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add test_Expand and simply the gserver/tests/CMakeLists #5076
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| /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. | ||
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| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
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| http://www.apache.org/licenses/LICENSE-2.0 | ||
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| 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. */ | ||
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| #include <gtest/gtest.h> | ||
| #include <string> | ||
| #include <vector> | ||
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| #include "LayerGradUtil.h" | ||
| #include "paddle/testing/TestUtil.h" | ||
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| using namespace paddle; // NOLINT | ||
| using namespace std; // NOLINT | ||
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| // Do one forward pass of expand layer and check to see if its output | ||
| // matches the given result.(Test onlyCPU currently.) | ||
| void doOneExpandTest(string trans_type, | ||
| bool hasSubseq, | ||
| bool useGpu, | ||
| Argument& input1, | ||
| Argument& input2, | ||
| Argument& result) { | ||
| FLAGS_use_gpu = false; | ||
| // Setting up the expand layer | ||
| TestConfig config; | ||
| config.layerConfig.set_type("expand"); | ||
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| auto inputType1 = | ||
| trans_type == "non-seq" ? INPUT_DENSE_DIM_DATA : INPUT_SEQUENCE_DATA; | ||
| config.inputDefs.push_back({inputType1, "layer0", 1, 0}); | ||
| auto inputType2 = | ||
| hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA; | ||
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| config.inputDefs.push_back({inputType2, "layer1", 1, 0}); | ||
| config.layerConfig.add_inputs(); | ||
| config.layerConfig.add_inputs(); | ||
| config.layerConfig.set_trans_type(trans_type); | ||
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| // data layer initialize | ||
| std::vector<DataLayerPtr> dataLayers; | ||
| LayerMap layerMap; | ||
| vector<Argument> datas; | ||
| initDataLayer( | ||
| config, &dataLayers, &datas, &layerMap, "expand", 1, false, useGpu); | ||
| dataLayers[0]->getOutput() = input1; | ||
| dataLayers[1]->getOutput() = input2; | ||
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| // test layer initialize | ||
| std::vector<ParameterPtr> parameters; | ||
| LayerPtr expandLayer; | ||
| initTestLayer(config, &layerMap, ¶meters, &expandLayer); | ||
| expandLayer->forward(PASS_GC); | ||
| checkMatrixEqual(expandLayer->getOutputValue(), result.value); | ||
| } | ||
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| TEST(Layer, ExpandLayerFwd) { | ||
| bool useGpu = false; | ||
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| // Assume batch_size =3 in all cases. | ||
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| // CPU case 1. non-seq expand to seq | ||
| // input1 = 1,2,3 | ||
| // input2 = [4,5],[6],[7,8,9] | ||
| // result = [1,1],[2],[3,3,3] | ||
| Argument input1, input2, result; | ||
| input1.value = Matrix::create(3, 1, false, useGpu); | ||
| real input1Data[] = {1, 2, 3}; | ||
| input1.value->setData(input1Data); | ||
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| input2.value = Matrix::create(6, 1, false, useGpu); | ||
| real input2Data[] = {4, 5, 6, 7, 8, 9}; | ||
| input2.value->setData(input2Data); | ||
| input2.sequenceStartPositions = ICpuGpuVector::create(4, useGpu); | ||
| int input2Seq[] = {0, 2, 3, 6}; | ||
| input2.sequenceStartPositions->copyFrom(input2Seq, 4, useGpu); | ||
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| result.value = Matrix::create(6, 1, false, useGpu); | ||
| real resultData[] = {1, 1, 2, 3, 3, 3}; | ||
| result.value->setData(resultData); | ||
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| doOneExpandTest("non-seq", false, useGpu, input1, input2, result); | ||
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| // CPU case 2. non-seq expand to sub-seq | ||
| // NOTE: input1.batch_size == input2.sequencelength in this case. | ||
| // i.e, input1 expands by input2.sequence | ||
| // input1 = 1,2,3 | ||
| // input2 = [[4,5]],[[6]],[[7],[8,9]] | ||
| // result = [[1,1]],[[2]],[[3],[3,3]] | ||
| input2.subSequenceStartPositions = ICpuGpuVector::create(5, useGpu); | ||
| int input2SubSeq[] = {0, 2, 3, 4, 6}; | ||
| input2.subSequenceStartPositions->copyFrom(input2SubSeq, 5, useGpu); | ||
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| doOneExpandTest("non-seq", true, useGpu, input1, input2, result); | ||
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| // CPU case 3. seq expand to sub-seq | ||
| // input1 = [1,2],[3],[4] | ||
| // input2 = [[4,5]],[[6]],[[7],[8,9]] | ||
| // result = [[1,1]],[[2]],[[3],[4,4]] | ||
| Matrix::resizeOrCreate(input1.value, 4, 1, false, useGpu); | ||
| real input1Data_case3[] = {1, 2, 3, 4}; | ||
| input1.value->setData(input1Data_case3); | ||
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| input1.sequenceStartPositions = ICpuGpuVector::create(4, useGpu); | ||
| int input1Seq[] = {0, 2, 3, 4}; | ||
| input1.sequenceStartPositions->copyFrom(input1Seq, 4, useGpu); | ||
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| real resultData_case3[] = {1, 1, 2, 3, 4, 4}; | ||
| result.value->setData(resultData_case3); | ||
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| doOneExpandTest("seq", true, useGpu, input1, input2, result); | ||
| } | ||
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| int main(int argc, char** argv) { | ||
| testing::InitGoogleTest(&argc, argv); | ||
| initMain(argc, argv); | ||
| return RUN_ALL_TESTS(); | ||
| } | ||
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如果input1 = [1,2],[3]
result应该等于什么？[[1,1]],[[2]],[[3],[3,3]]？