caoying03
8 years ago
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84627bb934
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/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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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
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include <gtest/gtest.h>
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#include "ModelConfig.pb.h"
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#include "paddle/gserver/layers/DataLayer.h"
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#include "paddle/trainer/Trainer.h"
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#include "LayerGradUtil.h"
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#include "paddle/testing/TestUtil.h"
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using namespace paddle; // NOLINT
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using namespace std; // NOLINT
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DECLARE_int32(gpu_id);
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DECLARE_bool(thread_local_rand_use_global_seed);
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const int MAX_SEQ_NUM = 5;
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const int MAX_SEQ_LEN = 5;
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const int MAX_BEAM_SIZE = 3;
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vector<real> randSampling(real range, int n) {
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CHECK_GE(range, n);
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vector<real> num(range);
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iota(begin(num), end(num), 0.);
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if (range == n) return num;
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random_shuffle(begin(num), end(num));
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num.resize(n);
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sort(begin(num), end(num));
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return num;
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}
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void genSeqInfo(vector<int>& seqStartPos, vector<int>& subSeqStartPos) {
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seqStartPos.resize(1, 0);
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subSeqStartPos.resize(1, 0);
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// srand((size_t)(time(NULL)));
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srand(1);
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int seqNum = 1 + (rand() % MAX_SEQ_NUM);
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for (int i = 0; i < seqNum; ++i) {
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int subSeqNum = 1 + (rand() % MAX_SEQ_NUM);
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for (int j = 0; j < subSeqNum; ++j)
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subSeqStartPos.push_back(subSeqStartPos.back() +
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(1 + (rand() % MAX_SEQ_LEN)));
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seqStartPos.push_back(subSeqStartPos.back());
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}
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}
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/*
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generate start indices according to sequence start positions.
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*/
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void genStarts(vector<int>& seqStartPos,
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vector<vector<real>>& starts,
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size_t beamSize) {
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starts.clear();
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starts.resize(seqStartPos.size() - 1, vector<real>(beamSize, -1.));
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for (size_t i = 0; i < seqStartPos.size() - 1; ++i) {
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int seqLen = seqStartPos[i + 1] - seqStartPos[i];
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vector<real> randStarts =
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randSampling(seqLen, min(seqLen, static_cast<int>(beamSize)));
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copy(begin(randStarts), end(randStarts), begin(starts[i]));
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}
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}
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/*
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generate end indices according to sequence start positions and start indices.
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*/
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void genEnds(vector<int>& seqStartPos,
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vector<vector<real>>& starts,
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vector<vector<real>>& ends,
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size_t beamSize) {
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CHECK_EQ(seqStartPos.size() - 1, starts.size());
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ends.clear();
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ends.resize(seqStartPos.size() - 1, vector<real>(beamSize, -1.));
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for (size_t i = 0; i < starts.size(); ++i) {
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for (size_t j = 0; j < starts[i].size(); ++j) {
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int seqLen = seqStartPos[i + 1] - seqStartPos[i];
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CHECK_GE(seqLen - 1, starts[i][j]);
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if (starts[i][j] == -1.) break;
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if (starts[i][j] == (seqLen - 1)) {
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ends[i][j] = starts[i][j];
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} else {
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ends[i][j] = starts[i][j] + randSampling(seqLen - starts[i][j], 1)[0];
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}
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}
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}
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}
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void genTestData(vector<int>& seqStartPos,
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vector<int>& subSeqStartPos,
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vector<vector<real>>& starts,
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vector<vector<real>>& ends,
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bool hasSubseq) {
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size_t beamSize = MAX_BEAM_SIZE;
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genSeqInfo(seqStartPos, subSeqStartPos);
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genStarts(hasSubseq ? subSeqStartPos : seqStartPos, starts, beamSize);
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genEnds(hasSubseq ? subSeqStartPos : seqStartPos, starts, ends, beamSize);
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}
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template <typename T>
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void flatten2dVector(vector<vector<T>>& inVec, vector<T>& outVec) {
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size_t totalSize{0};
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for (auto const& items : inVec) totalSize += items.size();
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outVec.reserve(totalSize);
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for (auto& items : inVec)
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move(items.begin(), items.end(), back_inserter(outVec));
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}
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void testSeqSliceLayer(bool hasSubseq,
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bool useGpu,
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vector<int>& seqStartPos,
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vector<int>& subSeqStartPos,
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vector<vector<real>>& starts,
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vector<vector<real>>& ends) {
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// layer size is not crutial for this layer,
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// so here use a small layer size in the unittest.
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const size_t layerSize{4};
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TestConfig config;
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config.layerConfig.set_type("seq_slice");
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config.layerConfig.set_size(layerSize);
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// add the first input
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MatrixPtr seqInputPtr =
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Matrix::create(hasSubseq ? subSeqStartPos.back() : seqStartPos.back(),
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layerSize,
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false,
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false);
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seqInputPtr->randomizeUniform();
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if (hasSubseq) {
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config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA,
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"seq_input",
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seqInputPtr,
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seqStartPos,
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subSeqStartPos});
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} else {
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config.inputDefs.push_back(
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{INPUT_SELF_DEFINE_DATA, "seq_input", seqInputPtr, seqStartPos});
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}
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config.layerConfig.add_inputs();
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// add start indices
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if (starts.size()) {
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vector<real> startsToVec;
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flatten2dVector(starts, startsToVec);
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MatrixPtr startMatrixPtr =
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Matrix::create(starts.size(), starts[0].size(), false, false);
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startMatrixPtr->copyFrom(startsToVec.data(), startsToVec.size());
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config.inputDefs.push_back(
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{INPUT_SELF_DEFINE_DATA, "starts", startMatrixPtr});
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config.layerConfig.add_inputs();
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}
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// add end indices
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if (ends.size()) {
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vector<real> endsToVec;
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flatten2dVector(ends, endsToVec);
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MatrixPtr endMatrixPtr =
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Matrix::create(ends.size(), ends[0].size(), false, false);
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config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA, "ends", endMatrixPtr});
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config.layerConfig.add_inputs();
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}
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testLayerGrad(config, "seq_slice", /*batchSize*/ 100, false, useGpu, false);
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}
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TEST(Layer, SeqSliceLayer) {
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vector<int> seqStartPos;
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vector<int> subSeqStartPos;
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vector<vector<real>> starts;
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vector<vector<real>> ends;
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genSeqInfo(seqStartPos, subSeqStartPos);
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for (bool hasSubseq : {false, true}) {
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genTestData(seqStartPos, subSeqStartPos, starts, ends, hasSubseq);
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for (bool useGpu : {false, true}) {
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vector<vector<real>> tmp;
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testSeqSliceLayer(
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hasSubseq, useGpu, seqStartPos, subSeqStartPos, tmp, ends);
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testSeqSliceLayer(
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hasSubseq, useGpu, seqStartPos, subSeqStartPos, starts, tmp);
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testSeqSliceLayer(
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hasSubseq, useGpu, seqStartPos, subSeqStartPos, starts, ends);
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}
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}
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}
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int main(int argc, char** argv) {
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initMain(argc, argv);
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hl_start();
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hl_init(FLAGS_gpu_id);
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FLAGS_thread_local_rand_use_global_seed = true;
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srand(1);
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testing::InitGoogleTest(&argc, argv);
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return RUN_ALL_TESTS();
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}
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