cavis/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests4.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

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

* 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
******************************************************************************/
//
// @author raver119@gmail.com
//
#include "testlayers.h"
#include <ops/declarable/CustomOperations.h>
#include <helpers/helper_hash.h>
#include <NDArray.h>
#include <array/NDArrayList.h>
using namespace nd4j;
using namespace nd4j::graph;
class DeclarableOpsTests4 : public testing::Test {
public:
DeclarableOpsTests4() {
printf("\n");
fflush(stdout);
nd4j::ops::adjust_hue op0;
nd4j::ops::adjust_saturation op1;
}
};
template <typename T>
class TypedDeclarableOpsTests4 : public testing::Test {
public:
TypedDeclarableOpsTests4() {
printf("\n");
fflush(stdout);
nd4j::ops::adjust_hue op0;
nd4j::ops::adjust_saturation op1;
}
};
typedef ::testing::Types<double, float> TestingTypes;
TYPED_TEST_CASE(TypedDeclarableOpsTests4, TestingTypes);
TYPED_TEST(TypedDeclarableOpsTests4, Test_Pooling_Parity_1) {
auto x = NDArrayFactory::create<TypeParam>('c', {2, 4, 4, 2});
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 2}, {6.f, 7.f, 10.f, 11.f, 22.f, 23.f, 26.f, 27.f, 38.f, 39.f, 42.f, 43.f, 54.f, 55.f, 58.f, 59.f});
x.linspace(1);
nd4j::ops::avgpool2d op;
auto result = op.execute({&x}, {}, {2, 2, 2, 2, 0, 0, 1, 1, 1, 1, 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;
}
TYPED_TEST(TypedDeclarableOpsTests4, Test_Pooling_Parity_2) {
auto x = NDArrayFactory::create<TypeParam>('c', {2, 4, 4, 2});
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 2}, {6.f, 7.f, 10.f, 11.f, 22.f, 23.f, 26.f, 27.f, 38.f, 39.f, 42.f, 43.f, 54.f, 55.f, 58.f, 59.f});
x.linspace(1);
nd4j::ops::avgpool2d op;
auto result = op.execute({&x}, {}, {2, 2, 2, 2, 0, 0, 1, 1, 0, 1, 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;
}
TYPED_TEST(TypedDeclarableOpsTests4, Test_Pooling_Parity_5) {
auto x = NDArrayFactory::create<TypeParam>('c', {2, 5, 5, 2});
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 3, 3, 2}, {7.f, 8.f, 11.f, 12.f, 14.f, 15.f, 27.f, 28.f, 31.f, 32.f, 34.f, 35.f, 42.f, 43.f, 46.f, 47.f, 49.f, 50.f, 57.f, 58.f, 61.f, 62.f, 64.f, 65.f, 77.f, 78.f, 81.f, 82.f, 84.f, 85.f, 92.f, 93.f, 96.f, 97.f, 99.f, 100.f,});
x.linspace(1);
nd4j::ops::avgpool2d op;
auto result = op.execute({&x}, {}, {2, 2, 2, 2, 0, 0, 1, 1, 1, 0, 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;
}
TYPED_TEST(TypedDeclarableOpsTests4, Test_Pooling_Parity_6) {
auto x = NDArrayFactory::create<TypeParam>('c', {2, 5, 5, 2});
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 2}, {7.f, 8.f, 11.f, 12.f, 27.f, 28.f, 31.f, 32.f, 57.f, 58.f, 61.f, 62.f, 77.f, 78.f, 81.f, 82.f});
x.linspace(1);
nd4j::ops::avgpool2d op;
auto result = op.execute({&x}, {}, {2, 2, 2, 2, 0, 0, 1, 1, 0, 1, 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;
}
TYPED_TEST(TypedDeclarableOpsTests4, Test_Pooling_Parity_8) {
auto x = NDArrayFactory::create<TypeParam>('c', {2, 2, 5, 5});
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 2, 3, 3}, {1.f, 2.5f, 4.5f, 8.5f, 10.f, 12.f, 18.5f, 20.f, 22.f, 26.f, 27.5f, 29.5f, 33.5f, 35.f, 37.f, 43.5f, 45.f, 47.f, 51.f, 52.5f, 54.5f, 58.5f, 60.f, 62.f, 68.5f, 70.f, 72.f, 76.f, 77.5f, 79.5f, 83.5f, 85.f, 87.f, 93.5f, 95.f, 97.f});
x.linspace(1);
nd4j::ops::avgpool2d op;
auto result = op.execute({&x}, {}, {2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 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;
}
TYPED_TEST(TypedDeclarableOpsTests4, Test_Pooling_Parity_9) {
auto x = NDArrayFactory::create<TypeParam>('c', {2, 2, 5, 5});
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 2, 3, 3}, {0.25f, 1.25f, 2.25f, 4.25f, 10.f, 12.f, 9.25f, 20.f, 22.f, 6.5f, 13.75f, 14.75, 16.75f, 35.f, 37.f, 21.75f, 45.f, 47.f, 12.75f, 26.25f, 27.25f, 29.25f, 60.f, 62.f, 34.25f, 70.f, 72.f, 19.f, 38.75f, 39.75f, 41.75f, 85.f, 87.f, 46.75f, 95.f, 97.f});
x.linspace(1);
nd4j::ops::avgpool2d op;
auto result = op.execute({&x}, {}, {2, 2, 2, 2, 1, 1, 1, 1, 0, 1, 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;
}
TYPED_TEST(TypedDeclarableOpsTests4, Test_Pooling_Parity_10) {
auto x = NDArrayFactory::create<TypeParam>('c', {2, 2, 5, 5});
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 2, 3, 3}, {4.f, 6.f, 7.5f, 14.f, 16.f, 17.5f, 21.5f, 23.5f, 25.f, 29.f, 31.f, 32.5f, 39.f, 41.f, 42.5f, 46.5f, 48.5f, 50.f, 54.f, 56.f, 57.5f, 64.f, 66.f, 67.5f, 71.5f, 73.5f, 75.f, 79.f, 81.f, 82.5f, 89.f, 91.f, 92.5f, 96.5f, 98.5f, 100.f});
x.linspace(1);
nd4j::ops::avgpool2d op;
auto result = op.execute({&x}, {}, {2, 2, 2, 2, 0, 0, 1, 1, 1, 0, 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;
}
TYPED_TEST(TypedDeclarableOpsTests4, Test_Pooling_Parity_11) {
auto x = NDArrayFactory::create<TypeParam>('c', {1, 1, 3, 3});
auto exp = NDArrayFactory::create<TypeParam>('c', {1, 1, 2, 2}, {3, 4, 6, 7});
x.linspace(1);
nd4j::ops::avgpool2d op;
auto result = op.execute({&x}, {}, {2, 2, 1, 1, 0, 0, 1, 1, 0, 0, 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;
}
TYPED_TEST(TypedDeclarableOpsTests4, Test_Pooling_Parity_12) {
auto x = NDArrayFactory::create<TypeParam>('c', {1, 1, 3, 3});
auto exp = NDArrayFactory::create<TypeParam>('c', {1, 1, 3, 3}, {3.f, 4.f, 4.5f, 6.f, 7.f, 7.5f, 7.5f, 8.5f, 9.f});
x.linspace(1);
nd4j::ops::avgpool2d op;
auto result = op.execute({&x}, {}, {2, 2, 1, 1, 0, 0, 1, 1, 1, 0, 0});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
//z->printShapeInfo("z shape:");
//z->printBuffer("z buffer:");
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_BiasAdd_NHWC_1) {
auto x = NDArrayFactory::create<double>('c', {2, 3, 3, 2});
auto bias = NDArrayFactory::create<double>('c', {1, 2}, {1, 2});
auto exp = NDArrayFactory::create<double>('c', {2, 3, 3, 2}, {1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f});
nd4j::ops::biasadd op;
auto result = op.execute({&x, &bias}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
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(DeclarableOpsTests4, Test_BiasAdd_NCHW_1) {
auto x = NDArrayFactory::create<double>('c', {2, 2, 3, 3});
auto bias = NDArrayFactory::create<double>('c', {1, 2}, {1, 2});
auto exp = NDArrayFactory::create<double>('c', {2, 2, 3, 3}, {1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f});
nd4j::ops::biasadd op;
auto result = op.execute({&x, &bias}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
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(DeclarableOpsTests4, Test_Fill_1) {
auto x = NDArrayFactory::create<int>('c', {1, 3}, {3, 2, 4});
auto v = NDArrayFactory::create<double>(2.);
auto exp = NDArrayFactory::create<double>('c', {3, 2, 4});
exp.assign(2.0f);
nd4j::ops::fill op;
auto result = op.execute({&x, &v}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
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(DeclarableOpsTests4, Test_Reshape_Again) {
auto x = NDArrayFactory::create<double>('c', {4, 3});
auto exp = NDArrayFactory::create<double>('c', {4, 3});
x.linspace(1);
exp.linspace(1);
nd4j::ops::reshape op;
auto result = op.execute({&x}, {}, {-99, 4, 3});
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_Gemv_Transpose_1) {
auto x = NDArrayFactory::create<double>('c', {4, 3});
auto y = NDArrayFactory::create<double>('c', {4, 1});
auto exp = NDArrayFactory::create<double>('c',{ 3, 1}, {70, 80, 90});
x.linspace(1);
y.linspace(1);
nd4j::ops::matmul op;
auto result = op.execute({&x, &y}, {}, {1, 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(DeclarableOpsTests4, Test_Split_1) {
auto x = NDArrayFactory::create<double>('c', {5, 30});
auto sizes = NDArrayFactory::create<int>('c', {1, 3}, {4, 15, 11});
std::vector<Nd4jLong> list0({0,0, 0,4});
std::vector<Nd4jLong> list1({0,0, 4,19});
std::vector<Nd4jLong> list2({0,0, 19,30});
auto sub0 = x(list0, true);
auto sub1 = x(list1, true);
auto sub2 = x(list2, true);
sub0.assign(0.0);
sub1.assign(1.0);
sub2.assign(2.0);
nd4j::ops::split_v op;
auto result = op.execute({&x, &sizes}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
ASSERT_EQ(3, result->size());
auto z0 = result->at(0);
auto z1 = result->at(1);
auto z2 = result->at(2);
ASSERT_TRUE(sub0.isSameShape(z0));
ASSERT_TRUE(sub1.isSameShape(z1));
ASSERT_TRUE(sub2.isSameShape(z2));
ASSERT_TRUE(sub0.equalsTo(z0));
ASSERT_TRUE(sub1.equalsTo(z1));
ASSERT_TRUE(sub2.equalsTo(z2));
delete result;
}
// special test for TF mode, when axis goes first
TEST_F(DeclarableOpsTests4, Test_Split_2) {
auto x = NDArrayFactory::create<double>('c', {5, 12});
auto axis = NDArrayFactory::create<double>('c', {1, 1}, {1.f});
std::vector<Nd4jLong> list0 = {0,0, 0,3};
std::vector<Nd4jLong> list1 = {0,0, 3,6};
std::vector<Nd4jLong> list2 = {0,0, 6,9};
std::vector<Nd4jLong> list3 = {0,0, 9,12};
auto sub0 = x(list0, true);
auto sub1 = x(list1, true);
auto sub2 = x(list2, true);
auto sub3 = x(list3, true);
sub0.assign(0.0f);
sub1.assign(1.0f);
sub2.assign(2.0f);
sub3.assign(3.0f);
nd4j::ops::split op;
auto result = op.execute({&axis, &x}, {}, {4});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z0 = result->at(0);
auto z1 = result->at(1);
auto z2 = result->at(2);
auto z3 = result->at(3);
ASSERT_TRUE(sub0.isSameShape(z0));
ASSERT_TRUE(sub1.isSameShape(z1));
ASSERT_TRUE(sub2.isSameShape(z2));
ASSERT_TRUE(sub3.isSameShape(z3));
ASSERT_TRUE(sub0.equalsTo(z0));
ASSERT_TRUE(sub1.equalsTo(z1));
ASSERT_TRUE(sub2.equalsTo(z2));
ASSERT_TRUE(sub3.equalsTo(z3));
delete result;
}
// special test for TF mode, when axis goes first
TEST_F(DeclarableOpsTests4, Test_Split_3) {
auto x = NDArrayFactory::create<double>('c', {6, 12});
auto axis = NDArrayFactory::create<double>('c', {1, 1}, {0.f});
std::vector<Nd4jLong> list0 = {0,2, 0,0};
std::vector<Nd4jLong> list1 = {2,4, 0,0};
std::vector<Nd4jLong> list2 = {4,6, 0,0};
auto sub0 = x(list0, true);
auto sub1 = x(list1, true);
auto sub2 = x(list2, true);
sub0.assign(0.0f);
sub1.assign(1.0f);
sub2.assign(2.0f);
nd4j::ops::split op;
auto result = op.execute({&axis, &x}, {}, {3});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z0 = result->at(0);
auto z1 = result->at(1);
auto z2 = result->at(2);
ASSERT_TRUE(sub0.isSameShape(z0));
ASSERT_TRUE(sub1.isSameShape(z1));
ASSERT_TRUE(sub2.isSameShape(z2));
ASSERT_TRUE(sub0.equalsTo(z0));
ASSERT_TRUE(sub1.equalsTo(z1));
ASSERT_TRUE(sub2.equalsTo(z2));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_Stack_4) {
auto t = NDArrayFactory::create<double>('c', {2, 3, 5});
auto u = NDArrayFactory::create<double>('c', {2, 3, 5});
auto v = NDArrayFactory::create<double>('c', {2, 3, 5});
auto exp = NDArrayFactory::create<double>('c', {3, 2, 3, 5});
nd4j::ops::stack op;
auto result = op.execute({&t, &u, &v}, {}, {-4});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_Squeeze_args_1) {
auto x = NDArrayFactory::create<double>('c', {2, 1, 1, 1, 2}, {1, 2, 3, 4});
auto exp = NDArrayFactory::create<double>('c', {2, 1, 2}, {1, 2, 3, 4});
nd4j::ops::squeeze op;
auto result = op.execute({&x}, {}, {1, 3});
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(DeclarableOpsTests4, Test_Squeeze_args_2) {
auto x = NDArrayFactory::create<double>('c', {2, 1, 1, 1, 2}, {1, 2, 3, 4});
auto y = NDArrayFactory::create<double>('c', {2}, {1.f, 3.f});
auto exp = NDArrayFactory::create<double>('c', {2, 1, 2}, {1, 2, 3, 4});
nd4j::ops::squeeze op;
auto result = op.execute({&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(DeclarableOpsTests4, Test_Squeeze_args_3) {
auto x = NDArrayFactory::create<double>('c', {2, 1, 1, 1, 2}, {1, 2, 3, 4});
auto exp = NDArrayFactory::create<double>('c', {2, 1, 2}, {1, 2, 3, 4});
nd4j::ops::squeeze op;
auto result = op.execute({&x}, {}, {-2, -3});
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(DeclarableOpsTests4, Test_BiasAdd_1) {
auto x = NDArrayFactory::create<double>('c', {2, 3});
auto row = NDArrayFactory::create<double>('c', {3}, {1, 2, 3});
auto exp = NDArrayFactory::create<double>('c', {2, 3}, {1, 2, 3, 1, 2, 3});
nd4j::ops::biasadd op;
auto result = op.execute({&x, &row}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_1D_1) {
auto x = NDArrayFactory::create<double>('c', {2, 3});
nd4j::ops::unstack op;
auto result = op.execute({&x}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
ASSERT_EQ(3, result->size());
for (int e = 0; e < 3; e++)
ASSERT_EQ(1, result->at(e)->rankOf());
delete result;
}
TEST_F(DeclarableOpsTests4, Test_SpaceToDepth_1) {
auto x = NDArrayFactory::create<double>('c', {1, 2, 2, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto exp = NDArrayFactory::create<double>('c', {1, 1, 1, 12}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
nd4j::ops::space_to_depth op;
auto result = op.execute({&x}, {}, {2, 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(DeclarableOpsTests4, Test_SpaceToDepth_2) {
auto x = NDArrayFactory::create<double>('c', {1, 3, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto exp = NDArrayFactory::create<double>('c', {1, 12, 1, 1}, {1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12});
nd4j::ops::space_to_depth op;
auto result = op.execute({&x}, {}, {2, 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(DeclarableOpsTests4, Test_DepthToSpace_1) {
auto x = NDArrayFactory::create<double>('c', {1, 1, 1, 12}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto exp = NDArrayFactory::create<double>('c', {1, 2, 2, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
nd4j::ops::depth_to_space op;
auto result = op.execute({&x}, {}, {2, 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(DeclarableOpsTests4, Test_DepthToSpace_2) {
auto x = NDArrayFactory::create<double>('c', {1, 12, 1, 1}, {1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12});
auto exp = NDArrayFactory::create<double>('c', {1, 3, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
nd4j::ops::depth_to_space op;
auto result = op.execute({&x}, {}, {2, 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(DeclarableOpsTests4, Test_DepthToSpace_3) {
auto x = NDArrayFactory::create<double>('c', {4, 4, 16, 16});
auto exp = NDArrayFactory::create<double>('c', {4, 16, 64, 1});
nd4j::ops::depth_to_space op;
auto result = op.execute({&x}, {}, {4, 1});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_Cross_1) {
auto a = NDArrayFactory::create<double>('c', {3}, {1, 2, 3});
auto b = NDArrayFactory::create<double>('c', {3}, {6, 7, 8});
auto exp = NDArrayFactory::create<double>('c', {3}, {-5, 10, -5});
nd4j::ops::cross op;
auto result = op.execute({&a, &b}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
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(DeclarableOpsTests4, Test_Cross_2) {
auto a = NDArrayFactory::create<double>('c', {2, 3}, {1, 2, 3, 1, 2, 3});
auto b = NDArrayFactory::create<double>('c', {2, 3}, {6, 7, 8, 6, 7, 8});
auto exp = NDArrayFactory::create<double>('c', {2, 3}, {-5, 10, -5, -5, 10, -5});
nd4j::ops::cross op;
auto result = op.execute({&a, &b}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
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(DeclarableOpsTests4, Test_Cross_3) {
auto a = NDArrayFactory::create<double>('c', {3, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9});
auto b = NDArrayFactory::create<double>('c', {3, 3}, {2, 3, 4, 7, 6, 5, 6, 3, 2});
auto exp = NDArrayFactory::create<double>('c', {3, 3}, { -1, 2, -1, -11, 22, -11, -11, 40, -27});
nd4j::ops::cross op;
auto result = op.execute({&a, &b}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
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(DeclarableOpsTests4, Test_Matmul_YATS_1) {
auto a = NDArrayFactory::create<double>('c', {3, 4}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto b = NDArrayFactory::create<double>('c', {4}, {1, 2, 3, 4});
auto exp = NDArrayFactory::create<double>('c', {3}, {30, 70, 110});
nd4j::ops::matmul op;
auto result = op.execute({&a, &b}, {}, {});
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(DeclarableOpsTests4, Test_Matmul_YATS_2) {
auto a = NDArrayFactory::create<double>('c', {4}, {1, 2, 3, 4});
auto b = NDArrayFactory::create<double>('c', {4, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto exp = NDArrayFactory::create<double>('c', {3}, {70, 80, 90});
nd4j::ops::matmul op;
auto result = op.execute({&a, &b}, {}, {});
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(DeclarableOpsTests4, Test_Matmul_YATS_3) {
auto a = NDArrayFactory::create<double>('c', {1, 4}, {1, 2, 3, 4});
auto b = NDArrayFactory::create<double>('c', {4, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto exp = NDArrayFactory::create<double>('c', {1, 3}, {70, 80, 90});
nd4j::ops::matmul op;
auto result = op.execute({&a, &b}, {}, {});
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(DeclarableOpsTests4, Test_Add_119) {
auto a = NDArrayFactory::create<double>('c', {1, 4}, {1, 2, 3, 4});
auto b = NDArrayFactory::create<double>('c', {4}, {1, 2, 3, 4});
auto exp = NDArrayFactory::create<double>('c', {1, 4}, {2, 4, 6, 8});
nd4j::ops::add op;
auto result = op.execute({&a, &b}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_EQ(2, z->rankOf());
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_Reshape_Negative_1) {
auto x = NDArrayFactory::create<double>('c', {2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8});
auto shape = NDArrayFactory::create<Nd4jLong>('c', {2}, {-1, 2});
auto exp = NDArrayFactory::create<double>('c', {4, 2}, {1, 2, 3, 4, 5, 6, 7, 8});
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(DeclarableOpsTests4, Test_TileToShape_1) {
auto x = NDArrayFactory::create<double>('c', {2, 1, 3});
auto exp = NDArrayFactory::create<double>('c', {2, 4, 3}, {1.f, 2.f, 3.f,1.f, 2.f, 3.f,1.f, 2.f, 3.f,1.f, 2.f, 3.f,
4.f, 5.f, 6.f,4.f, 5.f, 6.f,4.f, 5.f, 6.f,4.f, 5.f, 6.f});
x.linspace(1.f);
nd4j::ops::tile_to_shape op;
auto result = op.execute({&x},{}, {2, 4, 3}, {}, false, nd4j::DataType::DOUBLE);
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_StridedSlice_Alex_1) {
auto x = NDArrayFactory::create<double>('c', {3, 4, 5});
x.linspace(1);
auto exp = NDArrayFactory::create<double>('c', {1,3,4,5});
exp.linspace(1);
nd4j::ops::strided_slice op;
auto result = op.execute({&x}, {}, {0,0,0,1,0, -999,0,0,0, -999,3,4,5, -999,1,1,1});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_StridedSlice_Alex_2) {
auto x = NDArrayFactory::create<double>('c', {3, 4, 5});
auto begin = NDArrayFactory::create<double>('c', {4}, {-999,0,0,0});
auto end = NDArrayFactory::create<double>('c', {4}, {-999,3,4,5});
auto stride = NDArrayFactory::create<double>('c', {4}, {-999,1,1,1});
x.linspace(1);
auto exp = NDArrayFactory::create<double>('c', {1,3,4,5});
exp.linspace(1);
nd4j::ops::strided_slice op;
auto result = op.execute({&x, &begin, &end, &stride}, {}, {0,0,0,1,0});
ASSERT_EQ(Status::OK(), result->status());
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_StridedSlice_Alex_3) {
auto x = NDArrayFactory::create<double>('c', {1}, {10});
auto begin = NDArrayFactory::create<int>('c', {1}, {(int)0});
auto end = NDArrayFactory::create<int>('c', {1}, {(int)0});
auto stride = NDArrayFactory::create<int>('c', {1}, {1});
//x.linspace(1);
//auto exp = NDArrayFactory::create<double>('c', {1,3,4,5});
//exp.linspace(1);
nd4j::ops::strided_slice op;
auto result = op.execute({&x, &begin, &end, &stride}, {}, {1,0,0,0,0});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
z->printShapeInfo("Emply shape expected");
ASSERT_TRUE(z->isEmpty());
delete result;
}
TEST_F(DeclarableOpsTests4, Test_StridedSlice_Alex_4) {
auto x = NDArrayFactory::create<double>('c', {1,3}, {1, 2, 3});
auto begin = NDArrayFactory::create<double>('c', {2}, {0, 0});
auto end = NDArrayFactory::create<double>('c', {2}, {0,1});
auto stride = NDArrayFactory::create<double>('c', {2}, {1,1});
// x.linspace(1);
auto exp = NDArrayFactory::create<double>('c', {1}, {1});
//exp.linspace(1);
nd4j::ops::strided_slice op;
auto result = op.execute({&x, &begin, &end, &stride}, {}, {1,0,1,0,2});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
z->printBuffer("Strided Slice");
z->printShapeInfo("Vector size 1 shape expected");
exp.printShapeInfo("Expected shape");
ASSERT_TRUE(z->lengthOf() == 1);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, parallel_stack_test1) {
auto x1 = NDArrayFactory::create<double>('c', {2,2,2});
auto x2 = NDArrayFactory::create<double>('c', {2,2,2});
auto x3 = NDArrayFactory::create<double>('c', {2,2,2});
x1.linspace(1);
x2.linspace(9);
x3.linspace(17);
auto expected = NDArrayFactory::create<double>('c', {3,2,2,2});
expected.linspace(1);
nd4j::ops::parallel_stack op;
auto results = op.execute({&x1, &x2, &x3}, {}, {});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, parallel_stack_test2) {
auto x1 = NDArrayFactory::create<double>('c', {1,2}, {1,2});
auto x2 = NDArrayFactory::create<double>('c', {1,2}, {3,4});
auto x3 = NDArrayFactory::create<double>('c', {1,2}, {5,6});
auto expected = NDArrayFactory::create<double>('c', {3,1,2}, {1,2,3,4,5,6});
nd4j::ops::parallel_stack op;
auto results = op.execute({&x1, &x2, &x3}, {}, {});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, parallel_stack_test3) {
auto x1 = NDArrayFactory::create<double>('c', {2,1}, {1,2});
auto x2 = NDArrayFactory::create<double>('c', {2,1}, {3,4});
auto x3 = NDArrayFactory::create<double>('c', {2,1}, {5,6});
auto expected = NDArrayFactory::create<double>('c', {3,2,1}, {1,2,3,4,5,6});
nd4j::ops::parallel_stack op;
auto results = op.execute({&x1, &x2, &x3}, {}, {});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
\
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, parallel_stack_test4) {
auto x1 = NDArrayFactory::create<double>('c', {2}, {1,2});
auto x2 = NDArrayFactory::create<double>('c', {2}, {3,4});
auto x3 = NDArrayFactory::create<double>('c', {2}, {5,6});
auto expected = NDArrayFactory::create<double>('c', {3,2}, {1,2,3,4,5,6});
nd4j::ops::parallel_stack op;
auto results = op.execute({&x1, &x2, &x3}, {}, {});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, parallel_stack_test5) {
auto x1 = NDArrayFactory::create<double>('c', {1}, {1});
auto x2 = NDArrayFactory::create<double>('c', {1}, {3});
auto x3 = NDArrayFactory::create<double>('c', {1}, {5});
auto expected = NDArrayFactory::create<double>('c', {3,1}, {1,3,5});
nd4j::ops::parallel_stack op;
auto results = op.execute({&x1, &x2, &x3}, {}, {});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, parallel_stack_test6) {
auto x1 = NDArrayFactory::create<double>(1.);
auto x2 = NDArrayFactory::create<double>(3.);
auto x3 = NDArrayFactory::create<double>(5.);
auto expected = NDArrayFactory::create<double>('c', {3}, {1,3,5});
nd4j::ops::parallel_stack op;
auto results = op.execute({&x1, &x2, &x3}, {}, {});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, parallel_stack_test7) {
auto x1 = NDArrayFactory::create<double>(1.);
auto expected = NDArrayFactory::create<double>('c', {1}, {1.});
nd4j::ops::parallel_stack op;
auto results = op.execute({&x1}, {}, {});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, meshgrid_test1) {
auto in0 = NDArrayFactory::create<double>('c', {2}, {1, 2});
auto in1 = NDArrayFactory::create<double>('c', {3}, {10, 20, 30});
auto in2 = NDArrayFactory::create<double>('c', {4}, {100, 200, 300, 400});
auto exp0 = NDArrayFactory::create<double>('c', {2,3,4}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2});
auto exp1 = NDArrayFactory::create<double>('c', {2,3,4}, {10, 10, 10, 10, 20, 20, 20, 20, 30, 30, 30, 30, 10, 10, 10, 10, 20, 20, 20, 20, 30, 30, 30, 30});
auto exp2 = NDArrayFactory::create<double>('c', {2,3,4}, {100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400});
nd4j::ops::meshgrid op;
auto results = op.execute({&in0, &in1, &in2}, {}, {0});
auto out0 = results->at(0);
auto out1 = results->at(1);
auto out2 = results->at(2);
// out0->printIndexedBuffer();
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp0.isSameShape(out0));
ASSERT_TRUE(exp0.equalsTo(out0));
ASSERT_TRUE(exp1.isSameShape(out1));
ASSERT_TRUE(exp1.equalsTo(out1));
ASSERT_TRUE(exp2.isSameShape(out2));
ASSERT_TRUE(exp2.equalsTo(out2));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, meshgrid_test2) {
auto in0 = NDArrayFactory::create<double>('c', {2}, {1, 2});
auto in1 = NDArrayFactory::create<double>('c', {3}, {10, 20, 30});
auto in2 = NDArrayFactory::create<double>('c', {4}, {100, 200, 300, 400});
auto exp0 = NDArrayFactory::create<double>('c', {3,2,4}, {1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2});
auto exp1 = NDArrayFactory::create<double>('c', {3,2,4}, {10, 10, 10, 10, 10, 10, 10, 10, 20, 20, 20, 20, 20, 20, 20, 20, 30, 30, 30, 30, 30, 30, 30, 30});
auto exp2 = NDArrayFactory::create<double>('c', {3,2,4}, {100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400});
nd4j::ops::meshgrid op;
auto results = op.execute({&in0, &in1, &in2}, {}, {});
auto out0 = results->at(0);
auto out1 = results->at(1);
auto out2 = results->at(2);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp0.isSameShape(out0));
ASSERT_TRUE(exp0.equalsTo(out0));
ASSERT_TRUE(exp1.isSameShape(out1));
ASSERT_TRUE(exp1.equalsTo(out1));
ASSERT_TRUE(exp2.isSameShape(out2));
ASSERT_TRUE(exp2.equalsTo(out2));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, meshgrid_test3) {
auto in0 = NDArrayFactory::create<double>('c', {2}, {1, 2});
auto in1 = NDArrayFactory::create<double>('c', {1,3}, {10, 20, 30});
auto in2 = NDArrayFactory::create<double>('c', {2,2}, {100, 200, 300, 400});
auto exp0 = NDArrayFactory::create<double>('c', {3,2,4}, {1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2});
auto exp1 = NDArrayFactory::create<double>('c', {3,2,4}, {10, 10, 10, 10, 10, 10, 10, 10, 20, 20, 20, 20, 20, 20, 20, 20, 30, 30, 30, 30, 30, 30, 30, 30});
auto exp2 = NDArrayFactory::create<double>('c', {3,2,4}, {100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400});
nd4j::ops::meshgrid op;
auto results = op.execute({&in0, &in1, &in2}, {}, {});
auto out0 = results->at(0);
auto out1 = results->at(1);
auto out2 = results->at(2);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp0.isSameShape(out0));
ASSERT_TRUE(exp0.equalsTo(out0));
ASSERT_TRUE(exp1.isSameShape(out1));
ASSERT_TRUE(exp1.equalsTo(out1));
ASSERT_TRUE(exp2.isSameShape(out2));
ASSERT_TRUE(exp2.equalsTo(out2));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, meshgrid_test4) {
auto in0 = NDArrayFactory::create<double>('c', {1,2}, {1, 2});
auto in1 = NDArrayFactory::create<double>('c', {3,1}, {10, 20, 30});
auto in2 = NDArrayFactory::create<double>('c', {1,4,1}, {100, 200, 300, 400});
auto exp0 = NDArrayFactory::create<double>('c', {2,3,4}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2});
auto exp1 = NDArrayFactory::create<double>('c', {2,3,4}, {10, 10, 10, 10, 20, 20, 20, 20, 30, 30, 30, 30, 10, 10, 10, 10, 20, 20, 20, 20, 30, 30, 30, 30});
auto exp2 = NDArrayFactory::create<double>('c', {2,3,4}, {100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400});
nd4j::ops::meshgrid op;
auto results = op.execute({&in0, &in1, &in2}, {}, {0});
auto out0 = results->at(0);
auto out1 = results->at(1);
auto out2 = results->at(2);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp0.isSameShape(out0));
ASSERT_TRUE(exp0.equalsTo(out0));
ASSERT_TRUE(exp1.isSameShape(out1));
ASSERT_TRUE(exp1.equalsTo(out1));
ASSERT_TRUE(exp2.isSameShape(out2));
ASSERT_TRUE(exp2.equalsTo(out2));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, meshgrid_test5) {
auto in0 = NDArrayFactory::create<double>(1);
auto in1 = NDArrayFactory::create<double>(2);
auto in2 = NDArrayFactory::create<double>(3);
auto exp0 = NDArrayFactory::create<double>('c', {1,1,1}, {1});
auto exp1 = NDArrayFactory::create<double>('c', {1,1,1}, {2});
auto exp2 = NDArrayFactory::create<double>('c', {1,1,1}, {3});
nd4j::ops::meshgrid op;
auto results = op.execute({&in0, &in1, &in2}, {}, {0});
auto out0 = results->at(0);
auto out1 = results->at(1);
auto out2 = results->at(2);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp0.isSameShape(out0));
ASSERT_TRUE(exp0.equalsTo(out0));
ASSERT_TRUE(exp1.isSameShape(out1));
ASSERT_TRUE(exp1.equalsTo(out1));
ASSERT_TRUE(exp2.isSameShape(out2));
ASSERT_TRUE(exp2.equalsTo(out2));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, meshgrid_test6) {
auto in0 = NDArrayFactory::create<double>('c', {2,2},{1,2,3,4});
auto in1 = NDArrayFactory::create<double>(5);
auto in2 = NDArrayFactory::create<double>(6);
auto exp0 = NDArrayFactory::create<double>('c', {4,1,1}, {1,2,3,4});
auto exp1 = NDArrayFactory::create<double>('c', {4,1,1}, {5,5,5,5});
auto exp2 = NDArrayFactory::create<double>('c', {4,1,1}, {6,6,6,6});
nd4j::ops::meshgrid op;
auto results = op.execute({&in0, &in1, &in2}, {}, {0});
auto out0 = results->at(0);
auto out1 = results->at(1);
auto out2 = results->at(2);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp0.isSameShape(out0));
ASSERT_TRUE(exp0.equalsTo(out0));
ASSERT_TRUE(exp1.isSameShape(out1));
ASSERT_TRUE(exp1.equalsTo(out1));
ASSERT_TRUE(exp2.isSameShape(out2));
ASSERT_TRUE(exp2.equalsTo(out2));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, meshgrid_test7) {
auto in0 = NDArrayFactory::create<double>('c', {2,2},{1,2,3,4});
auto in1 = NDArrayFactory::create<double>(5);
auto in2 = NDArrayFactory::create<double>(6);
auto exp0 = NDArrayFactory::create<double>('c', {1,4,1}, {1,2,3,4});
auto exp1 = NDArrayFactory::create<double>('c', {1,4,1}, {5,5,5,5});
auto exp2 = NDArrayFactory::create<double>('c', {1,4,1}, {6,6,6,6});
nd4j::ops::meshgrid op;
auto results = op.execute({&in0, &in1, &in2}, {}, {1});
auto out0 = results->at(0);
auto out1 = results->at(1);
auto out2 = results->at(2);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp0.isSameShape(out0));
ASSERT_TRUE(exp0.equalsTo(out0));
ASSERT_TRUE(exp1.isSameShape(out1));
ASSERT_TRUE(exp1.equalsTo(out1));
ASSERT_TRUE(exp2.isSameShape(out2));
ASSERT_TRUE(exp2.equalsTo(out2));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, meshgrid_test8) {
auto in0 = NDArrayFactory::create<double>(5);
auto exp0 = NDArrayFactory::create<double>('c', {1}, {5});
nd4j::ops::meshgrid op;
auto results = op.execute({&in0}, {}, {0});
auto out0 = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp0.isSameShape(out0));
ASSERT_TRUE(exp0.equalsTo(out0));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, meshgrid_test9) {
auto in0 = NDArrayFactory::create<double>(5);
auto exp0 = NDArrayFactory::create<double>('c', {1}, {5});
nd4j::ops::meshgrid op;
auto results = op.execute({&in0}, {}, {1});
auto out0 = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp0.isSameShape(out0));
ASSERT_TRUE(exp0.equalsTo(out0));
delete results;
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, WeightedCrossEntropyWithLogits_1) {
auto input = NDArrayFactory::create<double>('c', {2, 3}, {11.f, 13.f, 4.f, 15.f, 6.f, 3.f});
auto targets = NDArrayFactory::create<double>('c', {2, 3}, {15.5f, 15.7f, 5.f , 15.f, 5.f, 6.f});
auto weight = NDArrayFactory::create<double>(0.7f);
auto expected = NDArrayFactory::create<double>('c', {2, 3}, {-159.50006, -191.1, -16.009075, -210., -24.001238, -15.03887});
//Targets {15.5f, 15.7f, 5.f , 15.f, 5.f, 6.f};
//----------
//Inputs {11.f, 13.f, 4.f, 15.f, 6.f, 3.f};
//----------
//Weights [0.7]
//Result {-159.50006, -191.1, -16.009075, -210., -24.001238, -15.03887}
nd4j::ops::weighted_cross_entropy_with_logits op;
auto results = op.execute({&targets, &input, &weight}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
auto output = results->at(0);
// output->printIndexedBuffer();
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, WeightedCrossEntropyWithLogits_2) {
auto input = NDArrayFactory::create<double>('c', {2, 3}, {11.f, 13.f, 4.f, 15.f, 6.f, 3.f});
auto targets = NDArrayFactory::create<double>('c', {2, 3}, {15.5f, 15.7f, 5.f, 15.f, 5.f, 6.f});
auto weights = NDArrayFactory::create<double>({0.5f, 0.7f, 1.0f}) ;
auto expected = NDArrayFactory::create<double>('c', {2, 3}, {-159.5001f, -191.1f, -15.98185f, -210.f, -24.001238f, -14.951412f});
nd4j::ops::weighted_cross_entropy_with_logits op;
auto results = op.execute({&targets, &input, &weights}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
auto output = results->at(0);
output->printIndexedBuffer("Result is ");
expected.printIndexedBuffer("Expected is ");
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, lstm_test1) {
const int time = 5;
const int batchSize = 3;
const int inSize = 3;
const int numProj = 3;
const int numUnits = 3;
auto x = NDArrayFactory::create<double>('c', {time, batchSize, inSize});
auto h0 = NDArrayFactory::create<double>('c', {batchSize, numProj});
auto c0 = NDArrayFactory::create<double>('c', {batchSize, numUnits});
auto Wx = NDArrayFactory::create<double>('c', {inSize, 4*numUnits});
auto Wh = NDArrayFactory::create<double>('c', {numProj, 4*numUnits});
auto Wc = NDArrayFactory::create<double>('c', {3*numUnits});
auto Wp = NDArrayFactory::create<double>('c', {numUnits, numProj});
auto b = NDArrayFactory::create<double>('c', {4*numUnits});
x.linspace(0.5, 0.5);
h0 = 1.;
c0 = 2.;
Wx = 0.003;
Wh = 0.006;
Wc = 0.;
Wp = 0.;
b = 0.5;
auto expH = NDArrayFactory::create<double>('c', {time, batchSize, numProj}, {0.57574,0.57574,0.57574,0.58006,0.58006,0.58006,0.58434,0.58434,0.58434,
0.55114,0.55114,0.55114,0.55732,0.55732,0.55732,0.56338,0.56338,0.56338,
0.53763,0.53763,0.53763,0.54534,0.54534,0.54534,0.55287,0.55287,0.55287,
0.53626,0.53626,0.53626,0.54487,0.54487,0.54487,0.55327,0.55327,0.55327,
0.54484,0.54484,0.54484,0.55379,0.55379,0.55379,0.5625 ,0.5625 ,0.5625});
auto expClast = NDArrayFactory::create<double>('c', {1, batchSize, numProj}, {1.1589154,1.1589154,1.1589154,1.1892855,1.1892855,1.1892855,1.219861 ,1.219861 ,1.219861});
nd4j::ops::lstm op;
auto results = op.execute({&x, &h0, &c0, &Wx, &Wh, &Wc, &Wp, &b}, {0., 0., 0.}, {0, 0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *h = results->at(0);
auto *c = results->at(1);
auto cLast = (*c)({4,5,0,0,0,0},true);
ASSERT_TRUE(expH.isSameShape(h));
ASSERT_TRUE(expH.equalsTo(h));
ASSERT_TRUE(expClast.isSameShape(&cLast));
ASSERT_TRUE(expClast.equalsTo(&cLast));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, relu6_test1) {
auto input = NDArrayFactory::create<double>('c', {2,4}, {-13.,10,-5,0,2,7,6,12});
auto expected = NDArrayFactory::create<double>('c', {2,4}, {0., 6., 0., 0.,2., 6., 6., 6.});
nd4j::ops::relu6 op;
auto results = op.execute({&input}, {0.}, {}, {}, false, nd4j::DataType::DOUBLE);
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto output = results->at(0);
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, relu6_bp_test1) {
auto input = NDArrayFactory::create<double>('c', {2,4}, {-13.,10, -5, 0, 2, 7, 6, 5});
auto gradO = NDArrayFactory::create<double>('c', {2,4}, {-1., -2., 0., 4., 5., 6., 7., 8.});
auto expected = NDArrayFactory::create<double>('c', {2,4}, {0., 0., 0., 0., 5., 0., 0., 8.});
nd4j::ops::relu6_bp op;
auto results = op.execute({&input, &gradO}, {0.}, {}, {}, false, nd4j::DataType::DOUBLE);
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto output = results->at(0);
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
////////////////////////////////////////////////////////////////////////////////
TYPED_TEST(TypedDeclarableOpsTests4, LrnTest_1) {
auto x = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 2}, { 5.5, 0., 0.3, 5.5,
8.6, 0., 0., 0.4,
1.5, 1., 1.3, 1.5,
2.6, 2., 3., 1.4}
);
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 2}, {
0.98386997, 0., 0.05358852, 0.9824562,
0.99330735, 0., 0., 0.37139067,
0.72760683, 0.4850712, 0.5848977, 0.67488194,
0.7581754, 0.58321184, 0.86747235, 0.4048204}
);
nd4j::ops::lrn op;
auto results = op.execute({&x}, {1.0, 1.0, 0.5}, {5}, {}, false, nd4j::DataType::DOUBLE);
auto out = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp.isSameShape(out));
// out->printIndexedBuffer("LRN out");
// exp.printIndexedBuffer("LRN exp");
ASSERT_TRUE(exp.equalsTo(out));
delete results;
}
////////////////////////////////////////////////////////////////////////////////
TYPED_TEST(TypedDeclarableOpsTests4, LrnTest_2) {
auto x = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 2}, { 5.5, 0., 0.3, 5.5,
8.6, 0., 0., 0.4,
1.5, 1., 1.3, 1.5,
2.6, 2., 3., 1.4});
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 2}, {
0.98386997, 0., 0.05358852, 0.9824562,
0.99330735, 0., 0., 0.37139067,
0.72760683, 0.4850712, 0.5848977, 0.67488194,
0.7581754, 0.58321184, 0.86747235, 0.4048204});
nd4j::ops::lrn op;
auto results = op.execute({&x}, {1.0, 1.0, 0.5}, {2}, {}, false, nd4j::DataType::DOUBLE);
auto out = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp.isSameShape(out));
// out->printIndexedBuffer("LRN out");
// exp.printIndexedBuffer("LRN exp");
ASSERT_TRUE(exp.equalsTo(out));
delete results;
}
////////////////////////////////////////////////////////////////////////////////
TYPED_TEST(TypedDeclarableOpsTests4, LrnTest_3) {
auto x = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 4}, {
5.5, 0., 0.3, 5.5,
1.5, 0., 1.3, 6.5,
8.6, 0., 0., 0.4,
2.5, 1., 0.3, 4.5,
1.5, 1., 1.3, 1.5,
3.5, 0., 1.3, 2.5,
2.6, 2., 3., 1.4,
4.5, 1., 0.3, 0.5}
);
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 4}, {
0.9824562, 0., 0.03822664, 0.9824562,
0.67488194, 0., 0.18924236, 0.96960944,
0.99330735, 0., 0., 0.37139067,
0.86567914, 0.18702209, 0.05610663, 0.9520745,
0.6154575, 0.34942827, 0.45425674, 0.6154575,
0.905509, 0. , 0.2824086, 0.8361251,
0.57063663, 0.41959068, 0.629386, 0.3504383,
0.9520745, 0.21039814, 0.06311944, 0.3268602 }
);
nd4j::ops::lrn op;
auto results = op.execute({&x}, {1.0, 1.0, 0.5}, {2}, {}, false, nd4j::DataType::DOUBLE);
auto out = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp.isSameShape(out));
// out->printIndexedBuffer("LRN out");
// exp.printIndexedBuffer("LRN exp");
ASSERT_TRUE(exp.equalsTo(out));
delete results;
}
////////////////////////////////////////////////////////////////////////////////
TYPED_TEST(TypedDeclarableOpsTests4, LrnTest_4) {
auto x = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 4}, {
5.5, 0., 0.3, 5.5,
1.5, 0., 1.3, 6.5,
8.6, 0., 0., 0.4,
2.5, 1., 0.3, 4.5,
1.5, 1., 1.3, 1.5,
3.5, 0., 1.3, 2.5,
2.6, 2., 3., 1.4,
4.5, 1., 0.3, 0.5}
);
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 4}, {
0.70082176, 0., 0.03822664, 0.70082176,
0.21835658, 0., 0.18924236, 0.9462118,
0.9922489, 0., 0., 0.04615111,
0.46755522, 0.18702209, 0.05610663, 0.8415994,
0.5241424, 0.34942827, 0.45425674, 0.5241424,
0.76033086, 0., 0.2824086, 0.54309344,
0.54546785, 0.41959068, 0.629386, 0.29371348,
0.94679165, 0.21039814, 0.06311944, 0.10519907}
);
nd4j::ops::lrn op;
auto results = op.execute({&x}, {1.0, 1.0, 0.5}, {5}, {}, false, nd4j::DataType::DOUBLE);
auto out = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp.isSameShape(out));
// out->printIndexedBuffer("LRN out");
// exp.printIndexedBuffer("LRN exp");
ASSERT_TRUE(exp.equalsTo(out));
delete results;
}
////////////////////////////////////////////////////////////////////////////////
TYPED_TEST(TypedDeclarableOpsTests4, LrnTest_5) {
auto x = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 4}, {
5.5,0., 0.3, 5.5,
1.5,0., 1.3, 6.5,
8.6,0., 0., 0.4,
2.5,1., 0.3, 4.5,
1.5,1., 1.3, 1.5,
3.5,0., 1.3, 2.5,
2.6,2., 3., 1.4,
4.5,1., 0.3, 0.5}
);
auto eps = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 4}, {
0.70082176, 0., 0.03822664, 0.70082176,
0.21835658, 0., 0.18924236, 0.9462118,
0.9922489, 0., 0. , 0.04615111,
0.46755522, 0.18702209, 0.05610663, 0.8415994,
0.5241424, 0.34942827, 0.45425674, 0.5241424,
0.76033086, 0., 0.2824086 , 0.54309344,
0.54546785, 0.41959068, 0.629386 , 0.29371348,
0.94679165, 0.21039814, 0.06311944, 0.10519907}
);
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 4});
nd4j::ops::lrn_bp op;
auto results = op.execute({&x, &eps}, {1.0, 1.0, 0.5}, {5}, {}, false, typeid(TypeParam) == typeid(float) ? nd4j::DataType::FLOAT32 : nd4j::DataType::DOUBLE);
auto out = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp.isSameShape(out));
// out->printIndexedBuffer("LRN out");
// exp.printIndexedBuffer("LRN exp");
// ASSERT_TRUE(exp.equalsTo(out));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, tri_test1) {
const int rows = 3;
const int cols = 5;
auto expected = NDArrayFactory::create<float>('c', {rows, cols}, {1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0});
nd4j::ops::tri op;
auto results = op.execute({}, {}, {rows, cols});
auto output = results->at(0);
// output->printIndexedBuffer();
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, tri_test2) {
const int rows = 3;
const int cols = 5;
const int diag = 2;
auto expected = NDArrayFactory::create<float>('c', {rows, cols}, {1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1});
nd4j::ops::tri op;
auto results = op.execute({}, {}, {rows, cols, diag});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, tri_test3) {
const int rows = 3;
const int cols = 5;
const int diag = -1;
auto expected = NDArrayFactory::create<float>('c', {rows, cols}, {0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0});
nd4j::ops::tri op;
auto results = op.execute({}, {}, {rows, cols, diag});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, tri_test4) {
const int rows = 3;
const int cols = 5;
const int diag = -2;
auto expected = NDArrayFactory::create<float>('c', {rows, cols}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0});
nd4j::ops::tri op;
auto results = op.execute({}, {}, {rows, cols, diag});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, tri_test5) {
const int rows = 5;
auto expected = NDArrayFactory::create<float>('c', {rows, rows}, {1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1});
nd4j::ops::tri op;
auto results = op.execute({}, {}, {rows});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, tri_test6) {
const int rows = 3;
const int cols = 5;
const int diag = -20;
auto expected = NDArrayFactory::create<float>('c', {rows, cols}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0});
nd4j::ops::tri op;
auto results = op.execute({}, {}, {rows, cols, diag});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, tri_test7) {
const int rows = 3;
const int cols = 5;
const int diag = 20;
auto expected = NDArrayFactory::create<float>('c', {rows, cols}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
nd4j::ops::tri op;
auto results = op.execute({}, {}, {rows, cols, diag});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, triu_test1) {
auto input = NDArrayFactory::create<double>('c', {4, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto expected = NDArrayFactory::create<double>('c', {4, 3}, {1, 2, 3, 0, 5, 6, 0, 0, 9, 0, 0, 0});
nd4j::ops::triu op;
auto results = op.execute({&input}, {}, {});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, triu_test2) {
auto input = NDArrayFactory::create<double>('c', {4, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto expected = NDArrayFactory::create<double>('c', {4, 3}, {1, 2, 3,4, 5, 6,0, 8, 9,0, 0, 12});
nd4j::ops::triu op;
auto results = op.execute({&input}, {}, {-1});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, triu_test3) {
auto input = NDArrayFactory::create<double>('c', {2, 3, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto expected = NDArrayFactory::create<double>('c', {2, 3, 2}, {1, 2,3, 4,0, 6,7, 8,9,10,0,12});
nd4j::ops::triu op;
auto results = op.execute({&input}, {}, {-1});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, triu_test4) {
auto input = NDArrayFactory::create<double>('c', {2, 3, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto expected = NDArrayFactory::create<double>('c', {2, 3, 2}, {1, 2,0, 4,0, 0,7, 8,0, 10,0, 0});
nd4j::ops::triu op;
auto results = op.execute({&input}, {}, {});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, triu_test5) {
auto input = NDArrayFactory::create<double>('c', {2, 3, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto expected = NDArrayFactory::create<double>('c', {2, 3, 2}, {0, 2,0, 0,0, 0,0, 8,0, 0,0, 0});
nd4j::ops::triu op;
auto results = op.execute({&input}, {}, {1});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, triu_test6) {
auto input = NDArrayFactory::create<double>('c', {2, 3, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto expected = NDArrayFactory::create<double>('c', {2, 3, 2}, {0, 0,0, 0,0, 0,0, 0,0, 0,0, 0});
nd4j::ops::triu op;
auto results = op.execute({&input}, {}, {10});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, triu_test7) {
auto input = NDArrayFactory::create<double>('c', {2, 3, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto expected = NDArrayFactory::create<double>('c', {2, 3, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
nd4j::ops::triu op;
auto results = op.execute({&input}, {}, {-10});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, triu_test8) {
auto input = NDArrayFactory::create<double>('c', {6}, {1, 2, 3, 4, 5, 6});
auto expected = NDArrayFactory::create<double>('c', {6, 6}, {1, 2, 3, 4, 5, 6,0, 2, 3, 4, 5, 6,0, 0, 3, 4, 5, 6,0, 0, 0, 4, 5, 6,0, 0, 0, 0, 5, 6,0, 0, 0, 0, 0, 6});
nd4j::ops::triu op;
auto results = op.execute({&input}, {}, {});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, triu_test9) {
auto input = NDArrayFactory::create<double>('c', {6}, {1, 2, 3, 4, 5, 6});
auto expected = NDArrayFactory::create<double>('c', {6, 6}, {1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 0, 2, 3, 4, 5, 6, 0, 0, 3, 4, 5, 6});
nd4j::ops::triu op;
auto results = op.execute({&input}, {}, {-3});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, triu_test10) {
auto input = NDArrayFactory::create<double>('c', {6}, {1, 2, 3, 4, 5, 6});
auto expected = NDArrayFactory::create<double>('c', {6, 6}, {0, 0, 0, 4, 5, 6, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0});
nd4j::ops::triu op;
auto results = op.execute({&input}, {}, {3});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, triu_test11) {
auto input = NDArrayFactory::create<double>('c', {6}, {1, 2, 3, 4, 5, 6});
auto expected = NDArrayFactory::create<double>('c', {6, 6}, {1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6});
nd4j::ops::triu op;
auto results = op.execute({&input}, {}, {-58});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, triu_bp_test1) {
auto input = NDArrayFactory::create<double>('c', {2, 3, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto gradO = NDArrayFactory::create<double>('c', {2, 3, 2});
gradO = 0.5;
auto expected = NDArrayFactory::create<double>('c', {2, 3, 2}, {0.,0.5,0.,0. ,0.,0. ,0.,0.5,0.,0. ,0.,0.});
nd4j::ops::triu_bp op;
auto results = op.execute({&input, &gradO}, {}, {1});
auto gradI = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(gradI));
ASSERT_TRUE(expected.equalsTo(gradI));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, triu_bp_test2) {
auto input = NDArrayFactory::create<double>('c', {2, 3, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto gradO = NDArrayFactory::create<double>('c', {2, 3, 2});
gradO = 0.5;
auto expected = NDArrayFactory::create<double>('c', {2, 3, 2}, {0.5,0.5,0. ,0.5,0. ,0. ,0.5,0.5,0. ,0.5,0. ,0.});
nd4j::ops::triu_bp op;
auto results = op.execute({&input, &gradO}, {}, {});
auto gradI = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(gradI));
ASSERT_TRUE(expected.equalsTo(gradI));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, triu_bp_test3) {
auto input = NDArrayFactory::create<double>('c', {6}, {1, 2, 3, 4, 5, 6});
auto gradO = NDArrayFactory::create<double>('c', {6,6});
gradO = 0.5;
auto expected = NDArrayFactory::create<double>('c', {6,6}, {0.5, 0.5, 0.5, 0.5, 0.5, 0.5,0.5, 0.5, 0.5, 0.5, 0.5, 0.5,0.5, 0.5, 0.5, 0.5, 0.5, 0.5,0. , 0.5, 0.5, 0.5, 0.5, 0.5,0. , 0. , 0.5, 0.5, 0.5, 0.5,0. , 0. , 0. , 0.5, 0.5, 0.5});
nd4j::ops::triu_bp op;
auto results = op.execute({&input, &gradO}, {}, {-2});
auto gradI = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(gradI));
ASSERT_TRUE(expected.equalsTo(gradI));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, triu_bp_test4) {
auto input = NDArrayFactory::create<double>('c', {2,3}, {1, 2, 3, 4, 5, 6});
auto gradO = NDArrayFactory::create<double>('c', {2,3});
gradO = 0.5;
auto expected = NDArrayFactory::create<double>('c', {2,3}, {0., 0., 0., 0., 0., 0.});
nd4j::ops::triu_bp op;
auto results = op.execute({&input, &gradO}, {}, {10});
auto gradI = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(gradI));
ASSERT_TRUE(expected.equalsTo(gradI));
delete results;
}