/* ******************************************************************************
*
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership.
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// Created by raver on 8/4/2018.
//
#include "testlayers.h"
#include <ops/declarable/CustomOperations.h>
#include <array/NDArray.h>
#include <ops/ops.h>
#include <helpers/GradCheck.h>
#include <helpers/MmulHelper.h>
#include <ops/declarable/helpers/image_resize.h>
using namespace sd;
class DeclarableOpsTests11 : public testing::Test {
public:
DeclarableOpsTests11() {
printf("\n");
fflush(stdout);
}
};
TEST_F(DeclarableOpsTests11, test_listdiff_1) {
auto x = NDArrayFactory::create<int>('c', {4}, {0, 1, 2, 3});
auto y = NDArrayFactory::create<int>('c',{2}, {3, 1});
sd::ops::listdiff op;
auto result = op.evaluate({&x, &y}, {}, {});
ASSERT_EQ(Status::OK(), result.status());
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, log_loss_grad_test1) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {-12.49997,-13.04346, -13.63635, -14.28571,-14.99999,-15.78947, -16.66666, -17.64705,-18.75 ,-20. , -21.42857, -23.07692,
-24.99999,-27.27272, -29.99999, -33.33332,-37.49999,-42.85713, -49.99998, -59.99998,-74.99995,-99.99992,-149.99986,-299.99911});
NDArray dLdwExp('c', {2,3,4}, {3.21887, 4.96807, 6.10512, 6.80726, 7.15461, 7.19051, 6.93973, 6.41584, 5.62456, 4.56548, 3.2326 , 1.61444,
-0.30659, -2.55529, -5.16569, -8.18417,-11.67468,-15.72734,-20.47379,-26.11644,-32.9902 ,-41.71318,-53.64824,-73.05434});
NDArray dLdlExp('c', {2,3,4}, {1.58903, 1.22117, 0.99621, 0.82911, 0.69315, 0.57634, 0.47223, 0.37689, 0.28768, 0.20273, 0.12058, 0.04002,
-0.04002,-0.12058,-0.20273,-0.28768,-0.37689,-0.47223,-0.57634,-0.69315,-0.82911,-0.99621,-1.22117,-1.58903});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::log_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {0}, {});
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));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, log_loss_grad_test2) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,1,4}, sd::DataType::DOUBLE);
NDArray dLdwExp('c', {2,1,4}, {15.99805, 16.72406, 16.27746, 14.83754,-44.97147,-59.99582,-79.28771,-107.35497});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::log_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, log_loss_grad_test3) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights(sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {-12.49997,-13.04346, -13.63635, -14.28571,-14.99999,-15.78947, -16.66666, -17.64705,-18.75 ,-20. , -21.42857, -23.07692,
-24.99999,-27.27272, -29.99999, -33.33332,-37.49999,-42.85713, -49.99998, -59.99998,-74.99995,-99.99992,-149.99986,-299.99911});
NDArray dLdwExp('c', {}, std::vector<double>{-227.77286});
NDArray dLdlExp('c', {2,3,4}, {1.58903, 1.22117, 0.99621, 0.82911, 0.69315, 0.57634, 0.47223, 0.37689, 0.28768, 0.20273, 0.12058, 0.04002,
-0.04002,-0.12058,-0.20273,-0.28768,-0.37689,-0.47223,-0.57634,-0.69315,-0.82911,-0.99621,-1.22117,-1.58903});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::log_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {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));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, log_loss_grad_test4) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE);
NDArray dLdwExp('c', {1,3,1}, {4.8876 , -46.29156, -186.36887});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::log_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
// dLdw->printIndexedBuffer();
// dLdw->printShapeInfo();
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, log_loss_grad_test5) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {-1.04166,-1.08696, -1.13636, -1.19048,-1.25 ,-1.31579, -1.38889, -1.47059,-1.5625 ,-1.66667, -1.78571, -1.92308,
-2.08333,-2.27273, -2.5 , -2.77778,-3.125 ,-3.57143, -4.16667, -5. ,-6.25 ,-8.33333,-12.49999,-24.99993});
NDArray dLdwExp('c', {2,3,4}, {1.05912, 1.20488, 1.29964, 1.35815, 1.3871 , 1.39009, 1.36919, 1.32553, 1.25959, 1.17133, 1.06026, 0.92541,
0.76533, 0.57794, 0.3604 , 0.10886,-0.18201,-0.51973,-0.91527,-1.38549,-1.95831,-2.68522,-3.67981,-5.29698});
NDArray dLdlExp('c', {2,3,4}, {0.13242, 0.10176, 0.08302, 0.06909, 0.05776, 0.04803, 0.03935, 0.03141, 0.02397, 0.01689, 0.01005, 0.00334,
-0.00334,-0.01005,-0.01689,-0.02397,-0.03141,-0.03935,-0.04803,-0.05776,-0.06909,-0.08302,-0.10176,-0.13242});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::log_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {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));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, log_loss_grad_test6) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE);
NDArray dLdwExp('c', {1,3,1}, {6.73432, 2.46939,-9.20372});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::log_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, log_loss_grad_test7) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights(sd::DataType::DOUBLE);
NDArray dLdwExp('c', {}, std::vector<double>{0.});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::log_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, log_loss_grad_test8) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {0. , 0. , 0. , 0. ,-1.5 ,-1.57895, -1.66667, -1.76471,-1.875 ,-2. , -2.14286, -2.30769,
-2.5 ,-2.72727, -3. , -3.33333,-3.75 ,-4.28571, -5. , -6. ,-7.49999,-9.99999,-14.99999,-29.99991});
NDArray dLdwExp('c', {2,3,4}, {1.56625, 1.74117, 1.85487, 1.92509, 1.95982, 1.96341, 1.93833, 1.88594, 1.80682, 1.70091, 1.56762, 1.4058 ,
1.2137 , 0.98883, 0.72779, 0.42594, 0.07689,-0.32837,-0.80302,-1.36728,-2.05466,-2.92696,-4.12046,-6.06107});
NDArray dLdlExp('c', {2,3,4}, {0. , 0. , 0. , 0. , 0.06931, 0.05763, 0.04722, 0.03769, 0.02877, 0.02027, 0.01206, 0.004,
-0.004 ,-0.01206,-0.02027,-0.02877,-0.03769,-0.04722,-0.05763,-0.06931,-0.08291,-0.09962,-0.12212,-0.1589});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
weights.p(0, 0.);
weights.p(1, 0.);
weights.p(2, 0.);
weights.p(3, 0.);
sd::ops::log_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {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));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, log_loss_grad_test9) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {-0.52083,-0.54348,-0.56818, -0.59524,-0.625 ,-0.65789,-0.69444, -0.73529,-0.78125,-0.83333,-0.89286, -0.96154,
-1.04167,-1.13636,-1.25 , -1.38889,-1.5625 ,-1.78571,-2.08333, -2.5 ,-3.125 ,-4.16666,-6.24999,-12.49996});
NDArray dLdwExp('c', {2,3,4}, {0.13412, 0.207 , 0.25438, 0.28364, 0.29811, 0.2996 , 0.28916, 0.26733, 0.23436, 0.19023, 0.13469, 0.06727,
-0.01277,-0.10647,-0.21524,-0.34101,-0.48645,-0.65531,-0.85307,-1.08819,-1.37459,-1.73805,-2.23534,-3.04393});
NDArray dLdlExp('c', {2,3,4}, {0.06621, 0.05088, 0.04151, 0.03455, 0.02888, 0.02401, 0.01968, 0.0157 , 0.01199, 0.00845, 0.00502, 0.00167,
-0.00167,-0.00502,-0.00845,-0.01199,-0.0157 ,-0.01968,-0.02401,-0.02888,-0.03455,-0.04151,-0.05088,-0.06621});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::log_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {3});
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));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, log_loss_grad_test10) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {1,1}, sd::DataType::DOUBLE);
NDArray dLdwExp('c', {1,1}, std::vector<double>{-9.49054});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::log_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, log_loss_grad_test11) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE);
NDArray dLdwExp('c', {1,3,1}, {0.20365,-1.92882,-7.76537});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::log_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, log_loss_grad_test12) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, { 0. , 0. , 0. , 0. ,-0.75 ,-0.789473,-0.833333, -0.882353,-0.9375 ,-1. ,-1.071428, -1.153846,
-1.25 ,-1.363636,-1.5 , -1.666666,-1.875 ,-2.142857,-2.499999, -2.999999,-3.749997,-4.999997,-7.499993,-14.999956});
NDArray dLdwExp('c', {2,3,4}, {0.16094, 0.2484 , 0.30526, 0.34036, 0.35773, 0.35953, 0.34699, 0.32079, 0.28123, 0.22827, 0.16163, 0.08072,
-0.01533,-0.12776,-0.25828,-0.40921,-0.58373,-0.78637,-1.02369,-1.30582,-1.64951,-2.08566,-2.68241,-3.65272});
NDArray dLdlExp('c', {2,3,4}, {0. , 0. , 0. , 0. , 0.03466, 0.02882, 0.02361, 0.01884, 0.01438, 0.01014, 0.00603, 0.002 ,
-0.002 ,-0.00603,-0.01014,-0.01438,-0.01884,-0.02361,-0.02882,-0.03466,-0.04146,-0.04981,-0.06106,-0.07945});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
weights.r<double>(0) = 0.;
weights.r<double>(1) = 0.;
weights.r<double>(2) = 0.;
weights.r<double>(3) = 0.;
sd::ops::log_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {3});
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));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, log_loss_grad_test13) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,1}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
-2.08333,-2.27273, -2.5 , -2.77778,-3.125 ,-3.57143, -4.16667, -5. ,-6.25 ,-8.33333,-12.49999,-24.99993});
NDArray dLdwExp('c', {2,3,1}, {1.75828, 2.30839, 1.25309, -1.35098, -6.16602,-16.78383});
NDArray dLdlExp('c', {2,3,4}, {0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
-0.00334,-0.01005,-0.01689,-0.02397,-0.03141,-0.03935,-0.04803,-0.05776,-0.06909,-0.08302,-0.10176,-0.13242});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
weights.r<double>(0) = 0.;
weights.r<double>(1) = 0.;
weights.r<double>(2) = 0.;
sd::ops::log_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {3});
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));
}
TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test1) {
NDArray input = NDArrayFactory::create<float>('c', {1, 7, 7, 1}, {
1.f, 2.1f, 3.15f, 4.2f, 5.15f, 6.1f, 7.f,
8.f, 9.1f, 10.f, 11.f, 12.9f, 13.1f, 14.f,
15.f, 16.f, 17.f, 18.f, 19.f, 20.f, 21.f,
22.f, 23.f, 24.f, 25.f, 26.f, 27.f, 28.f,
30.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f,
37.f, 38.f, 39.f, 40.f, 41.f, 42.f, 43.f,
44.f, 45.f, 46.f, 47.f, 48.f, 49.f, 50.f
});
NDArray expected = NDArrayFactory::create<float>('c', {1, 30, 30, 1}, {
1.f, 1.1976162f, 1.4174359f, 1.6775769f, 1.9961575f, 2.3283265f,
2.550918f, 2.7360606f, 2.9655411f, 3.2929654f, 3.5441515f, 3.7380352f,
3.948995f, 4.248106f, 4.5073795f, 4.6843743f, 4.8572845f, 5.104302f,
5.3869915f, 5.581401f, 5.7539616f, 5.974285f, 6.272836f, 6.5204263f,
6.718899f, 6.8871036f, 7.039068f, 7.099216f, 7.0784245f, 7.0281887f,
2.247592f, 2.446947f, 2.6694887f, 2.9312382f, 3.248216f, 3.5745337f,
3.78931f, 3.9656973f, 4.186417f, 4.5046535f, 4.740569f, 4.9217057f,
5.133866f, 5.459533f, 5.7744613f, 6.0197873f, 6.254011f, 6.535633f,
6.8097296f, 6.9607787f, 7.0749416f, 7.241601f, 7.5094895f, 7.7499495f,
7.954571f, 8.131972f, 8.286526f, 8.346463f, 8.325745f, 8.275683f,
3.6286845f, 3.830573f, 4.0569587f, 4.3211575f, 4.6364856f, 4.9556503f,
5.160583f, 5.3258467f, 5.535462f, 5.84216f, 6.058749f, 6.223753f,
6.437597f, 6.797369f, 7.1836042f, 7.5164022f, 7.8290343f, 8.154773f,
8.417635f, 8.512958f, 8.5521f, 8.649708f, 8.87788f, 9.108794f,
9.320926f, 9.509781f, 9.667375f, 9.72694f, 9.706349f, 9.656599f,
5.276778f, 5.480438f, 5.709702f, 5.9754477f, 6.288551f, 6.6005697f,
6.796207f, 6.9511423f, 7.1503997f, 7.4461427f, 7.644651f, 7.794562f,
8.009684f, 8.400473f, 8.851847f, 9.26469f, 9.649218f, 10.015648f,
10.268647f, 10.313368f, 10.2843275f, 10.319379f, 10.512033f, 10.734956f,
10.954604f, 11.154507f, 11.315369f, 11.374779f, 11.354242f, 11.304622f,
7.325373f, 7.5284843f, 7.757575f, 8.022221f, 8.331997f, 8.638187f,
8.827649f, 8.976217f, 9.168955f, 9.45726f, 9.6442375f, 9.784517f,
9.999621f, 10.407702f, 10.896234f, 11.355122f, 11.781423f, 12.172186f,
12.420712f, 12.4374485f, 12.370511f, 12.371386f, 12.545973f, 12.766424f,
12.992249f, 13.20012f, 13.364252f, 13.424109f, 13.40342f, 13.353425f,
9.493208f, 9.692467f, 9.9169445f, 10.176801f, 10.482199f, 10.78547f,
10.974367f, 11.123442f, 11.31637f, 11.603645f, 11.790616f, 11.930889f,
12.144082f, 12.546447f, 13.024898f, 13.4723f, 13.889232f, 14.276275f,
14.528972f, 14.555555f, 14.50145f, 14.515459f, 14.700572f, 14.927055f,
15.156046f, 15.366046f, 15.532901f, 15.594008f, 15.5728855f, 15.521847f,
10.970133f, 11.163599f, 11.380694f, 11.633735f, 11.935032f, 12.238887f,
12.43254f, 12.588294f, 12.787534f, 13.079956f, 13.27752f, 13.426631f,
13.636713f, 14.013844f, 14.441672f, 14.827978f, 15.191209f, 15.549808f,
15.81343f, 15.881828f, 15.883522f, 15.950411f, 16.16933f, 16.40794f,
16.636436f, 16.842583f, 17.010887f, 17.07363f, 17.05194f, 16.999537f,
12.219155f, 12.406129f, 12.614796f, 12.860335f, 13.157928f, 13.464224f,
13.665207f, 13.830567f, 14.039036f, 14.339629f, 14.552863f, 14.715049f,
14.921564f, 15.264454f, 15.622843f, 15.924977f, 16.213829f, 16.532364f,
16.8099f, 16.934835f, 17.012146f, 17.150164f, 17.413412f, 17.666712f,
17.892765f, 18.09207f, 18.261044f, 18.325531f, 18.303238f, 18.249378f,
13.7663965f, 13.947391f, 14.148263f, 14.386917f, 14.681246f, 14.990087f,
15.198166f, 15.372728f, 15.590062f, 15.898583f, 16.126892f, 16.301655f,
16.50487f, 16.815214f, 17.107498f, 17.329458f, 17.547403f, 17.827654f,
18.118288f, 18.296928f, 18.4461f, 18.651634f, 18.956806f, 19.22382f,
19.447308f, 19.639887f, 19.809319f, 19.875397f, 19.852556f, 19.797365f,
15.9419365f, 16.118704f, 16.314133f, 16.547867f, 16.839561f, 17.14954f,
17.361883f, 17.542162f, 17.764957f, 18.078188f, 18.315733f, 18.498205f,
18.699116f, 18.988684f, 19.238989f, 19.410137f, 19.583265f, 19.839512f,
20.13878f, 20.35177f, 20.546844f, 20.795671f, 21.128067f, 21.404358f,
21.626736f, 21.8155f, 21.98561f, 22.052843f, 22.029604f, 21.973448f,
17.53522f, 17.71077f, 17.904636f, 18.13695f, 18.42784f, 18.738056f,
18.951529f, 19.133352f, 19.357613f, 19.672083f, 19.912102f, 20.096638f,
20.296894f, 20.580765f, 20.819603f, 20.976887f, 21.137802f, 21.387535f,
21.689209f, 21.911621f, 22.119276f, 22.37999f, 22.71991f, 22.998823f,
23.22097f, 23.40876f, 23.57911f, 23.646685f, 23.623325f, 23.566887f,
18.746353f, 18.922657f, 19.117487f, 19.350685f, 19.64207f, 19.952137f,
20.164913f, 20.345781f, 20.569134f, 20.88284f, 21.12133f, 21.30459f,
21.505253f, 21.792645f, 22.038572f, 22.204426f, 22.37289f, 22.626648f,
22.926834f, 23.143423f, 23.343302f, 23.596668f, 23.931936f, 24.209232f,
24.431519f, 24.619913f, 24.79011f, 24.857473f, 24.83419f, 24.777927f,
20.16656f, 20.344206f, 20.540766f, 20.775532f, 21.067804f, 21.377607f,
21.589132f, 21.768297f, 21.99003f, 22.302366f, 22.538124f, 22.719105f,
22.920494f, 23.214176f, 23.472767f, 23.653934f, 23.83589f, 24.096842f,
24.394371f, 24.600555f, 24.786541f, 25.026773f, 25.353731f, 25.62813f,
25.850672f, 26.04014f, 26.210072f, 26.277063f, 26.253906f, 26.197956f,
22.363024f, 22.54125f, 22.738552f, 22.973991f, 23.266647f, 23.57634f,
23.787327f, 23.96576f, 24.186796f, 24.498543f, 24.733124f, 24.913122f,
25.114826f, 25.411213f, 25.675262f, 25.863028f, 26.050789f, 26.314838f,
26.611223f, 26.812925f, 26.992926f, 27.227505f, 27.550882f, 27.824034f,
28.046684f, 28.236614f, 28.406433f, 28.473265f, 28.450163f, 28.394344f,
24.429443f, 24.60767f, 24.80497f, 25.04041f, 25.333065f, 25.642756f,
25.853743f, 26.032173f, 26.25321f, 26.564959f, 26.79954f, 26.97954f,
27.181242f, 27.47763f, 27.74168f, 27.929441f, 28.117207f, 28.381254f,
28.677637f, 28.879343f, 29.059345f, 29.293922f, 29.617298f, 29.890451f,
30.113104f, 30.303034f, 30.472853f, 30.539684f, 30.516582f, 30.460762f,
26.f, 26.178228f, 26.375526f, 26.61097f, 26.903624f, 27.213314f,
27.424305f, 27.602734f, 27.823772f, 28.135519f, 28.3701f, 28.550098f,
28.7518f, 29.04819f, 29.312237f, 29.5f, 29.687763f, 29.951813f,
30.2482f, 30.449903f, 30.629902f, 30.864483f, 31.187859f, 31.461012f,
31.683659f, 31.873592f, 32.043407f, 32.11024f, 32.087135f, 32.03132f,
27.570559f, 27.748787f, 27.946087f, 28.181528f, 28.474184f, 28.783876f,
28.994865f, 29.173294f, 29.39433f, 29.70608f, 29.940659f, 30.120655f,
30.32236f, 30.618746f, 30.882797f, 31.070557f, 31.25832f, 31.522371f,
31.818754f, 32.02046f, 32.20046f, 32.43504f, 32.758415f, 33.031567f,
33.25422f, 33.44415f, 33.613964f, 33.680794f, 33.657696f, 33.60188f,
29.636976f, 29.815207f, 30.0125f, 30.247944f, 30.5406f, 30.85029f,
31.061283f, 31.239712f, 31.46075f, 31.7725f, 32.00708f, 32.187077f,
32.38878f, 32.685165f, 32.949215f, 33.13698f, 33.32474f, 33.58879f,
33.885178f, 34.086884f, 34.26688f, 34.501457f, 34.824837f, 35.09799f,
35.320637f, 35.510574f, 35.68039f, 35.747215f, 35.724117f, 35.6683f,
31.83344f, 32.011665f, 32.20897f, 32.444412f, 32.73707f, 33.046757f,
33.257744f, 33.436176f, 33.657207f, 33.96896f, 34.203537f, 34.383537f,
34.58524f, 34.88163f, 35.145676f, 35.33344f, 35.521206f, 35.785255f,
36.081642f, 36.28334f, 36.46334f, 36.69792f, 37.021297f, 37.294453f,
37.517097f, 37.707027f, 37.876846f, 37.94368f, 37.920578f, 37.864758f,
33.253647f, 33.431873f, 33.62917f, 33.864613f, 34.15727f, 34.466957f,
34.677948f, 34.856377f, 35.077415f, 35.38916f, 35.623745f, 35.803745f,
36.005447f, 36.301834f, 36.565884f, 36.753647f, 36.941406f, 37.205456f,
37.50184f, 37.703545f, 37.883545f, 38.118122f, 38.4415f, 38.714653f,
38.9373f, 39.127235f, 39.297054f, 39.363884f, 39.340782f, 39.28496f,
34.464783f, 34.64301f, 34.840305f, 35.075752f, 35.368404f, 35.6781f,
35.889088f, 36.067516f, 36.28855f, 36.6003f, 36.834885f, 37.014877f,
37.216583f, 37.51297f, 37.77702f, 37.964783f, 38.152546f, 38.416595f,
38.71298f, 38.914684f, 39.094685f, 39.32926f, 39.652645f, 39.925793f,
40.14844f, 40.338375f, 40.508194f, 40.575024f, 40.55192f, 40.496105f,
36.058067f, 36.23629f, 36.43359f, 36.669033f, 36.961685f, 37.271378f,
37.48237f, 37.6608f, 37.881836f, 38.19359f, 38.42817f, 38.608162f,
38.809868f, 39.10625f, 39.3703f, 39.558064f, 39.74583f, 40.00988f,
40.306267f, 40.50797f, 40.68797f, 40.92255f, 41.245926f, 41.519077f,
41.741722f, 41.931652f, 42.101475f, 42.168304f, 42.145203f, 42.089386f,
38.315002f, 38.493233f, 38.690533f, 38.925976f, 39.218628f, 39.52832f,
39.739307f, 39.917736f, 40.138775f, 40.45052f, 40.685104f, 40.865097f,
41.066803f, 41.36319f, 41.627243f, 41.815002f, 42.002766f, 42.26682f,
42.5632f, 42.764908f, 42.944904f, 43.179485f, 43.50286f, 43.776016f,
43.998665f, 44.188595f, 44.358418f, 44.425247f, 44.402145f, 44.34633f,
40.22708f, 40.40531f, 40.602608f, 40.83805f, 41.130707f, 41.440395f,
41.651382f, 41.82982f, 42.050854f, 42.3626f, 42.597183f, 42.77718f,
42.97888f, 43.27527f, 43.53932f, 43.72708f, 43.914845f, 44.178894f,
44.47528f, 44.676983f, 44.856983f, 45.09156f, 45.41494f, 45.68809f,
45.91074f, 46.100674f, 46.270493f, 46.337322f, 46.31422f, 46.2584f,
41.785618f, 41.963844f, 42.161144f, 42.396584f, 42.68924f, 42.998936f,
43.209923f, 43.388355f, 43.609394f, 43.921143f, 44.15572f, 44.335716f,
44.53742f, 44.833805f, 45.09786f, 45.285614f, 45.473377f, 45.737427f,
46.033817f, 46.235523f, 46.415524f, 46.650105f, 46.973476f, 47.24663f,
47.469276f, 47.65921f, 47.82903f, 47.895855f, 47.872753f, 47.81694f,
43.11514f, 43.293365f, 43.490665f, 43.726105f, 44.018764f, 44.328457f,
44.539444f, 44.717873f, 44.93891f, 45.25066f, 45.48524f, 45.665237f,
45.86694f, 46.163326f, 46.427376f, 46.615143f, 46.802902f, 47.066956f,
47.363342f, 47.56505f, 47.74505f, 47.979626f, 48.302998f, 48.576153f,
48.798798f, 48.98873f, 49.158546f, 49.225376f, 49.202282f, 49.146458f,
44.303867f, 44.482094f, 44.679394f, 44.914833f, 45.207493f, 45.51718f,
45.72817f, 45.9066f, 46.12764f, 46.439384f, 46.673965f, 46.853966f,
47.055668f, 47.352055f, 47.6161f, 47.803867f, 47.99163f, 48.25568f,
48.552063f, 48.75377f, 48.933773f, 49.16835f, 49.491726f, 49.764877f,
49.987526f, 50.17746f, 50.347275f, 50.4141f, 50.391006f, 50.335186f,
44.771675f, 44.949905f, 45.1472f, 45.382645f, 45.6753f, 45.98499f,
46.195976f, 46.374413f, 46.595448f, 46.907196f, 47.141773f, 47.321774f,
47.523476f, 47.819862f, 48.08391f, 48.27168f, 48.459446f, 48.72349f,
49.019882f, 49.22158f, 49.401585f, 49.63616f, 49.959538f, 50.232693f,
50.455338f, 50.64527f, 50.81509f, 50.88192f, 50.858818f, 50.803f,
44.609966f, 44.788193f, 44.985493f, 45.220936f, 45.51359f, 45.82328f,
46.03427f, 46.2127f, 46.433743f, 46.74549f, 46.98007f, 47.160065f,
47.36177f, 47.658157f, 47.922207f, 48.10997f, 48.297733f, 48.561783f,
48.858166f, 49.059875f, 49.239872f, 49.47445f, 49.79783f, 50.07098f,
50.293625f, 50.48356f, 50.653378f, 50.720203f, 50.6971f, 50.64128f,
44.219246f, 44.397472f, 44.594772f, 44.83021f, 45.122868f, 45.43256f,
45.643543f, 45.82198f, 46.04302f, 46.354763f, 46.589344f, 46.76934f,
46.971046f, 47.267433f, 47.531483f, 47.719242f, 47.907005f, 48.17105f,
48.467438f, 48.66914f, 48.849144f, 49.08372f, 49.4071f, 49.680256f,
49.902905f, 50.092834f, 50.262653f, 50.329483f, 50.30638f, 50.25057f});
auto size = NDArrayFactory::create<int>({30, 30});
sd::ops::resize_bicubic op;
auto results = op.evaluate({&input, &size}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Resized to 30x30");
// expected.printBuffer("Expect for 30x30");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
}
TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test2) {
NDArray input = NDArrayFactory::create<double>('c', {2, 5, 4, 3});
NDArray expected = NDArrayFactory::create<float>('c', {2, 10, 8, 3}, {
1.000000f, 2.000000f, 3.000000f, 2.218750f, 3.218750f, 4.218750f, 4.000000f, 5.000000f, 6.000000f,
5.500000f, 6.500000f, 7.500000f, 7.000000f, 8.000000f, 9.000000f, 8.781250f, 9.781250f, 10.781250f,
10.000000f, 11.000000f, 12.000000f, 10.281250f, 11.281250f, 12.281250f, 5.875000f, 6.875000f, 7.875000f,
7.093750f, 8.093750f, 9.093750f, 8.875000f, 9.875000f, 10.875000f, 10.375000f, 11.375000f, 12.375000f,
11.875000f, 12.875000f, 13.875000f, 13.656250f, 14.656250f, 15.656250f, 14.875000f, 15.875000f, 16.875000f,
15.156250f, 16.156250f, 17.156250f, 13.000000f, 14.000000f, 15.000000f, 14.218750f, 15.218750f, 16.218750f,
16.000000f, 17.000000f, 18.000000f, 17.500000f, 18.500000f, 19.500000f, 19.000000f, 20.000000f, 21.000000f,
20.781250f, 21.781250f, 22.781250f, 22.000000f, 23.000000f, 24.000000f, 22.281250f, 23.281250f, 24.281250f,
19.000000f, 20.000000f, 21.000000f, 20.218750f, 21.218750f, 22.218750f, 22.000000f, 23.000000f, 24.000000f,
23.500000f, 24.500000f, 25.500000f, 25.000000f, 26.000000f, 27.000000f, 26.781250f, 27.781250f, 28.781250f,
28.000000f, 29.000000f, 30.000000f, 28.281250f, 29.281250f, 30.281250f, 25.000000f, 26.000000f, 27.000000f,
26.218750f, 27.218750f, 28.218750f, 28.000000f, 29.000000f, 30.000000f, 29.500000f, 30.500000f, 31.500000f,
31.000000f, 32.000000f, 33.000000f, 32.781250f, 33.781250f, 34.781250f, 34.000000f, 35.000000f, 36.000000f,
34.281250f, 35.281250f, 36.281250f, 31.000000f, 32.000000f, 33.000000f, 32.218750f, 33.218750f, 34.218750f,
34.000000f, 35.000000f, 36.000000f, 35.500000f, 36.500000f, 37.500000f, 37.000000f, 38.000000f, 39.000000f,
38.781250f, 39.781250f, 40.781250f, 40.000000f, 41.000000f, 42.000000f, 40.281250f, 41.281250f, 42.281250f,
37.000000f, 38.000000f, 39.000000f, 38.218750f, 39.218750f, 40.218750f, 40.000000f, 41.000000f, 42.000000f,
41.500000f, 42.500000f, 43.500000f, 43.000000f, 44.000000f, 45.000000f, 44.781250f, 45.781250f, 46.781250f,
46.000000f, 47.000000f, 48.000000f, 46.281250f, 47.281250f, 48.281250f, 44.125000f, 45.125000f, 46.125000f,
45.343750f, 46.343750f, 47.343750f, 47.125000f, 48.125000f, 49.125000f, 48.625000f, 49.625000f, 50.625000f,
50.125000f, 51.125000f, 52.125000f, 51.906250f, 52.906250f, 53.906250f, 53.125000f, 54.125000f, 55.125000f,
53.406250f, 54.406250f, 55.406250f, 49.000000f, 50.000000f, 51.000000f, 50.218750f, 51.218750f, 52.218750f,
52.000000f, 53.000000f, 54.000000f, 53.500000f, 54.500000f, 55.500000f, 55.000000f, 56.000000f, 57.000000f,
56.781250f, 57.781250f, 58.781250f, 58.000000f, 59.000000f, 60.000000f, 58.281250f, 59.281250f, 60.281250f,
50.125000f, 51.125000f, 52.125000f, 51.343750f, 52.343750f, 53.343750f, 53.125000f, 54.125000f, 55.125000f,
54.625000f, 55.625000f, 56.625000f, 56.125000f, 57.125000f, 58.125000f, 57.906250f, 58.906250f, 59.906250f,
59.125000f, 60.125000f, 61.125000f, 59.406250f, 60.406250f, 61.406250f, 61.000000f, 62.000000f, 63.000000f,
62.218750f, 63.218750f, 64.218750f, 64.000000f, 65.000000f, 66.000000f, 65.500000f, 66.500000f, 67.500000f,
67.000000f, 68.000000f, 69.000000f, 68.781250f, 69.781250f, 70.781250f, 70.000000f, 71.000000f, 72.000000f,
70.281250f, 71.281250f, 72.281250f, 65.875000f, 66.875000f, 67.875000f, 67.093750f, 68.093750f, 69.093750f,
68.875000f, 69.875000f, 70.875000f, 70.375000f, 71.375000f, 72.375000f, 71.875000f, 72.875000f, 73.875000f,
73.656250f, 74.656250f, 75.656250f, 74.875000f, 75.875000f, 76.875000f, 75.156250f, 76.156250f, 77.156250f,
73.000000f, 74.000000f, 75.000000f, 74.218750f, 75.218750f, 76.218750f, 76.000000f, 77.000000f, 78.000000f,
77.500000f, 78.500000f, 79.500000f, 79.000000f, 80.000000f, 81.000000f, 80.781250f, 81.781250f, 82.781250f,
82.000000f, 83.000000f, 84.000000f, 82.281250f, 83.281250f, 84.281250f, 79.000000f, 80.000000f, 81.000000f,
80.218750f, 81.218750f, 82.218750f, 82.000000f, 83.000000f, 84.000000f, 83.500000f, 84.500000f, 85.500000f,
85.000000f, 86.000000f, 87.000000f, 86.781250f, 87.781250f, 88.781250f, 88.000000f, 89.000000f, 90.000000f,
88.281250f, 89.281250f, 90.281250f, 85.000000f, 86.000000f, 87.000000f, 86.218750f, 87.218750f, 88.218750f,
88.000000f, 89.000000f, 90.000000f, 89.500000f, 90.500000f, 91.500000f, 91.000000f, 92.000000f, 93.000000f,
92.781250f, 93.781250f, 94.781250f, 94.000000f, 95.000000f, 96.000000f, 94.281250f, 95.281250f, 96.281250f,
91.000000f, 92.000000f, 93.000000f, 92.218750f, 93.218750f, 94.218750f, 94.000000f, 95.000000f, 96.000000f,
95.500000f, 96.500000f, 97.500000f, 97.000000f, 98.000000f, 99.000000f, 98.781250f, 99.781250f, 100.781250f,
100.000000f, 101.000000f, 102.000000f, 100.281250f, 101.281250f, 102.281250f, 97.000000f, 98.000000f,
99.000000f, 98.218750f, 99.218750f, 100.218750f, 100.000000f, 101.000000f, 102.000000f, 101.500000f,
102.500000f, 103.500000f, 103.000000f, 104.000000f, 105.000000f, 104.781250f, 105.781250f, 106.781250f,
106.000000f, 107.000000f, 108.000000f, 106.281250f, 107.281250f, 108.281250f, 104.125000f, 105.125000f,
106.125000f, 105.343750f, 106.343750f, 107.343750f, 107.125000f, 108.125000f, 109.125000f, 108.625000f,
109.625000f, 110.625000f, 110.125000f, 111.125000f, 112.125000f, 111.906250f, 112.906250f, 113.906250f,
113.125000f, 114.125000f, 115.125000f, 113.406250f, 114.406250f, 115.406250f, 109.000000f, 110.000000f,
111.000000f, 110.218750f, 111.218750f, 112.218750f, 112.000000f, 113.000000f, 114.000000f, 113.500000f,
114.500000f, 115.500000f, 115.000000f, 116.000000f, 117.000000f, 116.781250f, 117.781250f, 118.781250f,
118.000000f, 119.000000f, 120.000000f, 118.281250f, 119.281250f, 120.281250f, 110.125000f, 111.125000f,
112.125000f, 111.343750f, 112.343750f, 113.343750f, 113.125000f, 114.125000f, 115.125000f, 114.625000f,
115.625000f, 116.625000f, 116.125000f, 117.125000f, 118.125000f, 117.906250f, 118.906250f, 119.906250f,
119.125000f, 120.125000f, 121.125000f, 119.406250f, 120.406250f, 121.406250f
}); //input = 1.f;
input.linspace(1);
auto size = NDArrayFactory::create<int>({10, 8});
sd::ops::resize_bicubic op;
auto results = op.evaluate({&input, &size}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Resized to 10x8");
// expected.printBuffer("Expect for 10x8");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
}
TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test3) {
NDArray input = NDArrayFactory::create<double>('c', {1, 3, 3, 4});
NDArray expected = NDArrayFactory::create<float>('c', {1, 6, 6, 4}, {
1.000000f, 2.000000f, 3.000000f, 4.000000f, 2.625000f, 3.625000f, 4.625000f, 5.625000f, 5.000000f,
6.000000f, 7.000000f, 8.000000f, 7.375000f, 8.375000f, 9.375000f, 10.375000f, 9.000000f, 10.000000f,
11.000000f, 12.000000f, 9.375000f, 10.375000f, 11.375000f, 12.375000f, 5.875000f, 6.875000f, 7.875000f,
8.875000f, 7.500000f, 8.500000f, 9.500000f, 10.500000f, 9.875000f, 10.875000f, 11.875000f, 12.875000f,
12.250000f, 13.250000f, 14.250000f, 15.250000f, 13.875000f, 14.875000f, 15.875000f, 16.875000f, 14.250000f,
15.250000f, 16.250000f, 17.250000f, 13.000000f, 14.000000f, 15.000000f, 16.000000f, 14.625000f, 15.625000f,
16.625000f, 17.625000f, 17.000000f, 18.000000f, 19.000000f, 20.000000f, 19.375000f, 20.375000f, 21.375000f,
22.375000f, 21.000000f, 22.000000f, 23.000000f, 24.000000f, 21.375000f, 22.375000f, 23.375000f, 24.375000f,
20.125000f, 21.125000f, 22.125000f, 23.125000f, 21.750000f, 22.750000f, 23.750000f, 24.750000f, 24.125000f,
25.125000f, 26.125000f, 27.125000f, 26.500000f, 27.500000f, 28.500000f, 29.500000f, 28.125000f, 29.125000f,
30.125000f, 31.125000f, 28.500000f, 29.500000f, 30.500000f, 31.500000f, 25.000000f, 26.000000f, 27.000000f,
28.000000f, 26.625000f, 27.625000f, 28.625000f, 29.625000f, 29.000000f, 30.000000f, 31.000000f, 32.000000f,
31.375000f, 32.375000f, 33.375000f, 34.375000f, 33.000000f, 34.000000f, 35.000000f, 36.000000f, 33.375000f,
34.375000f, 35.375000f, 36.375000f, 26.125000f, 27.125000f, 28.125000f, 29.125000f, 27.750000f, 28.750000f,
29.750000f, 30.750000f, 30.125000f, 31.125000f, 32.125000f, 33.125000f, 32.500000f, 33.500000f, 34.500000f,
35.500000f, 34.125000f, 35.125000f, 36.125000f, 37.125000f, 34.500000f, 35.500000f, 36.500000f, 37.500000f
});
input.linspace(1);
auto size = NDArrayFactory::create<int>({6, 6});
sd::ops::resize_bicubic op;
auto results = op.evaluate({&input, &size}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Resized to 6x6");
// expected.printBuffer("Expect for 6x6");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
}
TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test4) {
NDArray input = NDArrayFactory::create<double>('c', {1, 3, 4, 3});
NDArray expected = NDArrayFactory::create<float>('c', {1, 6, 8, 3}, {
1.000000f, 2.000000f, 3.000000f, 2.218750f, 3.218750f, 4.218750f, 4.000000f, 5.000000f, 6.000000f,
5.500000f, 6.500000f, 7.500000f, 7.000000f, 8.000000f, 9.000000f, 8.781250f, 9.781250f, 10.781250f,
10.000000f, 11.000000f, 12.000000f, 10.281250f, 11.281250f, 12.281250f, 5.875000f, 6.875000f, 7.875000f,
7.093750f, 8.093750f, 9.093750f, 8.875000f, 9.875000f, 10.875000f, 10.375000f, 11.375000f, 12.375000f,
11.875000f, 12.875000f, 13.875000f, 13.656250f, 14.656250f, 15.656250f, 14.875000f, 15.875000f, 16.875000f,
15.156250f, 16.156250f, 17.156250f, 13.000000f, 14.000000f, 15.000000f, 14.218750f, 15.218750f, 16.218750f,
16.000000f, 17.000000f, 18.000000f, 17.500000f, 18.500000f, 19.500000f, 19.000000f, 20.000000f, 21.000000f,
20.781250f, 21.781250f, 22.781250f, 22.000000f, 23.000000f, 24.000000f, 22.281250f, 23.281250f, 24.281250f,
20.125000f, 21.125000f, 22.125000f, 21.343750f, 22.343750f, 23.343750f, 23.125000f, 24.125000f, 25.125000f,
24.625000f, 25.625000f, 26.625000f, 26.125000f, 27.125000f, 28.125000f, 27.906250f, 28.906250f, 29.906250f,
29.125000f, 30.125000f, 31.125000f, 29.406250f, 30.406250f, 31.406250f, 25.000000f, 26.000000f, 27.000000f,
26.218750f, 27.218750f, 28.218750f, 28.000000f, 29.000000f, 30.000000f, 29.500000f, 30.500000f, 31.500000f,
31.000000f, 32.000000f, 33.000000f, 32.781250f, 33.781250f, 34.781250f, 34.000000f, 35.000000f, 36.000000f,
34.281250f, 35.281250f, 36.281250f, 26.125000f, 27.125000f, 28.125000f, 27.343750f, 28.343750f, 29.343750f,
29.125000f, 30.125000f, 31.125000f, 30.625000f, 31.625000f, 32.625000f, 32.125000f, 33.125000f, 34.125000f,
33.906250f, 34.906250f, 35.906250f, 35.125000f, 36.125000f, 37.125000f, 35.406250f, 36.406250f, 37.406250f
});
input.linspace(1);
auto size = NDArrayFactory::create<int>({6, 8});
sd::ops::resize_bicubic op;
auto results = op.evaluate({&input, &size}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Resized to 6x8");
// expected.printBuffer("Expect for 6x8");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
}
TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test5) {
NDArray input = NDArrayFactory::create<double>('c', {1, 4, 4, 3});
NDArray expected = NDArrayFactory::create<float>('c', {1, 8, 8, 3}, {
1.000000f, 2.000000f, 3.000000f, 2.218750f, 3.218750f, 4.218750f, 4.000000f, 5.000000f, 6.000000f,
5.500000f, 6.500000f, 7.500000f, 7.000000f, 8.000000f, 9.000000f, 8.781250f, 9.781250f, 10.781250f,
10.000000f, 11.000000f, 12.000000f, 10.281250f, 11.281250f, 12.281250f, 5.875000f, 6.875000f, 7.875000f,
7.093750f, 8.093750f, 9.093750f, 8.875000f, 9.875000f, 10.875000f, 10.375000f, 11.375000f, 12.375000f,
11.875000f, 12.875000f, 13.875000f, 13.656250f, 14.656250f, 15.656250f, 14.875000f, 15.875000f, 16.875000f,
15.156250f, 16.156250f, 17.156250f, 13.000000f, 14.000000f, 15.000000f, 14.218750f, 15.218750f, 16.218750f,
16.000000f, 17.000000f, 18.000000f, 17.500000f, 18.500000f, 19.500000f, 19.000000f, 20.000000f, 21.000000f,
20.781250f, 21.781250f, 22.781250f, 22.000000f, 23.000000f, 24.000000f, 22.281250f, 23.281250f, 24.281250f,
19.000000f, 20.000000f, 21.000000f, 20.218750f, 21.218750f, 22.218750f, 22.000000f, 23.000000f, 24.000000f,
23.500000f, 24.500000f, 25.500000f, 25.000000f, 26.000000f, 27.000000f, 26.781250f, 27.781250f, 28.781250f,
28.000000f, 29.000000f, 30.000000f, 28.281250f, 29.281250f, 30.281250f, 25.000000f, 26.000000f, 27.000000f,
26.218750f, 27.218750f, 28.218750f, 28.000000f, 29.000000f, 30.000000f, 29.500000f, 30.500000f, 31.500000f,
31.000000f, 32.000000f, 33.000000f, 32.781250f, 33.781250f, 34.781250f, 34.000000f, 35.000000f, 36.000000f,
34.281250f, 35.281250f, 36.281250f, 32.125000f, 33.125000f, 34.125000f, 33.343750f, 34.343750f, 35.343750f,
35.125000f, 36.125000f, 37.125000f, 36.625000f, 37.625000f, 38.625000f, 38.125000f, 39.125000f, 40.125000f,
39.906250f, 40.906250f, 41.906250f, 41.125000f, 42.125000f, 43.125000f, 41.406250f, 42.406250f, 43.406250f,
37.000000f, 38.000000f, 39.000000f, 38.218750f, 39.218750f, 40.218750f, 40.000000f, 41.000000f, 42.000000f,
41.500000f, 42.500000f, 43.500000f, 43.000000f, 44.000000f, 45.000000f, 44.781250f, 45.781250f, 46.781250f,
46.000000f, 47.000000f, 48.000000f, 46.281250f, 47.281250f, 48.281250f, 38.125000f, 39.125000f, 40.125000f,
39.343750f, 40.343750f, 41.343750f, 41.125000f, 42.125000f, 43.125000f, 42.625000f, 43.625000f, 44.625000f,
44.125000f, 45.125000f, 46.125000f, 45.906250f, 46.906250f, 47.906250f, 47.125000f, 48.125000f, 49.125000f,
47.406250f, 48.406250f, 49.406250f,
});
input.linspace(1);
auto size = NDArrayFactory::create<int>({8, 8});
sd::ops::resize_bicubic op;
auto results = op.evaluate({&input, &size}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Resized to 8x8");
// expected.printBuffer("Expect for 8x8");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
}
TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test6) {
NDArray input = NDArrayFactory::create<float>('c', {7, 7, 1}, {
1.f, 2.1f, 3.15f, 4.2f, 5.15f, 6.1f, 7.f,
8.f, 9.1f, 10.f, 11.f, 12.9f, 13.1f, 14.f,
15.f, 16.f, 17.f, 18.f, 19.f, 20.f, 21.f,
22.f, 23.f, 24.f, 25.f, 26.f, 27.f, 28.f,
30.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f,
37.f, 38.f, 39.f, 40.f, 41.f, 42.f, 43.f,
44.f, 45.f, 46.f, 47.f, 48.f, 49.f, 50.f
});
NDArray expected = NDArrayFactory::create<float>('c', {30, 30, 1}, {
1.000000f, 1.197616f, 1.417436f, 1.677577f, 1.996158f, 2.328327f, 2.550918f, 2.736061f, 2.965541f,
3.292965f, 3.544151f, 3.738035f, 3.948995f, 4.248106f, 4.507379f, 4.684374f, 4.857284f, 5.104302f,
5.386991f, 5.581401f, 5.753962f, 5.974285f, 6.272836f, 6.520426f, 6.718899f, 6.887104f, 7.039068f,
7.099216f, 7.078424f, 7.028189f, 2.247592f, 2.446947f, 2.669489f, 2.931238f, 3.248216f, 3.574534f,
3.789310f, 3.965697f, 4.186417f, 4.504653f, 4.740569f, 4.921706f, 5.133866f, 5.459533f, 5.774461f,
6.019787f, 6.254011f, 6.535633f, 6.809730f, 6.960779f, 7.074942f, 7.241601f, 7.509489f, 7.749949f,
7.954571f, 8.131972f, 8.286526f, 8.346463f, 8.325745f, 8.275683f, 3.628684f, 3.830573f, 4.056959f,
4.321157f, 4.636486f, 4.955650f, 5.160583f, 5.325847f, 5.535462f, 5.842160f, 6.058749f, 6.223753f,
6.437597f, 6.797369f, 7.183604f, 7.516402f, 7.829034f, 8.154773f, 8.417635f, 8.512958f, 8.552100f,
8.649708f, 8.877880f, 9.108794f, 9.320926f, 9.509781f, 9.667375f, 9.726940f, 9.706349f, 9.656599f,
5.276778f, 5.480438f, 5.709702f, 5.975448f, 6.288551f, 6.600570f, 6.796207f, 6.951142f, 7.150400f,
7.446143f, 7.644651f, 7.794562f, 8.009684f, 8.400473f, 8.851847f, 9.264690f, 9.649218f, 10.015648f,
10.268647f, 10.313368f, 10.284327f, 10.319379f, 10.512033f, 10.734956f, 10.954604f, 11.154507f, 11.315369f,
11.374779f, 11.354242f, 11.304622f, 7.325373f, 7.528484f, 7.757575f, 8.022221f, 8.331997f, 8.638187f,
8.827649f, 8.976217f, 9.168955f, 9.457260f, 9.644237f, 9.784517f, 9.999621f, 10.407702f, 10.896234f,
11.355122f, 11.781423f, 12.172186f, 12.420712f, 12.437449f, 12.370511f, 12.371386f, 12.545973f, 12.766424f,
12.992249f, 13.200120f, 13.364252f, 13.424109f, 13.403420f, 13.353425f, 9.493208f, 9.692467f, 9.916944f,
10.176801f, 10.482199f, 10.785470f, 10.974367f, 11.123442f, 11.316370f, 11.603645f, 11.790616f, 11.930889f,
12.144082f, 12.546447f, 13.024898f, 13.472300f, 13.889232f, 14.276275f, 14.528972f, 14.555555f, 14.501450f,
14.515459f, 14.700572f, 14.927055f, 15.156046f, 15.366046f, 15.532901f, 15.594008f, 15.572885f, 15.521847f,
10.970133f, 11.163599f, 11.380694f, 11.633735f, 11.935032f, 12.238887f, 12.432540f, 12.588294f, 12.787534f,
13.079956f, 13.277520f, 13.426631f, 13.636713f, 14.013844f, 14.441672f, 14.827978f, 15.191209f, 15.549808f,
15.813430f, 15.881828f, 15.883522f, 15.950411f, 16.169330f, 16.407940f, 16.636436f, 16.842583f, 17.010887f,
17.073630f, 17.051940f, 16.999537f, 12.219155f, 12.406129f, 12.614796f, 12.860335f, 13.157928f, 13.464224f,
13.665207f, 13.830567f, 14.039036f, 14.339629f, 14.552863f, 14.715049f, 14.921564f, 15.264454f, 15.622843f,
15.924977f, 16.213829f, 16.532364f, 16.809900f, 16.934835f, 17.012146f, 17.150164f, 17.413412f, 17.666712f,
17.892765f, 18.092070f, 18.261044f, 18.325531f, 18.303238f, 18.249378f, 13.766397f, 13.947391f, 14.148263f,
14.386917f, 14.681246f, 14.990087f, 15.198166f, 15.372728f, 15.590062f, 15.898583f, 16.126892f, 16.301655f,
16.504870f, 16.815214f, 17.107498f, 17.329458f, 17.547403f, 17.827654f, 18.118288f, 18.296928f, 18.446100f,
18.651634f, 18.956806f, 19.223820f, 19.447308f, 19.639887f, 19.809319f, 19.875397f, 19.852556f, 19.797365f,
15.941937f, 16.118704f, 16.314133f, 16.547867f, 16.839561f, 17.149540f, 17.361883f, 17.542162f, 17.764957f,
18.078188f, 18.315733f, 18.498205f, 18.699116f, 18.988684f, 19.238989f, 19.410137f, 19.583265f, 19.839512f,
20.138780f, 20.351770f, 20.546844f, 20.795671f, 21.128067f, 21.404358f, 21.626736f, 21.815500f, 21.985610f,
22.052843f, 22.029604f, 21.973448f, 17.535220f, 17.710770f, 17.904636f, 18.136950f, 18.427840f, 18.738056f,
18.951529f, 19.133352f, 19.357613f, 19.672083f, 19.912102f, 20.096638f, 20.296894f, 20.580765f, 20.819603f,
20.976887f, 21.137802f, 21.387535f, 21.689209f, 21.911621f, 22.119276f, 22.379990f, 22.719910f, 22.998823f,
23.220970f, 23.408760f, 23.579110f, 23.646685f, 23.623325f, 23.566887f, 18.746353f, 18.922657f, 19.117487f,
19.350685f, 19.642070f, 19.952137f, 20.164913f, 20.345781f, 20.569134f, 20.882840f, 21.121330f, 21.304590f,
21.505253f, 21.792645f, 22.038572f, 22.204426f, 22.372890f, 22.626648f, 22.926834f, 23.143423f, 23.343302f,
23.596668f, 23.931936f, 24.209232f, 24.431519f, 24.619913f, 24.790110f, 24.857473f, 24.834190f, 24.777927f,
20.166560f, 20.344206f, 20.540766f, 20.775532f, 21.067804f, 21.377607f, 21.589132f, 21.768297f, 21.990030f,
22.302366f, 22.538124f, 22.719105f, 22.920494f, 23.214176f, 23.472767f, 23.653934f, 23.835890f, 24.096842f,
24.394371f, 24.600555f, 24.786541f, 25.026773f, 25.353731f, 25.628130f, 25.850672f, 26.040140f, 26.210072f,
26.277063f, 26.253906f, 26.197956f, 22.363024f, 22.541250f, 22.738552f, 22.973991f, 23.266647f, 23.576340f,
23.787327f, 23.965760f, 24.186796f, 24.498543f, 24.733124f, 24.913122f, 25.114826f, 25.411213f, 25.675262f,
25.863028f, 26.050789f, 26.314838f, 26.611223f, 26.812925f, 26.992926f, 27.227505f, 27.550882f, 27.824034f,
28.046684f, 28.236614f, 28.406433f, 28.473265f, 28.450163f, 28.394344f, 24.429443f, 24.607670f, 24.804970f,
25.040410f, 25.333065f, 25.642756f, 25.853743f, 26.032173f, 26.253210f, 26.564959f, 26.799540f, 26.979540f,
27.181242f, 27.477630f, 27.741680f, 27.929441f, 28.117207f, 28.381254f, 28.677637f, 28.879343f, 29.059345f,
29.293922f, 29.617298f, 29.890451f, 30.113104f, 30.303034f, 30.472853f, 30.539684f, 30.516582f, 30.460762f,
26.000000f, 26.178228f, 26.375526f, 26.610970f, 26.903624f, 27.213314f, 27.424305f, 27.602734f, 27.823772f,
28.135519f, 28.370100f, 28.550098f, 28.751800f, 29.048190f, 29.312237f, 29.500000f, 29.687763f, 29.951813f,
30.248200f, 30.449903f, 30.629902f, 30.864483f, 31.187859f, 31.461012f, 31.683659f, 31.873592f, 32.043407f,
32.110240f, 32.087135f, 32.031320f, 27.570559f, 27.748787f, 27.946087f, 28.181528f, 28.474184f, 28.783876f,
28.994865f, 29.173294f, 29.394330f, 29.706080f, 29.940659f, 30.120655f, 30.322360f, 30.618746f, 30.882797f,
31.070557f, 31.258320f, 31.522371f, 31.818754f, 32.020460f, 32.200460f, 32.435040f, 32.758415f, 33.031567f,
33.254220f, 33.444150f, 33.613964f, 33.680794f, 33.657696f, 33.601880f, 29.636976f, 29.815207f, 30.012500f,
30.247944f, 30.540600f, 30.850290f, 31.061283f, 31.239712f, 31.460750f, 31.772500f, 32.007080f, 32.187077f,
32.388780f, 32.685165f, 32.949215f, 33.136980f, 33.324740f, 33.588790f, 33.885178f, 34.086884f, 34.266880f,
34.501457f, 34.824837f, 35.097990f, 35.320637f, 35.510574f, 35.680390f, 35.747215f, 35.724117f, 35.668300f,
31.833440f, 32.011665f, 32.208970f, 32.444412f, 32.737070f, 33.046757f, 33.257744f, 33.436176f, 33.657207f,
33.968960f, 34.203537f, 34.383537f, 34.585240f, 34.881630f, 35.145676f, 35.333440f, 35.521206f, 35.785255f,
36.081642f, 36.283340f, 36.463340f, 36.697920f, 37.021297f, 37.294453f, 37.517097f, 37.707027f, 37.876846f,
37.943680f, 37.920578f, 37.864758f, 33.253647f, 33.431873f, 33.629170f, 33.864613f, 34.157270f, 34.466957f,
34.677948f, 34.856377f, 35.077415f, 35.389160f, 35.623745f, 35.803745f, 36.005447f, 36.301834f, 36.565884f,
36.753647f, 36.941406f, 37.205456f, 37.501840f, 37.703545f, 37.883545f, 38.118122f, 38.441500f, 38.714653f,
38.937300f, 39.127235f, 39.297054f, 39.363884f, 39.340782f, 39.284960f, 34.464783f, 34.643010f, 34.840305f,
35.075752f, 35.368404f, 35.678100f, 35.889088f, 36.067516f, 36.288550f, 36.600300f, 36.834885f, 37.014877f,
37.216583f, 37.512970f, 37.777020f, 37.964783f, 38.152546f, 38.416595f, 38.712980f, 38.914684f, 39.094685f,
39.329260f, 39.652645f, 39.925793f, 40.148440f, 40.338375f, 40.508194f, 40.575024f, 40.551920f, 40.496105f,
36.058067f, 36.236290f, 36.433590f, 36.669033f, 36.961685f, 37.271378f, 37.482370f, 37.660800f, 37.881836f,
38.193590f, 38.428170f, 38.608162f, 38.809868f, 39.106250f, 39.370300f, 39.558064f, 39.745830f, 40.009880f,
40.306267f, 40.507970f, 40.687970f, 40.922550f, 41.245926f, 41.519077f, 41.741722f, 41.931652f, 42.101475f,
42.168304f, 42.145203f, 42.089386f, 38.315002f, 38.493233f, 38.690533f, 38.925976f, 39.218628f, 39.528320f,
39.739307f, 39.917736f, 40.138775f, 40.450520f, 40.685104f, 40.865097f, 41.066803f, 41.363190f, 41.627243f,
41.815002f, 42.002766f, 42.266820f, 42.563200f, 42.764908f, 42.944904f, 43.179485f, 43.502860f, 43.776016f,
43.998665f, 44.188595f, 44.358418f, 44.425247f, 44.402145f, 44.346330f, 40.227080f, 40.405310f, 40.602608f,
40.838050f, 41.130707f, 41.440395f, 41.651382f, 41.829820f, 42.050854f, 42.362600f, 42.597183f, 42.777180f,
42.978880f, 43.275270f, 43.539320f, 43.727080f, 43.914845f, 44.178894f, 44.475280f, 44.676983f, 44.856983f,
45.091560f, 45.414940f, 45.688090f, 45.910740f, 46.100674f, 46.270493f, 46.337322f, 46.314220f, 46.258400f,
41.785618f, 41.963844f, 42.161144f, 42.396584f, 42.689240f, 42.998936f, 43.209923f, 43.388355f, 43.609394f,
43.921143f, 44.155720f, 44.335716f, 44.537420f, 44.833805f, 45.097860f, 45.285614f, 45.473377f, 45.737427f,
46.033817f, 46.235523f, 46.415524f, 46.650105f, 46.973476f, 47.246630f, 47.469276f, 47.659210f, 47.829030f,
47.895855f, 47.872753f, 47.816940f, 43.115140f, 43.293365f, 43.490665f, 43.726105f, 44.018764f, 44.328457f,
44.539444f, 44.717873f, 44.938910f, 45.250660f, 45.485240f, 45.665237f, 45.866940f, 46.163326f, 46.427376f,
46.615143f, 46.802902f, 47.066956f, 47.363342f, 47.565050f, 47.745050f, 47.979626f, 48.302998f, 48.576153f,
48.798798f, 48.988730f, 49.158546f, 49.225376f, 49.202282f, 49.146458f, 44.303867f, 44.482094f, 44.679394f,
44.914833f, 45.207493f, 45.517180f, 45.728170f, 45.906600f, 46.127640f, 46.439384f, 46.673965f, 46.853966f,
47.055668f, 47.352055f, 47.616100f, 47.803867f, 47.991630f, 48.255680f, 48.552063f, 48.753770f, 48.933773f,
49.168350f, 49.491726f, 49.764877f, 49.987526f, 50.177460f, 50.347275f, 50.414100f, 50.391006f, 50.335186f,
44.771675f, 44.949905f, 45.147200f, 45.382645f, 45.675300f, 45.984990f, 46.195976f, 46.374413f, 46.595448f,
46.907196f, 47.141773f, 47.321774f, 47.523476f, 47.819862f, 48.083910f, 48.271680f, 48.459446f, 48.723490f,
49.019882f, 49.221580f, 49.401585f, 49.636160f, 49.959538f, 50.232693f, 50.455338f, 50.645270f, 50.815090f,
50.881920f, 50.858818f, 50.803000f, 44.609966f, 44.788193f, 44.985493f, 45.220936f, 45.513590f, 45.823280f,
46.034270f, 46.212700f, 46.433743f, 46.745490f, 46.980070f, 47.160065f, 47.361770f, 47.658157f, 47.922207f,
48.109970f, 48.297733f, 48.561783f, 48.858166f, 49.059875f, 49.239872f, 49.474450f, 49.797830f, 50.070980f,
50.293625f, 50.483560f, 50.653378f, 50.720203f, 50.697100f, 50.641280f, 44.219246f, 44.397472f, 44.594772f,
44.830210f, 45.122868f, 45.432560f, 45.643543f, 45.821980f, 46.043020f, 46.354763f, 46.589344f, 46.769340f,
46.971046f, 47.267433f, 47.531483f, 47.719242f, 47.907005f, 48.171050f, 48.467438f, 48.669140f, 48.849144f,
49.083720f, 49.407100f, 49.680256f, 49.902905f, 50.092834f, 50.262653f, 50.329483f, 50.306380f, 50.250570f
});
auto size = NDArrayFactory::create<int>({30, 30});
sd::ops::resize_bicubic op;
auto results = op.evaluate({&input, &size}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Resized to 30x30");
// expected.printBuffer("Expect for 30x30");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
}
TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test7) {
NDArray input = NDArrayFactory::create<double>('c', {2, 5, 5, 1}, {
0.2303, 0.7950, 0.8171, 0.0451, 0.3690, 0.6846, 0.2727, 0.2770, 0.2381, 0.9511,
0.4116, 0.3997, 0.4075, 0.6275, 0.8018, 0.0678, 0.6221, 0.2982, 0.1524, 0.2613,
0.7425, 0.6036, 0.7926, 0.5838, 0.1361, 0.4154, 0.3634, 0.3741, 0.2088, 0.2989,
0.3982, 0.5618, 0.7266, 0.1089, 0.2922, 0.3306, 0.2869, 0.6638, 0.3091, 0.9312,
0.0240, 0.2893, 0.5632, 0.9625, 0.4189, 0.3854, 0.2743, 0.6754, 0.8820, 0.8699});
NDArray expected = NDArrayFactory::create<float>('c', {2, 9, 9, 1}, {
0.2303f, 0.54569f, 0.840649f, 0.92725444f, 0.65660673f,
0.16641647f, 0.06117659f, 0.33279106f, 0.4023279f, 0.5139505f,
0.49821317f, 0.4906872f, 0.537642f, 0.4070102f, 0.13030615f,
0.258801f, 0.65352744f, 0.773368f, 0.69225276f, 0.44177493f,
0.21910316f, 0.22368976f, 0.24221404f, 0.21399781f, 0.5114972f,
0.9169859f, 1.0511527f, 0.5608501f, 0.41315168f, 0.2913824f,
0.2966933f, 0.38585684f, 0.48849702f, 0.71013063f, 0.9086001f,
0.9794303f, 0.29625386f, 0.39427578f, 0.45971435f, 0.39693952f,
0.40860707f, 0.51061106f, 0.6181093f, 0.67309624f, 0.69564015f,
0.06012487f, 0.3863805f, 0.58993465f, 0.40679216f, 0.22607432f,
0.20093678f, 0.25901243f, 0.3615362f, 0.39371052f, 0.24176767f,
0.4868709f, 0.650651f, 0.5493148f, 0.3825456f, 0.27788478f,
0.18927254f, 0.16692996f, 0.15432167f, 0.677519f, 0.6236242f,
0.61700624f, 0.7214321f, 0.7307374f, 0.6251454f, 0.3924176f,
0.17802659f, 0.10231908f, 0.81192374f, 0.66878575f, 0.6118803f,
0.7797006f, 0.8396968f, 0.72889954f, 0.44547448f, 0.16794783f,
0.07125802f, 0.4154f, 0.38504714f, 0.3623221f, 0.3862173f,
0.3397379f, 0.23285517f, 0.21876639f, 0.2892362f, 0.30817088f,
0.41268015f, 0.45587808f, 0.51991886f, 0.60977113f, 0.49489656f,
0.21313031f, 0.11297428f, 0.2167207f, 0.23940037f, 0.39337245f,
0.46112412f, 0.583034f, 0.76207364f, 0.6326203f, 0.22189438f,
0.12071565f, 0.3275853f, 0.3794855f, 0.38497013f, 0.35049653f,
0.41895086f, 0.671095f, 0.62119365f, 0.22362521f, 0.30189657f,
0.72530353f, 0.85048175f, 0.2524255f, 0.2182264f, 0.2964637f,
0.5361996f, 0.6255393f, 0.46424767f, 0.5741281f, 0.8408146f,
0.92403257f, 0.04648584f, 0.14959256f, 0.32215607f, 0.46194845f,
0.6642166f, 0.83560026f, 0.7663391f, 0.5284251f, 0.4573109f,
0.10357999f, 0.17442937f, 0.32116935f, 0.45530772f, 0.7163773f,
0.9856574f, 0.8976148f, 0.5538923f, 0.45173654f, 0.34958175f,
0.2680429f, 0.30470955f, 0.51233786f, 0.75128907f, 0.86736864f,
0.8982046f, 0.83254474f, 0.8168574f, 0.4225865f, 0.2956836f,
0.29948136f, 0.5276342f, 0.76461166f, 0.8442875f, 0.907862f,
0.9139262f, 0.92068815f
});
auto size = NDArrayFactory::create<int>({9, 9});
sd::ops::resize_bicubic op;
auto results = op.evaluate({&input, &size}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Resized to 9x9");
// expected.printBuffer("Expect for 9x9");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
}
TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test8) {
NDArray input = NDArrayFactory::create<double>('c', {2, 5, 5, 1}, {
0.23028551377579154, 0.7949972231516509, 0.8171307820461517, 0.04507309923418412, 0.3689673597428338,
0.6845757584903018, 0.27268547668219667, 0.2770196372806053, 0.2381478370531429, 0.9511201914609859,
0.41160882670429033, 0.3997152563642703, 0.4074505147711718, 0.6274595060113246, 0.8017922711300232,
0.06782045852179475, 0.6220772280691722, 0.2982335327629251, 0.1523603480424196, 0.2612986044295986,
0.7424762244324299, 0.6036156464824591, 0.7926371071102005, 0.5838270656432538, 0.13607200219168547,
0.4154002170215956, 0.36340617544852116, 0.37405031188276827, 0.20880251686544882, 0.298919946410666,
0.39820758164277126, 0.5617728968896589, 0.72660225993937, 0.10888245916813699, 0.29215797784445496,
0.3305531351746034, 0.28693451964931715, 0.6637635348315494, 0.30913418229827583, 0.9312186188801752,
0.0239594182399363, 0.2892942758780874, 0.5631691110629038, 0.9625499752246309, 0.4189439089689968,
0.3854304088214935, 0.27426304203925045, 0.6754051704648238, 0.8820362490795286, 0.8699337744328859});
auto testData = NDArrayFactory::create<float>('c', {2,9,9,1}, {
0.230286514f, 0.510566354f, 0.794997215f, 0.931386113f, 0.817130804f, 0.402811885f, 0.045073099f, 0.134639814f, 0.368967354f,
0.483021289f, 0.501266003f, 0.521932304f, 0.572325349f, 0.534847379f, 0.267853439f, 0.105112493f, 0.349290252f, 0.674043298f,
0.684575737f, 0.478224277f, 0.272685468f, 0.239882097f, 0.27701965f, 0.191148892f, 0.23814784f, 0.590989769f, 0.951120198f,
0.622912169f, 0.441326082f, 0.266387194f, 0.232538164f, 0.301838756f, 0.356378645f, 0.495445013f, 0.756725252f, 0.981704295f,
0.411608815f, 0.40493685f, 0.399715245f, 0.381842017f, 0.407450527f, 0.501836538f, 0.627459526f, 0.735251725f, 0.801792264f,
0.150875032f, 0.357000858f, 0.524536073f, 0.450354964f, 0.318719596f, 0.319606483f, 0.385957927f, 0.46392554f, 0.529285908f,
0.06782046f, 0.375309169f, 0.622077227f, 0.525792599f, 0.298233539f, 0.184723631f, 0.15236035f, 0.193153858f, 0.261298597f,
0.372918189f, 0.512539625f, 0.63369292f, 0.628733814f, 0.535196245f, 0.436597466f, 0.323553175f, 0.215942055f, 0.148014024f,
0.742476225f, 0.655325174f, 0.603615642f, 0.704684138f, 0.79263711f, 0.747929871f, 0.583827078f, 0.340373576f, 0.136071995f,
0.415400207f, 0.388405323f, 0.363406181f, 0.379345775f, 0.374050319f, 0.28397581f, 0.208802521f, 0.238369256f, 0.298919946f,
0.413146496f, 0.444389015f, 0.488355637f, 0.568351328f, 0.556217432f, 0.345546633f, 0.140068889f, 0.148834035f, 0.23562704f,
0.398207575f, 0.464537472f, 0.561772883f, 0.717433035f, 0.726602256f, 0.416013002f, 0.108882457f, 0.142608985f, 0.292157978f,
0.391511708f, 0.389470309f, 0.442729384f, 0.651181757f, 0.737665415f, 0.41685915f, 0.138383076f, 0.342548877f, 0.659080088f,
0.330553144f, 0.273416102f, 0.286934525f, 0.50450629f, 0.663763523f, 0.463456154f, 0.309134185f, 0.586929917f, 0.931218624f,
0.137025774f, 0.169145152f, 0.263757467f, 0.436182201f, 0.597053051f, 0.657990932f, 0.662163854f, 0.68354249f, 0.692712903f,
0.023959421f, 0.130951077f, 0.289294273f, 0.413664877f, 0.563169122f, 0.839498401f, 0.962549984f, 0.728188932f, 0.418943912f,
0.175951749f, 0.198239252f, 0.281999886f, 0.420836329f, 0.609856486f, 0.863734365f, 0.983550847f, 0.825015843f, 0.596413136f,
0.385430396f, 0.292239636f, 0.274263054f, 0.445040524f, 0.675405145f, 0.817462444f, 0.882036269f, 0.895356655f, 0.869933784f
});
auto size = NDArrayFactory::create<int>({9, 9});
sd::ops::resize_bicubic op;
auto results = op.evaluate({&input, &size}, {}, {}, {true, false});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Resized to 9x9");
// testData.printBuffer("Expect for 9x9");
ASSERT_TRUE(testData.isSameShape(result));
ASSERT_TRUE(testData.equalsTo(result));
}
TEST_F(DeclarableOpsTests11, ImageResizeArea_Test1) {
NDArray input = NDArrayFactory::create<double>('c', {1, 3, 3, 4});
NDArray expected = NDArrayFactory::create<float>('c', {1, 6, 6, 4}, {
1.f, 2.f, 3.f, 4.f,
1.f, 2.f, 3.f, 4.f,
5.f, 6.f, 7.f, 8.f,
5.f, 6.f, 7.f, 8.f,
9.f, 10.f, 11.f, 12.f,
9.f, 10.f, 11.f, 12.f,
1.f, 2.f, 3.f, 4.f,
1.f, 2.f, 3.f, 4.f,
5.f, 6.f, 7.f, 8.f,
5.f, 6.f, 7.f, 8.f,
9.f, 10.f, 11.f, 12.f,
9.f, 10.f, 11.f, 12.f,
13.f, 14.f, 15.f, 16.f,
13.f, 14.f, 15.f, 16.f,
17.f, 18.f, 19.f, 20.f,
17.f, 18.f, 19.f, 20.f,
21.f, 22.f, 23.f, 24.f,
21.f, 22.f, 23.f, 24.f,
13.f, 14.f, 15.f, 16.f,
13.f, 14.f, 15.f, 16.f,
17.f, 18.f, 19.f, 20.f,
17.f, 18.f, 19.f, 20.f,
21.f, 22.f, 23.f, 24.f,
21.f, 22.f, 23.f, 24.f,
25.f, 26.f, 27.f, 28.f,
25.f, 26.f, 27.f, 28.f,
29.f, 30.f, 31.f, 32.f,
29.f, 30.f, 31.f, 32.f,
33.f, 34.f, 35.f, 36.f,
33.f, 34.f, 35.f, 36.f,
25.f, 26.f, 27.f, 28.f,
25.f, 26.f, 27.f, 28.f,
29.f, 30.f, 31.f, 32.f,
29.f, 30.f, 31.f, 32.f,
33.f, 34.f, 35.f, 36.f,
33.f, 34.f, 35.f, 36.f });
input.linspace(1);
auto size = NDArrayFactory::create<int>({6, 6});
sd::ops::resize_area op;
auto results = op.evaluate({&input, &size}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Area Resized to 6x6");
// expected.printBuffer("Area Expect for 6x6");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
}
TEST_F(DeclarableOpsTests11, ImageResizeArea_Test2) {
NDArray input = NDArrayFactory::create<float>('c', {1, 3, 3, 1});
NDArray expected = NDArrayFactory::create<float>('c', {1, 6, 6, 1}, {
1.f, 1.f, 2.f, 2.f, 3.f, 3.f,
1.f, 1.f, 2.f, 2.f, 3.f, 3.f,
4.f, 4.f, 5.f, 5.f, 6.f, 6.f,
4.f, 4.f, 5.f, 5.f, 6.f, 6.f,
7.f, 7.f, 8.f, 8.f, 9.f, 9.f,
7.f, 7.f, 8.f, 8.f, 9.f, 9.f
});
input.linspace(1);
auto size = NDArrayFactory::create<int>({6, 6});
sd::ops::resize_area op;
auto results = op.evaluate({&input, &size}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Area Resized to 6x6");
// expected.printBuffer("Area Expect for 6x6");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
}
TEST_F(DeclarableOpsTests11, ImageResizeArea_Test3) {
NDArray input = NDArrayFactory::create<float>('c', {1, 3, 3, 3});
NDArray expected = NDArrayFactory::create<float>('c', {1, 6, 6, 3}, {
1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f,
1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f,
10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f,
10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f,
19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f,
19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f
});
input.linspace(1);
auto size = NDArrayFactory::create<int>({6, 6});
sd::ops::resize_area op;
auto results = op.evaluate({&input, &size}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Area Resized to 6x6");
// expected.printBuffer("Area Expect for 6x6");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
}
TEST_F(DeclarableOpsTests11, ImageResizeArea_Test4) {
NDArray input = NDArrayFactory::create<float>('c', {2, 3, 3, 3}, {
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27
});
NDArray expected = NDArrayFactory::create<float>('c', {2, 6, 6, 3}, {
1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f,
1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f,
10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f,
10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f,
19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f,
19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f,
1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f,
1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f,
10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f,
10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f,
19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f,
19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f
});
//input.linspace(1);
auto size = NDArrayFactory::create<int>({6, 6});
sd::ops::resize_area op;
auto results = op.evaluate({&input, &size}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Area Resized to 6x6");
// expected.printBuffer("Area Expect for 6x6");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
}
TEST_F(DeclarableOpsTests11, ImageResizeArea_Test5) {
NDArray input = NDArrayFactory::create<int>('c', {2, 3, 3, 3}, {
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27
});
NDArray expected = NDArrayFactory::create<float>('c', {2, 6, 6, 3}, {
1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f,
1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f,
10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f,
10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f,
19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f,
19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f,
1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f,
1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f,
10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f,
10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f,
19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f,
19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f
});
//input.linspace(1);
auto size = NDArrayFactory::create<int>({6, 6});
sd::ops::resize_area op;
auto results = op.evaluate({&input, &size}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Area Resized to 6x6");
// expected.printBuffer("Area Expect for 6x6");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
}
TEST_F(DeclarableOpsTests11, ImageResizeArea_Test6) {
NDArray input = NDArrayFactory::create<int>('c', {2, 3, 3, 1}, {
1, 2, 3, 4, 5, 6, 7, 8, 9,
1, 2, 3, 4, 5, 6, 7, 8, 9
});
NDArray expected = NDArrayFactory::create<float>('c', {2, 6, 6, 1}, {
1.f, 1.f, 1.5f, 2.f, 2.f, 3.f,
1.f, 1.f, 1.5f, 2.f, 2.f, 3.f,
2.5f, 2.5f, 3.f, 3.5f, 3.5f, 4.5f,
4.f, 4.f, 4.5f, 5.f, 5.f, 6.f,
4.f, 4.f, 4.5f, 5.f, 5.f, 6.f,
7.f, 7.f, 7.5f, 8.f, 8.f, 9.f,
1.f, 1.f, 1.5f, 2.f, 2.f, 3.f,
1.f, 1.f, 1.5f, 2.f, 2.f, 3.f,
2.5f, 2.5f, 3.f, 3.5f, 3.5f, 4.5f,
4.f, 4.f, 4.5f, 5.f, 5.f, 6.f,
4.f, 4.f, 4.5f, 5.f, 5.f, 6.f,
7.f, 7.f, 7.5f, 8.f, 8.f, 9.f
});
//input.linspace(1);
auto size = NDArrayFactory::create<int>({6, 6});
sd::ops::resize_area op;
auto results = op.evaluate({&input, &size}, {}, {}, {true});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Area Resized to 6x6");
// expected.printBuffer("Area Expect for 6x6");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
}
TEST_F(DeclarableOpsTests11, ImageResizeArea_Test7) {
NDArray input = NDArrayFactory::create<int>('c', {2, 3, 3, 1}, {
1, 2, 3, 4, 5, 6, 7, 8, 9,
1, 2, 3, 4, 5, 6, 7, 8, 9
});
NDArray expected = NDArrayFactory::create<float>('c', {2, 6, 6, 1}, {
1.f, 1.f, 1.5f, 2.f, 2.f, 3.f,
1.f, 1.f, 1.5f, 2.f, 2.f, 3.f,
2.5f, 2.5f, 3.f, 3.5f, 3.5f, 4.5f,
4.f, 4.f, 4.5f, 5.f, 5.f, 6.f,
4.f, 4.f, 4.5f, 5.f, 5.f, 6.f,
7.f, 7.f, 7.5f, 8.f, 8.f, 9.f,
1.f, 1.f, 1.5f, 2.f, 2.f, 3.f,
1.f, 1.f, 1.5f, 2.f, 2.f, 3.f,
2.5f, 2.5f, 3.f, 3.5f, 3.5f, 4.5f,
4.f, 4.f, 4.5f, 5.f, 5.f, 6.f,
4.f, 4.f, 4.5f, 5.f, 5.f, 6.f,
7.f, 7.f, 7.5f, 8.f, 8.f, 9.f
});
//input.linspace(1);
// auto size = NDArrayFactory::create<int>({6, 6});
sd::ops::resize_area op;
auto results = op.evaluate({&input}, {}, {6, 6}, {true});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Area Resized to 6x6");
// expected.printBuffer("Area Expect for 6x6");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
}
TEST_F(DeclarableOpsTests11, ImageResizeArea_Test8) {
NDArray input = NDArrayFactory::create<int>('c', {1, 3, 3, 1}, {
1, 2, 3, 4, 5, 6, 7, 8, 9
});
NDArray expected = NDArrayFactory::create<float>('c', {1, 6, 6, 1}, {
1.f, 1.f, 1.5f, 2.f, 2.f, 3.f,
1.f, 1.f, 1.5f, 2.f, 2.f, 3.f,
2.5f, 2.5f, 3.f, 3.5f, 3.5f, 4.5f,
4.f, 4.f, 4.5f, 5.f, 5.f, 6.f,
4.f, 4.f, 4.5f, 5.f, 5.f, 6.f,
7.f, 7.f, 7.5f, 8.f, 8.f, 9.f
});
//input.linspace(1);
// auto size = NDArrayFactory::create<int>({6, 6});
sd::ops::resize_area op;
auto results = op.evaluate({&input}, {}, {6, 6}, {true});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Area Resized to 6x6");
// expected.printBuffer("Area Expect for 6x6");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
}
TEST_F(DeclarableOpsTests11, ResizeImages_Test8) {
NDArray input = NDArrayFactory::create<int>('c', {1, 3, 3, 1}, {
1, 2, 3, 4, 5, 6, 7, 8, 9
});
NDArray expected = NDArrayFactory::create<float>('c', {1, 6, 6, 1}, {
// 1.f, 1.f, 2.f, 2.f, 3.f, 3.f, 1.f, 1.f, 2.f, 2.f, 3.f, 3.f, 4.f, 4.f, 5.f, 5.f, 6.f, 6.f, 4.f, 4.f, 5.f, 5.f,
// 6.f, 6.f, 7.f, 7.f, 8.f, 8.f, 9.f, 9.f, 7.f, 7.f, 8.f, 8.f, 9.f, 9.f
1.f , 1.f , 1.5f, 2.f , 2.f, 3.f, 1.f , 1.f , 1.5f, 2.f , 2.f, 3.f,
2.5f, 2.5f, 3.f, 3.5f, 3.5f, 4.5f, 4.f , 4.f , 4.5f , 5.f, 5.f, 6.f ,
4.f, 4.f, 4.5f , 5.f, 5.f, 6.f, 7.f , 7.f , 7.5f , 8.f , 8.f , 9.f
});
//input.linspace(1);
// auto size = NDArrayFactory::create<int>({6, 6});
sd::ops::resize_images op;
auto results = op.evaluate({&input}, {}, {6, 8, ops::helpers::kResizeArea}, {true, true}); // resize_area to 6x8 with align corners and preserve aspect ratio of input image
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result->printBuffer("Area Resized to 6x6");
// expected.printBuffer("Area Expect for 6x6");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, ImageResizeArea_Test9) {
NDArray input = NDArrayFactory::create<int>('c', {1, 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
});
NDArray expected = NDArrayFactory::create<float>('c', {1, 10, 10, 4}, {
1.000000f, 2.000000f, 3.000000f, 4.000000f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 1.000000f, 2.000000f,
3.000000f, 4.000000f, 3.666667f, 4.666667f, 5.666667f, 6.666667f, 5.000000f, 6.000000f, 7.000000f, 8.000000f,
5.000000f, 6.000000f, 7.000000f, 8.000000f, 6.333336f, 7.333336f, 8.333336f, 9.333337f, 9.000000f, 10.000000f,
11.000000f, 12.000000f, 9.000000f, 10.000000f, 11.000000f, 12.000000f, 8.999998f, 9.999998f, 10.999998f, 11.999998f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 3.666667f, 4.666667f, 5.666667f, 6.666667f, 5.000000f, 6.000000f, 7.000000f, 8.000000f, 5.000000f, 6.000000f, 7.000000f, 8.000000f, 6.333336f, 7.333336f, 8.333336f, 9.333337f, 9.000000f, 10.000000f, 11.000000f, 12.000000f, 9.000000f, 10.000000f, 11.000000f, 12.000000f, 8.999998f, 9.999998f, 10.999998f, 11.999998f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 3.666667f, 4.666667f, 5.666667f, 6.666667f, 5.000000f, 6.000000f, 7.000000f, 8.000000f, 5.000000f, 6.000000f, 7.000000f, 8.000000f, 6.333336f, 7.333336f, 8.333336f, 9.333337f, 9.000000f, 10.000000f, 11.000000f, 12.000000f, 9.000000f, 10.000000f, 11.000000f, 12.000000f, 8.999998f, 9.999998f, 10.999998f, 11.999998f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 3.666667f, 4.666667f, 5.666667f, 6.666667f, 5.000000f, 6.000000f, 7.000000f, 8.000000f, 5.000000f, 6.000000f, 7.000000f, 8.000000f, 6.333336f, 7.333336f, 8.333336f, 9.333337f, 9.000000f, 10.000000f, 11.000000f, 12.000000f, 9.000000f, 10.000000f, 11.000000f, 12.000000f, 8.999998f, 9.999998f, 10.999998f, 11.999998f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 3.666667f, 4.666667f, 5.666667f, 6.666667f, 5.000000f, 6.000000f, 7.000000f, 8.000000f, 5.000000f, 6.000000f, 7.000000f, 8.000000f, 6.333336f, 7.333336f, 8.333336f, 9.333336f, 8.999999f, 9.999999f, 11.000000f, 11.999999f, 8.999999f, 9.999999f, 11.000000f, 11.999999f, 8.999998f, 9.999997f, 10.999997f, 11.999997f, 13.000003f, 14.000004f, 15.000003f, 16.000004f, 13.000003f, 14.000004f, 15.000003f, 16.000004f, 13.000003f, 14.000004f, 15.000003f, 16.000004f, 15.666671f, 16.666672f, 17.666672f, 18.666672f, 17.000006f, 18.000004f, 19.000006f, 20.000004f, 17.000006f, 18.000004f, 19.000006f, 20.000004f, 18.333344f, 19.333344f, 20.333345f, 21.333344f, 21.000006f, 22.000006f, 23.000006f, 24.000006f, 21.000006f, 22.000006f, 23.000006f, 24.000006f, 21.000002f, 22.000000f, 23.000002f, 24.000000f, 13.000000f, 14.000001f, 15.000000f, 16.000000f, 13.000000f, 14.000001f, 15.000000f, 16.000000f, 13.000000f, 14.000001f, 15.000000f, 16.000000f, 15.666667f, 16.666668f, 17.666668f, 18.666668f, 17.000002f, 18.000000f, 19.000002f, 20.000000f, 17.000002f, 18.000000f, 19.000002f, 20.000000f, 18.333340f, 19.333340f, 20.333342f, 21.333340f, 21.000002f, 22.000000f, 22.999998f, 24.000000f, 21.000002f, 22.000000f, 22.999998f, 24.000000f, 20.999996f, 21.999996f, 22.999994f, 23.999996f, 13.000000f, 14.000001f, 15.000000f, 16.000000f, 13.000000f, 14.000001f, 15.000000f, 16.000000f, 13.000000f, 14.000001f, 15.000000f, 16.000000f, 15.666667f, 16.666668f, 17.666668f, 18.666668f, 17.000002f, 18.000000f, 19.000002f, 20.000000f, 17.000002f, 18.000000f, 19.000002f, 20.000000f, 18.333340f, 19.333340f, 20.333342f, 21.333340f, 21.000002f, 22.000000f, 22.999998f, 24.000000f, 21.000002f, 22.000000f, 22.999998f, 24.000000f, 20.999996f, 21.999996f, 22.999994f, 23.999996f, 13.000000f, 14.000001f, 15.000000f, 16.000000f, 13.000000f, 14.000001f, 15.000000f, 16.000000f, 13.000000f, 14.000001f, 15.000000f, 16.000000f, 15.666667f, 16.666668f, 17.666668f, 18.666668f, 17.000002f, 18.000000f, 19.000002f, 20.000000f, 17.000002f, 18.000000f, 19.000002f, 20.000000f, 18.333340f, 19.333340f, 20.333342f, 21.333340f, 21.000002f, 22.000000f, 22.999998f, 24.000000f, 21.000002f, 22.000000f, 22.999998f, 24.000000f, 20.999996f, 21.999996f, 22.999994f, 23.999996f, 12.999995f, 13.999995f, 14.999994f, 15.999994f, 12.999995f, 13.999995f, 14.999994f, 15.999994f, 12.999995f, 13.999995f, 14.999994f, 15.999994f, 15.666661f, 16.666662f, 17.666660f, 18.666660f, 16.999994f, 17.999994f, 18.999992f, 19.999992f, 16.999994f, 17.999994f, 18.999992f, 19.999992f, 18.333334f, 19.333332f, 20.333334f, 21.333332f, 20.999992f, 21.999992f, 22.999990f, 23.999992f, 20.999992f, 21.999992f, 22.999990f, 23.999992f, 20.999989f, 21.999989f, 22.999987f, 23.999987f
});
//input.linspace(1);
auto size = NDArrayFactory::create<int>({10, 10});
sd::ops::resize_area op;
auto results = op.evaluate({&input, &size}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Area Resized to 10x10");
// expected.printBuffer("Area Expect for 6x6");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, ImageResizeArea_Test10) {
NDArray input = NDArrayFactory::create<int>('c', {1, 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
});
NDArray expected = NDArrayFactory::create<float>('c', {1, 10, 10, 4}, {
1.000000f, 2.000000f, 3.000000f, 4.000000f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 3.666667f, 4.666667f, 5.666667f, 6.666667f, 5.000000f, 6.000000f, 7.000000f, 8.000000f, 5.000000f, 6.000000f, 7.000000f, 8.000000f, 6.333336f, 7.333336f, 8.333336f, 9.333337f, 9.000000f, 10.000000f, 11.000000f, 12.000000f, 9.000000f, 10.000000f, 11.000000f, 12.000000f, 8.999998f, 9.999998f, 10.999998f, 11.999998f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 3.666667f, 4.666667f, 5.666667f, 6.666667f, 5.000000f, 6.000000f, 7.000000f, 8.000000f, 5.000000f, 6.000000f, 7.000000f, 8.000000f, 6.333336f, 7.333336f, 8.333336f, 9.333337f, 9.000000f, 10.000000f, 11.000000f, 12.000000f, 9.000000f, 10.000000f, 11.000000f, 12.000000f, 8.999998f, 9.999998f, 10.999998f, 11.999998f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 3.666667f, 4.666667f, 5.666667f, 6.666667f, 5.000000f, 6.000000f, 7.000000f, 8.000000f, 5.000000f, 6.000000f, 7.000000f, 8.000000f, 6.333336f, 7.333336f, 8.333336f, 9.333337f, 9.000000f, 10.000000f, 11.000000f, 12.000000f, 9.000000f, 10.000000f, 11.000000f, 12.000000f, 8.999998f, 9.999998f, 10.999998f, 11.999998f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 3.666667f, 4.666667f, 5.666667f, 6.666667f, 5.000000f, 6.000000f, 7.000000f, 8.000000f, 5.000000f, 6.000000f, 7.000000f, 8.000000f, 6.333336f, 7.333336f, 8.333336f, 9.333337f, 9.000000f, 10.000000f, 11.000000f, 12.000000f, 9.000000f, 10.000000f, 11.000000f, 12.000000f, 8.999998f, 9.999998f, 10.999998f, 11.999998f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 1.000000f, 2.000000f, 3.000000f, 4.000000f, 3.666667f, 4.666667f, 5.666667f, 6.666667f, 5.000000f, 6.000000f, 7.000000f, 8.000000f, 5.000000f, 6.000000f, 7.000000f, 8.000000f, 6.333336f, 7.333336f, 8.333336f, 9.333336f, 8.999999f, 9.999999f, 11.000000f, 11.999999f, 8.999999f, 9.999999f, 11.000000f, 11.999999f, 8.999998f, 9.999997f, 10.999997f, 11.999997f, 13.000003f, 14.000004f, 15.000003f, 16.000004f, 13.000003f, 14.000004f, 15.000003f, 16.000004f, 13.000003f, 14.000004f, 15.000003f, 16.000004f, 15.666671f, 16.666672f, 17.666672f, 18.666672f, 17.000006f, 18.000004f, 19.000006f, 20.000004f, 17.000006f, 18.000004f, 19.000006f, 20.000004f, 18.333344f, 19.333344f, 20.333345f, 21.333344f, 21.000006f, 22.000006f, 23.000006f, 24.000006f, 21.000006f, 22.000006f, 23.000006f, 24.000006f, 21.000002f, 22.000000f, 23.000002f, 24.000000f, 13.000000f, 14.000001f, 15.000000f, 16.000000f, 13.000000f, 14.000001f, 15.000000f, 16.000000f, 13.000000f, 14.000001f, 15.000000f, 16.000000f, 15.666667f, 16.666668f, 17.666668f, 18.666668f, 17.000002f, 18.000000f, 19.000002f, 20.000000f, 17.000002f, 18.000000f, 19.000002f, 20.000000f, 18.333340f, 19.333340f, 20.333342f, 21.333340f, 21.000002f, 22.000000f, 22.999998f, 24.000000f, 21.000002f, 22.000000f, 22.999998f, 24.000000f, 20.999996f, 21.999996f, 22.999994f, 23.999996f, 13.000000f, 14.000001f, 15.000000f, 16.000000f, 13.000000f, 14.000001f, 15.000000f, 16.000000f, 13.000000f, 14.000001f, 15.000000f, 16.000000f, 15.666667f, 16.666668f, 17.666668f, 18.666668f, 17.000002f, 18.000000f, 19.000002f, 20.000000f, 17.000002f, 18.000000f, 19.000002f, 20.000000f, 18.333340f, 19.333340f, 20.333342f, 21.333340f, 21.000002f, 22.000000f, 22.999998f, 24.000000f, 21.000002f, 22.000000f, 22.999998f, 24.000000f, 20.999996f, 21.999996f, 22.999994f, 23.999996f, 13.000000f, 14.000001f, 15.000000f, 16.000000f, 13.000000f, 14.000001f, 15.000000f, 16.000000f, 13.000000f, 14.000001f, 15.000000f, 16.000000f, 15.666667f, 16.666668f, 17.666668f, 18.666668f, 17.000002f, 18.000000f, 19.000002f, 20.000000f, 17.000002f, 18.000000f, 19.000002f, 20.000000f, 18.333340f, 19.333340f, 20.333342f, 21.333340f, 21.000002f, 22.000000f, 22.999998f, 24.000000f, 21.000002f, 22.000000f, 22.999998f, 24.000000f, 20.999996f, 21.999996f, 22.999994f, 23.999996f, 12.999995f, 13.999995f, 14.999994f, 15.999994f, 12.999995f, 13.999995f, 14.999994f, 15.999994f, 12.999995f, 13.999995f, 14.999994f, 15.999994f, 15.666661f, 16.666662f, 17.666660f, 18.666660f, 16.999994f, 17.999994f, 18.999992f, 19.999992f, 16.999994f, 17.999994f, 18.999992f, 19.999992f, 18.333334f, 19.333332f, 20.333334f, 21.333332f, 20.999992f, 21.999992f, 22.999990f, 23.999992f, 20.999992f, 21.999992f, 22.999990f, 23.999992f, 20.999989f, 21.999989f, 22.999987f, 23.999987f
});
//input.linspace(1);
//auto size = NDArrayFactory::create<int>({10, 10});
sd::ops::resize_area op;
auto results = op.evaluate({&input}, {}, {10, 10});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Area Resized to 10x10");
// expected.printBuffer("Area Expect for 6x6");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, ImageResizeArea_Test11) {
NDArray input = NDArrayFactory::create<int>('c', {1, 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
});
// NDArray expected = NDArrayFactory::create<float>('c', {1, 6, 9, 4}, {
// 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 3.666667, 4.666667, 5.666667, 6.666667, 5.000000, 6.000000, 7.000000, 8.000000, 5.000000, 6.000000, 7.000000, 8.000000, 6.333336, 7.333336, 8.333336, 9.333337, 9.000000, 10.000000, 11.000000, 12.000000, 9.000000, 10.000000, 11.000000, 12.000000, 8.999998, 9.999998, 10.999998, 11.999998, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 3.666667, 4.666667, 5.666667, 6.666667, 5.000000, 6.000000, 7.000000, 8.000000, 5.000000, 6.000000, 7.000000, 8.000000, 6.333336, 7.333336, 8.333336, 9.333337, 9.000000, 10.000000, 11.000000, 12.000000, 9.000000, 10.000000, 11.000000, 12.000000, 8.999998, 9.999998, 10.999998, 11.999998, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 3.666667, 4.666667, 5.666667, 6.666667, 5.000000, 6.000000, 7.000000, 8.000000, 5.000000, 6.000000, 7.000000, 8.000000, 6.333336, 7.333336, 8.333336, 9.333337, 9.000000, 10.000000, 11.000000, 12.000000, 9.000000, 10.000000, 11.000000, 12.000000, 8.999998, 9.999998, 10.999998, 11.999998, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 3.666667, 4.666667, 5.666667, 6.666667, 5.000000, 6.000000, 7.000000, 8.000000, 5.000000, 6.000000, 7.000000, 8.000000, 6.333336, 7.333336, 8.333336, 9.333337, 9.000000, 10.000000, 11.000000, 12.000000, 9.000000, 10.000000, 11.000000, 12.000000, 8.999998, 9.999998, 10.999998, 11.999998, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 3.666667, 4.666667, 5.666667, 6.666667, 5.000000, 6.000000, 7.000000, 8.000000, 5.000000, 6.000000, 7.000000, 8.000000, 6.333336, 7.333336, 8.333336, 9.333336, 8.999999, 9.999999, 11.000000, 11.999999, 8.999999, 9.999999, 11.000000, 11.999999, 8.999998, 9.999997, 10.999997, 11.999997, 13.000003, 14.000004, 15.000003, 16.000004, 13.000003, 14.000004, 15.000003, 16.000004, 13.000003, 14.000004, 15.000003, 16.000004, 15.666671, 16.666672, 17.666672, 18.666672, 17.000006, 18.000004, 19.000006, 20.000004, 17.000006, 18.000004, 19.000006, 20.000004, 18.333344, 19.333344, 20.333345, 21.333344, 21.000006, 22.000006, 23.000006, 24.000006, 21.000006, 22.000006, 23.000006, 24.000006, 21.000002, 22.000000, 23.000002, 24.000000, 13.000000, 14.000001, 15.000000, 16.000000, 13.000000, 14.000001, 15.000000, 16.000000, 13.000000, 14.000001, 15.000000, 16.000000, 15.666667, 16.666668, 17.666668, 18.666668, 17.000002, 18.000000, 19.000002, 20.000000, 17.000002, 18.000000, 19.000002, 20.000000, 18.333340, 19.333340, 20.333342, 21.333340, 21.000002, 22.000000, 22.999998, 24.000000, 21.000002, 22.000000, 22.999998, 24.000000, 20.999996, 21.999996, 22.999994, 23.999996, 13.000000, 14.000001, 15.000000, 16.000000, 13.000000, 14.000001, 15.000000, 16.000000, 13.000000, 14.000001, 15.000000, 16.000000, 15.666667, 16.666668, 17.666668, 18.666668, 17.000002, 18.000000, 19.000002, 20.000000, 17.000002, 18.000000, 19.000002, 20.000000, 18.333340, 19.333340, 20.333342, 21.333340, 21.000002, 22.000000, 22.999998, 24.000000, 21.000002, 22.000000, 22.999998, 24.000000, 20.999996, 21.999996, 22.999994, 23.999996, 13.000000, 14.000001, 15.000000, 16.000000, 13.000000, 14.000001, 15.000000, 16.000000, 13.000000, 14.000001, 15.000000, 16.000000, 15.666667, 16.666668, 17.666668, 18.666668, 17.000002, 18.000000, 19.000002, 20.000000, 17.000002, 18.000000, 19.000002, 20.000000, 18.333340, 19.333340, 20.333342, 21.333340, 21.000002, 22.000000, 22.999998, 24.000000, 21.000002, 22.000000, 22.999998, 24.000000, 20.999996, 21.999996, 22.999994, 23.999996, 12.999995, 13.999995, 14.999994, 15.999994, 12.999995, 13.999995, 14.999994, 15.999994, 12.999995, 13.999995, 14.999994, 15.999994, 15.666661, 16.666662, 17.666660, 18.666660, 16.999994, 17.999994, 18.999992, 19.999992, 16.999994, 17.999994, 18.999992, 19.999992, 18.333334, 19.333332, 20.333334, 21.333332, 20.999992, 21.999992, 22.999990, 23.999992, 20.999992, 21.999992, 22.999990, 23.999992, 20.999989, 21.999989, 22.999987, 23.999987
//
// });
//input.linspace(1);
//auto size = NDArrayFactory::create<int>({10, 10});
sd::ops::resize_area op;
auto results = op.evaluate({&input}, {}, {6, 9});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Area Resized to 6x9");
// expected.printBuffer("Area Expect for 6x6");
// ASSERT_TRUE(expected.isSameShape(result));
// ASSERT_TRUE(expected.equalsTo(result));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, ImageResizeArea_Test12) {
NDArray input = NDArrayFactory::create<int>('c', {1, 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
});
// NDArray expected = NDArrayFactory::create<float>('c', {1, 6, 9, 4}, {
// 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 3.666667, 4.666667, 5.666667, 6.666667, 5.000000, 6.000000, 7.000000, 8.000000, 5.000000, 6.000000, 7.000000, 8.000000, 6.333336, 7.333336, 8.333336, 9.333337, 9.000000, 10.000000, 11.000000, 12.000000, 9.000000, 10.000000, 11.000000, 12.000000, 8.999998, 9.999998, 10.999998, 11.999998, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 3.666667, 4.666667, 5.666667, 6.666667, 5.000000, 6.000000, 7.000000, 8.000000, 5.000000, 6.000000, 7.000000, 8.000000, 6.333336, 7.333336, 8.333336, 9.333337, 9.000000, 10.000000, 11.000000, 12.000000, 9.000000, 10.000000, 11.000000, 12.000000, 8.999998, 9.999998, 10.999998, 11.999998, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 3.666667, 4.666667, 5.666667, 6.666667, 5.000000, 6.000000, 7.000000, 8.000000, 5.000000, 6.000000, 7.000000, 8.000000, 6.333336, 7.333336, 8.333336, 9.333337, 9.000000, 10.000000, 11.000000, 12.000000, 9.000000, 10.000000, 11.000000, 12.000000, 8.999998, 9.999998, 10.999998, 11.999998, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 3.666667, 4.666667, 5.666667, 6.666667, 5.000000, 6.000000, 7.000000, 8.000000, 5.000000, 6.000000, 7.000000, 8.000000, 6.333336, 7.333336, 8.333336, 9.333337, 9.000000, 10.000000, 11.000000, 12.000000, 9.000000, 10.000000, 11.000000, 12.000000, 8.999998, 9.999998, 10.999998, 11.999998, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 3.666667, 4.666667, 5.666667, 6.666667, 5.000000, 6.000000, 7.000000, 8.000000, 5.000000, 6.000000, 7.000000, 8.000000, 6.333336, 7.333336, 8.333336, 9.333336, 8.999999, 9.999999, 11.000000, 11.999999, 8.999999, 9.999999, 11.000000, 11.999999, 8.999998, 9.999997, 10.999997, 11.999997, 13.000003, 14.000004, 15.000003, 16.000004, 13.000003, 14.000004, 15.000003, 16.000004, 13.000003, 14.000004, 15.000003, 16.000004, 15.666671, 16.666672, 17.666672, 18.666672, 17.000006, 18.000004, 19.000006, 20.000004, 17.000006, 18.000004, 19.000006, 20.000004, 18.333344, 19.333344, 20.333345, 21.333344, 21.000006, 22.000006, 23.000006, 24.000006, 21.000006, 22.000006, 23.000006, 24.000006, 21.000002, 22.000000, 23.000002, 24.000000, 13.000000, 14.000001, 15.000000, 16.000000, 13.000000, 14.000001, 15.000000, 16.000000, 13.000000, 14.000001, 15.000000, 16.000000, 15.666667, 16.666668, 17.666668, 18.666668, 17.000002, 18.000000, 19.000002, 20.000000, 17.000002, 18.000000, 19.000002, 20.000000, 18.333340, 19.333340, 20.333342, 21.333340, 21.000002, 22.000000, 22.999998, 24.000000, 21.000002, 22.000000, 22.999998, 24.000000, 20.999996, 21.999996, 22.999994, 23.999996, 13.000000, 14.000001, 15.000000, 16.000000, 13.000000, 14.000001, 15.000000, 16.000000, 13.000000, 14.000001, 15.000000, 16.000000, 15.666667, 16.666668, 17.666668, 18.666668, 17.000002, 18.000000, 19.000002, 20.000000, 17.000002, 18.000000, 19.000002, 20.000000, 18.333340, 19.333340, 20.333342, 21.333340, 21.000002, 22.000000, 22.999998, 24.000000, 21.000002, 22.000000, 22.999998, 24.000000, 20.999996, 21.999996, 22.999994, 23.999996, 13.000000, 14.000001, 15.000000, 16.000000, 13.000000, 14.000001, 15.000000, 16.000000, 13.000000, 14.000001, 15.000000, 16.000000, 15.666667, 16.666668, 17.666668, 18.666668, 17.000002, 18.000000, 19.000002, 20.000000, 17.000002, 18.000000, 19.000002, 20.000000, 18.333340, 19.333340, 20.333342, 21.333340, 21.000002, 22.000000, 22.999998, 24.000000, 21.000002, 22.000000, 22.999998, 24.000000, 20.999996, 21.999996, 22.999994, 23.999996, 12.999995, 13.999995, 14.999994, 15.999994, 12.999995, 13.999995, 14.999994, 15.999994, 12.999995, 13.999995, 14.999994, 15.999994, 15.666661, 16.666662, 17.666660, 18.666660, 16.999994, 17.999994, 18.999992, 19.999992, 16.999994, 17.999994, 18.999992, 19.999992, 18.333334, 19.333332, 20.333334, 21.333332, 20.999992, 21.999992, 22.999990, 23.999992, 20.999992, 21.999992, 22.999990, 23.999992, 20.999989, 21.999989, 22.999987, 23.999987
//
// });
//input.linspace(1);
//auto size = NDArrayFactory::create<int>({10, 10});
sd::ops::resize_area op;
auto results = op.evaluate({&input}, {}, {10, 15});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Area Resized to 6x9");
// expected.printBuffer("Area Expect for 6x6");
// ASSERT_TRUE(expected.isSameShape(result));
// ASSERT_TRUE(expected.equalsTo(result));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, ImageResizeArea_Test13) {
NDArray input = NDArrayFactory::create<int>('c', {1, 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
});
// NDArray expected = NDArrayFactory::create<float>('c', {1, 8, 8, 4}, {
// 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 3.666667, 4.666667, 5.666667, 6.666667, 5.000000, 6.000000, 7.000000, 8.000000, 5.000000, 6.000000, 7.000000, 8.000000, 6.333336, 7.333336, 8.333336, 9.333337, 9.000000, 10.000000, 11.000000, 12.000000, 9.000000, 10.000000, 11.000000, 12.000000, 8.999998, 9.999998, 10.999998, 11.999998, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 3.666667, 4.666667, 5.666667, 6.666667, 5.000000, 6.000000, 7.000000, 8.000000, 5.000000, 6.000000, 7.000000, 8.000000, 6.333336, 7.333336, 8.333336, 9.333337, 9.000000, 10.000000, 11.000000, 12.000000, 9.000000, 10.000000, 11.000000, 12.000000, 8.999998, 9.999998, 10.999998, 11.999998, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 3.666667, 4.666667, 5.666667, 6.666667, 5.000000, 6.000000, 7.000000, 8.000000, 5.000000, 6.000000, 7.000000, 8.000000, 6.333336, 7.333336, 8.333336, 9.333337, 9.000000, 10.000000, 11.000000, 12.000000, 9.000000, 10.000000, 11.000000, 12.000000, 8.999998, 9.999998, 10.999998, 11.999998, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 3.666667, 4.666667, 5.666667, 6.666667, 5.000000, 6.000000, 7.000000, 8.000000, 5.000000, 6.000000, 7.000000, 8.000000, 6.333336, 7.333336, 8.333336, 9.333337, 9.000000, 10.000000, 11.000000, 12.000000, 9.000000, 10.000000, 11.000000, 12.000000, 8.999998, 9.999998, 10.999998, 11.999998, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 1.000000, 2.000000, 3.000000, 4.000000, 3.666667, 4.666667, 5.666667, 6.666667, 5.000000, 6.000000, 7.000000, 8.000000, 5.000000, 6.000000, 7.000000, 8.000000, 6.333336, 7.333336, 8.333336, 9.333336, 8.999999, 9.999999, 11.000000, 11.999999, 8.999999, 9.999999, 11.000000, 11.999999, 8.999998, 9.999997, 10.999997, 11.999997, 13.000003, 14.000004, 15.000003, 16.000004, 13.000003, 14.000004, 15.000003, 16.000004, 13.000003, 14.000004, 15.000003, 16.000004, 15.666671, 16.666672, 17.666672, 18.666672, 17.000006, 18.000004, 19.000006, 20.000004, 17.000006, 18.000004, 19.000006, 20.000004, 18.333344, 19.333344, 20.333345, 21.333344, 21.000006, 22.000006, 23.000006, 24.000006, 21.000006, 22.000006, 23.000006, 24.000006, 21.000002, 22.000000, 23.000002, 24.000000, 13.000000, 14.000001, 15.000000, 16.000000, 13.000000, 14.000001, 15.000000, 16.000000, 13.000000, 14.000001, 15.000000, 16.000000, 15.666667, 16.666668, 17.666668, 18.666668, 17.000002, 18.000000, 19.000002, 20.000000, 17.000002, 18.000000, 19.000002, 20.000000, 18.333340, 19.333340, 20.333342, 21.333340, 21.000002, 22.000000, 22.999998, 24.000000, 21.000002, 22.000000, 22.999998, 24.000000, 20.999996, 21.999996, 22.999994, 23.999996, 13.000000, 14.000001, 15.000000, 16.000000, 13.000000, 14.000001, 15.000000, 16.000000, 13.000000, 14.000001, 15.000000, 16.000000, 15.666667, 16.666668, 17.666668, 18.666668, 17.000002, 18.000000, 19.000002, 20.000000, 17.000002, 18.000000, 19.000002, 20.000000, 18.333340, 19.333340, 20.333342, 21.333340, 21.000002, 22.000000, 22.999998, 24.000000, 21.000002, 22.000000, 22.999998, 24.000000, 20.999996, 21.999996, 22.999994, 23.999996, 13.000000, 14.000001, 15.000000, 16.000000, 13.000000, 14.000001, 15.000000, 16.000000, 13.000000, 14.000001, 15.000000, 16.000000, 15.666667, 16.666668, 17.666668, 18.666668, 17.000002, 18.000000, 19.000002, 20.000000, 17.000002, 18.000000, 19.000002, 20.000000, 18.333340, 19.333340, 20.333342, 21.333340, 21.000002, 22.000000, 22.999998, 24.000000, 21.000002, 22.000000, 22.999998, 24.000000, 20.999996, 21.999996, 22.999994, 23.999996, 12.999995, 13.999995, 14.999994, 15.999994, 12.999995, 13.999995, 14.999994, 15.999994, 12.999995, 13.999995, 14.999994, 15.999994, 15.666661, 16.666662, 17.666660, 18.666660, 16.999994, 17.999994, 18.999992, 19.999992, 16.999994, 17.999994, 18.999992, 19.999992, 18.333334, 19.333332, 20.333334, 21.333332, 20.999992, 21.999992, 22.999990, 23.999992, 20.999992, 21.999992, 22.999990, 23.999992, 20.999989, 21.999989, 22.999987, 23.999987
//
// });
//input.linspace(1);
//auto size = NDArrayFactory::create<int>({10, 10});
sd::ops::resize_area op;
auto results = op.evaluate({&input}, {}, {9, 9});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Area Resized to 8x8");
// expected.printBuffer("Area Expect for 6x6");
// ASSERT_TRUE(expected.isSameShape(result));
// ASSERT_TRUE(expected.equalsTo(result));
}
TEST_F(DeclarableOpsTests11, ImageResizeArea_Test14) {
NDArray input = NDArrayFactory::create<int>('c', {1, 5, 5, 1}, {
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
});
auto size = NDArrayFactory::create<int>({8, 7});
NDArray expected = NDArrayFactory::create<float>('c', {1, 8, 7, 1}, {
1.f, 1.6f , 2.1999993f, 2.9999995f , 3.8f , 4.399997f, 5.f , 2.9999995f , 3.5999997f , 4.199999f,
4.9999995f, 5.8f , 6.3999963f , 7.f , 5.999999f , 6.6f, 7.1999984f , 7.9999995f , 8.8f,
9.399994f, 10.f , 10.f, 10.6f , 11.199998f, 12.f, 12.8f, 13.399992f , 14.f, 12.f , 12.599999f,
13.199998f , 13.999998f , 14.800002f , 15.399991f , 16.f , 15.999999f , 16.599998f , 17.199995f,
18.f , 18.800003f , 19.399986f , 20.000002f , 19.f , 19.599998f , 20.199997f ,
20.999998f , 21.800003f , 22.399984f , 23.000002f , 20.999998f ,
21.599998f , 22.199995f , 22.999998f , 23.800001f , 24.399984f ,
25.f
}); //input.linspace(1);
// auto size = NDArrayFactory::create<int>({6, 6});
sd::ops::resize_area op;
auto results = op.evaluate({&input, &size}, {}, {false});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Area Resized to 8x7");
// expected.printBuffer("Area Expect for 8x7");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
}
TEST_F(DeclarableOpsTests11, ImageResizeArea_Test15) {
NDArray input = NDArrayFactory::create<int>('c', {1, 5, 5, 1}, {
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
});
//auto size = NDArrayFactory::create<int>({8, 7});
NDArray expected = NDArrayFactory::create<float>('c', {1, 8, 7, 1}, {
1.f, 1.6f , 2.1999993f, 2.9999995f , 3.8f , 4.399997f, 5.f , 2.9999995f , 3.5999997f , 4.199999f,
4.9999995f, 5.8f , 6.3999963f , 7.f , 5.999999f , 6.6f, 7.1999984f , 7.9999995f , 8.8f,
9.399994f, 10.f , 10.f, 10.6f , 11.199998f, 12.f, 12.8f, 13.399992f , 14.f, 12.f , 12.599999f,
13.199998f , 13.999998f , 14.800002f , 15.399991f , 16.f , 15.999999f , 16.599998f , 17.199995f,
18.f , 18.800003f , 19.399986f , 20.000002f , 19.f , 19.599998f , 20.199997f ,
20.999998f , 21.800003f , 22.399984f , 23.000002f , 20.999998f , 21.599998f , 22.199995f ,
22.999998f , 23.800001f , 24.399984f , 25.f
});
sd::ops::resize_area op;
auto results = op.evaluate({&input}, {}, {8, 7}, {false});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
NDArray* result = results.at(0);
// result.printBuffer("Area Resized to 8x7");
// expected.printBuffer("Area Expect for 8x7");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, summaryStatsData_test1) {
functions::summarystats::SummaryStatsData<double> var1;
functions::summarystats::SummaryStatsData<double> var2;
var2.n = var2.mean = var2.M2 = var2.M3 = var2.M4 = var2.bias = 5;
functions::summarystats::SummaryStatsData<double>* arr = new functions::summarystats::SummaryStatsData<double>[2];
arr[0] = var1;
arr[1] = var2;
arr[0] = arr[1];
functions::summarystats::SummaryStatsData<double> var3(var1);
ASSERT_TRUE(arr[0].n == arr[0].mean && arr[0].M2 == arr[0].M3 && arr[0].n == 5);
ASSERT_TRUE(arr[1].n == arr[1].mean && arr[1].M2 == arr[1].M3 && arr[1].n == 5);
ASSERT_TRUE(var3.n == var3.mean && var3.M2 == var3.M3 && var3.n == 0);
delete []arr;
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, Solve_Test_1) {
auto a = NDArrayFactory::create<float>('c', {3, 3}, {
2.f, -1.f, -2.f, -4.f, 6.f, 3.f, -4.f, -2.f, 8.f
});
auto b = NDArrayFactory::create<float>('c', {3, 1}, {
2.f, 4.f, 3.f
});
auto exp = NDArrayFactory::create<float>('c', {3, 1}, {
7.625f, 3.25f, 5.f
});
sd::ops::solve op;
auto res = op.evaluate({&a, &b});
ASSERT_EQ(res.status(), ND4J_STATUS_OK);
auto z = res.at(0);
// z->printIndexedBuffer("Solve of 3x3");
ASSERT_TRUE(exp.equalsTo(z));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, Solve_Test_2) {
auto a = NDArrayFactory::create<float>('c', {4, 4}, {
1.f, 1.f, 1.f, 1.f,
0.f, 1.f, 1.f, 0.f,
0.f, 0.f, 2.f, 1.f,
0.f, 0.f, 0.f, 3.f,
});
auto b = NDArrayFactory::create<float>('c', {4, 1}, {
2.f, 4.f, 2.f, 4.f
});
auto exp = NDArrayFactory::create<float>('c', {4, 1}, {
-3.3333333f, 3.6666666f, 0.333333f, 1.3333333f
});
sd::ops::solve op;
auto res = op.evaluate({&a, &b});
ASSERT_EQ(res.status(), ND4J_STATUS_OK);
auto z = res.at(0);
// z->printIndexedBuffer("Solve 4x4");
ASSERT_TRUE(exp.equalsTo(z));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, Solve_Test_3) {
auto a = NDArrayFactory::create<float>('c', {2, 4, 4}, {
1.f, 1.f, 1.f, 1.f,
0.f, 1.f, 1.f, 0.f,
0.f, 0.f, 2.f, 1.f,
0.f, 0.f, 0.f, 3.f,
3.f, 0.f, 0.f, 0.f,
2.f, 1.f, 0.f, 0.f,
1.f, 0.f, 1.f, 0.f,
1.f, 1.f, 1.f, 1.f
});
auto b = NDArrayFactory::create<float>('c', {2, 4, 1}, {
2.f, 4.f, 2.f, 4.f,
4.f, 2.f, 4.f, 2.f
});
auto exp = NDArrayFactory::create<float>('c', {2, 4, 1}, {
-3.3333333f, 3.6666666f, 0.333333f, 1.3333333f,
1.333333f, -0.6666667f, 2.6666667f, -1.3333333f
});
sd::ops::solve op;
auto res = op.evaluate({&a, &b});
ASSERT_EQ(res.status(), ND4J_STATUS_OK);
auto z = res.at(0);
// z->printIndexedBuffer("Solve 4x4");
ASSERT_TRUE(exp.equalsTo(z));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, Solve_Test_4) {
auto a = NDArrayFactory::create<float>('c', {2, 2, 2}, {
0.7788f, 0.8012f, 0.7244f, 0.2309f,
0.7271f, 0.1804f, 0.5056f, 0.8925f
});
auto b = NDArrayFactory::create<float>('c', {2, 2, 2}, {
0.7717f, 0.9281f, 0.9846f, 0.4838f,
0.6433f, 0.6041f, 0.6501f, 0.7612f
});
auto exp = NDArrayFactory::create<float>('c', {2, 2, 2}, {
// 1.524494767f, 0.432706356f,-0.518630624f, 0.737760842f,
// 0.819143713f, 0.720401764f, 0.264349997f, 0.444699198f
1.5245394f, 0.4326952f, -0.51873577f, 0.7377896f,
0.81915987f, 0.72049433f, 0.2643504f, 0.44472617f
});
sd::ops::solve op;
auto res = op.evaluate({&a, &b});
ASSERT_EQ(res.status(), ND4J_STATUS_OK);
auto z = res.at(0);
// z->printBuffer("4 Solve 4x4");
// exp.printBuffer("4 Expec 4x4");
ASSERT_TRUE(exp.equalsTo(z));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, Solve_Test_4_1) {
auto a = NDArrayFactory::create<float>('c', {2, 2, 2}, {
0.7788f, 0.8012f, 0.7244f, 0.2309f,
0.7271f, 0.1804f, 0.5056f, 0.8925f
});
auto b = NDArrayFactory::create<float>('c', {2, 2, 2}, {
0.7717f, 0.9281f, 0.9846f, 0.4838f, 0.6433f, 0.6041f, 0.6501f, 0.7612f
});
auto exp = NDArrayFactory::create<float>('c', {2, 2, 2}, {
1.3357621f, 0.3399364f, -0.37077796f, 0.91573375f,
0.4400987f, 0.2766527f, 0.6394467f, 0.79696566f
});
sd::ops::solve op;
auto res = op.evaluate({&a, &b}, {true});
ASSERT_EQ(res.status(), ND4J_STATUS_OK);
auto z = res.at(0);
// z->printBuffer("4 Solve 4x4");
// exp.printBuffer("4 Expec 4x4");
ASSERT_TRUE(exp.equalsTo(z));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, Solve_Test_4_2) {
auto a = NDArrayFactory::create<float>('c', {3, 3}, {
0.7788f, 0.8012f, 0.7244f,
0.2309f, 0.7271f, 0.1804f,
0.5056f, 0.8925f, 0.5461f
});
auto b = NDArrayFactory::create<float>('c', {3, 3}, {
0.7717f, 0.9281f, 0.9846f,
0.4838f, 0.6433f, 0.6041f,
0.6501f, 0.7612f, 0.7605f
});
auto exp = NDArrayFactory::create<float>('c', {3, 3}, {
0.99088347f, 1.1917052f, 1.2642528f,
0.35071516f, 0.50630623f, 0.42935497f,
-0.30013534f, -0.53690606f, -0.47959247f
});
sd::ops::triangular_solve op;
auto res = op.evaluate({&a, &b}, {true, false});
ASSERT_EQ(res.status(), ND4J_STATUS_OK);
auto z = res.at(0);
// z->printBuffer("4_2 Triangular_Solve 3x3");
// exp.printBuffer("4_2 Triangular_Expec 3x3");
ASSERT_TRUE(exp.equalsTo(z));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, Solve_Test_4_3) {
auto a = NDArrayFactory::create<float>('c', {3, 3}, {
0.7788f, 0.8012f, 0.7244f,
0.2309f, 0.7271f, 0.1804f,
0.5056f, 0.8925f, 0.5461f
});
auto b = NDArrayFactory::create<float>('c', {3, 3}, {
0.7717f, 0.9281f, 0.9846f,
0.4838f, 0.6433f, 0.6041f,
0.6501f, 0.7612f, 0.7605f
});
auto exp = NDArrayFactory::create<float>('c', {3, 3}, {
0.45400196f, 0.53174824f, 0.62064564f,
-0.79585856f, -0.82621557f, -0.87855506f,
1.1904413f, 1.3938838f, 1.3926021f
});
sd::ops::triangular_solve op;
auto res = op.evaluate({&a, &b}, {true, true});
ASSERT_EQ(res.status(), ND4J_STATUS_OK);
auto z = res.at(0);
// z->printBuffer("4_3 Triangular_Solve 3x3");
// exp.printBuffer("4_3 Triangular_Expec 3x3");
ASSERT_TRUE(exp.equalsTo(z));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, Solve_Test_4_4) {
auto a = NDArrayFactory::create<float>('c', {3, 3}, {
0.7788f, 0.8012f, 0.7244f,
0.2309f, 0.7271f, 0.1804f,
0.5056f, 0.8925f, 0.5461f
});
auto b = NDArrayFactory::create<float>('c', {3, 3}, {
0.7717f, 0.9281f, 0.9846f,
0.4838f, 0.6433f, 0.6041f,
0.6501f, 0.7612f, 0.7605f
});
auto exp = NDArrayFactory::create<float>('c', {3, 3}, {
0.8959121f, 1.6109066f, 1.7501404f,
0.49000582f, 0.66842675f, 0.5577021f,
-0.4398522f, -1.1899745f, -1.1392052f
});
sd::ops::solve op;
auto res = op.evaluate({&a, &b}, {false});
ASSERT_EQ(res.status(), ND4J_STATUS_OK);
auto z = res.at(0);
// z->printBuffer("4_4 Solve 3x3");
// exp.printBuffer("4_4 Expec 3x3");
ASSERT_TRUE(exp.equalsTo(z));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, Solve_Test_4_5) {
auto a = NDArrayFactory::create<float>('c', {3, 3}, {
0.7788f, 0.8012f, 0.7244f,
0.2309f, 0.7271f, 0.1804f,
0.5056f, 0.8925f, 0.5461f
});
auto b = NDArrayFactory::create<float>('c', {3, 3}, {
0.7717f, 0.9281f, 0.9846f,
0.4838f, 0.6433f, 0.6041f,
0.6501f, 0.7612f, 0.7605f
});
auto exp = NDArrayFactory::create<float>('c', {3, 3}, {
1.5504692f, 1.8953944f, 2.2765768f,
0.03399149f, 0.2883001f, 0.5377323f,
-0.8774802f, -1.2155888f, -1.8049058f
});
sd::ops::solve op;
auto res = op.evaluate({&a, &b}, {true, true});
ASSERT_EQ(res.status(), ND4J_STATUS_OK);
auto z = res.at(0);
// z->printBuffer("4_5 Solve 3x3");
// exp.printBuffer("4_5 Expec 3x3");
ASSERT_TRUE(exp.equalsTo(z));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, Solve_Test_4_6) {
auto a = NDArrayFactory::create<float>('c', {3, 3}, {
0.7788f, 0.8012f, 0.7244f,
0.2309f, 0.7271f, 0.1804f,
0.5056f, 0.8925f, 0.5461f
});
auto b = NDArrayFactory::create<float>('c', {3, 3}, {
0.7717f, 0.9281f, 0.9846f,
0.4838f, 0.6433f, 0.6041f,
0.6501f, 0.7612f, 0.7605f
});
auto exp = NDArrayFactory::create<float>('c', {3, 3}, {
0.99088347f, 1.1917052f, 1.2642528f,
-0.426483f, -0.42840624f, -0.5622601f,
0.01692283f, -0.04538865f, -0.09868701f
});
sd::ops::triangular_solve op;
auto res = op.evaluate({&a, &b}, {false, true});
ASSERT_EQ(res.status(), ND4J_STATUS_OK);
auto z = res.at(0);
// z->printBuffer("4_6 Solve 3x3");
// exp.printBuffer("4_6 Expec 3x3");
ASSERT_TRUE(exp.equalsTo(z));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, Solve_Test_4_7) {
auto a = NDArrayFactory::create<float>('c', {3, 3}, {
// 0.7788f, 0.2309f, 0.5056f,
// 0.8012f, 0.7271f, 0.8925f,
// 0.7244f, 0.1804f, 0.5461f
0.7788f, 0.2309f, 0.5056f,
0.8012f, 0.7271f, 0.8925f,
0.7244f, 0.1804f, 0.5461f
});
auto b = NDArrayFactory::create<float>('c', {3, 3}, {
0.7717f, 0.9281f, 0.9846f,
0.4838f, 0.6433f, 0.6041f,
0.6501f, 0.7612f, 0.7605f
});
auto exp = NDArrayFactory::create<float>('c', {3, 3}, {
0.99088347f, 1.1917052f, 1.2642528f,
-0.426483f, -0.42840624f, -0.5622601f,
0.01692283f, -0.04538865f, -0.09868701f
});
sd::ops::triangular_solve op;
auto res = op.evaluate({&a, &b}, {true, false});
ASSERT_EQ(res.status(), ND4J_STATUS_OK);
auto z = res.at(0);
// z->printBuffer("4_7 Solve 3x3");
// exp.printBuffer("4_7 Expec 3x3");
ASSERT_TRUE(exp.equalsTo(z));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, Solve_Test_5) {
auto a = NDArrayFactory::create<float>('c', {3, 3}, {
0.7788f, 0.8012f, 0.7244f,
0.2309f, 0.7271f, 0.1804f,
0.5056f, 0.8925f, 0.5461f
});
auto b = NDArrayFactory::create<float>('c', {3, 3}, {
0.7717f, 0.9281f, 0.9846f,
0.4838f, 0.6433f, 0.6041f,
0.6501f, 0.7612f, 0.7605f
});
auto exp = NDArrayFactory::create<float>('c', {3, 3}, {
1.5504692f, 1.8953944f, 2.2765768f,
0.03399149f, 0.2883001f, 0.5377323f,
-0.8774802f, -1.2155888f, -1.8049058f
});
sd::ops::solve op;
auto res = op.evaluate({&a, &b}, {true});
ASSERT_EQ(res.status(), ND4J_STATUS_OK);
auto z = res.at(0);
// z->printBuffer("4 Solve 4x4");
// exp.printBuffer("4 Expec 4x4");
ASSERT_TRUE(exp.equalsTo(z));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, SolveLS_Test_1) {
auto a = NDArrayFactory::create<double>('c', {2,2, 2}, {
1.f, 2.f, 3.f, 4.f,
5.f, 6.f, 7.f, 8.f
});
auto b = NDArrayFactory::create<double>('c', {2, 2, 1}, {
3.f, 7.f, 11.f, 15.f
});
auto exp = NDArrayFactory::create<double>('c', {2, 2, 1}, {
0.8311695f, 1.0909086f, 0.9205573f, 1.0630057f
});
sd::ops::lstsq op;
auto res = op.evaluate({&a, &b}, {0.5}, {}, {true});
ASSERT_EQ(res.status(), ND4J_STATUS_OK);
auto z = res.at(0);
// z->printIndexedBuffer("LS Solve 2x2");
// exp.printIndexedBuffer("LS Expec 2x2");
ASSERT_TRUE(exp.equalsTo(z, 1.e-4));
}
TEST_F(DeclarableOpsTests11, SolveLS_Test_2) {
auto a = NDArrayFactory::create<float>('c', {2,2, 2}, {
1.f, 2.f, 3.f, 4.f,
5.f, 6.f, 7.f, 8.f
});
auto b = NDArrayFactory::create<float>('c', {2, 2, 1}, {
3.f, 7.f, 11.f, 15.f
});
auto exp = NDArrayFactory::create<float>('c', {2, 2, 1}, {
0.8311695f, 1.0909086f, 0.9205573f, 1.0630057f
});
sd::ops::lstsq op;
auto res = op.evaluate({&a, &b}, {0.5}, {}, {true});
ASSERT_EQ(res.status(), ND4J_STATUS_OK);
auto z = res.at(0);
// z->printIndexedBuffer("2LS Solve 2x2");
// exp.printIndexedBuffer("2LS Expec 2x2");
ASSERT_TRUE(exp.equalsTo(z, 1.e-4));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, Cholesky_Test_2x2x2) {
auto a = NDArrayFactory::create<float>('c', {2,2, 2}, {
10.f, 14.f,
14.f, 20.f,
74.f, 86.f,
86.f, 100.f
});
auto exp = NDArrayFactory::create<float>('c', {2, 2, 2}, {
3.1622777f, 0.f, 4.427189f, 0.6324552f,
8.602325f, 0.f, 9.997296f, 0.23252854f
});
sd::ops::cholesky op;
auto res = op.evaluate({&a});
ASSERT_EQ(res.status(), ND4J_STATUS_OK);
auto z = res.at(0);
z->printIndexedBuffer("L matrix is");
exp.printIndexedBuffer("L expected is");
ASSERT_TRUE(exp.equalsTo(z));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, Cholesky_Test_2x2x2_2) {
auto a = NDArrayFactory::create<float>('c', {2,2, 2}, {
10.5f, 14.f,
14.f, 20.5f,
74.5f, 86.f,
86.f, 100.5f
});
auto exp = NDArrayFactory::create<float>('c', {2, 2, 2}, {
3.2403703f, 0.f, 4.3204937f, 1.3540066f,
8.631338f, 0.f, 9.963693f, 1.1067207f
});
sd::ops::cholesky op;
auto res = op.evaluate({&a});
ASSERT_EQ(res.status(), ND4J_STATUS_OK);
auto z = res.at(0);
// z->printIndexedBuffer("L matrix is");
// exp.printIndexedBuffer("L expected is");
MmulHelper::matmul(z, z, &exp, false, true);
ASSERT_TRUE(exp.equalsTo(a));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test1) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {-0.96, -1.92, -2.88, -3.84, -4.8 , -5.76, -6.72, -7.68, -8.64, -9.6 ,-10.56,-11.52,
-12.48,-13.44,-14.4 ,-15.36,-16.32,-17.28,-18.24,-19.2 ,-20.16,-21.12,-22.08,-23.04});
NDArray dLdwExp('c', {2,3,4}, {0.9216 , 3.6864 , 8.2944 , 14.7456 , 23.04 , 33.1776 , 45.1584 , 58.9824 , 74.6496 , 92.16 ,111.51361,132.7104 ,
155.75038,180.63359,207.35999,235.9296 ,266.34238,298.59842,332.6976 ,368.64001,406.4256 ,446.05444,487.5264 ,530.84161});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::mean_sqerr_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0});
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(dLdpExp.isSameShape(-*dLdl));
ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test2) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,1,4}, sd::DataType::DOUBLE);
NDArray dLdwExp('c', {2,1,4}, {98.61121,129.024 , 164.9664 , 206.4384 , 828.51837,925.28644,1027.58398,1135.41113});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::mean_sqerr_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test3) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights(sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {-0.96, -1.92, -2.88, -3.84, -4.8 , -5.76, -6.72, -7.68, -8.64, -9.6 ,-10.56,-11.52,
-12.48,-13.44,-14.4 ,-15.36,-16.32,-17.28,-18.24,-19.2 ,-20.16,-21.12,-22.08,-23.04});
NDArray dLdwExp('c', {}, std::vector<double>{4515.84});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::mean_sqerr_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {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(dLdpExp.isSameShape(-*dLdl));
ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test4) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE);
NDArray dLdwExp('c', {1,3,1}, {807.32153, 1426.63684, 2281.88159});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::mean_sqerr_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test5) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {-0.08,-0.16,-0.24,-0.32,-0.4 ,-0.48,-0.56,-0.64,-0.72,-0.8 ,-0.88,-0.96,
-1.04,-1.12,-1.2 ,-1.28,-1.36,-1.44,-1.52,-1.6 ,-1.68,-1.76,-1.84,-1.92});
NDArray dLdwExp('c', {2,3,4}, {-15.6032,-15.3728,-14.9888,-14.4512,-13.76 ,-12.9152,-11.9168,-10.7648, -9.4592, -8. , -6.3872, -4.6208,
-2.7008, -0.6272, 1.6 , 3.9808, 6.5152, 9.2032, 12.0448, 15.04 , 18.1888, 21.4912, 24.9472, 28.5568});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::mean_sqerr_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {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(dLdpExp.isSameShape(-*dLdl));
ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test6) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE);
NDArray dLdwExp('c', {1,3,1}, {-58.16319, -6.5536 , 64.71682});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::mean_sqerr_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test7) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights(sd::DataType::DOUBLE);
NDArray dLdwExp('c', {}, std::vector<double>{0.});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::mean_sqerr_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test8) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {0. ,0. ,0. ,0. ,-0.48 ,-0.576,-0.672,-0.768,-0.864,-0.96 ,-1.056,-1.152,
-1.248,-1.344,-1.44 ,-1.536,-1.632,-1.728,-1.824,-1.92 ,-2.016,-2.112,-2.208,-2.304});
NDArray dLdwExp('c', {2,3,4}, {-22.3488 ,-22.07232,-21.61152,-20.9664 ,-20.13696,-19.1232 ,-17.92512,-16.54272,-14.976 ,-13.22496,-11.2896 , -9.16992,
-6.86592, -4.3776 , -1.70496, 1.152 , 4.19328, 7.41888, 10.8288 , 14.42304, 18.2016 , 22.16449, 26.31168, 30.6432 });
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
weights.p(0, 0.);
weights.p(1, 0.);
weights.p(2, 0.);
weights.p(3, 0.);
sd::ops::mean_sqerr_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {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(dLdpExp.isSameShape(-*dLdl));
ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test9) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {-0.04,-0.08,-0.12,-0.16,-0.2 ,-0.24,-0.28,-0.32,-0.36,-0.4 ,-0.44,-0.48,
-0.52,-0.56,-0.6 ,-0.64,-0.68,-0.72,-0.76,-0.8 ,-0.84,-0.88,-0.92,-0.96});
NDArray dLdwExp('c', {2,3,4}, {0.0384, 0.1536, 0.3456, 0.6144, 0.96 , 1.3824, 1.8816, 2.4576, 3.1104, 3.84 , 4.6464, 5.5296,
6.4896, 7.5264, 8.64 , 9.8304,11.0976,12.4416,13.8624,15.36 ,16.9344,18.5856,20.3136,22.1184});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::mean_sqerr_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3});
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(dLdpExp.isSameShape(-*dLdl));
ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test10) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {1,1}, sd::DataType::DOUBLE);
NDArray dLdwExp('c', {1,1}, std::vector<double>{188.16});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::mean_sqerr_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test11) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE);
NDArray dLdwExp('c', {1,3,1}, {33.6384 ,59.4432 ,95.07841});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::mean_sqerr_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test12) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {0.,0.,0.,0., -0.24 ,-0.288,-0.336,-0.384,-0.432,-0.48 ,-0.528,-0.576,
-0.624,-0.672,-0.72 ,-0.768,-0.816,-0.864,-0.912,-0.96 ,-1.008,-1.056,-1.104,-1.152});
NDArray dLdwExp('c', {2,3,4}, {0.04608, 0.18432, 0.41472, 0.73728, 1.152 , 1.65888, 2.25792, 2.94912, 3.73248, 4.608 , 5.57568, 6.63552,
7.78752, 9.03168,10.368 ,11.79648,13.31712,14.92992,16.63488,18.432 ,20.32128,22.30272,24.37632,26.54208});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
weights.r<double>(0) = 0.;
weights.r<double>(1) = 0.;
weights.r<double>(2) = 0.;
weights.r<double>(3) = 0.;
sd::ops::mean_sqerr_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3});
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(dLdpExp.isSameShape(-*dLdl));
ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test13) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,1}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
-1.04,-1.12,-1.2 ,-1.28,-1.36,-1.44,-1.52,-1.6 ,-1.68,-1.76,-1.84,-1.92});
NDArray dLdwExp('c', {2,3,1}, {2.304 , 13.3632 , 34.2528 , 64.97279,105.5232 ,155.90401});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
weights.r<double>(0) = 0.;
weights.r<double>(1) = 0.;
weights.r<double>(2) = 0.;
sd::ops::mean_sqerr_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3});
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(dLdpExp.isSameShape(-*dLdl));
ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl));
}
TEST_F(DeclarableOpsTests11, SquaredSubtractTest_Test1) {
auto x = NDArrayFactory::create<float>('c', {4}, {0, 1, 2, 3});
auto y = NDArrayFactory::create<float>('c',{4}, {3, 2, 1, 0});
auto exp = NDArrayFactory::create<float>('c', {4}, {9, 1,1, 9});
sd::ops::squaredsubtract op;
auto result = op.evaluate({&x, &y}, {}, {});
ASSERT_EQ(Status::OK(), result.status());
ASSERT_TRUE(exp.equalsTo(result.at(0)));
}
TEST_F(DeclarableOpsTests11, SquaredSubtractTest_Test2) {
auto x = NDArrayFactory::create<float>('c', {2, 4}, {0, 1, 2, 3, 0, 1, 2, 3});
auto y = NDArrayFactory::create<float>('c',{4}, {3, 2, 1, 0});
auto exp = NDArrayFactory::create<float>('c', {2, 4}, {9, 1,1, 9, 9, 1, 1, 9});
sd::ops::squaredsubtract op;
auto result = op.evaluate({&x, &y}, {}, {});
ASSERT_EQ(Status::OK(), result.status());
ASSERT_TRUE(exp.equalsTo(result.at(0)));
}
TEST_F(DeclarableOpsTests11, SquaredSubtractTest_Test3) {
auto x = NDArrayFactory::create<float>('c', {2, 4}, {0, 1, 2, 3, 0, 1, 2, 3});
auto y = NDArrayFactory::create<float>('c',{4}, {3, 2, 1, 0});
auto exp = NDArrayFactory::create<float>('c', {2, 4}, {-6, -4, 6, 24, -30, -12, 14, 48});
auto eps = NDArrayFactory::create<float>('c', {2, 4}, {1,2,3,4,5,6,7,8});
sd::ops::squaredsubtract_bp op;
auto result = op.evaluate({&x, &y, &eps}, {}, {});
ASSERT_EQ(Status::OK(), result.status());
ASSERT_TRUE(exp.equalsTo(result.at(0)));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test1) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,
-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5});
NDArray dLdwExp('c', {2,3,4}, {0.96, 1.92, 2.88, 3.84, 4.8 , 5.76, 6.72, 7.68, 8.64, 9.6 ,10.56,11.52,
12.48,13.44,14.4 ,15.36,16.32,17.28,18.24,19.2 ,20.16,21.12,22.08,23.04});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::absolute_difference_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0});
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(dLdpExp.isSameShape(-*dLdl));
ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test2) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,1,4}, sd::DataType::DOUBLE);
NDArray dLdwExp('c', {2,1,4}, {14.4 , 17.28, 20.16, 23.04, 48.96, 51.84, 54.72, 57.6});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::absolute_difference_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test3) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights(sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,
-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5});
NDArray dLdwExp('c', {}, std::vector<double>{288.});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::absolute_difference_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {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(dLdpExp.isSameShape(-*dLdl));
ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test4) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE);
NDArray dLdwExp('c', {1,3,1}, {65.28, 96., 126.72001});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::absolute_difference_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test5) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,
-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167});
NDArray dLdwExp('c', {2,3,4}, {-0.92,-0.84,-0.76,-0.68,-0.6 ,-0.52,-0.44,-0.36,-0.28,-0.2 ,-0.12,-0.04,
0.04, 0.12, 0.2 , 0.28, 0.36, 0.44, 0.52, 0.6 , 0.68, 0.76, 0.84, 0.92});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::absolute_difference_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {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(dLdpExp.isSameShape(-*dLdl));
ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test6) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE);
NDArray dLdwExp('c', {1,3,1}, {-2.56, 0., 2.56});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::absolute_difference_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test7) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights(sd::DataType::DOUBLE);
NDArray dLdwExp('c', {}, std::vector<double>{0.});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::absolute_difference_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test8) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {-0. ,-0. ,-0. ,-0. ,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,
-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05});
NDArray dLdwExp('c', {2,3,4}, {-1.296,-1.2 ,-1.104,-1.008,-0.912,-0.816,-0.72 ,-0.624,-0.528,-0.432,-0.336,-0.24 ,
-0.144,-0.048, 0.048, 0.144, 0.24 , 0.336, 0.432, 0.528, 0.624, 0.72 , 0.816, 0.912});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
weights.p(0, 0.);
weights.p(1, 0.);
weights.p(2, 0.);
weights.p(3, 0.);
sd::ops::absolute_difference_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {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(dLdpExp.isSameShape(-*dLdl));
ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test9) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {-0.02083, -0.02083, -0.02083, -0.02083,-0.02083, -0.02083, -0.02083, -0.02083,-0.02083, -0.02083, -0.02083, -0.02083,
-0.02083, -0.02083, -0.02083, -0.02083,-0.02083, -0.02083, -0.02083, -0.02083,-0.02083, -0.02083, -0.02083, -0.02083});
NDArray dLdwExp('c', {2,3,4}, {0.04, 0.08, 0.12, 0.16, 0.2 , 0.24, 0.28, 0.32,0.36, 0.4 , 0.44, 0.48,
0.52, 0.56, 0.6 , 0.64,0.68, 0.72, 0.76, 0.8 ,0.84, 0.88, 0.92, 0.96});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::absolute_difference_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3});
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(dLdpExp.isSameShape(-*dLdl));
ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test10) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {1,1}, sd::DataType::DOUBLE);
NDArray dLdwExp('c', {1,1}, std::vector<double>{12.});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::absolute_difference_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test11) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE);
NDArray dLdwExp('c', {1,3,1}, {2.72, 4., 5.28});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::absolute_difference_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test12) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {0., 0., 0., 0., -0.025, -0.025, -0.025, -0.025,-0.025, -0.025, -0.025, -0.025,
-0.025, -0.025, -0.025, -0.025,-0.025, -0.025, -0.025, -0.025,-0.025, -0.025, -0.025, -0.025});
NDArray dLdwExp('c', {2,3,4}, {0.048, 0.096, 0.144, 0.192,0.24 , 0.288, 0.336, 0.384,0.432, 0.48 , 0.528, 0.576,
0.624, 0.672, 0.72 , 0.768,0.816, 0.864, 0.912, 0.96 ,1.008, 1.056, 1.104, 1.152});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
weights.r<double>(0) = 0.;
weights.r<double>(1) = 0.;
weights.r<double>(2) = 0.;
weights.r<double>(3) = 0.;
sd::ops::absolute_difference_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3});
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(dLdpExp.isSameShape(-*dLdl));
ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test13) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,1}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {0., 0., 0., 0., 0., 0., 0., 0.,0., 0., 0., 0.,
-0.04167, -0.04167, -0.04167, -0.04167,-0.04167, -0.04167, -0.04167, -0.04167,-0.04167, -0.04167, -0.04167, -0.04167});
NDArray dLdwExp('c', {2,3,1}, {0.8 ,2.08,3.36,4.64,5.92,7.2 });
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
weights.r<double>(0) = 0.;
weights.r<double>(1) = 0.;
weights.r<double>(2) = 0.;
sd::ops::absolute_difference_loss_grad op;
auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3});
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(dLdpExp.isSameShape(-*dLdl));
ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, BFloat16_Test_1) {
NDArray x = NDArrayFactory::create<bfloat16>('c', {2,3,4});
NDArray y = NDArrayFactory::create<bfloat16>('c', {2,3,4});//('c', {2,3,4}, sd::DataType::BFLOAT16);
NDArray exp = NDArrayFactory::create<bfloat16>('c', {2,3,4});//('c', {2,3,4}, sd::DataType::BFLOAT16);
x.linspace(1);
y.linspace(1);
exp.linspace(2,2);
sd::ops::add op;
auto results = op.evaluate({&x, &y}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto res = results.at(0);
ASSERT_TRUE(res->equalsTo(exp));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, BFloat16_Test_2) {
NDArray x = NDArrayFactory::create<float16>('c', {2,3,4});
NDArray y = NDArrayFactory::create<bfloat16>('c', {2,3,4});//('c', {2,3,4}, sd::DataType::BFLOAT16);
NDArray exp = NDArrayFactory::create<float16>('c', {2,3,4});//('c', {2,3,4}, sd::DataType::BFLOAT16);
x.linspace(1);
y.linspace(1);
exp.linspace(2,2);
sd::ops::add op;
auto results = op.evaluate({&x, &y}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto res = results.at(0);
ASSERT_TRUE(res->equalsTo(exp));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, BFloat16_Test_3) {
NDArray x('c', {2,3,4}, sd::DataType::BFLOAT16);
NDArray y('c', {2,3,4}, sd::DataType::BFLOAT16);
NDArray exp('c', {2,3,4}, sd::DataType::BFLOAT16);
x.linspace(1);
y.linspace(1);
exp.linspace(2,2);
sd::ops::add op;
auto results = op.evaluate({&x, &y}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto res = results.at(0);
ASSERT_TRUE(res->equalsTo(exp));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test1) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {-0.25999, -0.755 , -1.25 , -1.745 , -2.24001, -2.73502, -3.23004, -3.72508, -4.22014, -4.71523, -5.21034, -5.70548,
-6.20066, -6.69587, -7.19113, -7.68643, -8.18177, -8.67717, -9.17262, -9.66813,-10.1637 ,-10.65932,-11.15501,-11.65077});
NDArray dLdwExp('c', {2,3,4}, {0.73395, 0.75335, 0.69315, 0.55335, 0.33395, 0.03495, -0.34366, -0.80186, -1.33967, -1.95708, -2.65411, -3.43074,
-4.28698, -5.22285, -6.23833, -7.33343, -8.50815, -9.76251,-11.0965 ,-12.51013,-14.00341,-15.57633,-17.2289 ,-18.96113});
NDArray dLdlExp('c', {2,3,4}, {0.04, 0.02,-0. ,-0.02,-0.04,-0.06,-0.08,-0.1 ,-0.12,-0.14,-0.16,-0.18,
-0.2 ,-0.22,-0.24,-0.26,-0.28,-0.3 ,-0.32,-0.34,-0.36,-0.38,-0.4 ,-0.42});
logits.linspace(-0.08, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::sigm_cross_entropy_loss_grad op;
auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {0});
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));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test2) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,1,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {-0.18499,-0.53 ,-0.875 ,-1.22 ,-1.56501,-1.91002,-2.25504,-2.60008,-2.94514,-3.29023,-3.63534,-3.98048,
-4.32566,-4.67087,-5.01613,-5.36143,-5.70677,-6.05217,-6.39762,-6.74313,-7.0887 ,-7.43432,-7.78001,-8.12577});
NDArray dLdwExp('c', {2,1,4}, {0.43622, -0.19079, -0.98462, -1.94525,-18.09855,-20.72768,-23.52373,-26.48669});
NDArray dLdlExp('c', {2,3,4}, {0.028, 0.014, -0. , -0.014,-0.028, -0.042, -0.056, -0.07 ,-0.084, -0.098, -0.112, -0.126,
-0.14 , -0.154, -0.168, -0.182,-0.196, -0.21 , -0.224, -0.238,-0.252, -0.266, -0.28 , -0.294});
logits.linspace(-0.08, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::sigm_cross_entropy_loss_grad op;
auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {0});
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));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test3) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights(sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {-0.18499,-0.53 ,-0.875 ,-1.22 ,-1.56501,-1.91002,-2.25504,-2.60008,-2.94514,-3.29023,-3.63534,-3.98048,
-4.32566,-4.67087,-5.01613,-5.36143,-5.70677,-6.05217,-6.39762,-6.74313,-7.0887 ,-7.43432,-7.78001,-8.12577});
NDArray dLdwExp('c', {}, std::vector<double>{-91.52109});
NDArray dLdlExp('c', {2,3,4}, {0.028, 0.014, -0., -0.014,-0.028, -0.042, -0.056, -0.07 ,-0.084, -0.098, -0.112, -0.126,
-0.14 , -0.154, -0.168, -0.182,-0.196, -0.21 , -0.224, -0.238,-0.252, -0.266, -0.28 , -0.294});
logits.linspace(-0.08, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::sigm_cross_entropy_loss_grad op;
auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {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));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test4) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE);
NDArray dLdwExp('c', {1,3,1}, {-12.54779,-28.13393,-50.83936});
logits.linspace(-0.08, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::sigm_cross_entropy_loss_grad op;
auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test5) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {-0.01542,-0.04417,-0.07292,-0.10167,-0.13042,-0.15917,-0.18792,-0.21667,-0.24543,-0.27419,-0.30294,-0.33171,
-0.36047,-0.38924,-0.41801,-0.44679,-0.47556,-0.50435,-0.53314,-0.56193,-0.59072,-0.61953,-0.64833,-0.67715});
NDArray dLdwExp('c', {2,3,4}, {0.37794, 0.37906, 0.37554, 0.36739, 0.35461, 0.33719, 0.31514, 0.28846, 0.25714, 0.22119, 0.18061, 0.13539,
0.08553, 0.03104,-0.02808,-0.09184,-0.16023,-0.23326,-0.31093,-0.39323,-0.48017,-0.57175,-0.66796,-0.76881});
NDArray dLdlExp('c', {2,3,4}, {0.00233, 0.00117,-0.,-0.00117,-0.00233,-0.0035 ,-0.00467,-0.00583,-0.007 ,-0.00817,-0.00933,-0.0105,
-0.01167,-0.01283,-0.014 ,-0.01517,-0.01633,-0.0175 ,-0.01867,-0.01983,-0.021 ,-0.02217,-0.02333,-0.0245});
logits.linspace(-0.08, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::sigm_cross_entropy_loss_grad op;
auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {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));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test6) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE);
NDArray dLdwExp('c', {1,3,1}, {1.4966 , 0.19776,-1.69436});
logits.linspace(-0.08, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::sigm_cross_entropy_loss_grad op;
auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test7) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights(sd::DataType::DOUBLE);
NDArray dLdwExp('c', {}, std::vector<double>{0.});
logits.linspace(-0.08, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::sigm_cross_entropy_loss_grad op;
auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test8) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, { 0. , 0. , 0. , 0. ,-0.1565 ,-0.191 ,-0.2255 ,-0.26001,-0.29451,-0.32902,-0.36353,-0.39805,
-0.43257,-0.46709,-0.50161,-0.53614,-0.57068,-0.60522,-0.63976,-0.67431,-0.70887,-0.74343,-0.778 ,-0.81258});
NDArray dLdwExp('c', {2,3,4}, {0.54353, 0.54487, 0.54065, 0.53087, 0.51553, 0.49463, 0.46817, 0.43615, 0.39857, 0.35543, 0.30672, 0.25246,
0.19264, 0.12725, 0.0563 ,-0.02021,-0.10228,-0.18992,-0.28312,-0.38188,-0.48621,-0.5961 ,-0.71156,-0.83258});
NDArray dLdlExp('c', {2,3,4}, {-0. ,-0. , 0. , 0. ,-0.0028,-0.0042,-0.0056,-0.007 ,-0.0084,-0.0098,-0.0112,-0.0126,
-0.014 ,-0.0154,-0.0168,-0.0182,-0.0196,-0.021 ,-0.0224,-0.0238,-0.0252,-0.0266,-0.028 ,-0.0294});
logits.linspace(-0.08, 0.04);
labels.linspace(1);
weights.assign(0.5);
weights.p(0, 0.);
weights.p(1, 0.);
weights.p(2, 0.);
weights.p(3, 0.);
sd::ops::sigm_cross_entropy_loss_grad op;
auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {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));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test9) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {-0.00771, -0.02208, -0.03646, -0.05083,-0.06521, -0.07958, -0.09396, -0.10834,-0.12271, -0.13709, -0.15147, -0.16585,
-0.18024, -0.19462, -0.20901, -0.22339,-0.23778, -0.25217, -0.26657, -0.28096,-0.29536, -0.30976, -0.32417, -0.33857});
NDArray dLdwExp('c', {2,3,4}, {0.03008, 0.03064, 0.02888, 0.02481, 0.01841, 0.00971, -0.00132, -0.01466,-0.03032, -0.0483 , -0.06859, -0.0912 ,
-0.11612, -0.14337, -0.17293, -0.20481,-0.23901, -0.27552, -0.31435, -0.35551,-0.39898, -0.44476, -0.49287, -0.5433 });
NDArray dLdlExp('c', {2,3,4}, {0.00117, 0.00058, -0. , -0.00058,-0.00117, -0.00175, -0.00233, -0.00292,-0.0035 , -0.00408, -0.00467, -0.00525,
-0.00583, -0.00642, -0.007 , -0.00758,-0.00817, -0.00875, -0.00933, -0.00992,-0.0105 , -0.01108, -0.01167, -0.01225});
logits.linspace(-0.08, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::sigm_cross_entropy_loss_grad op;
auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {3});
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));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test10) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {1,1}, sd::DataType::DOUBLE);
NDArray dLdwExp('c', {1,1}, std::vector<double>{-3.81338});
logits.linspace(-0.08, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::sigm_cross_entropy_loss_grad op;
auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test11) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE);
NDArray dLdwExp('c', {1,3,1}, {-0.52282,-1.17225,-2.11831});
logits.linspace(-0.08, 0.04);
labels.linspace(1);
weights.assign(0.5);
sd::ops::sigm_cross_entropy_loss_grad op;
auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdw = results.at(1);
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test12) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {0. , 0. , 0. , 0. ,-0.07825, -0.0955 , -0.11275, -0.13 ,-0.14726, -0.16451, -0.18177, -0.19902,
-0.21628, -0.23354, -0.25081, -0.26807,-0.28534, -0.30261, -0.31988, -0.33716,-0.35443, -0.37172, -0.389 , -0.40629});
NDArray dLdwExp('c', {2,3,4}, {0.0361 , 0.03677, 0.03466, 0.02977, 0.0221 , 0.01165, -0.00158, -0.01759,-0.03638, -0.05795, -0.08231, -0.10944,
-0.13935, -0.17204, -0.20752, -0.24577,-0.28681, -0.33063, -0.37723, -0.42661,-0.47877, -0.53372, -0.59144, -0.65196});
NDArray dLdlExp('c', {2,3,4}, {-0. , -0. , 0. , 0. ,-0.0014, -0.0021, -0.0028, -0.0035,-0.0042, -0.0049, -0.0056, -0.0063,
-0.007 , -0.0077, -0.0084, -0.0091,-0.0098, -0.0105, -0.0112, -0.0119,-0.0126, -0.0133, -0.014 , -0.0147});
logits.linspace(-0.08, 0.04);
labels.linspace(1);
weights.assign(0.5);
weights.r<double>(0) = 0.;
weights.r<double>(1) = 0.;
weights.r<double>(2) = 0.;
weights.r<double>(3) = 0.;
sd::ops::sigm_cross_entropy_loss_grad op;
auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {3});
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));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test13) {
NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2,3,1}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
-0.36047, -0.38924, -0.41801, -0.44679,-0.47556, -0.50435, -0.53314, -0.56193,-0.59072, -0.61953, -0.64833, -0.67715});
NDArray dLdwExp('c', {2,3,1}, {0.22882, 0.02428,-0.4768 ,-1.27447,-2.36878,-3.75981,});
NDArray dLdlExp('c', {2,3,4}, {-0. , -0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.,
-0.01167, -0.01283, -0.014 , -0.01517,-0.01633, -0.0175 , -0.01867, -0.01983,-0.021 , -0.02217, -0.02333, -0.0245});
logits.linspace(-0.08, 0.04);
labels.linspace(1);
weights.assign(0.5);
weights.r<double>(0) = 0.;
weights.r<double>(1) = 0.;
weights.r<double>(2) = 0.;
sd::ops::sigm_cross_entropy_loss_grad op;
auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {3});
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));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, BFloat16_Test_4) {
NDArray x = NDArrayFactory::create<float>('c', {2,3,4});
NDArray y = NDArrayFactory::create<bfloat16>('c', {2,3,4});//('c', {2,3,4}, sd::DataType::BFLOAT16);
NDArray exp = NDArrayFactory::create<float>('c', {2,3,4});//('c', {2,3,4}, sd::DataType::BFLOAT16);
x.linspace(1);
y.linspace(1);
exp.linspace(2,2);
sd::ops::add op;
auto results = op.evaluate({&x, &y}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto res = results.at(0);
ASSERT_TRUE(res->equalsTo(exp));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, BFloat16_Test_5) {
NDArray x = NDArrayFactory::create<float>('c', {2,3,4});
NDArray y = NDArrayFactory::create<bfloat16>('c', {2,3,4});//('c', {2,3,4}, sd::DataType::BFLOAT16);
NDArray exp = NDArrayFactory::create<float>('c', {2,3,4});//('c', {2,3,4}, sd::DataType::BFLOAT16);
x.linspace(2, 2);
y.linspace(1);
exp.linspace(1);
sd::ops::subtract op;
auto results = op.evaluate({&x, &y}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto res = results.at(0);
ASSERT_TRUE(res->equalsTo(exp));
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, BFloat16_Test_6) {
NDArray x = NDArrayFactory::create<bfloat16>('c', {2,3,4});
NDArray y = NDArrayFactory::create<double>('c', {2,3,4});//('c', {2,3,4}, sd::DataType::BFLOAT16);
NDArray exp = NDArrayFactory::create<bfloat16>('c', {2,3,4});//('c', {2,3,4}, sd::DataType::BFLOAT16);
x.linspace(2, 2);
y.linspace(1);
exp.linspace(1);
sd::ops::subtract op;
auto results = op.evaluate({&x, &y}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto res = results.at(0);
ASSERT_TRUE(res->equalsTo(exp));
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test1) {
NDArray labels('c', {2,4}, {0,0,1,0, 0,1,0,0}, sd::DataType::INT32);
NDArray logits('c', {2,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,4}, {0.1176, 0.1224, -0.3726, 0.1326, 0.1176, -0.3776, 0.1274, 0.1326});
NDArray dLdwExp('c', {2}, {1.36729, 1.40729});
logits.linspace(-0.08, 0.04);
weights.assign(0.5);
sd::ops::softmax_cross_entropy_loss_grad op;
auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {0});
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));
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test2) {
NDArray labels('c', {4}, {0,0,1,0}, sd::DataType::INT32);
NDArray logits('c', {4}, sd::DataType::DOUBLE);
NDArray weights('c', {1}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {4}, {0.125, 0.125, -0.375, 0.125});
NDArray dLdwExp('c', {1}, std::vector<double>{1.38629});
logits = 2.;
weights.assign(0.5);
sd::ops::softmax_cross_entropy_loss_grad op;
auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {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));
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test3) {
NDArray labels('c', {4}, {0,0,1,0}, sd::DataType::INT32);
NDArray logits('c', {4}, sd::DataType::DOUBLE);
NDArray weights('c', {}, std::vector<double>{0}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {4}, {0.125, 0.125, -0.375, 0.125});
NDArray dLdwExp('c', {}, std::vector<double>{1.38629});
logits = 2.;
weights.assign(0.5);
sd::ops::softmax_cross_entropy_loss_grad op;
auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {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));
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test4) {
NDArray labels('c', {4}, {0,0,1,0}, sd::DataType::INT32);
NDArray logits('c', {4}, sd::DataType::DOUBLE);
NDArray weights('c', {}, std::vector<double>{0}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {4}, {0.23521, 0.2448 , -0.7452 , 0.26519});
NDArray dLdwExp('c', {}, std::vector<double>{0.});
logits.linspace(-0.08, 0.04);
weights = 0.5;
sd::ops::softmax_cross_entropy_loss_grad op;
auto results = op.evaluate({&logits, &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));
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test5) {
NDArray labels('c', {4}, {0,0,1,0}, sd::DataType::INT32);
NDArray logits('c', {4}, sd::DataType::DOUBLE);
NDArray weights('c', {1}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {4}, {0.1176, 0.1224, -0.3726, 0.1326});
NDArray dLdwExp('c', {1}, std::vector<double>{1.36729});
logits.linspace(-0.08, 0.04);
weights = 0.5;
sd::ops::softmax_cross_entropy_loss_grad op;
auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {3});
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));
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test6) {
NDArray labels('c', {2,4}, {0,0,1,0, 0,1,0,0}, sd::DataType::INT32);
NDArray logits('c', {2,4}, sd::DataType::DOUBLE);
NDArray weights('c', {2}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,4}, {0.0801, 0.0849, -0.2601, 0.0951, 0.0801, -0.2651, 0.0899, 0.0951});
NDArray dLdwExp('c', {2}, {-0.014000, 0.014000});
logits.linspace(-0.08, 0.04);
weights.assign(0.5);
sd::ops::softmax_cross_entropy_loss_grad op;
auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {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));
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test7) {
NDArray labels('c', {2,3,4}, {1,0,0,0, 0,1,0,0, 0,0,1,0, 0,0,0,1, 1,0,0,0, 0,1,0,0}, sd::DataType::INT32);
NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray weights('c', {1,3}, {0.5, 0., 1.5});
NDArray dLdpExp('c', {2,3,4}, {-0.0956 , 0.0306 , 0.03185, 0.03315, 0.,-0., 0., 0., 0.0882 , 0.0918 ,-0.27945, 0.09945,
0.0294 , 0.0306 , 0.03185,-0.09185,-0., 0., 0., 0., 0.0882 ,-0.2832 , 0.09555, 0.09945});
NDArray dLdwExp('c', {1,3}, {0.69365, 0.71365, 0.69365});
logits.linspace(-0.08, 0.04);
sd::ops::softmax_cross_entropy_loss_grad op;
auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {3});
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));
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test8) {
NDArray labels('c', {2,3,4,5}, {1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,
0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,
0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0}, sd::DataType::INT32);
NDArray logits('c', {2,3,4,5}, sd::DataType::DOUBLE);
NDArray weights('c', {1,1,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4,5}, {-0.03399, 0.00799, 0.00832, 0.00866, 0.00901, 0.00768,-0.03367, 0.00832, 0.00866, 0.00901, 0.00768, 0.00799,-0.03335,
0.00866, 0.00901, 0.00768, 0.00799, 0.00832,-0.03301, 0.00901, 0.00768, 0.00799, 0.00832, 0.00866,-0.03265,-0.03399,
0.00799, 0.00832, 0.00866, 0.00901, 0.00768,-0.03367, 0.00832, 0.00866, 0.00901, 0.00768, 0.00799,-0.03335, 0.00866,
0.00901, 0.00768, 0.00799, 0.00832,-0.03301, 0.00901, 0.00768, 0.00799, 0.00832, 0.00866,-0.03265,-0.03399, 0.00799,
0.00832, 0.00866, 0.00901, 0.00768,-0.03367, 0.00832, 0.00866, 0.00901, 0.00768, 0.00799,-0.03335, 0.00866, 0.00901,
0.00768, 0.00799, 0.00832,-0.03301, 0.00901, 0.00768, 0.00799, 0.00832, 0.00866,-0.03265,-0.03399, 0.00799, 0.00832,
0.00866, 0.00901, 0.00768,-0.03367, 0.00832, 0.00866, 0.00901, 0.00768, 0.00799,-0.03335, 0.00866, 0.00901, 0.00768,
0.00799, 0.00832,-0.03301, 0.00901, 0.00768, 0.00799, 0.00832, 0.00866,-0.03265,-0.03399, 0.00799, 0.00832, 0.00866,
0.00901, 0.00768,-0.03367, 0.00832, 0.00866, 0.00901, 0.00768, 0.00799,-0.03335, 0.00866, 0.00901, 0.00768, 0.00799, 0.00832,-0.03301, 0.00901});
NDArray dLdwExp('c', {1,1,4}, {0.005, 0.00167, -0.00167, -0.005});
logits.linspace(-0.08, 0.04);
weights.assign(0.5);
sd::ops::softmax_cross_entropy_loss_grad op;
auto results = op.evaluate({&logits, &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);
// dLdp->printIndexedBuffer();
// ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
// ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, SafeDivideMixed_Test1) {
NDArray labels('c', {2, 3}, {1.0, 2.0, 3.0, -1.0, 2.0, 1.0});
auto sumDiff = labels.reduceAlongDimension(reduce::Sum, {1}, true);
NDArray numOfNonZero(sumDiff.shapeInfo(), sd::DataType::INT64, false);
numOfNonZero.assign(1);
sumDiff.applyPairwiseTransform(pairwise::SafeDivide, numOfNonZero, sumDiff);
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test1) {
NDArray labels('c', {2,3,4}, {1,0,0,0, 0,1,0,0, 0,0,1,0, 0,0,0,1, 1,0,0,0, 0,1,0,0});
NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {-0.76479, 0.2448, 0.2548, 0.26519, 0.23521,-0.7552, 0.2548, 0.26519, 0.23521, 0.2448,-0.7452, 0.26519,
0.23521, 0.2448, 0.2548,-0.73481,-0.76479, 0.2448, 0.2548, 0.26519, 0.23521,-0.7552, 0.2548, 0.26519});
logits.linspace(-0.08, 0.04);
sd::ops::softmax_cross_entropy_loss_with_logits_grad op;
auto results = op.evaluate({&logits, &labels}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdp = results.at(0);
ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test2) {
NDArray labels('c', {2,3,4}, {1,0,0,0, 0,1,0,1, 0,0,1,0, 0,0,0,1, 1,0,1,0, 0,1,0,0});
NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {-0.71836, 0.28164, 0.28164, 0.28164, 0.33051, -0.66949, 0.33051, -0.66949, 0.38785, 0.38785, -0.61215, 0.38785,
0.28164, 0.28164, 0.28164, -0.71836,-0.66949, 0.33051, -0.66949, 0.33051, 0.38785, -0.61215, 0.38785, 0.38785});
logits.linspace(-0.08, 0.04);
sd::ops::softmax_cross_entropy_loss_with_logits_grad op;
auto results = op.evaluate({&logits, &labels}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdp = results.at(0);
ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test3) {
NDArray labels('c', {2,3}, {1,0,0, 0,1,1});
NDArray logits('c', {2,3}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3}, {-0.52996, 0.47004, 0.47004, 0.52996, -0.47004, -0.47004});
logits.linspace(-0.08, 0.04);
sd::ops::softmax_cross_entropy_loss_with_logits_grad op;
auto results = op.evaluate({&logits, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdp = results.at(0);
ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test4) {
NDArray labels('c', {2,1}, {1,1});
NDArray logits('c', {2,1}, {-0.04, 0.04});
NDArray dLdpExp('c', {2,1}, {0., 0.});
sd::ops::softmax_cross_entropy_loss_with_logits_grad op;
auto results = op.evaluate({&logits, &labels}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdp = results.at(0);
ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test5) {
NDArray labels('c', {2,1}, std::vector<double>{1,0});
NDArray logits('c', {2,1}, {-0.04, 0.04});
NDArray dLdpExp('c', {2,1}, {-0.51999, 0.51999});
sd::ops::softmax_cross_entropy_loss_with_logits_grad op;
auto results = op.evaluate({&logits, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdp = results.at(0);
ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test6) {
NDArray labels('c', {1,2}, {1,1.});
NDArray logits('c', {1,2}, {-0.04, 0.04});
NDArray dLdpExp('c', {1,2}, {0, 0.});
sd::ops::softmax_cross_entropy_loss_with_logits_grad op;
auto results = op.evaluate({&logits, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdp = results.at(0);
ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test7) {
NDArray labels('c', {2}, {0,1});
NDArray logits('c', {2}, {-0.04, 0.04});
NDArray dLdpExp('c', {2}, {0.48001, -0.48001});
sd::ops::softmax_cross_entropy_loss_with_logits_grad op;
auto results = op.evaluate({&logits, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdp = results.at(0);
ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test8) {
NDArray labels('c', {1}, std::vector<double>{1});
NDArray logits('c', {1}, std::vector<double>{0.04});
NDArray dLdpExp('c', {1}, std::vector<double>{0});
sd::ops::softmax_cross_entropy_loss_with_logits_grad op;
auto results = op.evaluate({&logits, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdp = results.at(0);
ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, Multiply_BP_Test1) {
NDArray x('c', {3,4,5}, sd::DataType::DOUBLE);
NDArray y('c', {1,1,1}, sd::DataType::DOUBLE);
NDArray dLdp('c', {3,4,5}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {3,4,5}, sd::DataType::DOUBLE);
x.assign(1.0);//linspace(0.1, 0.1);
y.assign(1.0);
dLdp.assign(1.0);
dLdpExp.assign(1.0);
sd::ops::multiply_bp op;
auto results = op.evaluate({&x, &y, &dLdp}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdo = results.at(0);
ASSERT_TRUE(dLdpExp.isSameShape(dLdo));
ASSERT_TRUE(dLdpExp.equalsTo(dLdo));
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, sparseSoftmaxCrossEntropyWithLogits_grad_test1) {
NDArray labels('c', {2}, {2,1}, sd::DataType::INT64);
NDArray logits('c', {2,3}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3}, {0.30061, 0.33222, -0.63283, 0.30061, -0.66778, 0.36717});
logits.linspace(0.1, 0.1);
sd::ops::sparse_softmax_cross_entropy_loss_with_logits_grad op;
auto results = op.evaluate({&labels, &logits}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdp = results.at(0);
ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, sparseSoftmaxCrossEntropyWithLogits_grad_test2) {
NDArray labels('c', {2}, {0,1}, sd::DataType::INT64);
NDArray logits('c', {2,3}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3}, {-0.69939, 0.33222, 0.36717, 0.30061, -0.66778, 0.36717});
logits.linspace(-0.1, 0.1);
sd::ops::sparse_softmax_cross_entropy_loss_with_logits_grad op;
auto results = op.evaluate({&labels, &logits}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdp = results.at(0);
ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, sparseSoftmaxCrossEntropyWithLogits_grad_test3) {
NDArray labels('c', {}, std::vector<double>{1}, sd::DataType::INT64);
NDArray logits('c', {2}, {-0.2, 0.3});
NDArray dLdpExp('c', {2}, {0.37754, -0.37754});
sd::ops::sparse_softmax_cross_entropy_loss_with_logits_grad op;
auto results = op.evaluate({&labels, &logits}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdp = results.at(0);
ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, sparseSoftmaxCrossEntropyWithLogits_grad_test4) {
NDArray labels('c', {2,3}, {0,1,1, 3,3,2}, sd::DataType::INT64);
NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE);
NDArray dLdpExp('c', {2,3,4}, {-0.78616, 0.23633, 0.26118, 0.28865, 0.21384, -0.76367, 0.26118, 0.28865, 0.21384, -0.76367, 0.26118, 0.28865,
0.21384, 0.23633, 0.26118, -0.71135, 0.21384, 0.23633, 0.26118, -0.71135, 0.21384, 0.23633, -0.73882, 0.28865});
logits.linspace(-0.5, 0.1);
sd::ops::sparse_softmax_cross_entropy_loss_with_logits_grad op;
auto results = op.evaluate({&labels, &logits}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdp = results.at(0);
ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
}
/////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests11, sparseSoftmaxCrossEntropyWithLogits_grad_test5) {
NDArray labels('c', {1,1}, std::vector<double>({0}), sd::DataType::INT64);
NDArray logits('c', {1,1,2}, {-0.3,0.2});
NDArray dLdpExp('c', {1,1,2}, {-0.62246, 0.62246});
sd::ops::sparse_softmax_cross_entropy_loss_with_logits_grad op;
auto results = op.evaluate({&labels, &logits}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, results.status());
auto *dLdp = results.at(0);
ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
}