2019-06-06 14:21:15 +02:00
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/*******************************************************************************
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* Copyright (c) 2015-2018 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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// Created by raver on 8/4/2018.
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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 <ConstantTadHelper.h>
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#include <helpers/PointersManager.h>
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using namespace nd4j;
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class DeclarableOpsTests12 : public testing::Test {
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public:
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DeclarableOpsTests12() {
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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(DeclarableOpsTests12, test_any_validation_1) {
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auto x = NDArrayFactory::create<double>('c', {2, 1}, {1.0, 2.0});
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auto y = NDArrayFactory::create<int>('c', {2}, {1, 0});
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nd4j::ops::transpose 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(x.dataType(), z->dataType());
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delete result;
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}
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/////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test1) {
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NDArray labels('c', {2,4}, {0,1,1,0,1,0,1,0});
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NDArray predictions('c', {2,4}, nd4j::DataType::DOUBLE);
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NDArray weights('c', {2,1}, nd4j::DataType::DOUBLE);
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NDArray dLdpExp('c', {2,4}, {-0. , -0.5, -0.5, -0., -0.5, -0. , -0.5, -0.});
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NDArray dLdwExp('c', {2,1}, {1.2, -0.2});
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predictions.linspace(-0.4, 0.2);
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weights.assign(0.5);
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nd4j::ops::cosine_distance_loss_grad op;
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auto results = op.execute({&predictions, &weights, &labels}, {}, {0, -1});
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ASSERT_EQ(ND4J_STATUS_OK, results->status());
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auto *dLdp = results->at(0);
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auto *dLdw = results->at(1);
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auto *dLdl = results->at(2);
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ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
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ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
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ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
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ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
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delete results;
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}
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/////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test2) {
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NDArray labels('c', {2,4}, {-0.1, 0.3, 2, -1.4, 2.5, -3, 1.2, 2.2});
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NDArray predictions('c', {2,4}, nd4j::DataType::DOUBLE);
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NDArray weights('c', {1,4}, nd4j::DataType::DOUBLE);
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NDArray dLdpExp('c', {2,4}, {0.05, -0.15, -1. , 0.7 ,-1.25, 1.5 , -0.6 , -1.1 });
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NDArray dLdwExp('c', {1,4}, {-0.04, 2.86, 0.04, -0.92});
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NDArray dLdlExp('c', {2,4}, {0.2, 0.1, 0. , -0.1, -0.2, -0.3, -0.4, -0.5});
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predictions.linspace(-0.4, 0.2);
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weights.assign(0.5);
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nd4j::ops::cosine_distance_loss_grad op;
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auto results = op.execute({&predictions, &weights, &labels}, {}, {0, 0});
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ASSERT_EQ(ND4J_STATUS_OK, results->status());
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auto *dLdp = results->at(0);
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auto *dLdw = results->at(1);
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auto *dLdl = results->at(2);
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ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
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ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
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ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
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ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
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ASSERT_TRUE(dLdlExp.isSameShape(dLdl));
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ASSERT_TRUE(dLdlExp.equalsTo(dLdl));
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delete results;
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}
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/////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test3) {
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NDArray labels('c', {4}, {-0.1, 0.3, 2, -1.4});
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NDArray predictions('c', {4}, nd4j::DataType::DOUBLE);
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NDArray weights('c', {1}, nd4j::DataType::DOUBLE);
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NDArray dLdpExp('c', {4}, {0.05, -0.15, -1., 0.7});
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NDArray dLdwExp('c', {1}, {1.3});
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NDArray dLdlExp('c', {4}, {0.2, 0.1, -0. , -0.1});
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predictions.linspace(-0.4, 0.2);
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weights.assign(0.5);
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nd4j::ops::cosine_distance_loss_grad op;
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auto results = op.execute({&predictions, &weights, &labels}, {}, {0, 0});
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ASSERT_EQ(ND4J_STATUS_OK, results->status());
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auto *dLdp = results->at(0);
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auto *dLdw = results->at(1);
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auto *dLdl = results->at(2);
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ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
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ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
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ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
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ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
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ASSERT_TRUE(dLdlExp.isSameShape(dLdl));
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ASSERT_TRUE(dLdlExp.equalsTo(dLdl));
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delete results;
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}
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/////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test4) {
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NDArray labels('c', {1,4}, {-0.1, 0.3, 2, -1.4});
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NDArray predictions('c', {1,4}, nd4j::DataType::DOUBLE);
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2019-06-15 13:34:34 +02:00
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NDArray weights('c', {}, {0.}, nd4j::DataType::DOUBLE);
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2019-06-06 14:21:15 +02:00
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NDArray dLdpExp('c', {1,4}, {0.05, -0.15, -1., 0.7});
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2019-06-15 13:34:34 +02:00
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NDArray dLdwExp('c', {}, {1.3});
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2019-06-06 14:21:15 +02:00
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NDArray dLdlExp('c', {1,4}, {0.2, 0.1, -0. , -0.1});
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predictions.linspace(-0.4, 0.2);
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weights.assign(0.5);
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nd4j::ops::cosine_distance_loss_grad op;
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auto results = op.execute({&predictions, &weights, &labels}, {}, {1, 1});
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ASSERT_EQ(ND4J_STATUS_OK, results->status());
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auto *dLdp = results->at(0);
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auto *dLdw = results->at(1);
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auto *dLdl = results->at(2);
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ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
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ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
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ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
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ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
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ASSERT_TRUE(dLdlExp.isSameShape(dLdl));
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ASSERT_TRUE(dLdlExp.equalsTo(dLdl));
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delete results;
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}
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/////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test5) {
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NDArray labels('c', {4}, {-0.1, 0.3, 2, -1.4}, nd4j::DataType::DOUBLE);
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NDArray predictions('c', {4}, nd4j::DataType::DOUBLE);
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NDArray weights('c', {1,1}, nd4j::DataType::DOUBLE);
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NDArray dLdpExp('c', {4}, {0.1, -0.3, -2. , 1.4});
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NDArray dLdwExp('c', {1,1}, {0.});
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NDArray dLdlExp('c', {4}, {0.4, 0.2, -0. , -0.2});
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predictions.linspace(-0.4, 0.2);
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weights = 0.5;
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nd4j::ops::cosine_distance_loss_grad op;
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auto results = op.execute({&predictions, &weights, &labels}, {}, {2, 0});
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ASSERT_EQ(ND4J_STATUS_OK, results->status());
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auto *dLdp = results->at(0);
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auto *dLdw = results->at(1);
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auto *dLdl = results->at(2);
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ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
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ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
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ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
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ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
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ASSERT_TRUE(dLdlExp.isSameShape(dLdl));
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ASSERT_TRUE(dLdlExp.equalsTo(dLdl));
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delete results;
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}
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/////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test6) {
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NDArray labels('c', {4,1}, {-0.1, 0.3, 2, -1.4}, nd4j::DataType::DOUBLE);
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NDArray predictions('c', {4,1}, nd4j::DataType::DOUBLE);
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NDArray weights('c', {4,1}, nd4j::DataType::DOUBLE);
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NDArray dLdpExp('c', {4,1}, {0.0125, -0.0375, -0.25 , 0.175});
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NDArray dLdwExp('c', {4,1}, {0.24 , 0.265, 0.25 , 0.32});
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NDArray dLdlExp('c', {4,1}, {0.05 , 0.025, -0. , -0.025});
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predictions.linspace(-0.4, 0.2);
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weights = 0.5;
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nd4j::ops::cosine_distance_loss_grad op;
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auto results = op.execute({&predictions, &weights, &labels}, {}, {3, 1});
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ASSERT_EQ(ND4J_STATUS_OK, results->status());
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auto *dLdp = results->at(0);
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auto *dLdw = results->at(1);
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auto *dLdl = results->at(2);
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ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
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ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
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ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
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ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
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ASSERT_TRUE(dLdlExp.isSameShape(dLdl));
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ASSERT_TRUE(dLdlExp.equalsTo(dLdl));
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delete results;
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}
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/////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test7) {
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NDArray labels('c', {2,3,4}, {-0.1, 0.3, 2, -1.4, 2.5, -3, 1.2, 2.2,-0.1, 0.3, 2, -3.4, 2.5, -3, 1.2, 2.2,-0.2, 0.3, 2, -1.4, 2.7, -3, 1.2, 4.2});
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NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
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NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE);
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NDArray dLdpExp('c', {2,3,4}, {0.00833, -0.025 , -0.16667, 0.11667,-0.20833, 0.25 , -0.1 , -0.18333, 0.00833, -0.025 , -0.16667, 0.28333,
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-0.20833, 0.25 , -0.1 , -0.18333, 0.01667, -0.025 , -0.16667, 0.11667,-0.225 , 0.25 , -0.1 , -0.35 });
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NDArray dLdwExp('c', {1,3,1}, {0.50444, 0.89778, -1.40222});
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NDArray dLdlExp('c', {2,3,4}, {0.03333, 0.01667, -0. , -0.01667,-0.03333, -0.05 , -0.06667, -0.08333,-0.1, -0.11667, -0.13333, -0.15,
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-0.16667, -0.18333, -0.2 , -0.21667,-0.23333, -0.25 , -0.26667, -0.28333,-0.3, -0.31667, -0.33333, -0.35 });
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predictions.linspace(-0.4, 0.2);
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weights = 0.5;
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nd4j::ops::cosine_distance_loss_grad op;
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auto results = op.execute({&predictions, &weights, &labels}, {}, {2, 0});
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ASSERT_EQ(ND4J_STATUS_OK, results->status());
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auto *dLdp = results->at(0);
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auto *dLdw = results->at(1);
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auto *dLdl = results->at(2);
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ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
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ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
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ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
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ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
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ASSERT_TRUE(dLdlExp.isSameShape(dLdl));
|
|
|
|
|
ASSERT_TRUE(dLdlExp.equalsTo(dLdl));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test8) {
|
|
|
|
|
|
|
|
|
|
NDArray labels('c', {2,3,4}, {-0.1, 0.3, 2, -1.4, 2.5, -3, 1.2, 2.2,-0.1, 0.3, 2, -3.4, 2.5, -3, 1.2, 2.2,-0.2, 0.3, 2, -1.4, 2.7, -3, 1.2, 4.2});
|
|
|
|
|
NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray weights('c', {2,1,1}, nd4j::DataType::DOUBLE);
|
|
|
|
|
|
|
|
|
|
NDArray dLdpExp('c', {2,3,4}, {0.00625, -0.01875, -0.125 , 0.0875,-0.15625, 0.1875 , -0.075 , -0.1375, 0.00625, -0.01875, -0.125 , 0.2125,
|
|
|
|
|
-0.15625, 0.1875 , -0.075 , -0.1375, 0.0125 , -0.01875, -0.125 , 0.0875,-0.16875, 0.1875 , -0.075 , -0.2625});
|
|
|
|
|
NDArray dLdwExp('c', {2,1,1}, {0.57, -3.2175});
|
|
|
|
|
NDArray dLdlExp('c', {2,3,4}, {0.025, 0.0125, -0. , -0.0125,-0.025, -0.0375, -0.05, -0.0625,-0.075, -0.0875, -0.1 , -0.1125,
|
|
|
|
|
-0.125, -0.1375, -0.15, -0.1625,-0.175, -0.1875, -0.2 , -0.2125,-0.225, -0.2375, -0.25, -0.2625});
|
|
|
|
|
|
|
|
|
|
predictions.linspace(-0.4, 0.2);
|
|
|
|
|
weights = 0.5;
|
|
|
|
|
|
|
|
|
|
nd4j::ops::cosine_distance_loss_grad op;
|
|
|
|
|
|
|
|
|
|
auto results = op.execute({&predictions, &weights, &labels}, {}, {3, 1});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto *dLdp = results->at(0);
|
|
|
|
|
auto *dLdw = results->at(1);
|
|
|
|
|
auto *dLdl = results->at(2);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
|
|
|
|
|
ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
|
|
|
|
|
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
|
|
|
|
|
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
|
|
|
|
|
ASSERT_TRUE(dLdlExp.isSameShape(dLdl));
|
|
|
|
|
ASSERT_TRUE(dLdlExp.equalsTo(dLdl));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test9) {
|
|
|
|
|
|
|
|
|
|
NDArray labels('c', {2,3,4}, {-0.1, 0.3, 2, -1.4, 2.5, -3, 1.2, 2.2,-0.1, 0.3, 2, -3.4, 2.5, -3, 1.2, 2.2,-0.2, 0.3, 2, -1.4, 2.7, -3, 1.2, 4.2});
|
|
|
|
|
NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray weights('c', {2,3,1}, nd4j::DataType::DOUBLE);
|
|
|
|
|
|
|
|
|
|
NDArray dLdpExp('c', {2,3,4}, {0.05, -0.15, -1. , 0.7,-1.25, 1.5 , -0.6 , -1.1, 0.05, -0.15, -1. , 1.7,
|
|
|
|
|
-1.25, 1.5 , -0.6 , -1.1, 0.1 , -0.15, -1. , 0.7,-1.35, 1.5 , -0.6 , -2.1});
|
|
|
|
|
NDArray dLdwExp('c', {2,3,1}, {1.3 , -1.36, 3.62, -6. , -0.98,-19.76});
|
|
|
|
|
NDArray dLdlExp('c', {2,3,4}, {0.2, 0.1, -0. , -0.1,-0.2, -0.3, -0.4, -0.5,-0.6, -0.7, -0.8, -0.9,
|
|
|
|
|
-1. , -1.1, -1.2, -1.3,-1.4, -1.5, -1.6, -1.7,-1.8, -1.9, -2. , -2.1});
|
|
|
|
|
|
|
|
|
|
predictions.linspace(-0.4, 0.2);
|
|
|
|
|
weights = 0.5;
|
|
|
|
|
|
|
|
|
|
nd4j::ops::cosine_distance_loss_grad op;
|
|
|
|
|
|
|
|
|
|
auto results = op.execute({&predictions, &weights, &labels}, {}, {0, 2});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto *dLdp = results->at(0);
|
|
|
|
|
auto *dLdw = results->at(1);
|
|
|
|
|
auto *dLdl = results->at(2);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
|
|
|
|
|
ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
|
|
|
|
|
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
|
|
|
|
|
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
|
|
|
|
|
ASSERT_TRUE(dLdlExp.isSameShape(dLdl));
|
|
|
|
|
ASSERT_TRUE(dLdlExp.equalsTo(dLdl));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, hinge_loss_14) {
|
|
|
|
|
|
|
|
|
|
NDArray logits('c', {3,4}, nd4j::DataType::DOUBLE);
|
2019-06-15 13:34:34 +02:00
|
|
|
NDArray weights('c', {}, {1.});
|
2019-06-06 14:21:15 +02:00
|
|
|
NDArray labels('c', {3,4}, {0,1,1,0,1,0,1,0,1,0,1,0});
|
|
|
|
|
|
2019-06-15 13:34:34 +02:00
|
|
|
NDArray output('c', {}, {0.}, nd4j::DataType::DOUBLE);
|
2019-06-06 14:21:15 +02:00
|
|
|
|
|
|
|
|
logits.linspace(1.);
|
|
|
|
|
weights.assign(1.);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::hinge_loss op;
|
|
|
|
|
Nd4jStatus status = op.execute({&logits, &weights, &labels}, {&output}, {}, {1}, {});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(output.e<double>(0) == 47.);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, TestDivideBP_1) {
|
|
|
|
|
|
|
|
|
|
NDArray x('c', {3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray y = NDArrayFactory::create<double>(2.);
|
|
|
|
|
NDArray eps('c', {3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
|
|
|
|
|
NDArray output1('c', {3, 4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray output2(nd4j::DataType::DOUBLE);
|
|
|
|
|
|
|
|
|
|
x.linspace(2., 2.);
|
|
|
|
|
eps.linspace(1.);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::divide_bp op;
|
|
|
|
|
Nd4jStatus status = op.execute({&x, &y, &eps}, {&output1, &output2}, {}, {}, {});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
|
output1.printIndexedBuffer("DivideBP X out");
|
|
|
|
|
output2.printIndexedBuffer("DivideBP Y out");
|
|
|
|
|
//ASSERT_TRUE(output.e<double>(0) == 47.);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, TestDivideBP_2) {
|
|
|
|
|
|
|
|
|
|
NDArray x('c', {3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray y = NDArrayFactory::create<double>('c', {3,4});
|
|
|
|
|
NDArray eps('c', {3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray exp1('c', {3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray exp2('c', {3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray output1('c', {3, 4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray output2('c', {3, 4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
exp1.assign(1.);
|
|
|
|
|
exp2.assign(-2.);
|
|
|
|
|
x.linspace(2., 2.);
|
|
|
|
|
y.linspace(1.);
|
|
|
|
|
eps.linspace(1.);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::divide_bp op;
|
|
|
|
|
Nd4jStatus status = op.execute({&x, &y, &eps}, {&output1, &output2}, {}, {}, {});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
|
output1.printIndexedBuffer("2DivideBP X out");
|
|
|
|
|
output2.printIndexedBuffer("2DivideBP Y out");
|
|
|
|
|
ASSERT_TRUE(output1.equalsTo(exp1));
|
|
|
|
|
ASSERT_TRUE(output2.equalsTo(exp2));
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, TestReverseDivideBP_1) {
|
|
|
|
|
|
|
|
|
|
NDArray x('c', {3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray y = NDArrayFactory::create<double>(2.);
|
|
|
|
|
NDArray eps('c', {3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
|
|
|
|
|
NDArray output1('c', {3, 4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray output2(nd4j::DataType::DOUBLE);
|
|
|
|
|
|
|
|
|
|
x.linspace(2., 2.);
|
|
|
|
|
eps.linspace(1.);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::reversedivide_bp op;
|
|
|
|
|
Nd4jStatus status = op.execute({&y, &x, &eps}, {&output2, &output1}, {}, {}, {});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
|
output1.printIndexedBuffer("RDivideBP X out");
|
|
|
|
|
output2.printIndexedBuffer("RDivideBP Y out");
|
|
|
|
|
//ASSERT_TRUE(output.e<double>(0) == 47.);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, TestReverseDivideBP_2) {
|
|
|
|
|
|
|
|
|
|
NDArray x('c', {3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray y = NDArrayFactory::create<double>('c', {3,4});
|
|
|
|
|
NDArray eps('c', {3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray exp1('c', {3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray exp2('c', {3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
|
|
|
|
|
NDArray output1('c', {3, 4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray output2('c', {3, 4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
|
|
|
|
|
x.linspace(2., 2.);
|
|
|
|
|
y.linspace(1.);
|
|
|
|
|
eps.linspace(1.);
|
|
|
|
|
exp1.assign(1.);
|
|
|
|
|
exp2.assign(-2.);
|
|
|
|
|
nd4j::ops::reversedivide_bp op;
|
|
|
|
|
Nd4jStatus status = op.execute({&y, &x, &eps}, {&output2, &output1}, {}, {}, {});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
|
output1.printIndexedBuffer("2RDivideBP X out");
|
|
|
|
|
output2.printIndexedBuffer("2RDivideBP Y out");
|
|
|
|
|
ASSERT_TRUE(output1.equalsTo(exp1));
|
|
|
|
|
ASSERT_TRUE(output2.equalsTo(exp2));
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, TestSliceBP_1) {
|
|
|
|
|
|
|
|
|
|
NDArray x('c', {3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray eps('c', {2,2}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray exp('c', {3,4}, {0., 0., 0., 0., 0., 1.,1., 0., 0., 1., 1., 0.});
|
|
|
|
|
//NDArray exp2('c', {3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
|
|
|
|
|
NDArray output('c', {3, 4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
//NDArray output2('c', {3, 4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
output.assign(119.113);
|
|
|
|
|
x.linspace(1.);
|
|
|
|
|
eps.assign(1.);
|
|
|
|
|
//exp1.assign(1.);
|
|
|
|
|
//exp2.assign(-2.);
|
|
|
|
|
nd4j::ops::slice_bp op;
|
|
|
|
|
Nd4jStatus status = op.execute({&x, &eps}, {&output}, {}, {1,1,2,2}, {});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
|
output.printIndexedBuffer("SLICE_BP out");
|
|
|
|
|
ASSERT_TRUE(output.equalsTo(exp));
|
|
|
|
|
//ASSERT_TRUE(output2.equalsTo(exp2));
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, TestConfusionZero_1) {
|
|
|
|
|
|
|
|
|
|
NDArray x('c', {2}, {1,2}, nd4j::DataType::INT64);
|
|
|
|
|
NDArray i('c', {2}, {0,2}, nd4j::DataType::INT64);
|
|
|
|
|
//NDArray eps('c', {2,2}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray exp('c', {4,4}, {0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0}, nd4j::DataType::INT64);
|
|
|
|
|
//NDArray exp2('c', {3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
|
|
|
|
|
NDArray output('c', {4, 4}, nd4j::DataType::INT64);
|
|
|
|
|
//NDArray output2('c', {3, 4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
output.assign(119.113);
|
|
|
|
|
x.linspace(1.);
|
|
|
|
|
//eps.assign(1.);
|
|
|
|
|
//exp1.assign(1.);
|
|
|
|
|
//exp2.assign(-2.);
|
|
|
|
|
nd4j::ops::confusion_matrix op;
|
|
|
|
|
Nd4jStatus status = op.execute({&x, &i}, {&output}, {}, {4}, {});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
|
output.printIndexedBuffer("Confusion out");
|
|
|
|
|
ASSERT_TRUE(output.equalsTo(exp));
|
|
|
|
|
//ASSERT_TRUE(output2.equalsTo(exp2));
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, TestMaximumBP_1) {
|
|
|
|
|
|
|
|
|
|
NDArray x('c', {3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray y('c', {3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray eps('c', {3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray exp1('c', {3,4}, {0, 0, 0, 0, 0, 0, 7, 8, 9, 10, 11, 12}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray exp2('c', {3,4}, {1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0}, nd4j::DataType::DOUBLE);
|
|
|
|
|
|
|
|
|
|
NDArray output1('c', {3, 4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray output2('c', {3, 4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
output1.assign(119);
|
|
|
|
|
x.linspace(1.);
|
|
|
|
|
y.linspace(12., -1.);
|
|
|
|
|
x.printBuffer("X");
|
|
|
|
|
y.printBuffer("Y");
|
|
|
|
|
eps.linspace(1.);
|
|
|
|
|
//exp1.assign(1.);
|
|
|
|
|
//exp2.assign(-2.);
|
|
|
|
|
nd4j::ops::maximum_bp op;
|
|
|
|
|
Nd4jStatus status = op.execute({&x, &y, &eps}, {&output1, &output2}, {}, {}, {});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
|
output1.printIndexedBuffer("X max");
|
|
|
|
|
output2.printIndexedBuffer("Y max");
|
|
|
|
|
ASSERT_TRUE(output1.equalsTo(exp1));
|
|
|
|
|
ASSERT_TRUE(output2.equalsTo(exp2));
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, TestMinimumBP_1) {
|
|
|
|
|
|
|
|
|
|
NDArray x('c', {3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray y('c', {3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray eps('c', {3,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray exp1('c', {3,4}, {0, 0, 0, 0, 0, 0, 7, 8, 9, 10, 11, 12}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray exp2('c', {3,4}, {1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0}, nd4j::DataType::DOUBLE);
|
|
|
|
|
|
|
|
|
|
NDArray output1('c', {3, 4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray output2('c', {3, 4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
output1.assign(119);
|
|
|
|
|
x.linspace(1.);
|
|
|
|
|
y.linspace(12., -1.);
|
|
|
|
|
x.printBuffer("X");
|
|
|
|
|
y.printBuffer("Y");
|
|
|
|
|
eps.linspace(1.);
|
|
|
|
|
//exp1.assign(1.);
|
|
|
|
|
//exp2.assign(-2.);
|
|
|
|
|
nd4j::ops::minimum_bp op;
|
|
|
|
|
Nd4jStatus status = op.execute({&x, &y, &eps}, {&output2, &output1}, {}, {}, {});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
|
output2.printIndexedBuffer("X min");
|
|
|
|
|
output1.printIndexedBuffer("Y min");
|
|
|
|
|
ASSERT_TRUE(output1.equalsTo(exp1));
|
|
|
|
|
ASSERT_TRUE(output2.equalsTo(exp2));
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, reverse_test15) {
|
|
|
|
|
|
|
|
|
|
NDArray x('c', {5}, {1,2,3,4,5}, nd4j::DataType::DOUBLE);
|
2019-06-15 13:34:34 +02:00
|
|
|
NDArray axis('c', {}, {0}, nd4j::DataType::INT32);
|
2019-06-06 14:21:15 +02:00
|
|
|
NDArray z('c', {5}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray exp('c', {5}, {5,4,3,2,1}, nd4j::DataType::DOUBLE);
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
nd4j::ops::reverse op;
|
|
|
|
|
// auto result = op.execute({&x, &axis}, {}, {1}, {});
|
|
|
|
|
Nd4jStatus status = op.execute({&x, &axis}, {&z}, {}, {1}, {});
|
|
|
|
|
// auto z = result->at(0);
|
|
|
|
|
// z->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(Status::OK(), status);
|
|
|
|
|
ASSERT_TRUE(exp.isSameShape(z));
|
|
|
|
|
ASSERT_TRUE(exp.equalsTo(z));
|
|
|
|
|
// delete result;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, mirrorPad_test17) {
|
|
|
|
|
|
|
|
|
|
NDArray x('c', {2,3}, {1,2,3,4,5,6}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray padding('c', {2,2}, {1,1,2,2}, nd4j::DataType::INT32);
|
|
|
|
|
NDArray z('c', {4,7}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray exp1('c', {4,7}, {6, 5, 4, 5, 6, 5, 4,3, 2, 1, 2, 3, 2, 1,6, 5, 4, 5, 6, 5, 4,3, 2, 1, 2, 3, 2, 1}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray exp2('c', {4,7}, {2, 1, 1, 2, 3, 3, 2,2, 1, 1, 2, 3, 3, 2,5, 4, 4, 5, 6, 6, 5,5, 4, 4, 5, 6, 6, 5}, nd4j::DataType::DOUBLE);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::mirror_pad op;
|
|
|
|
|
Nd4jStatus status = op.execute({&x, &padding}, {&z}, {}, {0}, {}); // reflect
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(Status::OK(), status);
|
|
|
|
|
ASSERT_TRUE(exp1.isSameShape(z));
|
|
|
|
|
ASSERT_TRUE(exp1.equalsTo(z));
|
|
|
|
|
|
|
|
|
|
z = 0.;
|
|
|
|
|
status = op.execute({&x, &padding}, {&z}, {}, {1}, {}); // symmetric
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(Status::OK(), status);
|
|
|
|
|
ASSERT_TRUE(exp2.isSameShape(z));
|
|
|
|
|
ASSERT_TRUE(exp2.equalsTo(z));
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, mirrorPad_test18) {
|
|
|
|
|
|
|
|
|
|
NDArray x('c', {3}, {1,2,3}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray padding('c', {2}, {1,1}, nd4j::DataType::INT32);
|
|
|
|
|
NDArray z('c', {5}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray exp('c', {5}, {2,1,2,3,2}, nd4j::DataType::DOUBLE);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::mirror_pad op;
|
|
|
|
|
Nd4jStatus status = op.execute({&x, &padding}, {&z}, {}, {0}, {}); // reflect
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(Status::OK(), status);
|
|
|
|
|
ASSERT_TRUE(exp.isSameShape(z));
|
|
|
|
|
ASSERT_TRUE(exp.equalsTo(z));
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, relu_1) {
|
|
|
|
|
|
|
|
|
|
NDArray input('c', {1,5,5,6}, { 0.557449, 0.768277, 1.094015, -0.557449, -0.768277, -1.094015,0.563735, 0.900299, 0.789979, -0.563735, -0.900299, -0.789979,
|
|
|
|
|
0.142528, 0.959611, 0.877506, -0.142528, -0.959611, -0.877506,0.448742, 0.995377, 1.171543, -0.448742, -0.995377, -1.171543,
|
|
|
|
|
0.603772, 0.799391, 0.560310, -0.603772, -0.799391, -0.560310,0.529753, 0.906786, 0.737630, -0.529753, -0.906786, -0.737630,
|
|
|
|
|
0.221464, 0.824996, 0.472221, -0.221464, -0.824996, -0.472221,0.427730, 0.397933, 0.714365, -0.427730, -0.397933, -0.714365,
|
|
|
|
|
0.488365, 1.016589, 0.744197, -0.488365, -1.016589, -0.744197,0.789846, 0.940837, 0.838412, -0.789846, -0.940837, -0.838412,
|
|
|
|
|
0.404485, 0.677328, 0.754997, -0.404485, -0.677328, -0.754997,0.436760, 0.794765, 0.729766, -0.436760, -0.794765, -0.729766,
|
|
|
|
|
0.588081, 0.652226, 0.725522, -0.588081, -0.652226, -0.725522,0.374457, 1.225813, 1.053411, -0.374457, -1.225813, -1.053411,
|
|
|
|
|
0.300958, 0.599417, 0.633234, -0.300958, -0.599417, -0.633234,0.241993, 1.025464, 0.695378, -0.241993, -1.025464, -0.695378,
|
|
|
|
|
0.236289, 0.907919, 1.012100, -0.236289, -0.907919, -1.012100,0.627402, 0.565187, 0.766926, -0.627402, -0.565187, -0.766926,
|
|
|
|
|
0.133276, 0.326284, 0.102804, -0.133276, -0.326284, -0.102804,0.426913, 0.256251, 0.305241, -0.426913, -0.256251, -0.305241,
|
|
|
|
|
0.177977, 0.841799, 0.800615, -0.177977, -0.841799, -0.800615,0.001991, 0.518389, 0.439322, -0.001991, -0.518389, -0.439322,
|
|
|
|
|
0.166846, 0.508224, 0.486687, -0.166846, -0.508224, -0.486687,0.167493, 0.930932, 0.868717, -0.167493, -0.930932, -0.868717,
|
|
|
|
|
0.174864, 0.444607, 0.445000, -0.174864, -0.444607, -0.445000}, nd4j::DataType::FLOAT32);
|
|
|
|
|
|
|
|
|
|
NDArray expected('c', {1,5,5,6}, { 0.557449, 0.768277, 1.094015, 0., 0., 0., 0.563735, 0.900299, 0.789979, 0., 0., 0.,
|
|
|
|
|
0.142528, 0.959611, 0.877506, 0., 0., 0., 0.448742, 0.995377, 1.171543, 0., 0., 0.,
|
|
|
|
|
0.603772, 0.799391, 0.560310, 0., 0., 0., 0.529753, 0.906786, 0.737630, 0., 0., 0.,
|
|
|
|
|
0.221464, 0.824996, 0.472221, 0., 0., 0., 0.427730, 0.397933, 0.714365, 0., 0., 0.,
|
|
|
|
|
0.488365, 1.016589, 0.744197, 0., 0., 0., 0.789846, 0.940837, 0.838412, 0., 0., 0.,
|
|
|
|
|
0.404485, 0.677328, 0.754997, 0., 0., 0., 0.436760, 0.794765, 0.729766, 0., 0., 0.,
|
|
|
|
|
0.588081, 0.652226, 0.725522, 0., 0., 0., 0.374457, 1.225813, 1.053411, 0., 0., 0.,
|
|
|
|
|
0.300958, 0.599417, 0.633234, 0., 0., 0., 0.241993, 1.025464, 0.695378, 0., 0., 0.,
|
|
|
|
|
0.236289, 0.907919, 1.012100, 0., 0., 0., 0.627402, 0.565187, 0.766926, 0., 0., 0.,
|
|
|
|
|
0.133276, 0.326284, 0.102804, 0., 0., 0., 0.426913, 0.256251, 0.305241, 0., 0., 0.,
|
|
|
|
|
0.177977, 0.841799, 0.800615, 0., 0., 0., 0.001991, 0.518389, 0.439322, 0., 0., 0.,
|
|
|
|
|
0.166846, 0.508224, 0.486687, 0., 0., 0., 0.167493, 0.930932, 0.868717, 0., 0., 0.,
|
|
|
|
|
0.174864, 0.444607, 0.445000, 0., 0., 0.}, nd4j::DataType::FLOAT32);
|
|
|
|
|
|
|
|
|
|
NDArray z('c', {1,5,5,6}, nd4j::DataType::FLOAT32);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::relu op;
|
|
|
|
|
Nd4jStatus status = op.execute({&input}, {&z}, {0}, {}, {});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(&z));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(z));
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
#include "ops/declarable/helpers/multiUnique.h"
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, multiUnique_1) {
|
|
|
|
|
|
|
|
|
|
NDArray input1('c', {3,5}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15}, nd4j::DataType::INT32);
|
|
|
|
|
NDArray input2('c', {3,4}, {1,2,3,4,5,6,7,8,9,10,11,12}, nd4j::DataType::INT32);
|
|
|
|
|
NDArray input3('c', {2,3}, {10,11,12,13,14,15}, nd4j::DataType::INT32);
|
|
|
|
|
NDArray input4('c', {1,5}, {7,8,9,10,11}, nd4j::DataType::INT32);
|
|
|
|
|
NDArray input5('c', {5,3}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15}, nd4j::DataType::INT32);
|
|
|
|
|
|
|
|
|
|
//NDArray indices('c', {1}, {2}, nd4j::DataType::INT32);
|
|
|
|
|
//NDArray expected('c', {1,5}, {11, 12, 13, 14, 15.}, nd4j::DataType::FLOAT32);
|
|
|
|
|
|
|
|
|
|
std::vector<NDArray*> arrayList({&input1, &input2, &input3, &input4, &input5});
|
|
|
|
|
|
|
|
|
|
ASSERT_FALSE(nd4j::ops::helpers::multiUnique(arrayList));
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, multiUnique_2) {
|
|
|
|
|
|
|
|
|
|
NDArray input1('c', {3,5}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15}, nd4j::DataType::INT32);
|
|
|
|
|
NDArray input2('c', {3,4}, {21,22,23,24,25,26,27,28,29,210,211,212}, nd4j::DataType::INT32);
|
|
|
|
|
NDArray input3('c', {2,3}, {310,311,312,313,314,315}, nd4j::DataType::INT32);
|
|
|
|
|
NDArray input4('c', {1,5}, {47,48,49,410,411}, nd4j::DataType::INT32);
|
|
|
|
|
NDArray input5('c', {5,3}, {51,52,53,54,55,56,57,58,59,510,511,512,513,514,515}, nd4j::DataType::INT32);
|
|
|
|
|
|
|
|
|
|
//NDArray indices('c', {1}, {2}, nd4j::DataType::INT32);
|
|
|
|
|
//NDArray expected('c', {1,5}, {11, 12, 13, 14, 15.}, nd4j::DataType::FLOAT32);
|
|
|
|
|
|
|
|
|
|
std::vector<NDArray*> arrayList({&input1, &input2, &input3, &input4, &input5});
|
|
|
|
|
ASSERT_TRUE(nd4j::ops::helpers::multiUnique(arrayList));
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, tensormmul_6) {
|
|
|
|
|
|
[WIP] multi-device support (#80)
* fix pad javadoc and @see links. (#72)
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* [WIP] More fixes (#73)
* special tests for ConstantTadHelper/ConstantShapeHelper
Signed-off-by: raver119 <raver119@gmail.com>
* release methods for data buffers
Signed-off-by: raver119 <raver119@gmail.com>
* delete temporary buffer Java side
Signed-off-by: raver119 <raver119@gmail.com>
* delete temporary buffer Java side
Signed-off-by: raver119 <raver119@gmail.com>
* delete temporary TadPack C++/Java side (#74)
Signed-off-by: raver119 <raver119@gmail.com>
* Zoo model TF import test updates (#75)
* argLine fix, update compression_gru comment
* updated comment for xception
* undid but commented argLine change
* updated xlnet comment
* copyright headers
* - new NDArray methods like()/ulike() (#77)
- fix for depthwise_conv2d_bp + special test
Signed-off-by: raver119 <raver119@gmail.com>
* upsampling2d fix CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* DL4J trace logging (#79)
* MLN/CG trace logging for debugging
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tiny tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* strided_slice_bp shape fn leak fix
Signed-off-by: raver119 <raver119@gmail.com>
* SameDiff fixes and naming (#78)
* remove SDVariable inplace methods
* import methods
* npe fix in OpVal
* removed SameDiff inplace ops from tests
* Naming updates, moved to centralized methods in SameDiff, should use op_#:# for everything
* quick fixes
* javadoc
* SDVariable eval with placeholders
* use regex match
* better matching
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* fix javadoc. (#76)
* fix javadoc.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* replace most @see with @link s.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* 4 additional tests
Signed-off-by: raver119 <raver119@gmail.com>
* launch context reorganization
Signed-off-by: raver119 <raver119@gmail.com>
* LaunchContext reorganization
Signed-off-by: raver119 <raver119@gmail.com>
* per-device LaunchContext
Signed-off-by: raver119 <raver119@gmail.com>
* Various DL4J/ND4J fixes (#81)
* #7954 Force refresh of UI when switching tabs on overview page
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8017 Concurrent modification exception (synchronize) fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8033 Don't initialize updater in middle of writing memory crash dump
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8208 Fix shape checks for ND4J int[] creator methods
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #6385 #7992 Keras import naming fixes + cleanup
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8016 Upsampling3D - add NDHWC format support
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* Refactor NativeOps.h to export C functions
* Actually export functions from NativeOps.h
* Adapt the Java wrappers in ND4J generated with JavaCPP
* Create C wrappers for some of the C++ classes currently used by ND4J
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* remove duplicate code in createBufferDetached. (#83)
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* Keras model import - updater lr fix (#84)
* Keras model import - updater lr fix
Signed-off-by: eraly <susan.eraly@gmail.com>
* Keras model import - updater lr fix, cleanup
Signed-off-by: eraly <susan.eraly@gmail.com>
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* Fix functions of OpaqueVariablesSet
* thread-local buffers/affinity
Signed-off-by: raver119 <raver119@gmail.com>
* thread safety for LaunchContext
Signed-off-by: raver119 <raver119@gmail.com>
* more of thread safety
Signed-off-by: raver119 <raver119@gmail.com>
* one more multi threaded test
Signed-off-by: raver119 <raver119@gmail.com>
* SameDiff Convolution Config validation, better output methods (#82)
* Conv Config validation & tests
Signed-off-by: Ryan Nett <rnett@skymind.io>
* stackOutputs utility method
Signed-off-by: Ryan Nett <rnett@skymind.io>
* use constructor for validation, support negative kernel sizes (infered from weights)
Signed-off-by: Ryan Nett <rnett@skymind.io>
* better output methods
Signed-off-by: Ryan Nett <rnett@skymind.io>
* move output to be with fit and evaluate
Signed-off-by: Ryan Nett <rnett@skymind.io>
* fixes
Signed-off-by: Ryan Nett <rnett@skymind.io>
* more fixes
Signed-off-by: Ryan Nett <rnett@skymind.io>
* refactor duplicate code from pad methods. (#86)
* refactor duplicate code from pad methods.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* replace switch with if.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* Various ND4J/DL4J fixes and improvements (#87)
* Reshape and reallocate - small fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Reshape and reallocate - small fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #6488 ElementWiseVertex broadcast support
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Constructors and broadcast supported it Transforms.max/min
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8054 ElementWiseVertex now supports broadcast inputs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8057 Nd4j.create overload dtype fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7551 ND4J Shape validation fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Numpy boolean import (#91)
* numpy bool type
Signed-off-by: raver119 <raver119@gmail.com>
* numpy bool java side
Signed-off-by: raver119 <raver119@gmail.com>
* remove create method with unused parameter. (#89)
* remove create method with unused parameter.
* removed more unused methods.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* removing more unused code.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* last removal of unused code.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* remove createSparse methods. (#92)
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* Various ND4J/DL4J fixes (#90)
* Deprecate Old*Op instances
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8063 #8054 Broadcast exceptions + cleanup inplace ops
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Remove bad test condition
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7993 Fix shape function issue in crop_and_resize op
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* DL4J SameDiff lambda layer fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8029 Fix for pnorm backprop math
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8038 Fix Op profiler NaN/Inf triggering + add tests (#93)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* createUninitializedDetached refactoring. (#94)
* wip
* update interface, add null implementations.
* Breaking one test in a weird way.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* createUninitializedDetached refactored.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* cuda build fix for issues introduced by recent refactoring
Signed-off-by: raver119 <raver119@gmail.com>
* [WIP] More of CUDA (#95)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* Implementation of hashcode cuda helper. Working edition.
* Fixed parallel test input arangements.
* Fixed tests for hashcode op.
* Fixed shape calculation for image:crop_and_resize op and test.
* NativeOps tests. Initial test suite.
* Added tests for indexReduce methods.
* Added test on execBroadcast with NDArray as dimensions.
* Added test on execBroadcastBool with NDArray as dimensions.
* Added tests on execPairwiseTransform and execPairwiseTransofrmBool.
* Added tests for execReduce with scalar results.
* Added reduce tests for non-empty dims array.
* Added tests for reduce3.
* Added tests for execScalar.
* Added tests for execSummaryStats.
* - provide cpu/cuda code for batch_to_space
- testing it
Signed-off-by: Yurii <yurii@skymind.io>
* - remove old test for batch_to_space (had wrong format and numbers were not checked)
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed complilation errors with test.
* Added test for execTransformFloat.
* Added test for execTransformSame.
* Added test for execTransformBool.
* Added test for execTransformStrict.
* Added tests for execScalar/execScalarBool with TADs.
* Added test for flatten.
* - provide cpu/cuda code for space_to_Batch operaion
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for concat.
* comment unnecessary stuff in s_t_b
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for specialConcat.
* Added tests for memcpy/set routines.
* Fixed pullRow cuda test.
* Added pullRow test.
* Added average test.
* - correct typo in NDArray::applyPairwiseTransform(nd4j::pairwise::BoolOps op...)
Signed-off-by: Yurii <yurii@skymind.io>
* - debugging and fixing cuda tests in JavaInteropTests file
Signed-off-by: Yurii <yurii@skymind.io>
* - correct some tests
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for shuffle.
* Fixed ops declarations.
* Restored omp and added shuffle test.
* Added convertTypes test.
* Added tests for execRandom. Eliminated usage of RandomBuffer with NativeOps.
* Added sort tests.
* Added tests for execCustomOp.
* - further debuging and fixing tests terminated with crash
Signed-off-by: Yurii <yurii@skymind.io>
* Added tests for calculateOutputShapes.
* Addded Benchmarks test.
* Commented benchmark tests.
* change assertion
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for apply_sgd op. Added cpu helper for that op.
* Implement cuda helper for aplly_sgd op. Fixed tests for NativeOps.
* Added test for assign broadcastable.
* Added tests for assign_bp op.
* Added tests for axpy op.
* - assign/execScalar/execTransformAny signature change
- minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed axpy op.
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* - fix tests for nativeOps::concat
Signed-off-by: Yurii <yurii@skymind.io>
* sequential transform/scalar
Signed-off-by: raver119 <raver119@gmail.com>
* allow nested parallelism
Signed-off-by: raver119 <raver119@gmail.com>
* assign_bp leak fix
Signed-off-by: raver119 <raver119@gmail.com>
* block setRNG fix
Signed-off-by: raver119 <raver119@gmail.com>
* enable parallelism by default
Signed-off-by: raver119 <raver119@gmail.com>
* enable nested parallelism by default
Signed-off-by: raver119 <raver119@gmail.com>
* Added cuda implementation for row_count helper.
* Added implementation for tnse gains op helper.
* - take into account possible situations when input arrays are empty in reduce_ cuda stuff
Signed-off-by: Yurii <yurii@skymind.io>
* Implemented tsne/edge_forces op cuda-based helper. Parallelized cpu-based helper for edge_forces.
* Added kernel for tsne/symmetrized op heleper.
* Implementation of tsne/symmetrized op cuda helper. Working edition.
* Eliminated waste printfs.
* Added test for broadcastgradientargs op.
* host-only fallback for empty reduce float
Signed-off-by: raver119 <raver119@gmail.com>
* - some tests fixes
Signed-off-by: Yurii <yurii@skymind.io>
* - correct the rest of reduce_ stuff
Signed-off-by: Yurii <yurii@skymind.io>
* - further correction of reduce_ stuff
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for Cbow op. Also added cuda implementation for cbow helpers.
* - improve code of stack operation for scalar case
Signed-off-by: Yurii <yurii@skymind.io>
* - provide cuda kernel for gatherND operation
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of cbow helpers with cuda kernels.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* - further correction of cuda stuff
Signed-off-by: Yurii <yurii@skymind.io>
* Implementatation of cbow op helper with cuda kernels. Working edition.
* Skip random testing for cudablas case.
* lstmBlockCell context fix
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for ELU and ELU_BP ops.
* Added tests for eq_scalar, gt_scalar, gte_scalar and lte_scalar ops.
* Added tests for neq_scalar.
* Added test for noop.
* - further work on clipbynorm_bp
Signed-off-by: Yurii <yurii@skymind.io>
* - get rid of concat op call, use instead direct concat helper call
Signed-off-by: Yurii <yurii@skymind.io>
* lstmBlockCell context fix
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for lrelu and lrelu_bp.
* Added tests for selu and selu_bp.
* Fixed lrelu derivative helpers.
* - some corrections in lstm
Signed-off-by: Yurii <yurii@skymind.io>
* operator * result shape fix
Signed-off-by: raver119 <raver119@gmail.com>
* - correct typo in lstmCell
Signed-off-by: Yurii <yurii@skymind.io>
* few tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA inverse broadcast bool fix
Signed-off-by: raver119 <raver119@gmail.com>
* disable MMAP test for CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* BooleanOp syncToDevice
Signed-off-by: raver119 <raver119@gmail.com>
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* additional data types for im2col/col2im
Signed-off-by: raver119 <raver119@gmail.com>
* Added test for firas_sparse op.
* one more RandomBuffer test excluded
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for flatten op.
* Added test for Floor op.
* bunch of tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* mmulDot tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* Implemented floordiv_bp op and tests.
* Fixed scalar case with cuda implementation for bds.
* - work on cuda kernel for clip_by_norm backprop op is completed
Signed-off-by: Yurii <yurii@skymind.io>
* Eliminate cbow crach.
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminated abortion with batched nlp test.
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed shared flag initializing.
* disabled bunch of cpu workspaces tests
Signed-off-by: raver119 <raver119@gmail.com>
* scalar operators fix: missing registerSpecialUse call
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed logdet for cuda and tests.
* - correct clipBynorm_bp
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed crop_and_resize shape datatype.
* - correct some mmul tests
Signed-off-by: Yurii <yurii@skymind.io>
* build fix
Signed-off-by: raver119 <raver119@gmail.com>
* exclude two methods for JNI
Signed-off-by: raver119 <raver119@gmail.com>
* exclude two methods for JNI
Signed-off-by: raver119 <raver119@gmail.com>
* exclude two methods for JNI (#97)
Signed-off-by: raver119 <raver119@gmail.com>
* temporary stack fix
Signed-off-by: raver119 <raver119@gmail.com>
* round robin affinity test
Signed-off-by: raver119 <raver119@gmail.com>
* get rid of legacy CudaContext methods
Signed-off-by: raver119 <raver119@gmail.com>
* get rid of legacy ContextPool classes/methods
Signed-off-by: raver119 <raver119@gmail.com>
* one legacy test removed
Signed-off-by: raver119 <raver119@gmail.com>
* few more fields rearranged
Signed-off-by: raver119 <raver119@gmail.com>
* OpaqueLaunchContext
Signed-off-by: raver119 <raver119@gmail.com>
* OpaqueLaunchContext++
Signed-off-by: raver119 <raver119@gmail.com>
* more of OpaqueLaunchContext methods
Signed-off-by: raver119 <raver119@gmail.com>
* LaunchContext -> CudaContext
Signed-off-by: raver119 <raver119@gmail.com>
* AffinityManger changes
Signed-off-by: raver119 <raver119@gmail.com>
* AffinityManger changes
Signed-off-by: raver119 <raver119@gmail.com>
* cusolver handles
Signed-off-by: raver119 <raver119@gmail.com>
* typo
Signed-off-by: raver119 <raver119@gmail.com>
* cusolver method
Signed-off-by: raver119 <raver119@gmail.com>
* cusolver handle propagated
Signed-off-by: raver119 <raver119@gmail.com>
* blas/solver handles
Signed-off-by: raver119 <raver119@gmail.com>
* one more test
Signed-off-by: raver119 <raver119@gmail.com>
* legacy concat implementations replaced with new CustomOp
Signed-off-by: raver119 <raver119@gmail.com>
* one more test
Signed-off-by: raver119 <raver119@gmail.com>
* concat now uses way more blocks
Signed-off-by: raver119 <raver119@gmail.com>
* print
Signed-off-by: raver119 <raver119@gmail.com>
* no more triple template mmul
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of kernels have dtypes reconsidered
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of kernels have dtypes reconsidered
Signed-off-by: raver119 <raver119@gmail.com>
* bitonic sort reorganized
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of cpu stuff removed from cuda scope
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of cpu stuff removed from cuda scope
Signed-off-by: raver119 <raver119@gmail.com>
* type conversions moved to generic impl
Signed-off-by: raver119 <raver119@gmail.com>
* cpu data types pass
Signed-off-by: raver119 <raver119@gmail.com>
* non_max_suppression
Signed-off-by: raver119 <raver119@gmail.com>
* sortByValue fix
Signed-off-by: raver119 <raver119@gmail.com>
* ignore all mixed datatype tests for mmul
Signed-off-by: raver119 <raver119@gmail.com>
* special handling of OpProfiler exceptions
Signed-off-by: raver119 <raver119@gmail.com>
* - one failing concat test in cpp
- Nd4j.tile now uses op internally
Signed-off-by: raver119 <raver119@gmail.com>
* get back dtype exception for legacy arrays deserialization
Signed-off-by: raver119 <raver119@gmail.com>
2019-08-14 15:52:34 +02:00
|
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NDArray x('c', {1}, {2}, nd4j::DataType::FLOAT32);
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NDArray y('c', {2,1,2}, {1,2,3,4}, nd4j::DataType::FLOAT32);
|
2019-06-06 14:21:15 +02:00
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NDArray exp('c', {2,2}, {2,4,6,8}, nd4j::DataType::FLOAT32);
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nd4j::ops::tensormmul op;
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auto results = op.execute({&x, &y}, {}, {1,0, 1,1});
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ASSERT_EQ(ND4J_STATUS_OK, results->status());
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auto *result = results->at(0);
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// exp.printShapeInfo();
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// result->printShapeInfo();
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// result->printIndexedBuffer();
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ASSERT_TRUE(exp.isSameShape(result));
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ASSERT_TRUE(exp.equalsTo(result));
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delete results;
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}
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////////////////////////////////////////////////////////////////////////////////
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|
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TEST_F(DeclarableOpsTests12, concat_test10) {
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NDArray x0('c', {1,4,5}, nd4j::DataType::FLOAT32);
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NDArray x1('c', {2,4,5}, nd4j::DataType::FLOAT32);
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NDArray z('f', {3,4,5}, nd4j::DataType::FLOAT32);
|
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x0 = 0.;
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x1 = 1.;
|
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|
|
nd4j::ops::concat op;
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|
|
|
auto status = op.execute({&x0, &x1}, {&z}, {}, {0}, {});
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, concat_14) {
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NDArray x0('c', {1,6}, {1,2,3,4,5,6});
|
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|
NDArray x1('c', {1,6}, {7,8,9,10,11,12});
|
|
|
|
|
NDArray output('f', {2,6}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray exp('c', {2,6}, {1,2,3,4,5,6,7,8,9,10,11,12});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::concat op;
|
|
|
|
|
|
|
|
|
|
auto status = op.execute({&x0, &x1}, {&output}, {}, {0}, {});
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
|
// output.printBuffer();
|
|
|
|
|
// output.printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(exp.equalsTo(output));
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, concat_15) {
|
|
|
|
|
|
|
|
|
|
NDArray x0('c', {1,4}, {1,2,3,4});
|
|
|
|
|
NDArray x1('c', {1,4}, {5,6,7,8});
|
|
|
|
|
NDArray output('c', {2,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray exp('c', {2,4}, {1,2,3,4,5,6,7,8});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::concat op;
|
|
|
|
|
|
|
|
|
|
auto status = op.execute({&x0, &x1}, {&output}, {}, {0}, {});
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
|
// output.printBuffer();
|
|
|
|
|
// output.printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(exp.equalsTo(output));
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, reduceMeanBp_4) {
|
|
|
|
|
|
|
|
|
|
NDArray x('c', {3,5}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15});
|
|
|
|
|
NDArray gradO('c', {5}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray exp('c', {3,5}, nd4j::DataType::DOUBLE);
|
|
|
|
|
|
|
|
|
|
gradO = 1.;
|
|
|
|
|
exp = 0.333333;
|
|
|
|
|
|
|
|
|
|
nd4j::ops::reduce_mean_bp op;
|
|
|
|
|
auto result = op.execute({&x, &gradO}, {}, {0});
|
|
|
|
|
auto output = result->at(0);
|
|
|
|
|
|
|
|
|
|
// output->printShapeInfo();
|
|
|
|
|
// output->printIndexedBuffer();
|
|
|
|
|
ASSERT_TRUE(exp.isSameShape(output));
|
|
|
|
|
ASSERT_TRUE(exp.equalsTo(output));
|
|
|
|
|
|
|
|
|
|
delete result;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, reduceMeanBp_5) {
|
|
|
|
|
|
|
|
|
|
NDArray x('c', {3,5}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15});
|
|
|
|
|
NDArray gradO('c', {3}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray exp('c', {3,5}, nd4j::DataType::DOUBLE);
|
|
|
|
|
|
|
|
|
|
gradO = 1.;
|
|
|
|
|
exp = 0.2;
|
|
|
|
|
|
|
|
|
|
nd4j::ops::reduce_mean_bp op;
|
|
|
|
|
auto result = op.execute({&x, &gradO}, {}, {1});
|
|
|
|
|
auto output = result->at(0);
|
|
|
|
|
|
|
|
|
|
// output->printShapeInfo();
|
|
|
|
|
// output->printIndexedBuffer();
|
|
|
|
|
ASSERT_TRUE(exp.isSameShape(output));
|
|
|
|
|
ASSERT_TRUE(exp.equalsTo(output));
|
|
|
|
|
|
|
|
|
|
delete result;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, reduceSqnormBp_1) {
|
|
|
|
|
|
|
|
|
|
NDArray x('c', {8,6,4}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray gradO('c', {8,6,1}, nd4j::DataType::DOUBLE);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::reduce_sqnorm_bp op;
|
|
|
|
|
auto result = op.execute({&x, &gradO}, {1}, {2});
|
|
|
|
|
ASSERT_EQ(Status::OK(), result->status());
|
|
|
|
|
|
|
|
|
|
delete result;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pullRows_1) {
|
|
|
|
|
|
|
|
|
|
NDArray x('c', {5, 1}, {0,1,2,3,4});
|
|
|
|
|
NDArray z('c', {4, 1}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray exp('c', {4, 1}, {0,2,3,4});
|
|
|
|
|
|
|
|
|
|
Nd4jLong indexes[] = {0,2,3,4};
|
|
|
|
|
PointersManager pm(LaunchContext::defaultContext(), "pullRows");
|
|
|
|
|
auto pidx = reinterpret_cast<Nd4jLong *>(pm.replicatePointer(indexes, 4 * sizeof(Nd4jLong)));
|
|
|
|
|
|
|
|
|
|
std::vector<int> dims = {1};
|
|
|
|
|
|
|
|
|
|
auto xTadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x.getShapeInfo(), dims);
|
|
|
|
|
auto zTadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(z.getShapeInfo(), dims);
|
|
|
|
|
|
|
|
|
|
Nd4jPointer nativeStart[2];
|
|
|
|
|
|
|
|
|
|
#ifdef __CUDABLAS__
|
2019-08-02 19:01:03 +02:00
|
|
|
nativeStart[1] = (x.getContext()->getCudaStream());
|
2019-06-06 14:21:15 +02:00
|
|
|
#endif
|
|
|
|
|
|
2019-07-22 13:34:08 +02:00
|
|
|
pullRows(nativeStart, x.buffer(), x.getShapeInfo(), x.getSpecialBuffer(), x.getSpecialShapeInfo(),
|
2019-06-06 14:21:15 +02:00
|
|
|
z.buffer(), z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(),
|
|
|
|
|
4, pidx,
|
|
|
|
|
xTadPack.platformShapeInfo(), xTadPack.platformOffsets(),
|
|
|
|
|
zTadPack.platformShapeInfo(), zTadPack.platformOffsets());
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(z.equalsTo(exp));
|
|
|
|
|
pm.synchronize();
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pullRows_2) {
|
|
|
|
|
|
|
|
|
|
NDArray arr('f', {5, 2}, {0,1,2,3,4,5,6,7,8,9});
|
|
|
|
|
NDArray* y = arr.dup('c');
|
|
|
|
|
NDArray x = (*y)({0,0, 0,1}, true); // view, points on first column of y, shape is {5,1}
|
|
|
|
|
|
|
|
|
|
NDArray z('c', {4, 1}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray exp('c', {4, 1}, {0,2,3,4});
|
|
|
|
|
|
|
|
|
|
Nd4jLong indexes[] = {0,2,3,4};
|
|
|
|
|
PointersManager pm(LaunchContext::defaultContext(), "pullRows");
|
|
|
|
|
auto pidx = reinterpret_cast<Nd4jLong *>(pm.replicatePointer(indexes, 4 * sizeof(Nd4jLong)));
|
|
|
|
|
|
|
|
|
|
std::vector<int> dims = {1};
|
|
|
|
|
|
|
|
|
|
auto xTadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x.getShapeInfo(), dims);
|
|
|
|
|
auto zTadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(z.getShapeInfo(), dims);
|
|
|
|
|
|
|
|
|
|
Nd4jPointer nativeStart[2];
|
|
|
|
|
#ifdef __CUDABLAS__
|
2019-08-02 19:01:03 +02:00
|
|
|
nativeStart[1] = (x.getContext()->getCudaStream());
|
2019-06-06 14:21:15 +02:00
|
|
|
#endif
|
2019-07-22 13:34:08 +02:00
|
|
|
pullRows(nativeStart, x.buffer(), x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(),
|
2019-06-06 14:21:15 +02:00
|
|
|
z.buffer(), z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(),
|
|
|
|
|
4, pidx,
|
|
|
|
|
xTadPack.platformShapeInfo(), xTadPack.platformOffsets(),
|
|
|
|
|
zTadPack.platformShapeInfo(), zTadPack.platformOffsets());
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(z.equalsTo(exp));
|
|
|
|
|
pm.synchronize();
|
|
|
|
|
delete y;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, softmax_9) {
|
|
|
|
|
NDArray arrC('c', {5,2}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6, -0.7, 0.8, -0.9, 1}, nd4j::DataType::FLOAT32);
|
|
|
|
|
NDArray* arrF = arrC.dup('f');
|
|
|
|
|
|
|
|
|
|
NDArray outCC('c', {5,2}, nd4j::DataType::FLOAT32);
|
|
|
|
|
NDArray outCF('f', {5,2}, nd4j::DataType::FLOAT32);
|
|
|
|
|
NDArray outFC('c', {5,2}, nd4j::DataType::FLOAT32);
|
|
|
|
|
NDArray outFF('c', {5,2}, nd4j::DataType::FLOAT32);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::softmax op;
|
|
|
|
|
auto status1 = op.execute({&arrC}, {&outCC}, {}, {}, {});
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status1);
|
|
|
|
|
auto status2 = op.execute({&arrC}, {&outCF}, {}, {}, {});
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status2);
|
|
|
|
|
auto status3 = op.execute({arrF}, {&outFC}, {}, {}, {});
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status3);
|
|
|
|
|
auto status4 = op.execute({arrF}, {&outFF}, {}, {}, {});
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status4);
|
|
|
|
|
|
|
|
|
|
// outCC.printIndexedBuffer("\n");
|
|
|
|
|
// outCF.printIndexedBuffer("\n");
|
|
|
|
|
// outFC.printIndexedBuffer("\n");
|
|
|
|
|
// outFF.printIndexedBuffer("\n");
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(outCC, outCF);
|
|
|
|
|
ASSERT_EQ(outCC, outFC);
|
|
|
|
|
ASSERT_EQ(outCC, outFF);
|
|
|
|
|
|
|
|
|
|
delete arrF;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, lrn_bp_1) {
|
|
|
|
|
|
|
|
|
|
NDArray input('c', {2,3,4,10});
|
|
|
|
|
NDArray gradO('c', {2,3,4,10});
|
|
|
|
|
NDArray exp('c', {2,3,4,10}, {1.00438418e-02, 5.25184907e-03, 1.78685773e-03, -1.14537543e-03, -4.00071684e-03, -5.31899510e-03, -4.97647980e-03, -4.42161644e-03, -3.95395281e-03, -3.59310722e-03, 2.91823584e-04, -2.18498681e-05, -3.12092161e-04, -6.07360795e-04, -9.36298165e-04,
|
|
|
|
|
-1.02553482e-03, -7.91735307e-04, -6.15672267e-04, -4.71792649e-04, -3.42114770e-04, 4.29357824e-05, -5.46473675e-05, -1.48361753e-04, -2.47166492e-04, -3.61090642e-04, -3.81607766e-04, -2.89086485e-04, -2.17203109e-04, -1.56231865e-04, -9.91634734e-05,
|
|
|
|
|
8.99407951e-06, -3.76849275e-05, -8.32021178e-05, -1.31939698e-04, -1.89008832e-04, -1.96661276e-04, -1.47534331e-04, -1.08789405e-04, -7.53896020e-05, -4.36357586e-05,
|
|
|
|
|
1.23124300e-06, -2.60028974e-05, -5.27824741e-05, -8.17063192e-05, -1.15871291e-04, -1.19515295e-04, -8.91248055e-05, -6.49499125e-05, -4.39216528e-05, -2.37579407e-05, -9.34046056e-07, -1.87477999e-05, -3.63574763e-05, -5.54830040e-05, -7.82010393e-05,
|
|
|
|
|
-8.02115537e-05, -5.95739621e-05, -4.30659420e-05, -2.86241393e-05, -1.47010251e-05, -1.52835810e-06, -1.40790498e-05, -2.65316012e-05, -4.01083526e-05, -5.62983550e-05, -5.75223821e-05, -4.25982689e-05, -3.06141737e-05, -2.00884024e-05, -9.90276021e-06,
|
|
|
|
|
-1.61666367e-06, -1.09328157e-05, -2.02010433e-05, -3.03347279e-05, -4.24536738e-05, -4.32532870e-05, -3.19610226e-05, -2.28673853e-05, -1.48570880e-05, -7.08444895e-06,
|
|
|
|
|
-1.53552355e-06, -8.72318924e-06, -1.58886232e-05, -2.37402273e-05, -3.31507035e-05, -3.37014644e-05, -2.48602537e-05, -1.77248403e-05, -1.14254890e-05, -5.30027773e-06, -1.40318230e-06, -7.11624580e-06, -1.28209140e-05, -1.90826468e-05, -2.66006646e-05,
|
|
|
|
|
-2.69959855e-05, -1.98865000e-05, -1.41387427e-05, -9.05554589e-06, -4.10473058e-06, -1.26330860e-06, -5.91293519e-06, -1.05618501e-05, -1.56718652e-05, -2.18157675e-05, -2.21090413e-05, -1.62681827e-05, -1.15394150e-05, -7.35144840e-06, -3.26711961e-06,
|
|
|
|
|
-1.13179840e-06, -4.98940426e-06, -8.85062400e-06, -1.30997241e-05, -1.82144904e-05, -1.84380206e-05, -1.35542105e-05, -9.59566933e-06, -6.08572736e-06, -2.65887866e-06,
|
|
|
|
|
-1.01367493e-06, -4.26561428e-06, -7.52358210e-06, -1.11123145e-05, -1.54364170e-05, -1.56106762e-05, -1.14666063e-05, -8.10436813e-06, -5.12021325e-06, -2.20401580e-06, -9.09635219e-07, -3.68808492e-06, -6.47385696e-06, -9.54499774e-06, -1.32485484e-05,
|
|
|
|
|
-1.33870126e-05, -9.82651000e-06, -6.93532820e-06, -4.36710525e-06, -1.85539375e-06, -8.18735487e-07, -3.22003825e-06, -5.62928972e-06, -8.28724023e-06, -1.14948289e-05, -1.16066676e-05, -8.51461300e-06, -6.00201292e-06, -3.76846447e-06, -1.58258263e-06,
|
|
|
|
|
-7.39498375e-07, -2.83553072e-06, -4.93973403e-06, -7.26259532e-06, -1.00675643e-05, -1.01591886e-05, -7.44886802e-06, -5.24508141e-06, -3.28481428e-06, -1.36524977e-06,
|
|
|
|
|
-6.70378654e-07, -2.51585061e-06, -4.36947221e-06, -6.41683391e-06, -8.89049170e-06, -8.96649362e-06, -6.57134478e-06, -4.62275193e-06, -2.88851857e-06, -1.18941352e-06, -6.09944266e-07, -2.24723408e-06, -3.89250545e-06, -5.71062310e-06, -7.90838203e-06,
|
|
|
|
|
-7.97212033e-06, -5.84020108e-06, -4.10491293e-06, -2.55976192e-06, -1.04521314e-06, -5.56935277e-07, -2.01937837e-06, -3.48954882e-06, -5.11487451e-06, -7.08044308e-06, -7.13442114e-06, -5.22460778e-06, -3.66942504e-06, -2.28403951e-06, -9.25535005e-07,
|
|
|
|
|
-5.10270809e-07, -1.82444705e-06, -3.14605040e-06, -4.60769843e-06, -6.37601988e-06, -6.42213308e-06, -4.70144141e-06, -3.29971408e-06, -2.05053857e-06, -8.25151346e-07,
|
|
|
|
|
-4.69036365e-07, -1.65639949e-06, -2.85086708e-06, -4.17237243e-06, -5.77171340e-06, -5.81141694e-06, -4.25308644e-06, -2.98317354e-06, -1.85106614e-06, -7.40148607e-07, -4.32460268e-07, -1.51051631e-06, -2.59534818e-06, -3.79594053e-06, -5.24941379e-06,
|
|
|
|
|
-5.28384317e-06, -3.86593183e-06, -2.71007866e-06, -1.67932183e-06, -6.67554332e-07, -3.99893480e-07, -1.38306928e-06, -2.37269478e-06, -3.46823890e-06, -4.79492701e-06, -4.82497671e-06, -3.52932648e-06, -2.47282924e-06, -1.53039912e-06, -6.05077048e-07,
|
|
|
|
|
-3.70789934e-07, -1.27108103e-06, -2.17750403e-06, -3.18120783e-06, -4.39700398e-06, -4.42338614e-06, -3.23483960e-06, -2.26541715e-06, -1.40042869e-06, -5.50929371e-07});
|
|
|
|
|
input.linspace(1);
|
|
|
|
|
gradO = 1;
|
|
|
|
|
|
|
|
|
|
nd4j::ops::lrn_bp op;
|
|
|
|
|
|
|
|
|
|
auto results = op.execute({&input, &gradO}, {1., 1., 1}, {5});
|
|
|
|
|
auto gradI = results->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(*gradI, exp);
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, lrn_bp_2) {
|
|
|
|
|
|
|
|
|
|
NDArray input('c', {2,3,4,10});
|
|
|
|
|
NDArray gradO('c', {2,3,4,10});
|
|
|
|
|
NDArray exp('c', {2,3,4,10}, {-1.06179598e-03, -2.70050880e-03, -4.02126182e-03, -2.58826977e-03, -2.16024881e-03, -2.20575323e-03, -2.75954953e-03, -4.42477595e-03, -2.89176637e-03, -9.46942251e-04, -1.32603094e-03, -3.34868953e-03, -4.98152524e-03, -3.21313459e-03, -2.68880837e-03, -2.75207381e-03, -3.45109636e-03, -5.54159656e-03, -3.61320702e-03, -1.16457068e-03,
|
|
|
|
|
-1.70158676e-03, -4.26037982e-03, -6.33032294e-03, -4.09416296e-03, -3.43742501e-03, -3.52900685e-03, -4.43827361e-03, -7.13911094e-03, -4.64041065e-03, -1.46419462e-03, -2.26016506e-03, -5.59943309e-03, -8.30824208e-03, -5.39253885e-03, -4.54709725e-03, -4.68666852e-03, -5.91615774e-03, -9.53640230e-03, -6.17204653e-03, -1.89000927e-03,
|
|
|
|
|
-3.14102764e-03, -7.67878769e-03, -1.13740638e-02, -7.41857197e-03, -6.29213545e-03, -6.51977258e-03, -8.27047508e-03, -1.33656031e-02, -8.59564263e-03, -2.51553906e-03, -4.64272872e-03, -1.11560747e-02, -1.64905936e-02, -1.08321551e-02, -9.26420093e-03, -9.67171416e-03, -1.23506878e-02, -2.00199075e-02, -1.27442302e-02, -3.45497206e-03,
|
|
|
|
|
-7.49545777e-03, -1.76018942e-02, -2.59558801e-02, -1.72390267e-02, -1.49321631e-02, -1.57669969e-02, -2.03234926e-02, -3.30405571e-02, -2.06389092e-02, -4.78462130e-03, -1.38390735e-02, -3.14943902e-02, -4.63354364e-02, -3.13667879e-02, -2.77508944e-02, -2.98541505e-02, -3.89749333e-02, -6.32867143e-02, -3.77952419e-02, -5.26650995e-03,
|
|
|
|
|
-3.16195861e-02, -6.90807998e-02, -1.01725549e-01, -7.13700354e-02, -6.54785037e-02, -7.25797564e-02, -9.49372798e-02, -1.47399038e-01, -7.21285641e-02, 2.15010419e-02, -8.06625858e-02, -1.79638922e-01, -2.66877055e-01, -1.64447501e-01, -1.00968637e-01, -2.75682062e-02, 1.13596700e-01, 3.32260162e-01, 5.96845448e-01, 8.13161016e-01,
|
|
|
|
|
9.52381015e-01, 8.13161016e-01, 5.96845508e-01, 3.32260162e-01, 1.13596708e-01, -2.75682174e-02, -1.37202948e-01, -2.71326721e-01, -1.84127048e-01, -7.94974267e-02, 3.29870060e-02, -7.39035010e-02, -1.60488203e-01, -1.04997143e-01, -8.06594491e-02, -7.25797564e-02, -7.87955597e-02, -1.11791104e-01, -7.58660138e-02, -3.48676592e-02,
|
|
|
|
|
-4.96974029e-03, -4.04525958e-02, -6.82792515e-02, -4.20900472e-02, -3.21968049e-02, -2.98541524e-02, -3.36477235e-02, -4.95737195e-02, -3.37007530e-02, -1.48636252e-02, -4.92655952e-03, -2.17927732e-02, -3.49853337e-02, -2.15152260e-02, -1.66727621e-02, -1.57669988e-02, -1.81730352e-02, -2.73226351e-02, -1.85334161e-02, -7.91355036e-03,
|
|
|
|
|
-3.57114570e-03, -1.33136865e-02, -2.09431648e-02, -1.29161589e-02, -1.01064872e-02, -9.67171136e-03, -1.12970043e-02, -1.71830691e-02, -1.16271935e-02, -4.84848116e-03, -2.59314431e-03, -8.91274121e-03, -1.38697922e-02, -8.58002994e-03, -6.75992295e-03, -6.51977304e-03, -7.68158771e-03, -1.17703741e-02, -7.94785097e-03, -3.25604435e-03,
|
|
|
|
|
-1.94202550e-03, -6.36530807e-03, -9.84015409e-03, -6.10316684e-03, -4.83274320e-03, -4.68666898e-03, -5.55526093e-03, -8.55536573e-03, -5.76688722e-03, -2.33053416e-03, -1.50016253e-03, -4.76644421e-03, -7.33569637e-03, -4.55961144e-03, -3.62428720e-03, -3.52900638e-03, -4.20164689e-03, -6.49448857e-03, -4.37143166e-03, -1.74761284e-03,
|
|
|
|
|
-1.19028054e-03, -3.69978836e-03, -5.67591935e-03, -3.53418733e-03, -2.81759514e-03, -2.75207404e-03, -3.28776496e-03, -5.09600528e-03, -3.42601724e-03, -1.35771628e-03, -9.65878542e-04, -2.95373448e-03, -4.52052988e-03, -2.81889434e-03, -2.25270819e-03, -2.20575323e-03, -2.64216494e-03, -4.10421193e-03, -2.75646802e-03, -1.08450721e-03,
|
|
|
|
|
-7.98697409e-04, -2.41194153e-03, -3.68447183e-03, -2.30037421e-03, -1.84193184e-03, -1.80714857e-03, -2.16938392e-03, -3.37567786e-03, -2.26523401e-03, -8.85842834e-04, -6.71049987e-04, -2.00629188e-03, -3.06024216e-03, -1.91263494e-03, -1.53396139e-03, -1.50748459e-03, -1.81288645e-03, -2.82496959e-03, -1.89429161e-03, -7.36965681e-04,
|
|
|
|
|
-5.71501616e-04, -1.69480499e-03, -2.58198148e-03, -1.61517004e-03, -1.29717519e-03, -1.27655920e-03, -1.53747783e-03, -2.39865575e-03, -1.60740130e-03, -6.22576685e-04, -4.92433901e-04, -1.45049067e-03, -2.20754091e-03, -1.38200901e-03, -1.11122860e-03, -1.09486456e-03, -1.32032647e-03, -2.06194492e-03, -1.38099224e-03, -5.32818493e-04});
|
|
|
|
|
|
|
|
|
|
input.linspace(-10, 0.1);
|
|
|
|
|
gradO = 1;
|
|
|
|
|
|
|
|
|
|
nd4j::ops::lrn_bp op;
|
|
|
|
|
|
|
|
|
|
auto results = op.execute({&input, &gradO}, {1., 1., 1}, {2});
|
|
|
|
|
auto gradI = results->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(*gradI, exp);
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, lrn_bp_3) {
|
|
|
|
|
|
|
|
|
|
NDArray input('c', {2,3,4,10});
|
|
|
|
|
NDArray gradO('c', {2,3,4,10});
|
|
|
|
|
NDArray exp('c', {2,3,4,10}, {-6.78180193e-04, -1.06947345e-03, -1.50362519e-03, -1.47711602e-03, -1.45060697e-03, -1.42409769e-03, -1.39758852e-03, -1.37107936e-03, -8.79839936e-04, -4.27795108e-04, -8.62496032e-04, -1.34585891e-03, -1.88281795e-03, -1.84591592e-03, -1.80901436e-03, -1.77211256e-03, -1.73521065e-03, -1.69830909e-03, -1.08184782e-03, -5.13895764e-04,
|
|
|
|
|
-1.13227055e-03, -1.74428569e-03, -2.42520543e-03, -2.37169350e-03, -2.31818156e-03, -2.26466986e-03, -2.21115816e-03, -2.15764646e-03, -1.36136822e-03, -6.26647263e-04, -1.54878304e-03, -2.34815548e-03, -3.23930010e-03, -3.15753091e-03, -3.07576265e-03, -2.99399323e-03, -2.91222427e-03, -2.83045508e-03, -1.76287338e-03, -7.75904860e-04,
|
|
|
|
|
-2.23870482e-03, -3.32566188e-03, -4.54067392e-03, -4.40674182e-03, -4.27281018e-03, -4.13887901e-03, -4.00494691e-03, -3.87101574e-03, -2.36659218e-03, -9.72117065e-04, -3.49745504e-03, -5.05724549e-03, -6.80746930e-03, -6.56589260e-03, -6.32431870e-03, -6.08274434e-03, -5.84116904e-03, -5.59959421e-03, -3.32604628e-03, -1.21081201e-03,
|
|
|
|
|
-6.14068285e-03, -8.55270587e-03, -1.12749329e-02, -1.07723922e-02, -1.02698486e-02, -9.76730697e-03, -9.26476624e-03, -8.76222178e-03, -4.94601438e-03, -1.37539487e-03, -1.30690653e-02, -1.72132626e-02, -2.19351258e-02, -2.06174850e-02, -1.92998387e-02, -1.79821979e-02, -1.66645572e-02, -1.53469117e-02, -7.72346184e-03, -5.22134826e-04,
|
|
|
|
|
-3.99478227e-02, -4.78655733e-02, -5.70126995e-02, -5.16961850e-02, -4.63796593e-02, -4.10631336e-02, -3.57466117e-02, -3.04300785e-02, -9.11374856e-03, 1.14024431e-02, -2.35893592e-01, -2.17480078e-01, -1.88097835e-01, -1.38812393e-01, -8.95269737e-02, -4.02415469e-02, 9.04385652e-03, 5.83292767e-02, 1.78530529e-01, 2.96026409e-01,
|
|
|
|
|
4.16666657e-01, 2.79557735e-01, 1.36546940e-01, 7.49502778e-02, 1.33536234e-02, -4.82430384e-02, -1.09839723e-01, -1.71436355e-01, -2.33033031e-01, -2.74476141e-01, 1.54189002e-02, -8.10869783e-03, -3.24862264e-02, -3.88403721e-02, -4.51945364e-02, -5.15486896e-02, -5.79028539e-02, -6.42570183e-02, -5.45457527e-02, -4.61437553e-02,
|
|
|
|
|
-2.29711179e-04, -8.06892477e-03, -1.63567103e-02, -1.78351123e-02, -1.93135180e-02, -2.07919199e-02, -2.22703181e-02, -2.37487257e-02, -1.87229179e-02, -1.43175106e-02, -1.37000845e-03, -5.16320160e-03, -9.21433326e-03, -9.76086594e-03, -1.03073996e-02, -1.08539313e-02, -1.14004640e-02, -1.19469995e-02, -9.08647850e-03, -6.55380823e-03,
|
|
|
|
|
-1.23490533e-03, -3.45137389e-03, -5.83263952e-03, -6.09064987e-03, -6.34865928e-03, -6.60666777e-03, -6.86467718e-03, -7.12268520e-03, -5.30054048e-03, -3.67741752e-03, -9.94500006e-04, -2.44303374e-03, -4.00528917e-03, -4.14666394e-03, -4.28803731e-03, -4.42941114e-03, -4.57078544e-03, -4.71215881e-03, -3.45545518e-03, -2.33156094e-03,
|
|
|
|
|
-7.93270417e-04, -1.81236281e-03, -2.91444198e-03, -3.00004939e-03, -3.08565609e-03, -3.17126350e-03, -3.25687067e-03, -3.34247784e-03, -2.42513884e-03, -1.60246110e-03, -6.39747130e-04, -1.39506557e-03, -2.21352675e-03, -2.26921216e-03, -2.32489733e-03, -2.38058274e-03, -2.43626791e-03, -2.49195332e-03, -1.79354590e-03, -1.16592250e-03,
|
|
|
|
|
-5.23828785e-04, -1.10576022e-03, -1.73730974e-03, -1.77553250e-03, -1.81375467e-03, -1.85197743e-03, -1.89020019e-03, -1.92842260e-03, -1.37922564e-03, -8.84913374e-04, -4.35433642e-04, -8.97393096e-04, -1.39935245e-03, -1.42670958e-03, -1.45406683e-03, -1.48142409e-03, -1.50878134e-03, -1.53613824e-03, -1.09309505e-03, -6.93831593e-04,
|
|
|
|
|
-3.66991735e-04, -7.42538832e-04, -1.15100679e-03, -1.17125409e-03, -1.19150116e-03, -1.21174823e-03, -1.23199564e-03, -1.25224248e-03, -8.87364266e-04, -5.58210537e-04, -3.13144788e-04, -6.24410110e-04, -9.63238359e-04, -9.78639582e-04, -9.94040747e-04, -1.00944215e-03, -1.02484343e-03, -1.04024459e-03, -7.34565372e-04, -4.58585098e-04,
|
|
|
|
|
-2.70129647e-04, -5.32291830e-04, -8.17865424e-04, -8.29851197e-04, -8.41836852e-04, -8.53822567e-04, -8.65808397e-04, -8.77794111e-04, -6.18013146e-04, -3.83307983e-04, -2.35282409e-04, -4.59096394e-04, -7.03040219e-04, -7.12549896e-04, -7.22059398e-04, -7.31569016e-04, -7.41078693e-04, -7.50588137e-04, -5.27105702e-04, -3.25074652e-04});
|
|
|
|
|
|
|
|
|
|
input.linspace(-10, 0.1);
|
|
|
|
|
gradO = 1;
|
|
|
|
|
|
|
|
|
|
nd4j::ops::lrn_bp op;
|
|
|
|
|
|
|
|
|
|
auto results = op.execute({&input, &gradO}, {1., 1., 1}, {7});
|
|
|
|
|
auto gradI = results->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(*gradI, exp);
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, lrn_bp_4) {
|
|
|
|
|
|
|
|
|
|
NDArray input('c', {2,3,4,10});
|
|
|
|
|
NDArray gradO('c', {2,3,4,10});
|
|
|
|
|
NDArray exp('c', {2,3,4,10}, {-0.00119282, -0.00116995, -0.00114708, -0.00112421, -0.00110134, -0.00107847, -0.00105559, -0.00103272, -0.00100985, -0.00098698, -0.00150102, -0.00146918, -0.00143734, -0.0014055 , -0.00137366, -0.00134182, -0.00130998, -0.00127814, -0.0012463 , -0.00121446,
|
|
|
|
|
-0.00194534,-0.00189916, -0.00185299, -0.00180681, -0.00176064, -0.00171446, -0.00166829, -0.00162211, -0.00157593, -0.00152976, -0.0026189 , -0.00254833, -0.00247776, -0.00240719, -0.00233662, -0.00226605, -0.00219548, -0.00212491, -0.00205434, -0.00198377,
|
|
|
|
|
-0.00370962, -0.00359401, -0.00347839, -0.00336277, -0.00324716, -0.00313154, -0.00301593, -0.00290031, -0.00278469, -0.00266908, -0.00564327, -0.00543464, -0.00522602, -0.00501739, -0.00480876, -0.00460013, -0.0043915 , -0.00418288, -0.00397425, -0.00376562,
|
|
|
|
|
-0.00955302, -0.00911865, -0.00868428, -0.00824992, -0.00781555, -0.00738118, -0.00694682, -0.00651245, -0.00607808, -0.00564371, -0.01927758, -0.01813637, -0.01699515, -0.01585394, -0.01471272, -0.01357151, -0.01243029, -0.01128908, -0.01014786, -0.00900664,
|
|
|
|
|
-0.05409876, -0.04945958, -0.04482041, -0.04018124, -0.03554206, -0.03090289, -0.02626371, -0.02162454, -0.01698537, -0.01234619, -0.26145172, -0.214688 , -0.16792431, -0.12116055, -0.07439683, -0.02763309, 0.01913062, 0.06589434, 0.11265809, 0.15942183,
|
|
|
|
|
0.25974026, 0.19902176, 0.13830325, 0.07758474, 0.01686624, -0.04385226, -0.10457078, -0.16528927, -0.22600779, -0.2867263 , -0.01177884, -0.0173331 , -0.02288735, -0.02844159, -0.03399584, -0.0395501 , -0.04510435, -0.05065861, -0.05621284, -0.0617671 ,
|
|
|
|
|
-0.00944993, -0.01073084, -0.01201174, -0.01329265, -0.01457355, -0.01585446, -0.01713536, -0.01841627, -0.01969717, -0.02097807, -0.00589878, -0.00637122, -0.00684368, -0.00731612, -0.00778858, -0.00826102, -0.00873347, -0.00920592, -0.00967837, -0.01015082,
|
|
|
|
|
-0.00390961, -0.00413245, -0.00435528, -0.00457812, -0.00480095, -0.00502378, -0.00524662, -0.00546945, -0.00569229, -0.00591512, -0.00275609, -0.00287813, -0.00300018, -0.00312222, -0.00324427, -0.00336631, -0.00348836, -0.0036104 , -0.00373245, -0.00385449,
|
|
|
|
|
-0.00203982, -0.00211371, -0.00218759, -0.00226147, -0.00233536, -0.00240924, -0.00248312, -0.00255701, -0.00263089, -0.00270478, -0.00156781, -0.00161586, -0.00166391, -0.00171197, -0.00176002, -0.00180807, -0.00185612, -0.00190417, -0.00195223, -0.00200028,
|
|
|
|
|
-0.00124141, -0.00127439, -0.00130737, -0.00134035, -0.00137333, -0.00140631, -0.00143929, -0.00147227, -0.00150525, -0.00153822, -0.00100674, -0.00103034, -0.00105394, -0.00107754, -0.00110115, -0.00112475, -0.00114835, -0.00117195, -0.00119556, -0.00121916,
|
|
|
|
|
-0.00083255, -0.00085002, -0.00086748, -0.00088495, -0.00090242, -0.00091989, -0.00093735, -0.00095482, -0.00097229, -0.00098976, -0.0006998 , -0.00071308, -0.00072637, -0.00073965, -0.00075294, -0.00076623, -0.00077951, -0.0007928 , -0.00080609, -0.00081937,
|
|
|
|
|
-0.00059635, -0.00060669, -0.00061703, -0.00062737, -0.00063771, -0.00064805, -0.00065839, -0.00066873, -0.00067906, -0.0006894 , -0.0005142 , -0.0005224 , -0.00053061, -0.00053881, -0.00054701, -0.00055522, -0.00056342, -0.00057162, -0.00057983, -0.00058803});
|
|
|
|
|
|
|
|
|
|
input.linspace(-10, 0.1);
|
|
|
|
|
gradO = 1;
|
|
|
|
|
|
|
|
|
|
nd4j::ops::lrn_bp op;
|
|
|
|
|
|
|
|
|
|
auto results = op.execute({&input, &gradO}, {1., 1., 1}, {12});
|
|
|
|
|
auto gradI = results->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(*gradI, exp);
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, lrn_bp_5) {
|
|
|
|
|
|
|
|
|
|
NDArray input('c', {2,2,2,5});
|
|
|
|
|
NDArray gradO('c', {2,2,2,5});
|
|
|
|
|
NDArray exp('c', {2,2,2,5}, {6.2497472e-03, -3.4008762e-03, -1.5232352e-02, 2.3018382e-04, 1.3257053e-02, 7.1492628e-03, -5.4330104e-03, -2.0878183e-02, 1.5153568e-03, 2.0571884e-02,
|
|
|
|
|
6.7926152e-03, -1.0990440e-02, -3.2685306e-02, 7.2436016e-03, 4.2120241e-02, -1.3439789e-02, -3.4284033e-02, -4.4852167e-02, 8.8073254e-02, 2.2223940e-01,
|
|
|
|
|
4.0824831e-01, 2.1201703e-01, 3.8555145e-02, -3.1969927e-02, -3.0673094e-02, 5.2034661e-02, 1.0463811e-02, -3.6619946e-02, -1.3280880e-02, 5.9767403e-03,
|
|
|
|
|
2.3028374e-02, 2.0452859e-03, -2.2533152e-02, -6.1039329e-03, 7.2805062e-03, 1.4290780e-02, 3.8017845e-04, -1.6107092e-02,-3.6896234e-03, 6.4357026e-03});
|
|
|
|
|
input.linspace(-20, 1);
|
|
|
|
|
// gradO.linspace(0.1, 0.1);
|
|
|
|
|
gradO = 1;
|
|
|
|
|
|
|
|
|
|
nd4j::ops::lrn_bp op;
|
|
|
|
|
|
|
|
|
|
auto results = op.execute({&input, &gradO}, {1., 1., 0.5}, {2});
|
|
|
|
|
auto gradI = results->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(*gradI, exp);
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, lrn_bp_6) {
|
|
|
|
|
|
|
|
|
|
NDArray input('c', {1,1,1,5}, {1, 2., 3, 4, 5});
|
|
|
|
|
NDArray gradO('c', {1,1,1,5});
|
|
|
|
|
NDArray exp('c', {1,1,1,5}, {0.06926288, 0.04360996, 0.01795704, -0.00769587, -0.0333488});
|
|
|
|
|
// gradO.linspace(-1.5, 0.1);
|
|
|
|
|
gradO = 1;
|
|
|
|
|
|
|
|
|
|
nd4j::ops::lrn_bp op;
|
|
|
|
|
|
|
|
|
|
auto results = op.execute({&input, &gradO}, {1., 2., 0.5}, {10});
|
|
|
|
|
auto gradI = results->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(*gradI, exp);
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, lrn_bp_7) {
|
|
|
|
|
|
|
|
|
|
NDArray input('c', {2,2,2,5});
|
|
|
|
|
NDArray gradO('c', {2,2,2,5});
|
|
|
|
|
|
|
|
|
|
input.linspace(-20, 1);
|
|
|
|
|
gradO.linspace(-1.5, 0.1);
|
|
|
|
|
|
|
|
|
|
const OpArgsHolder argsHolderFF({&input}, {1,2,0.5}, {2});
|
|
|
|
|
const OpArgsHolder argsHolderBP({&input, &gradO}, {1,2,0.5}, {2});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::lrn opFF;
|
|
|
|
|
nd4j::ops::lrn_bp opBP;
|
|
|
|
|
|
|
|
|
|
const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(isGradCorrect);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, lrn_bp_8) {
|
|
|
|
|
|
|
|
|
|
NDArray input('c', {1,1,1,5}, {1, 2, 3, 4, 5});
|
|
|
|
|
NDArray gradO('c', {1,1,1,5}, {2, 3, 4, 5, 6});
|
|
|
|
|
|
|
|
|
|
const OpArgsHolder argsHolderFF({&input}, {1,2,0.5}, {2});
|
|
|
|
|
const OpArgsHolder argsHolderBP({&input, &gradO}, {1,2,0.5}, {2});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::lrn opFF;
|
|
|
|
|
nd4j::ops::lrn_bp opBP;
|
|
|
|
|
|
|
|
|
|
const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(isGradCorrect);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, lrn_bp_9) {
|
|
|
|
|
|
|
|
|
|
NDArray input('c', {1,1,1,5}, {1,2,3,4,5});
|
|
|
|
|
NDArray gradO('c', {1,1,1,5}, {1, 1, 1, 1, 1});
|
|
|
|
|
NDArray exp('c', {1,1,1,5}, {0.1084472 , 0.03816165, 0.00978456, -0.01859251,-0.02511311});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::lrn_bp op;
|
|
|
|
|
|
|
|
|
|
auto results = op.execute({&input, &gradO}, {1., 2., 0.5}, {3});
|
|
|
|
|
auto gradI = results->at(0);
|
|
|
|
|
|
|
|
|
|
// for (int i = 0; i < exp.lengthOf(); ++i)
|
|
|
|
|
// printf("%10.5f %10.5f\n", exp.e<double>(i), gradI->e<double>(i));
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(*gradI, exp);
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, lrn_bp_10) {
|
|
|
|
|
|
|
|
|
|
NDArray input('c', {1,1,1,1}, {1});
|
|
|
|
|
NDArray gradO('c', {1,1,1,1}, {1});
|
|
|
|
|
NDArray exp('c', {1,1,1,1}, {0.19245008});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::lrn_bp op;
|
|
|
|
|
|
|
|
|
|
auto results = op.execute({&input, &gradO}, {1., 2., 0.5}, {1});
|
|
|
|
|
auto gradI = results->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(*gradI, exp);
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, lrn_1) {
|
|
|
|
|
|
|
|
|
|
NDArray input('c', {2,2,2,5});
|
|
|
|
|
NDArray exp('c', {2,2,2,5}, {-0.42923987, -0.3623817 , -0.3152079 , -0.34268343, -0.3836809, -0.43648192, -0.3652726 , -0.31428117, -0.3379276 , -0.3731494 ,
|
|
|
|
|
-0.45129365, -0.37083852, -0.3111639 , -0.3260225 , -0.34698898, -0.4975186 , -0.3831305 , -0.2847474 , -0.25607377, -0.18569534,
|
|
|
|
|
0., 0.18569534, 0.25607377, 0.38411066, 0.52075565,0.33633637, 0.32117262, 0.30966178, 0.37259716, 0.45631808,
|
|
|
|
|
0.36986336, 0.33643705, 0.31394684, 0.36608824, 0.43857202, 0.3821113 , 0.34197718, 0.31508508, 0.36284128, 0.4303756 });
|
|
|
|
|
|
|
|
|
|
input.linspace(-20, 1);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::lrn op;
|
|
|
|
|
|
|
|
|
|
auto results = op.execute({&input}, {1., 2., 0.5}, {2});
|
|
|
|
|
auto output = results->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(*output, exp);
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, lrn_2) {
|
|
|
|
|
|
|
|
|
|
NDArray input('c', {1,1,1,5}, {1, 2., 3, 4, 5});
|
|
|
|
|
NDArray exp('c', {1,1,1,5}, {0.09530295, 0.1906059 , 0.28590885, 0.3812118 , 0.47651473});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::lrn op;
|
|
|
|
|
|
|
|
|
|
auto results = op.execute({&input}, {0.1, 2., 0.5}, {5});
|
|
|
|
|
auto output = results->at(0);
|
|
|
|
|
ASSERT_EQ(*output, exp);
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, lrn_3) {
|
|
|
|
|
|
|
|
|
|
NDArray input('c', {1,1,1,1}, {1.});
|
|
|
|
|
NDArray exp('c', {1,1,1,1}, {0.69006556});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::lrn op;
|
|
|
|
|
|
|
|
|
|
auto results = op.execute({&input}, {0.1, 2., 0.5}, {5});
|
|
|
|
|
auto output = results->at(0);
|
|
|
|
|
ASSERT_EQ(*output, exp);
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, lrn_4) {
|
|
|
|
|
|
|
|
|
|
NDArray input('c', {1,1,1,1}, {1.});
|
|
|
|
|
NDArray exp('c', {1,1,1,1}, {0.69006556});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::lrn op;
|
|
|
|
|
|
|
|
|
|
auto results = op.execute({&input}, {0.1, 2., 0.5}, {0});
|
|
|
|
|
auto output = results->at(0);
|
|
|
|
|
ASSERT_EQ(*output, exp);
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, lrn_5) {
|
|
|
|
|
|
|
|
|
|
NDArray input('c', {1,1,1,5}, {1, 2., 3, 4, 5});
|
|
|
|
|
NDArray exp('c', {1,1,1,5}, {0.69006556, 0.70272833, 0.7051508 , 0.7060045 , 0.7064008});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::lrn op;
|
|
|
|
|
|
|
|
|
|
auto results = op.execute({&input}, {0.1, 2., 0.5}, {0});
|
|
|
|
|
auto output = results->at(0);
|
|
|
|
|
ASSERT_EQ(*output, exp);
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, inTopK_1) {
|
|
|
|
|
|
|
|
|
|
NDArray x('c', {4, 5}, {11.0, 14.0, 6.0, 9.0, 3.5, 7.0, 21.0, 3.0, 15.0, 6.0, 9.0, 3.5, 7.0, 11.0, 13.0, 5.0, 16.0, 9.0, 13.5, 7.0});
|
|
|
|
|
NDArray y('c', {4}, {0, 0, 0, 0}, nd4j::DataType::INT64);
|
|
|
|
|
NDArray z('c', {4}, {1, 1, 1, 1}, nd4j::DataType::BOOL);
|
|
|
|
|
|
|
|
|
|
NDArray expV('c', {4}, {1, 0, 0, 0}, nd4j::DataType::BOOL);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::in_top_k op;
|
|
|
|
|
Nd4jStatus status = op.execute({&x, &y, }, {&z}, {}, {2}, {});
|
|
|
|
|
|
|
|
|
|
// z.printIndexedBuffer();
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expV.isSameShape(z));
|
|
|
|
|
ASSERT_TRUE(expV.equalsTo(z));
|
|
|
|
|
}
|
|
|
|
|
|
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
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, inTopK_2) {
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {4, 5});
|
|
|
|
|
auto idx = NDArrayFactory::create<Nd4jLong>('c', {4});
|
|
|
|
|
|
|
|
|
|
auto exp = NDArrayFactory::create<bool>({0, 0, 0, 1});
|
|
|
|
|
|
|
|
|
|
int exclusive, reverse;
|
|
|
|
|
input.linspace(1);
|
|
|
|
|
idx.linspace(1);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::in_top_k op;
|
|
|
|
|
|
|
|
|
|
auto res = op.execute({&input, &idx}, {}, {1}, {}, false, nd4j::DataType::BOOL);
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(res->status(), ND4J_STATUS_OK);
|
|
|
|
|
//res->at(0)->printIndexedBuffer("IN_TOP_K output");
|
|
|
|
|
ASSERT_TRUE(res->at(0)->equalsTo(&exp));
|
|
|
|
|
delete res;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, inTopK_3) {
|
|
|
|
|
auto x = NDArrayFactory::create<double>('c', {2, 3}, {1.0, 11.0, 3.0, 14.0, 5.0, 6.0});
|
|
|
|
|
auto y = NDArrayFactory::create<Nd4jLong>('c', {2}, {1, 1});
|
|
|
|
|
auto expV = NDArrayFactory::create<bool>('c', {2}, {true, false});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::in_top_k op;
|
|
|
|
|
auto result = op.execute({&x, &y}, {}, {2});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, result->status());
|
|
|
|
|
ASSERT_EQ(1, result->size());
|
|
|
|
|
|
|
|
|
|
auto v = result->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expV.isSameShape(v));
|
|
|
|
|
ASSERT_TRUE(expV.equalsTo(v));
|
|
|
|
|
|
|
|
|
|
delete result;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, inTopK_4) {
|
|
|
|
|
auto x = NDArrayFactory::create<double>('c', {6, 4}, {11.0, 3.0, 14.0, 5.0, 6.0, 9.0, 3.5, 7.0, 21.0, 3.0, 14.0, 15.0, 6.0, 9.0, 3.5, 7.0, 11.0, 13.0, 14.0, 5.0, 16.0, 9.0, 13.5, 7.0} );
|
|
|
|
|
auto y = NDArrayFactory::create<Nd4jLong>('c', {6}, {0, 0, 0, 0, 0, 0});
|
|
|
|
|
auto expV = NDArrayFactory::create<bool>('c', {6}, {true, false, true, false, false, true});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::in_top_k op;
|
|
|
|
|
auto result = op.execute({&x, &y}, {}, {2});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, result->status());
|
|
|
|
|
ASSERT_EQ(1, result->size());
|
|
|
|
|
|
|
|
|
|
auto v = result->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expV.isSameShape(v));
|
|
|
|
|
ASSERT_TRUE(expV.equalsTo(v));
|
|
|
|
|
|
|
|
|
|
delete result;
|
|
|
|
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, inTopK_5) {
|
|
|
|
|
auto x = NDArrayFactory::create<double>('f', {6, 4}, {11.0, 3.0, 14.0, 5.0, 6.0, 9.0, 3.5, 7.0, 21.0, 3.0, 14.0, 15.0, 6.0, 9.0, 3.5, 7.0, 11.0, 13.0, 14.0, 5.0, 16.0, 9.0, 13.5, 7.0} );
|
|
|
|
|
auto y = NDArrayFactory::create<Nd4jLong>('f', {6}, {0, 0, 0, 0, 0, 0});
|
|
|
|
|
auto expV = NDArrayFactory::create<bool>('f', {6}, {1, 0, 0, 0, 0, 0 });
|
|
|
|
|
|
|
|
|
|
nd4j::ops::in_top_k op;
|
|
|
|
|
auto result = op.execute({&x, &y}, {}, {2});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, result->status());
|
|
|
|
|
ASSERT_EQ(1, result->size());
|
|
|
|
|
|
|
|
|
|
auto v = result->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expV.isSameShape(v));
|
|
|
|
|
ASSERT_TRUE(expV.equalsTo(v));
|
|
|
|
|
|
|
|
|
|
delete result;
|
|
|
|
|
}
|
|
|
|
|
|
2019-06-06 14:21:15 +02:00
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, cube_1) {
|
|
|
|
|
|
|
|
|
|
NDArray x('c', {2, 3}, {1., 2., 3., 4., 5, 6});
|
|
|
|
|
NDArray exp('c', {2, 3}, {1., 8., 27., 64., 125, 216});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::cube op;
|
|
|
|
|
|
|
|
|
|
auto result = op.execute({&x}, {}, {});
|
|
|
|
|
|
|
|
|
|
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(DeclarableOpsTests12, cube_bp_1) {
|
|
|
|
|
|
|
|
|
|
NDArray x('c', {2, 3}, {1., 2., 3., 4., 5, 6});
|
|
|
|
|
NDArray gradO('c', {2, 3}, nd4j::DataType::DOUBLE);
|
|
|
|
|
NDArray exp('c', {2, 3}, {1.5, 6., 13.5, 24., 37.5, 54});
|
|
|
|
|
|
|
|
|
|
gradO = 0.5;
|
|
|
|
|
|
|
|
|
|
nd4j::ops::cube_bp op;
|
|
|
|
|
|
|
|
|
|
auto result = op.execute({&x, &gradO}, {}, {});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, result->status());
|
|
|
|
|
|
|
|
|
|
auto z = result->at(0);
|
|
|
|
|
// z->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(exp.isSameShape(z));
|
|
|
|
|
ASSERT_TRUE(exp.equalsTo(z));
|
|
|
|
|
|
|
|
|
|
delete result;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
// CONSTANT mode 2D
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests1) {
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
NDArray input('c', {2,3}, {1,2,3,4,5,6}, nd4j::DataType::FLOAT32);
|
|
|
|
|
NDArray paddings('c', {2,2}, {1,1,2,2}, nd4j::DataType::INT32);
|
|
|
|
|
NDArray expected('c', {4,7}, {0,0,0,0,0,0,0, 0,0,1,2,3,0,0, 0,0,4,5,6,0,0, 0,0,0,0,0,0,0}, nd4j::DataType::FLOAT32);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {0});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
// result->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
// REFLECT mode 2D
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests2) {
|
|
|
|
|
|
|
|
|
|
float inBuff[] = {1,2,3,4,5,6};
|
|
|
|
|
int padBuff[] = {1,1,2,2};
|
|
|
|
|
float expBuff[] = {6,5,4,5,6,5,4, 3,2,1,2,3,2,1, 6,5,4,5,6,5,4, 3,2,1,2,3,2,1};
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<float>(inBuff, 'c', {2,3});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>(padBuff, 'c', {2,2});
|
|
|
|
|
auto expected = NDArrayFactory::create<float>(expBuff, 'c', {4,7});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {1});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
// result->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
// SYMMETRIC mode 2D
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests3) {
|
|
|
|
|
|
|
|
|
|
float inBuff[] = {1,2,3,4,5,6};
|
|
|
|
|
int padBuff[] = {1,1,2,2};
|
|
|
|
|
float expBuff[] = {2,1,1,2,3,3,2, 2,1,1,2,3,3,2, 5,4,4,5,6,6,5, 5,4,4,5,6,6,5};
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<float>(inBuff, 'c', {2,3});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>(padBuff, 'c', {2,2});
|
|
|
|
|
auto expected = NDArrayFactory::create<float>(expBuff, 'c', {4,7});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {2});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
// result->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
// CONSTANT mode 3D
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests4) {
|
|
|
|
|
|
|
|
|
|
float inBuff[] = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18};
|
|
|
|
|
int padBuff[] = {1,1,2,2,2,2};
|
|
|
|
|
float expBuff[] = {0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0,
|
|
|
|
|
0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 1, 2, 3,0,0,0,0, 4, 5, 6,0,0,0,0,
|
|
|
|
|
7, 8, 9,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0,10,11,12,0,
|
|
|
|
|
0,0,0,13,14,15,0,0,0,0,16,17,18,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0,
|
|
|
|
|
0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0};
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<float>(inBuff, 'c', {2,3,3});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>(padBuff, 'c', {3,2});
|
|
|
|
|
auto expected = NDArrayFactory::create<float>(expBuff, 'c', {4,7,7});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {0});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
// result->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
// for(int i = 0; i < expected.lengthOf(); ++i) {
|
|
|
|
|
// float one = expected.e<float>(i);
|
|
|
|
|
// float two = result->e<float>(i);
|
|
|
|
|
// if(one != two)
|
|
|
|
|
// printf("%i : %f, %f\n", i, one, two);
|
|
|
|
|
// }
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
// REFLECT mode 3D
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests5) {
|
|
|
|
|
|
|
|
|
|
float inBuff[] = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18};
|
|
|
|
|
int padBuff[] = {1,1,2,2,2,2};
|
|
|
|
|
float expBuff[] = {18,17,16,17,18,17,16, 15,14,13,14,15,14,13, 12,11,10,11,12,11,10, 15,14,13,14,15,14,13, 18,17,16,17,18,17,16, 15,14,13,14,15,14,13, 12,11,10,11,12,11,10, 9, 8, 7, 8, 9, 8, 7, 6, 5, 4, 5, 6, 5, 4, 3, 2, 1, 2, 3, 2, 1, 6, 5, 4, 5, 6, 5, 4, 9, 8, 7, 8, 9, 8, 7, 6, 5, 4, 5, 6, 5, 4, 3, 2, 1, 2, 3, 2, 1, 18,17,16,17,18,17,16, 15,14,13,14,15,14,13, 12,11,10,11,12,11,10, 15,14,13,14,15,14,13, 18,17,16,17,18,17,16, 15,14,13,14,15,14,13, 12,11,10,11,12,11,10, 9, 8, 7, 8, 9, 8, 7, 6, 5, 4, 5, 6, 5, 4, 3, 2, 1, 2, 3, 2, 1, 6, 5, 4, 5, 6, 5, 4, 9, 8, 7, 8, 9, 8, 7, 6, 5, 4, 5, 6, 5, 4, 3, 2, 1, 2, 3, 2, 1};
|
|
|
|
|
auto input = NDArrayFactory::create<float>(inBuff, 'c', {2,3,3});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>(padBuff, 'c', {3,2});
|
|
|
|
|
auto expected = NDArrayFactory::create<float>(expBuff, 'c', {4,7,7});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {1});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
// result->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
// SYMMETRIC mode 3D
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests6) {
|
|
|
|
|
|
|
|
|
|
float inBuff[] = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18};
|
|
|
|
|
int padBuff[] = {1,1,2,2,2,2};
|
|
|
|
|
float expBuff[] = {5, 4, 4, 5, 6, 6, 5, 2, 1, 1, 2, 3, 3, 2, 2, 1, 1, 2, 3, 3, 2, 5, 4, 4, 5, 6, 6, 5, 8, 7, 7, 8, 9, 9, 8, 8, 7, 7, 8, 9, 9, 8, 5, 4, 4, 5, 6, 6, 5, 5, 4, 4, 5, 6, 6, 5, 2, 1, 1, 2, 3, 3, 2, 2, 1, 1, 2, 3, 3, 2, 5, 4, 4, 5, 6, 6, 5, 8, 7, 7, 8, 9, 9, 8, 8, 7, 7, 8, 9, 9, 8, 5, 4, 4, 5, 6, 6, 5, 14,13,13,14,15,15,14, 11,10,10,11,12,12,11, 11,10,10,11,12,12,11, 14,13,13,14,15,15,14, 17,16,16,17,18,18,17, 17,16,16,17,18,18,17, 14,13,13,14,15,15,14, 14,13,13,14,15,15,14, 11,10,10,11,12,12,11, 11,10,10,11,12,12,11, 14,13,13,14,15,15,14, 17,16,16,17,18,18,17, 17,16,16,17,18,18,17, 14,13,13,14,15,15,14};
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<float>(inBuff, 'c', {2,3,3});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>(padBuff, 'c', {3,2});
|
|
|
|
|
auto expected = NDArrayFactory::create<float>(expBuff, 'c', {4,7,7});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {2});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
// result->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
// CONSTANT mode 4D
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests7)
|
|
|
|
|
{
|
|
|
|
|
|
|
|
|
|
float inBuff[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
|
|
|
|
|
int padBuff[] = {1, 1, 1, 1, 1, 1, 1, 1};
|
|
|
|
|
float expBuff[] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 10, 0, 0, 11, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 13, 14, 0, 0, 15, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
|
|
|
|
|
auto input = NDArrayFactory::create<float>(inBuff, 'c', {2, 2, 2, 2});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>(padBuff, 'c', {4, 2});
|
|
|
|
|
auto expected = NDArrayFactory::create<float>(expBuff, 'c', {4, 4, 4, 4});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {0});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto *result = results->at(0);
|
|
|
|
|
// result->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
// REFLECT mode 4D
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests8)
|
|
|
|
|
{
|
|
|
|
|
|
|
|
|
|
float inBuff[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
|
|
|
|
|
int padBuff[] = {1, 1, 1, 1, 1, 1, 1, 1};
|
|
|
|
|
float expBuff[] = {16, 15, 16, 15, 14, 13, 14, 13, 16, 15, 16, 15, 14, 13, 14, 13, 12, 11, 12, 11, 10, 9, 10, 9, 12, 11, 12, 11, 10, 9, 10, 9, 16, 15, 16, 15, 14, 13, 14, 13, 16, 15, 16, 15, 14, 13, 14, 13, 12, 11, 12, 11, 10, 9, 10, 9, 12, 11, 12, 11, 10, 9, 10, 9, 8, 7, 8, 7, 6, 5, 6, 5, 8, 7, 8, 7, 6, 5, 6, 5, 4, 3, 4, 3, 2, 1, 2, 1, 4, 3, 4, 3, 2, 1, 2, 1, 8, 7, 8, 7, 6, 5, 6, 5, 8, 7, 8, 7, 6, 5, 6, 5, 4, 3, 4, 3, 2, 1, 2, 1, 4, 3, 4, 3, 2, 1, 2, 1, 16, 15, 16, 15, 14, 13, 14, 13, 16, 15, 16, 15, 14, 13, 14, 13, 12, 11, 12, 11, 10, 9, 10, 9, 12, 11, 12, 11, 10, 9, 10, 9, 16, 15, 16, 15, 14, 13, 14, 13, 16, 15, 16, 15, 14, 13, 14, 13, 12, 11, 12, 11, 10, 9, 10, 9, 12, 11, 12, 11, 10, 9, 10, 9, 8, 7, 8, 7, 6, 5, 6, 5, 8, 7, 8, 7, 6, 5, 6, 5, 4, 3, 4, 3, 2, 1, 2, 1, 4, 3, 4, 3, 2, 1, 2, 1, 8, 7, 8, 7, 6, 5, 6, 5, 8, 7, 8, 7, 6, 5, 6, 5, 4, 3, 4, 3, 2, 1, 2, 1, 4, 3, 4, 3, 2, 1, 2, 1};
|
|
|
|
|
auto input = NDArrayFactory::create<float>(inBuff, 'c', {2, 2, 2, 2});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>(padBuff, 'c', {4, 2});
|
|
|
|
|
auto expected = NDArrayFactory::create<float>(expBuff, 'c', {4, 4, 4, 4});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {1});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto *result = results->at(0);
|
|
|
|
|
// result->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////
|
|
|
|
|
// SYMMETRIC mode 4D
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests9)
|
|
|
|
|
{
|
|
|
|
|
|
|
|
|
|
float inBuff[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
|
|
|
|
|
int padBuff[] = {1, 1, 1, 1, 1, 1, 1, 1};
|
|
|
|
|
float expBuff[] = {1, 1, 2, 2, 1, 1, 2, 2, 3, 3, 4, 4, 3, 3, 4, 4, 1, 1, 2, 2, 1, 1, 2, 2, 3, 3, 4, 4, 3, 3, 4, 4, 5, 5, 6, 6, 5, 5, 6, 6, 7, 7, 8, 8, 7, 7, 8, 8, 5, 5, 6, 6, 5, 5, 6, 6, 7, 7, 8, 8, 7, 7, 8, 8, 1, 1, 2, 2, 1, 1, 2, 2, 3, 3, 4, 4, 3, 3, 4, 4, 1, 1, 2, 2, 1, 1, 2, 2, 3, 3, 4, 4, 3, 3, 4, 4, 5, 5, 6, 6, 5, 5, 6, 6, 7, 7, 8, 8, 7, 7, 8, 8, 5, 5, 6, 6, 5, 5, 6, 6, 7, 7, 8, 8, 7, 7, 8, 8, 9, 9, 10, 10, 9, 9, 10, 10, 11, 11, 12, 12, 11, 11, 12, 12, 9, 9, 10, 10, 9, 9, 10, 10, 11, 11, 12, 12, 11, 11, 12, 12, 13, 13, 14, 14, 13, 13, 14, 14, 15, 15, 16, 16, 15, 15, 16, 16, 13, 13, 14, 14, 13, 13, 14, 14, 15, 15, 16, 16, 15, 15, 16, 16, 9, 9, 10, 10, 9, 9, 10, 10, 11, 11, 12, 12, 11, 11, 12, 12, 9, 9, 10, 10, 9, 9, 10, 10, 11, 11, 12, 12, 11, 11, 12, 12, 13, 13, 14, 14, 13, 13, 14, 14, 15, 15, 16, 16, 15, 15, 16, 16, 13, 13, 14, 14, 13, 13, 14, 14, 15, 15, 16, 16, 15, 15, 16, 16};
|
|
|
|
|
auto input = NDArrayFactory::create<float>(inBuff, 'c', {2, 2, 2, 2});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>(padBuff, 'c', {4, 2});
|
|
|
|
|
auto expected = NDArrayFactory::create<float>(expBuff, 'c', {4, 4, 4, 4});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {2});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto *result = results->at(0);
|
|
|
|
|
// result->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests10) {
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {2,3,4});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>('c', {3,2}, {0,0, 0,1, 0,0});
|
|
|
|
|
auto expected = NDArrayFactory::create<double>('c', {2,4,4}, {1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,0.,0.,0.,0.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,0.,0.,0.,0.});
|
|
|
|
|
|
|
|
|
|
input = 1.f;
|
|
|
|
|
//input.assign(1.);
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {0});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests11) {
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {2,3,4});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>('c', {3,2}, {0,0, 0,1, 0,0});
|
|
|
|
|
auto expected = NDArrayFactory::create<double>('c', {2,4,4}, {1., 2., 3., 4., 5., 6., 7., 8., 9.,10.,11.,12., 5., 6., 7., 8.,13.,14.,15.,16.,17.,18.,19.,20.,21.,22.,23.,24.,17.,18.,19.,20.});
|
|
|
|
|
|
|
|
|
|
input.linspace(1.f);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {1});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests12) {
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {2,3,4,5});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>('c', {4,2}, {0,0, 0,1, 0,1, 0,0});
|
|
|
|
|
auto expected = NDArrayFactory::create<double>('c', {2,4,5,5}, { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 16., 17., 18., 19., 20.,
|
|
|
|
|
21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 36., 37., 38., 39., 40.,
|
|
|
|
|
41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 56., 57., 58., 59., 60.,
|
|
|
|
|
41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 56., 57., 58., 59., 60.,
|
|
|
|
|
61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 76., 77., 78., 79., 80.,
|
|
|
|
|
81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99.,100., 96., 97., 98., 99.,100.,
|
|
|
|
|
101.,102.,103.,104.,105.,106.,107.,108.,109.,110.,111.,112.,113.,114.,115.,116.,117.,118.,119.,120.,116.,117.,118.,119.,120.,
|
|
|
|
|
101.,102.,103.,104.,105.,106.,107.,108.,109.,110.,111.,112.,113.,114.,115.,116.,117.,118.,119.,120.,116.,117.,118.,119.,120.});
|
|
|
|
|
input.linspace(1.f);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {2});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
// result->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests13) {
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {5});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>('c', {1,2}, {2,3});
|
|
|
|
|
auto expected = NDArrayFactory::create<double>('c', {10}, {3., 2., 1., 2., 3., 4., 5., 4., 3., 2.});
|
|
|
|
|
input.linspace(1.f);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {1});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
// result->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests14) {
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {1,5});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>('c', {2,2}, {0,0,2,3});
|
|
|
|
|
auto expected = NDArrayFactory::create<double>('c', {1,10}, {2., 1., 1., 2., 3., 4., 5., 5., 4., 3.});
|
|
|
|
|
input.linspace(1.f);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {2});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests15) {
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {1,5});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>('c', {2,2}, {1,1,0,0});
|
|
|
|
|
auto expected = NDArrayFactory::create<double>('c', {3,5}, {1., 2., 3., 4., 5., 1., 2., 3., 4., 5., 1., 2., 3., 4., 5.});
|
|
|
|
|
input.linspace(1.f);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {2});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests16) {
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {5,1});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>('c', {2,2}, {2,3,0,0});
|
|
|
|
|
auto expected = NDArrayFactory::create<double>('c', {10,1}, {3., 2., 1., 2., 3., 4., 5., 4., 3., 2.});
|
|
|
|
|
input.linspace(1.f);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {1});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests17) {
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {5,1});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>('c', {2,2}, {0,0,1,0});
|
|
|
|
|
auto expected = NDArrayFactory::create<double>('c', {5,2}, {1.,1., 2.,2., 3.,3., 4.,4., 5.,5.});
|
|
|
|
|
input.linspace(1.f);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {2});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests18) {
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {5});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>('c', {1,2}, {0,0});
|
|
|
|
|
auto expected = NDArrayFactory::create<double>('c', {5}, {1.,2.,3.,4.,5.});
|
|
|
|
|
input.linspace(1.f);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {1});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests19) {
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {5,1});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>('c', {2,2}, {0,0,0,0});
|
|
|
|
|
auto expected = NDArrayFactory::create<double>('c', {5,1}, {1., 2., 3., 4., 5.});
|
|
|
|
|
input.linspace(1.f);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {1});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests20) {
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {1,5});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>('c', {2,2}, {0,0,0,0});
|
|
|
|
|
auto expected = NDArrayFactory::create<double>('c', {1,5}, {1., 2., 3., 4., 5.});
|
|
|
|
|
input.linspace(1.f);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {1});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests21) {
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {1,3,1,5});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>('c', {4,2}, {0,0, 0,1, 0,1, 0,0});
|
|
|
|
|
auto expected = NDArrayFactory::create<double>('c', {1,4,2,5}, {1., 2., 3., 4., 5., 1., 2., 3., 4., 5., 6., 7., 8., 9.,10., 6., 7., 8., 9.,10.,
|
|
|
|
|
11.,12.,13.,14.,15.,11.,12.,13.,14.,15.,11.,12.,13.,14.,15.,11.,12.,13.,14.,15.});
|
|
|
|
|
input.linspace(1.f);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {2});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
// result->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests22) {
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {1,1});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>('c', {2,2}, {0,0, 0,0});
|
|
|
|
|
auto expected = NDArrayFactory::create<double>('c', {1,1}, {1.});
|
|
|
|
|
|
|
|
|
|
input.linspace(1.f);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {0});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
// result->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests23) {
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {1,1});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>('c', {2,2}, {0,0, 1,0});
|
|
|
|
|
auto expected = NDArrayFactory::create<double>('c', {1,2}, {0.,1.});
|
|
|
|
|
|
|
|
|
|
input.linspace(1.f);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {0});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
// result->printShapeInfo("r");
|
|
|
|
|
// expected.printShapeInfo("e");
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests24) {
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {1});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>('c', {1,2}, {0,0});
|
|
|
|
|
auto expected = NDArrayFactory::create<double>('c', {1}, {1.});
|
|
|
|
|
|
|
|
|
|
input.linspace(1.f);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {0});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests25) {
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {1});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>('c', {1,2}, {1,1});
|
|
|
|
|
auto expected = NDArrayFactory::create<double>('c', {3}, {1.,1.,1});
|
|
|
|
|
|
|
|
|
|
input.linspace(1.f);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {2});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests26) {
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {1});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>('c', {1,2}, {3,2});
|
|
|
|
|
auto expected = NDArrayFactory::create<double>('c', {6}, {0., 0., 0., 1., 0., 0.});
|
|
|
|
|
|
|
|
|
|
input.linspace(1.f);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {0});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests27) {
|
|
|
|
|
|
|
|
|
|
NDArray input('c', {2,3}, nd4j::DataType::FLOAT32);
|
|
|
|
|
NDArray paddings('c', {2,2}, {0,0,0,1}, nd4j::DataType::INT32);
|
|
|
|
|
NDArray exp('c', {2,4}, {1,1,1,0,1,1,1,0}, nd4j::DataType::FLOAT32);
|
|
|
|
|
NDArray z('c', {2,4}, nd4j::DataType::FLOAT32);
|
|
|
|
|
input = 1.;
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
Nd4jStatus status = op.execute({&input, &paddings}, {&z}, {0}, {0}, {}); // constant
|
|
|
|
|
// z.printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
|
ASSERT_TRUE(exp.isSameShapeStrict(&z));
|
|
|
|
|
ASSERT_TRUE(exp.equalsTo(z));
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests28) {
|
|
|
|
|
|
|
|
|
|
NDArray input('c', {1,111,111,32}, nd4j::DataType::FLOAT32);
|
|
|
|
|
NDArray paddings('c', {4,2}, {0,0,0,1,0,1,0,0}, nd4j::DataType::INT32);
|
|
|
|
|
NDArray z('c', {1,112,112,32}, nd4j::DataType::FLOAT32);
|
|
|
|
|
input = 1.;
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
Nd4jStatus status = op.execute({&input, &paddings}, {&z}, {0}, {0}, {}); // constant
|
|
|
|
|
// z.printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
NDArray sum = z.reduceNumber(nd4j::reduce::Sum);
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
|
ASSERT_EQ(sum.e<float>(0), 111*111*32);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests29) {
|
|
|
|
|
|
|
|
|
|
auto in = NDArrayFactory::create<double>({1., 1., 1., 1., 1.});
|
|
|
|
|
// auto pad = NDArrayFactory::create<double>('c', {1, 2}, {1., 1.});// = Nd4j.create(new double[]{1, 1}, new long[]{1, 2});
|
|
|
|
|
auto pad = NDArrayFactory::create<int>('c', {1, 2}, {1, 1});
|
|
|
|
|
// auto value(10.0);
|
|
|
|
|
|
|
|
|
|
auto exp = NDArrayFactory::create<double>({10., 1., 1., 1., 1., 1., 10.});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
|
|
|
|
|
auto res = op.execute({&in, &pad}, {10.0}, {0});
|
|
|
|
|
ASSERT_EQ(res->status(), ND4J_STATUS_OK);
|
|
|
|
|
ASSERT_TRUE(exp.equalsTo(res->at(0)));
|
|
|
|
|
delete res;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests30) {
|
|
|
|
|
|
|
|
|
|
auto in = NDArrayFactory::create<double>({1., 11., 111., 11., 1.});
|
|
|
|
|
auto pad = NDArrayFactory::create<int>('c', {1, 2}, {1, 1});
|
|
|
|
|
|
|
|
|
|
auto exp = NDArrayFactory::create<double>({1., 1., 11., 111., 11., 1., 1.});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
|
|
|
|
|
auto res = op.execute({&in, &pad}, {10.0}, {2});
|
|
|
|
|
ASSERT_EQ(res->status(), ND4J_STATUS_OK);
|
|
|
|
|
ASSERT_TRUE(exp.equalsTo(res->at(0)));
|
|
|
|
|
delete res;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests31) {
|
|
|
|
|
|
|
|
|
|
auto in = NDArrayFactory::create<double>({1., 11., 111., 1111., 11111.});
|
|
|
|
|
// auto pad = NDArrayFactory::create<double>('c', {1, 2}, {1., 1.});// = Nd4j.create(new double[]{1, 1}, new long[]{1, 2});
|
|
|
|
|
auto pad = NDArrayFactory::create<int>('c', {1, 2}, {1, 1});
|
|
|
|
|
// auto value(10.0);
|
|
|
|
|
|
|
|
|
|
auto exp = NDArrayFactory::create<double>({11., 1., 11., 111., 1111., 11111., 1111.});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
|
|
|
|
|
auto res = op.execute({&in, &pad}, {10.0}, {1});
|
|
|
|
|
ASSERT_EQ(res->status(), ND4J_STATUS_OK);
|
|
|
|
|
ASSERT_TRUE(exp.equalsTo(res->at(0)));
|
|
|
|
|
delete res;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
///////////////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests32) {
|
|
|
|
|
|
|
|
|
|
auto in = NDArrayFactory::create<double>('c', {3,3}, {1., 2., 3., 4., 5.,6,7,8,9});
|
|
|
|
|
auto pad = NDArrayFactory::create<int>('c', {2,2}, {1, 2, 2, 3});
|
|
|
|
|
|
|
|
|
|
auto exp = NDArrayFactory::create<double>('c', {6,8}, {2, 1, 1, 2, 3, 3, 2, 1, 2, 1, 1, 2, 3, 3, 2, 1, 5, 4, 4, 5, 6, 6, 5, 4, 8, 7, 7, 8, 9, 9, 8, 7, 8, 7, 7, 8, 9, 9, 8, 7, 5, 4, 4, 5, 6, 6, 5, 4});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
|
|
|
|
|
auto res = op.execute({&in, &pad}, {10.0}, {2});
|
|
|
|
|
ASSERT_EQ(res->status(), ND4J_STATUS_OK);
|
|
|
|
|
ASSERT_TRUE(exp.equalsTo(res->at(0)));
|
|
|
|
|
delete res;
|
|
|
|
|
}
|
|
|
|
|
///////////////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests33) {
|
|
|
|
|
|
|
|
|
|
auto in = NDArrayFactory::create<double>('c', {2,3,4}, {1, 2, 3, 4,5, 6, 7, 8,9,10,11,12,13, 14, 15, 16,17, 18, 19, 20,21, 22, 23, 24});
|
|
|
|
|
|
|
|
|
|
auto pad = NDArrayFactory::create<int>('c', {3,2}, {1, 2, 2, 3, 3,3});
|
|
|
|
|
|
|
|
|
|
auto exp = NDArrayFactory::create<double>('c', {5,8,10}, { 7,6,5,5,6,7,8,8,7,6., 3,2,1,1,2,3,4,4,3,2., 3,2,1,1,2,3,4,4,3,2., 7,6,5,5,6,7,8,8,7,6., 11,10,9,9,10,11,12,12,11,10.,
|
|
|
|
|
11,10,9,9,10,11,12,12,11,10., 7,6,5,5,6,7,8,8,7,6., 3,2,1,1,2,3,4,4,3,2., 7,6,5,5,6,7,8,8,7,6., 3,2,1,1,2,3,4,4,3,2.,
|
|
|
|
|
3,2,1,1,2,3,4,4,3,2., 7,6,5,5,6,7,8,8,7,6., 11,10,9,9,10,11,12,12,11,10., 11,10,9,9,10,11,12,12,11,10.,7,6,5,5,6,7,8,8,7,6.,
|
|
|
|
|
3,2,1,1,2,3,4,4,3,2., 19,18,17,17,18,19,20,20,19,18., 15,14,13,13,14,15,16,16,15,14., 15,14,13,13,14,15,16,16,15,14.,
|
|
|
|
|
19,18,17,17,18,19,20,20,19,18., 23,22,21,21,22,23,24,24,23,22., 23,22,21,21,22,23,24,24,23,22., 19,18,17,17,18,19,20,20,19,18.,
|
|
|
|
|
15,14,13,13,14,15,16,16,15,14., 19,18,17,17,18,19,20,20,19,18., 15,14,13,13,14,15,16,16,15,14., 15,14,13,13,14,15,16,16,15,14.,
|
|
|
|
|
19,18,17,17,18,19,20,20,19,18., 23,22,21,21,22,23,24,24,23,22., 23,22,21,21,22,23,24,24,23,22., 19,18,17,17,18,19,20,20,19,18.,
|
|
|
|
|
15,14,13,13,14,15,16,16,15,14., 7,6,5,5,6,7,8,8,7,6., 3,2,1,1,2,3,4,4,3,2., 3,2,1,1,2,3,4,4,3,2., 7,6,5,5,6,7,8,8,7,6.,
|
|
|
|
|
11,10,9,9,10,11,12,12,11,10., 11,10,9,9,10,11,12,12,11,10., 7,6,5,5,6,7,8,8,7,6., 3,2,1,1,2,3,4,4,3,2.});
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
|
|
|
|
|
auto res = op.execute({&in, &pad}, {10.0}, {2});
|
|
|
|
|
ASSERT_EQ(res->status(), ND4J_STATUS_OK);
|
|
|
|
|
ASSERT_TRUE(exp.equalsTo(res->at(0)));
|
|
|
|
|
delete res;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, pad_tests34) {
|
|
|
|
|
|
|
|
|
|
NDArray input('c', {5}, {0.778786, 0.801198, 0.724375, 0.230894, 0.727141}, nd4j::DataType::FLOAT32);
|
|
|
|
|
NDArray paddings('c', {1,2}, {1,1}, nd4j::DataType::INT32);
|
|
|
|
|
NDArray expected('c', {7}, {10., 0.778786, 0.801198, 0.724375, 0.230894, 0.727141, 10.}, nd4j::DataType::FLOAT32);
|
|
|
|
|
NDArray z('c', {7}, nd4j::DataType::FLOAT32);
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
Nd4jStatus status = op.execute({&input, &paddings}, {&z}, {10}, {0}, {}); // constant
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(&z));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(z));
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
// CONSTANT mode 2D
|
|
|
|
|
TEST_F(DeclarableOpsTests12, Pad_1) {
|
|
|
|
|
|
|
|
|
|
float inBuff[] = {1,2,3,4,5,6};
|
|
|
|
|
int padBuff[] = {1,1,2,2};
|
|
|
|
|
float expBuff[] = {0,0,0,0,0,0,0, 0,0,1,2,3,0,0, 0,0,4,5,6,0,0, 0,0,0,0,0,0,0};
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<float>(inBuff, 'c', {2,3});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>(padBuff, 'c', {2,2});
|
|
|
|
|
auto expected = NDArrayFactory::create<float>(expBuff, 'c', {4,7});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {0});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
// result->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
// REFLECT mode 2D
|
|
|
|
|
TEST_F(DeclarableOpsTests12, Pad_2) {
|
|
|
|
|
|
|
|
|
|
float inBuff[] = {1,2,3,4,5,6};
|
|
|
|
|
int padBuff[] = {1,1,2,2};
|
|
|
|
|
float expBuff[] = {6,5,4,5,6,5,4, 3,2,1,2,3,2,1, 6,5,4,5,6,5,4, 3,2,1,2,3,2,1};
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<float>(inBuff, 'c', {2,3});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>(padBuff, 'c', {2,2});
|
|
|
|
|
auto expected = NDArrayFactory::create<float>(expBuff, 'c', {4,7});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {1});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
// result->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
// SYMMETRIC mode 2D
|
|
|
|
|
TEST_F(DeclarableOpsTests12, Pad_3) {
|
|
|
|
|
|
|
|
|
|
float inBuff[] = {1,2,3,4,5,6};
|
|
|
|
|
int padBuff[] = {1,1,2,2};
|
|
|
|
|
float expBuff[] = {2,1,1,2,3,3,2, 2,1,1,2,3,3,2, 5,4,4,5,6,6,5, 5,4,4,5,6,6,5};
|
|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<float>(inBuff, 'c', {2,3});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>(padBuff, 'c', {2,2});
|
|
|
|
|
auto expected = NDArrayFactory::create<float>(expBuff, 'c', {4,7});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {2});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
// result->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
// CONSTANT mode 3D
|
|
|
|
|
TEST_F(DeclarableOpsTests12, Pad_4) {
|
|
|
|
|
|
|
|
|
|
float inBuff[] = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18};
|
|
|
|
|
int padBuff[] = {1,1,2,2,2,2};
|
|
|
|
|
float expBuff[] = {0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 1, 2, 3,0,0,0,0, 4, 5, 6,0,0,0,0, 7, 8, 9,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0,10,11,12,0,0,0,0,13,14,15,0,0,0,0,16,17,18,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0,0,0, 0, 0, 0,0,0};
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|
|
auto input = NDArrayFactory::create<float>(inBuff, 'c', {2,3,3});
|
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|
|
auto paddings = NDArrayFactory::create<int>(padBuff, 'c', {3,2});
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|
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|
|
auto expected = NDArrayFactory::create<float>(expBuff, 'c', {4,7,7});
|
|
|
|
|
|
|
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|
|
nd4j::ops::pad op;
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|
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|
|
auto results = op.execute({&input, &paddings}, {}, {0});
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|
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|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
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|
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|
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|
|
auto result = results->at(0);
|
|
|
|
|
// result->printIndexedBuffer();
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|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
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|
|
ASSERT_TRUE(expected.equalsTo(result));
|
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|
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|
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|
|
delete results;
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|
|
}
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////////////////////////////////////////////////////////////////////
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|
// REFLECT mode 3D
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TEST_F(DeclarableOpsTests12, Pad_5) {
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float inBuff[] = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18};
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int padBuff[] = {1,1,2,2,2,2};
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|
|
float expBuff[] = {18,17,16,17,18,17,16, 15,14,13,14,15,14,13, 12,11,10,11,12,11,10, 15,14,13,14,15,14,13, 18,17,16,17,18,17,16, 15,14,13,14,15,14,13, 12,11,10,11,12,11,10, 9, 8, 7, 8, 9, 8, 7, 6, 5, 4, 5, 6, 5, 4, 3, 2, 1, 2, 3, 2, 1, 6, 5, 4, 5, 6, 5, 4, 9, 8, 7, 8, 9, 8, 7, 6, 5, 4, 5, 6, 5, 4, 3, 2, 1, 2, 3, 2, 1, 18,17,16,17,18,17,16, 15,14,13,14,15,14,13, 12,11,10,11,12,11,10, 15,14,13,14,15,14,13, 18,17,16,17,18,17,16, 15,14,13,14,15,14,13, 12,11,10,11,12,11,10, 9, 8, 7, 8, 9, 8, 7, 6, 5, 4, 5, 6, 5, 4, 3, 2, 1, 2, 3, 2, 1, 6, 5, 4, 5, 6, 5, 4, 9, 8, 7, 8, 9, 8, 7, 6, 5, 4, 5, 6, 5, 4, 3, 2, 1, 2, 3, 2, 1};
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|
auto input = NDArrayFactory::create<float>(inBuff, 'c', {2,3,3});
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|
auto paddings = NDArrayFactory::create<int>(padBuff, 'c', {3,2});
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|
|
auto expected = NDArrayFactory::create<float>(expBuff, 'c', {4,7,7});
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|
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|
|
|
|
|
|
nd4j::ops::pad op;
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|
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|
|
auto results = op.execute({&input, &paddings}, {}, {1});
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|
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|
|
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|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
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|
|
|
auto result = results->at(0);
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|
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|
|
// result->printIndexedBuffer();
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|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
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|
|
|
|
////////////////////////////////////////////////////////////////////
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|
|
|
// SYMMETRIC mode 3D
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|
|
TEST_F(DeclarableOpsTests12, Pad_6) {
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|
|
float inBuff[] = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18};
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|
|
|
int padBuff[] = {1,1,2,2,2,2};
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|
|
|
float expBuff[] = {5, 4, 4, 5, 6, 6, 5, 2, 1, 1, 2, 3, 3, 2, 2, 1, 1, 2, 3, 3, 2, 5, 4, 4, 5, 6, 6, 5, 8, 7, 7, 8, 9, 9, 8, 8, 7, 7, 8, 9, 9, 8, 5, 4, 4, 5, 6, 6, 5, 5, 4, 4, 5, 6, 6, 5, 2, 1, 1, 2, 3, 3, 2, 2, 1, 1, 2, 3, 3, 2, 5, 4, 4, 5, 6, 6, 5, 8, 7, 7, 8, 9, 9, 8, 8, 7, 7, 8, 9, 9, 8, 5, 4, 4, 5, 6, 6, 5, 14,13,13,14,15,15,14, 11,10,10,11,12,12,11, 11,10,10,11,12,12,11, 14,13,13,14,15,15,14, 17,16,16,17,18,18,17, 17,16,16,17,18,18,17, 14,13,13,14,15,15,14, 14,13,13,14,15,15,14, 11,10,10,11,12,12,11, 11,10,10,11,12,12,11, 14,13,13,14,15,15,14, 17,16,16,17,18,18,17, 17,16,16,17,18,18,17, 14,13,13,14,15,15,14};
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|
|
|
|
|
|
|
|
|
auto input = NDArrayFactory::create<float>(inBuff, 'c', {2,3,3});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>(padBuff, 'c', {3,2});
|
|
|
|
|
auto expected = NDArrayFactory::create<float>(expBuff, 'c', {4,7,7});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {2});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto result = results->at(0);
|
|
|
|
|
// result->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
// CONSTANT mode 4D
|
|
|
|
|
TEST_F(DeclarableOpsTests12, Pad_7)
|
|
|
|
|
{
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|
|
|
|
|
|
|
|
|
float inBuff[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
|
|
|
|
|
int padBuff[] = {1, 1, 1, 1, 1, 1, 1, 1};
|
|
|
|
|
float expBuff[] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 10, 0, 0, 11, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 13, 14, 0, 0, 15, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
|
|
|
|
|
auto input = NDArrayFactory::create<float>(inBuff, 'c', {2, 2, 2, 2});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>(padBuff, 'c', {4, 2});
|
|
|
|
|
auto expected = NDArrayFactory::create<float>(expBuff, 'c', {4, 4, 4, 4});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {0});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto *result = results->at(0);
|
|
|
|
|
// result->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
|
|
|
// REFLECT mode 4D
|
|
|
|
|
TEST_F(DeclarableOpsTests12, Pad_8)
|
|
|
|
|
{
|
|
|
|
|
|
|
|
|
|
float inBuff[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
|
|
|
|
|
int padBuff[] = {1, 1, 1, 1, 1, 1, 1, 1};
|
|
|
|
|
float expBuff[] = {16, 15, 16, 15, 14, 13, 14, 13, 16, 15, 16, 15, 14, 13, 14, 13, 12, 11, 12, 11, 10, 9, 10, 9, 12, 11, 12, 11, 10, 9, 10, 9, 16, 15, 16, 15, 14, 13, 14, 13, 16, 15, 16, 15, 14, 13, 14, 13, 12, 11, 12, 11, 10, 9, 10, 9, 12, 11, 12, 11, 10, 9, 10, 9, 8, 7, 8, 7, 6, 5, 6, 5, 8, 7, 8, 7, 6, 5, 6, 5, 4, 3, 4, 3, 2, 1, 2, 1, 4, 3, 4, 3, 2, 1, 2, 1, 8, 7, 8, 7, 6, 5, 6, 5, 8, 7, 8, 7, 6, 5, 6, 5, 4, 3, 4, 3, 2, 1, 2, 1, 4, 3, 4, 3, 2, 1, 2, 1, 16, 15, 16, 15, 14, 13, 14, 13, 16, 15, 16, 15, 14, 13, 14, 13, 12, 11, 12, 11, 10, 9, 10, 9, 12, 11, 12, 11, 10, 9, 10, 9, 16, 15, 16, 15, 14, 13, 14, 13, 16, 15, 16, 15, 14, 13, 14, 13, 12, 11, 12, 11, 10, 9, 10, 9, 12, 11, 12, 11, 10, 9, 10, 9, 8, 7, 8, 7, 6, 5, 6, 5, 8, 7, 8, 7, 6, 5, 6, 5, 4, 3, 4, 3, 2, 1, 2, 1, 4, 3, 4, 3, 2, 1, 2, 1, 8, 7, 8, 7, 6, 5, 6, 5, 8, 7, 8, 7, 6, 5, 6, 5, 4, 3, 4, 3, 2, 1, 2, 1, 4, 3, 4, 3, 2, 1, 2, 1};
|
|
|
|
|
auto input = NDArrayFactory::create<float>(inBuff, 'c', {2, 2, 2, 2});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>(padBuff, 'c', {4, 2});
|
|
|
|
|
auto expected = NDArrayFactory::create<float>(expBuff, 'c', {4, 4, 4, 4});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {1});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto *result = results->at(0);
|
|
|
|
|
// result->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////
|
|
|
|
|
// SYMMETRIC mode 4D
|
|
|
|
|
TEST_F(DeclarableOpsTests12, Pad_9)
|
|
|
|
|
{
|
|
|
|
|
|
|
|
|
|
float inBuff[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
|
|
|
|
|
int padBuff[] = {1, 1, 1, 1, 1, 1, 1, 1};
|
|
|
|
|
float expBuff[] = {1, 1, 2, 2, 1, 1, 2, 2, 3, 3, 4, 4, 3, 3, 4, 4, 1, 1, 2, 2, 1, 1, 2, 2, 3, 3, 4, 4, 3, 3, 4, 4, 5, 5, 6, 6, 5, 5, 6, 6, 7, 7, 8, 8, 7, 7, 8, 8, 5, 5, 6, 6, 5, 5, 6, 6, 7, 7, 8, 8, 7, 7, 8, 8, 1, 1, 2, 2, 1, 1, 2, 2, 3, 3, 4, 4, 3, 3, 4, 4, 1, 1, 2, 2, 1, 1, 2, 2, 3, 3, 4, 4, 3, 3, 4, 4, 5, 5, 6, 6, 5, 5, 6, 6, 7, 7, 8, 8, 7, 7, 8, 8, 5, 5, 6, 6, 5, 5, 6, 6, 7, 7, 8, 8, 7, 7, 8, 8, 9, 9, 10, 10, 9, 9, 10, 10, 11, 11, 12, 12, 11, 11, 12, 12, 9, 9, 10, 10, 9, 9, 10, 10, 11, 11, 12, 12, 11, 11, 12, 12, 13, 13, 14, 14, 13, 13, 14, 14, 15, 15, 16, 16, 15, 15, 16, 16, 13, 13, 14, 14, 13, 13, 14, 14, 15, 15, 16, 16, 15, 15, 16, 16, 9, 9, 10, 10, 9, 9, 10, 10, 11, 11, 12, 12, 11, 11, 12, 12, 9, 9, 10, 10, 9, 9, 10, 10, 11, 11, 12, 12, 11, 11, 12, 12, 13, 13, 14, 14, 13, 13, 14, 14, 15, 15, 16, 16, 15, 15, 16, 16, 13, 13, 14, 14, 13, 13, 14, 14, 15, 15, 16, 16, 15, 15, 16, 16};
|
|
|
|
|
auto input = NDArrayFactory::create<float>(inBuff, 'c', {2, 2, 2, 2});
|
|
|
|
|
auto paddings = NDArrayFactory::create<int>(padBuff, 'c', {4, 2});
|
|
|
|
|
auto expected = NDArrayFactory::create<float>(expBuff, 'c', {4, 4, 4, 4});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
auto results = op.execute({&input, &paddings}, {}, {2});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, results->status());
|
|
|
|
|
|
|
|
|
|
auto *result = results->at(0);
|
|
|
|
|
// result->printIndexedBuffer();
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(expected.isSameShapeStrict(result));
|
|
|
|
|
ASSERT_TRUE(expected.equalsTo(result));
|
|
|
|
|
|
|
|
|
|
delete results;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
TEST_F(DeclarableOpsTests12, Test_Expose_1) {
|
|
|
|
|
auto input0 = NDArrayFactory::create<float>('c', {2, 3}, {1, 2, 3, 6, 5, 4});
|
|
|
|
|
auto input1 = NDArrayFactory::create<float>('c', {2, 3}, {3, 2, 1, 4, 5, 6});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::expose op;
|
|
|
|
|
|
|
|
|
|
auto result = op.execute({&input0, &input1}, {}, {});
|
|
|
|
|
|
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, result->status());
|
|
|
|
|
|
|
|
|
|
auto z0 = result->at(0);
|
|
|
|
|
auto z1 = result->at(1);
|
|
|
|
|
|
|
|
|
|
ASSERT_TRUE(input0.equalsTo(z0));
|
|
|
|
|
ASSERT_TRUE(input1.equalsTo(z1));
|
|
|
|
|
|
|
|
|
|
delete result;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
|
TEST_F(DeclarableOpsTests12, Pad_SGO_Test_1) {
|
|
|
|
|
|
|
|
|
|
auto in = NDArrayFactory::create<double>({1., 1., 1., 1., 1.});
|
|
|
|
|
// auto pad = NDArrayFactory::create<double>('c', {1, 2}, {1., 1.});// = Nd4j.create(new double[]{1, 1}, new long[]{1, 2});
|
|
|
|
|
auto pad = NDArrayFactory::create<int>('c', {1, 2}, {1, 1});
|
|
|
|
|
// auto value(10.0);
|
|
|
|
|
|
|
|
|
|
auto exp = NDArrayFactory::create<double>({10., 1., 1., 1., 1., 1., 10.});
|
|
|
|
|
|
|
|
|
|
nd4j::ops::pad op;
|
|
|
|
|
|
|
|
|
|
auto res = op.execute({&in, &pad}, {10.0}, {0});
|
|
|
|
|
ASSERT_EQ(res->status(), ND4J_STATUS_OK);
|
|
|
|
|
// res->at(0)->printIndexedBuffer("PAD_SGO");
|
|
|
|
|
// exp.printIndexedBuffer("PAD_EXP");
|
|
|
|
|
ASSERT_TRUE(exp.equalsTo(res->at(0)));
|
|
|
|
|
delete res;
|
|
|
|
|
}
|
