + assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l2)); + assertEquals(new Adam(0.005), l2.getIUpdater()); + + assertEquals(BackpropType.TruncatedBPTT, net.getLayerWiseConfigurations().getBackpropType()); + assertEquals(50, net.getLayerWiseConfigurations().getTbpttBackLength()); + assertEquals(50, net.getLayerWiseConfigurations().getTbpttFwdLength()); + + INDArray outExp; + File f2 = Resources.asFile("regression_testing/100b6/GravesLSTMCharModelingExample_Output_100b6.bin"); + try (DataInputStream dis = new DataInputStream(new FileInputStream(f2))) { + outExp = Nd4j.read(dis); + } + + INDArray in; + File f3 = Resources.asFile("regression_testing/100b6/GravesLSTMCharModelingExample_Input_100b6.bin"); + try (DataInputStream dis = new DataInputStream(new FileInputStream(f3))) { + in = Nd4j.read(dis); + } + + INDArray outAct = net.output(in); + + assertEquals(outExp, outAct); + } + + @Test + public void testVae() throws Exception { + + File f = Resources.asFile("regression_testing/100b6/VaeMNISTAnomaly_100b6.bin"); + MultiLayerNetwork net = MultiLayerNetwork.load(f, true); + + VariationalAutoencoder l0 = (VariationalAutoencoder) net.getLayer(0).conf().getLayer(); + assertEquals(new ActivationLReLU(), l0.getActivationFn()); + assertEquals(32, l0.getNOut()); + assertArrayEquals(new int[]{256, 256}, l0.getEncoderLayerSizes()); + assertArrayEquals(new int[]{256, 256}, l0.getDecoderLayerSizes()); + assertEquals(new WeightInitXavier(), l0.getWeightInitFn()); + assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0)); + assertEquals(new Adam(1e-3), l0.getIUpdater()); + + INDArray outExp; + File f2 = Resources.asFile("regression_testing/100b6/VaeMNISTAnomaly_Output_100b6.bin"); + try (DataInputStream dis = new DataInputStream(new FileInputStream(f2))) { + outExp = Nd4j.read(dis); + } + + INDArray in; + File f3 = Resources.asFile("regression_testing/100b6/VaeMNISTAnomaly_Input_100b6.bin"); + try (DataInputStream dis = new DataInputStream(new FileInputStream(f3))) { + in = Nd4j.read(dis); + } + + INDArray outAct = net.output(in); + + assertEquals(outExp, outAct); + } + + + @Test + public void testYoloHouseNumber() throws Exception { + + File f = Resources.asFile("regression_testing/100b6/HouseNumberDetection_100b6.bin"); + ComputationGraph net = ComputationGraph.load(f, true); + + int nBoxes = 5; + int nClasses = 10; + + ConvolutionLayer cl = (ConvolutionLayer) ((LayerVertex) net.getConfiguration().getVertices() + .get("convolution2d_9")).getLayerConf().getLayer(); + assertEquals(nBoxes * (5 + nClasses), cl.getNOut()); + assertEquals(new ActivationIdentity(), cl.getActivationFn()); + assertEquals(ConvolutionMode.Same, cl.getConvolutionMode()); + assertEquals(new WeightInitXavier(), cl.getWeightInitFn()); + assertArrayEquals(new int[]{1, 1}, cl.getKernelSize()); + + INDArray outExp; + File f2 = Resources.asFile("regression_testing/100b6/HouseNumberDetection_Output_100b6.bin"); + try (DataInputStream dis = new DataInputStream(new FileInputStream(f2))) { + outExp = Nd4j.read(dis); + } + + INDArray in; + File f3 = Resources.asFile("regression_testing/100b6/HouseNumberDetection_Input_100b6.bin"); + try (DataInputStream dis = new DataInputStream(new FileInputStream(f3))) { + in = Nd4j.read(dis); + } + + INDArray outAct = net.outputSingle(in); + + boolean eq = outExp.equalsWithEps(outAct.castTo(outExp.dataType()), 1e-3); + assertTrue(eq); + } + + @Test + public void testSyntheticCNN() throws Exception { + + File f = Resources.asFile("regression_testing/100b6/SyntheticCNN_100b6.bin"); + MultiLayerNetwork net = MultiLayerNetwork.load(f, true); + + ConvolutionLayer l0 = (ConvolutionLayer) net.getLayer(0).conf().getLayer(); + assertEquals(new ActivationReLU(), l0.getActivationFn()); + assertEquals(4, l0.getNOut()); + assertEquals(new WeightInitXavier(), l0.getWeightInitFn()); + assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0)); + assertEquals(new Adam(0.005), l0.getIUpdater()); + assertArrayEquals(new int[]{3, 3}, l0.getKernelSize()); + assertArrayEquals(new int[]{2, 1}, l0.getStride()); + assertArrayEquals(new int[]{1, 1}, l0.getDilation()); + assertArrayEquals(new int[]{0, 0}, l0.getPadding()); + + SeparableConvolution2D l1 = (SeparableConvolution2D) net.getLayer(1).conf().getLayer(); + assertEquals(new ActivationReLU(), l1.getActivationFn()); + assertEquals(8, l1.getNOut()); + assertEquals(new WeightInitXavier(), l1.getWeightInitFn()); + assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l1)); + assertEquals(new Adam(0.005), l1.getIUpdater()); + assertArrayEquals(new int[]{3, 3}, l1.getKernelSize()); + assertArrayEquals(new int[]{1, 1}, l1.getStride()); + assertArrayEquals(new int[]{1, 1}, l1.getDilation()); + assertArrayEquals(new int[]{0, 0}, l1.getPadding()); + assertEquals(ConvolutionMode.Same, l1.getConvolutionMode()); + assertEquals(1, l1.getDepthMultiplier()); + + SubsamplingLayer l2 = (SubsamplingLayer) net.getLayer(2).conf().getLayer(); + assertArrayEquals(new int[]{3, 3}, l2.getKernelSize()); + assertArrayEquals(new int[]{2, 2}, l2.getStride()); + assertArrayEquals(new int[]{1, 1}, l2.getDilation()); + assertArrayEquals(new int[]{0, 0}, l2.getPadding()); + assertEquals(PoolingType.MAX, l2.getPoolingType()); + + ZeroPaddingLayer l3 = (ZeroPaddingLayer) net.getLayer(3).conf().getLayer(); + assertArrayEquals(new int[]{4, 4, 4, 4}, l3.getPadding()); + + Upsampling2D l4 = (Upsampling2D) net.getLayer(4).conf().getLayer(); + assertArrayEquals(new int[]{3, 3}, l4.getSize()); + + DepthwiseConvolution2D l5 = (DepthwiseConvolution2D) net.getLayer(5).conf().getLayer(); + assertEquals(new ActivationReLU(), l5.getActivationFn()); + assertEquals(16, l5.getNOut()); + assertEquals(new WeightInitXavier(), l5.getWeightInitFn()); + assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l5)); + assertEquals(new Adam(0.005), l5.getIUpdater()); + assertArrayEquals(new int[]{3, 3}, l5.getKernelSize()); + assertArrayEquals(new int[]{1, 1}, l5.getStride()); + assertArrayEquals(new int[]{1, 1}, l5.getDilation()); + assertArrayEquals(new int[]{0, 0}, l5.getPadding()); + assertEquals(2, l5.getDepthMultiplier()); + + SubsamplingLayer l6 = (SubsamplingLayer) net.getLayer(6).conf().getLayer(); + assertArrayEquals(new int[]{2, 2}, l6.getKernelSize()); + assertArrayEquals(new int[]{2, 2}, l6.getStride()); + assertArrayEquals(new int[]{1, 1}, l6.getDilation()); + assertArrayEquals(new int[]{0, 0}, l6.getPadding()); + assertEquals(PoolingType.MAX, l6.getPoolingType()); + + Cropping2D l7 = (Cropping2D) net.getLayer(7).conf().getLayer(); + assertArrayEquals(new int[]{3, 3, 2, 2}, l7.getCropping()); + + ConvolutionLayer l8 = (ConvolutionLayer) net.getLayer(8).conf().getLayer(); + assertEquals(4, l8.getNOut()); + assertEquals(new WeightInitXavier(), l8.getWeightInitFn()); + assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l8)); + assertEquals(new Adam(0.005), l8.getIUpdater()); + assertArrayEquals(new int[]{4, 4}, l8.getKernelSize()); + assertArrayEquals(new int[]{1, 1}, l8.getStride()); + assertArrayEquals(new int[]{1, 1}, l8.getDilation()); + assertArrayEquals(new int[]{0, 0}, l8.getPadding()); + + CnnLossLayer l9 = (CnnLossLayer) net.getLayer(9).conf().getLayer(); + assertEquals(new WeightInitXavier(), l9.getWeightInitFn()); + assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l9)); + assertEquals(new Adam(0.005), l9.getIUpdater()); + assertEquals(new LossMAE(), l9.getLossFn()); + + INDArray outExp; + File f2 = Resources.asFile("regression_testing/100b6/SyntheticCNN_Output_100b6.bin"); + try (DataInputStream dis = new DataInputStream(new FileInputStream(f2))) { + outExp = Nd4j.read(dis); + } + + INDArray in; + File f3 = Resources.asFile("regression_testing/100b6/SyntheticCNN_Input_100b6.bin"); + try (DataInputStream dis = new DataInputStream(new FileInputStream(f3))) { + in = Nd4j.read(dis); + } + + INDArray outAct = net.output(in); + + //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); + } +}