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

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* 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
******************************************************************************/
//
// @author raver119@gmail.com
//
#include <helpers/shape.h>
#include "testlayers.h"
#include <ops/declarable/headers/shape.h>
using namespace nd4j;
using namespace nd4j::graph;
class ShapeTests : public testing::Test {
public:
};
TEST_F(ShapeTests, Test_Basics_1) {
Nd4jLong shape[] = {2, 5, 3, 3, 1, 0, 1, 99};
ASSERT_EQ(2, shape::rank(shape));
ASSERT_EQ(1, shape::elementWiseStride(shape));
ASSERT_EQ(5, shape::sizeAt(shape, 0));
ASSERT_EQ(3, shape::sizeAt(shape, 1));
ASSERT_EQ('c', shape::order(shape));
}
TEST_F(ShapeTests, Test_Basics_2) {
Nd4jLong shape[] = {4, 2, 3, 4, 5, 60, 20, 5, 1, 0, -1, 102};
ASSERT_EQ(4, shape::rank(shape));
ASSERT_EQ(-1, shape::elementWiseStride(shape));
ASSERT_EQ(2, shape::sizeAt(shape, 0));
ASSERT_EQ(3, shape::sizeAt(shape, 1));
ASSERT_EQ(4, shape::sizeAt(shape, 2));
ASSERT_EQ(5, shape::sizeAt(shape, 3));
ASSERT_EQ('f', shape::order(shape));
}
TEST_F(ShapeTests, Test_tadLength_1) {
Nd4jLong shape[] = {4, 2, 3, 4, 5, 60, 20, 5, 1, 0, -1, 102};
int axis[] = {2, 3};
ASSERT_EQ(20, shape::tadLength(shape, axis, 2));
}
TEST_F(ShapeTests, Test_ShapeEquality_1) {
Nd4jLong shape[] = {4, 2, 3, 4, 5, 60, 20, 5, 1, 0, -1, 102};
Nd4jLong shape_GOOD[] = {4, 2, 3, 4, 5, 60, 20, 5, 1, 0, 1, 99};
Nd4jLong shape_BAD[] = {4, 3, 3, 4, 5, 60, 20, 5, 1, 0, -1, 102};
ASSERT_TRUE(shape::equalsSoft(shape, shape_GOOD));
ASSERT_FALSE(shape::equalsSoft(shape, shape_BAD));
}
TEST_F(ShapeTests, Test_ShapeEquality_2) {
Nd4jLong shape[] = {4, 2, 3, 4, 5, 60, 20, 5, 1, 0, -1, 102};
Nd4jLong shape_GOOD[] = {4, 2, 3, 4, 5, 60, 20, 5, 1, 0, -1, 102};
Nd4jLong shape_BAD[] = {4, 2, 3, 4, 5, 60, 20, 5, 1, 0, -1, 99};
ASSERT_TRUE(shape::equalsStrict(shape, shape_GOOD));
ASSERT_FALSE(shape::equalsStrict(shape, shape_BAD));
}
TEST_F(ShapeTests, Test_Ind2SubC_1) {
Nd4jLong shape[] = {3, 5};
Nd4jLong c0[2];
shape::index2coords(2, shape, 0, c0);
ASSERT_EQ(0, c0[0]);
ASSERT_EQ(0, c0[1]);
Nd4jLong c1[2];
shape::index2coords(2, shape, 1, c1);
ASSERT_EQ(0, c1[0]);
ASSERT_EQ(1, c1[1]);
Nd4jLong c6[2];
shape::index2coords(2, shape, 5, c6);
ASSERT_EQ(1, c6[0]);
ASSERT_EQ(0, c6[1]);
}
TEST_F(ShapeTests, Test_Ind2Sub_1) {
Nd4jLong shape[] = {3, 5};
Nd4jLong c0[2];
shape::index2coords(2, shape, 0, c0, 'f');
ASSERT_EQ(0, c0[0]);
ASSERT_EQ(0, c0[1]);
Nd4jLong c1[2];
shape::index2coords(2, shape, 1, c1, 'f');
ASSERT_EQ(1, c1[0]);
ASSERT_EQ(0, c1[1]);
Nd4jLong c6[2];
shape::index2coords(2, shape, 5, c6, 'f');
ASSERT_EQ(2, c6[0]);
ASSERT_EQ(1, c6[1]);
}
TEST_F(ShapeTests, Test_ShapeDetector_1) {
Nd4jLong shape[] = {2, 5, 3, 3, 1, 0, 1, 99};
ASSERT_TRUE(shape::isMatrix(shape));
}
TEST_F(ShapeTests, Test_ShapeDetector_2) {
Nd4jLong shape[] = {3, 2, 5, 3, 15, 3, 1, 0, 1, 99};
ASSERT_FALSE(shape::isMatrix(shape));
}
TEST_F(ShapeTests, Test_ShapeDetector_3) {
Nd4jLong shape[] = {2, 1, 3, 3, 1, 0, 1, 99};
ASSERT_FALSE(shape::isColumnVector(shape));
ASSERT_TRUE(shape::isVector(shape));
ASSERT_TRUE(shape::isRowVector(shape));
ASSERT_FALSE(shape::isMatrix(shape));
}
TEST_F(ShapeTests, Test_ShapeDetector_4) {
Nd4jLong shape[] = {2, 3, 1, 1, 1, 0, 1, 99};
ASSERT_TRUE(shape::isColumnVector(shape));
ASSERT_TRUE(shape::isVector(shape));
ASSERT_FALSE(shape::isRowVector(shape));
ASSERT_FALSE(shape::isMatrix(shape));
}
TEST_F(ShapeTests, Test_ShapeDetector_5) {
Nd4jLong shape[] = {2, 1, 1, 1, 1, 0, 1, 99};
ASSERT_TRUE(shape::isScalar(shape));
ASSERT_FALSE(shape::isMatrix(shape));
// edge case here. Technicaly it's still a vector with length of 1
ASSERT_TRUE(shape::isVector(shape));
}
TEST_F(ShapeTests, Test_ShapeDetector_6) {
Nd4jLong shape[] = {2, 1, 1, 1, 1, 0, 1, 99};
ASSERT_EQ(8, shape::shapeInfoLength(shape));
ASSERT_EQ(64, shape::shapeInfoByteLength(shape));
}
TEST_F(ShapeTests, Test_ShapeDetector_7) {
Nd4jLong shape[] = {3, 1, 1, 1, 1, 1, 1, 0, 1, 99};
ASSERT_EQ(10, shape::shapeInfoLength(shape));
ASSERT_EQ(80, shape::shapeInfoByteLength(shape));
}
TEST_F(ShapeTests, Test_Transpose_1) {
Nd4jLong shape[] = {3, 2, 5, 3, 15, 3, 1, 0, 1, 99};
Nd4jLong exp[] = {3, 3, 5, 2, 1, 3, 15, 0, 1, 102};
shape::transposeInplace(shape);
ASSERT_TRUE(shape::equalsStrict(exp, shape));
}
TEST_F(ShapeTests, Test_Transpose_2) {
Nd4jLong shape[] = {2, 5, 3, 3, 1, 0, 1, 99};
Nd4jLong exp[] = {2, 3, 5, 1, 3, 0, 1, 102};
shape::transposeInplace(shape);
ASSERT_TRUE(shape::equalsStrict(exp, shape));
}
TEST_F(ShapeTests, Test_Transpose_3) {
Nd4jLong shape[] = {2, 1, 3, 3, 1, 0, 1, 99};
Nd4jLong exp[] = {2, 3, 1, 1, 3, 0, 1, 102};
shape::transposeInplace(shape);
ASSERT_TRUE(shape::equalsStrict(exp, shape));
}
TEST_F(ShapeTests, Test_Transpose_4) {
Nd4jLong shape[] = {4, 2, 3, 4, 5, 5, 4, 3, 2, 0, 1, 99};
Nd4jLong exp[] = {4, 5, 4, 3, 2, 2, 3, 4, 5, 0, 1, 102};
shape::transposeInplace(shape);
ASSERT_TRUE(shape::equalsStrict(exp, shape));
}
TEST_F(ShapeTests, Test_Edge_1) {
auto x = NDArrayFactory::create<float>('f', {1, 4, 1, 4});
x.linspace(1);
x.reshapei('c', {4, 4});
//x.printShapeInfo("reshape0");
//x.printIndexedBuffer("x i");
//x.printBuffer("x r");
x.reshapei({4, 1, 1, 4});
//x.printShapeInfo("reshape1");
}
TEST_F(ShapeTests, Test_Edge_2) {
auto x = NDArrayFactory::create<float>('c', {1, 4, 1, 3});
x.reshapei('c', {3, 4});
//x.printShapeInfo("reshape0");
x.reshapei({3, 1, 1, 4});
//x.printShapeInfo("reshape1");
}
TEST_F(ShapeTests, Test_Remove_Index_1) {
int array[] = {1, 2, 3};
int idx[] = {0};
int result[2];
shape::removeIndex(array, idx, 3, 1, result);
ASSERT_EQ(2, result[0]);
ASSERT_EQ(3, result[1]);
}
TEST_F(ShapeTests, Test_Remove_Index_2) {
int array[] = {1, 2, 3};
int idx[] = {1};
int result[2];
shape::removeIndex(array, idx, 3, 1, result);
ASSERT_EQ(1, result[0]);
ASSERT_EQ(3, result[1]);
}
TEST_F(ShapeTests, Test_Remove_Index_3) {
int array[] = {1, 2, 3};
int idx[] = {2};
int result[2];
shape::removeIndex(array, idx, 3, 1, result);
ASSERT_EQ(1, result[0]);
ASSERT_EQ(2, result[1]);
}
TEST_F(ShapeTests, Test_Remove_Index_4) {
int array[] = {1, 2, 3};
int idx[] = {0, 2};
int result[1];
shape::removeIndex(array, idx, 3, 2, result);
ASSERT_EQ(2, result[0]);
}
TEST_F(ShapeTests, Test_Remove_Index_5) {
int array[] = {1, 2, 3};
int idx[] = {1, 0};
int result[1];
shape::removeIndex(array, idx, 3, 2, result);
ASSERT_EQ(3, result[0]);
}
TEST_F(ShapeTests, Test_Remove_Index_6) {
int array[] = {1, 2, 3};
int idx[] = {1, 2};
int result[1];
shape::removeIndex(array, idx, 3, 2, result);
ASSERT_EQ(1, result[0]);
}
TEST_F(ShapeTests, Tests_Transpose_119_1) {
auto x = NDArrayFactory::create<float>('c', {3, 2});
auto y = NDArrayFactory::create<float>('c', {2}, {1.0f, 0.0f});
auto z = NDArrayFactory::create<float>('c', {2, 3});
x.linspace(1.f);
auto e = x.permute({1, 0});
e.streamline('c');
nd4j::ops::transpose op;
auto result = op.execute({&x, &y}, {&z}, {}, {}, {});
ASSERT_EQ(Status::OK(), result);
ASSERT_TRUE(e.isSameShape(z));
ASSERT_TRUE(e.equalsTo(z));
}
TEST_F(ShapeTests, Tests_Transpose_119_2) {
auto x = NDArrayFactory::create<float>('c', {3, 5});
x.linspace(1.f);
auto exp = x.transpose();
nd4j::ops::transpose op;
auto result = op.execute({&x},{}, {});
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(ShapeTests, Tests_Transpose_119_3) {
auto x = NDArrayFactory::create<float>('c', {3, 5});
x.linspace(1.f);
auto z = NDArrayFactory::create<float>('c', {5, 3});
auto exp = x.transpose();
nd4j::ops::transpose op;
auto result = op.execute({&x}, {&z}, {}, {}, {});
ASSERT_EQ(Status::OK(), result);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
}