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remove old inference C++ tests (#24368)

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release/2.0-alpha
Tao Luo 5 years ago committed by GitHub
parent aa0f254fbe
commit 63da846de0
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GPG Key ID: 4AEE18F83AFDEB23
11 changed files with 1 additions and 940 deletions
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6
paddle/fluid/inference/CMakeLists.txt
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@ -55,12 +55,8 @@ endif()
# C inference API
add_subdirectory(capi)
if(WITH_TESTING)
# tests/book depends the models that generated by python/paddle/fluid/tests/book
add_subdirectory(tests/book)
if(WITH_INFERENCE_API_TEST)
if(WITH_TESTING AND WITH_INFERENCE_API_TEST)
add_subdirectory(tests/api)
endif()
endif()
if(NOT ON_INFER)

50
paddle/fluid/inference/tests/book/CMakeLists.txt
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@ -1,50 +0,0 @@
function(inference_test TARGET_NAME)
set(options "")
set(oneValueArgs "")
set(multiValueArgs ARGS)
cmake_parse_arguments(inference_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(arg_list "")
if(inference_test_ARGS)
foreach(arg ${inference_test_ARGS})
list(APPEND arg_list "_${arg}")
endforeach()
else()
list(APPEND arg_list "_")
endif()
foreach(arg ${arg_list})
string(REGEX REPLACE "^_$" "" arg "${arg}")
cc_test(test_inference_${TARGET_NAME}${arg}
SRCS test_inference_${TARGET_NAME}.cc
DEPS paddle_fluid_api
ARGS --dirname=${PYTHON_TESTS_DIR}/book/${TARGET_NAME}${arg}.inference.model)
set_tests_properties(test_inference_${TARGET_NAME}${arg}
PROPERTIES DEPENDS test_${TARGET_NAME})
set_tests_properties(test_inference_${TARGET_NAME}${arg}
PROPERTIES LABELS "RUN_TYPE=DIST")
endforeach()
endfunction(inference_test)
####################
# Inference tests here depend on fluid/tests/book. If users want to run
# individual test with ctest, they need to run tests in fluid/tests/book
# first to generate saved model.
####################
# This unittest is buggy!
#inference_test(fit_a_line)
inference_test(image_classification ARGS vgg resnet)
inference_test(label_semantic_roles)
inference_test(recognize_digits ARGS mlp conv)
inference_test(recommender_system)
#inference_test(rnn_encoder_decoder)
#inference_test(understand_sentiment ARGS conv)
inference_test(word2vec)
# This is an unly work around to make this test run
# TODO(TJ): clean me up
cc_test(test_inference_nlp
SRCS test_inference_nlp.cc
DEPS paddle_fluid_api
ARGS
--model_path=${PADDLE_BINARY_DIR}/python/paddle/fluid/tests/book/recognize_digits_mlp.inference.model)
set_tests_properties(test_inference_nlp PROPERTIES LABELS "RUN_TYPE=DIST")

91
paddle/fluid/inference/tests/book/test_inference_fit_a_line.cc
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/* 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 "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/fluid/inference/tests/test_helper.h"
#include "paddle/fluid/inference/tests/test_multi_thread_helper.h"
DEFINE_string(dirname, "", "Directory of the inference model.");
TEST(inference, fit_a_line) {
if (FLAGS_dirname.empty()) {
LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model";
}
LOG(INFO) << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
std::string dirname = FLAGS_dirname;
// 0. Call `paddle::framework::InitDevices()` initialize all the devices
// In unittests, this is done in paddle/testing/paddle_gtest_main.cc
for (int num_threads : {1, 2}) {
std::vector<std::vector<paddle::framework::LoDTensor*>> cpu_feeds;
cpu_feeds.resize(num_threads);
for (int i = 0; i < num_threads; ++i) {
auto* input = new paddle::framework::LoDTensor();
// The second dim of the input tensor should be 13
// The input data should be >= 0
int64_t batch_size = 10;
SetupTensor<float>(input, {batch_size, 13}, static_cast<float>(0),
static_cast<float>(10));
cpu_feeds[i].push_back(input);
}
std::vector<std::vector<paddle::framework::FetchType*>> cpu_fetchs1;
cpu_fetchs1.resize(num_threads);
for (int i = 0; i < num_threads; ++i) {
auto* output = new paddle::framework::FetchType();
cpu_fetchs1[i].push_back(output);
}
// Run inference on CPU
LOG(INFO) << "--- CPU Runs (num_threads: " << num_threads << "): ---";
if (num_threads == 1) {
TestInference<paddle::platform::CPUPlace>(dirname, cpu_feeds[0],
cpu_fetchs1[0]);
} else {
TestMultiThreadInference<paddle::platform::CPUPlace>(
dirname, cpu_feeds, cpu_fetchs1, num_threads);
}
#ifdef PADDLE_WITH_CUDA
std::vector<std::vector<paddle::framework::FetchType*>> cpu_fetchs2;
cpu_fetchs2.resize(num_threads);
for (int i = 0; i < num_threads; ++i) {
auto* output = new paddle::framework::FetchType();
cpu_fetchs2[i].push_back(output);
}
// Run inference on CUDA GPU
LOG(INFO) << "--- GPU Runs (num_threads: " << num_threads << "): ---";
if (num_threads == 1) {
TestInference<paddle::platform::CUDAPlace>(dirname, cpu_feeds[0],
cpu_fetchs2[0]);
} else {
TestMultiThreadInference<paddle::platform::CUDAPlace>(
dirname, cpu_feeds, cpu_fetchs2, num_threads);
}
for (int i = 0; i < num_threads; ++i) {
CheckError<float>(
BOOST_GET(paddle::framework::LoDTensor, *cpu_fetchs1[i][0]),
BOOST_GET(paddle::framework::LoDTensor, *cpu_fetchs2[i][0]));
delete cpu_fetchs2[i][0];
}
#endif
for (int i = 0; i < num_threads; ++i) {
delete cpu_feeds[i][0];
delete cpu_fetchs1[i][0];
}
} // num_threads-loop
}

100
paddle/fluid/inference/tests/book/test_inference_image_classification.cc
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/* 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 "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/fluid/inference/tests/test_helper.h"
DEFINE_string(dirname, "", "Directory of the inference model.");
DEFINE_string(fp16_dirname, "", "Directory of the float16 inference model.");
DEFINE_int32(batch_size, 1, "Batch size of input data");
DEFINE_int32(repeat, 1, "Running the inference program repeat times");
DEFINE_bool(skip_cpu, false, "Skip the cpu test");
TEST(inference, image_classification) {
if (FLAGS_dirname.empty() || FLAGS_batch_size < 1 || FLAGS_repeat < 1) {
LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model "
"--batch_size=1 --repeat=1";
}
LOG(INFO) << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
std::string dirname = FLAGS_dirname;
// 0. Call `paddle::framework::InitDevices()` initialize all the devices
// In unittests, this is done in paddle/testing/paddle_gtest_main.cc
const bool is_combined = false;
std::vector<std::vector<int64_t>> feed_target_shapes =
GetFeedTargetShapes(dirname, is_combined);
paddle::framework::LoDTensor input;
// Use normilized image pixels as input data,
// which should be in the range [0.0, 1.0].
feed_target_shapes[0][0] = FLAGS_batch_size;
paddle::framework::DDim input_dims =
paddle::framework::make_ddim(feed_target_shapes[0]);
LOG(INFO) << input_dims;
SetupTensor<float>(&input, input_dims, static_cast<float>(0),
static_cast<float>(1));
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&input);
paddle::framework::FetchType output1;
if (!FLAGS_skip_cpu) {
std::vector<paddle::framework::FetchType*> cpu_fetchs1;
cpu_fetchs1.push_back(&output1);
// Run inference on CPU
LOG(INFO) << "--- CPU Runs: ---";
LOG(INFO) << "Batch size is " << FLAGS_batch_size;
TestInference<paddle::platform::CPUPlace, false, true>(
dirname, cpu_feeds, cpu_fetchs1, FLAGS_repeat, is_combined);
LOG(INFO) << BOOST_GET(paddle::framework::LoDTensor, output1).dims();
}
#ifdef PADDLE_WITH_CUDA
paddle::framework::FetchType output2;
std::vector<paddle::framework::FetchType*> cpu_fetchs2;
cpu_fetchs2.push_back(&output2);
// Run inference on CUDA GPU
LOG(INFO) << "--- GPU Runs: ---";
LOG(INFO) << "Batch size is " << FLAGS_batch_size;
TestInference<paddle::platform::CUDAPlace, false, true>(
dirname, cpu_feeds, cpu_fetchs2, FLAGS_repeat, is_combined);
LOG(INFO) << BOOST_GET(paddle::framework::LoDTensor, output2).dims();
if (!FLAGS_skip_cpu) {
CheckError<float>(BOOST_GET(paddle::framework::LoDTensor, output1),
BOOST_GET(paddle::framework::LoDTensor, output2));
}
// float16 inference requires cuda GPUs with >= 5.3 compute capability
if (!FLAGS_fp16_dirname.empty() &&
paddle::platform::GetCUDAComputeCapability(0) >= 53) {
paddle::framework::FetchType output3;
std::vector<paddle::framework::FetchType*> cpu_fetchs3;
cpu_fetchs3.push_back(&output3);
LOG(INFO) << "--- GPU Runs in float16 mode: ---";
LOG(INFO) << "Batch size is " << FLAGS_batch_size;
TestInference<paddle::platform::CUDAPlace, false, true>(
FLAGS_fp16_dirname, cpu_feeds, cpu_fetchs3, FLAGS_repeat);
CheckError<float>(BOOST_GET(paddle::framework::LoDTensor, output2),
BOOST_GET(paddle::framework::LoDTensor, output3));
}
#endif
}

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paddle/fluid/inference/tests/book/test_inference_label_semantic_roles.cc
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/* 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 "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/fluid/inference/tests/test_helper.h"
DEFINE_string(dirname, "", "Directory of the inference model.");
TEST(inference, label_semantic_roles) {
if (FLAGS_dirname.empty()) {
LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model";
}
LOG(INFO) << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
std::string dirname = FLAGS_dirname;
// 0. Call `paddle::framework::InitDevices()` initialize all the devices
// In unittests, this is done in paddle/testing/paddle_gtest_main.cc
paddle::framework::LoDTensor word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1,
ctx_p2, mark;
paddle::framework::LoD lod{{0, 4, 10}};
int64_t word_dict_len = 44068;
int64_t predicate_dict_len = 3162;
int64_t mark_dict_len = 2;
SetupLoDTensor(&word, lod, static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(&predicate, lod, static_cast<int64_t>(0),
static_cast<int64_t>(predicate_dict_len - 1));
SetupLoDTensor(&ctx_n2, lod, static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(&ctx_n1, lod, static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(&ctx_0, lod, static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(&ctx_p1, lod, static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(&ctx_p2, lod, static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(&mark, lod, static_cast<int64_t>(0),
static_cast<int64_t>(mark_dict_len - 1));
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&word);
cpu_feeds.push_back(&predicate);
cpu_feeds.push_back(&ctx_n2);
cpu_feeds.push_back(&ctx_n1);
cpu_feeds.push_back(&ctx_0);
cpu_feeds.push_back(&ctx_p1);
cpu_feeds.push_back(&ctx_p2);
cpu_feeds.push_back(&mark);
paddle::framework::FetchType output1;
std::vector<paddle::framework::FetchType*> cpu_fetchs1;
cpu_fetchs1.push_back(&output1);
// Run inference on CPU
TestInference<paddle::platform::CPUPlace>(dirname, cpu_feeds, cpu_fetchs1);
auto output1_tensor = BOOST_GET(paddle::framework::LoDTensor, output1);
LOG(INFO) << output1_tensor.lod();
LOG(INFO) << output1_tensor.dims();
#ifdef PADDLE_WITH_CUDA
paddle::framework::FetchType output2;
std::vector<paddle::framework::FetchType*> cpu_fetchs2;
cpu_fetchs2.push_back(&output2);
// Run inference on CUDA GPU
TestInference<paddle::platform::CUDAPlace>(dirname, cpu_feeds, cpu_fetchs2);
auto output2_tensor = BOOST_GET(paddle::framework::LoDTensor, output2);
LOG(INFO) << output2_tensor.lod();
LOG(INFO) << output2_tensor.dims();
CheckError<float>(output1_tensor, output2_tensor);
#endif
}

244
paddle/fluid/inference/tests/book/test_inference_nlp.cc
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@ -1,244 +0,0 @@
/* 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 <time.h>
#include <fstream>
#include <thread> // NOLINT
#include "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/fluid/inference/tests/test_helper.h"
#include "paddle/fluid/platform/cpu_helper.h"
#include "paddle/fluid/framework/feed_fetch_method.h"
DEFINE_string(model_path, "", "Directory of the inference model.");
DEFINE_string(data_file, "", "File of input index data.");
DEFINE_int32(repeat, 100, "Running the inference program repeat times");
DEFINE_bool(prepare_vars, true, "Prepare variables before executor");
DEFINE_int32(num_threads, 1, "Number of threads should be used");
DECLARE_bool(use_mkldnn);
DECLARE_int32(paddle_num_threads);
inline double GetCurrentMs() {
struct timeval time;
gettimeofday(&time, NULL);
return 1e+3 * time.tv_sec + 1e-3 * time.tv_usec;
}
// This function just give dummy data for recognize_digits model.
size_t DummyData(std::vector<paddle::framework::LoDTensor>* out) {
paddle::framework::LoDTensor input;
SetupTensor<float>(&input, {1, 1, 28, 28}, -1.f, 1.f);
out->emplace_back(input);
return 1;
}
// Load the input word index data from file and save into LodTensor.
// Return the size of words.
size_t LoadData(std::vector<paddle::framework::LoDTensor>* out,
const std::string& filename) {
if (filename.empty()) {
return DummyData(out);
}
size_t sz = 0;
std::fstream fin(filename);
std::string line;
out->clear();
while (getline(fin, line)) {
std::istringstream iss(line);
std::vector<int64_t> ids;
std::string field;
while (getline(iss, field, ' ')) {
ids.push_back(stoi(field));
}
if (ids.size() >= 1024) {
// Synced with NLP guys, they will ignore input larger then 1024
continue;
}
paddle::framework::LoDTensor words;
paddle::framework::LoD lod{{0, ids.size()}};
words.set_lod(lod);
int64_t* pdata = words.mutable_data<int64_t>(
{static_cast<int64_t>(ids.size()), 1}, paddle::platform::CPUPlace());
memcpy(pdata, ids.data(), words.numel() * sizeof(int64_t));
out->emplace_back(words);
sz += ids.size();
}
return sz;
}
// Split input data samples into small pieces jobs as balanced as possible,
// according to the number of threads.
void SplitData(
const std::vector<paddle::framework::LoDTensor>& datasets,
std::vector<std::vector<const paddle::framework::LoDTensor*>>* jobs,
const int num_threads) {
size_t s = 0;
jobs->resize(num_threads);
while (s < datasets.size()) {
for (auto it = jobs->begin(); it != jobs->end(); it++) {
it->emplace_back(&datasets[s]);
s++;
if (s >= datasets.size()) {
break;
}
}
}
}
void ThreadRunInfer(
const int tid, paddle::framework::Scope* scope,
const std::vector<std::vector<const paddle::framework::LoDTensor*>>& jobs) {
// maybe framework:ProgramDesc is not thread-safe
paddle::platform::CPUPlace place;
paddle::framework::Executor executor(place);
auto& sub_scope = scope->NewScope();
auto inference_program =
paddle::inference::Load(&executor, scope, FLAGS_model_path);
auto ctx = executor.Prepare(*inference_program, /*block_id*/ 0);
executor.CreateVariables(*inference_program, &sub_scope, /*block_id*/ 0);
const std::vector<std::string>& feed_target_names =
inference_program->GetFeedTargetNames();
const std::vector<std::string>& fetch_target_names =
inference_program->GetFetchTargetNames();
PADDLE_ENFORCE_EQ(fetch_target_names.size(), 1UL);
std::map<std::string, paddle::framework::FetchType*> fetch_targets;
paddle::framework::FetchType outtensor;
fetch_targets[fetch_target_names[0]] = &outtensor;
std::map<std::string, const paddle::framework::LoDTensor*> feed_targets;
PADDLE_ENFORCE_EQ(feed_target_names.size(), 1UL);
// map the data of feed_targets to feed_holder
for (auto* op : inference_program->Block(0).AllOps()) {
if (op->Type() == "feed") {
std::string feed_target_name = op->Output("Out")[0];
int idx = BOOST_GET_CONST(int, op->GetAttr("col"));
paddle::framework::SetFeedVariable(scope, *feed_targets[feed_target_name],
"feed", idx);
}
}
auto& inputs = jobs[tid];
auto start_ms = GetCurrentMs();
for (size_t i = 0; i < inputs.size(); ++i) {
feed_targets[feed_target_names[0]] = inputs[i];
executor.RunPreparedContext(ctx.get(), &sub_scope,
false /*create_local_scope*/);
}
auto stop_ms = GetCurrentMs();
// obtain the data of fetch_targets from fetch_holder
for (auto* op : inference_program->Block(0).AllOps()) {
if (op->Type() == "fetch") {
std::string fetch_target_name = op->Input("X")[0];
int idx = BOOST_GET_CONST(int, op->GetAttr("col"));
*fetch_targets[fetch_target_name] =
BOOST_GET(paddle::framework::LoDTensor,
paddle::framework::GetFetchVariable(*scope, "fetch", idx));
}
}
scope->DeleteScope(&sub_scope);
LOG(INFO) << "Tid: " << tid << ", process " << inputs.size()
<< " samples, avg time per sample: "
<< (stop_ms - start_ms) / inputs.size() << " ms";
}
TEST(inference, nlp) {
if (FLAGS_model_path.empty()) {
LOG(FATAL) << "Usage: ./example --model_path=path/to/your/model";
}
if (FLAGS_data_file.empty()) {
LOG(WARNING) << "No data file provided, will use dummy data!"
<< "Note: if you use nlp model, please provide data file.";
}
LOG(INFO) << "Model Path: " << FLAGS_model_path;
LOG(INFO) << "Data File: " << FLAGS_data_file;
std::vector<paddle::framework::LoDTensor> datasets;
size_t num_total_words = LoadData(&datasets, FLAGS_data_file);
LOG(INFO) << "Number of samples (seq_len<1024): " << datasets.size();
LOG(INFO) << "Total number of words: " << num_total_words;
// 0. Call `paddle::framework::InitDevices()` initialize all the devices
std::unique_ptr<paddle::framework::Scope> scope(
new paddle::framework::Scope());
paddle::platform::SetNumThreads(FLAGS_paddle_num_threads);
double start_ms = 0, stop_ms = 0;
if (FLAGS_num_threads > 1) {
std::vector<std::vector<const paddle::framework::LoDTensor*>> jobs;
SplitData(datasets, &jobs, FLAGS_num_threads);
std::vector<std::unique_ptr<std::thread>> threads;
start_ms = GetCurrentMs();
for (int i = 0; i < FLAGS_num_threads; ++i) {
threads.emplace_back(
new std::thread(ThreadRunInfer, i, scope.get(), std::ref(jobs)));
}
for (int i = 0; i < FLAGS_num_threads; ++i) {
threads[i]->join();
}
stop_ms = GetCurrentMs();
} else {
// 1. Define place, executor, scope
paddle::platform::CPUPlace place;
paddle::framework::Executor executor(place);
// 2. Initialize the inference_program and load parameters
std::unique_ptr<paddle::framework::ProgramDesc> inference_program;
inference_program = InitProgram(&executor, scope.get(), FLAGS_model_path,
/*model combined*/ false);
// always prepare context
std::unique_ptr<paddle::framework::ExecutorPrepareContext> ctx;
ctx = executor.Prepare(*inference_program, 0);
if (FLAGS_prepare_vars) {
executor.CreateVariables(*inference_program, scope.get(), 0);
}
// preapre fetch
const std::vector<std::string>& fetch_target_names =
inference_program->GetFetchTargetNames();
PADDLE_ENFORCE_EQ(fetch_target_names.size(), 1UL);
std::map<std::string, paddle::framework::FetchType*> fetch_targets;
paddle::framework::FetchType outtensor;
fetch_targets[fetch_target_names[0]] = &outtensor;
// prepare feed
const std::vector<std::string>& feed_target_names =
inference_program->GetFeedTargetNames();
PADDLE_ENFORCE_EQ(feed_target_names.size(), 1UL);
std::map<std::string, const paddle::framework::LoDTensor*> feed_targets;
// feed data and run
start_ms = GetCurrentMs();
for (size_t i = 0; i < datasets.size(); ++i) {
feed_targets[feed_target_names[0]] = &(datasets[i]);
executor.RunPreparedContext(ctx.get(), scope.get(), &feed_targets,
&fetch_targets, !FLAGS_prepare_vars);
}
stop_ms = GetCurrentMs();
LOG(INFO) << "Tid: 0, process " << datasets.size()
<< " samples, avg time per sample: "
<< (stop_ms - start_ms) / datasets.size() << " ms";
}
LOG(INFO) << "Total inference time with " << FLAGS_num_threads
<< " threads : " << (stop_ms - start_ms) / 1000.0
<< " sec, QPS: " << datasets.size() / ((stop_ms - start_ms) / 1000);
}

70
paddle/fluid/inference/tests/book/test_inference_recognize_digits.cc
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/* 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 "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/fluid/inference/tests/test_helper.h"
DEFINE_string(dirname, "", "Directory of the inference model.");
DEFINE_int32(batch_size, 1, "Batch size of input data");
DEFINE_int32(repeat, 1, "Running the inference program repeat times");
TEST(inference, recognize_digits) {
if (FLAGS_dirname.empty() || FLAGS_batch_size < 1 || FLAGS_repeat < 1) {
LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model "
"--batch_size=1 --repeat=1";
}
LOG(INFO) << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
std::string dirname = FLAGS_dirname;
// 0. Call `paddle::framework::InitDevices()` initialize all the devices
// In unittests, this is done in paddle/testing/paddle_gtest_main.cc
paddle::framework::LoDTensor input;
// Use normilized image pixels as input data,
// which should be in the range [-1.0, 1.0].
SetupTensor<float>(&input, {FLAGS_batch_size, 1, 28, 28},
static_cast<float>(-1), static_cast<float>(1));
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&input);
for (auto is_combined : {false, true}) {
paddle::framework::FetchType output1;
std::vector<paddle::framework::FetchType*> cpu_fetchs1;
cpu_fetchs1.push_back(&output1);
// Run inference on CPU
LOG(INFO) << "--- CPU Runs: is_combined=" << is_combined << " ---";
TestInference<paddle::platform::CPUPlace>(dirname, cpu_feeds, cpu_fetchs1,
FLAGS_repeat, is_combined);
auto output1_tensor = BOOST_GET(paddle::framework::LoDTensor, output1);
LOG(INFO) << output1_tensor.dims();
#ifdef PADDLE_WITH_CUDA
paddle::framework::FetchType output2;
std::vector<paddle::framework::FetchType*> cpu_fetchs2;
cpu_fetchs2.push_back(&output2);
// Run inference on CUDA GPU
LOG(INFO) << "--- GPU Runs: is_combined=" << is_combined << " ---";
TestInference<paddle::platform::CUDAPlace>(dirname, cpu_feeds, cpu_fetchs2,
FLAGS_repeat, is_combined);
auto output2_tensor = BOOST_GET(paddle::framework::LoDTensor, output2);
LOG(INFO) << output2_tensor.dims();
CheckError<float>(output1_tensor, output2_tensor);
#endif
}
}

89
paddle/fluid/inference/tests/book/test_inference_recommender_system.cc
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/* 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 "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/fluid/inference/tests/test_helper.h"
DEFINE_string(dirname, "", "Directory of the inference model.");
TEST(inference, recommender_system) {
if (FLAGS_dirname.empty()) {
LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model";
}
LOG(INFO) << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
std::string dirname = FLAGS_dirname;
// 0. Call `paddle::framework::InitDevices()` initialize all the devices
// In unittests, this is done in paddle/testing/paddle_gtest_main.cc
int64_t batch_size = 1;
paddle::framework::LoDTensor user_id, gender_id, age_id, job_id, movie_id,
category_id, movie_title;
// Use the first data from paddle.dataset.movielens.test() as input
std::vector<int64_t> user_id_data = {1};
SetupTensor<int64_t>(&user_id, {batch_size, 1}, user_id_data);
std::vector<int64_t> gender_id_data = {1};
SetupTensor<int64_t>(&gender_id, {batch_size, 1}, gender_id_data);
std::vector<int64_t> age_id_data = {0};
SetupTensor<int64_t>(&age_id, {batch_size, 1}, age_id_data);
std::vector<int64_t> job_id_data = {10};
SetupTensor<int64_t>(&job_id, {batch_size, 1}, job_id_data);
std::vector<int64_t> movie_id_data = {783};
SetupTensor<int64_t>(&movie_id, {batch_size, 1}, movie_id_data);
std::vector<int64_t> category_id_data = {10, 8, 9};
SetupLoDTensor<int64_t>(&category_id, {3, 1}, {{0, 3}}, category_id_data);
std::vector<int64_t> movie_title_data = {1069, 4140, 2923, 710, 988};
SetupLoDTensor<int64_t>(&movie_title, {5, 1}, {{0, 5}}, movie_title_data);
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&user_id);
cpu_feeds.push_back(&gender_id);
cpu_feeds.push_back(&age_id);
cpu_feeds.push_back(&job_id);
cpu_feeds.push_back(&movie_id);
cpu_feeds.push_back(&category_id);
cpu_feeds.push_back(&movie_title);
paddle::framework::FetchType output1;
std::vector<paddle::framework::FetchType*> cpu_fetchs1;
cpu_fetchs1.push_back(&output1);
// Run inference on CPU
TestInference<paddle::platform::CPUPlace>(dirname, cpu_feeds, cpu_fetchs1);
auto output1_tensor = BOOST_GET(paddle::framework::LoDTensor, output1);
LOG(INFO) << output1_tensor.dims();
#ifdef PADDLE_WITH_CUDA
paddle::framework::FetchType output2;
std::vector<paddle::framework::FetchType*> cpu_fetchs2;
cpu_fetchs2.push_back(&output2);
// Run inference on CUDA GPU
TestInference<paddle::platform::CUDAPlace>(dirname, cpu_feeds, cpu_fetchs2);
auto output2_tensor = BOOST_GET(paddle::framework::LoDTensor, output2);
LOG(INFO) << output2_tensor.dims();
CheckError<float>(output1_tensor, output2_tensor);
#endif
}

67
paddle/fluid/inference/tests/book/test_inference_rnn_encoder_decoder.cc
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/* 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 "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/fluid/inference/tests/test_helper.h"
DEFINE_string(dirname, "", "Directory of the inference model.");
TEST(inference, rnn_encoder_decoder) {
if (FLAGS_dirname.empty()) {
LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model";
}
LOG(INFO) << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
std::string dirname = FLAGS_dirname;
// 0. Call `paddle::framework::InitDevices()` initialize all the devices
// In unittests, this is done in paddle/testing/paddle_gtest_main.cc
paddle::framework::LoDTensor word_data, trg_word;
paddle::framework::LoD lod{{0, 4, 10}};
SetupLoDTensor(&word_data, lod, static_cast<int64_t>(0),
static_cast<int64_t>(1));
SetupLoDTensor(&trg_word, lod, static_cast<int64_t>(0),
static_cast<int64_t>(1));
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&word_data);
cpu_feeds.push_back(&trg_word);
paddle::framework::FetchType output1;
std::vector<paddle::framework::FetchType*> cpu_fetchs1;
cpu_fetchs1.push_back(&output1);
// Run inference on CPU
TestInference<paddle::platform::CPUPlace>(dirname, cpu_feeds, cpu_fetchs1);
auto output1_tensor = BOOST_GET(paddle::framework::LoDTensor, output1);
LOG(INFO) << output1_tensor.lod();
LOG(INFO) << output1_tensor.dims();
#ifdef PADDLE_WITH_CUDA
paddle::framework::FetchType output2;
std::vector<paddle::framework::FetchType*> cpu_fetchs2;
cpu_fetchs2.push_back(&output2);
// Run inference on CUDA GPU
TestInference<paddle::platform::CUDAPlace>(dirname, cpu_feeds, cpu_fetchs2);
auto output2_tensor = BOOST_GET(paddle::framework::LoDTensor, output2);
LOG(INFO) << output2_tensor.lod();
LOG(INFO) << output2_tensor.dims();
CheckError<float>(output1_tensor, output2_tensor);
#endif
}

65
paddle/fluid/inference/tests/book/test_inference_understand_sentiment.cc
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/* 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 "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/fluid/inference/tests/test_helper.h"
DEFINE_string(dirname, "", "Directory of the inference model.");
TEST(inference, understand_sentiment) {
if (FLAGS_dirname.empty()) {
LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model";
}
LOG(INFO) << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
std::string dirname = FLAGS_dirname;
// 0. Call `paddle::framework::InitDevices()` initialize all the devices
// In unittests, this is done in paddle/testing/paddle_gtest_main.cc
paddle::framework::LoDTensor words;
paddle::framework::LoD lod{{0, 4, 10}};
int64_t word_dict_len = 5147;
SetupLoDTensor(&words, lod, static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&words);
paddle::framework::FetchType output1;
std::vector<paddle::framework::FetchType*> cpu_fetchs1;
cpu_fetchs1.push_back(&output1);
// Run inference on CPU
TestInference<paddle::platform::CPUPlace>(dirname, cpu_feeds, cpu_fetchs1);
auto output1_tensor = BOOST_GET(paddle::framework::LoDTensor, output1);
LOG(INFO) << output1_tensor.lod();
LOG(INFO) << output1_tensor.dims();
#ifdef PADDLE_WITH_CUDA
paddle::framework::FetchType output2;
std::vector<paddle::framework::FetchType*> cpu_fetchs2;
cpu_fetchs2.push_back(&output2);
// Run inference on CUDA GPU
TestInference<paddle::platform::CUDAPlace>(dirname, cpu_feeds, cpu_fetchs2);
auto output2_tensor = BOOST_GET(paddle::framework::LoDTensor, output2);
LOG(INFO) << output2_tensor.lod();
LOG(INFO) << output2_tensor.dims();
CheckError<float>(output1_tensor, output2_tensor);
#endif
}

70
paddle/fluid/inference/tests/book/test_inference_word2vec.cc
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/* 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 "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/fluid/inference/tests/test_helper.h"
DEFINE_string(dirname, "", "Directory of the inference model.");
TEST(inference, word2vec) {
if (FLAGS_dirname.empty()) {
LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model";
}
LOG(INFO) << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
std::string dirname = FLAGS_dirname;
// 0. Call `paddle::framework::InitDevices()` initialize all the devices
// In unittests, this is done in paddle/testing/paddle_gtest_main.cc
paddle::framework::LoDTensor first_word, second_word, third_word, fourth_word;
paddle::framework::LoD lod{{0, 1}};
int64_t dict_size = 2073; // The size of dictionary
SetupLoDTensor(&first_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(&second_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(&third_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(&fourth_word, lod, static_cast<int64_t>(0), dict_size - 1);
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&first_word);
cpu_feeds.push_back(&second_word);
cpu_feeds.push_back(&third_word);
cpu_feeds.push_back(&fourth_word);
paddle::framework::FetchType output1;
std::vector<paddle::framework::FetchType*> cpu_fetchs1;
cpu_fetchs1.push_back(&output1);
// Run inference on CPU
TestInference<paddle::platform::CPUPlace>(dirname, cpu_feeds, cpu_fetchs1);
auto output1_tensor = BOOST_GET(paddle::framework::LoDTensor, output1);
LOG(INFO) << output1_tensor.lod();
LOG(INFO) << output1_tensor.dims();
#ifdef PADDLE_WITH_CUDA
paddle::framework::FetchType output2;
std::vector<paddle::framework::FetchType*> cpu_fetchs2;
cpu_fetchs2.push_back(&output2);
// Run inference on CUDA GPU
TestInference<paddle::platform::CUDAPlace>(dirname, cpu_feeds, cpu_fetchs2);
auto output2_tensor = BOOST_GET(paddle::framework::LoDTensor, output2);
LOG(INFO) << output2_tensor.lod();
LOG(INFO) << output2_tensor.dims();
CheckError<float>(output1_tensor, output2_tensor);
#endif
}
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