未验证 提交 140fc1e9 编写于 作者: T Tao Luo 提交者: GitHub

Merge pull request #15392 from luotao1/pyramid_dnn

add pyramid_dnn c++ inference test
......@@ -86,6 +86,11 @@ set(MM_DNN_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/mm_dnn")
download_model_and_data(${MM_DNN_INSTALL_DIR} "MM_DNN_model.tar.gz" "MM_DNN_data.txt.tar.gz")
inference_analysis_api_test(test_analyzer_mm_dnn ${MM_DNN_INSTALL_DIR} analyzer_mm_dnn_tester.cc)
# Pyramid DNN
set(PYRAMID_DNN_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/pyramid_dnn")
download_model_and_data(${PYRAMID_DNN_INSTALL_DIR} "PyramidDNN_model.tar.gz" "PyramidDNN_data.txt.tar.gz")
inference_analysis_api_test(test_analyzer_pyramid_dnn ${PYRAMID_DNN_INSTALL_DIR} analyzer_pyramid_dnn_tester.cc)
# text_classification
set(TEXT_CLASSIFICATION_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/text_classification")
download_model_and_data(${TEXT_CLASSIFICATION_INSTALL_DIR} "text-classification-Senta.tar.gz" "text_classification_data.txt.tar.gz")
......
// 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/tests/api/tester_helper.h"
namespace paddle {
namespace inference {
using contrib::AnalysisConfig;
struct DataRecord {
std::vector<std::vector<int64_t>> query_basic, query_phrase, title_basic,
title_phrase;
std::vector<size_t> lod1, lod2, lod3, lod4;
size_t batch_iter{0}, batch_size{1}, num_samples; // total number of samples
DataRecord() = default;
explicit DataRecord(const std::string &path, int batch_size = 1)
: batch_size(batch_size) {
Load(path);
}
DataRecord NextBatch() {
DataRecord data;
size_t batch_end = batch_iter + batch_size;
// NOTE skip the final batch, if no enough data is provided.
if (batch_end <= query_basic.size()) {
GetInputPerBatch(query_basic, &data.query_basic, &data.lod1, batch_iter,
batch_end);
GetInputPerBatch(query_phrase, &data.query_phrase, &data.lod2, batch_iter,
batch_end);
GetInputPerBatch(title_basic, &data.title_basic, &data.lod3, batch_iter,
batch_end);
GetInputPerBatch(title_phrase, &data.title_phrase, &data.lod4, batch_iter,
batch_end);
}
batch_iter += batch_size;
return data;
}
void Load(const std::string &path) {
std::ifstream file(path);
std::string line;
int num_lines = 0;
while (std::getline(file, line)) {
std::vector<std::string> data;
split(line, ';', &data);
// load query data
std::vector<int64_t> query_basic_data;
split_to_int64(data[1], ' ', &query_basic_data);
std::vector<int64_t> query_phrase_data;
split_to_int64(data[2], ' ', &query_phrase_data);
// load title data
std::vector<int64_t> title_basic_data;
split_to_int64(data[3], ' ', &title_basic_data);
std::vector<int64_t> title_phrase_data;
split_to_int64(data[4], ' ', &title_phrase_data);
// filter the empty data
bool flag =
data[1].size() && data[2].size() && data[3].size() && data[4].size();
if (flag) {
query_basic.push_back(std::move(query_basic_data));
query_phrase.push_back(std::move(query_phrase_data));
title_basic.push_back(std::move(title_basic_data));
title_phrase.push_back(std::move(title_phrase_data));
num_lines++;
}
}
num_samples = num_lines;
}
};
void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
int batch_size) {
PaddleTensor query_basic_tensor, query_phrase_tensor, title_basic_tensor,
title_phrase_tensor;
query_basic_tensor.name = "query_basic";
query_phrase_tensor.name = "query_phrase";
title_basic_tensor.name = "pos_title_basic";
title_phrase_tensor.name = "pos_title_phrase";
auto one_batch = data->NextBatch();
// assign data
TensorAssignData<int64_t>(&query_basic_tensor, one_batch.query_basic,
one_batch.lod1);
TensorAssignData<int64_t>(&query_phrase_tensor, one_batch.query_phrase,
one_batch.lod2);
TensorAssignData<int64_t>(&title_basic_tensor, one_batch.title_basic,
one_batch.lod3);
TensorAssignData<int64_t>(&title_phrase_tensor, one_batch.title_phrase,
one_batch.lod4);
// Set inputs.
input_slots->assign({query_basic_tensor, query_phrase_tensor,
title_basic_tensor, title_phrase_tensor});
for (auto &tensor : *input_slots) {
tensor.dtype = PaddleDType::INT64;
}
}
void SetConfig(contrib::AnalysisConfig *cfg) {
cfg->SetModel(FLAGS_infer_model);
cfg->DisableGpu();
cfg->SwitchSpecifyInputNames();
cfg->SwitchIrOptim();
}
void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
std::vector<PaddleTensor> input_slots;
int epoch = FLAGS_test_all_data ? data.num_samples / FLAGS_batch_size : 1;
LOG(INFO) << "number of samples: " << epoch * FLAGS_batch_size;
for (int bid = 0; bid < epoch; ++bid) {
PrepareInputs(&input_slots, &data, FLAGS_batch_size);
(*inputs).emplace_back(input_slots);
}
}
// Easy for profiling independently.
TEST(Analyzer_Pyramid_DNN, profile) {
contrib::AnalysisConfig cfg;
SetConfig(&cfg);
std::vector<PaddleTensor> outputs;
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
TestPrediction(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
input_slots_all, &outputs, FLAGS_num_threads);
if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
PADDLE_ENFORCE_EQ(outputs.size(), 1UL);
size_t size = GetSize(outputs[0]);
PADDLE_ENFORCE_GT(size, 0);
float *result = static_cast<float *>(outputs[0].data.data());
// output is probability, which is in (0, 1).
for (size_t i = 0; i < size; i++) {
EXPECT_GT(result[i], 0);
EXPECT_LT(result[i], 1);
}
}
}
// Check the fuse status
TEST(Analyzer_Pyramid_DNN, fuse_statis) {
contrib::AnalysisConfig cfg;
SetConfig(&cfg);
int num_ops;
auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
auto fuse_statis = GetFuseStatis(
static_cast<AnalysisPredictor *>(predictor.get()), &num_ops);
}
// Compare result of NativeConfig and AnalysisConfig
TEST(Analyzer_Pyramid_DNN, compare) {
contrib::AnalysisConfig cfg;
SetConfig(&cfg);
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
CompareNativeAndAnalysis(
reinterpret_cast<const PaddlePredictor::Config *>(&cfg), input_slots_all);
}
// Compare Deterministic result
TEST(Analyzer_Pyramid_DNN, compare_determine) {
AnalysisConfig cfg;
SetConfig(&cfg);
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
CompareDeterministic(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
input_slots_all);
}
} // namespace inference
} // namespace paddle
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