cavis/libnd4j/tests_cpu/layers_tests/ParityOpsTests.cpp
Alex Black 1170827c18 Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)

* Modified strided_slice op to properly work with empty-like shapes.

* Fixed test for reduce_mean with empty-like input.

* [WIP] Last merge (#15)

* correct logsoftmax looss (#2)

* Small SameDiff listener fix (#4)

* Various fixes (#6)

* #7839 Fix for asXMatrix and tests

* #7866 EmbeddingSequenceLayer dtype fix + test

* #7856 SameDiff save/load stream methods

* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration

* EvaluationBinary 3d/4d

* More evaluation 3d/4d tests

* #7847 Evaluation empty checks

* Small test ifx

* #7848 Fix median edge case

* Improve DL4J samediff layer tests

* [WIP] FastText wrapper implemented (#8)

* FastText implemented

* Some fixes

* Fix shapes for wordsNearest

* Validation of input vectors

* Fixes

* Fixed test

* Thread tagged

* Some tweaks

* setContextClassLoader for DeallocatorServiceThread

* Numpy format tests (#1)

* Various fixes (#11)

* #7852 SameDiff gather fix

* #7892 SameDiff placeholder to constant conversion

* #7890 validate input rank for MLN/CG init methods

* Fix broken permute shape calculation

* Permute and gather fixes

* Tests

* #7850 LogSumExp fix + test

* Handful of test fixes

* Empty arrays with non-scalar shapes (#10)

* minor rearrangements for lambdas

* empty tensors with non-scalar shapes

* numpy empty tensors with non-scalar shapes

* few more empty tweaks

* Small fixes

* conv3d signature update

* micro fix in batchnorm mkldnn

* Import fixes

* Fix

* MKL-DNN update

* Small fill fix

* fill with empty input + test

* Fixes

* Small error improvement

* Fix

* one special test

* couple of fixes for lstm

* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone

* Fixes

* FP16

* Unsigned

* BFloat16

* Fill op - empty tweaks

* - couple of fixes for empty arrays construction
- stack updated

* strided slice fix

* one transform test

* provide method for reducing shapeInfo in case of input array is empty

* Fixed reduceAlongDimensions to use empty input properly.

* couple of broadcast tests

* couple of tests broadcast tests + tweak to make them pass

* add check of non-empty to methods producing sub-arrays

* Fixed reshapeC with zeros in shape.

* complete empty check in reduce_... legacy ops

* Concat and cumsum/prod

* Tweak to empty shape inference on import

* add empty check to the rest of reduce legacy ops

* one more test

* correct typo in evalReduceShapeInfoEmpty

* Added tests for reduce_* ops to tests with zero shapes.

* few more tests for empty reductions

* Fixed strided_slice op with empty case and tests.

* one more empty reduction test

* Fixed strided_slice test.

* add empty check to NDArray::reshapei

* infOrMax

* empty min/max with infinity tests

* made unstack working correctly with empty arrays

* few IndexReduce tests + tweaks for empty shapes

* add test for empty concat

* few tests fixed

* Validation fix for reductions on empty shapes

* Reverse fix

* Reduction shape calc fixes

* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs

* Range fix

* - NDArray constructor updated for scalars/empty arrays
- few tests fixed

* More fixes

* Empty creator fixes

* concat fix

* concat fix

* TF import tests: allow 'both all NaN' and 'both all inf' to pass

* Slice, zero fraction, and reshape fixes

* transpose, gather

* Zero fraction

* scalar cast fix

* Empty reduction axis support

* few more tests fixed

* Fixed input checks conforming with TF for concat op and tests.

* few tests fixed

* matmul scalar shape fix

* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.

* broadcast bool fix

* few more tests

* few more tests

* correct evalReduceShapeInfoEmpty

* argmax/argmin + tests

* one more empty edge case + one more test

* argmax/argmin/realdiv_bp tweaks

* empty reshape test + fix

* Helper fixes

* Small fixes

* Gather test fix

* Gather test fix

* Small fixes

* reduce scalar zero values

* scalar mean workaround

* Remove debug code

* along dim mean workaround

* one more test

* - equalsTo() tweak for empty arrays
- one more test

* broadcast tweaks

* [WIP] Fixing outstanding issues for NLP (#9)

* Avoid using not-inited objects

* Test fixed.

* Redundant method avoided for models like FastText

* KMeans++ implementation

* KMeans++ implementation

* Disable parallel execution

* KMeans++

* Tests

* Dev branch merge (#16)

* SameDiff: convertDataType and gradient check util improvements (#12)

* GradCheck util improvements

* StopGradient constructor + test

* SameDiff: Add datatype conversion

* Javadoc and add DataType.isNumerical()

* Small fix

* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)

* TFGraphTestAllHelper: check intermediates in execution order

* Add missing debug listener

* [WIP] lstmBlock fix + other changes (#13)

- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite

* Small test fix

* CheckNumerics op wrapper

* Fix some issues on master (#17)

* Fix DataVec test issue

* Fix issue with dl4j SameDiff output layer

* Dtype fix for lambda layers

* #7912 BertIterator dtype fix (use float32 not global default)

* [WIP] Next set of CUDA stuff (#7)

New CUDA implementations and improvements

* bad file

* Dev branch master merge (#23)

* SameDiff: convertDataType and gradient check util improvements (#12)

* GradCheck util improvements

* StopGradient constructor + test

* SameDiff: Add datatype conversion

* Javadoc and add DataType.isNumerical()

* Small fix

* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)

* TFGraphTestAllHelper: check intermediates in execution order

* Add missing debug listener

* [WIP] lstmBlock fix + other changes (#13)

- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite

* Small test fix

* CheckNumerics op wrapper

* Compatibility of deserialization (#18)

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* SameDiff: add activation gradient checking support for debugging (#19)

* SameDiff gradient checker: first pass on activation gradient checks

* Fixes + tests for activation gradient checking

* Javadoc

* [WIP] Some nd4j data type corrections (#20)

* Adjust data type

* Set correct Data type.

* Size of proper data type.

* fix averaged cpu load (#22)

* SameDiff ops, TF import and fixes (#24)

* CheckNumerics tests + fixes + misc fixes

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* Fake quant

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* Fixes

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* FakeQuantWithMinMaxArgs

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* CheckNumerics fix

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* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)

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* Small fix

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* Javadoc

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* Exception tweak

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* fix

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* Fix for out of scope stack allocated var use

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* Ignores

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* Ignore for known failing test (already logged issue)

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* Merge upstream to fork (#25)

* Add thousand-separator commas to TotalParams (#7915)

* Add thousand-separator commas to TotalParams

The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.

* Add thousand-separator commas to MultiLayerNetwork

Corresponding change to MultiLayerNetwork

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* Update contributing and issue/PR templates (#7934)

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* Fix link to AdaDelta paper (#7942)

Fix link to AdaDelta paper hosted on matthewzeiler.com

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* Fixes, and ignores for known/logged failing issues (#7943)

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* SameDiff + DL4J/SameDiff: Multiple fixes (#28)

* #7919 HDF5 attribute buffer length fix

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* #7909 Arbiter constructor exception ux improvements

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* #7925 RNN output layer length checks

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* #7939 Add listener for validating inputs are not incorrectly modified

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* #7939 Integrate NonInplaceValidationListener into tests

* #7844 DL4J SameDiff fixes for variable minibatch size

* DL4J SameDiff fixes - ensure gradient for input placeholder is available

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* Tweaks to ExternalErrorsFunction - use placeholders, make more robust

* Another fix

* More fixes

* More SameDiff/DL4J fixes

* Scope out scalar array creation in BaseScalarOp

* Remove debug code

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* [WIP] Final dev branch merge (#29)

* SameDiff: convertDataType and gradient check util improvements (#12)

* GradCheck util improvements

* StopGradient constructor + test

* SameDiff: Add datatype conversion

* Javadoc and add DataType.isNumerical()

* Small fix

* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)

* TFGraphTestAllHelper: check intermediates in execution order

* Add missing debug listener

* [WIP] lstmBlock fix + other changes (#13)

- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite

* Small test fix

* CheckNumerics op wrapper

* Compatibility of deserialization (#18)

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* SameDiff: add activation gradient checking support for debugging (#19)

* SameDiff gradient checker: first pass on activation gradient checks

* Fixes + tests for activation gradient checking

* Javadoc

* [WIP] Some nd4j data type corrections (#20)

* Adjust data type

* Set correct Data type.

* Size of proper data type.

* fix averaged cpu load (#22)

* [WIP] Multiple dataset iterators (#27)

* Splitting dataset into arbitrary number

* Fixes

* Multiple split of iterator

* Test

* Test

* Some fixes

* signature change

* one more tweak

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* one more test for sequential use of DataSetIteratorSplitter

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* Fixes

* Fixes

* one more test for Alexander

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* Some fixes

* Some fixes

* one more test for Alexander

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* minor test fix

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* Some fixes

* Some fixes

* couple of assertions tweaked

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* MDS splitter test :/

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* Minor refactoring

* Multi dataset

* Some fixes

* More tests

* Small number of test fixes/improvements (failures on CI) (#31)

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* [WIP] More CUDA stuff (#26)

* initial commit

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* LRN BP CUDA

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* less memory

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* Fixed bug with crop_and_resize op helper.

* get rid of unnecessary index-calculation dunction

Signed-off-by: Yurii <yurii@skymind.io>

* Fixed sort with nth_element cuda-based helper.

* Refactored nth_element.

* Refactored nth_element op and tests.

* Modified usage of dim array with sortTad routine.

* Refactored main routine of helper for non_max_image_suppression op.

* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.

* fix vol2col cuda kernel

* meh

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* topK concept

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* unsorted topK with scanWitdh of 1

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* correct vol2col tests

* sorted/unsorted topK

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* implementation and fixing col2im/col2vol

* Corrected usage flags with input/output with reverse op.

* dup is const now

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* percentile op

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* group tests for mapool2d

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* special test for george

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* less threads for sortTad

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* provide conv2d for cuda

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* remove auther in sort tad kernel code

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* provide depthwise_conv2d for cuda

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* - max_pooling_with_argmax
- null check for special use

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* dts cuda

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* provide sconv2d for cuda

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* std cuda

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* Refactored non_max_suppression op to conform TF implementation.

* Improved suppression helper.

* provide pooling3d for cuda

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* minor lstm rearrangements

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* more of minor lstm rearrangements

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* (bi)dynamic_rnn

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* templates init order

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* Refactored non_max_suppression op.

* Added cuda kernel for non_max_suppression.

* CPU sort by key/value

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* CPU sort TAD by key/value

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* CPU sort TAD by key/value tests

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* Eliminate compiler error with cuda implementation.

* - repaired gradCheck in cuda
- provide conv2d_bp for cuda

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* missed signature

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* provide depthwise_conv2d_bp for cuda

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* Implementation of lup helper with cuda kernel. Initial commit.

* further work on backprops for convolutions

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* CUDA linear sort by key/val

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* CUDA tad sort by key/val

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* start providing of backprop for pooling2d/3d

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* Added atomicAdd for bool datatype.

* dynamic partition concept

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* dynamic partition concept

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* dynamic partition scalar CUDA

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* important comment

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* fix pooling2d/3d backprop helpers

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* Added non-linear test with dynamic_partition.

* Improved test for dynamic_partition.

* dynamic_partition TAD concept

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* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix

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* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d

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* dynamic_stitch CUDA vector case

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* dynamic_stitch CUDA TAD case concept

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* dynamic_stitch CUDA TAD case impl

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* Added tests for dynamic_stitch 3D-4D cases.

* minor tests tweaks

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* Fixed type check for dynamic stitch.

* min/max bp

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* rewrite code for upsampling2d/3d cpu

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* reduce min/max/norm_max bp

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* lup implementation. Additional enhancements.

* provide code for upsamling2d/3d backprop

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* weightedCrossEntropyWithLogits

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* Fixed template math atomicMul for 64bit ints.

* Refactored dynamic_partition_bp op.

* inverseBroadcast fix

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* DynamicPartitionBP test datatype fixed.

* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA

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2019-06-27 18:37:04 +03:00

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/*******************************************************************************
* Copyright (c) 2015-2018 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
******************************************************************************/
//
// Created by raver119 on 12.10.2017.
//
#include "testlayers.h"
#include <NDArray.h>
#include <ops/declarable/CustomOperations.h>
using namespace nd4j;
using namespace nd4j::ops;
class ParityOpsTests : public testing::Test {
public:
};
TEST_F(ParityOpsTests, TestZeroAs1) {
auto x = NDArrayFactory::create<float>('c', {10, 10});
x.assign(1.0);
auto exp = NDArrayFactory::create<float>('c', {10, 10});
exp.assign(0.0f);
nd4j::ops::zeros_as op;
auto result = op.execute({&x}, {}, {});
auto z = result->at(0);
ASSERT_TRUE(z->isSameShape(&x));
ASSERT_TRUE(z->equalsTo(&exp));
delete result;
}
TEST_F(ParityOpsTests, TestMaximum1) {
auto x = NDArrayFactory::create<float>('c', {10, 10});
x.assign(1.0);
auto y = NDArrayFactory::create<float>('c', {10, 10});
y.assign(2.0);
nd4j::ops::maximum op;
auto result = op.execute({&x, &y}, {}, {});
auto z = result->at(0);
ASSERT_TRUE(y.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, TestMinimum1) {
auto x = NDArrayFactory::create<float>('c', {10, 10});
x.assign(1.0f);
auto y = NDArrayFactory::create<float>('c', {10, 10});
y.assign(-2.0f);
nd4j::ops::minimum op;
auto result = op.execute({&x, &y}, {}, {});
auto z = result->at(0);
ASSERT_TRUE(y.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, TestTear1) {
auto input = NDArrayFactory::create<float>('c', {10, 5});
auto tads = input.allTensorsAlongDimension({1});
for (int e = 0; e < tads->size(); e++) {
ASSERT_EQ(5, tads->at(e)->lengthOf());
tads->at(e)->assign((float) e + 1);
}
nd4j::ops::tear op;
auto result = op.execute({&input}, {}, {1});
ASSERT_EQ(10, result->size());
for (int e = 0; e < result->size(); e++)
ASSERT_TRUE(tads->at(e)->equalsTo(result->at(e)));
delete result;
delete tads;
}
TEST_F(ParityOpsTests, TestUnstack1) {
auto input = NDArrayFactory::create<float>('c', {10, 5});
auto tads = input.allTensorsAlongDimension({1});
for (int e = 0; e < tads->size(); e++) {
ASSERT_EQ(5, tads->at(e)->lengthOf());
tads->at(e)->assign((float) e + 1);
}
nd4j::ops::unstack op;
auto result = op.execute({&input}, {}, {0});
ASSERT_EQ(10, result->size());
// result->at(0)->printShapeInfo("rz");
// tads->at(0)->printShapeInfo("re");
for (int e = 0; e < result->size(); e++)
ASSERT_TRUE(tads->at(e)->equalsTo(result->at(e)));
delete result;
delete tads;
}
TEST_F(ParityOpsTests, TestUnstack2) {
auto input = NDArrayFactory::create<float>('c', {5,2,6});
auto tads = input.allTensorsAlongDimension({0,1});
for (int e = 0; e < tads->size(); e++) {
ASSERT_EQ(10, tads->at(e)->lengthOf());
tads->at(e)->assign((float) e + 1);
}
nd4j::ops::unstack op;
auto result = op.execute({&input}, {}, {2});
ASSERT_EQ(6, result->size());
for (int e = 0; e < result->size(); e++)
ASSERT_TRUE(tads->at(e)->equalsTo(result->at(e)));
delete result;
delete tads;
}
TEST_F(ParityOpsTests, TestUnstack3) {
auto input = NDArrayFactory::create<float>('c', {3,2,3});
auto exp = NDArrayFactory::create<float>('c', {3, 2}, {1.f, 4., 7., 10.f, 13.f, 16.f});
input.linspace(1);
nd4j::ops::unstack op;
auto result = op.execute({&input}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, TestUnstack4) {
auto input = NDArrayFactory::create<float>('c', {3,2,3});
auto exp = NDArrayFactory::create<float>('c', {3, 3}, { 1, 2, 3, 7, 8, 9, 13, 14, 15.});
input.linspace(1);
nd4j::ops::unstack op;
auto result = op.execute({&input}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, TestUnstack5) {
auto input = NDArrayFactory::create<float>('c', {3,2,3});
auto exp = NDArrayFactory::create<float>('c', {2, 3}, { 1, 2, 3, 4, 5, 6});
input.linspace(1);
nd4j::ops::unstack op;
auto result = op.execute({&input}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, TestUnstack6) {
auto input = NDArrayFactory::create<float>('c', {1, 1, 1});
auto exp = NDArrayFactory::create<float>('c', {1, 1}, {1});
input.linspace(1);
nd4j::ops::unstack op;
auto result = op.execute({&input}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, TestUnstack7) {
auto input = NDArrayFactory::create<float>('c', {1, 1, 1});
auto exp = NDArrayFactory::create<float>('c', {1, 1}, {1});
input.linspace(1);
nd4j::ops::unstack op;
auto result = op.execute({&input}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, TestUnstack8) {
auto input = NDArrayFactory::create<float>('c', {1, 1});
auto exp = NDArrayFactory::create<float>('c', {1}, {1});
input.linspace(1);
nd4j::ops::unstack op;
auto result = op.execute({&input}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, TestUnstack9) {
auto input = NDArrayFactory::create<float>('c', {1, 1});
auto exp = NDArrayFactory::create<float>('c', {1}, {1});
input.linspace(1);
nd4j::ops::unstack op;
auto result = op.execute({&input}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, TestUnstack10) {
auto input = NDArrayFactory::create<float>('c', {3, 0, 2});
auto exp = NDArrayFactory::create<float>('c', {0,2});
nd4j::ops::unstack op;
auto result = op.execute({&input}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
ASSERT_TRUE(exp.isSameShape(result->at(0)));
ASSERT_TRUE(exp.isSameShape(result->at(1)));
ASSERT_TRUE(exp.isSameShape(result->at(2)));
delete result;
}
////////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, TestUnstack11) {
auto input = NDArrayFactory::create<float>('c', {3, 0, 2});
auto exp = NDArrayFactory::create<float>('c', {3,0});
nd4j::ops::unstack op;
auto result = op.execute({&input}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
ASSERT_TRUE(exp.isSameShape(result->at(0)));
ASSERT_TRUE(exp.isSameShape(result->at(1)));
delete result;
}
////////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, TestUnstack12) {
auto input = NDArrayFactory::create<float>('c', {3, 0, 2});
nd4j::ops::unstack op;
auto result = op.execute({&input}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
ASSERT_TRUE(result->size() == 0);
delete result;
}
TEST_F(ParityOpsTests, ExpandDimsTest1) {
auto input = NDArrayFactory::create<float>('c', {5, 5});
input.linspace(1);
auto reshaped = input.reshape('c', {5, 1, 5});
nd4j::ops::expand_dims op;
auto result = op.execute({&input}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(reshaped.isSameShape(z));
ASSERT_TRUE(reshaped.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, ExpandDimsTest2) {
auto input = NDArrayFactory::create<float>('c', {3, 4});
input.linspace(1);
auto reshaped = input.reshape('c', {1, 3, 4});
nd4j::ops::expand_dims op;
auto result = op.execute({&input}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(reshaped.isSameShape(z));
ASSERT_TRUE(reshaped.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, ExpandDimsTest3) {
auto input = NDArrayFactory::create<float>('c', {3, 4});
input.linspace(1);
auto reshaped = input.reshape('c', {3, 1, 4});
nd4j::ops::expand_dims op;
auto result = op.execute({&input}, {}, {-2});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(reshaped.isSameShape(z));
ASSERT_TRUE(reshaped.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, ExpandDimsTest4) {
auto input = NDArrayFactory::create<float>('c', {3, 4});
input.linspace(1);
auto reshaped = input.reshape('c', {1, 3, 4});
nd4j::ops::expand_dims op;
auto result = op.execute({&input}, {}, {-3});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(reshaped.isSameShape(z));
ASSERT_TRUE(reshaped.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_Shape_1) {
auto x = NDArrayFactory::create<float>('c', {3, 4, 5, 6});
auto exp = NDArrayFactory::create<Nd4jLong>('c', {4}, {3, 4, 5, 6});
nd4j::ops::shape_of op;
auto result = op.execute({&x}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
z->printShapeInfo("z shape");
z->printIndexedBuffer(" z buffr");
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_Equals_1) {
auto x = NDArrayFactory::create<float>('c', {1, 5}, {1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<float>('c', {1, 5}, {1, 0, 3, 0, 5});
auto exp = NDArrayFactory::create<bool>('c', {1, 5}, {1, 0, 1, 0, 1});
nd4j::ops::equals op;
auto result = op.execute({&x, &y}, {}, {}, {}, false, nd4j::DataType::BOOL);
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_NotEquals_1) {
auto x = NDArrayFactory::create<float>('c', {1, 5}, {1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<float>('c', {1, 5}, {1, 0, 3, 0, 5});
auto exp = NDArrayFactory::create<bool>('c', {1, 5}, {0, 1, 0, 1, 0});
nd4j::ops::not_equals op;
auto result = op.execute({&x, &y}, {}, {}, {}, false, nd4j::DataType::BOOL);
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_Less_1) {
auto x = NDArrayFactory::create<float>('c', {1, 5}, {1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<float>('c', {1, 5}, {5, 4, 3, 2, 1});
auto exp = NDArrayFactory::create<bool>('c', {1, 5}, {1, 1, 0, 0, 0});
nd4j::ops::less op;
auto result = op.execute({&x, &y}, {}, {}, {}, false, nd4j::DataType::BOOL);
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_LessEquals_1) {
auto x = NDArrayFactory::create<float>('c', {1, 5}, {1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<float>('c', {1, 5}, {5, 4, 3, 2, 1});
auto exp = NDArrayFactory::create<bool>('c', {1, 5}, {1, 1, 1, 0, 0});
nd4j::ops::less_equal op;
auto result = op.execute({&x, &y}, {}, {}, {}, false, nd4j::DataType::BOOL);
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_GreaterEquals_1) {
auto x = NDArrayFactory::create<float>('c', {1, 5}, {1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<float>('c', {1, 5}, {5, 4, 3, 2, 1});
auto exp = NDArrayFactory::create<bool>('c', {1, 5}, {0, 0, 1, 1, 1});
nd4j::ops::greater_equal op;
auto result = op.execute({&x, &y}, {}, {}, {}, false, nd4j::DataType::BOOL);
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_GreaterEquals_2) {
auto x = NDArrayFactory::create<double>('c', {1, 5}, {1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<double>('c', {1, 5}, {5, 4, 3, 2, 1});
auto exp = NDArrayFactory::create<bool>('c', {1, 5}, {0, 0, 1, 1, 1});
nd4j::ops::greater_equal op;
auto result = op.execute({&x, &y}, {}, {}, {}, false);
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_Greater_1) {
auto x = NDArrayFactory::create<float>('c', {1, 5}, {1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<float>('c', {1, 5}, {5, 4, 3, 2, 1});
auto exp = NDArrayFactory::create<bool>('c', {1, 5}, {0, 0, 0, 1, 1});
nd4j::ops::greater op;
auto result = op.execute({&x, &y}, {}, {}, {}, false, nd4j::DataType::BOOL);
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_Where_1) {
auto mask = NDArrayFactory::create<bool>('c', {3, 3}, {1, 1, 1, 0, 0, 0, 1, 1, 1});
auto x = NDArrayFactory::create<float>('c', {3, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9});
auto y = NDArrayFactory::create<float>('c', {3, 3}, {9, 8, 7, 6, 5, 4, 3, 2, 1});
auto exp = NDArrayFactory::create<float>('c', {3, 3}, {1, 2, 3, 6, 5, 4, 7, 8, 9});
nd4j::ops::Where op;
auto result = op.execute({&mask, &x, &y}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
// z->printIndexedBuffer("result");
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_Where_2) {
auto mask = NDArrayFactory::create<bool>('c', {1, 3}, {1, 0, 0});
auto x = NDArrayFactory::create<float>('c', {3, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9});
auto y = NDArrayFactory::create<float>('c', {3, 3}, {9, 8, 7, 6, 5, 4, 3, 2, 1});
auto exp = NDArrayFactory::create<float>('c', {3, 3}, {1, 2, 3, 6, 5, 4, 3, 2, 1});
nd4j::ops::Where op;
auto result = op.execute({&mask, &x, &y}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_Where_3) {
auto mask = NDArrayFactory::create<bool>('c', {2, 2, 3}, {0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1});
auto exp = NDArrayFactory::create<Nd4jLong>('c', {5, 3}, {0, 0, 1, 0, 0, 2, 0, 1, 1, 1, 0, 0, 1, 1, 2});
nd4j::ops::Where op;
auto result = op.execute({&mask}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
// z->printShapeInfo("z");
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_Select_1) {
auto mask = NDArrayFactory::create<bool>('c', {1, 3}, {1, 0, 0});
auto x = NDArrayFactory::create<float>('c', {3, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9});
auto y = NDArrayFactory::create<float>('c', {3, 3}, {9, 8, 7, 6, 5, 4, 3, 2, 1});
auto exp = NDArrayFactory::create<float>('c', {3, 3}, {1, 2, 3, 6, 5, 4, 3, 2, 1});
nd4j::ops::select op;
auto result = op.execute({&mask, &x, &y}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_Select_2) {
auto mask = NDArrayFactory::create<bool>('c', {2, 2}, {1, 0, 1, 0});
auto x = NDArrayFactory::create<float>('c', {2, 2}, {1, 2, 3, 4 });
auto y = NDArrayFactory::create<float>('c', {2, 2}, {9, 8, 7, 6});
auto exp = NDArrayFactory::create<float>('c', {2, 2}, {1, 8, 3, 6});
nd4j::ops::select op;
auto result = op.execute({&mask, &x, &y}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_Select_3) {
auto mask = NDArrayFactory::create<bool>('c', {1, 1}, {false});
auto x = NDArrayFactory::create<float>('c', {1, 1}, {1});
auto y = NDArrayFactory::create<float>('c', {1, 1}, {2});
auto exp = NDArrayFactory::create<float>('c', {1, 1}, {2});
nd4j::ops::select op;
auto result = op.execute({&mask, &x, &y}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_Reshape_TF_1) {
auto x = NDArrayFactory::create<int>('c', {2, 2}, {1, 2, 3, 4});
auto shape = NDArrayFactory::create<int>('c', {1, 3}, {1, 2, 2});
auto exp = NDArrayFactory::create<int>('c', {1, 2, 2}, {1, 2, 3, 4});
nd4j::ops::reshape op;
auto result = op.execute({&x, &shape}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_Bias_Add_1) {
auto x = NDArrayFactory::create<float>('c', {10, 5});
x.assign(0.0);
auto bias = NDArrayFactory::create<float>('c', {1, 5}, {1, 2, 3, 4, 5});
nd4j::ops::biasadd op;
auto result = op.execute({&x, &bias}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
auto tads = z->allTensorsAlongDimension({1});
for (int e = 0; e < tads->size(); e++) {
ASSERT_TRUE(bias.equalsTo(tads->at(e)));
}
delete tads;
delete result;
}
TEST_F(ParityOpsTests, Test_Scatter_Add_1) {
auto matrix = NDArrayFactory::create<float>('c', {2, 2}, {1, 2, 3, 4});
NDArray idc('c', {1}, {0}, nd4j::DataType::INT64);
auto updates = NDArrayFactory::create<float>('c', {1, 2}, {1, 1});
auto exp = NDArrayFactory::create<float>('c', {2, 2}, {2, 3, 3, 4});
nd4j::ops::scatter_add op;
auto result = op.execute({&matrix, &idc, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_Scatter_Add_2) {
auto vec = NDArrayFactory::create<float>('c', {4}, {1, 2, 3, 4});
NDArray idc('c', {1, 4}, {0, 1, 2, 3}, nd4j::DataType::INT64);
auto updates = NDArrayFactory::create<float>('c', {1, 4}, {1, 1, 1, 1});
auto exp = NDArrayFactory::create<float>('c', {1, 4}, {2, 3, 4, 5});
nd4j::ops::scatter_add op;
auto result = op.execute({&vec, &idc, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_Scatter_Add_3) {
auto matrix = NDArrayFactory::create<float>('c', {2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8});
NDArray idc('c', {1}, {0}, nd4j::DataType::INT64);
auto updates = NDArrayFactory::create<float>('c', {1, 2, 2}, {1, 1, 1, 1});
auto exp = NDArrayFactory::create<float>('c', {2, 2, 2}, {2, 3, 4, 5, 5, 6, 7, 8});
nd4j::ops::scatter_add op;
auto result = op.execute({&matrix, &idc, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_Scatter_Add_4) {
auto matrix = NDArrayFactory::create<float>('c', {2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8});
NDArray idc('c', {1, 2}, {0, 0}, nd4j::DataType::INT64);
auto updates = NDArrayFactory::create<float>('c', {1, 2, 2, 2}, {1, 1, 1, 1, 1, 1, 1, 1});
auto exp = NDArrayFactory::create<float>('c', {2, 2, 2}, {3, 4, 5, 6, 5, 6, 7, 8});
nd4j::ops::scatter_add op;
auto result = op.execute({&matrix, &idc, &updates}, {}, {}, {true});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_Scatter_Add_5) {
auto matrix = NDArrayFactory::create<float>('c', {2, 2, 3}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
NDArray idc('c', {2, 2}, {1, 1, 0, 0}, nd4j::DataType::INT64);
auto updates = NDArrayFactory::create<float>('c', {2, 2, 2, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto exp = NDArrayFactory::create<float>('c', {2, 2, 3}, {9., 11., 13.,15., 17., 19., 9., 11., 13.,15., 17., 19.});
nd4j::ops::scatter_add op;
auto result = op.execute({&matrix, &idc, &updates}, {}, {}, {true});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
// z->printBuffer();
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_Scatter_Add_6) {
auto matrix = NDArrayFactory::create<float>('c', {2, 2, 2}, {1, 1, 1, 1, 1, 1, 1, 1});
NDArray idc('c', {2, 2}, {1, 1, 0, 0}, nd4j::DataType::INT64);
auto updates = NDArrayFactory::create<float>('c', {2, 2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8});
auto exp = NDArrayFactory::create<float>('c', {2, 2, 2}, {7, 9, 11, 13, 7, 9, 11, 13});
nd4j::ops::scatter_add op;
auto result = op.execute({&matrix, &idc, &updates}, {}, {}, {true});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, Test_Scatter_Add_7) {
auto matrix = NDArrayFactory::create<float>('c', {10, 3}, {1.f,2.f,3.f,4.f,5.f,6.f,7.f,8.f,9.f,10.f,11.f,12.f,13.f,14.f,15.f,16.f,17.f,18.f,19.f,20.f,21.f,22.f,23.f,24.f,25.f,26.f,27.f,28.f,29.f,30.f});
NDArray idc('c', {}, {5}, nd4j::DataType::INT64);
auto updates = NDArrayFactory::create<float>('c', {3}, {10.f, 20.f, 30.f});
auto exp = NDArrayFactory::create<float>('c', {10, 3}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f,11.f,12.f, 13.f,14.f,15.f, 26.f,37.f,48.f, 19.f,20.f,21.f, 22.f,23.f,24.f, 25.f,26.f,27.f, 28.f,29.f,30.f});
nd4j::ops::scatter_add op;
auto result = op.execute({&matrix, &idc, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, Test_Scatter_Add_8) {
NDArray input('c', {8}, {1,1,1,1,1,1,1,1}, nd4j::DataType::FLOAT32);
NDArray indices('c', {4}, {1, 1, 1, 1}, nd4j::DataType::INT32);
NDArray updates('c', {4}, {1,2,3,4}, nd4j::DataType::FLOAT32);
NDArray expected('c', {8}, {1.f, 11.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f}, nd4j::DataType::FLOAT32);
NDArray z('c', {8}, nd4j::DataType::FLOAT32);
nd4j::ops::scatter_add op;
Nd4jStatus status = op.execute({&input, &indices, &updates}, {&z}, {}, {}, {true});
// z.printBuffer();
ASSERT_EQ(ND4J_STATUS_OK, status);
ASSERT_TRUE(expected.isSameShapeStrict(&z));
ASSERT_TRUE(expected.equalsTo(z));
}
////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterMax_test1) {
auto matrix = NDArrayFactory::create<float>('c', {2, 2}, {1, 2, 3, 4});
NDArray idc('c', {1}, {0.}, nd4j::DataType::INT64);
auto updates = NDArrayFactory::create<float>('c', {1, 2}, {10, 1});
auto exp = NDArrayFactory::create<float>('c', {2, 2}, {10, 2, 3, 4});
nd4j::ops::scatter_max op;
auto result = op.execute({&matrix, &idc, &updates}, {}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, scatterMax_test2) {
auto vec = NDArrayFactory::create<float>('c', {4}, {1, 2, 3, 4});
NDArray idc('c', {1, 4}, {0, 1, 2, 3}, nd4j::DataType::INT64);
auto updates = NDArrayFactory::create<float>('c', {1, 4}, {10, 1, 30, 1});
auto exp = NDArrayFactory::create<float>('c', {1, 4}, {10, 2, 30, 4});
nd4j::ops::scatter_max op;
auto result = op.execute({&vec, &idc, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, scatterMax_test3) {
auto matrix = NDArrayFactory::create<float>('c', {2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8});
NDArray idc('c', {1}, {0}, nd4j::DataType::INT64);
auto updates = NDArrayFactory::create<float>('c', {1, 2, 2}, {10, 1, 30, 1});
auto exp = NDArrayFactory::create<float>('c', {2, 2, 2}, {10, 2, 30, 4, 5, 6, 7, 8});
nd4j::ops::scatter_max op;
auto result = op.execute({&matrix, &idc, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, scatterMax_test4) {
auto matrix = NDArrayFactory::create<float>('c', {2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8});
NDArray idc('c', {1,2}, {0,0}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {1, 2, 2, 2}, {1,10,1,10, 1,1,10,1.});
auto exp = NDArrayFactory::create<float>('c', {2, 2, 2}, {1, 10, 10, 10, 5, 6, 7, 8});
nd4j::ops::scatter_max op;
auto result = op.execute({&matrix, &idc, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, scatterMax_test5) {
auto matrix = NDArrayFactory::create<float>('c', {2, 2, 3}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
NDArray idc('c', {2, 2}, {1, 1, 0, 0}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {2, 2, 2, 3}, {2,10,1,10, 2,10,1,10, 2,10,1,10, 10,2,10,1, 10,2,10,1, 10,2,10,1.});
auto exp = NDArrayFactory::create<float>('c', {2, 2, 3}, {10, 2, 10, 2, 10, 2, 2, 10, 2, 10, 2, 10});
nd4j::ops::scatter_max op;
auto result = op.execute({&matrix, &idc, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, scatterMax_test6) {
auto matrix = NDArrayFactory::create<float>('c', {2, 2, 2}, {1, 1, 1, 1, 1, 1, 1, 1});
NDArray idc('c', {2, 2}, {1, 1, 0, 0}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {2, 2, 2, 2}, {0,2,0,2, 0,2,0,2, 2,0,2,0., 2,0,2,0});
auto exp = NDArrayFactory::create<float>('c', {2, 2, 2}, {2, 1, 2, 1, 1, 2, 1, 2});
nd4j::ops::scatter_max op;
auto result = op.execute({&matrix, &idc, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, scatterMin_test1) {
auto matrix = NDArrayFactory::create<float>('c', {2, 2}, {1, 2, 3, 4});
NDArray idc('c', {1}, {0}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {1, 2}, {-1, 1});
auto exp = NDArrayFactory::create<float>('c', {2, 2}, {-1, 1, 3, 4});
nd4j::ops::scatter_min op;
auto result = op.execute({&matrix, &idc, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, scatterMin_test2) {
auto vec = NDArrayFactory::create<float>('c', {4}, {1, 2, 3, 4});
NDArray idc('c', {1, 4}, {0, 1, 2, 3}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {1, 4}, {10, 1, 30, 1});
auto exp = NDArrayFactory::create<float>('c', {1, 4}, {1, 1, 3, 1});
nd4j::ops::scatter_min op;
auto result = op.execute({&vec, &idc, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, scatterMin_test3) {
auto matrix = NDArrayFactory::create<float>('c', {2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8});
NDArray idc('c', {1}, {0}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {1, 2, 2}, {10, 1, 30, 2});
auto exp = NDArrayFactory::create<float>('c', {2, 2, 2}, {1, 1, 3, 2, 5, 6, 7, 8});
nd4j::ops::scatter_min op;
auto result = op.execute({&matrix, &idc, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, scatterMin_test4) {
auto matrix = NDArrayFactory::create<float>('c', {2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8});
NDArray idc('c', {1,2}, {0,0}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {1, 2, 2, 2}, {1,10,1,10, 1,1,10,1.});
auto exp = NDArrayFactory::create<float>('c', {2, 2, 2}, {1, 1, 1, 1, 5, 6, 7, 8});
nd4j::ops::scatter_min op;
auto result = op.execute({&matrix, &idc, &updates}, {}, {}, {true});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
// z->printBuffer();
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_test1) {
NDArray indices('c', {2, 1}, {1., 0.}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {2, 4}, {10.f, 20.f, 30.f, 40.f, 50.f, 60.f, 70.f, 80.f});
auto shape = NDArrayFactory::create<int>('c', {2}, {3, 4});
auto exp = NDArrayFactory::create<float>('c', {3, 4}, {50.f, 60.f, 70.f, 80.f, 10.f, 20.f, 30.f, 40.f, 0.f, 0.f, 0.f, 0.f});
nd4j::ops::scatter_nd op;
auto result = op.execute({&indices, &updates, &shape}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
// z->printBuffer();
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_test2) {
NDArray indices('c', {3, 1}, {4., 2., 0.}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {3, 4});
auto shape = NDArrayFactory::create<int>('c', {2}, {5, 4});
auto exp = NDArrayFactory::create<float>('c', {5, 4}, {9.f,10.f,11.f,12.f, 0.f, 0.f, 0.f, 0.f, 5.f, 6.f, 7.f, 8.f, 0.f, 0.f, 0.f, 0.f, 1.f, 2.f, 3.f, 4.f});
updates.linspace(1.f);
nd4j::ops::scatter_nd op;
auto result = op.execute({&indices, &updates, &shape}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_test3) {
NDArray indices('c', {2, 3, 1}, {0., 2., 7., 3., 6., 9.}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {2,3, 3,4});
auto shape = NDArrayFactory::create<int>('c', {3}, {10, 3, 4});
auto exp = NDArrayFactory::create<float>('c', {10, 3, 4}, {1.f, 2.f, 3.f, 4., 5.f, 6.f, 7.f, 8., 9.f, 10.f, 11.f, 12., 0.f, 0.f, 0.f, 0., 0.f, 0.f, 0.f, 0., 0.f, 0.f, 0.f, 0.,
13.f, 14.f, 15.f, 16.,17.f, 18.f, 19.f, 20.,21.f, 22.f, 23.f, 24.,37.f, 38.f, 39.f, 40.,41.f, 42.f, 43.f, 44.,45.f, 46.f, 47.f, 48.,
0.f, 0.f, 0.f, 0., 0.f, 0.f, 0.f, 0., 0.f, 0.f, 0.f, 0., 0.f, 0.f, 0.f, 0., 0.f, 0.f, 0.f, 0., 0.f, 0.f, 0.f, 0.,
49.f, 50.f, 51.f, 52.,53.f, 54.f, 55.f, 56.,57.f, 58.f, 59.f, 60.,25.f, 26.f, 27.f, 28.,29.f, 30.f, 31.f, 32.,33.f, 34.f, 35.f, 36.,
0.f, 0.f, 0.f, 0., 0.f, 0.f, 0.f, 0., 0.f, 0.f, 0.f, 0.,61.f, 62.f, 63.f, 64.,65.f, 66.f, 67.f, 68.,69.f, 70.f, 71.f, 72.,});
updates.linspace(1.f);
nd4j::ops::scatter_nd op;
auto result = op.execute({&indices, &updates, &shape}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_test4) {
NDArray indices('c', {4, 1}, {4., 3., 1., 7.}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {4}, {9.f, 10.f, 11.f, 12.f});
auto shape = NDArrayFactory::create<int>('c', {1}, {8});
auto exp = NDArrayFactory::create<float>('c', {8}, {0.f, 11.f, 0.f, 10.f, 9.f, 0.f, 0.f, 12.f});
nd4j::ops::scatter_nd op;
auto result = op.execute({&indices, &updates, &shape}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_test5) {
NDArray indices('c', {4, 1}, {1, 1, 1, 1}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {4}, {1.f, 2.f, 3.f, 4.f});
auto shape = NDArrayFactory::create<int>('c', {1}, {8});
auto exp = NDArrayFactory::create<float>('c', {8}, {0.f, 10.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f});
nd4j::ops::scatter_nd op;
auto result = op.execute({&indices, &updates, &shape}, {}, {}, {true});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
// z->printBuffer();
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_test6) {
NDArray indices('c', {3, 2}, {0,1,1,0,3,2}, nd4j::DataType::INT32);
NDArray updates('c', {3, 2, 3}, nd4j::DataType::FLOAT32);
NDArray shape('c', {4}, {5,4,2,3}, nd4j::DataType::INT32);
NDArray exp('c', {5,4,2,3}, {0., 0., 0.,0., 0., 0.,1., 2., 3.,4., 5., 6.,0., 0., 0.,0., 0., 0., 0., 0., 0.,0., 0., 0.,
7., 8., 9., 10., 11., 12., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 13., 14., 15., 16., 17., 18., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.}, nd4j::DataType::FLOAT32);
updates.linspace(1);
nd4j::ops::scatter_nd op;
auto result = op.execute({&indices, &updates, &shape}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
// z->printBuffer();
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_test7) {
NDArray indices('c', {4,3,2}, {0,1,1,0,3,2,1,0,0,1,1,0,3,2,1,0,0,1,1,0,3,2,1,0}, nd4j::DataType::INT32);
NDArray updates('c', {4,3,2,3}, nd4j::DataType::FLOAT32);
NDArray shape('c', {4}, {5,4,2,3}, nd4j::DataType::INT32);
NDArray exp('c', {5,4,2,3}, {0., 0., 0., 0., 0., 0., 75., 78., 81., 84., 87., 90., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
222., 228., 234., 240., 246., 252., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 111., 114., 117., 120., 123., 126., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.}, nd4j::DataType::FLOAT32);
updates.linspace(1);
nd4j::ops::scatter_nd op;
auto result = op.execute({&indices, &updates, &shape}, {}, {}, {true});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
// z->printBuffer();
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_test8) {
NDArray indices('c', {3, 2}, {0,0, 1,1, 2,2}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {3}, {1.f, 2.f, 3.f});
auto shape = NDArrayFactory::create<int>('c', {2}, {6,4});
auto exp = NDArrayFactory::create<float>('c', {6,4}, {1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0});
nd4j::ops::scatter_nd op;
auto result = op.execute({&indices, &updates, &shape}, {}, {true});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
// z->printBuffer();
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_add_test1) {
auto input = NDArrayFactory::create<float>('c', {8}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f});
NDArray indices('c', {4, 1}, {4., 3., 1., 7.}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {4}, {9.f, 10.f, 11.f, 12.f});
auto exp = NDArrayFactory::create<float>('c', {8}, {1.f, 13.f, 3.f, 14.f, 14.f, 6.f, 7.f, 20.f});
nd4j::ops::scatter_nd_add op;
auto result = op.execute({&input, &indices, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_add_test2) {
auto input = NDArrayFactory::create<float>('c', {6, 4});
NDArray indices('c', {3, 3, 2}, {0.f,0.f, 1.f,1.f, 2.f,2.f, 3.f,3.f, 4.f,0.f, 5.f,1.f, 0.f,2.f, 1.f,3.f, 2.f,0.f}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {3,3});
auto exp = NDArrayFactory::create<float>('c', {6,4}, {1.f,0.f,7.f,0.f, 0.f,2.f,0.f,8.f, 9.f,0.f,3.f,0.f, 0.f,0.f,0.f,4.f, 5.f,0.f,0.f,0.f, 0.f,6.f,0.f,0.f});
input = 0.f;
updates.linspace(1.f);
nd4j::ops::scatter_nd_add op;
auto result = op.execute({&input, &indices, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
// z->printIndexedBuffer();
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_add_test3) {
auto input = NDArrayFactory::create<float>('c', {6, 4});
NDArray indices('c', {2, 3, 1}, {5.f, 1.f, 2.f, 3.f, 4.f, 0.f}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {2,3,4});
auto exp = NDArrayFactory::create<float>('c', {6,4}, {21.f, 22.f, 23.f, 24.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f,13.f, 14.f, 15.f, 16.f,17.f, 18.f, 19.f, 20.f, 1.f, 2.f, 3.f, 4.f});
input = 0.f;
updates.linspace(1.f);
nd4j::ops::scatter_nd_add op;
auto result = op.execute({&input, &indices, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
//////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_add_test4) {
auto input = NDArrayFactory::create<float>('c', {6, 4, 5});
NDArray indices('c', {3, 3, 2}, {0.f,0.f, 1.f,1.f, 2.f,2.f, 3.f,3.f, 4.f,0.f, 5.f,1.f, 0.f,2.f, 1.f,3.f, 2.f,0.f}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {3,3,5});
auto exp = NDArrayFactory::create<float>('c', {6,4,5}, {1.f, 2.f, 3.f, 4.f, 5.f, 0.f, 0.f, 0.f, 0.f, 0.f,31.f, 32.f, 33.f, 34.f, 35.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 6.f, 7.f, 8.f, 9.f, 10.f, 0.f, 0.f, 0.f, 0.f, 0.f,36.f, 37.f, 38.f, 39.f, 40.f,
41.f, 42.f, 43.f, 44.f, 45.f, 0.f, 0.f, 0.f, 0.f, 0.f,11.f, 12.f, 13.f, 14.f, 15.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,16.f, 17.f, 18.f, 19.f, 20.f,
21.f, 22.f, 23.f, 24.f, 25.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f,26.f, 27.f, 28.f, 29.f, 30.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f});
input = 0.f;
updates.linspace(1.f);
nd4j::ops::scatter_nd_add op;
auto result = op.execute({&input, &indices, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
//////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_add_test5) {
auto input = NDArrayFactory::create<float>('c', {6,5,4,3,2});
NDArray indices('c', {2,2,3}, {0.f,0.f,0.f, 1.f,1.f,1.f, 2.f,2.f,2.f, 3.f,3.f,3.f}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {2,2,3,2});
auto exp = NDArrayFactory::create<float>('c', {6,5,4,3,2}, { 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 7.f, 8.f, 9.f, 10.f,11.f, 12.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,13.f, 14.f,15.f, 16.f,17.f, 18.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,19.f, 20.f,21.f, 22.f,23.f, 24.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f});
input = 0.f;
updates.linspace(1.f);
nd4j::ops::scatter_nd_add op;
auto result = op.execute({&input, &indices, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_sub_test1) {
auto input = NDArrayFactory::create<float>('c', {8}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f});
NDArray indices('c', {4, 1}, {4.f, 3.f, 1.f, 7.f}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {4}, {9.f, 10.f, 11.f, 12.f});
auto exp = NDArrayFactory::create<float>('c', {8}, {1.f, -9.f, 3.f, -6.f, -4.f, 6.f, 7.f, -4.f});
nd4j::ops::scatter_nd_sub op;
auto result = op.execute({&input, &indices, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_sub_test2) {
auto input = NDArrayFactory::create<float>('c', {6, 4});
NDArray indices('c', {3, 3, 2}, {0.f,0.f, 1.f,1.f, 2.f,2.f, 3.f,3.f, 4.f,0.f, 5.f,1.f, 0.f,2.f, 1.f,3.f, 2.f,0.f}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {3,3});
auto exp = NDArrayFactory::create<float>('c', {6,4}, {-1.f,0.f,-7.f,0.f, 0.f,-2.f,0.f,-8.f, -9.f,0.f,-3.f,0.f, 0.f,0.f,0.f,-4.f, -5.f,0.f,0.f,0.f, 0.f,-6.f,0.f,0.f});
input = 0.f;
updates.linspace(1.f);
nd4j::ops::scatter_nd_sub op;
auto result = op.execute({&input, &indices, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
// z->printIndexedBuffer();
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_sub_test3) {
auto input = NDArrayFactory::create<float>('c', {6, 4});
NDArray indices('c', {2, 3, 1}, {5.f, 1.f, 2.f, 3.f,4.f, 0.f}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {2,3,4});
auto exp = NDArrayFactory::create<float>('c', {6,4}, {-21.f,-22.f,-23.f,-24., -5.f, -6.f, -7.f, -8., -9.f,-10.f,-11.f,-12., -13.f,-14.f,-15.f,-16., -17.f,-18.f,-19.f,-20., -1.f, -2.f, -3.f, -4.f});
input = 0.f;
updates.linspace(1.f);
nd4j::ops::scatter_nd_sub op;
auto result = op.execute({&input, &indices, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
//////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_sub_test4) {
auto input = NDArrayFactory::create<float>('c', {6, 4, 5});
NDArray indices('c', {3, 3, 2}, {0.f,0.f, 1.f,1.f, 2.f,2.f, 3.f,3.f, 4.f,0.f, 5.f,1.f, 0.f,2.f, 1.f,3.f, 2.f,0.f}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {3,3,5});
auto exp = NDArrayFactory::create<float>('c', {6,4,5}, {-1.f, -2.f, -3.f, -4.f, -5.f, 0.f, 0.f, 0.f, 0.f, 0.f,-31.f, -32.f, -33.f, -34.f, -35.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, -6.f, -7.f, -8.f, -9.f, -10.f, 0.f, 0.f, 0.f, 0.f, 0.f,-36.f, -37.f, -38.f, -39.f, -40.f,
-41.f, -42.f, -43.f, -44.f, -45.f, 0.f, 0.f, 0.f, 0.f, 0.f,-11.f, -12.f, -13.f, -14.f, -15.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,-16.f, -17.f, -18.f, -19.f, -20.f,
-21.f, -22.f, -23.f, -24.f, -25.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f,-26.f, -27.f, -28.f, -29.f, -30.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f});
input = 0.f;
updates.linspace(1.f);
nd4j::ops::scatter_nd_sub op;
auto result = op.execute({&input, &indices, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
//////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_sub_test5) {
auto input = NDArrayFactory::create<float>('c', {6,5,4,3,2});
NDArray indices('c', {2,2,3}, {0.f,0.f,0.f, 1.f,1.f,1.f, 2.f,2.f,2.f, 3.f,3.f,3.f}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {2,2,3,2});
auto exp = NDArrayFactory::create<float>('c', {6,5,4,3,2}, { -1.f, -2.f, -3.f, -4.f, -5.f, -6.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, -7.f, -8.f, -9.f, -10.f,-11.f, -12.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,-13.f, -14.f,-15.f, -16.f,-17.f, -18.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,-19.f, -20.f,-21.f, -22.f,-23.f,-24.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f});
input = 0.f;
updates.linspace(1.f);
nd4j::ops::scatter_nd_sub op;
auto result = op.execute({&input, &indices, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_update_test1) {
auto input = NDArrayFactory::create<float>('c', {8}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f});
NDArray indices('c', {4, 1}, {4.f, 3.f, 1.f, 7.f}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {4}, {9.f, 10.f, 11.f, 12.f});
auto exp = NDArrayFactory::create<float>('c', {8}, {1.f, 11.f, 3.f, 10.f, 9.f, 6.f, 7.f, 12.f});
nd4j::ops::scatter_nd_update op;
auto result = op.execute({&input, &indices, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_update_test2) {
auto input = NDArrayFactory::create<float>('c', {6, 4});
NDArray indices('c', {3, 3, 2}, {0.f,0.f, 1.f,1.f, 2.f,2.f, 3.f,3.f, 4.f,0.f, 5.f,1.f, 0.f,2.f, 1.f,3.f, 2.f,0.f}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {3,3});
auto exp = NDArrayFactory::create<float>('c', {6,4}, {1.f,-1.f,7.f,-1.f, -1.f,2.f,-1.f,8.f, 9.f,-1.f,3.f,-1.f, -1.f,-1.f,-1.f,4.f, 5.f,-1.f,-1.f,-1.f, -1.f,6.f,-1.f,-1.f});
input = -1.f;
updates.linspace(1.f);
nd4j::ops::scatter_nd_update op;
auto result = op.execute({&input, &indices, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
// z->printIndexedBuffer();
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_update_test3) {
auto input = NDArrayFactory::create<float>('c', {6, 4});
NDArray indices('c', {2, 3, 1}, {5.f, 1.f, 2.f, 3.f, 4.f, 0.f}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {2,3,4});
auto exp = NDArrayFactory::create<float>('c', {6,4}, {21.f, 22.f, 23.f, 24.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f,13.f, 14.f, 15.f, 16.f,17.f, 18.f, 19.f, 20.f, 1.f, 2.f, 3.f, 4.f,});
input = -1.f;
updates.linspace(1.f);
nd4j::ops::scatter_nd_update op;
auto result = op.execute({&input, &indices, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
// z->printBuffer();
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
//////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_update_test4) {
auto input = NDArrayFactory::create<float>('c', {6, 4, 5});
NDArray indices('c', {3, 3, 2}, {0.f,0.f, 1.f,1.f, 2.f,2.f, 3.f,3.f, 4.f,0.f, 5.f,1.f, 0.f,2.f, 1.f,3.f, 2.f,0.f}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {3,3,5});
auto exp = NDArrayFactory::create<float>('c', {6,4,5}, {1.f, 2.f, 3.f, 4.f, 5.f, -1.f, -1.f, -1.f, -1.f, -1.f,31.f, 32.f, 33.f, 34.f, 35.f, -1.f, -1.f, -1.f, -1.f, -1.f,
-1.f, -1.f, -1.f, -1.f, -1.f, 6.f, 7.f, 8.f, 9.f, 10.f, -1.f, -1.f, -1.f, -1.f, -1.f,36.f, 37.f, 38.f, 39.f, 40.f,
41.f, 42.f, 43.f, 44.f, 45.f, -1.f, -1.f, -1.f, -1.f, -1.f,11.f, 12.f, 13.f, 14.f, 15.f, -1.f, -1.f, -1.f, -1.f, -1.f,
-1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f,16.f, 17.f, 18.f, 19.f, 20.f,
21.f, 22.f, 23.f, 24.f, 25.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f,
-1.f, -1.f, -1.f, -1.f, -1.f,26.f, 27.f, 28.f, 29.f, 30.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f});
input = -1.f;
updates.linspace(1.f);
nd4j::ops::scatter_nd_update op;
auto result = op.execute({&input, &indices, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
//////////////////////////////////////////////////////////////////////
TEST_F(ParityOpsTests, scatterND_update_test5) {
auto input = NDArrayFactory::create<float>('c', {6,5,4,3,2});
NDArray indices('c', {2,2,3}, {0.f,0.f,0.f, 1.f,1.f,1.f, 2.f,2.f,2.f, 3.f,3.f,3.f}, nd4j::DataType::INT32);
auto updates = NDArrayFactory::create<float>('c', {2,2,3,2});
auto exp = NDArrayFactory::create<float>('c', {6,5,4,3,2}, { 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f,
-1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f,
-1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f,
-1.f, -1.f, -1.f, -1.f, -1.f, -1.f, 7.f, 8.f, 9.f, 10.f,11.f, 12.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f,
-1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f,
-1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f,
-1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f,13.f, 14.f,15.f, 16.f,17.f, 18.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f,
-1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f,
-1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f,
-1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f,19.f, 20.f,21.f, 22.f,23.f, 24.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f,
-1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f,
-1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f,
-1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f,
-1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f,
-1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f});
input = -1.f;
updates.linspace(1.f);
nd4j::ops::scatter_nd_update op;
auto result = op.execute({&input, &indices, &updates}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(ParityOpsTests, scatter_update_1) {
auto matrix = NDArrayFactory::create_<float>('c', {3, 2});
auto updates = NDArrayFactory::create_<float>('c', {2, 2});
updates->assign(1.0);
//updates.printBuffer("Updates");
auto variableSpace = new VariableSpace();
variableSpace->putVariable(-1, matrix);
variableSpace->putVariable(-2, updates);
variableSpace->putVariable(1, new Variable(&matrix));
auto block = new Context(1, variableSpace, false);
block->fillInputs({-1, -2});
std::vector<int>* arguments = block->getIArguments();
arguments->push_back(0);
arguments->push_back(1);
arguments->push_back(1);
arguments->push_back(2);
arguments->push_back(1);
arguments->push_back(2);
nd4j::ops::scatter_update op;
Nd4jStatus result = op.execute(block);
ASSERT_EQ(ND4J_STATUS_OK, result);
delete block;
delete variableSpace;
}