[WIP] Ignored TF OP Import test updates (#37)
* new issue for b_d_s (old is closed), new issue for boolean_mask (old is fixed) Signed-off-by: Ryan Nett <rnett@skymind.io> * date update Signed-off-by: Ryan Nett <rnett@skymind.io> * "embedding_lookup/.*multiple.*" is passing Signed-off-by: Ryan Nett <rnett@skymind.io> * new bincount issue (old was fixed) Signed-off-by: Ryan Nett <rnett@skymind.io> * date update Signed-off-by: Ryan Nett <rnett@skymind.io> * where op comment Signed-off-by: Ryan Nett <rnett@skymind.io> * where passing Signed-off-by: Ryan Nett <rnett@skymind.io> * ignore gan Signed-off-by: Ryan Nett <rnett@skymind.io> * scatter_nd/rank2shape_2indices passing Signed-off-by: Ryan Nett <rnett@skymind.io> * topk update, test fix (need to verify) Signed-off-by: Ryan Nett <rnett@skymind.io> * batch_to_space issue update Signed-off-by: Ryan Nett <rnett@skymind.io> * updates, no change Signed-off-by: Ryan Nett <rnett@skymind.io> * date updates Signed-off-by: Ryan Nett <rnett@skymind.io> * batch_to_space is fixed Signed-off-by: Ryan Nett <rnett@skymind.io> * equality function helper Signed-off-by: Ryan Nett <rnett@skymind.io> * topk passing Signed-off-by: Ryan Nett <rnett@skymind.io> * dropout equality check Signed-off-by: Ryan Nett <rnett@skymind.io> * adding ignores for zoo models Signed-off-by: Ryan Nett <rnett@skymind.io> * ignore libnd4j rnn tests Signed-off-by: Ryan Nett <rnett@skymind.io> * added batch to space test Signed-off-by: Ryan Nett <rnett@skymind.io> * issue comments Signed-off-by: Ryan Nett <rnett@skymind.io>master
Ryan Nett
AlexDBlack
parent
dde50ee570
commit
c135883162
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@ -16,6 +16,7 @@
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package org.nd4j.imports.TFGraphs;
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import com.google.common.primitives.Doubles;
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import lombok.extern.slf4j.Slf4j;
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import lombok.val;
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import org.apache.commons.io.FilenameUtils;
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@ -181,7 +182,8 @@ public class TFGraphTestAllHelper {
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if (tfPred.dataType() != nd4jPred.dataType())
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nd4jPred = nd4jPred.castTo(tfPred.dataType());
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boolean eq = tfPred.equals(nd4jPred);
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boolean eq = getEqualityFunction(modelName, outputNode, tfPred, nd4jPred).apply(tfPred, nd4jPred);
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if(!eq){
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//Check for both NaN, both inf
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if(tfPred.dataType().isFPType() && tfPred.equalShapes(nd4jPred) && tfPred.isNaN().castTo(DataType.INT).sumNumber().intValue() == tfPred.length()
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@ -714,4 +716,50 @@ public class TFGraphTestAllHelper {
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}
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return null;
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}
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public static boolean equalsWithEps(double a, double b){
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return Math.abs(a - b) <= 0.00001;
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}
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public static BiFunction<INDArray, INDArray, Boolean> getEqualityFunction(String modelName, String varName, INDArray tf, INDArray sd){
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if(modelName.startsWith("topk")){
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return (t, s) -> Nd4j.sort(t, true).equals(Nd4j.sort(s, true));
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}
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if(modelName.startsWith("alpha_dropout") || modelName.startsWith("layers_dropout"))
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return (t, s) -> {
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double[] tfNums = t.ravel().toDoubleVector();
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double[] sdNums = s.ravel().toDoubleVector();
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Double seen1 = null, seen2 = null;
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for(int i = 0 ; i < tfNums.length ; i++){
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if(!equalsWithEps(tfNums[i], sdNums[i])){
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// if we have only seen one inequality so far, figure out which is the dropout
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if(seen1 != null && seen2 != null){
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if(equalsWithEps(tfNums[i], seen1) || equalsWithEps(sdNums[i], seen1)) // the dropout is in seen1
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seen2 = null;
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else if(equalsWithEps(tfNums[i], seen2) || equalsWithEps(sdNums[i], seen2)){ // the dropout is in seen2
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seen1 = seen2;
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seen2 = null;
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} else // neither match
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return false;
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}
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if(seen1 != null){
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if(!equalsWithEps(tfNums[i], seen1) && !equalsWithEps(sdNums[i], seen1))
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return false;
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} else {
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seen1 = tfNums[i];
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seen2 = sdNums[i];
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}
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}
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}
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return true;
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};
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return Object::equals;
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}
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}
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@ -67,50 +67,29 @@ public class TFGraphTestAllSameDiff { //Note: Can't extend BaseNd4jTest here a
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public static final String[] IGNORE_REGEXES = new String[]{
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//Still failing: 2019/04/08 - https://github.com/deeplearning4j/deeplearning4j/issues/6322 and https://github.com/deeplearning4j/deeplearning4j/issues/6958 issue 1
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//Still failing: 2019/07/01 - https://github.com/deeplearning4j/deeplearning4j/issues/6322 and https://github.com/eclipse/deeplearning4j/issues/7955
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"broadcast_dynamic_shape/1_4",
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"broadcast_dynamic_shape/2,2_1",
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//Failing 2019/04/09 - JVM Crash - https://github.com/deeplearning4j/deeplearning4j/issues/7495
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//Failing 2019/07/01 - Libnd4j Concat sizing issue - https://github.com/eclipse/deeplearning4j/issues/7963
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"boolean_mask/.*",
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//Failing 2019/04/08 - Issue 10, https://github.com/deeplearning4j/deeplearning4j/issues/6958
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//Failing 2019/07/01 - Issue 10, https://github.com/deeplearning4j/deeplearning4j/issues/6958
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"slogdet/.*",
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//Failing 2019/04/08 - Issue 11 - https://github.com/deeplearning4j/deeplearning4j/issues/6958 also exception
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//Failing 2019/07/01 - https://github.com/eclipse/deeplearning4j/issues/7965
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"bincount/.*",
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//Failures as of 2019/04/08: due to bad gather op - Issue 12 https://github.com/deeplearning4j/deeplearning4j/issues/6958
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"embedding_lookup/.*multiple.*",
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//Failing 2019/04/08 - Issue 15 https://github.com/deeplearning4j/deeplearning4j/issues/6958
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"where/cond_only.*",
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//scatter_nd: a few cases failing as of 2019/04/08
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"scatter_nd/rank2shape_2indices",
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"scatter_nd/rank3shape_2indices",
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//TODO floormod and truncatemod behave differently - i.e., "c" vs. "python" semantics. Need to check implementations too
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"truncatemod/.*",
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//2019/04/08 - This is simply an order issue - need to account for this in test (TF gives no order guarantees)
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"topk/.*",
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//Still failing as of 2019/04/08 - https://github.com/deeplearning4j/deeplearning4j/issues/6447
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"cnn1d_layers/channels_first_b2_k2_s1_d2_SAME",
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"cnn2d_layers/channels_first_b1_k12_s1_d12_SAME",
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//2019/01/16 - These have a random component so can't be validated using simple .equals... should still be compared, however to check range is sensible etc
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"alpha_dropout/.*",
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"layers_dropout/.*",
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//Still failing as of 2019/04/08 - https://github.com/deeplearning4j/deeplearning4j/issues/6464 - not sure if related to: https://github.com/deeplearning4j/deeplearning4j/issues/6447
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//Still failing as of 2019/07/02 - https://github.com/deeplearning4j/deeplearning4j/issues/6464 - not sure if related to: https://github.com/deeplearning4j/deeplearning4j/issues/6447
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"cnn2d_nn/nhwc_b1_k12_s12_d12_SAME",
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//2019/01/08 - No tensorflow op found for SparseTensorDenseAdd
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//2019/07/02 - No tensorflow op found for SparseTensorDenseAdd
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"confusion/.*",
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//2019/04/08 - Couple of tests failing (InferenceSession issues)
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//2019/07/02 - Couple of tests failing (InferenceSession issues)
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"rnn/bstack/d_.*",
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//2019/05/21 - Failing on AVX2/512 intermittently (Linux, OSX), passing elsewhere
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@ -52,7 +52,7 @@ public class TFGraphTestList {
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public TemporaryFolder testDir = new TemporaryFolder();
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public static String[] modelNames = new String[]{
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"cond/cond_true"
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"cnn2d_nn/nhwc_b1_k12_s12_d12_SAME"
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};
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@After
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@ -57,6 +57,28 @@ public class TFGraphTestZooModels { //Note: Can't extend BaseNd4jTest here as we
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public static TemporaryFolder classTestDir = new TemporaryFolder();
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public static final String[] IGNORE_REGEXES = {
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//2019/06/28 - Output incorrect, can't debug b/c https://github.com/eclipse/deeplearning4j/issues/7957
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"cifar10_gan_85",
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//2019/07/03 - java.lang.NullPointerException: varName is marked @NonNull but is null
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// https://github.com/eclipse/deeplearning4j/issues/7975
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"alexnet",
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//2019/07/03 - Out of Memory error
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"compression_residual_gru",
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//2019/07/03 - calculateOutputDataTypes() has not been implemented for org.nd4j.linalg.api.ops.impl.image.ResizeNearestNeighbor
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// https://github.com/eclipse/deeplearning4j/issues/7976
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"deeplabv3_xception_ade20k_train",
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//2019/07/03 - o.n.i.g.t.TFGraphMapper - No TensorFlow descriptor found for tensor "sample_sequence/model/h0/attn/MatMul", op "BatchMatMulV2"
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//org.nd4j.linalg.exception.ND4JIllegalStateException: No tensorflow op found for Multinomial possibly missing operation class?
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// @ TFGraphMapper.mapNodeType(TFGraphMapper.java:593)
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// Missing Multinormal op, see https://github.com/eclipse/deeplearning4j/issues/7913
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"gpt-2_117M",
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//2019/05/15 - "Invalid shape for op shape_of: shape has invalid values <= 0: shape=[0]"
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//Also: https://github.com/deeplearning4j/deeplearning4j/issues/7112
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"ssd_mobilenet_v1_0.75_depth_300x300_coco14_sync_2018_07_03",
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@ -69,10 +91,19 @@ public class TFGraphTestZooModels { //Note: Can't extend BaseNd4jTest here as we
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"faster_rcnn_resnet101_coco_2018_01_28",
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//2019/06/24 - JVM crash on linux-x86_64-cpu-avx2 and -avx512 CI machines only - runs fine elsewhere
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"deeplabv3_pascal_train_aug_2018_01_04"
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"deeplabv3_pascal_train_aug_2018_01_04",
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};
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public static final String[] IGNORE_REGEXES_LIBND4J_EXEC_ONLY = {
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// Graph wasn't topsorted for all Keras RNNs (possible TF's too)
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// https://github.com/eclipse/deeplearning4j/issues/7974
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"PorV-RNN",
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"temperature_bidirectional_63",
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"temperature_stacked_63",
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"text_gen_81",
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// 2019/05/20 - Buffer is too big to export? https://github.com/deeplearning4j/deeplearning4j/issues/7760
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// File: C:/DL4J/Git/deeplearning4j/libnd4j/blasbuild/cpu/flatbuffers-src/include/flatbuffers/flatbuffers.h, Line 668
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//Expression: size() < FLATBUFFERS_MAX_BUFFER_SIZE
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@ -70,6 +70,7 @@ import org.nd4j.linalg.api.ops.impl.transforms.any.IsMax;
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import org.nd4j.linalg.api.ops.impl.transforms.bool.MatchConditionTransform;
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import org.nd4j.linalg.api.ops.impl.transforms.comparison.CompareAndSet;
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import org.nd4j.linalg.api.ops.impl.transforms.comparison.Eps;
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import org.nd4j.linalg.api.ops.impl.transforms.custom.BatchToSpace;
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import org.nd4j.linalg.api.ops.impl.transforms.pairwise.BinaryRelativeError;
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import org.nd4j.linalg.api.ops.impl.transforms.pairwise.Set;
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import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.Axpy;
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@ -7871,6 +7872,22 @@ public class Nd4jTestsC extends BaseNd4jTest {
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arr[i][j][k][m] = (float) cnt++;
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}
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@Test
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public void testBatchToSpace(){
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INDArray out = Nd4j.create(DataType.FLOAT, 2, 4, 5);
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DynamicCustomOp c = new BatchToSpace();
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c.addInputArgument(
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Nd4j.rand(DataType.FLOAT, new int[]{4, 4, 3}),
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Nd4j.createFromArray(1, 2),
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Nd4j.createFromArray(new int[][]{ new int[]{0, 0}, new int[]{0, 1} })
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);
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c.addOutputArgument(out);
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Nd4j.getExecutioner().exec(c);
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assertEquals(Nd4j.createFromArray(1f, 3f, 4f), out);
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}
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private static INDArray fwd(INDArray input, INDArray W, INDArray b){
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INDArray ret = Nd4j.createUninitialized(input.size(0), W.size(1));
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