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cavis/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests4.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
******************************************************************************/
//
// @author raver119@gmail.com
//
#include "testlayers.h"
#include <ops/declarable/CustomOperations.h>
#include <helpers/helper_hash.h>
#include <NDArray.h>
#include <array/NDArrayList.h>
using namespace nd4j;
using namespace nd4j::graph;
class DeclarableOpsTests4 : public testing::Test {
public:
DeclarableOpsTests4() {
printf("\n");
fflush(stdout);
nd4j::ops::adjust_hue op0;
nd4j::ops::adjust_saturation op1;
}
};
template <typename T>
class TypedDeclarableOpsTests4 : public testing::Test {
public:
TypedDeclarableOpsTests4() {
printf("\n");
fflush(stdout);
nd4j::ops::adjust_hue op0;
nd4j::ops::adjust_saturation op1;
}
};
typedef ::testing::Types<double, float> TestingTypes;
TYPED_TEST_CASE(TypedDeclarableOpsTests4, TestingTypes);
TYPED_TEST(TypedDeclarableOpsTests4, Test_Pooling_Parity_1) {
auto x = NDArrayFactory::create<TypeParam>('c', {2, 4, 4, 2});
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 2}, {6.f, 7.f, 10.f, 11.f, 22.f, 23.f, 26.f, 27.f, 38.f, 39.f, 42.f, 43.f, 54.f, 55.f, 58.f, 59.f});
x.linspace(1);
nd4j::ops::avgpool2d op;
auto result = op.execute({&x}, {}, {2, 2, 2, 2, 0, 0, 1, 1, 1, 1, 1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TYPED_TEST(TypedDeclarableOpsTests4, Test_Pooling_Parity_2) {
auto x = NDArrayFactory::create<TypeParam>('c', {2, 4, 4, 2});
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 2}, {6.f, 7.f, 10.f, 11.f, 22.f, 23.f, 26.f, 27.f, 38.f, 39.f, 42.f, 43.f, 54.f, 55.f, 58.f, 59.f});
x.linspace(1);
nd4j::ops::avgpool2d op;
auto result = op.execute({&x}, {}, {2, 2, 2, 2, 0, 0, 1, 1, 0, 1, 1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TYPED_TEST(TypedDeclarableOpsTests4, Test_Pooling_Parity_5) {
auto x = NDArrayFactory::create<TypeParam>('c', {2, 5, 5, 2});
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 3, 3, 2}, {7.f, 8.f, 11.f, 12.f, 14.f, 15.f, 27.f, 28.f, 31.f, 32.f, 34.f, 35.f, 42.f, 43.f, 46.f, 47.f, 49.f, 50.f, 57.f, 58.f, 61.f, 62.f, 64.f, 65.f, 77.f, 78.f, 81.f, 82.f, 84.f, 85.f, 92.f, 93.f, 96.f, 97.f, 99.f, 100.f,});
x.linspace(1);
nd4j::ops::avgpool2d op;
auto result = op.execute({&x}, {}, {2, 2, 2, 2, 0, 0, 1, 1, 1, 0, 1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TYPED_TEST(TypedDeclarableOpsTests4, Test_Pooling_Parity_6) {
auto x = NDArrayFactory::create<TypeParam>('c', {2, 5, 5, 2});
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 2}, {7.f, 8.f, 11.f, 12.f, 27.f, 28.f, 31.f, 32.f, 57.f, 58.f, 61.f, 62.f, 77.f, 78.f, 81.f, 82.f});
x.linspace(1);
nd4j::ops::avgpool2d op;
auto result = op.execute({&x}, {}, {2, 2, 2, 2, 0, 0, 1, 1, 0, 1, 1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TYPED_TEST(TypedDeclarableOpsTests4, Test_Pooling_Parity_8) {
auto x = NDArrayFactory::create<TypeParam>('c', {2, 2, 5, 5});
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 2, 3, 3}, {1.f, 2.5f, 4.5f, 8.5f, 10.f, 12.f, 18.5f, 20.f, 22.f, 26.f, 27.5f, 29.5f, 33.5f, 35.f, 37.f, 43.5f, 45.f, 47.f, 51.f, 52.5f, 54.5f, 58.5f, 60.f, 62.f, 68.5f, 70.f, 72.f, 76.f, 77.5f, 79.5f, 83.5f, 85.f, 87.f, 93.5f, 95.f, 97.f});
x.linspace(1);
nd4j::ops::avgpool2d op;
auto result = op.execute({&x}, {}, {2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TYPED_TEST(TypedDeclarableOpsTests4, Test_Pooling_Parity_9) {
auto x = NDArrayFactory::create<TypeParam>('c', {2, 2, 5, 5});
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 2, 3, 3}, {0.25f, 1.25f, 2.25f, 4.25f, 10.f, 12.f, 9.25f, 20.f, 22.f, 6.5f, 13.75f, 14.75, 16.75f, 35.f, 37.f, 21.75f, 45.f, 47.f, 12.75f, 26.25f, 27.25f, 29.25f, 60.f, 62.f, 34.25f, 70.f, 72.f, 19.f, 38.75f, 39.75f, 41.75f, 85.f, 87.f, 46.75f, 95.f, 97.f});
x.linspace(1);
nd4j::ops::avgpool2d op;
auto result = op.execute({&x}, {}, {2, 2, 2, 2, 1, 1, 1, 1, 0, 1, 0});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TYPED_TEST(TypedDeclarableOpsTests4, Test_Pooling_Parity_10) {
auto x = NDArrayFactory::create<TypeParam>('c', {2, 2, 5, 5});
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 2, 3, 3}, {4.f, 6.f, 7.5f, 14.f, 16.f, 17.5f, 21.5f, 23.5f, 25.f, 29.f, 31.f, 32.5f, 39.f, 41.f, 42.5f, 46.5f, 48.5f, 50.f, 54.f, 56.f, 57.5f, 64.f, 66.f, 67.5f, 71.5f, 73.5f, 75.f, 79.f, 81.f, 82.5f, 89.f, 91.f, 92.5f, 96.5f, 98.5f, 100.f});
x.linspace(1);
nd4j::ops::avgpool2d op;
auto result = op.execute({&x}, {}, {2, 2, 2, 2, 0, 0, 1, 1, 1, 0, 0});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TYPED_TEST(TypedDeclarableOpsTests4, Test_Pooling_Parity_11) {
auto x = NDArrayFactory::create<TypeParam>('c', {1, 1, 3, 3});
auto exp = NDArrayFactory::create<TypeParam>('c', {1, 1, 2, 2}, {3, 4, 6, 7});
x.linspace(1);
nd4j::ops::avgpool2d op;
auto result = op.execute({&x}, {}, {2, 2, 1, 1, 0, 0, 1, 1, 0, 0, 0});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TYPED_TEST(TypedDeclarableOpsTests4, Test_Pooling_Parity_12) {
auto x = NDArrayFactory::create<TypeParam>('c', {1, 1, 3, 3});
auto exp = NDArrayFactory::create<TypeParam>('c', {1, 1, 3, 3}, {3.f, 4.f, 4.5f, 6.f, 7.f, 7.5f, 7.5f, 8.5f, 9.f});
x.linspace(1);
nd4j::ops::avgpool2d op;
auto result = op.execute({&x}, {}, {2, 2, 1, 1, 0, 0, 1, 1, 1, 0, 0});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
//z->printShapeInfo("z shape:");
//z->printBuffer("z buffer:");
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_BiasAdd_NHWC_1) {
auto x = NDArrayFactory::create<double>('c', {2, 3, 3, 2});
auto bias = NDArrayFactory::create<double>('c', {1, 2}, {1, 2});
auto exp = NDArrayFactory::create<double>('c', {2, 3, 3, 2}, {1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f});
nd4j::ops::biasadd op;
auto result = op.execute({&x, &bias}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_BiasAdd_NCHW_1) {
auto x = NDArrayFactory::create<double>('c', {2, 2, 3, 3});
auto bias = NDArrayFactory::create<double>('c', {1, 2}, {1, 2});
auto exp = NDArrayFactory::create<double>('c', {2, 2, 3, 3}, {1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f});
nd4j::ops::biasadd op;
auto result = op.execute({&x, &bias}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_Fill_1) {
auto x = NDArrayFactory::create<int>('c', {1, 3}, {3, 2, 4});
auto v = NDArrayFactory::create<double>(2.);
auto exp = NDArrayFactory::create<double>('c', {3, 2, 4});
exp.assign(2.0f);
nd4j::ops::fill op;
auto result = op.execute({&x, &v}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_Reshape_Again) {
auto x = NDArrayFactory::create<double>('c', {4, 3});
auto exp = NDArrayFactory::create<double>('c', {4, 3});
x.linspace(1);
exp.linspace(1);
nd4j::ops::reshape op;
auto result = op.execute({&x}, {}, {-99, 4, 3});
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_Gemv_Transpose_1) {
auto x = NDArrayFactory::create<double>('c', {4, 3});
auto y = NDArrayFactory::create<double>('c', {4, 1});
auto exp = NDArrayFactory::create<double>('c',{ 3, 1}, {70, 80, 90});
x.linspace(1);
y.linspace(1);
nd4j::ops::matmul op;
auto result = op.execute({&x, &y}, {}, {1, 0});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_Split_1) {
auto x = NDArrayFactory::create<double>('c', {5, 30});
auto sizes = NDArrayFactory::create<int>('c', {1, 3}, {4, 15, 11});
std::vector<Nd4jLong> list0({0,0, 0,4});
std::vector<Nd4jLong> list1({0,0, 4,19});
std::vector<Nd4jLong> list2({0,0, 19,30});
auto sub0 = x(list0, true);
auto sub1 = x(list1, true);
auto sub2 = x(list2, true);
sub0.assign(0.0);
sub1.assign(1.0);
sub2.assign(2.0);
nd4j::ops::split_v op;
auto result = op.execute({&x, &sizes}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
ASSERT_EQ(3, result->size());
auto z0 = result->at(0);
auto z1 = result->at(1);
auto z2 = result->at(2);
ASSERT_TRUE(sub0.isSameShape(z0));
ASSERT_TRUE(sub1.isSameShape(z1));
ASSERT_TRUE(sub2.isSameShape(z2));
ASSERT_TRUE(sub0.equalsTo(z0));
ASSERT_TRUE(sub1.equalsTo(z1));
ASSERT_TRUE(sub2.equalsTo(z2));
delete result;
}
// special test for TF mode, when axis goes first
TEST_F(DeclarableOpsTests4, Test_Split_2) {
auto x = NDArrayFactory::create<double>('c', {5, 12});
auto axis = NDArrayFactory::create<double>('c', {1, 1}, {1.f});
std::vector<Nd4jLong> list0 = {0,0, 0,3};
std::vector<Nd4jLong> list1 = {0,0, 3,6};
std::vector<Nd4jLong> list2 = {0,0, 6,9};
std::vector<Nd4jLong> list3 = {0,0, 9,12};
auto sub0 = x(list0, true);
auto sub1 = x(list1, true);
auto sub2 = x(list2, true);
auto sub3 = x(list3, true);
sub0.assign(0.0f);
sub1.assign(1.0f);
sub2.assign(2.0f);
sub3.assign(3.0f);
nd4j::ops::split op;
auto result = op.execute({&axis, &x}, {}, {4});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z0 = result->at(0);
auto z1 = result->at(1);
auto z2 = result->at(2);
auto z3 = result->at(3);
ASSERT_TRUE(sub0.isSameShape(z0));
ASSERT_TRUE(sub1.isSameShape(z1));
ASSERT_TRUE(sub2.isSameShape(z2));
ASSERT_TRUE(sub3.isSameShape(z3));
ASSERT_TRUE(sub0.equalsTo(z0));
ASSERT_TRUE(sub1.equalsTo(z1));
ASSERT_TRUE(sub2.equalsTo(z2));
ASSERT_TRUE(sub3.equalsTo(z3));
delete result;
}
// special test for TF mode, when axis goes first
TEST_F(DeclarableOpsTests4, Test_Split_3) {
auto x = NDArrayFactory::create<double>('c', {6, 12});
auto axis = NDArrayFactory::create<double>('c', {1, 1}, {0.f});
std::vector<Nd4jLong> list0 = {0,2, 0,0};
std::vector<Nd4jLong> list1 = {2,4, 0,0};
std::vector<Nd4jLong> list2 = {4,6, 0,0};
auto sub0 = x(list0, true);
auto sub1 = x(list1, true);
auto sub2 = x(list2, true);
sub0.assign(0.0f);
sub1.assign(1.0f);
sub2.assign(2.0f);
nd4j::ops::split op;
auto result = op.execute({&axis, &x}, {}, {3});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z0 = result->at(0);
auto z1 = result->at(1);
auto z2 = result->at(2);
ASSERT_TRUE(sub0.isSameShape(z0));
ASSERT_TRUE(sub1.isSameShape(z1));
ASSERT_TRUE(sub2.isSameShape(z2));
ASSERT_TRUE(sub0.equalsTo(z0));
ASSERT_TRUE(sub1.equalsTo(z1));
ASSERT_TRUE(sub2.equalsTo(z2));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_Stack_4) {
auto t = NDArrayFactory::create<double>('c', {2, 3, 5});
auto u = NDArrayFactory::create<double>('c', {2, 3, 5});
auto v = NDArrayFactory::create<double>('c', {2, 3, 5});
auto exp = NDArrayFactory::create<double>('c', {3, 2, 3, 5});
nd4j::ops::stack op;
auto result = op.execute({&t, &u, &v}, {}, {-4});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_Squeeze_args_1) {
auto x = NDArrayFactory::create<double>('c', {2, 1, 1, 1, 2}, {1, 2, 3, 4});
auto exp = NDArrayFactory::create<double>('c', {2, 1, 2}, {1, 2, 3, 4});
nd4j::ops::squeeze op;
auto result = op.execute({&x}, {}, {1, 3});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_Squeeze_args_2) {
auto x = NDArrayFactory::create<double>('c', {2, 1, 1, 1, 2}, {1, 2, 3, 4});
auto y = NDArrayFactory::create<double>('c', {2}, {1.f, 3.f});
auto exp = NDArrayFactory::create<double>('c', {2, 1, 2}, {1, 2, 3, 4});
nd4j::ops::squeeze op;
auto result = op.execute({&x, &y}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_Squeeze_args_3) {
auto x = NDArrayFactory::create<double>('c', {2, 1, 1, 1, 2}, {1, 2, 3, 4});
auto exp = NDArrayFactory::create<double>('c', {2, 1, 2}, {1, 2, 3, 4});
nd4j::ops::squeeze op;
auto result = op.execute({&x}, {}, {-2, -3});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_BiasAdd_1) {
auto x = NDArrayFactory::create<double>('c', {2, 3});
auto row = NDArrayFactory::create<double>('c', {3}, {1, 2, 3});
auto exp = NDArrayFactory::create<double>('c', {2, 3}, {1, 2, 3, 1, 2, 3});
nd4j::ops::biasadd op;
auto result = op.execute({&x, &row}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_1D_1) {
auto x = NDArrayFactory::create<double>('c', {2, 3});
nd4j::ops::unstack op;
auto result = op.execute({&x}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
ASSERT_EQ(3, result->size());
for (int e = 0; e < 3; e++)
ASSERT_EQ(1, result->at(e)->rankOf());
delete result;
}
TEST_F(DeclarableOpsTests4, Test_SpaceToDepth_1) {
auto x = NDArrayFactory::create<double>('c', {1, 2, 2, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto exp = NDArrayFactory::create<double>('c', {1, 1, 1, 12}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
nd4j::ops::space_to_depth op;
auto result = op.execute({&x}, {}, {2, 1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_SpaceToDepth_2) {
auto x = NDArrayFactory::create<double>('c', {1, 3, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto exp = NDArrayFactory::create<double>('c', {1, 12, 1, 1}, {1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12});
nd4j::ops::space_to_depth op;
auto result = op.execute({&x}, {}, {2, 0});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_DepthToSpace_1) {
auto x = NDArrayFactory::create<double>('c', {1, 1, 1, 12}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto exp = NDArrayFactory::create<double>('c', {1, 2, 2, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
nd4j::ops::depth_to_space op;
auto result = op.execute({&x}, {}, {2, 1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_DepthToSpace_2) {
auto x = NDArrayFactory::create<double>('c', {1, 12, 1, 1}, {1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12});
auto exp = NDArrayFactory::create<double>('c', {1, 3, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
nd4j::ops::depth_to_space op;
auto result = op.execute({&x}, {}, {2, 0});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_DepthToSpace_3) {
auto x = NDArrayFactory::create<double>('c', {4, 4, 16, 16});
auto exp = NDArrayFactory::create<double>('c', {4, 16, 64, 1});
nd4j::ops::depth_to_space op;
auto result = op.execute({&x}, {}, {4, 1});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_Cross_1) {
auto a = NDArrayFactory::create<double>('c', {3}, {1, 2, 3});
auto b = NDArrayFactory::create<double>('c', {3}, {6, 7, 8});
auto exp = NDArrayFactory::create<double>('c', {3}, {-5, 10, -5});
nd4j::ops::cross op;
auto result = op.execute({&a, &b}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_Cross_2) {
auto a = NDArrayFactory::create<double>('c', {2, 3}, {1, 2, 3, 1, 2, 3});
auto b = NDArrayFactory::create<double>('c', {2, 3}, {6, 7, 8, 6, 7, 8});
auto exp = NDArrayFactory::create<double>('c', {2, 3}, {-5, 10, -5, -5, 10, -5});
nd4j::ops::cross op;
auto result = op.execute({&a, &b}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_Cross_3) {
auto a = NDArrayFactory::create<double>('c', {3, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9});
auto b = NDArrayFactory::create<double>('c', {3, 3}, {2, 3, 4, 7, 6, 5, 6, 3, 2});
auto exp = NDArrayFactory::create<double>('c', {3, 3}, { -1, 2, -1, -11, 22, -11, -11, 40, -27});
nd4j::ops::cross op;
auto result = op.execute({&a, &b}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_Matmul_YATS_1) {
auto a = NDArrayFactory::create<double>('c', {3, 4}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto b = NDArrayFactory::create<double>('c', {4}, {1, 2, 3, 4});
auto exp = NDArrayFactory::create<double>('c', {3}, {30, 70, 110});
nd4j::ops::matmul op;
auto result = op.execute({&a, &b}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_Matmul_YATS_2) {
auto a = NDArrayFactory::create<double>('c', {4}, {1, 2, 3, 4});
auto b = NDArrayFactory::create<double>('c', {4, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto exp = NDArrayFactory::create<double>('c', {3}, {70, 80, 90});
nd4j::ops::matmul op;
auto result = op.execute({&a, &b}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_Matmul_YATS_3) {
auto a = NDArrayFactory::create<double>('c', {1, 4}, {1, 2, 3, 4});
auto b = NDArrayFactory::create<double>('c', {4, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
auto exp = NDArrayFactory::create<double>('c', {1, 3}, {70, 80, 90});
nd4j::ops::matmul op;
auto result = op.execute({&a, &b}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_Add_119) {
auto a = NDArrayFactory::create<double>('c', {1, 4}, {1, 2, 3, 4});
auto b = NDArrayFactory::create<double>('c', {4}, {1, 2, 3, 4});
auto exp = NDArrayFactory::create<double>('c', {1, 4}, {2, 4, 6, 8});
nd4j::ops::add op;
auto result = op.execute({&a, &b}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_EQ(2, z->rankOf());
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_Reshape_Negative_1) {
auto x = NDArrayFactory::create<double>('c', {2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8});
auto shape = NDArrayFactory::create<Nd4jLong>('c', {2}, {-1, 2});
auto exp = NDArrayFactory::create<double>('c', {4, 2}, {1, 2, 3, 4, 5, 6, 7, 8});
nd4j::ops::reshape op;
auto result = op.execute({&x, &shape}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_TileToShape_1) {
auto x = NDArrayFactory::create<double>('c', {2, 1, 3});
auto exp = NDArrayFactory::create<double>('c', {2, 4, 3}, {1.f, 2.f, 3.f,1.f, 2.f, 3.f,1.f, 2.f, 3.f,1.f, 2.f, 3.f,
4.f, 5.f, 6.f,4.f, 5.f, 6.f,4.f, 5.f, 6.f,4.f, 5.f, 6.f});
x.linspace(1.f);
nd4j::ops::tile_to_shape op;
auto result = op.execute({&x},{}, {2, 4, 3}, {}, false, nd4j::DataType::DOUBLE);
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_StridedSlice_Alex_1) {
auto x = NDArrayFactory::create<double>('c', {3, 4, 5});
x.linspace(1);
auto exp = NDArrayFactory::create<double>('c', {1,3,4,5});
exp.linspace(1);
nd4j::ops::strided_slice op;
auto result = op.execute({&x}, {}, {0,0,0,1,0, -999,0,0,0, -999,3,4,5, -999,1,1,1});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_StridedSlice_Alex_2) {
auto x = NDArrayFactory::create<double>('c', {3, 4, 5});
auto begin = NDArrayFactory::create<double>('c', {4}, {-999,0,0,0});
auto end = NDArrayFactory::create<double>('c', {4}, {-999,3,4,5});
auto stride = NDArrayFactory::create<double>('c', {4}, {-999,1,1,1});
x.linspace(1);
auto exp = NDArrayFactory::create<double>('c', {1,3,4,5});
exp.linspace(1);
nd4j::ops::strided_slice op;
auto result = op.execute({&x, &begin, &end, &stride}, {}, {0,0,0,1,0});
ASSERT_EQ(Status::OK(), result->status());
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests4, Test_StridedSlice_Alex_3) {
[WIP] INDArray hashCode() impl (#50) * initial commit Signed-off-by: raver119@gmail.com <raver119@gmail.com> * one more initial commit Signed-off-by: raver119 <raver119@gmail.com> * parallel hashCode prototype Signed-off-by: raver119 <raver119@gmail.com> * longBytes for hashCode Signed-off-by: raver119 <raver119@gmail.com> * INDArray hashCode java side Signed-off-by: raver119 <raver119@gmail.com> * few tests fixed for MSVC Signed-off-by: raver119 <raver119@gmail.com> * Small gradcheck validation util fix - hash names not SDVariables Signed-off-by: Alex Black <blacka101@gmail.com> * Small fix + ignore for logged issue Signed-off-by: Alex Black <blacka101@gmail.com> * - scrollable iterator fix - sptree hashset replaced with collection Signed-off-by: raver119 <raver119@gmail.com> * hashcode exception removed * int hashCode for java
2019-07-10 13:32:12 +02:00
int axis = 0;
Eclipse Migration Initial Commit
2019-06-06 14:21:15 +02:00
auto x = NDArrayFactory::create<double>('c', {1}, {10});
[WIP] INDArray hashCode() impl (#50) * initial commit Signed-off-by: raver119@gmail.com <raver119@gmail.com> * one more initial commit Signed-off-by: raver119 <raver119@gmail.com> * parallel hashCode prototype Signed-off-by: raver119 <raver119@gmail.com> * longBytes for hashCode Signed-off-by: raver119 <raver119@gmail.com> * INDArray hashCode java side Signed-off-by: raver119 <raver119@gmail.com> * few tests fixed for MSVC Signed-off-by: raver119 <raver119@gmail.com> * Small gradcheck validation util fix - hash names not SDVariables Signed-off-by: Alex Black <blacka101@gmail.com> * Small fix + ignore for logged issue Signed-off-by: Alex Black <blacka101@gmail.com> * - scrollable iterator fix - sptree hashset replaced with collection Signed-off-by: raver119 <raver119@gmail.com> * hashcode exception removed * int hashCode for java
2019-07-10 13:32:12 +02:00
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});
Eclipse Migration Initial Commit
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);
z->printShapeInfo("Emply shape expected");
ASSERT_TRUE(z->isEmpty());
delete result;
}
TEST_F(DeclarableOpsTests4, Test_StridedSlice_Alex_4) {
auto x = NDArrayFactory::create<double>('c', {1,3}, {1, 2, 3});
auto begin = NDArrayFactory::create<double>('c', {2}, {0, 0});
auto end = NDArrayFactory::create<double>('c', {2}, {0,1});
auto stride = NDArrayFactory::create<double>('c', {2}, {1,1});
// x.linspace(1);
auto exp = NDArrayFactory::create<double>('c', {1}, {1});
//exp.linspace(1);
nd4j::ops::strided_slice op;
auto result = op.execute({&x, &begin, &end, &stride}, {}, {1,0,1,0,2});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
z->printBuffer("Strided Slice");
z->printShapeInfo("Vector size 1 shape expected");
exp.printShapeInfo("Expected shape");
ASSERT_TRUE(z->lengthOf() == 1);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, parallel_stack_test1) {
auto x1 = NDArrayFactory::create<double>('c', {2,2,2});
auto x2 = NDArrayFactory::create<double>('c', {2,2,2});
auto x3 = NDArrayFactory::create<double>('c', {2,2,2});
x1.linspace(1);
x2.linspace(9);
x3.linspace(17);
auto expected = NDArrayFactory::create<double>('c', {3,2,2,2});
expected.linspace(1);
nd4j::ops::parallel_stack op;
auto results = op.execute({&x1, &x2, &x3}, {}, {});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, parallel_stack_test2) {
auto x1 = NDArrayFactory::create<double>('c', {1,2}, {1,2});
auto x2 = NDArrayFactory::create<double>('c', {1,2}, {3,4});
auto x3 = NDArrayFactory::create<double>('c', {1,2}, {5,6});
auto expected = NDArrayFactory::create<double>('c', {3,1,2}, {1,2,3,4,5,6});
nd4j::ops::parallel_stack op;
auto results = op.execute({&x1, &x2, &x3}, {}, {});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, parallel_stack_test3) {
auto x1 = NDArrayFactory::create<double>('c', {2,1}, {1,2});
auto x2 = NDArrayFactory::create<double>('c', {2,1}, {3,4});
auto x3 = NDArrayFactory::create<double>('c', {2,1}, {5,6});
auto expected = NDArrayFactory::create<double>('c', {3,2,1}, {1,2,3,4,5,6});
nd4j::ops::parallel_stack op;
auto results = op.execute({&x1, &x2, &x3}, {}, {});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
\
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, parallel_stack_test4) {
auto x1 = NDArrayFactory::create<double>('c', {2}, {1,2});
auto x2 = NDArrayFactory::create<double>('c', {2}, {3,4});
auto x3 = NDArrayFactory::create<double>('c', {2}, {5,6});
auto expected = NDArrayFactory::create<double>('c', {3,2}, {1,2,3,4,5,6});
nd4j::ops::parallel_stack op;
auto results = op.execute({&x1, &x2, &x3}, {}, {});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, parallel_stack_test5) {
auto x1 = NDArrayFactory::create<double>('c', {1}, {1});
auto x2 = NDArrayFactory::create<double>('c', {1}, {3});
auto x3 = NDArrayFactory::create<double>('c', {1}, {5});
auto expected = NDArrayFactory::create<double>('c', {3,1}, {1,3,5});
nd4j::ops::parallel_stack op;
auto results = op.execute({&x1, &x2, &x3}, {}, {});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, parallel_stack_test6) {
auto x1 = NDArrayFactory::create<double>(1.);
auto x2 = NDArrayFactory::create<double>(3.);
auto x3 = NDArrayFactory::create<double>(5.);
auto expected = NDArrayFactory::create<double>('c', {3}, {1,3,5});
nd4j::ops::parallel_stack op;
auto results = op.execute({&x1, &x2, &x3}, {}, {});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, parallel_stack_test7) {
auto x1 = NDArrayFactory::create<double>(1.);
auto expected = NDArrayFactory::create<double>('c', {1}, {1.});
nd4j::ops::parallel_stack op;
auto results = op.execute({&x1}, {}, {});
auto output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, meshgrid_test1) {
auto in0 = NDArrayFactory::create<double>('c', {2}, {1, 2});
auto in1 = NDArrayFactory::create<double>('c', {3}, {10, 20, 30});
auto in2 = NDArrayFactory::create<double>('c', {4}, {100, 200, 300, 400});
auto exp0 = NDArrayFactory::create<double>('c', {2,3,4}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2});
auto exp1 = NDArrayFactory::create<double>('c', {2,3,4}, {10, 10, 10, 10, 20, 20, 20, 20, 30, 30, 30, 30, 10, 10, 10, 10, 20, 20, 20, 20, 30, 30, 30, 30});
auto exp2 = NDArrayFactory::create<double>('c', {2,3,4}, {100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400});
nd4j::ops::meshgrid op;
auto results = op.execute({&in0, &in1, &in2}, {}, {0});
auto out0 = results->at(0);
auto out1 = results->at(1);
auto out2 = results->at(2);
// out0->printIndexedBuffer();
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp0.isSameShape(out0));
ASSERT_TRUE(exp0.equalsTo(out0));
ASSERT_TRUE(exp1.isSameShape(out1));
ASSERT_TRUE(exp1.equalsTo(out1));
ASSERT_TRUE(exp2.isSameShape(out2));
ASSERT_TRUE(exp2.equalsTo(out2));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, meshgrid_test2) {
auto in0 = NDArrayFactory::create<double>('c', {2}, {1, 2});
auto in1 = NDArrayFactory::create<double>('c', {3}, {10, 20, 30});
auto in2 = NDArrayFactory::create<double>('c', {4}, {100, 200, 300, 400});
auto exp0 = NDArrayFactory::create<double>('c', {3,2,4}, {1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2});
auto exp1 = NDArrayFactory::create<double>('c', {3,2,4}, {10, 10, 10, 10, 10, 10, 10, 10, 20, 20, 20, 20, 20, 20, 20, 20, 30, 30, 30, 30, 30, 30, 30, 30});
auto exp2 = NDArrayFactory::create<double>('c', {3,2,4}, {100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400});
nd4j::ops::meshgrid op;
auto results = op.execute({&in0, &in1, &in2}, {}, {});
auto out0 = results->at(0);
auto out1 = results->at(1);
auto out2 = results->at(2);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp0.isSameShape(out0));
ASSERT_TRUE(exp0.equalsTo(out0));
ASSERT_TRUE(exp1.isSameShape(out1));
ASSERT_TRUE(exp1.equalsTo(out1));
ASSERT_TRUE(exp2.isSameShape(out2));
ASSERT_TRUE(exp2.equalsTo(out2));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, meshgrid_test3) {
auto in0 = NDArrayFactory::create<double>('c', {2}, {1, 2});
auto in1 = NDArrayFactory::create<double>('c', {1,3}, {10, 20, 30});
auto in2 = NDArrayFactory::create<double>('c', {2,2}, {100, 200, 300, 400});
auto exp0 = NDArrayFactory::create<double>('c', {3,2,4}, {1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2});
auto exp1 = NDArrayFactory::create<double>('c', {3,2,4}, {10, 10, 10, 10, 10, 10, 10, 10, 20, 20, 20, 20, 20, 20, 20, 20, 30, 30, 30, 30, 30, 30, 30, 30});
auto exp2 = NDArrayFactory::create<double>('c', {3,2,4}, {100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400});
nd4j::ops::meshgrid op;
auto results = op.execute({&in0, &in1, &in2}, {}, {});
auto out0 = results->at(0);
auto out1 = results->at(1);
auto out2 = results->at(2);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp0.isSameShape(out0));
ASSERT_TRUE(exp0.equalsTo(out0));
ASSERT_TRUE(exp1.isSameShape(out1));
ASSERT_TRUE(exp1.equalsTo(out1));
ASSERT_TRUE(exp2.isSameShape(out2));
ASSERT_TRUE(exp2.equalsTo(out2));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, meshgrid_test4) {
auto in0 = NDArrayFactory::create<double>('c', {1,2}, {1, 2});
auto in1 = NDArrayFactory::create<double>('c', {3,1}, {10, 20, 30});
auto in2 = NDArrayFactory::create<double>('c', {1,4,1}, {100, 200, 300, 400});
auto exp0 = NDArrayFactory::create<double>('c', {2,3,4}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2});
auto exp1 = NDArrayFactory::create<double>('c', {2,3,4}, {10, 10, 10, 10, 20, 20, 20, 20, 30, 30, 30, 30, 10, 10, 10, 10, 20, 20, 20, 20, 30, 30, 30, 30});
auto exp2 = NDArrayFactory::create<double>('c', {2,3,4}, {100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400});
nd4j::ops::meshgrid op;
auto results = op.execute({&in0, &in1, &in2}, {}, {0});
auto out0 = results->at(0);
auto out1 = results->at(1);
auto out2 = results->at(2);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp0.isSameShape(out0));
ASSERT_TRUE(exp0.equalsTo(out0));
ASSERT_TRUE(exp1.isSameShape(out1));
ASSERT_TRUE(exp1.equalsTo(out1));
ASSERT_TRUE(exp2.isSameShape(out2));
ASSERT_TRUE(exp2.equalsTo(out2));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, meshgrid_test5) {
auto in0 = NDArrayFactory::create<double>(1);
auto in1 = NDArrayFactory::create<double>(2);
auto in2 = NDArrayFactory::create<double>(3);
auto exp0 = NDArrayFactory::create<double>('c', {1,1,1}, {1});
auto exp1 = NDArrayFactory::create<double>('c', {1,1,1}, {2});
auto exp2 = NDArrayFactory::create<double>('c', {1,1,1}, {3});
nd4j::ops::meshgrid op;
auto results = op.execute({&in0, &in1, &in2}, {}, {0});
auto out0 = results->at(0);
auto out1 = results->at(1);
auto out2 = results->at(2);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp0.isSameShape(out0));
ASSERT_TRUE(exp0.equalsTo(out0));
ASSERT_TRUE(exp1.isSameShape(out1));
ASSERT_TRUE(exp1.equalsTo(out1));
ASSERT_TRUE(exp2.isSameShape(out2));
ASSERT_TRUE(exp2.equalsTo(out2));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, meshgrid_test6) {
auto in0 = NDArrayFactory::create<double>('c', {2,2},{1,2,3,4});
auto in1 = NDArrayFactory::create<double>(5);
auto in2 = NDArrayFactory::create<double>(6);
auto exp0 = NDArrayFactory::create<double>('c', {4,1,1}, {1,2,3,4});
auto exp1 = NDArrayFactory::create<double>('c', {4,1,1}, {5,5,5,5});
auto exp2 = NDArrayFactory::create<double>('c', {4,1,1}, {6,6,6,6});
nd4j::ops::meshgrid op;
auto results = op.execute({&in0, &in1, &in2}, {}, {0});
auto out0 = results->at(0);
auto out1 = results->at(1);
auto out2 = results->at(2);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp0.isSameShape(out0));
ASSERT_TRUE(exp0.equalsTo(out0));
ASSERT_TRUE(exp1.isSameShape(out1));
ASSERT_TRUE(exp1.equalsTo(out1));
ASSERT_TRUE(exp2.isSameShape(out2));
ASSERT_TRUE(exp2.equalsTo(out2));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, meshgrid_test7) {
auto in0 = NDArrayFactory::create<double>('c', {2,2},{1,2,3,4});
auto in1 = NDArrayFactory::create<double>(5);
auto in2 = NDArrayFactory::create<double>(6);
auto exp0 = NDArrayFactory::create<double>('c', {1,4,1}, {1,2,3,4});
auto exp1 = NDArrayFactory::create<double>('c', {1,4,1}, {5,5,5,5});
auto exp2 = NDArrayFactory::create<double>('c', {1,4,1}, {6,6,6,6});
nd4j::ops::meshgrid op;
auto results = op.execute({&in0, &in1, &in2}, {}, {1});
auto out0 = results->at(0);
auto out1 = results->at(1);
auto out2 = results->at(2);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp0.isSameShape(out0));
ASSERT_TRUE(exp0.equalsTo(out0));
ASSERT_TRUE(exp1.isSameShape(out1));
ASSERT_TRUE(exp1.equalsTo(out1));
ASSERT_TRUE(exp2.isSameShape(out2));
ASSERT_TRUE(exp2.equalsTo(out2));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, meshgrid_test8) {
auto in0 = NDArrayFactory::create<double>(5);
auto exp0 = NDArrayFactory::create<double>('c', {1}, {5});
nd4j::ops::meshgrid op;
auto results = op.execute({&in0}, {}, {0});
auto out0 = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp0.isSameShape(out0));
ASSERT_TRUE(exp0.equalsTo(out0));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, meshgrid_test9) {
auto in0 = NDArrayFactory::create<double>(5);
auto exp0 = NDArrayFactory::create<double>('c', {1}, {5});
nd4j::ops::meshgrid op;
auto results = op.execute({&in0}, {}, {1});
auto out0 = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(exp0.isSameShape(out0));
ASSERT_TRUE(exp0.equalsTo(out0));
delete results;
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, WeightedCrossEntropyWithLogits_1) {
auto input = NDArrayFactory::create<double>('c', {2, 3}, {11.f, 13.f, 4.f, 15.f, 6.f, 3.f});
auto targets = NDArrayFactory::create<double>('c', {2, 3}, {15.5f, 15.7f, 5.f , 15.f, 5.f, 6.f});
auto weight = NDArrayFactory::create<double>(0.7f);
auto expected = NDArrayFactory::create<double>('c', {2, 3}, {-159.50006, -191.1, -16.009075, -210., -24.001238, -15.03887});
//Targets {15.5f, 15.7f, 5.f , 15.f, 5.f, 6.f};
//----------
//Inputs {11.f, 13.f, 4.f, 15.f, 6.f, 3.f};
//----------
//Weights [0.7]
//Result {-159.50006, -191.1, -16.009075, -210., -24.001238, -15.03887}
nd4j::ops::weighted_cross_entropy_with_logits op;
auto results = op.execute({&targets, &input, &weight}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
auto output = results->at(0);
// output->printIndexedBuffer();
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expected.isSameShape(output));
ASSERT_TRUE(expected.equalsTo(output));
delete results;
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests4, WeightedCrossEntropyWithLogits_2) {
auto input = NDArrayFactory::create<double>('c', {2, 3}, {11.f, 13.f, 4.f, 15.f, 6.f, 3.f});
auto targets = NDArrayFactory::create<double>('c', {2, 3}, {15.5f, 15.7f, 5.f, 15.f, 5.f, 6.f});
auto weights = NDArrayFactory::create<double>({0.5f, 0.7f, 1.0f}) ;
auto expected = NDArrayFactory::create<double>('c', {2, 3}, {-159.5001f, -191.1f, -15.98185f, -210.f, -24.001238f, -14.951412f});
nd4j::ops::weighted_cross_entropy_with_logits op;
auto results = op.execute({&targets, &input, &weights}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
auto output = results->at(0);
Merge master to upstream (#7945) * Shugeo strided slice zeros (#14) * Modified strided_slice op to properly work with empty-like shapes. * Fixed test for reduce_mean with empty-like input. * [WIP] Last merge (#15) * correct logsoftmax looss (#2) * Small SameDiff listener fix (#4) * Various fixes (#6) * #7839 Fix for asXMatrix and tests * #7866 EmbeddingSequenceLayer dtype fix + test * #7856 SameDiff save/load stream methods * #7859 RegressionEvaluation rank 4 fix + tests + axis configuration * EvaluationBinary 3d/4d * More evaluation 3d/4d tests * #7847 Evaluation empty checks * Small test ifx * #7848 Fix median edge case * Improve DL4J samediff layer tests * [WIP] FastText wrapper implemented (#8) * FastText implemented * Some fixes * Fix shapes for wordsNearest * Validation of input vectors * Fixes * Fixed test * Thread tagged * Some tweaks * setContextClassLoader for DeallocatorServiceThread * Numpy format tests (#1) * Various fixes (#11) * #7852 SameDiff gather fix * #7892 SameDiff placeholder to constant conversion * #7890 validate input rank for MLN/CG init methods * Fix broken permute shape calculation * Permute and gather fixes * Tests * #7850 LogSumExp fix + test * Handful of test fixes * Empty arrays with non-scalar shapes (#10) * minor rearrangements for lambdas * empty tensors with non-scalar shapes * numpy empty tensors with non-scalar shapes * few more empty tweaks * Small fixes * conv3d signature update * micro fix in batchnorm mkldnn * Import fixes * Fix * MKL-DNN update * Small fill fix * fill with empty input + test * Fixes * Small error improvement * Fix * one special test * couple of fixes for lstm * Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone * Fixes * FP16 * Unsigned * BFloat16 * Fill op - empty tweaks * - couple of fixes for empty arrays construction - stack updated * strided slice fix * one transform test * provide method for reducing shapeInfo in case of input array is empty * Fixed reduceAlongDimensions to use empty input properly. * couple of broadcast tests * couple of tests broadcast tests + tweak to make them pass * add check of non-empty to methods producing sub-arrays * Fixed reshapeC with zeros in shape. * complete empty check in reduce_... legacy ops * Concat and cumsum/prod * Tweak to empty shape inference on import * add empty check to the rest of reduce legacy ops * one more test * correct typo in evalReduceShapeInfoEmpty * Added tests for reduce_* ops to tests with zero shapes. * few more tests for empty reductions * Fixed strided_slice op with empty case and tests. * one more empty reduction test * Fixed strided_slice test. * add empty check to NDArray::reshapei * infOrMax * empty min/max with infinity tests * made unstack working correctly with empty arrays * few IndexReduce tests + tweaks for empty shapes * add test for empty concat * few tests fixed * Validation fix for reductions on empty shapes * Reverse fix * Reduction shape calc fixes * SameDiff.generateOutputVariable: don't use shape function to determine number of outputs * Range fix * - NDArray constructor updated for scalars/empty arrays - few tests fixed * More fixes * Empty creator fixes * concat fix * concat fix * TF import tests: allow 'both all NaN' and 'both all inf' to pass * Slice, zero fraction, and reshape fixes * transpose, gather * Zero fraction * scalar cast fix * Empty reduction axis support * few more tests fixed * Fixed input checks conforming with TF for concat op and tests. * few tests fixed * matmul scalar shape fix * Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats. * broadcast bool fix * few more tests * few more tests * correct evalReduceShapeInfoEmpty * argmax/argmin + tests * one more empty edge case + one more test * argmax/argmin/realdiv_bp tweaks * empty reshape test + fix * Helper fixes * Small fixes * Gather test fix * Gather test fix * Small fixes * reduce scalar zero values * scalar mean workaround * Remove debug code * along dim mean workaround * one more test * - equalsTo() tweak for empty arrays - one more test * broadcast tweaks * [WIP] Fixing outstanding issues for NLP (#9) * Avoid using not-inited objects * Test fixed. * Redundant method avoided for models like FastText * KMeans++ implementation * KMeans++ implementation * Disable parallel execution * KMeans++ * Tests * Dev branch merge (#16) * SameDiff: convertDataType and gradient check util improvements (#12) * GradCheck util improvements * StopGradient constructor + test * SameDiff: Add datatype conversion * Javadoc and add DataType.isNumerical() * Small fix * Fix SameDiff TF import test cases intermediate naming (workaround for bad default) * TFGraphTestAllHelper: check intermediates in execution order * Add missing debug listener * [WIP] lstmBlock fix + other changes (#13) - fixes lstmBlock issue - changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer - CheckNumerics op - fixes for ReduceBool IsInfOrNan & IsFinite * Small test fix * CheckNumerics op wrapper * Fix some issues on master (#17) * Fix DataVec test issue * Fix issue with dl4j SameDiff output layer * Dtype fix for lambda layers * #7912 BertIterator dtype fix (use float32 not global default) * [WIP] Next set of CUDA stuff (#7) New CUDA implementations and improvements * bad file * Dev branch master merge (#23) * SameDiff: convertDataType and gradient check util improvements (#12) * GradCheck util improvements * StopGradient constructor + test * SameDiff: Add datatype conversion * Javadoc and add DataType.isNumerical() * Small fix * Fix SameDiff TF import test cases intermediate naming (workaround for bad default) * TFGraphTestAllHelper: check intermediates in execution order * Add missing debug listener * [WIP] lstmBlock fix + other changes (#13) - fixes lstmBlock issue - changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer - CheckNumerics op - fixes for ReduceBool IsInfOrNan & IsFinite * Small test fix * CheckNumerics op wrapper * Compatibility of deserialization (#18) Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com> * SameDiff: add activation gradient checking support for debugging (#19) * SameDiff gradient checker: first pass on activation gradient checks * Fixes + tests for activation gradient checking * Javadoc * [WIP] Some nd4j data type corrections (#20) * Adjust data type * Set correct Data type. * Size of proper data type. * fix averaged cpu load (#22) * SameDiff ops, TF import and fixes (#24) * CheckNumerics tests + fixes + misc fixes Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fake quant Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fixes Signed-off-by: AlexDBlack <blacka101@gmail.com> * FakeQuantWithMinMaxArgs Signed-off-by: AlexDBlack <blacka101@gmail.com> * CheckNumerics fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types) Signed-off-by: AlexDBlack <blacka101@gmail.com> * Small fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * Javadoc Signed-off-by: AlexDBlack <blacka101@gmail.com> * Exception tweak Signed-off-by: AlexDBlack <blacka101@gmail.com> * fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fix for out of scope stack allocated var use Signed-off-by: AlexDBlack <blacka101@gmail.com> * Ignores Signed-off-by: AlexDBlack <blacka101@gmail.com> * Ignore for known failing test (already logged issue) Signed-off-by: AlexDBlack <blacka101@gmail.com> * Merge upstream to fork (#25) * Add thousand-separator commas to TotalParams (#7915) * Add thousand-separator commas to TotalParams The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them. * Add thousand-separator commas to MultiLayerNetwork Corresponding change to MultiLayerNetwork Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com> * Update contributing and issue/PR templates (#7934) Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fix link to AdaDelta paper (#7942) Fix link to AdaDelta paper hosted on matthewzeiler.com Signed-off-by: Jxtps * Fixes, and ignores for known/logged failing issues (#7943) Signed-off-by: AlexDBlack <blacka101@gmail.com> * SameDiff + DL4J/SameDiff: Multiple fixes (#28) * #7919 HDF5 attribute buffer length fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * #7909 Arbiter constructor exception ux improvements Signed-off-by: AlexDBlack <blacka101@gmail.com> * #7925 RNN output layer length checks Signed-off-by: AlexDBlack <blacka101@gmail.com> * #7939 Add listener for validating inputs are not incorrectly modified Signed-off-by: AlexDBlack <blacka101@gmail.com> * #7939 Integrate NonInplaceValidationListener into tests * #7844 DL4J SameDiff fixes for variable minibatch size * DL4J SameDiff fixes - ensure gradient for input placeholder is available Signed-off-by: AlexDBlack <blacka101@gmail.com> * Tweaks to ExternalErrorsFunction - use placeholders, make more robust * Another fix * More fixes * More SameDiff/DL4J fixes * Scope out scalar array creation in BaseScalarOp * Remove debug code Signed-off-by: AlexDBlack <blacka101@gmail.com> * [WIP] Final dev branch merge (#29) * SameDiff: convertDataType and gradient check util improvements (#12) * GradCheck util improvements * StopGradient constructor + test * SameDiff: Add datatype conversion * Javadoc and add DataType.isNumerical() * Small fix * Fix SameDiff TF import test cases intermediate naming (workaround for bad default) * TFGraphTestAllHelper: check intermediates in execution order * Add missing debug listener * [WIP] lstmBlock fix + other changes (#13) - fixes lstmBlock issue - changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer - CheckNumerics op - fixes for ReduceBool IsInfOrNan & IsFinite * Small test fix * CheckNumerics op wrapper * Compatibility of deserialization (#18) Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com> * SameDiff: add activation gradient checking support for debugging (#19) * SameDiff gradient checker: first pass on activation gradient checks * Fixes + tests for activation gradient checking * Javadoc * [WIP] Some nd4j data type corrections (#20) * Adjust data type * Set correct Data type. * Size of proper data type. * fix averaged cpu load (#22) * [WIP] Multiple dataset iterators (#27) * Splitting dataset into arbitrary number * Fixes * Multiple split of iterator * Test * Test * Some fixes * signature change * one more tweak Signed-off-by: raver119 <raver119@gmail.com> * one more test for sequential use of DataSetIteratorSplitter Signed-off-by: raver119 <raver119@gmail.com> * Fixes * Fixes * one more test for Alexander Signed-off-by: raver119 <raver119@gmail.com> * Some fixes * Some fixes * one more test for Alexander Signed-off-by: raver119 <raver119@gmail.com> * minor test fix Signed-off-by: raver119 <raver119@gmail.com> * Some fixes * Some fixes * couple of assertions tweaked Signed-off-by: raver119 <raver119@gmail.com> * MDS splitter test :/ Signed-off-by: raver119 <raver119@gmail.com> * Minor refactoring * Multi dataset * Some fixes * More tests * Small number of test fixes/improvements (failures on CI) (#31) Signed-off-by: AlexDBlack <blacka101@gmail.com> * [WIP] More CUDA stuff (#26) * initial commit Signed-off-by: raver119 <raver119@gmail.com> * LRN BP CUDA Signed-off-by: raver119 <raver119@gmail.com> * less memory Signed-off-by: raver119 <raver119@gmail.com> * Fixed bug with crop_and_resize op helper. * get rid of unnecessary index-calculation dunction Signed-off-by: Yurii <yurii@skymind.io> * Fixed sort with nth_element cuda-based helper. * Refactored nth_element. * Refactored nth_element op and tests. * Modified usage of dim array with sortTad routine. * Refactored main routine of helper for non_max_image_suppression op. * non_max_image_suppression op helper with cuda kernel implementation. Initial revision. * fix vol2col cuda kernel * meh Signed-off-by: raver119 <raver119@gmail.com> * topK concept Signed-off-by: raver119 <raver119@gmail.com> * unsorted topK with scanWitdh of 1 Signed-off-by: raver119 <raver119@gmail.com> * correct vol2col tests * sorted/unsorted topK Signed-off-by: raver119 <raver119@gmail.com> * implementation and fixing col2im/col2vol * Corrected usage flags with input/output with reverse op. * dup is const now Signed-off-by: raver119 <raver119@gmail.com> * percentile op Signed-off-by: raver119 <raver119@gmail.com> * group tests for mapool2d Signed-off-by: Yurii <yurii@skymind.io> * special test for george Signed-off-by: raver119 <raver119@gmail.com> * less threads for sortTad Signed-off-by: raver119 <raver119@gmail.com> * provide conv2d for cuda Signed-off-by: Yurii <yurii@skymind.io> * remove auther in sort tad kernel code Signed-off-by: Yurii <yurii@skymind.io> * provide depthwise_conv2d for cuda Signed-off-by: Yurii <yurii@skymind.io> * - max_pooling_with_argmax - null check for special use Signed-off-by: raver119 <raver119@gmail.com> * dts cuda Signed-off-by: raver119 <raver119@gmail.com> * provide sconv2d for cuda Signed-off-by: Yurii <yurii@skymind.io> * std cuda Signed-off-by: raver119 <raver119@gmail.com> * Refactored non_max_suppression op to conform TF implementation. * Improved suppression helper. * provide pooling3d for cuda Signed-off-by: Yurii <yurii@skymind.io> * minor lstm rearrangements Signed-off-by: raver119 <raver119@gmail.com> * more of minor lstm rearrangements Signed-off-by: raver119 <raver119@gmail.com> * (bi)dynamic_rnn Signed-off-by: raver119 <raver119@gmail.com> * templates init order Signed-off-by: raver119 <raver119@gmail.com> * Refactored non_max_suppression op. * Added cuda kernel for non_max_suppression. * CPU sort by key/value Signed-off-by: raver119 <raver119@gmail.com> * CPU sort TAD by key/value Signed-off-by: raver119 <raver119@gmail.com> * CPU sort TAD by key/value tests Signed-off-by: raver119 <raver119@gmail.com> * Eliminate compiler error with cuda implementation. * - repaired gradCheck in cuda - provide conv2d_bp for cuda Signed-off-by: Yurii <yurii@skymind.io> * missed signature Signed-off-by: raver119 <raver119@gmail.com> * provide depthwise_conv2d_bp for cuda Signed-off-by: Yurii <yurii@skymind.io> * Implementation of lup helper with cuda kernel. Initial commit. * further work on backprops for convolutions Signed-off-by: Yurii <yurii@skymind.io> * CUDA linear sort by key/val Signed-off-by: raver119 <raver119@gmail.com> * CUDA tad sort by key/val Signed-off-by: raver119 <raver119@gmail.com> * start providing of backprop for pooling2d/3d Signed-off-by: Yurii <yurii@skymind.io> * Added atomicAdd for bool datatype. * dynamic partition concept Signed-off-by: raver119 <raver119@gmail.com> * dynamic partition concept Signed-off-by: raver119 <raver119@gmail.com> * dynamic partition scalar CUDA Signed-off-by: raver119 <raver119@gmail.com> * important comment Signed-off-by: raver119 <raver119@gmail.com> * fix pooling2d/3d backprop helpers Signed-off-by: Yurii <yurii@skymind.io> * Added non-linear test with dynamic_partition. * Improved test for dynamic_partition. * dynamic_partition TAD concept Signed-off-by: raver119 <raver119@gmail.com> * - dynamic_partition TAD CUDA impl - dynamic_partition TAD CPU fix Signed-off-by: raver119 <raver119@gmail.com> * - rewrite cpu code for usampling2d/3d - write cuda code for usampling2d/3d Signed-off-by: Yurii <yurii@skymind.io> * dynamic_stitch CUDA vector case Signed-off-by: raver119 <raver119@gmail.com> * dynamic_stitch CUDA TAD case concept Signed-off-by: raver119 <raver119@gmail.com> * dynamic_stitch CUDA TAD case impl Signed-off-by: raver119 <raver119@gmail.com> * Added tests for dynamic_stitch 3D-4D cases. * minor tests tweaks Signed-off-by: raver119 <raver119@gmail.com> * Fixed type check for dynamic stitch. * min/max bp Signed-off-by: raver119 <raver119@gmail.com> * rewrite code for upsampling2d/3d cpu Signed-off-by: Yurii <yurii@skymind.io> * reduce min/max/norm_max bp Signed-off-by: raver119 <raver119@gmail.com> * lup implementation. Additional enhancements. * provide code for upsamling2d/3d backprop Signed-off-by: Yurii <yurii@skymind.io> * weightedCrossEntropyWithLogits Signed-off-by: raver119 <raver119@gmail.com> * Fixed template math atomicMul for 64bit ints. * Refactored dynamic_partition_bp op. * inverseBroadcast fix Signed-off-by: raver119 <raver119@gmail.com> * DynamicPartitionBP test datatype fixed. * - nd4j_atomicMul Windows fix - cpu/NDArrayLambda.hpp excluded from CUDA Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
output->printIndexedBuffer("Result is ");
expected.printIndexedBuffer("Expected is ");
Eclipse Migration Initial Commit
2019-06-06 14:21:15 +02:00
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.});
Eclipse Migration Initial Commit
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;
}