sneaxiy
6 years ago
commit
fde9e0c27d
@ -0,0 +1,199 @@
|
|||||||
|
// 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 {
|
||||||
|
|
||||||
|
struct DataRecord {
|
||||||
|
std::vector<std::vector<int64_t>> title1_all, title2_all, title3_all, l1_all;
|
||||||
|
std::vector<std::vector<int64_t>> title1, title2, title3, l1;
|
||||||
|
std::vector<size_t> title1_lod, title2_lod, title3_lod, l1_lod;
|
||||||
|
size_t batch_iter{0};
|
||||||
|
size_t batch_size{1};
|
||||||
|
size_t 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 <= title1_all.size()) {
|
||||||
|
data.title1_all.assign(title1_all.begin() + batch_iter,
|
||||||
|
title1_all.begin() + batch_end);
|
||||||
|
data.title2_all.assign(title2_all.begin() + batch_iter,
|
||||||
|
title2_all.begin() + batch_end);
|
||||||
|
data.title3_all.assign(title3_all.begin() + batch_iter,
|
||||||
|
title3_all.begin() + batch_end);
|
||||||
|
data.l1_all.assign(l1_all.begin() + batch_iter,
|
||||||
|
l1_all.begin() + batch_end);
|
||||||
|
// Prepare LoDs
|
||||||
|
data.title1_lod.push_back(0);
|
||||||
|
data.title2_lod.push_back(0);
|
||||||
|
data.title3_lod.push_back(0);
|
||||||
|
data.l1_lod.push_back(0);
|
||||||
|
CHECK(!data.title1_all.empty());
|
||||||
|
CHECK(!data.title2_all.empty());
|
||||||
|
CHECK(!data.title3_all.empty());
|
||||||
|
CHECK(!data.l1_all.empty());
|
||||||
|
CHECK_EQ(data.title1_all.size(), data.title2_all.size());
|
||||||
|
CHECK_EQ(data.title1_all.size(), data.title3_all.size());
|
||||||
|
CHECK_EQ(data.title1_all.size(), data.l1_all.size());
|
||||||
|
for (size_t j = 0; j < data.title1_all.size(); j++) {
|
||||||
|
data.title1.push_back(data.title1_all[j]);
|
||||||
|
data.title2.push_back(data.title2_all[j]);
|
||||||
|
data.title3.push_back(data.title3_all[j]);
|
||||||
|
data.l1.push_back(data.l1_all[j]);
|
||||||
|
// calculate lod
|
||||||
|
data.title1_lod.push_back(data.title1_lod.back() +
|
||||||
|
data.title1_all[j].size());
|
||||||
|
data.title2_lod.push_back(data.title2_lod.back() +
|
||||||
|
data.title2_all[j].size());
|
||||||
|
data.title3_lod.push_back(data.title3_lod.back() +
|
||||||
|
data.title3_all[j].size());
|
||||||
|
data.l1_lod.push_back(data.l1_lod.back() + data.l1_all[j].size());
|
||||||
|
}
|
||||||
|
}
|
||||||
|
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)) {
|
||||||
|
num_lines++;
|
||||||
|
std::vector<std::string> data;
|
||||||
|
split(line, '\t', &data);
|
||||||
|
// load title1 data
|
||||||
|
std::vector<int64_t> title1_data;
|
||||||
|
split_to_int64(data[0], ' ', &title1_data);
|
||||||
|
// load title2 data
|
||||||
|
std::vector<int64_t> title2_data;
|
||||||
|
split_to_int64(data[1], ' ', &title2_data);
|
||||||
|
// load title3 data
|
||||||
|
std::vector<int64_t> title3_data;
|
||||||
|
split_to_int64(data[2], ' ', &title3_data);
|
||||||
|
// load l1 data
|
||||||
|
std::vector<int64_t> l1_data;
|
||||||
|
split_to_int64(data[3], ' ', &l1_data);
|
||||||
|
title1_all.push_back(std::move(title1_data));
|
||||||
|
title2_all.push_back(std::move(title2_data));
|
||||||
|
title3_all.push_back(std::move(title3_data));
|
||||||
|
l1_all.push_back(std::move(l1_data));
|
||||||
|
}
|
||||||
|
num_samples = num_lines;
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
|
||||||
|
int batch_size) {
|
||||||
|
PaddleTensor title1_tensor, title2_tensor, title3_tensor, l1_tensor;
|
||||||
|
title1_tensor.name = "title1";
|
||||||
|
title2_tensor.name = "title2";
|
||||||
|
title3_tensor.name = "title3";
|
||||||
|
l1_tensor.name = "l1";
|
||||||
|
auto one_batch = data->NextBatch();
|
||||||
|
int title1_size = one_batch.title1_lod[one_batch.title1_lod.size() - 1];
|
||||||
|
title1_tensor.shape.assign({title1_size, 1});
|
||||||
|
title1_tensor.lod.assign({one_batch.title1_lod});
|
||||||
|
int title2_size = one_batch.title2_lod[one_batch.title2_lod.size() - 1];
|
||||||
|
title2_tensor.shape.assign({title2_size, 1});
|
||||||
|
title2_tensor.lod.assign({one_batch.title2_lod});
|
||||||
|
int title3_size = one_batch.title3_lod[one_batch.title3_lod.size() - 1];
|
||||||
|
title3_tensor.shape.assign({title3_size, 1});
|
||||||
|
title3_tensor.lod.assign({one_batch.title3_lod});
|
||||||
|
int l1_size = one_batch.l1_lod[one_batch.l1_lod.size() - 1];
|
||||||
|
l1_tensor.shape.assign({l1_size, 1});
|
||||||
|
l1_tensor.lod.assign({one_batch.l1_lod});
|
||||||
|
|
||||||
|
// assign data
|
||||||
|
TensorAssignData<int64_t>(&title1_tensor, one_batch.title1);
|
||||||
|
TensorAssignData<int64_t>(&title2_tensor, one_batch.title2);
|
||||||
|
TensorAssignData<int64_t>(&title3_tensor, one_batch.title3);
|
||||||
|
TensorAssignData<int64_t>(&l1_tensor, one_batch.l1);
|
||||||
|
// Set inputs.
|
||||||
|
input_slots->assign({title1_tensor, title2_tensor, title3_tensor, l1_tensor});
|
||||||
|
for (auto &tensor : *input_slots) {
|
||||||
|
tensor.dtype = PaddleDType::INT64;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
void SetConfig(AnalysisConfig *cfg) {
|
||||||
|
cfg->model_dir = FLAGS_infer_model;
|
||||||
|
cfg->use_gpu = false;
|
||||||
|
cfg->device = 0;
|
||||||
|
cfg->specify_input_name = true;
|
||||||
|
cfg->enable_ir_optim = true;
|
||||||
|
}
|
||||||
|
|
||||||
|
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_seq_conv1, profile) {
|
||||||
|
AnalysisConfig cfg;
|
||||||
|
SetConfig(&cfg);
|
||||||
|
std::vector<PaddleTensor> outputs;
|
||||||
|
|
||||||
|
std::vector<std::vector<PaddleTensor>> input_slots_all;
|
||||||
|
SetInput(&input_slots_all);
|
||||||
|
TestPrediction(cfg, input_slots_all, &outputs, FLAGS_num_threads);
|
||||||
|
|
||||||
|
if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
|
||||||
|
// the first inference result
|
||||||
|
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_seq_conv1, fuse_statis) {
|
||||||
|
AnalysisConfig cfg;
|
||||||
|
SetConfig(&cfg);
|
||||||
|
int num_ops;
|
||||||
|
auto fuse_statis = GetFuseStatis(cfg, &num_ops);
|
||||||
|
}
|
||||||
|
|
||||||
|
// Compare result of NativeConfig and AnalysisConfig
|
||||||
|
TEST(Analyzer_seq_conv1, compare) {
|
||||||
|
AnalysisConfig cfg;
|
||||||
|
SetConfig(&cfg);
|
||||||
|
|
||||||
|
std::vector<std::vector<PaddleTensor>> input_slots_all;
|
||||||
|
SetInput(&input_slots_all);
|
||||||
|
CompareNativeAndAnalysis(cfg, input_slots_all);
|
||||||
|
}
|
||||||
|
|
||||||
|
} // namespace inference
|
||||||
|
} // namespace paddle
|
||||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in new issue