cavis/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests16.cpp

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/*******************************************************************************
* Copyright (c) 2015-2019 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 <NDArray.h>
#include <ops/ops.h>
#include <GradCheck.h>
#include <array>
using namespace nd4j;
class DeclarableOpsTests16 : public testing::Test {
public:
DeclarableOpsTests16() {
printf("\n");
fflush(stdout);
}
};
TEST_F(DeclarableOpsTests16, scatter_upd_1) {
auto x = NDArrayFactory::create<float>('c', {3}, {1.f, 1.f, 1.f});
auto y = NDArrayFactory::create<int>(0);
auto w = NDArrayFactory::create<float>(3.0f);
auto e = NDArrayFactory::create<float>('c', {3}, {3.f, 1.f, 1.f});
nd4j::ops::scatter_upd op;
auto result = op.execute({&x, &y, &w}, {}, {});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_EQ(e, *z);
delete result;
}
TEST_F(DeclarableOpsTests16, scatter_upd_2) {
NDArray x('c', {10, 3}, nd4j::DataType::FLOAT32);
NDArray indices('c', {2}, {2,5}, nd4j::DataType::INT32);
NDArray updates('c', {2, 3}, {100,101,102, 200,201,202}, nd4j::DataType::FLOAT32);
NDArray e('c', {10, 3}, {1,2,3, 4,5,6, 100,101,102, 10,11,12, 13,14,15, 200,201,202, 19,20,21, 22,23,24, 25,26,27, 28,29,30}, nd4j::DataType::FLOAT32);
x.linspace(1);
nd4j::ops::scatter_upd op;
auto result = op.execute({&x, &indices, &updates}, {}, {});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_EQ(e, *z);
delete result;
}
TEST_F(DeclarableOpsTests16, scatter_upd_3) {
NDArray x('c', {10, 3}, nd4j::DataType::FLOAT32);
NDArray indices('c', {2}, {20,5}, nd4j::DataType::INT32);
NDArray updates('c', {2, 3}, {100,101,102, 200,201,202}, nd4j::DataType::FLOAT32);
NDArray output('c', {10, 3}, nd4j::DataType::FLOAT32);
nd4j::ops::scatter_upd op;
ASSERT_ANY_THROW(op.execute({&x, &indices, &updates}, {&output}, {}, {}, {true, true}));
}
TEST_F(DeclarableOpsTests16, test_size_dtype_1) {
auto x = NDArrayFactory::create<float>('c', {3}, {1, 1, 1});
auto z = NDArrayFactory::create<float>(0.0f);
auto e = NDArrayFactory::create<float>(3.0f);
nd4j::ops::size op;
auto status = op.execute({&x}, {&z}, {}, {}, {});
ASSERT_EQ(Status::OK(), status);
ASSERT_EQ(e, z);
}
TEST_F(DeclarableOpsTests16, test_empty_noop_1) {
auto z = NDArrayFactory::empty<Nd4jLong>();
nd4j::ops::noop op;
auto status = op.execute({}, {&z}, {}, {}, {});
ASSERT_EQ(Status::OK(), status);
}
TEST_F(DeclarableOpsTests16, test_empty_noop_2) {
auto z = NDArrayFactory::empty<Nd4jLong>();
Context ctx(1);
ctx.setOutputArray(0, z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo());
nd4j::ops::noop op;
auto status = op.execute(&ctx);
ASSERT_EQ(Status::OK(), status);
}
TEST_F(DeclarableOpsTests16, test_svd_1) {
auto x = NDArrayFactory::create<float>('c', {3, 3}, {0.7787856f, 0.80119777f, 0.72437465f, 0.23089433f, 0.72714126f, 0.18039072f,0.50563407f, 0.89252293f, 0.5461209f});
auto z = NDArrayFactory::create<float>('c', {3});
nd4j::ops::svd op;
auto status = op.execute({&x}, {&z}, {}, {0, 0, 16}, {});
ASSERT_EQ(Status::OK(), status);
}
TEST_F(DeclarableOpsTests16, test_hamming_distance_1) {
auto x = NDArrayFactory::create<Nd4jLong>({37, 37, 37});
auto y = NDArrayFactory::create<Nd4jLong>({8723, 8723, 8723});
auto e = NDArrayFactory::create<Nd4jLong>(18);
nd4j::ops::bits_hamming_distance op;
auto result = op.execute({&x, &y}, {}, {});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_EQ(e, *z);
delete result;
}
TEST_F(DeclarableOpsTests16, test_knn_mindistance_1) {
auto input = NDArrayFactory::create<float>('c', {512});
auto low = NDArrayFactory::create<float>('c', {512});
auto high = NDArrayFactory::create<float>('c', {512});
auto output = NDArrayFactory::create<float>(0.0f);
input.linspace(1.0);
low.linspace(1.0);
high.linspace(1.0);
nd4j::ops::knn_mindistance op;
auto result = op.execute({&input, &low, &high}, {&output}, {}, {}, {});
ASSERT_EQ(Status::OK(), result);
}
TEST_F(DeclarableOpsTests16, test_empty_cast_1) {
auto x = NDArrayFactory::create<bool>('c', {1, 0, 2});
auto e = NDArrayFactory::create<Nd4jLong>('c', {1, 0, 2});
nd4j::ops::cast op;
auto result = op.execute({&x}, {}, {10});
ASSERT_EQ(Status::OK(), result->status());
ASSERT_EQ(e, *result->at(0));
delete result;
}
TEST_F(DeclarableOpsTests16, test_range_1) {
nd4j::ops::range op;
auto z = NDArrayFactory::create<float>('c', {200});
Context ctx(1);
ctx.setTArguments({-1.0, 1.0, 0.01});
ctx.setOutputArray(0, &z);
auto status = op.execute(&ctx);
ASSERT_EQ(Status::OK(), status);
}
TEST_F(DeclarableOpsTests16, test_range_2) {
nd4j::ops::range op;
auto z = NDArrayFactory::create<float>('c', {200});
double tArgs[] = {-1.0, 1.0, 0.01};
auto shapes = ::calculateOutputShapes2(nullptr, op.getOpHash(), nullptr, nullptr, 0, tArgs, 3, nullptr, 0, nullptr, 0);
shape::printShapeInfoLinear("Result", shapes->at(0));
ASSERT_TRUE(shape::shapeEquals(z.shapeInfo(), shapes->at(0)));
delete shapes;
}
TEST_F(DeclarableOpsTests16, test_reverse_1) {
std::vector<Nd4jLong> rows = {3, 5, 7, 8, 9, 10, 119, 211};
std::vector<Nd4jLong> columns = {6, 5, 10, 100, 153, 171, 635};
for (auto r : rows) {
for (auto c : columns) {
//nd4j_printf("Trying [%i, %i]\n", r, c);
auto array = NDArrayFactory::create<float>('c', {r, c});
auto exp = NDArrayFactory::create<float>('c', {r, c});
auto reversed = NDArrayFactory::create<float>('c', {r, c});
auto rowOriginal = NDArrayFactory::create<float>('c', {c});
auto rowReversed = NDArrayFactory::create<float>('c', {c});
for (int e = 0; e < c; e++) {
rowOriginal.p(e, (float) e);
rowReversed.p(c - e - 1, (float) e);
}
auto listI = array.allTensorsAlongDimension({1});
auto listE = exp.allTensorsAlongDimension({1});
for (int e = 0; e < r; e++) {
listI->at(e)->assign(rowOriginal);
listE->at(e)->assign(rowReversed);
}
delete listI;
delete listE;
nd4j::ops::reverse op;
Nd4jLong axis = 1;
auto status = op.execute({&array}, {&reversed}, {}, {axis}, {});
ASSERT_EQ(Status::OK(), status);
ASSERT_EQ(exp, reversed);
}
}
}