提交 63b38ca4 编写于 作者: T tensor-tang

add lac test

上级 663a11ac
......@@ -50,7 +50,7 @@ endfunction(inference_download_and_uncompress)
set(DITU_RNN_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/ditu_rnn_fluid%2Fmodel.tar.gz")
set(DITU_RNN_DATA_URL "http://paddle-inference-dist.bj.bcebos.com/ditu_rnn_fluid%2Fdata.txt.tar.gz")
set(DITU_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo/ditu_rnn" CACHE PATH "Ditu RNN model and data root." FORCE)
if (NOT EXISTS ${DITU_INSTALL_DIR})
if (NOT EXISTS ${DITU_INSTALL_DIR} AND WITH_TESTING)
inference_download_and_uncompress(${DITU_INSTALL_DIR} ${DITU_RNN_MODEL_URL} "ditu_rnn_fluid%2Fmodel.tar.gz")
inference_download_and_uncompress(${DITU_INSTALL_DIR} ${DITU_RNN_DATA_URL} "ditu_rnn_fluid%2Fdata.txt.tar.gz")
endif()
......@@ -86,7 +86,7 @@ inference_analysis_test(test_model_store_pass SRCS model_store_pass_tester.cc)
set(CHINESE_NER_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/chinese_ner_model.tar.gz")
set(CHINESE_NER_DATA_URL "http://paddle-inference-dist.bj.bcebos.com/chinese_ner-data.txt.tar.gz")
set(CHINESE_NER_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo/chinese_ner" CACHE PATH "Chinese ner model and data root." FORCE)
if (NOT EXISTS ${CHINESE_NER_INSTALL_DIR})
if (NOT EXISTS ${CHINESE_NER_INSTALL_DIR} AND WITH_TESTING)
inference_download_and_uncompress(${CHINESE_NER_INSTALL_DIR} ${CHINESE_NER_MODEL_URL} "chinese_ner_model.tar.gz")
inference_download_and_uncompress(${CHINESE_NER_INSTALL_DIR} ${CHINESE_NER_DATA_URL} "chinese_ner-data.txt.tar.gz")
endif()
......@@ -95,3 +95,16 @@ inference_analysis_test(test_analyzer_ner SRCS analyzer_ner_tester.cc
EXTRA_DEPS paddle_inference_api paddle_fluid_api
ARGS --infer_model=${CHINESE_NER_INSTALL_DIR}/model
--infer_data=${CHINESE_NER_INSTALL_DIR}/data.txt)
set(LAC_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/lac_model.tar.gz")
set(LAC_DATA_URL "http://paddle-inference-dist.bj.bcebos.com/lac_data.txt.tar.gz")
set(LAC_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo/lac" CACHE PATH "LAC model and data root." FORCE)
if (NOT EXISTS ${LAC_INSTALL_DIR} AND WITH_TESTING)
inference_download_and_uncompress(${LAC_INSTALL_DIR} ${LAC_MODEL_URL} "lac_model.tar.gz")
inference_download_and_uncompress(${LAC_INSTALL_DIR} ${LAC_DATA_URL} "lac_data.txt.tar.gz")
endif()
inference_analysis_test(test_analyzer_lac SRCS analyzer_lac_tester.cc
EXTRA_DEPS paddle_inference_api paddle_fluid_api
ARGS --infer_model=${LAC_INSTALL_DIR}/model
--infer_data=${LAC_INSTALL_DIR}/data.txt)
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// 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
//
// http://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.
#include "paddle/fluid/inference/analysis/analyzer.h"
#include <google/protobuf/text_format.h>
#include <gtest/gtest.h>
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/inference/analysis/ut_helper.h"
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/platform/profiler.h"
DEFINE_string(infer_model, "", "model path for LAC");
DEFINE_string(infer_data, "", "data file for LAC");
DEFINE_int32(batch_size, 1, "batch size.");
DEFINE_int32(burning, 0, "Burning before repeat.");
DEFINE_int32(repeat, 1, "Running the inference program repeat times.");
DEFINE_bool(test_all_data, false, "Test the all dataset in data file.");
namespace paddle {
namespace inference {
namespace analysis {
struct DataRecord {
std::vector<int64_t> data;
std::vector<size_t> lod;
// for dataset and nextbatch
size_t batch_iter{0};
std::vector<std::vector<size_t>> batched_lods;
std::vector<std::vector<int64_t>> batched_datas;
std::vector<std::vector<int64_t>> datasets;
DataRecord() = default;
explicit DataRecord(const std::string &path, int batch_size = 1) {
Load(path);
Prepare(batch_size);
batch_iter = 0;
}
void Load(const std::string &path) {
std::ifstream file(path);
std::string line;
int num_lines = 0;
datasets.resize(0);
while (std::getline(file, line)) {
num_lines++;
std::vector<std::string> data;
split(line, ';', &data);
std::vector<int64_t> words_ids;
split_to_int64(data[1], ' ', &words_ids);
datasets.emplace_back(words_ids);
}
}
void Prepare(int bs) {
if (bs == 1) {
batched_datas = datasets;
for (auto one_sentence : datasets) {
batched_lods.push_back({0, one_sentence.size()});
}
} else {
std::vector<int64_t> one_batch;
std::vector<size_t> lod{0};
int bs_id = 0;
for (auto one_sentence : datasets) {
bs_id++;
one_batch.insert(one_batch.end(), one_sentence.begin(),
one_sentence.end());
lod.push_back(lod.back() + one_sentence.size());
if (bs_id == bs) {
bs_id = 0;
batched_datas.push_back(one_batch);
batched_lods.push_back(lod);
one_batch.clear();
one_batch.resize(0);
lod.clear();
lod.resize(0);
lod.push_back(0);
}
}
if (one_batch.size() != 0) {
batched_datas.push_back(one_batch);
batched_lods.push_back(lod);
}
}
}
DataRecord NextBatch() {
DataRecord data;
data.data = batched_datas[batch_iter];
data.lod = batched_lods[batch_iter];
batch_iter++;
if (batch_iter >= batched_datas.size()) {
batch_iter = 0;
}
return data;
}
};
void GetOneBatch(std::vector<PaddleTensor> *input_slots, DataRecord *data,
int batch_size) {
auto one_batch = data->NextBatch();
PaddleTensor input_tensor;
input_tensor.name = "word";
input_tensor.shape.assign({static_cast<int>(one_batch.data.size()), 1});
input_tensor.lod.assign({one_batch.lod});
input_tensor.dtype = PaddleDType::INT64;
TensorAssignData<int64_t>(&input_tensor, {one_batch.data});
PADDLE_ENFORCE_EQ(batch_size, static_cast<int>(one_batch.lod.size() - 1));
input_slots->assign({input_tensor});
}
static void PrintTime(const double latency, const int bs, const int repeat) {
LOG(INFO) << "===========profile result===========";
LOG(INFO) << "batch_size: " << bs << ", repeat: " << repeat
<< ", avg latency: " << latency / repeat << "ms";
LOG(INFO) << "=====================================";
}
void BenchAllData(const std::string &model_path, const std::string &data_file,
const int batch_size, const int repeat) {
NativeConfig config;
config.model_dir = model_path;
config.use_gpu = false;
config.device = 0;
config.specify_input_name = true;
std::vector<PaddleTensor> input_slots, outputs_slots;
DataRecord data(data_file, batch_size);
auto predictor =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
GetOneBatch(&input_slots, &data, batch_size);
for (int i = 0; i < FLAGS_burning; i++) {
predictor->Run(input_slots, &outputs_slots);
}
Timer timer;
double sum = 0;
for (int i = 0; i < repeat; i++) {
for (size_t bid = 0; bid < data.batched_datas.size(); ++bid) {
GetOneBatch(&input_slots, &data, batch_size);
timer.tic();
predictor->Run(input_slots, &outputs_slots);
sum += timer.toc();
}
}
PrintTime(sum, batch_size, repeat);
}
const int64_t lac_ref_data[] = {24, 25, 25, 25, 38, 30, 31, 14, 15, 44, 24, 25,
25, 25, 25, 25, 44, 24, 25, 25, 25, 36, 42, 43,
44, 14, 15, 44, 14, 15, 44, 14, 15, 44, 38, 39,
14, 15, 44, 22, 23, 23, 23, 23, 23, 23, 23};
void TestLACPrediction(const std::string &model_path,
const std::string &data_file, const int batch_size,
const int repeat, bool test_all_data) {
if (test_all_data) {
BenchAllData(model_path, data_file, batch_size, repeat);
return;
}
NativeConfig config;
config.model_dir = model_path;
config.use_gpu = false;
config.device = 0;
config.specify_input_name = true;
std::vector<PaddleTensor> input_slots, outputs_slots;
DataRecord data(data_file, batch_size);
GetOneBatch(&input_slots, &data, batch_size);
auto predictor =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
for (int i = 0; i < FLAGS_burning; i++) {
predictor->Run(input_slots, &outputs_slots);
}
Timer timer;
timer.tic();
for (int i = 0; i < repeat; i++) {
predictor->Run(input_slots, &outputs_slots);
}
PrintTime(timer.toc(), batch_size, repeat);
EXPECT_EQ(outputs_slots.size(), 1UL);
auto &out = outputs_slots[0];
size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1,
[](int a, int b) { return a * b; });
size_t batch1_size = sizeof(lac_ref_data) / sizeof(int64_t);
PADDLE_ENFORCE_GT(size, 0);
EXPECT_GE(size, batch1_size);
int64_t *pdata = static_cast<int64_t *>(out.data.data());
for (size_t i = 0; i < batch1_size; ++i) {
EXPECT_EQ(pdata[i], lac_ref_data[i]);
}
}
TEST(Analyzer_LAC, native) {
LOG(INFO) << "LAC with native";
TestLACPrediction(FLAGS_infer_model, FLAGS_infer_data, FLAGS_batch_size,
FLAGS_repeat, FLAGS_test_all_data);
}
} // namespace analysis
} // namespace inference
} // namespace paddle
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