cavis/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests4.cpp

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2019-06-06 14:21:15 +02:00
/*******************************************************************************
* 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, biasadd_1) {
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auto x = NDArrayFactory::create<double>('c', {2, 3, 3, 2});
auto bias = NDArrayFactory::create<double>('c', {2}, {1, 2});
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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, biasadd_2) {
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auto x = NDArrayFactory::create<double>('c', {2, 2, 3, 3});
auto bias = NDArrayFactory::create<double>('c', {2}, {1, 2});
auto exp = NDArrayFactory::create<double>('c', {2, 2, 3, 3}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2});
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nd4j::ops::biasadd op;
auto result = op.execute({&x, &bias}, {}, {}, {true}, false, nd4j::DataType::DOUBLE);
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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, biasadd_3) {
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}, {}, {}, {true}, 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, biasadd_bp_1) {
NDArray x('c', {2,2,2,3}, {1.,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}, nd4j::DataType::FLOAT32);
NDArray gradO('c', {2,2,2,3}, nd4j::DataType::FLOAT32);
NDArray bias('c', {3}, {-1., -2, -3}, nd4j::DataType::FLOAT32);
NDArray expGradB('c', {3}, {9.2, 10. , 10.8}, nd4j::DataType::FLOAT32);
gradO.linspace(0.1, 0.1);
nd4j::ops::biasadd_bp op;
auto result = op.execute({&x, &bias, &gradO}, {}, {}, {false}); // NHWC
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto gradI = result->at(0);
auto gradB = result->at(1);
ASSERT_TRUE(gradI->isSameShape(gradO));
ASSERT_TRUE(gradI->equalsTo(gradO));
ASSERT_TRUE(gradB->isSameShape(expGradB));
ASSERT_TRUE(gradB->equalsTo(expGradB));
delete result;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, biasadd_bp_2) {
NDArray x('c', {2,3,2,2}, {1.,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}, nd4j::DataType::FLOAT32);
NDArray gradO('c', {2,3,2,2}, nd4j::DataType::FLOAT32);
NDArray bias('c', {3}, {-1., -2, -3}, nd4j::DataType::FLOAT32);
NDArray expGradB('c', {3}, {6.8, 10., 13.2}, nd4j::DataType::FLOAT32);
gradO.linspace(0.1, 0.1);
nd4j::ops::biasadd_bp op;
auto result = op.execute({&x, &bias, &gradO}, {}, {}, {true}); // NCHW
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto gradI = result->at(0);
auto gradB = result->at(1);
ASSERT_TRUE(gradI->isSameShape(gradO));
ASSERT_TRUE(gradI->equalsTo(gradO));
ASSERT_TRUE(gradB->isSameShape(expGradB));
ASSERT_TRUE(gradB->equalsTo(expGradB));
delete result;
}
TEST_F(DeclarableOpsTests4, biasadd_4) {
if (!Environment::getInstance()->isExperimentalBuild())
return;
auto x = NDArrayFactory::create<double>('c', {2, 3});
auto y = NDArrayFactory::create<float>('c', {3}, {1.f, 2.f, 3.f});
auto z = NDArrayFactory::create<float>('c', {2, 3});
auto exp = NDArrayFactory::create<float>('c', {2, 3}, {1.f, 2.f, 3.f, 1.f, 2.f, 3.f});
nd4j::ops::biasadd op;
auto status = op.execute({&x, &y}, {&z}, {}, {}, {true});
ASSERT_EQ(Status::OK(), status);
ASSERT_EQ(exp, z);
}
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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;
}
[WIP] More of CUDA (#95) * initial commit Signed-off-by: raver119 <raver119@gmail.com> * Implementation of hashcode cuda helper. Working edition. * Fixed parallel test input arangements. * Fixed tests for hashcode op. * Fixed shape calculation for image:crop_and_resize op and test. * NativeOps tests. Initial test suite. * Added tests for indexReduce methods. * Added test on execBroadcast with NDArray as dimensions. * Added test on execBroadcastBool with NDArray as dimensions. * Added tests on execPairwiseTransform and execPairwiseTransofrmBool. * Added tests for execReduce with scalar results. * Added reduce tests for non-empty dims array. * Added tests for reduce3. * Added tests for execScalar. * Added tests for execSummaryStats. * - provide cpu/cuda code for batch_to_space - testing it Signed-off-by: Yurii <yurii@skymind.io> * - remove old test for batch_to_space (had wrong format and numbers were not checked) Signed-off-by: Yurii <yurii@skymind.io> * Fixed complilation errors with test. * Added test for execTransformFloat. * Added test for execTransformSame. * Added test for execTransformBool. * Added test for execTransformStrict. * Added tests for execScalar/execScalarBool with TADs. * Added test for flatten. * - provide cpu/cuda code for space_to_Batch operaion Signed-off-by: Yurii <yurii@skymind.io> * Added test for concat. * comment unnecessary stuff in s_t_b Signed-off-by: Yurii <yurii@skymind.io> * Added test for specialConcat. * Added tests for memcpy/set routines. * Fixed pullRow cuda test. * Added pullRow test. * Added average test. * - correct typo in NDArray::applyPairwiseTransform(nd4j::pairwise::BoolOps op...) Signed-off-by: Yurii <yurii@skymind.io> * - debugging and fixing cuda tests in JavaInteropTests file Signed-off-by: Yurii <yurii@skymind.io> * - correct some tests Signed-off-by: Yurii <yurii@skymind.io> * Added test for shuffle. * Fixed ops declarations. * Restored omp and added shuffle test. * Added convertTypes test. * Added tests for execRandom. Eliminated usage of RandomBuffer with NativeOps. * Added sort tests. * Added tests for execCustomOp. * - further debuging and fixing tests terminated with crash Signed-off-by: Yurii <yurii@skymind.io> * Added tests for calculateOutputShapes. * Addded Benchmarks test. * Commented benchmark tests. * change assertion Signed-off-by: raver119 <raver119@gmail.com> * Added tests for apply_sgd op. Added cpu helper for that op. * Implement cuda helper for aplly_sgd op. Fixed tests for NativeOps. * Added test for assign broadcastable. * Added tests for assign_bp op. * Added tests for axpy op. * - assign/execScalar/execTransformAny signature change - minor test fix Signed-off-by: raver119 <raver119@gmail.com> * Fixed axpy op. * meh Signed-off-by: raver119 <raver119@gmail.com> * - fix tests for nativeOps::concat Signed-off-by: Yurii <yurii@skymind.io> * sequential transform/scalar Signed-off-by: raver119 <raver119@gmail.com> * allow nested parallelism Signed-off-by: raver119 <raver119@gmail.com> * assign_bp leak fix Signed-off-by: raver119 <raver119@gmail.com> * block setRNG fix Signed-off-by: raver119 <raver119@gmail.com> * enable parallelism by default Signed-off-by: raver119 <raver119@gmail.com> * enable nested parallelism by default Signed-off-by: raver119 <raver119@gmail.com> * Added cuda implementation for row_count helper. * Added implementation for tnse gains op helper. * - take into account possible situations when input arrays are empty in reduce_ cuda stuff Signed-off-by: Yurii <yurii@skymind.io> * Implemented tsne/edge_forces op cuda-based helper. Parallelized cpu-based helper for edge_forces. * Added kernel for tsne/symmetrized op heleper. * Implementation of tsne/symmetrized op cuda helper. Working edition. * Eliminated waste printfs. * Added test for broadcastgradientargs op. * host-only fallback for empty reduce float Signed-off-by: raver119 <raver119@gmail.com> * - some tests fixes Signed-off-by: Yurii <yurii@skymind.io> * - correct the rest of reduce_ stuff Signed-off-by: Yurii <yurii@skymind.io> * - further correction of reduce_ stuff Signed-off-by: Yurii <yurii@skymind.io> * Added test for Cbow op. Also added cuda implementation for cbow helpers. * - improve code of stack operation for scalar case Signed-off-by: Yurii <yurii@skymind.io> * - provide cuda kernel for gatherND operation Signed-off-by: Yurii <yurii@skymind.io> * Implementation of cbow helpers with cuda kernels. * minor tests tweaks Signed-off-by: raver119 <raver119@gmail.com> * minor tests tweaks Signed-off-by: raver119 <raver119@gmail.com> * - further correction of cuda stuff Signed-off-by: Yurii <yurii@skymind.io> * Implementatation of cbow op helper with cuda kernels. Working edition. * Skip random testing for cudablas case. * lstmBlockCell context fix Signed-off-by: raver119 <raver119@gmail.com> * Added tests for ELU and ELU_BP ops. * Added tests for eq_scalar, gt_scalar, gte_scalar and lte_scalar ops. * Added tests for neq_scalar. * Added test for noop. * - further work on clipbynorm_bp Signed-off-by: Yurii <yurii@skymind.io> * - get rid of concat op call, use instead direct concat helper call Signed-off-by: Yurii <yurii@skymind.io> * lstmBlockCell context fix Signed-off-by: raver119 <raver119@gmail.com> * Added tests for lrelu and lrelu_bp. * Added tests for selu and selu_bp. * Fixed lrelu derivative helpers. * - some corrections in lstm Signed-off-by: Yurii <yurii@skymind.io> * operator * result shape fix Signed-off-by: raver119 <raver119@gmail.com> * - correct typo in lstmCell Signed-off-by: Yurii <yurii@skymind.io> * few tests fixed Signed-off-by: raver119 <raver119@gmail.com> * CUDA inverse broadcast bool fix Signed-off-by: raver119 <raver119@gmail.com> * disable MMAP test for CUDA Signed-off-by: raver119 <raver119@gmail.com> * BooleanOp syncToDevice Signed-off-by: raver119 <raver119@gmail.com> * meh Signed-off-by: raver119 <raver119@gmail.com> * additional data types for im2col/col2im Signed-off-by: raver119 <raver119@gmail.com> * Added test for firas_sparse op. * one more RandomBuffer test excluded Signed-off-by: raver119 <raver119@gmail.com> * Added tests for flatten op. * Added test for Floor op. * bunch of tests fixed Signed-off-by: raver119 <raver119@gmail.com> * mmulDot tests fixed Signed-off-by: raver119 <raver119@gmail.com> * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * Implemented floordiv_bp op and tests. * Fixed scalar case with cuda implementation for bds. * - work on cuda kernel for clip_by_norm backprop op is completed Signed-off-by: Yurii <yurii@skymind.io> * Eliminate cbow crach. * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * Eliminated abortion with batched nlp test. * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * Fixed shared flag initializing. * disabled bunch of cpu workspaces tests Signed-off-by: raver119 <raver119@gmail.com> * scalar operators fix: missing registerSpecialUse call Signed-off-by: raver119 <raver119@gmail.com> * Fixed logdet for cuda and tests. * - correct clipBynorm_bp Signed-off-by: Yurii <yurii@skymind.io> * Fixed crop_and_resize shape datatype. * - correct some mmul tests Signed-off-by: Yurii <yurii@skymind.io>
2019-08-02 19:01:03 +02:00
TEST_F(DeclarableOpsTests4, Test_FirasSparce_1) {
auto x = NDArrayFactory::create<double>('c', {1, 81});
auto exp = NDArrayFactory::create<double>('c', {1, 2}, {0, 1});
x.p(51, 1);
x.p(52, 0);
x.p(60, 1);
x.p(61, 0);
nd4j::ops::firas_sparse op;
auto result = op.execute({&x}, {}, {0, 1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
// z->printIndexedBuffer("FIRAS");
// z->printShapeInfo("OUTSHAPE");
// ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_FlattenTests_1) {
auto x = NDArrayFactory::create<double>('c', {3, 3, 3, 3});
auto exp = NDArrayFactory::create<double>('c', {81});
x.linspace(1);
exp.linspace(1);
nd4j::ops::flatten op;
auto result = op.execute({&x}, {}, {'c'});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
// z->printIndexedBuffer("Flatten1");
// z->printShapeInfo("Flatten1 shape");
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_FlattenTests_2) {
auto x = NDArrayFactory::create<double>('c', {3, 3, 3, 3});
auto y = NDArrayFactory::create<double>('c', {3, 3});
auto exp = NDArrayFactory::create<double>('c', {90});
x.linspace(1);
y.linspace(82);
exp.linspace(1);
nd4j::ops::flatten op;
auto result = op.execute({&x, &y}, {}, {'c'});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
// z->printIndexedBuffer("Flatten2");
// z->printShapeInfo("Flatten2 shape");
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_FlattenTests_3) {
NDArray x('c', {2,2}, {1, 2, 3, 4}, nd4j::DataType::INT32);
NDArray y('f', {2,2}, nd4j::DataType::INT32);
NDArray exp('c', {8}, {1, 2, 3, 4, 1, 2, 3, 4}, nd4j::DataType::INT32);
y.assign(x);
nd4j::ops::flatten op;
auto result = op.execute({&x, &y}, {}, {'c'});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_FlattenTests_4) {
NDArray x('c', {2,2}, {1, 2, 3, 4}, nd4j::DataType::INT32);
NDArray y('f', {2,2}, nd4j::DataType::INT32);
NDArray exp('c', {8}, {1, 3, 2, 4, 1, 3, 2, 4}, nd4j::DataType::INT32);
y.assign(x);
nd4j::ops::flatten op;
auto result = op.execute({&x, &y}, {}, {'f'});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
[WIP] More of CUDA (#95) * initial commit Signed-off-by: raver119 <raver119@gmail.com> * Implementation of hashcode cuda helper. Working edition. * Fixed parallel test input arangements. * Fixed tests for hashcode op. * Fixed shape calculation for image:crop_and_resize op and test. * NativeOps tests. Initial test suite. * Added tests for indexReduce methods. * Added test on execBroadcast with NDArray as dimensions. * Added test on execBroadcastBool with NDArray as dimensions. * Added tests on execPairwiseTransform and execPairwiseTransofrmBool. * Added tests for execReduce with scalar results. * Added reduce tests for non-empty dims array. * Added tests for reduce3. * Added tests for execScalar. * Added tests for execSummaryStats. * - provide cpu/cuda code for batch_to_space - testing it Signed-off-by: Yurii <yurii@skymind.io> * - remove old test for batch_to_space (had wrong format and numbers were not checked) Signed-off-by: Yurii <yurii@skymind.io> * Fixed complilation errors with test. * Added test for execTransformFloat. * Added test for execTransformSame. * Added test for execTransformBool. * Added test for execTransformStrict. * Added tests for execScalar/execScalarBool with TADs. * Added test for flatten. * - provide cpu/cuda code for space_to_Batch operaion Signed-off-by: Yurii <yurii@skymind.io> * Added test for concat. * comment unnecessary stuff in s_t_b Signed-off-by: Yurii <yurii@skymind.io> * Added test for specialConcat. * Added tests for memcpy/set routines. * Fixed pullRow cuda test. * Added pullRow test. * Added average test. * - correct typo in NDArray::applyPairwiseTransform(nd4j::pairwise::BoolOps op...) Signed-off-by: Yurii <yurii@skymind.io> * - debugging and fixing cuda tests in JavaInteropTests file Signed-off-by: Yurii <yurii@skymind.io> * - correct some tests Signed-off-by: Yurii <yurii@skymind.io> * Added test for shuffle. * Fixed ops declarations. * Restored omp and added shuffle test. * Added convertTypes test. * Added tests for execRandom. Eliminated usage of RandomBuffer with NativeOps. * Added sort tests. * Added tests for execCustomOp. * - further debuging and fixing tests terminated with crash Signed-off-by: Yurii <yurii@skymind.io> * Added tests for calculateOutputShapes. * Addded Benchmarks test. * Commented benchmark tests. * change assertion Signed-off-by: raver119 <raver119@gmail.com> * Added tests for apply_sgd op. Added cpu helper for that op. * Implement cuda helper for aplly_sgd op. Fixed tests for NativeOps. * Added test for assign broadcastable. * Added tests for assign_bp op. * Added tests for axpy op. * - assign/execScalar/execTransformAny signature change - minor test fix Signed-off-by: raver119 <raver119@gmail.com> * Fixed axpy op. * meh Signed-off-by: raver119 <raver119@gmail.com> * - fix tests for nativeOps::concat Signed-off-by: Yurii <yurii@skymind.io> * sequential transform/scalar Signed-off-by: raver119 <raver119@gmail.com> * allow nested parallelism Signed-off-by: raver119 <raver119@gmail.com> * assign_bp leak fix Signed-off-by: raver119 <raver119@gmail.com> * block setRNG fix Signed-off-by: raver119 <raver119@gmail.com> * enable parallelism by default Signed-off-by: raver119 <raver119@gmail.com> * enable nested parallelism by default Signed-off-by: raver119 <raver119@gmail.com> * Added cuda implementation for row_count helper. * Added implementation for tnse gains op helper. * - take into account possible situations when input arrays are empty in reduce_ cuda stuff Signed-off-by: Yurii <yurii@skymind.io> * Implemented tsne/edge_forces op cuda-based helper. Parallelized cpu-based helper for edge_forces. * Added kernel for tsne/symmetrized op heleper. * Implementation of tsne/symmetrized op cuda helper. Working edition. * Eliminated waste printfs. * Added test for broadcastgradientargs op. * host-only fallback for empty reduce float Signed-off-by: raver119 <raver119@gmail.com> * - some tests fixes Signed-off-by: Yurii <yurii@skymind.io> * - correct the rest of reduce_ stuff Signed-off-by: Yurii <yurii@skymind.io> * - further correction of reduce_ stuff Signed-off-by: Yurii <yurii@skymind.io> * Added test for Cbow op. Also added cuda implementation for cbow helpers. * - improve code of stack operation for scalar case Signed-off-by: Yurii <yurii@skymind.io> * - provide cuda kernel for gatherND operation Signed-off-by: Yurii <yurii@skymind.io> * Implementation of cbow helpers with cuda kernels. * minor tests tweaks Signed-off-by: raver119 <raver119@gmail.com> * minor tests tweaks Signed-off-by: raver119 <raver119@gmail.com> * - further correction of cuda stuff Signed-off-by: Yurii <yurii@skymind.io> * Implementatation of cbow op helper with cuda kernels. Working edition. * Skip random testing for cudablas case. * lstmBlockCell context fix Signed-off-by: raver119 <raver119@gmail.com> * Added tests for ELU and ELU_BP ops. * Added tests for eq_scalar, gt_scalar, gte_scalar and lte_scalar ops. * Added tests for neq_scalar. * Added test for noop. * - further work on clipbynorm_bp Signed-off-by: Yurii <yurii@skymind.io> * - get rid of concat op call, use instead direct concat helper call Signed-off-by: Yurii <yurii@skymind.io> * lstmBlockCell context fix Signed-off-by: raver119 <raver119@gmail.com> * Added tests for lrelu and lrelu_bp. * Added tests for selu and selu_bp. * Fixed lrelu derivative helpers. * - some corrections in lstm Signed-off-by: Yurii <yurii@skymind.io> * operator * result shape fix Signed-off-by: raver119 <raver119@gmail.com> * - correct typo in lstmCell Signed-off-by: Yurii <yurii@skymind.io> * few tests fixed Signed-off-by: raver119 <raver119@gmail.com> * CUDA inverse broadcast bool fix Signed-off-by: raver119 <raver119@gmail.com> * disable MMAP test for CUDA Signed-off-by: raver119 <raver119@gmail.com> * BooleanOp syncToDevice Signed-off-by: raver119 <raver119@gmail.com> * meh Signed-off-by: raver119 <raver119@gmail.com> * additional data types for im2col/col2im Signed-off-by: raver119 <raver119@gmail.com> * Added test for firas_sparse op. * one more RandomBuffer test excluded Signed-off-by: raver119 <raver119@gmail.com> * Added tests for flatten op. * Added test for Floor op. * bunch of tests fixed Signed-off-by: raver119 <raver119@gmail.com> * mmulDot tests fixed Signed-off-by: raver119 <raver119@gmail.com> * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * Implemented floordiv_bp op and tests. * Fixed scalar case with cuda implementation for bds. * - work on cuda kernel for clip_by_norm backprop op is completed Signed-off-by: Yurii <yurii@skymind.io> * Eliminate cbow crach. * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * Eliminated abortion with batched nlp test. * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * Fixed shared flag initializing. * disabled bunch of cpu workspaces tests Signed-off-by: raver119 <raver119@gmail.com> * scalar operators fix: missing registerSpecialUse call Signed-off-by: raver119 <raver119@gmail.com> * Fixed logdet for cuda and tests. * - correct clipBynorm_bp Signed-off-by: Yurii <yurii@skymind.io> * Fixed crop_and_resize shape datatype. * - correct some mmul tests Signed-off-by: Yurii <yurii@skymind.io>
2019-08-02 19:01:03 +02:00
TEST_F(DeclarableOpsTests4, Test_FloorTests_1) {
auto x = NDArrayFactory::create<double>('c', {3, 3}, {1.5, 2.3, 3.4, 4.3, 5.9, 6.1, 7.2, 8.9, 9.7});
auto exp = NDArrayFactory::create<double>('c', {3,3});
exp.linspace(1);
nd4j::ops::Floor op;
auto result = op.execute({&x}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
// z->printIndexedBuffer("Flatten1");
// z->printShapeInfo("Flatten1 shape");
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
2019-06-06 14:21:15 +02:00
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_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) {
int axis = 0;
2019-06-06 14:21:15 +02:00
auto x = NDArrayFactory::create<double>('c', {1}, {10});
auto begin = NDArrayFactory::create<int>('c', {1}, {axis});
auto end = NDArrayFactory::create<int>('c', {1}, {axis});
Dev branch merge: dev_20190606 (#7904) * 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
2019-06-15 13:34:34 +02:00
auto stride = NDArrayFactory::create<int>('c', {1}, {1});
2019-06-06 14:21:15 +02:00
//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);
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);
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);
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;
[WIP] more CUDA stuff (#57) * initial commit Signed-off-by: raver119 <raver119@gmail.com> * Added gradcheck test for dynamic_partition_bp op. * - implementation of dilation op (cpu and cuda) Signed-off-by: Yurii <yurii@skymind.io> * Fixed broadcast_dynamic_shape 1D case and tests. * Fixed usage of default integer arguments. * Fixed dynamic_partition_bp op and tests. * Eliminated test with grad check for dynamic_partition_bp op. * start working on cuda svd - porting available corresponding api from cuSOLVER library Signed-off-by: Yurii <yurii@skymind.io> * provide prelu_bp Signed-off-by: Yurii <yurii@skymind.io> * - provide gruCell_bp (old version ??) Signed-off-by: Yurii <yurii@skymind.io> * - polishing cumsum_bp and cumprod_bp tests Signed-off-by: Yurii <yurii@skymind.io> * provide sparseSoftmaxCrossEntropyWithLogits and sparseSoftmaxCrossEntropyWithLogits_grad Signed-off-by: Yurii <yurii@skymind.io> * Fixed atomicMul with float input/output * implementation of cuda kernel for triu_bp operation Signed-off-by: Yurii <yurii@skymind.io> * Refactored lup helper to add parrallel computing. * cusolver libraries Signed-off-by: raver119 <raver119@gmail.com> * uncomment cuSolver APIs in svd.cu Signed-off-by: Yurii <yurii@skymind.io> * cusolver var Signed-off-by: raver119 <raver119@gmail.com> * - further work on cuSolver svd Signed-off-by: Yurii <yurii@skymind.io> * Implement usage of cuda solver to LUP decomposition. * - correct naames in lup functions Signed-off-by: Yurii <yurii@skymind.io> * correct svdQR cuda Signed-off-by: Yurii <yurii@skymind.io> * - provide transpositions of input matrices in case of c order in svdCudaQR Signed-off-by: Yurii <yurii@skymind.io> * Fixed implementation issues with LUP usign cuda solver. * Implementation of matrix_determinant helper with cuda kernels. Working revision. * Implemented log_matrix_determinant helper with cuda kernels. * - implementation of batched cuda svd Signed-off-by: Yurii <yurii@skymind.io> * Refactored cholesky helper and implementation of cuda solver cholesky batch. * - implementation of cuda kernel for tile bp Signed-off-by: Yurii <yurii@skymind.io> * Implementation of cholesky and logdet with cuda kernels. * - implementation of cuda kernel for sru_bidirectional Signed-off-by: Yurii <yurii@skymind.io> * Fixed cholesky helper. * Cholesky op helper implementation. Working double-based cublas implementation. * bad import excluded Signed-off-by: raver119 <raver119@gmail.com> * Finished with cuda implementation of cholesky helper and tests. * - implementation of cuda kernel for sru_bidirectional_backprop operation Signed-off-by: Yurii <yurii@skymind.io> * Implementation of matrix_inverse op helper with cuda kernels. The first revision. * - start working on gruCell_bp Signed-off-by: Yurii <yurii@skymind.io> * Implementation of matrix_inverse helper. * - further work on new gruCell_bp Signed-off-by: Yurii <yurii@skymind.io> * cuBLAS related fixes Signed-off-by: raver119 <raver119@gmail.com> * calculateOutputShapes() now passes device buffers as well Signed-off-by: raver119 <raver119@gmail.com> * special concat/average/accumulate init host pointers now Signed-off-by: raver119 <raver119@gmail.com> * few more tweaks Signed-off-by: raver119 <raver119@gmail.com> * additional CudaDataBufferFactory signatures certain for data types Signed-off-by: raver119 <raver119@gmail.com> * cuSolver host buffer Signed-off-by: raver119 <raver119@gmail.com> * buffer to buffer memcpy host ptr allocation Signed-off-by: raver119 <raver119@gmail.com>
2019-07-12 10:51:51 +02:00
auto expected = NDArrayFactory::create<double>('c', {2, 3, 2}, {0.,0.5,0.,0. ,0.,0. ,0.,0.5,0.,0. ,0.,0.});
2019-06-06 14:21:15 +02:00
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;
}