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6 years ago
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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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 "paddle/fluid/inference/tests/api/tester_helper.h"
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namespace paddle {
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namespace inference {
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struct DataRecord {
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std::vector<std::vector<int64_t>> title1_all, title2_all, title3_all, l1_all;
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std::vector<std::vector<int64_t>> title1, title2, title3, l1;
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std::vector<size_t> title1_lod, title2_lod, title3_lod, l1_lod;
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size_t batch_iter{0};
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size_t batch_size{1};
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size_t num_samples; // total number of samples
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DataRecord() = default;
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explicit DataRecord(const std::string &path, int batch_size = 1)
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: batch_size(batch_size) {
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Load(path);
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}
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DataRecord NextBatch() {
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DataRecord data;
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size_t batch_end = batch_iter + batch_size;
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// NOTE skip the final batch, if no enough data is provided.
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if (batch_end <= title1_all.size()) {
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data.title1_all.assign(title1_all.begin() + batch_iter,
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title1_all.begin() + batch_end);
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data.title2_all.assign(title2_all.begin() + batch_iter,
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title2_all.begin() + batch_end);
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data.title3_all.assign(title3_all.begin() + batch_iter,
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title3_all.begin() + batch_end);
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data.l1_all.assign(l1_all.begin() + batch_iter,
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l1_all.begin() + batch_end);
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// Prepare LoDs
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data.title1_lod.push_back(0);
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data.title2_lod.push_back(0);
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data.title3_lod.push_back(0);
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data.l1_lod.push_back(0);
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CHECK(!data.title1_all.empty());
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CHECK(!data.title2_all.empty());
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CHECK(!data.title3_all.empty());
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CHECK(!data.l1_all.empty());
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CHECK_EQ(data.title1_all.size(), data.title2_all.size());
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CHECK_EQ(data.title1_all.size(), data.title3_all.size());
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CHECK_EQ(data.title1_all.size(), data.l1_all.size());
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for (size_t j = 0; j < data.title1_all.size(); j++) {
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data.title1.push_back(data.title1_all[j]);
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data.title2.push_back(data.title2_all[j]);
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data.title3.push_back(data.title3_all[j]);
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data.l1.push_back(data.l1_all[j]);
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// calculate lod
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data.title1_lod.push_back(data.title1_lod.back() +
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data.title1_all[j].size());
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data.title2_lod.push_back(data.title2_lod.back() +
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data.title2_all[j].size());
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data.title3_lod.push_back(data.title3_lod.back() +
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data.title3_all[j].size());
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data.l1_lod.push_back(data.l1_lod.back() + data.l1_all[j].size());
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}
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}
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batch_iter += batch_size;
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return data;
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}
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void Load(const std::string &path) {
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std::ifstream file(path);
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std::string line;
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int num_lines = 0;
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while (std::getline(file, line)) {
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num_lines++;
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std::vector<std::string> data;
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split(line, '\t', &data);
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// load title1 data
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std::vector<int64_t> title1_data;
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split_to_int64(data[0], ' ', &title1_data);
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// load title2 data
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std::vector<int64_t> title2_data;
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split_to_int64(data[1], ' ', &title2_data);
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// load title3 data
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std::vector<int64_t> title3_data;
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split_to_int64(data[2], ' ', &title3_data);
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// load l1 data
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std::vector<int64_t> l1_data;
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split_to_int64(data[3], ' ', &l1_data);
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title1_all.push_back(std::move(title1_data));
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title2_all.push_back(std::move(title2_data));
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title3_all.push_back(std::move(title3_data));
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l1_all.push_back(std::move(l1_data));
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}
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num_samples = num_lines;
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}
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};
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void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
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int batch_size) {
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PaddleTensor title1_tensor, title2_tensor, title3_tensor, l1_tensor;
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title1_tensor.name = "title1";
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title2_tensor.name = "title2";
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title3_tensor.name = "title3";
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l1_tensor.name = "l1";
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auto one_batch = data->NextBatch();
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int title1_size = one_batch.title1_lod[one_batch.title1_lod.size() - 1];
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title1_tensor.shape.assign({title1_size, 1});
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title1_tensor.lod.assign({one_batch.title1_lod});
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int title2_size = one_batch.title2_lod[one_batch.title2_lod.size() - 1];
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title2_tensor.shape.assign({title2_size, 1});
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title2_tensor.lod.assign({one_batch.title2_lod});
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int title3_size = one_batch.title3_lod[one_batch.title3_lod.size() - 1];
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title3_tensor.shape.assign({title3_size, 1});
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title3_tensor.lod.assign({one_batch.title3_lod});
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int l1_size = one_batch.l1_lod[one_batch.l1_lod.size() - 1];
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l1_tensor.shape.assign({l1_size, 1});
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l1_tensor.lod.assign({one_batch.l1_lod});
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// assign data
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TensorAssignData<int64_t>(&title1_tensor, one_batch.title1);
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TensorAssignData<int64_t>(&title2_tensor, one_batch.title2);
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TensorAssignData<int64_t>(&title3_tensor, one_batch.title3);
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TensorAssignData<int64_t>(&l1_tensor, one_batch.l1);
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// Set inputs.
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input_slots->assign({title1_tensor, title2_tensor, title3_tensor, l1_tensor});
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for (auto &tensor : *input_slots) {
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tensor.dtype = PaddleDType::INT64;
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}
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}
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void SetConfig(AnalysisConfig *cfg) {
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cfg->model_dir = FLAGS_infer_model;
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cfg->use_gpu = false;
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cfg->device = 0;
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cfg->specify_input_name = true;
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cfg->enable_ir_optim = true;
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}
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void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
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DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
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std::vector<PaddleTensor> input_slots;
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int epoch = FLAGS_test_all_data ? data.num_samples / FLAGS_batch_size : 1;
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LOG(INFO) << "number of samples: " << epoch * FLAGS_batch_size;
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for (int bid = 0; bid < epoch; ++bid) {
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PrepareInputs(&input_slots, &data, FLAGS_batch_size);
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(*inputs).emplace_back(input_slots);
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}
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}
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// Easy for profiling independently.
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TEST(Analyzer_seq_conv1, profile) {
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AnalysisConfig cfg;
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SetConfig(&cfg);
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std::vector<PaddleTensor> outputs;
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std::vector<std::vector<PaddleTensor>> input_slots_all;
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SetInput(&input_slots_all);
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TestPrediction(cfg, input_slots_all, &outputs, FLAGS_num_threads);
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if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
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// the first inference result
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PADDLE_ENFORCE_EQ(outputs.size(), 1UL);
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size_t size = GetSize(outputs[0]);
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PADDLE_ENFORCE_GT(size, 0);
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float *result = static_cast<float *>(outputs[0].data.data());
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// output is probability, which is in (0, 1).
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for (size_t i = 0; i < size; i++) {
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EXPECT_GT(result[i], 0);
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EXPECT_LT(result[i], 1);
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}
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}
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}
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// Check the fuse status
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TEST(Analyzer_seq_conv1, fuse_statis) {
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AnalysisConfig cfg;
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SetConfig(&cfg);
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int num_ops;
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auto fuse_statis = GetFuseStatis(cfg, &num_ops);
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}
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// Compare result of NativeConfig and AnalysisConfig
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TEST(Analyzer_seq_conv1, compare) {
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AnalysisConfig cfg;
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SetConfig(&cfg);
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std::vector<std::vector<PaddleTensor>> input_slots_all;
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SetInput(&input_slots_all);
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CompareNativeAndAnalysis(cfg, input_slots_all);
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}
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} // namespace inference
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} // namespace paddle
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