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cavis/libnd4j/tests_cpu/layers_tests/NDArrayTests2.cpp

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Eclipse Migration Initial Commit
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
******************************************************************************/
//
// Created by raver119 on 21.11.17.
//
#include "testlayers.h"
#include <memory>
#include <NDArray.h>
#include <DebugHelper.h>
#include <ops/declarable/headers/parity_ops.h>
using namespace nd4j;
//////////////////////////////////////////////////////////////////////
class NDArrayTest2 : public testing::Test {
public:
};
TEST_F(NDArrayTest2, Test_ByteVector_1) {
auto x = NDArrayFactory::create<float>('c', {10, 10});
x.linspace(1);
auto vec = x.asByteVector();
auto restored = new NDArray((float *)vec.data(), x.shapeInfo(), x.getContext(), false);
ASSERT_TRUE(x.equalsTo(restored));
delete restored;
}
TEST_F(NDArrayTest2, Test_ByteVector_2) {
auto x = NDArrayFactory::create<bfloat16>('c', {10, 10});
x.linspace(1);
auto vec = x.asByteVector();
auto restored = new NDArray((bfloat16 *)vec.data(), x.shapeInfo(), x.getContext(), false);
ASSERT_TRUE(x.equalsTo(restored));
delete restored;
}
TEST_F(NDArrayTest2, Test_ByteVector_3) {
auto x = NDArrayFactory::create<double>('c', {10, 10});
x.linspace(1);
auto vec = x.asByteVector();
auto restored = new NDArray((double *)vec.data(), x.shapeInfo(), x.getContext(), false);
ASSERT_TRUE(x.equalsTo(restored));
delete restored;
}
TEST_F(NDArrayTest2, Test_Reshape_Scalar_1) {
auto x = NDArrayFactory::create<double>('c', {1, 1}, {1.0});
auto e = NDArrayFactory::create<double>(1.0);
x.reshapei({});
ASSERT_EQ(e, x);
ASSERT_EQ(e.rankOf(), x.rankOf());
}
TEST_F(NDArrayTest2, Test_Reshape_Scalar_2) {
auto x = NDArrayFactory::create<double>('c', {1, 1}, {1.0});
auto e = NDArrayFactory::create<double>('c', {1}, {1.0});
x.reshapei({1});
ASSERT_EQ(e, x);
ASSERT_EQ(e.rankOf(), x.rankOf());
}
TEST_F(NDArrayTest2, Test_IndexReduce_1) {
auto x = NDArrayFactory::create<float>('c', {1, 5}, {1, 2, 3, 4, 5});
ExtraArguments extras({3.0, 0.0, 10.0});
int idx = x.indexReduceNumber(indexreduce::FirstIndex, &extras).e<int>(0);
ASSERT_EQ(2, idx);
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, SetIdentity_test_1) {
auto x = NDArrayFactory::create<double>('c', {1, 5});
auto xExp = NDArrayFactory::create<double>('c', {1, 5}, {1, 0, 0, 0, 0});
x.setIdentity();
ASSERT_TRUE(x.equalsTo(&xExp));
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, SetIdentity_test_2) {
auto x = NDArrayFactory::create<double>('f', {1, 5});
auto xExp = NDArrayFactory::create<double>('f', {1, 5}, {1, 0, 0, 0, 0});
x.setIdentity();
ASSERT_TRUE(x.equalsTo(&xExp));
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, SetIdentity_test_3) {
auto x = NDArrayFactory::create<double>('f', {1, 1});
auto xExp = NDArrayFactory::create<double>('f', {1, 1}, {1});
x.setIdentity();
ASSERT_TRUE(x.equalsTo(&xExp));
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, SetIdentity_test_4) {
auto x = NDArrayFactory::create<double>('f', {2, 1});
auto xExp = NDArrayFactory::create<double>('f', {2, 1}, {1,0});
x.setIdentity();
ASSERT_TRUE(x.equalsTo(&xExp));
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, SetIdentity_test_5) {
auto x = NDArrayFactory::create<double>('f', {2, 2});
auto xExp = NDArrayFactory::create<double>('f', {2, 2}, {1,0,0,1});
x.setIdentity();
ASSERT_TRUE(x.equalsTo(&xExp));
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, SetIdentity_test_6) {
auto x = NDArrayFactory::create<float>('c', {3, 2});
auto xExp = NDArrayFactory::create<float>('c', {3, 2}, {1,0,0,1,0,0});
x.setIdentity();
ASSERT_TRUE(x.equalsTo(&xExp));
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, SetIdentity_test_7) {
auto x = NDArrayFactory::create<float>('c', {3, 4});
auto xExp = NDArrayFactory::create<float>('c', {3, 4}, {1.,0.,0.,0.,0.,1.,0.,0.,0.,0.,1.,0.});
x.setIdentity();
ASSERT_TRUE(x.equalsTo(&xExp));
}
#ifdef ALLOWED_3D_IDENTITY
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, SetIdentity_test_8) {
auto x = NDArrayFactory::create<float>('c', {3, 3, 3});
auto xExp = NDArrayFactory::create<float>('c', {3, 3, 3}, {1.,0.,0. ,0.,0.,0., 0.,0.,0., 0.,0.,0. ,0.,1.,0., 0.,0.,0., 0.,0.,0. ,0.,0.,0., 0.,0.,1.});
xExp.printIndexedBuffer("Identity8");
x.setIdentity();
ASSERT_TRUE(x.equalsTo(&xExp));
}
#endif
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, Test_AllReduce3_1) {
auto x = NDArrayFactory::create<float>('c', {2, 3}, {1, 2, 3, 1, 2, 3});
auto y = NDArrayFactory::create<float>('c', {2, 3}, {2, 3, 4, 2, 3, 4});
auto exp = NDArrayFactory::create<float>('c', {2, 2}, {1.73205, 1.73205, 1.73205, 1.73205});
auto z = x.applyAllReduce3(reduce3::EuclideanDistance, &y, {1}, nullptr);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete z;
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, Test_AllReduce3_2) {
auto x = NDArrayFactory::create<float>('c', {2, 3}, {1, 2, 3, 2, 3, 4 });
auto y = NDArrayFactory::create<float>('c', {2, 3}, {1, 2, 3, 2, 3, 4});
auto exp = NDArrayFactory::create<float>('c', {2, 2}, {0., 1.73205, 1.73205, 0.});
auto z = x.applyAllReduce3(reduce3::EuclideanDistance, &y, {1}, nullptr);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete z;
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, mmul_test1) {
auto x = NDArrayFactory::create<float>('c', {4, 1}, {1, 2, 3, 4});
auto y = NDArrayFactory::create<float>('c', {1, 4}, {1, 2, 3, 4});
auto exp = NDArrayFactory::create<float>('c', {4, 4}, {1,2, 3, 4,2,4, 6, 8,3,6, 9,12,4,8,12,16});
auto result = mmul(x, y);
ASSERT_TRUE(exp.isSameShape(&result));
ASSERT_TRUE(exp.equalsTo(&result));
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, mmul_test2) {
auto x = NDArrayFactory::create<float>('c', {4, 1}, {1, 2, 3, 4});
auto y = NDArrayFactory::create<float>('c', {1, 4}, {1, 2, 3, 4});
auto exp = NDArrayFactory::create<float>('c', {1, 1}, {30});
auto result = mmul(y ,x);
ASSERT_TRUE(exp.isSameShape(&result));
ASSERT_TRUE(exp.equalsTo(&result));
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, mmul_test3) {
auto x = NDArrayFactory::create<float>('c', {4, 1}, {1, 2, 3, 4});
auto exp = NDArrayFactory::create<float>('c', {4, 4}, {1. ,0.2 ,0.3 ,0.4 ,0.2,0.04,0.06,0.08,0.3,0.06,0.09,0.12,0.4,0.08,0.12,0.16});
auto w = NDArrayFactory::create<float>( x.ordering(), {(int)x.lengthOf(), 1}, x.getContext()); // column-vector
auto wT = NDArrayFactory::create<float>(x.ordering(), {1, (int)x.lengthOf()}, x.getContext()); // row-vector (transposed w)
w = x / (float)10.;
w.p(0, 1.);
wT.assign(&w);
auto result = mmul(w ,wT);
ASSERT_TRUE(exp.isSameShape(&result));
ASSERT_TRUE(exp.equalsTo(&result));
}
TEST_F(NDArrayTest2, Test_Streamline_1) {
auto x = NDArrayFactory::create<float>('c', {3, 4, 6});
auto y = NDArrayFactory::create<float>('c', {3, 4, 6});
x.linspace(1);
y.linspace(1);
x.permutei({1, 0, 2});
y.permutei({1, 0, 2});
y.streamline();
ASSERT_TRUE(x.isSameShape(&y));
ASSERT_TRUE(x.equalsTo(&y));
ASSERT_FALSE(x.isSameShapeStrict(&y));
}
TEST_F(NDArrayTest2, Test_Streamline_2) {
auto x = NDArrayFactory::create<double>('c', {3, 4, 6});
auto y = NDArrayFactory::create<double>('f', {3, 4, 6});
x.linspace(1);
y.linspace(1);
ASSERT_TRUE(x.isSameShape(&y));
ASSERT_TRUE(x.equalsTo(&y));
y.streamline('c');
ASSERT_TRUE(x.isSameShape(&y));
ASSERT_TRUE(x.equalsTo(&y));
}
TEST_F(NDArrayTest2, Test_Enforce_1) {
auto x = NDArrayFactory::create<float>('c', {4, 1, 1, 4});
auto exp = NDArrayFactory::create<float>('c', {4, 4});
x.linspace(1);
exp.linspace(1);
x.enforce({4, 4}, 'c');
ASSERT_TRUE(exp.isSameShapeStrict(&x));
ASSERT_TRUE(exp.equalsTo(&x));
}
TEST_F(NDArrayTest2, TestVector_1) {
auto x = NDArrayFactory::create<float>('c', {2, 3});
auto row = NDArrayFactory::create<float>('c', {3}, {1, 2, 3});
auto exp = NDArrayFactory::create<float>('c', {2, 3}, {1, 2, 3, 1, 2, 3});
x.addiRowVector(&row);
ASSERT_TRUE(exp.equalsTo(&x));
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, Operator_Plus_Test_5)
{
auto x = NDArrayFactory::create<float>('c', {8, 8, 8});
auto y = NDArrayFactory::create<float>('c', {8, 1, 8});
auto expected = NDArrayFactory::create<float>('c', {8, 8, 8});
x = 1.;
y = 2.;
expected = 3.;
auto result = x + y;
ASSERT_TRUE(expected.isSameShape(&result));
ASSERT_TRUE(expected.equalsTo(&result));
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, Operator_Plus_Test_6) {
auto x = NDArrayFactory::create<float>('c', {3, 3, 3});
auto y = NDArrayFactory::create<float>('c', {3, 1, 3});
auto expected = NDArrayFactory::create<float>('c', {3, 3, 3}, {2., 4., 6., 5., 7., 9., 8.,10.,12., 14.,16.,18.,17.,19.,21.,20.,22.,24., 26.,28.,30.,29.,31.,33.,32.,34.,36.});
x.linspace(1);
y.linspace(1);
auto result = x + y;
ASSERT_TRUE(expected.isSameShape(&result));
ASSERT_TRUE(expected.equalsTo(&result));
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, tileToShape_test1) {
auto x = NDArrayFactory::create<float>('c', {2, 2}, {1,2,3,4});
auto exp = NDArrayFactory::create<float>('c', {2, 2, 2}, {1,2,3,4,1,2,3,4});
x.tileToShape({2,2,2});
ASSERT_TRUE(x.isSameShape(&exp));
ASSERT_TRUE(x.equalsTo(&exp));
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, tileToShape_test2) {
auto x = NDArrayFactory::create<float>('c', {2, 1, 2}, {1,2,3,4});
auto exp = NDArrayFactory::create<float>('c', {2, 3, 2}, {1,2,1,2,1,2,3,4,3,4,3,4});
x.tileToShape({2,3,2});
ASSERT_TRUE(x.isSameShape(&exp));
ASSERT_TRUE(x.equalsTo(&exp));
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, tileToShape_test3) {
auto x = NDArrayFactory::create<float>('c', {2, 2}, {1,2,3,4});
auto result = NDArrayFactory::create<float>('c', {2, 2, 2});
auto exp = NDArrayFactory::create<float>('c', {2, 2, 2}, {1,2,3,4,1,2,3,4});
x.tileToShape({2,2,2}, &result);
// result.printIndexedBuffer();
ASSERT_TRUE(result.isSameShape(&exp));
ASSERT_TRUE(result.equalsTo(&exp));
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, tileToShape_test4) {
auto x = NDArrayFactory::create<float>('c', {2, 1, 2}, {1,2,3,4});
auto result = NDArrayFactory::create<float>('c', {2, 3, 2});
auto exp = NDArrayFactory::create<float>('c', {2, 3, 2}, {1,2,1,2,1,2,3,4,3,4,3,4});
x.tileToShape({2,3,2}, &result);
ASSERT_TRUE(result.isSameShape(&exp));
ASSERT_TRUE(result.equalsTo(&exp));
}
#ifndef __CUDABLAS__
TEST_F(NDArrayTest2, Test_TriplewiseLambda_1) {
auto t = NDArrayFactory::create<float>('c', {3, 3}, {1, 1, 1, 1, 1, 1, 1, 1, 1});
auto u = NDArrayFactory::create<float>('c', {3, 3}, {2, 2, 2, 2, 2, 2, 2, 2, 2});
auto v = NDArrayFactory::create<float>('c', {3, 3}, {3, 3, 3, 3, 3, 3, 3, 3, 3});
auto exp = NDArrayFactory::create<float>('c', {3, 3}, {7, 7, 7, 7, 7, 7, 7, 7, 7});
float extra = 1.0f;
auto la = LAMBDA_FFF(_t, _u, _v, extra) {
return _t + _u + _v + extra;
};
t.applyTriplewiseLambda<float>(&u, &v, la);
ASSERT_TRUE(t.equalsTo(&exp));
}
TEST_F(NDArrayTest2, Test_TriplewiseLambda_2) {
auto t = NDArrayFactory::create<float>('c', {3, 3}, {1, 1, 1, 1, 1, 1, 1, 1, 1});
auto u = NDArrayFactory::create<float>('f', {3, 3}, {2, 2, 2, 2, 2, 2, 2, 2, 2});
auto v = NDArrayFactory::create<float>('c', {3, 3}, {3, 3, 3, 3, 3, 3, 3, 3, 3});
auto exp = NDArrayFactory::create<float>('c', {3, 3}, {7, 7, 7, 7, 7, 7, 7, 7, 7});
float extra = 1.0f;
auto la = LAMBDA_FFF(_t, _u, _v, extra) {
return _t + _u + _v + extra;
};
t.applyTriplewiseLambda<float>(&u, &v, la);
ASSERT_TRUE(t.equalsTo(&exp));
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, Test_Indexed_Lambda) {
auto x = NDArrayFactory::create<float>('c', {2, 2});
auto exp = NDArrayFactory::create<float>('c', {2, 2}, {0, 1, 2, 3});
auto lambda = ILAMBDA_F(_x) {
return (float) _idx;
};
x.applyIndexedLambda<float>(lambda);
ASSERT_TRUE(exp.equalsTo(&x));
}
#endif
TEST_F(NDArrayTest2, Test_PermuteEquality_1) {
auto x = NDArrayFactory::create<float>('c', {1, 60});
auto exp = NDArrayFactory::create<float>('c', {3, 5, 4}, {1.0, 6.0, 11.0, 16.0, 2.0, 7.0, 12.0, 17.0, 3.0, 8.0, 13.0, 18.0, 4.0, 9.0, 14.0, 19.0, 5.0, 10.0, 15.0, 20.0, 21.0, 26.0, 31.0, 36.0, 22.0, 27.0, 32.0, 37.0, 23.0, 28.0, 33.0, 38.0, 24.0, 29.0, 34.0, 39.0, 25.0, 30.0, 35.0, 40.0, 41.0, 46.0, 51.0, 56.0, 42.0, 47.0, 52.0, 57.0, 43.0, 48.0, 53.0, 58.0, 44.0, 49.0, 54.0, 59.0, 45.0, 50.0, 55.0, 60.0});
x.linspace(1);
x.reshapei('c', {3, 4, 5});
x.permutei({0, 2, 1});
x.streamline();
// x.printShapeInfo("{0, 2, 1} shape");
// x.printBuffer("{0, 2, 1} data");
ASSERT_TRUE(exp.isSameShape(&x));
ASSERT_TRUE(exp.equalsTo(&x));
}
TEST_F(NDArrayTest2, Test_PermuteEquality_0) {
auto x = NDArrayFactory::create<float>('c', {1, 60});
x.linspace(1);
auto exp = NDArrayFactory::create<float>('c', {3, 4, 5}, {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0});
x.reshapei('c', {3, 4, 5});
x.permutei({0, 1, 2});
x.streamline();
// x.printShapeInfo("{0, 1, 2} shape");
// x.printBuffer("{0, 1, 2} data");
ASSERT_TRUE(exp.isSameShape(&x));
ASSERT_TRUE(exp.equalsTo(&x));
}
TEST_F(NDArrayTest2, Test_PermuteEquality_2) {
auto x = NDArrayFactory::create<float>('c', {1, 60});
x.linspace(1);
auto exp = NDArrayFactory::create<float>('c', {4, 3, 5}, {1.0, 2.0, 3.0, 4.0, 5.0, 21.0, 22.0, 23.0, 24.0, 25.0, 41.0, 42.0, 43.0, 44.0, 45.0, 6.0, 7.0, 8.0, 9.0, 10.0, 26.0, 27.0, 28.0, 29.0, 30.0, 46.0, 47.0, 48.0, 49.0, 50.0, 11.0, 12.0, 13.0, 14.0, 15.0, 31.0, 32.0, 33.0, 34.0, 35.0, 51.0, 52.0, 53.0, 54.0, 55.0, 16.0, 17.0, 18.0, 19.0, 20.0, 36.0, 37.0, 38.0, 39.0, 40.0, 56.0, 57.0, 58.0, 59.0, 60.0});
x.reshapei('c', {3, 4, 5});
x.permutei({1, 0, 2});
x.streamline();
// x.printShapeInfo("{1, 0, 2} shape");
// x.printBuffer("{1, 0, 2} data");
ASSERT_TRUE(exp.isSameShape(&x));
ASSERT_TRUE(exp.equalsTo(&x));
}
TEST_F(NDArrayTest2, Test_PermuteEquality_3) {
auto x = NDArrayFactory::create<float>('c', {1, 60});
x.linspace(1);
auto exp = NDArrayFactory::create<float>('c', {4, 5, 3}, {1.0, 21.0, 41.0, 2.0, 22.0, 42.0, 3.0, 23.0, 43.0, 4.0, 24.0, 44.0, 5.0, 25.0, 45.0, 6.0, 26.0, 46.0, 7.0, 27.0, 47.0, 8.0, 28.0, 48.0, 9.0, 29.0, 49.0, 10.0, 30.0, 50.0, 11.0, 31.0, 51.0, 12.0, 32.0, 52.0, 13.0, 33.0, 53.0, 14.0, 34.0, 54.0, 15.0, 35.0, 55.0, 16.0, 36.0, 56.0, 17.0, 37.0, 57.0, 18.0, 38.0, 58.0, 19.0, 39.0, 59.0, 20.0, 40.0, 60.0});
x.reshapei('c', {3, 4, 5});
x.permutei({1, 2, 0});
x.streamline();
// x.printShapeInfo("{1, 2, 0} shape");
// x.printBuffer("{1, 2, 0} data");
ASSERT_TRUE(exp.isSameShape(&x));
ASSERT_TRUE(exp.equalsTo(&x));
}
TEST_F(NDArrayTest2, Test_PermuteEquality_4) {
auto x = NDArrayFactory::create<float>('c', {1, 60});
x.linspace(1);
auto exp = NDArrayFactory::create<float>('c', {5, 3, 4}, {1.0, 6.0, 11.0, 16.0, 21.0, 26.0, 31.0, 36.0, 41.0, 46.0, 51.0, 56.0, 2.0, 7.0, 12.0, 17.0, 22.0, 27.0, 32.0, 37.0, 42.0, 47.0, 52.0, 57.0, 3.0, 8.0, 13.0, 18.0, 23.0, 28.0, 33.0, 38.0, 43.0, 48.0, 53.0, 58.0, 4.0, 9.0, 14.0, 19.0, 24.0, 29.0, 34.0, 39.0, 44.0, 49.0, 54.0, 59.0, 5.0, 10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 55.0, 60.0});
x.reshapei('c', {3, 4, 5});
x.permutei({2, 0, 1});
x.streamline();
// x.printShapeInfo("{2, 0, 1} shape");
// x.printBuffer("{2, 0, 1} data");
ASSERT_TRUE(exp.isSameShape(&x));
ASSERT_TRUE(exp.equalsTo(&x));
}
TEST_F(NDArrayTest2, Test_PermuteEquality_5) {
auto x = NDArrayFactory::create<float>('c', {1, 60});
x.linspace(1);
auto exp = NDArrayFactory::create<float>('c', {5, 4, 3},
{1.0, 21.0, 41.0, 6.0, 26.0, 46.0, 11.0, 31.0, 51.0, 16.0, 36.0, 56.0, 2.0, 22.0, 42.0, 7.0,
27.0, 47.0, 12.0, 32.0, 52.0, 17.0, 37.0, 57.0, 3.0, 23.0, 43.0, 8.0, 28.0, 48.0, 13.0, 33.0,
53.0, 18.0, 38.0, 58.0, 4.0, 24.0, 44.0, 9.0, 29.0, 49.0, 14.0, 34.0, 54.0, 19.0, 39.0, 59.0,
5.0, 25.0, 45.0, 10.0, 30.0, 50.0, 15.0, 35.0, 55.0, 20.0, 40.0, 60.0});
x.reshapei('c', {3, 4, 5});
x.permutei({2, 1, 0});
x.streamline();
// x.printShapeInfo("{2, 0, 1} shape");
// x.printBuffer("{2, 0, 1} data");
ASSERT_TRUE(exp.isSameShape(&x));
ASSERT_TRUE(exp.equalsTo(&x));
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, fillAsTriangular_test1) {
auto x = NDArrayFactory::create<float>('c', {4, 4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16});
auto exp = NDArrayFactory::create<float>('c', {4, 4}, {1,0,0,0,5,6,0,0,9,10,11,0 ,13,14,15,16});
x.fillAsTriangular<float>(0., 0, 0, 'u');
ASSERT_TRUE(exp.isSameShape(&x));
ASSERT_TRUE(exp.equalsTo(&x));
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, fillAsTriangular_test2) {
auto x = NDArrayFactory::create<float>('c', {4, 4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16});
auto exp = NDArrayFactory::create<float>('c', {4, 4}, {0,0,0,0,5,0,0,0,9,10,0 ,0 ,13,14,15,0});
x.fillAsTriangular<float>(0., 0, -1, 'u');
ASSERT_TRUE(exp.isSameShape(&x));
ASSERT_TRUE(exp.equalsTo(&x));
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, fillAsTriangular_test3) {
auto x = NDArrayFactory::create<float>('c', {4, 4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16});
auto exp = NDArrayFactory::create<float>('c', {4, 4}, {1,2,3,4,0,6,7,8,0,0 ,11,12,0 ,0 , 0,16});
x.fillAsTriangular<float>(0., 0, 0, 'l');
ASSERT_TRUE(exp.isSameShape(&x));
ASSERT_TRUE(exp.equalsTo(&x));
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, fillAsTriangular_test4) {
auto x = NDArrayFactory::create<float>('c', {4, 4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16});
auto exp = NDArrayFactory::create<float>('c', {4, 4}, {0,2,3,4,0,0,7,8,0,0 , 0,12, 0, 0, 0, 0});
x.fillAsTriangular<float>(0., 1, 0, 'l');
ASSERT_TRUE(exp.isSameShape(&x));
ASSERT_TRUE(exp.equalsTo(&x));
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, Test_DType_Conversion_1) {
auto x = NDArrayFactory::create<float>('c', {2, 3}, {1, 2, 3, 4, 5, 6});
auto xd = x.template asT<double>();
auto xf = xd->template asT<float>();
ASSERT_TRUE(x.isSameShape(xf));
ASSERT_TRUE(x.equalsTo(xf));
delete xf;
delete xd;
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, Test_ScalarArray_Assign_1) {
auto x = NDArrayFactory::create<float>('c', {2, 2});
auto y = NDArrayFactory::create<float>(2.0f);
auto exp = NDArrayFactory::create<float>('c', {2, 2}, {2.0f, 2.0f, 2.0f, 2.0f});
x.assign(y);
ASSERT_TRUE(exp.isSameShape(&x));
ASSERT_TRUE(exp.equalsTo(&x));
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, Test_Reshape_To_Vector_1) {
auto x = NDArrayFactory::create<float>('c', {2, 3}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f});
auto exp = NDArrayFactory::create<float>('c', {6}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f});
x.reshapei({-1});
ASSERT_TRUE(exp.isSameShape(x));
ASSERT_TRUE(exp.equalsTo(x));
}
TEST_F(NDArrayTest2, Test_toIndexedString_1) {
auto x = NDArrayFactory::create<float>('c', {2, 2}, {1.5f, 2.5f, 3.f, 4.5f});
auto str = x.asIndexedString();
std::string exp = "[1.5, 2.5, 3, 4.5]";
ASSERT_EQ(exp, str);
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, permute_test4) {
Nd4jLong arr1ShapeInfo[] = {6, 1, 1, 4, 3, 2, 2, 48, 48, 12, 4, 2, 1, 8192, 1, 99};
Nd4jLong arr2ShapeInfo[] = {6, 1, 2, 2, 1, 4, 3, 48, 2, 1, 48, 12, 4, 8192, 0, 99};
auto arr1Buffer = new float[786432];
auto arr2Buffer = new float[786432];
NDArray arr1(arr1Buffer, arr1ShapeInfo, nd4j::LaunchContext ::defaultContext());
NDArray arr2(arr2Buffer, arr2ShapeInfo, nd4j::LaunchContext ::defaultContext());
const std::vector<int> perm = {0, 4, 5, 1, 2, 3};
auto arr1P = arr1.permute(perm);
// arr1P->printShapeInfo();
// ASSERT_TRUE(arr1.isSameShapeStrict(&arr2));
ASSERT_TRUE(arr1P->isSameShapeStrict(&arr2));
delete arr1P;
delete []arr1Buffer;
delete []arr2Buffer;
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, TestStdDev3) {
// autoarray('c', {10, 10});
auto array = NDArrayFactory::create<double>('c', {2, 2}, {0.2946, 0.2084, 0.0345, 0.7368});
const int len = array.lengthOf();
double sum = 0.;
for(int i=0; i < len; ++i)
sum += array.e<double>(i);
const double mean = sum / len;
double diffSquared = 0.;
for(int i=0; i < len; ++i)
diffSquared += (array.e<double>(i) - mean) * (array.e<double>(i) - mean);
const double trueVariance = math::nd4j_sqrt<double, double>(diffSquared / len);
const double trueVarianceCorr = math::nd4j_sqrt<double, double>(diffSquared / (len - 1));
const double variance = array.varianceNumber(variance::SummaryStatsStandardDeviation, false).e<double>(0);
const double varianceCorr = array.varianceNumber(variance::SummaryStatsStandardDeviation, true).e<double>(0);
// printf("%s expected %.10f calculated %.10f\n","variance :", trueVariance, variance );
// printf("%s expected %.10f calculated %.10f\n","variance corrected:", trueVarianceCorr, varianceCorr);
ASSERT_NEAR(trueVariance, variance, 1e-8);
ASSERT_NEAR(trueVarianceCorr, varianceCorr, 1e-8);
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, Test_Linspace_1) {
auto exp = NDArrayFactory::create<double>('c',{1,5}, {1., 2., 3., 4., 5.});
auto x = NDArrayFactory::create<double>('c', {1, 5});
x.linspace(1);
ASSERT_TRUE(x.equalsTo(&exp));
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, Test_Linspace_2) {
auto exp = NDArrayFactory::create<double>('c',{1,5}, {1., 3., 5., 7., 9.});
auto x = NDArrayFactory::create<double>('c', {1, 5});
x.linspace(1, 2);
ASSERT_TRUE(x.equalsTo(&exp));
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, Test_Linspace_3) {
auto exp = NDArrayFactory::create<double>('c',{1,5}, {1., 4., 7., 10., 13.});
auto x = NDArrayFactory::create<double>('c', {1, 5});
x.linspace(1,3);
ASSERT_TRUE(x.equalsTo(&exp));
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, Test_Linspace_4) {
auto exp = NDArrayFactory::create<double>('c',{1,5}, {-1., -2., -3., -4., -5.});
auto x = NDArrayFactory::create<double>('c', {1, 5});
x.linspace(-1, -1);
ASSERT_TRUE(x.equalsTo(&exp));
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, Test_Linspace_5) {
auto exp = NDArrayFactory::create<double>('c',{1,5}, {9., 8., 7., 6., 5.});
auto x = NDArrayFactory::create<double>('c', {1, 5});
x.linspace(9, -1);
ASSERT_TRUE(x.equalsTo(&exp));
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, allTensorsAlongDimension_test1) {
auto x = NDArrayFactory::create<float>('c', {4}, {1, 2, 3, 4});
auto exp = NDArrayFactory::create<float>('c', {4}, {1, 2, 3, 4});
auto set = x.allTensorsAlongDimension({0});
// set->at(0)->printShapeInfo();
// set->at(0)->printIndexedBuffer();
ASSERT_TRUE(set->size() == 1);
ASSERT_TRUE(exp.isSameShape(set->at(0)));
ASSERT_TRUE(exp.equalsTo(set->at(0)));
delete set;
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, scalar_get_test1) {
auto scalar1 = NDArrayFactory::create(20.f);
NDArray arr('c', {2,2}, {0., 10., 20., 30.}, nd4j::DataType::FLOAT32);
NDArray scalar2 = arr.e(2);
ASSERT_TRUE(scalar1.isSameShape(scalar2));
ASSERT_TRUE(scalar1.equalsTo(scalar2));
ASSERT_TRUE(scalar1.dataType() == scalar2.dataType());
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, scalar_get_test2) {
auto scalar1 = NDArrayFactory::create(20.f);
NDArray arr('f', {2,2}, {0., 10., 20., 30.}, nd4j::DataType::FLOAT32);
NDArray scalar2 = arr.e(1);
ASSERT_TRUE(scalar1.isSameShape(scalar2));
ASSERT_TRUE(scalar1.equalsTo(scalar2));
ASSERT_TRUE(scalar1.dataType() == scalar2.dataType());
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, scalar_set_test1) {
NDArray scalar1 = NDArrayFactory::create(20.f);
NDArray arr('c', {2,2}, {0., 10., -20., 30.}, nd4j::DataType::FLOAT32);
NDArray exp('c', {2,2}, {0., 10., 20., 30.}, nd4j::DataType::FLOAT32);
arr.p(2, scalar1);
ASSERT_TRUE(exp.equalsTo(arr));
}
////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, scalar_set_test2) {
NDArray scalar1 = NDArrayFactory::create(20.f);
NDArray arr('f', {2,2}, {0., 10., -20., 30.}, nd4j::DataType::FLOAT32);
NDArray exp('f', {2,2}, {0., 10., 20., 30.}, nd4j::DataType::FLOAT32);
arr.p(1, scalar1);
ASSERT_TRUE(exp.equalsTo(arr));
}
TEST_F(NDArrayTest2, big_dup_test) {
// auto arr = NDArrayFactory::linspace<float>(1.0f, 10000000.0f, 100000000);
auto arr = NDArrayFactory::linspace<float>(1.0f, 1000.0f, 10000);
auto dup = arr->dup('c');
ASSERT_EQ(*arr, *dup);
delete arr;
delete dup;
}
TEST_F(NDArrayTest2, debugInfoTest_1) {
NDArray testArray('c', {2, 4, 4, 4}, {
91., 82., 37., 64., 55., 46., 73., 28., 119., 12., 112., 13., 14., 114., 16., 117.,
51., 42., 67., 24., 15., 56., 93., 28., 109., 82., 12., 113., 114., 14., 116., 11.,
31., 22., 87., 44., 55., 46., 73., 28., -119., 12., 112., 13., 14., 114., 16., 117.,
91., -82., 37., 64., -55.1, 0, 73., 28., -119., 12., 112., 13., 14., 114., 16.2, 117.,
91., -82., 37., 64., 55., 46., 73., 28., -119., 12., 112., 13., 14., 114., 16., 117.,
51., 42., 67., 24., 15., 0., 93., 28., 109., 82., 12., 113., 114., 14., 116., 11.,
31., 22., 87., 44., 55., 46., 73., 28., 119., 12., 112., 13., 14., 114., 16., 117.,
91., 82., 37., 64., -3, 0, 73., 28., 119., 12., 112., 13., 140., 110., 160., 107.}, nd4j::DataType::DOUBLE);
NDArray res(nd4j::DataType::DOUBLE);
DebugInfo info = DebugHelper::debugStatistics(&testArray);
DebugInfo exp; // = {}
nd4j::ops::reduce_min minOp;
nd4j::ops::reduce_mean meanOp;
nd4j::ops::reduce_max maxOp;
nd4j::ops::reduce_stdev stdevOp;
minOp.execute({&testArray}, {&res}, {}, {}, {});
exp._minValue = res.e<double>(0);
meanOp.execute({&testArray}, {&res}, {}, {}, {});
exp._meanValue = res.e<double>(0);
maxOp.execute({&testArray}, {&res}, {}, {}, {});
exp._maxValue = res.e<double>(0);
stdevOp.execute({&testArray}, {&res}, {}, {}, {});
exp._stdDevValue = res.e<double>(0);
exp._zeroCount = 3;
exp._negativeCount = 7;
exp._positiveCount = 118;
exp._infCount = 0;
exp._nanCount = 0;
printf("Output statistics %lf %lf %lf %lf\n", info._minValue, info._maxValue, info._meanValue, info._stdDevValue);
printf("Expect statistics %lf %lf %lf %lf\n", exp._minValue, exp._maxValue, exp._meanValue, exp._stdDevValue);
printf("%lld %lld %lld %lld %lld\n", info._zeroCount, info._negativeCount, info._positiveCount, info._infCount, info._nanCount);
ASSERT_EQ(exp, info);
}
TEST_F(NDArrayTest2, debugInfoTest_2) {
NDArray testArray('c', {2, 4, 4, 4}, {
91., 82., 37., 64., 55., 46., 73., 28., 119., 12., 112., 13., 14., 114., 16., 117.,
51., 42., 67., 24., 15., 56., 93., 28., 109., 82., 12., 113., 114., 14., 116., 11.,
31., 22., 87., 44., 55., 46., 73., 28., -119., 12., 112., 13., 14., 114., 16., 117.,
91., -82., 37., 64., -55.1, 0, 73., 28., -119., 12., 112., 13., 14., 114., 16.2, 117.,
91., -82., 37., 64., 55., 46., 73., 28., -119., 12., 112., 13., 14., 114., 16., 117.,
51., 42., 67., 24., 15., 0., 93., 28., 109., 82., 12., 113., 114., 14., 116., 11.,
31., 22., 87., 44., 55., 46., 73., 28., 119., 12., 112., 13., 14., 114., 16., 117.,
91., 82., 37., 64., -3, 0, 73., 28., 119., 12., 112., 13., 140., 110., 160., 107.}, nd4j::DataType::DOUBLE);
DebugInfo info;
DebugInfo exp; // = {}
exp._minValue = -119;
exp._maxValue = 160.;
exp._meanValue = 51.328906;
exp._stdDevValue = 52.385694;
exp._zeroCount = 3;
exp._negativeCount = 7;
exp._positiveCount = 118;
exp._infCount = 0;
exp._nanCount = 0;
DebugHelper::retrieveDebugStatistics(&info, &testArray);
printf("Output statistics %lf %lf %lf %lf\n", info._minValue, info._maxValue, info._meanValue, info._stdDevValue);
printf("Expect statistics %lf %lf %lf %lf\n", exp._minValue, exp._maxValue, exp._meanValue, exp._stdDevValue);
printf("%lld %lld %lld %lld %lld\n", info._zeroCount, info._negativeCount, info._positiveCount, info._infCount, info._nanCount);
//printf("%lf %lf %lf %lf\n", info._minValue, info._maxValue, info._meanValue, info._stdDevValue);
//printf("%lld %lld %lld %lld %lld\n", info._zeroCount, info._negativeCount, info._positiveCount, info._infCount, info._nanCount);
ASSERT_EQ(exp, info);
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, test_subarray_ews_1) {
NDArray x('c', {10, 5}, nd4j::DataType::FLOAT32);
auto subArr1 = x.subarray({NDIndex::all(), NDIndex::point(2)});
subArr1->printShapeInfo("subArr1");
ASSERT_EQ(5, subArr1->ews());
delete subArr1;
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, test_subarray_ews_2) {
NDArray x('f', {10, 5}, nd4j::DataType::FLOAT32);
auto subArr1 = x.subarray({NDIndex::all(), NDIndex::point(2)});
subArr1->printShapeInfo("subArr1");
ASSERT_EQ(1, subArr1->ews());
delete subArr1;
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, test_subarray_ews_3) {
NDArray x('c', {10, 5}, nd4j::DataType::FLOAT32);
auto subArr1 = x.subarray({NDIndex::point(2), NDIndex::all()});
subArr1->printShapeInfo("subArr1");
ASSERT_EQ(1, subArr1->ews());
delete subArr1;
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, test_subarray_ews_4) {
NDArray x('f', {10, 5}, nd4j::DataType::FLOAT32);
auto subArr1 = x.subarray({NDIndex::point(2), NDIndex::all()});
subArr1->printShapeInfo("subArr1");
ASSERT_EQ(10, subArr1->ews());
delete subArr1;
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, subarray_1) {
NDArray x('c', {2,3,4}, {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 y('f', {2,3,4}, {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);
Nd4jLong shapeExpX0[] = {1, 2, 12, 8192, 12, 99};
float buffExpX0[] = {1.000000, 13.000000};
float buffExpX1[] = {2.000000, 14.000000};
Nd4jLong shapeExpX2[] = {3, 2, 1, 1, 12, 4, 1, 8192, 12, 99};
float buffExpX2[] = {1.000000, 13.000000};
Nd4jLong shapeExpX3[] = {2, 2, 4, 12, 1, 8192, 0, 99};
float buffExpX3[] = {9.000000, 10.000000, 11.000000, 12.000000, 21.000000, 22.000000, 23.000000, 24.000000};
Nd4jLong shapeExpX4[] = {3, 2, 1, 4, 12, 4, 1, 8192, 0, 99};
float buffExpX4[] = {9.000000, 10.000000, 11.000000, 12.000000, 21.000000, 22.000000, 23.000000, 24.000000};
Nd4jLong shapeExpX5[] = {2, 2, 3, 12, 4, 8192, 0, 99};
float buffExpX5[] = {4.000000, 8.000000, 12.000000, 16.000000, 20.000000, 24.000000};
Nd4jLong shapeExpY0[] = {1, 2, 1, 8192, 1, 102};
float buffExpY0[] = {1.000000, 2.000000};
float buffExpY1[] = {7.000000, 8.000000};
Nd4jLong shapeExpY2[] = {3, 2, 1, 1, 1, 2, 6, 8192, 1, 102};
float buffExpY2[] = {1.000000, 2.000000};
Nd4jLong shapeExpY3[] = {2, 2, 4, 1, 6, 8192, 0, 102};
float buffExpY3[] = {5.000000, 11.000000, 17.000000, 23.000000, 6.000000, 12.000000, 18.000000, 24.000000};
Nd4jLong shapeExpY4[] = {3, 2, 1, 4, 1, 2, 6, 8192, 0, 102};
float buffExpY4[] = {5.000000, 11.000000, 17.000000, 23.000000, 6.000000, 12.000000, 18.000000, 24.000000};
Nd4jLong shapeExpY5[] = {2, 2, 3, 1, 2, 8192, 1, 102};
float buffExpY5[] = {19.000000, 21.000000, 23.000000, 20.000000, 22.000000, 24.000000};
NDArray x0 = x(0, {1,2});
for(int i = 0; i < shape::shapeInfoLength(x0.rankOf()); ++i)
ASSERT_TRUE(x0.getShapeInfo()[i] == shapeExpX0[i]);
for(int i = 0; i < x0.lengthOf(); ++i)
ASSERT_TRUE(x0.e<float>(i) == buffExpX0[i]);
NDArray x1 = x(1, {1,2});
for(int i = 0; i < shape::shapeInfoLength(x1.rankOf()); ++i)
ASSERT_TRUE(x1.getShapeInfo()[i] == shapeExpX0[i]);
for(int i = 0; i < x1.lengthOf(); ++i)
ASSERT_TRUE(x1.e<float>(i) == buffExpX1[i]);
NDArray x2 = x(0, {1,2}, true);
for(int i = 0; i < shape::shapeInfoLength(x2.rankOf()); ++i)
ASSERT_TRUE(x2.getShapeInfo()[i] == shapeExpX2[i]);
for(int i = 0; i < x2.lengthOf(); ++i)
ASSERT_TRUE(x2.e<float>(i) == buffExpX2[i]);
NDArray x3 = x(2, {1});
for(int i = 0; i < shape::shapeInfoLength(x3.rankOf()); ++i)
ASSERT_TRUE(x3.getShapeInfo()[i] == shapeExpX3[i]);
for(int i = 0; i < x3.lengthOf(); ++i)
ASSERT_TRUE(x3.e<float>(i) == buffExpX3[i]);
NDArray x4 = x(2, {1}, true);
for(int i = 0; i < shape::shapeInfoLength(x4.rankOf()); ++i)
ASSERT_TRUE(x4.getShapeInfo()[i] == shapeExpX4[i]);
for(int i = 0; i < x4.lengthOf(); ++i)
ASSERT_TRUE(x4.e<float>(i) == buffExpX4[i]);
NDArray x5 = x(3, {2});
for(int i = 0; i < shape::shapeInfoLength(x5.rankOf()); ++i)
ASSERT_TRUE(x5.getShapeInfo()[i] == shapeExpX5[i]);
for(int i = 0; i < x5.lengthOf(); ++i)
ASSERT_TRUE(x5.e<float>(i) == buffExpX5[i]);
// ******************* //
NDArray y0 = y(0, {1,2});
for(int i = 0; i < shape::shapeInfoLength(y0.rankOf()); ++i)
ASSERT_TRUE(y0.getShapeInfo()[i] == shapeExpY0[i]);
for(int i = 0; i < y0.lengthOf(); ++i)
ASSERT_TRUE(y0.e<float>(i) == buffExpY0[i]);
NDArray y1 = y(1, {1,2});
for(int i = 0; i < shape::shapeInfoLength(y1.rankOf()); ++i)
ASSERT_TRUE(y1.getShapeInfo()[i] == shapeExpY0[i]);
for(int i = 0; i < y1.lengthOf(); ++i)
ASSERT_TRUE(y1.e<float>(i) == buffExpY1[i]);
NDArray y2 = y(0, {1,2}, true);
for(int i = 0; i < shape::shapeInfoLength(y2.rankOf()); ++i)
ASSERT_TRUE(y2.getShapeInfo()[i] == shapeExpY2[i]);
for(int i = 0; i < y2.lengthOf(); ++i)
ASSERT_TRUE(y2.e<float>(i) == buffExpY2[i]);
NDArray y3 = y(2, {1});
for(int i = 0; i < shape::shapeInfoLength(y3.rankOf()); ++i)
ASSERT_TRUE(y3.getShapeInfo()[i] == shapeExpY3[i]);
for(int i = 0; i < y3.lengthOf(); ++i)
ASSERT_TRUE(y3.e<float>(i) == buffExpY3[i]);
NDArray y4 = y(2, {1}, true);
for(int i = 0; i < shape::shapeInfoLength(y4.rankOf()); ++i)
ASSERT_TRUE(y4.getShapeInfo()[i] == shapeExpY4[i]);
for(int i = 0; i < y4.lengthOf(); ++i)
ASSERT_TRUE(y4.e<float>(i) == buffExpY4[i]);
NDArray y5 = y(3, {2});
for(int i = 0; i < shape::shapeInfoLength(y5.rankOf()); ++i)
ASSERT_TRUE(y5.getShapeInfo()[i] == shapeExpY5[i]);
for(int i = 0; i < y5.lengthOf(); ++i)
ASSERT_TRUE(y5.e<float>(i) == buffExpY5[i]);
}
TEST_F(NDArrayTest2, test_subarray_interval_1) {
NDArray x('f', {10, 10}, nd4j::DataType::FLOAT32);
auto subArr1 = x.subarray({NDIndex::all(), NDIndex::interval(0,9)});
subArr1->printShapeInfo("subArr1");
ASSERT_EQ(10, subArr1->sizeAt(0));
ASSERT_EQ(9, subArr1->sizeAt(1));
delete subArr1;
}
TEST_F(NDArrayTest2, test_subarray_interval_2) {
NDArray x('c', {10, 10}, nd4j::DataType::FLOAT32);
auto subArr1 = x.subarray({NDIndex::all(), NDIndex::interval(0,9)});
subArr1->printShapeInfo("subArr1");
ASSERT_EQ(10, subArr1->sizeAt(0));
ASSERT_EQ(9, subArr1->sizeAt(1));
delete subArr1;
}
TEST_F(NDArrayTest2, test_subarray_3d_cf) {
NDArray f('f', {10, 20, 30}, nd4j::DataType::FLOAT32);
NDArray c('c', {10, 20, 30}, nd4j::DataType::FLOAT32);
auto subarrayF = f({0,0, 0,0, 2,3}, true);
subarrayF.printShapeInfo("F subarray shapeInfo");
auto subarrayC = c({2,3, 0,0, 0,0}, true);
subarrayC.printShapeInfo("C subarray shapeInfo");
}
TEST_F(NDArrayTest2, test_broadcast_row_1) {
auto x = NDArrayFactory::create<float>('c', {10, 5});
auto y = NDArrayFactory::create<float>('c', {5}, {1.f, 1.f, 1.f, 1.f, 1.f});
auto e = NDArrayFactory::create<float>('c', {10, 5});
e.assign(1.0f);
x += y;
ASSERT_EQ(e, x);
}
TEST_F(NDArrayTest2, test_broadcast_column_1) {
auto x = NDArrayFactory::create<float>('c', {5, 10});
auto y = NDArrayFactory::create<float>('c', {5, 1}, {1.f, 1.f, 1.f, 1.f, 1.f});
auto e = NDArrayFactory::create<float>('c', {5, 10});
e.assign(1.0f);
x += y;
ASSERT_EQ(e, x);
}
TEST_F(NDArrayTest2, test_broadcast_column_2) {
auto x = NDArrayFactory::create<float>('c', {5, 10});
auto y = NDArrayFactory::create<float>('c', {5, 1}, {1.f, 1.f, 1.f, 1.f, 1.f});
auto e = NDArrayFactory::create<float>('c', {5, 10});
e.assign(1.0f);
x.applyTrueBroadcast(BroadcastOpsTuple::Add(), &y, &x, false);
x.printShapeInfo();
x.printIndexedBuffer();
ASSERT_EQ(e, x);
}
TEST_F(NDArrayTest2, test_broadcast_column_3) {
auto x = NDArrayFactory::create<float>('c', {5, 10});
auto y = NDArrayFactory::create<float>('c', {5, 1}, {1.f, 1.f, 1.f, 1.f, 1.f});
auto e = NDArrayFactory::create<float>('c', {5, 10});
e.assign(1.0f);
x.applyTrueBroadcast(BroadcastOpsTuple::Add(), &y, &x);
ASSERT_EQ(e, x);
}
TEST_F(NDArrayTest2, test_broadcast_column_4) {
auto x = NDArrayFactory::create<float>('f', {10, 5});
auto y = NDArrayFactory::create<float>('f', {5}, {1.f, 1.f, 1.f, 1.f, 1.f});
auto e = NDArrayFactory::create<float>('f', {10, 5});
e.assign(1.0f);
x.applyTrueBroadcast(BroadcastOpsTuple::Add(), &y, &x);
ASSERT_EQ(e, x);
}
TEST_F(NDArrayTest2, test_not_tiled_1) {
auto x = NDArrayFactory::create<float>('c', {4, 12, 128, 128});
auto y = NDArrayFactory::create<float>('c', {4, 1, 128, 128});
auto e = NDArrayFactory::create<float>('c', {4, 12, 128, 128});
y.assign(1.0f);
e.assign(1.0f);
x += y;
ASSERT_EQ(e, x);
}
TEST_F(NDArrayTest2, test_not_tiled_2) {
auto x = NDArrayFactory::create<float>('c', {4, 128, 768});
auto y = NDArrayFactory::create<float>('c', {4, 128, 1});
auto e = NDArrayFactory::create<float>('c', {4, 128, 768});
y.assign(1.0f);
e.assign(1.0f);
x += y;
ASSERT_EQ(e, x);
}
TEST_F(NDArrayTest2, test_long_sum_1) {
auto x = NDArrayFactory::create<Nd4jLong>('c', {2, 2}, {1, 2, 3, 4});
auto z = x.reduceAlongDims(reduce::Sum, {0});
z.printIndexedBuffer("z long");
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, reshapei_1) {
Nd4jLong shapeInfo1[] = {6, 2,1,2,1,7,1, 7,7,14,28,1,1, 8192, 0, 99};
Nd4jLong shapeInfo2[] = {2, 4, 7, 7, 1, 8192, 1, 99};
auto buffer = new float[shape::length(shapeInfo1)];
NDArray x(buffer, shapeInfo1);
const bool canReshape = x.reshapei({4,7});
ASSERT_FALSE(canReshape);
ASSERT_TRUE(shape::equalsStrict(x.getShapeInfo(), shapeInfo2));
delete[] buffer;
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, reshapei_2) {
Nd4jLong shapeInfo1[] = {6, 1,2,1,2,7,1, 28,7,7,14,1,1, 8192, 0, 99};
Nd4jLong shapeInfo2[] = {2, 4, 7, 7, 1, 8192, 1, 99};
auto buffer = new float[shape::length(shapeInfo1)];
NDArray x(buffer, shapeInfo1);
const bool canReshape = x.reshapei({4,7});
ASSERT_FALSE(canReshape);
ASSERT_TRUE(shape::equalsStrict(x.getShapeInfo(), shapeInfo2));
delete[] buffer;
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, trueBroadcast_1) {
NDArray x('f', {2, 3}, {1., 2., 3., 4., 5., 6.});
NDArray y('f', {1, 3}, {5., 4., 3.});
NDArray z('c', {2, 3}, nd4j::DataType::DOUBLE);
auto exp = x - y;
x.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Subtract(), &y, &z, true);
// exp.printIndexedBuffer();
// z.printIndexedBuffer();
ASSERT_TRUE(exp.equalsTo(z));
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, reduce_1) {
NDArray arr6('f', {1, 1, 4, 4, 4, 4}, nd4j::DataType::DOUBLE);
NDArray exp('f', {1, 1, 4, 4}, nd4j::DataType::DOUBLE);
arr6.linspace(1);
NDArray* arr6s = arr6.reduceAlongDimension(nd4j::reduce::Sum, {2,3});
for (int i = 0; i < 4; i++) {
for (int j = 0; j < 4; j++) {
double sum = 0;
for (int x = 0; x < 4; x++) {
for (int y = 0; y < 4; y++) {
Nd4jLong indices[] = {0, 0, x, y, i, j};
Nd4jLong offset = shape::getOffset(0, arr6.shapeOf(), arr6.stridesOf(), indices, arr6.rankOf());
sum += ((double*)arr6.getBuffer())[offset];
}
}
exp.p<double>(0, 0, i, j, sum);
}
}
// arr6s->printShapeInfo();
// exp.printShapeInfo();
// exp.printIndexedBuffer();
// arr6s->printIndexedBuffer();
ASSERT_TRUE(exp.equalsTo(arr6s));
delete arr6s;
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayTest2, reduce3_1) {
NDArray x('c', {1,4}, {1,2,3,4});
NDArray y('c', {1,4}, {2,3,4,5});
NDArray exp('c', {4}, {1,1,1,1});
NDArray* z = x.applyReduce3(nd4j::reduce3::EuclideanDistance, &y, {0}, nullptr);
// z->printShapeInfo();
// z->printIndexedBuffer();
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete z;
}
TEST_F(NDArrayTest2, all_tads_1) {
auto x = NDArrayFactory::create<float>('c', {3, 5});
auto arrays = x.allTensorsAlongDimension({1});
ASSERT_EQ(3, arrays->size());
delete arrays;
}
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
TEST_F(NDArrayTest2, test_trueBroadcast_empty_1) {
auto x = NDArrayFactory::create<float>('c', {0, 2});
auto y = NDArrayFactory::create<float>('c', {1, 2});
auto z = x + y;
ASSERT_EQ(x, z);
}
TEST_F(NDArrayTest2, test_trueBroadcast_empty_2) {
auto x = NDArrayFactory::create<float>('c', {0, 2});
auto y = NDArrayFactory::create<float>('c', {1, 2});
auto z = y + x;
ASSERT_EQ(x, z);
}