Add C++ inference unittest of recommender system (#8227)
* Save the inference model in Python example of recommender_system. * Add infer() in Python unittest recommender_system. * Add C++ inference unittest of recommender_system.emailweixu-patch-1
Yiqun Liu
8 years ago
committed by
kexinzhao
parent
b257ca9a77
commit
1185a1b5ab
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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 <gtest/gtest.h>
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#include "gflags/gflags.h"
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#include "test_helper.h"
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DEFINE_string(dirname, "", "Directory of the inference model.");
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TEST(inference, recommender_system) {
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if (FLAGS_dirname.empty()) {
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LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model";
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}
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LOG(INFO) << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
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std::string dirname = FLAGS_dirname;
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// 0. Call `paddle::framework::InitDevices()` initialize all the devices
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// In unittests, this is done in paddle/testing/paddle_gtest_main.cc
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int64_t batch_size = 1;
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paddle::framework::LoDTensor user_id, gender_id, age_id, job_id, movie_id,
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category_id, movie_title;
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// Use the first data from paddle.dataset.movielens.test() as input
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std::vector<int64_t> user_id_data = {1};
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SetupTensor<int64_t>(user_id, {batch_size, 1}, user_id_data);
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std::vector<int64_t> gender_id_data = {1};
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SetupTensor<int64_t>(gender_id, {batch_size, 1}, gender_id_data);
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std::vector<int64_t> age_id_data = {0};
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SetupTensor<int64_t>(age_id, {batch_size, 1}, age_id_data);
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std::vector<int64_t> job_id_data = {10};
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SetupTensor<int64_t>(job_id, {batch_size, 1}, job_id_data);
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std::vector<int64_t> movie_id_data = {783};
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SetupTensor<int64_t>(movie_id, {batch_size, 1}, movie_id_data);
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std::vector<int64_t> category_id_data = {10, 8, 9};
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SetupLoDTensor<int64_t>(category_id, {3, 1}, {{0, 3}}, category_id_data);
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std::vector<int64_t> movie_title_data = {1069, 4140, 2923, 710, 988};
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SetupLoDTensor<int64_t>(movie_title, {5, 1}, {{0, 5}}, movie_title_data);
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std::vector<paddle::framework::LoDTensor*> cpu_feeds;
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cpu_feeds.push_back(&user_id);
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cpu_feeds.push_back(&gender_id);
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cpu_feeds.push_back(&age_id);
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cpu_feeds.push_back(&job_id);
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cpu_feeds.push_back(&movie_id);
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cpu_feeds.push_back(&category_id);
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cpu_feeds.push_back(&movie_title);
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paddle::framework::LoDTensor output1;
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std::vector<paddle::framework::LoDTensor*> cpu_fetchs1;
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cpu_fetchs1.push_back(&output1);
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// Run inference on CPU
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TestInference<paddle::platform::CPUPlace>(dirname, cpu_feeds, cpu_fetchs1);
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LOG(INFO) << output1.dims();
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#ifdef PADDLE_WITH_CUDA
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paddle::framework::LoDTensor output2;
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std::vector<paddle::framework::LoDTensor*> cpu_fetchs2;
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cpu_fetchs2.push_back(&output2);
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// Run inference on CUDA GPU
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TestInference<paddle::platform::CUDAPlace>(dirname, cpu_feeds, cpu_fetchs2);
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LOG(INFO) << output2.dims();
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CheckError<float>(output1, output2);
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#endif
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}
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@ -1 +1 @@
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recognize_digits_*.inference.model
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*.inference.model
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