feature/simple inference demo (#11105)
Yan Chunwei
7 years ago
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GitHub
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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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#
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inference_api_test(simple_on_word2vec ARGS test_word2vec)
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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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/*
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* This file contains a simple demo for how to take a model for inference.
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*/
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#include <glog/logging.h>
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#include <gtest/gtest.h>
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#include <memory>
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#include "paddle/contrib/inference/paddle_inference_api.h"
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namespace paddle {
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namespace demo {
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DEFINE_string(dirname, "", "Directory of the inference model.");
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void Main(bool use_gpu) {
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//# 1. Create PaddlePredictor with a config.
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NativeConfig config;
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config.model_dir = FLAGS_dirname + "word2vec.inference.model";
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config.use_gpu = use_gpu;
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config.fraction_of_gpu_memory = 0.15;
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config.device = 0;
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auto predictor =
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CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
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for (int batch_id = 0; batch_id < 3; batch_id++) {
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//# 2. Prepare input.
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int64_t data[4] = {1, 2, 3, 4};
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PaddleBuf buf{.data = data, .length = sizeof(data)};
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PaddleTensor tensor{.name = "",
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.shape = std::vector<int>({4, 1}),
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.data = buf,
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.dtype = PaddleDType::INT64};
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// For simplicity, we set all the slots with the same data.
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std::vector<PaddleTensor> slots(4, tensor);
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//# 3. Run
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std::vector<PaddleTensor> outputs;
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CHECK(predictor->Run(slots, &outputs));
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//# 4. Get output.
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ASSERT_EQ(outputs.size(), 1);
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LOG(INFO) << "output buffer size: " << outputs.front().data.length;
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const size_t num_elements = outputs.front().data.length / sizeof(float);
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// The outputs' buffers are in CPU memory.
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for (size_t i = 0; i < std::min(5UL, num_elements); i++) {
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LOG(INFO) << static_cast<float*>(outputs.front().data.data)[i];
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}
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}
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}
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TEST(demo, word2vec_cpu) { Main(false /*use_gpu*/); }
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TEST(demo, word2vec_gpu) { Main(true /*use_gpu*/); }
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} // namespace demo
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} // namespace paddle
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