Merge pull request #16399 from sfraczek/sfraczek/analyzer_int8_resnet50_test
create test for quantized resnet50revert-16555-model_data_cryption_link_all_lib
Tao Luo
6 years ago
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GitHub
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5b24002389
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/* Copyright (c) 2019 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 <fstream>
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#include <iostream>
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#include "paddle/fluid/inference/api/paddle_analysis_config.h"
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#include "paddle/fluid/inference/tests/api/tester_helper.h"
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DEFINE_int32(iterations, 0, "Number of iterations");
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namespace paddle {
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namespace inference {
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namespace analysis {
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void SetConfig(AnalysisConfig *cfg) {
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cfg->SetModel(FLAGS_infer_model);
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cfg->SetProgFile("__model__");
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cfg->DisableGpu();
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cfg->SwitchIrOptim();
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cfg->SwitchSpecifyInputNames(false);
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cfg->SetCpuMathLibraryNumThreads(FLAGS_paddle_num_threads);
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cfg->EnableMKLDNN();
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}
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template <typename T>
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class TensorReader {
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public:
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TensorReader(std::ifstream &file, size_t beginning_offset,
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std::vector<int> shape, std::string name)
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: file_(file), position(beginning_offset), shape_(shape), name_(name) {
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numel =
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std::accumulate(shape_.begin(), shape_.end(), 1, std::multiplies<T>());
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}
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PaddleTensor NextBatch() {
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PaddleTensor tensor;
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tensor.name = name_;
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tensor.shape = shape_;
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tensor.dtype = GetPaddleDType<T>();
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tensor.data.Resize(numel * sizeof(T));
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file_.seekg(position);
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file_.read(static_cast<char *>(tensor.data.data()), numel * sizeof(T));
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position = file_.tellg();
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if (file_.eof()) LOG(ERROR) << name_ << ": reached end of stream";
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if (file_.fail())
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throw std::runtime_error(name_ + ": failed reading file.");
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return tensor;
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}
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protected:
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std::ifstream &file_;
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size_t position;
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std::vector<int> shape_;
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std::string name_;
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size_t numel;
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};
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std::shared_ptr<std::vector<PaddleTensor>> GetWarmupData(
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const std::vector<std::vector<PaddleTensor>> &test_data, int num_images) {
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int test_data_batch_size = test_data[0][0].shape[0];
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CHECK_LE(static_cast<size_t>(num_images),
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test_data.size() * test_data_batch_size);
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PaddleTensor images;
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images.name = "input";
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images.shape = {num_images, 3, 224, 224};
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images.dtype = PaddleDType::FLOAT32;
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images.data.Resize(sizeof(float) * num_images * 3 * 224 * 224);
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PaddleTensor labels;
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labels.name = "labels";
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labels.shape = {num_images, 1};
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labels.dtype = PaddleDType::INT64;
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labels.data.Resize(sizeof(int64_t) * num_images);
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for (int i = 0; i < num_images; i++) {
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auto batch = i / test_data_batch_size;
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auto element_in_batch = i % test_data_batch_size;
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std::copy_n(static_cast<float *>(test_data[batch][0].data.data()) +
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element_in_batch * 3 * 224 * 224,
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3 * 224 * 224,
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static_cast<float *>(images.data.data()) + i * 3 * 224 * 224);
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std::copy_n(static_cast<int64_t *>(test_data[batch][1].data.data()) +
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element_in_batch,
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1, static_cast<int64_t *>(labels.data.data()) + i);
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}
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auto warmup_data = std::make_shared<std::vector<PaddleTensor>>(2);
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(*warmup_data)[0] = std::move(images);
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(*warmup_data)[1] = std::move(labels);
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return warmup_data;
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}
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void SetInput(std::vector<std::vector<PaddleTensor>> *inputs,
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int32_t batch_size = FLAGS_batch_size) {
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std::ifstream file(FLAGS_infer_data, std::ios::binary);
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if (!file) {
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FAIL() << "Couldn't open file: " << FLAGS_infer_data;
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}
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int64_t total_images{0};
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file.read(reinterpret_cast<char *>(&total_images), sizeof(total_images));
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LOG(INFO) << "Total images in file: " << total_images;
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std::vector<int> image_batch_shape{batch_size, 3, 224, 224};
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std::vector<int> label_batch_shape{batch_size, 1};
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auto labels_offset_in_file =
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static_cast<size_t>(file.tellg()) +
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sizeof(float) * total_images *
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std::accumulate(image_batch_shape.begin() + 1,
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image_batch_shape.end(), 1, std::multiplies<int>());
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TensorReader<float> image_reader(file, 0, image_batch_shape, "input");
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TensorReader<int64_t> label_reader(file, labels_offset_in_file,
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label_batch_shape, "label");
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auto iterations = total_images / batch_size;
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if (FLAGS_iterations > 0 && FLAGS_iterations < iterations)
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iterations = FLAGS_iterations;
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for (auto i = 0; i < iterations; i++) {
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auto images = image_reader.NextBatch();
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auto labels = label_reader.NextBatch();
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inputs->emplace_back(
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std::vector<PaddleTensor>{std::move(images), std::move(labels)});
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}
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}
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TEST(Analyzer_int8_resnet50, quantization) {
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AnalysisConfig cfg;
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SetConfig(&cfg);
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AnalysisConfig q_cfg;
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SetConfig(&q_cfg);
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std::vector<std::vector<PaddleTensor>> input_slots_all;
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SetInput(&input_slots_all, 100);
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std::shared_ptr<std::vector<PaddleTensor>> warmup_data =
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GetWarmupData(input_slots_all, 100);
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q_cfg.EnableMkldnnQuantizer();
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q_cfg.mkldnn_quantizer_config()->SetWarmupData(warmup_data);
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q_cfg.mkldnn_quantizer_config()->SetWarmupBatchSize(100);
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CompareQuantizedAndAnalysis(
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reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
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reinterpret_cast<const PaddlePredictor::Config *>(&q_cfg),
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input_slots_all);
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}
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TEST(Analyzer_int8_resnet50, profile) {
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AnalysisConfig cfg;
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SetConfig(&cfg);
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std::vector<std::vector<PaddleTensor>> input_slots_all;
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SetInput(&input_slots_all);
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std::shared_ptr<std::vector<PaddleTensor>> warmup_data =
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GetWarmupData(input_slots_all, 100);
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cfg.EnableMkldnnQuantizer();
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cfg.mkldnn_quantizer_config()->SetWarmupData(warmup_data);
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cfg.mkldnn_quantizer_config()->SetWarmupBatchSize(100);
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std::vector<PaddleTensor> outputs;
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TestPrediction(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
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input_slots_all, &outputs, FLAGS_num_threads);
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}
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} // namespace analysis
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} // namespace inference
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} // namespace paddle
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