Alex Black 68ea5f3688
Dev branch merge: dev_20190606 (#7904)
* correct logsoftmax looss (#2)

* Small SameDiff listener fix (#4)

* Various fixes (#6)

* #7839 Fix for asXMatrix and tests

* #7866 EmbeddingSequenceLayer dtype fix + test

* #7856 SameDiff save/load stream methods

* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration

* EvaluationBinary 3d/4d

* More evaluation 3d/4d tests

* #7847 Evaluation empty checks

* Small test ifx

* #7848 Fix median edge case

* Improve DL4J samediff layer tests

* [WIP] FastText wrapper implemented (#8)

* FastText implemented

* Some fixes

* Fix shapes for wordsNearest

* Validation of input vectors

* Fixes

* Fixed test

* Thread tagged

* Some tweaks

* setContextClassLoader for DeallocatorServiceThread

* Numpy format tests (#1)

* Various fixes (#11)

* #7852 SameDiff gather fix

* #7892 SameDiff placeholder to constant conversion

* #7890 validate input rank for MLN/CG init methods

* Fix broken permute shape calculation

* Permute and gather fixes

* Tests

* #7850 LogSumExp fix + test

* Handful of test fixes

* Empty arrays with non-scalar shapes (#10)

* minor rearrangements for lambdas

* empty tensors with non-scalar shapes

* numpy empty tensors with non-scalar shapes

* few more empty tweaks

* Small fixes

* conv3d signature update

* micro fix in batchnorm mkldnn

* Import fixes

* Fix

* MKL-DNN update

* Small fill fix

* fill with empty input + test

* Fixes

* Small error improvement

* Fix

* one special test

* couple of fixes for lstm

* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone

* Fixes

* FP16

* Unsigned

* BFloat16

* Fill op - empty tweaks

* - couple of fixes for empty arrays construction
- stack updated

* strided slice fix

* one transform test

* provide method for reducing shapeInfo in case of input array is empty

* Fixed reduceAlongDimensions to use empty input properly.

* couple of broadcast tests

* couple of tests broadcast tests + tweak to make them pass

* add check of non-empty to methods producing sub-arrays

* Fixed reshapeC with zeros in shape.

* complete empty check in reduce_... legacy ops

* Concat and cumsum/prod

* Tweak to empty shape inference on import

* add empty check to the rest of reduce legacy ops

* one more test

* correct typo in evalReduceShapeInfoEmpty

* Added tests for reduce_* ops to tests with zero shapes.

* few more tests for empty reductions

* Fixed strided_slice op with empty case and tests.

* one more empty reduction test

* Fixed strided_slice test.

* add empty check to NDArray::reshapei

* infOrMax

* empty min/max with infinity tests

* made unstack working correctly with empty arrays

* few IndexReduce tests + tweaks for empty shapes

* add test for empty concat

* few tests fixed

* Validation fix for reductions on empty shapes

* Reverse fix

* Reduction shape calc fixes

* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs

* Range fix

* - NDArray constructor updated for scalars/empty arrays
- few tests fixed

* More fixes

* Empty creator fixes

* concat fix

* concat fix

* TF import tests: allow 'both all NaN' and 'both all inf' to pass

* Slice, zero fraction, and reshape fixes

* transpose, gather

* Zero fraction

* scalar cast fix

* Empty reduction axis support

* few more tests fixed

* Fixed input checks conforming with TF for concat op and tests.

* few tests fixed

* matmul scalar shape fix

* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.

* broadcast bool fix

* few more tests

* few more tests

* correct evalReduceShapeInfoEmpty

* argmax/argmin + tests

* one more empty edge case + one more test

* argmax/argmin/realdiv_bp tweaks

* empty reshape test + fix

* Helper fixes

* Small fixes

* Gather test fix

* Gather test fix

* Small fixes

* reduce scalar zero values

* scalar mean workaround

* Remove debug code

* along dim mean workaround

* one more test

* - equalsTo() tweak for empty arrays
- one more test

* broadcast tweaks
2019-06-15 21:34:34 +10:00

212 lines
5.9 KiB
C++

/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// Created by raver on 6/18/2018.
//
#include "testlayers.h"
#include <ops/declarable/CustomOperations.h>
#include <NDArray.h>
// #include <array/NDArrayList.h>
using namespace nd4j;
class EmptyTests : public testing::Test {
public:
EmptyTests() {
printf("\n");
fflush(stdout);
}
};
TEST_F(EmptyTests, Test_Create_Empty_1) {
auto empty = NDArrayFactory::empty_<float>();
ASSERT_TRUE(empty->isEmpty());
ASSERT_EQ(0, empty->lengthOf());
ASSERT_TRUE(empty->buffer() == nullptr);
ASSERT_TRUE(shape::isEmpty(empty->shapeInfo()));
delete empty;
}
TEST_F(EmptyTests, Test_Create_Empty_2) {
auto empty = NDArrayFactory::empty<float>();
ASSERT_TRUE(empty.isEmpty());
ASSERT_EQ(0, empty.lengthOf());
ASSERT_TRUE(empty.buffer() == nullptr);
ASSERT_TRUE(shape::isEmpty(empty.shapeInfo()));
ASSERT_TRUE(empty.isEmpty());
}
TEST_F(EmptyTests, Test_Concat_1) {
// auto empty = NDArrayFactory::empty_<float>();
auto empty = new NDArray('c', {0}, nd4j::DataType::FLOAT32);//NDArrayFactory::create_<float>('c', {(Nd4jLong)0}};
auto vector = NDArrayFactory::create_<float>('c', {1}, {1.0f});
ASSERT_TRUE(empty->isEmpty());
nd4j::ops::concat op;
auto result = op.execute({empty, vector}, {}, {0});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
// z->printShapeInfo("z shape");
// z->printIndexedBuffer("z buffr");
ASSERT_EQ(*vector, *z);
delete empty;
delete vector;
delete result;
}
TEST_F(EmptyTests, Test_Concat_2) {
auto empty = new NDArray('c', {0}, nd4j::DataType::FLOAT32); //NDArrayFactory::empty_<float>();
auto scalar1 = NDArrayFactory::create_<float>('c', {1}, {1.0f});
auto scalar2 = NDArrayFactory::create_<float>('c', {1}, {2.0f});
auto exp = NDArrayFactory::create<float>('c', {2}, {1.f, 2.f});
ASSERT_TRUE(empty->isEmpty());
nd4j::ops::concat op;
auto result = op.execute({empty, scalar1, scalar2}, {}, {0});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
// z->printShapeInfo("z shape");
// z->printIndexedBuffer("z buffr");
ASSERT_EQ(exp, *z);
delete empty;
delete scalar1;
delete scalar2;
delete result;
}
TEST_F(EmptyTests, Test_Reshape_1) {
auto vector = NDArrayFactory::create<float>('c', {1}, {119.0f});
auto exp = NDArrayFactory::create<float>(119.f);
auto empty = NDArrayFactory::empty_<int>();
nd4j::ops::reshape op;
auto result = op.execute({&vector, empty}, {}, {});
ASSERT_EQ(Status::OK(), result->status());
ASSERT_EQ(exp, *result->at(0));
delete empty;
delete result;
}
TEST_F(EmptyTests, Test_Reshape_2) {
auto vector = NDArrayFactory::create<float>('c', {1}, {119.0f});
auto exp = NDArrayFactory::create<float>(119.0f);
auto empty = NDArrayFactory::empty_<Nd4jLong>();
nd4j::ops::reshape op;
auto result = op.execute({&vector, empty}, {}, {}, {}, true);
ASSERT_EQ(Status::OK(), result->status());
ASSERT_EQ(exp, *result->at(0));
ASSERT_EQ(exp, vector);
delete empty;
delete result;
}
TEST_F(EmptyTests, Test_Reshape_3) {
auto x = NDArrayFactory::create<float>('c', {1, 0, 0, 2});
auto y = NDArrayFactory::create<int>('c', {2}, {10, 0});
auto e = NDArrayFactory::create<float>('c', {10, 0});
nd4j::ops::reshape op;
auto result = op.execute({&x, &y}, {}, {});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_TRUE(e.isSameShape(z));
ASSERT_EQ(e, *z);
delete result;
}
TEST_F(EmptyTests, Test_dup_1) {
auto empty = NDArrayFactory::empty<int>();
auto dup = empty.dup();
ASSERT_TRUE(dup->isEmpty());
ASSERT_EQ(empty, *dup);
delete dup;
}
TEST_F(EmptyTests, test_shaped_empty_1) {
auto empty = NDArrayFactory::create<float>('c', {2, 0, 3});
std::vector<Nd4jLong> shape = {2, 0, 3};
ASSERT_EQ(nd4j::DataType::FLOAT32, empty.dataType());
ASSERT_EQ(0, empty.lengthOf());
ASSERT_TRUE(empty.isEmpty());
ASSERT_EQ(shape, empty.getShapeAsVector());
ASSERT_EQ(3, empty.rankOf());
}
TEST_F(EmptyTests, test_shaped_empty_2) {
auto empty = NDArrayFactory::create<float>('c', {0, 3});
std::vector<Nd4jLong> shape = {0, 3};
ASSERT_EQ(nd4j::DataType::FLOAT32, empty.dataType());
ASSERT_EQ(0, empty.lengthOf());
ASSERT_TRUE(empty.isEmpty());
ASSERT_EQ(shape, empty.getShapeAsVector());
ASSERT_EQ(2, empty.rankOf());
}
TEST_F(EmptyTests, test_shaped_empty_3) {
auto empty = NDArrayFactory::create<float>('c', {0});
std::vector<Nd4jLong> shape = {0};
ASSERT_EQ(nd4j::DataType::FLOAT32, empty.dataType());
ASSERT_EQ(0, empty.lengthOf());
ASSERT_TRUE(empty.isEmpty());
ASSERT_EQ(shape, empty.getShapeAsVector());
ASSERT_EQ(1, empty.rankOf());
}
TEST_F(EmptyTests, test_shaped_empty_4) {
auto shape = ConstantShapeHelper::getInstance()->vectorShapeInfo(0, nd4j::DataType::FLOAT32);
shape::printShapeInfoLinear("shape", shape);
NDArray array(shape, true, nd4j::LaunchContext::defaultContext());
std::vector<Nd4jLong> shapeOf({0});
ASSERT_TRUE(array.isEmpty());
ASSERT_EQ(1, array.rankOf());
ASSERT_EQ(shapeOf, array.getShapeAsVector());
}