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119 lines
3.8 KiB
119 lines
3.8 KiB
// Copyright (c) 2018 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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#include "paddle/fluid/inference/analysis/analyzer.h"
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#include <gflags/gflags.h>
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#include <glog/logging.h> // use glog instead of PADDLE_ENFORCE to avoid importing other paddle header files.
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#include <gtest/gtest.h>
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#include <fstream>
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#include "paddle/fluid/framework/ir/pass.h"
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#include "paddle/fluid/inference/analysis/ut_helper.h"
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#include "paddle/fluid/inference/api/helper.h"
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#include "paddle/fluid/inference/api/paddle_inference_api.h"
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#include "paddle/fluid/inference/api/paddle_inference_pass.h"
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#include "paddle/fluid/inference/api/timer.h"
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DEFINE_string(infer_model, "", "Directory of the inference model.");
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DEFINE_string(infer_data, "", "Path of the dataset.");
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DEFINE_int32(batch_size, 1, "batch size.");
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DEFINE_int32(repeat, 1, "How many times to repeat run.");
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DEFINE_int32(topn, -1, "Run top n batches of data to save time");
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namespace paddle {
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namespace inference {
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struct DataReader {
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explicit DataReader(const std::string &path)
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: file(new std::ifstream(path)) {}
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bool NextBatch(PaddleTensor *tensor, int batch_size) {
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PADDLE_ENFORCE_EQ(batch_size, 1);
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std::string line;
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tensor->lod.clear();
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tensor->lod.emplace_back(std::vector<size_t>({0}));
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std::vector<int64_t> data;
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for (int i = 0; i < batch_size; i++) {
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if (!std::getline(*file, line)) return false;
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inference::split_to_int64(line, ' ', &data);
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}
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tensor->lod.front().push_back(data.size());
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tensor->data.Resize(data.size() * sizeof(int64_t));
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memcpy(tensor->data.data(), data.data(), data.size() * sizeof(int64_t));
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tensor->shape.clear();
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tensor->shape.push_back(data.size());
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tensor->shape.push_back(1);
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return true;
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}
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std::unique_ptr<std::ifstream> file;
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};
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void Main(int batch_size) {
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// shape --
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// Create Predictor --
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AnalysisConfig config;
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config.model_dir = FLAGS_infer_model;
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config.use_gpu = false;
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config.enable_ir_optim = true;
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auto predictor =
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CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
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config);
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std::vector<PaddleTensor> input_slots(1);
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// one batch starts
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// data --
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auto &input = input_slots[0];
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input.dtype = PaddleDType::INT64;
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inference::Timer timer;
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double sum = 0;
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std::vector<PaddleTensor> output_slots;
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int num_batches = 0;
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for (int t = 0; t < FLAGS_repeat; t++) {
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DataReader reader(FLAGS_infer_data);
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while (reader.NextBatch(&input, FLAGS_batch_size)) {
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if (FLAGS_topn > 0 && num_batches > FLAGS_topn) break;
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timer.tic();
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CHECK(predictor->Run(input_slots, &output_slots));
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sum += timer.toc();
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++num_batches;
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}
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}
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PrintTime(batch_size, FLAGS_repeat, 1, 0, sum / FLAGS_repeat);
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// Get output
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LOG(INFO) << "get outputs " << output_slots.size();
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for (auto &output : output_slots) {
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LOG(INFO) << "output.shape: " << to_string(output.shape);
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// no lod ?
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CHECK_EQ(output.lod.size(), 0UL);
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LOG(INFO) << "output.dtype: " << output.dtype;
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std::stringstream ss;
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for (int i = 0; i < 5; i++) {
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ss << static_cast<float *>(output.data.data())[i] << " ";
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}
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LOG(INFO) << "output.data summary: " << ss.str();
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// one batch ends
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}
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}
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TEST(text_classification, basic) { Main(FLAGS_batch_size); }
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} // namespace inference
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} // namespace paddle
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