Xin Pan
7 years ago
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376c948e88
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2f0df56422
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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#pragma once
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#include <glog/logging.h>
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#include <memory>
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#include <string>
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#include <vector>
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#include "paddle/contrib/inference/paddle_inference_api.h"
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#include "paddle/fluid/framework/ddim.h"
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#include "paddle/fluid/framework/init.h"
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#include "paddle/fluid/framework/lod_tensor.h"
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#include "paddle/fluid/inference/io.h"
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#include "paddle/fluid/platform/profiler.h"
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namespace paddle {
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struct VisConfig : public PaddlePredictor::Config {
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int device;
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float fraction_of_gpu_memory;
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std::string prog_file;
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std::string param_file;
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bool share_variables;
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};
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/*
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* Do not use this, just a demo indicating how to customize a Predictor.
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*/
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class PaddlePredictorImpl : public PaddlePredictor {
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public:
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explicit PaddlePredictorImpl(const VisConfig &config) : config_(config) {}
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bool Init();
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bool Run(const std::vector<PaddleTensor> &inputs,
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std::vector<PaddleTensor> *output_data) override;
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std::unique_ptr<PaddlePredictor> Clone() override;
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~PaddlePredictorImpl() override{};
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private:
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bool InitShared(PaddlePredictorImpl *cls);
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bool SetFeed(const std::vector<PaddleTensor> &input_datas,
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std::vector<paddle::framework::LoDTensor> *feeds);
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bool GetFetch(const std::vector<paddle::framework::LoDTensor> &fetchs,
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std::vector<PaddleTensor> *output_data);
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VisConfig config_;
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paddle::platform::Place place_;
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std::unique_ptr<paddle::framework::Executor> executor_;
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std::unique_ptr<paddle::framework::Scope> scope_;
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std::unique_ptr<paddle::framework::ExecutorPrepareContext> ctx_;
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std::unique_ptr<paddle::framework::ProgramDesc> inference_program_;
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std::vector<std::string> feed_target_names_;
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std::vector<std::string> fetch_target_names_;
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};
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std::unique_ptr<PaddlePredictorImpl> CreatePaddlePredictorImpl(
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const VisConfig &config);
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} // namespace paddle
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@ -0,0 +1,83 @@
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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 <glog/logging.h>
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#include <gtest/gtest.h>
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#include "gflags/gflags.h"
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#include "paddle/contrib/inference/paddle_inference_api_impl.h"
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#include "paddle/fluid/inference/tests/test_helper.h"
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DEFINE_string(dirname, "", "Directory of the inference model.");
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namespace paddle {
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PaddleTensor LodTensorToPaddleTensor(framework::LoDTensor* t) {
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PaddleTensor pt;
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pt.data.data = t->data<void>();
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if (t->type() == typeid(int64_t)) {
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pt.data.length = t->numel() * sizeof(int64_t);
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pt.dtype = PaddleDType::INT64;
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} else if (t->type() == typeid(float)) {
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pt.data.length = t->numel() * sizeof(float);
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pt.dtype = PaddleDType::FLOAT32;
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} else {
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LOG(FATAL) << "unsupported type.";
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}
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pt.shape = framework::vectorize2int(t->dims());
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return pt;
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}
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TEST(paddle_inference_api_impl, word2vec) {
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VisConfig config;
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config.model_dir = FLAGS_dirname + "word2vec.inference.model";
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LOG(INFO) << "dirname " << config.model_dir;
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config.fraction_of_gpu_memory = 0.85;
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config.device = 0;
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config.share_variables = true;
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std::unique_ptr<PaddlePredictorImpl> predictor =
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CreatePaddlePredictorImpl(config);
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framework::LoDTensor first_word, second_word, third_word, fourth_word;
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framework::LoD lod{{0, 1}};
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int64_t dict_size = 2073; // The size of dictionary
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SetupLoDTensor(&first_word, lod, static_cast<int64_t>(0), dict_size - 1);
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SetupLoDTensor(&second_word, lod, static_cast<int64_t>(0), dict_size - 1);
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SetupLoDTensor(&third_word, lod, static_cast<int64_t>(0), dict_size - 1);
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SetupLoDTensor(&fourth_word, lod, static_cast<int64_t>(0), dict_size - 1);
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std::vector<PaddleTensor> cpu_feeds;
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cpu_feeds.push_back(LodTensorToPaddleTensor(&first_word));
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cpu_feeds.push_back(LodTensorToPaddleTensor(&second_word));
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cpu_feeds.push_back(LodTensorToPaddleTensor(&third_word));
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cpu_feeds.push_back(LodTensorToPaddleTensor(&fourth_word));
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std::vector<PaddleTensor> outputs;
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ASSERT_TRUE(predictor->Run(cpu_feeds, &outputs));
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ASSERT_EQ(outputs.size(), 1);
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for (size_t i = 0; i < outputs.size(); ++i) {
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size_t len = outputs[i].data.length;
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float* data = static_cast<float*>(outputs[i].data.data);
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for (int j = 0; j < len / sizeof(float); ++j) {
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ASSERT_LT(data[j], 1.0);
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ASSERT_GT(data[j], -1.0);
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}
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free(outputs[i].data.data);
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}
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}
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} // namespace paddle
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