240 lines
7.2 KiB
C++
240 lines
7.2 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2019 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author raver119@gmail.com
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//
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#include "testlayers.h"
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#include <ops/declarable/CustomOperations.h>
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#include <NDArray.h>
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#include <ops/ops.h>
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#include <GradCheck.h>
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#include <array>
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using namespace nd4j;
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class DeclarableOpsTests16 : public testing::Test {
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public:
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DeclarableOpsTests16() {
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printf("\n");
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fflush(stdout);
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}
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};
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TEST_F(DeclarableOpsTests16, scatter_upd_1) {
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auto x = NDArrayFactory::create<float>('c', {3}, {1.f, 1.f, 1.f});
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auto y = NDArrayFactory::create<int>(0);
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auto w = NDArrayFactory::create<float>(3.0f);
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auto e = NDArrayFactory::create<float>('c', {3}, {3.f, 1.f, 1.f});
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nd4j::ops::scatter_upd op;
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auto result = op.execute({&x, &y, &w}, {}, {});
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ASSERT_EQ(Status::OK(), result->status());
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auto z = result->at(0);
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ASSERT_EQ(e, *z);
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delete result;
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}
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TEST_F(DeclarableOpsTests16, scatter_upd_2) {
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NDArray x('c', {10, 3}, nd4j::DataType::FLOAT32);
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NDArray indices('c', {2}, {2,5}, nd4j::DataType::INT32);
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NDArray updates('c', {2, 3}, {100,101,102, 200,201,202}, nd4j::DataType::FLOAT32);
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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);
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x.linspace(1);
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nd4j::ops::scatter_upd op;
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auto result = op.execute({&x, &indices, &updates}, {}, {});
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ASSERT_EQ(Status::OK(), result->status());
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auto z = result->at(0);
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ASSERT_EQ(e, *z);
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delete result;
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}
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TEST_F(DeclarableOpsTests16, scatter_upd_3) {
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NDArray x('c', {10, 3}, nd4j::DataType::FLOAT32);
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NDArray indices('c', {2}, {20,5}, nd4j::DataType::INT32);
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NDArray updates('c', {2, 3}, {100,101,102, 200,201,202}, nd4j::DataType::FLOAT32);
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NDArray output('c', {10, 3}, nd4j::DataType::FLOAT32);
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nd4j::ops::scatter_upd op;
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ASSERT_ANY_THROW(op.execute({&x, &indices, &updates}, {&output}, {}, {}, {true, true}));
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}
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TEST_F(DeclarableOpsTests16, test_size_dtype_1) {
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auto x = NDArrayFactory::create<float>('c', {3}, {1, 1, 1});
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auto z = NDArrayFactory::create<float>(0.0f);
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auto e = NDArrayFactory::create<float>(3.0f);
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nd4j::ops::size op;
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auto status = op.execute({&x}, {&z}, {}, {}, {});
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ASSERT_EQ(Status::OK(), status);
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ASSERT_EQ(e, z);
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}
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TEST_F(DeclarableOpsTests16, test_empty_noop_1) {
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auto z = NDArrayFactory::empty<Nd4jLong>();
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nd4j::ops::noop op;
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auto status = op.execute({}, {&z}, {}, {}, {});
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ASSERT_EQ(Status::OK(), status);
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}
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TEST_F(DeclarableOpsTests16, test_empty_noop_2) {
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auto z = NDArrayFactory::empty<Nd4jLong>();
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Context ctx(1);
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ctx.setOutputArray(0, z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo());
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nd4j::ops::noop op;
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auto status = op.execute(&ctx);
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ASSERT_EQ(Status::OK(), status);
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}
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TEST_F(DeclarableOpsTests16, test_svd_1) {
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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});
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auto z = NDArrayFactory::create<float>('c', {3});
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nd4j::ops::svd op;
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auto status = op.execute({&x}, {&z}, {}, {0, 0, 16}, {});
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ASSERT_EQ(Status::OK(), status);
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}
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TEST_F(DeclarableOpsTests16, test_hamming_distance_1) {
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auto x = NDArrayFactory::create<Nd4jLong>({37, 37, 37});
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auto y = NDArrayFactory::create<Nd4jLong>({8723, 8723, 8723});
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auto e = NDArrayFactory::create<Nd4jLong>(18);
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nd4j::ops::bits_hamming_distance op;
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auto result = op.execute({&x, &y}, {}, {});
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ASSERT_EQ(Status::OK(), result->status());
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auto z = result->at(0);
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ASSERT_EQ(e, *z);
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delete result;
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}
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TEST_F(DeclarableOpsTests16, test_knn_mindistance_1) {
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auto input = NDArrayFactory::create<float>('c', {512});
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auto low = NDArrayFactory::create<float>('c', {512});
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auto high = NDArrayFactory::create<float>('c', {512});
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auto output = NDArrayFactory::create<float>(0.0f);
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input.linspace(1.0);
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low.linspace(1.0);
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high.linspace(1.0);
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nd4j::ops::knn_mindistance op;
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auto result = op.execute({&input, &low, &high}, {&output}, {}, {}, {});
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ASSERT_EQ(Status::OK(), result);
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}
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TEST_F(DeclarableOpsTests16, test_empty_cast_1) {
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auto x = NDArrayFactory::create<bool>('c', {1, 0, 2});
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auto e = NDArrayFactory::create<Nd4jLong>('c', {1, 0, 2});
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nd4j::ops::cast op;
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auto result = op.execute({&x}, {}, {10});
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ASSERT_EQ(Status::OK(), result->status());
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ASSERT_EQ(e, *result->at(0));
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delete result;
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}
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TEST_F(DeclarableOpsTests16, test_range_1) {
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nd4j::ops::range op;
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auto z = NDArrayFactory::create<float>('c', {200});
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Context ctx(1);
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ctx.setTArguments({-1.0, 1.0, 0.01});
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ctx.setOutputArray(0, &z);
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auto status = op.execute(&ctx);
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ASSERT_EQ(Status::OK(), status);
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}
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TEST_F(DeclarableOpsTests16, test_range_2) {
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nd4j::ops::range op;
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auto z = NDArrayFactory::create<float>('c', {200});
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double tArgs[] = {-1.0, 1.0, 0.01};
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auto shapes = ::calculateOutputShapes2(nullptr, op.getOpHash(), nullptr, nullptr, 0, tArgs, 3, nullptr, 0, nullptr, 0);
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shape::printShapeInfoLinear("Result", shapes->at(0));
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ASSERT_TRUE(shape::shapeEquals(z.shapeInfo(), shapes->at(0)));
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delete shapes;
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}
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TEST_F(DeclarableOpsTests16, test_reverse_1) {
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std::vector<Nd4jLong> rows = {3, 5, 7, 8, 9, 10, 119, 211};
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std::vector<Nd4jLong> columns = {6, 5, 10, 100, 153, 171, 635};
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for (auto r : rows) {
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for (auto c : columns) {
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//nd4j_printf("Trying [%i, %i]\n", r, c);
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auto array = NDArrayFactory::create<float>('c', {r, c});
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auto exp = NDArrayFactory::create<float>('c', {r, c});
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auto reversed = NDArrayFactory::create<float>('c', {r, c});
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auto rowOriginal = NDArrayFactory::create<float>('c', {c});
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auto rowReversed = NDArrayFactory::create<float>('c', {c});
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for (int e = 0; e < c; e++) {
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rowOriginal.p(e, (float) e);
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rowReversed.p(c - e - 1, (float) e);
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}
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auto listI = array.allTensorsAlongDimension({1});
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auto listE = exp.allTensorsAlongDimension({1});
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for (int e = 0; e < r; e++) {
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listI->at(e)->assign(rowOriginal);
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listE->at(e)->assign(rowReversed);
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}
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delete listI;
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delete listE;
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nd4j::ops::reverse op;
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Nd4jLong axis = 1;
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auto status = op.execute({&array}, {&reversed}, {}, {axis}, {});
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ASSERT_EQ(Status::OK(), status);
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ASSERT_EQ(exp, reversed);
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}
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}
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} |