[WIP] first tests

deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/convolution/Upsampling2DTest.java

@@ -0,0 +1,119 @@
+package org.deeplearning4j.nn.layers.convolution;
+
+import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
+import org.deeplearning4j.nn.api.Layer;
+import org.deeplearning4j.nn.conf.GradientNormalization;
+import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
+import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.inputs.InputType;
+import org.deeplearning4j.nn.conf.layers.OutputLayer;
+import org.deeplearning4j.nn.conf.layers.SubsamplingLayer;
+import org.deeplearning4j.nn.conf.layers.Upsampling2D;
+import org.deeplearning4j.nn.gradient.Gradient;
+import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
+import org.deeplearning4j.nn.weights.WeightInit;
+import org.junit.Test;
+import org.nd4j.linalg.activations.Activation;
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.dataset.DataSet;
+import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
+import org.nd4j.linalg.factory.Nd4j;
+import org.nd4j.linalg.primitives.Pair;
+
+import java.util.Arrays;
+
+import static org.junit.Assert.*;
+
+/**
+ * @author Max Pumperla
+ */
+public class Upsampling2DTest {
+
+    private int nExamples = 1;
+    private int depth = 20;
+    private int nChannelsIn = 1;
+    private int inputWidth = 28;
+    private int inputHeight = 28;
+
+    private int size = 2;
+    private int outputWidth = inputWidth * size;
+    private int outputHeight = inputHeight * size;
+
+    private INDArray epsilon = Nd4j.ones(nExamples, depth, outputHeight, outputWidth);
+
+
+    @Test
+    public void testUpsampling() throws Exception {
+
+        double[] outArray = new double[] {1., 1., 2., 2., 1., 1., 2., 2., 3., 3., 4., 4., 3., 3., 4., 4.};
+        INDArray containedExpectedOut = Nd4j.create(outArray, new int[] {1, 1, 4, 4});
+        INDArray containedInput = getContainedData();
+        System.out.println(containedInput);
+        System.out.println(containedExpectedOut);
+        INDArray input = getData();
+        Layer layer = getUpsamplingLayer();
+
+        INDArray containedOutput = layer.activate(containedInput);
+        assertTrue(Arrays.equals(containedExpectedOut.shape(), containedOutput.shape()));
+        assertEquals(containedExpectedOut, containedOutput);
+
+        INDArray output = layer.activate(input);
+        assertTrue(Arrays.equals(new int[] {nExamples, nChannelsIn, outputWidth, outputHeight},
+                        output.shape()));
+        assertEquals(nChannelsIn, output.size(1), 1e-4);
+    }
+
+
+    @Test
+    public void testUpsampling2DBackprop() throws Exception {
+        INDArray expectedContainedEpsilonInput =
+                        Nd4j.create(new double[] {1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.},
+                                new int[] {1, 1, 4, 4});
+
+        INDArray expectedContainedEpsilonResult = Nd4j.create(new double[] {1., 1., 1., 1.},
+                        new int[] {1, 1, 2, 2});
+
+        INDArray input = getContainedData();
+
+        Layer layer = getUpsamplingLayer();
+        layer.activate(input);
+
+        Pair<Gradient, INDArray> containedOutput = layer.backpropGradient(expectedContainedEpsilonInput);
+        assertEquals(expectedContainedEpsilonResult, containedOutput.getSecond());
+        assertEquals(null, containedOutput.getFirst().getGradientFor("W"));
+        assertEquals(expectedContainedEpsilonResult.shape().length, containedOutput.getSecond().shape().length);
+
+        INDArray input2 = getData();
+        layer.activate(input2);
+        int depth = input2.size(1);
+
+        epsilon = Nd4j.ones(5, depth, outputHeight, outputWidth);
+
+        Pair<Gradient, INDArray> out = layer.backpropGradient(epsilon);
+        assertEquals(input.shape().length, out.getSecond().shape().length);
+        assertEquals(depth, out.getSecond().size(1));
+    }
+
+
+    private Layer getUpsamplingLayer() {
+        NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder()
+                        .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer).seed(123)
+                        .layer(new Upsampling2D.Builder(size).build()).build();
+        return conf.getLayer().instantiate(conf, null, 0, null, true);
+    }
+
+    public INDArray getData() throws Exception {
+        DataSetIterator data = new MnistDataSetIterator(5, 5);
+        DataSet mnist = data.next();
+        nExamples = mnist.numExamples();
+        return mnist.getFeatureMatrix().reshape(nExamples, nChannelsIn, inputWidth, inputHeight);
+    }
+
+    private INDArray getContainedData() {
+        INDArray ret = Nd4j.create
+                (new double[] {1., 2., 3., 4.},
+                        new int[] {1, 1, 2, 2});
+        return ret;
+    }
+
+}