Merge pull request #13663 from luotao1/resnet50_ut
add resnet50 inference unit-testrevert-13637-optimize-opyreader
Tao Luo
6 years ago
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GitHub
commit
d770b9bda3
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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 <fstream>
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#include <iostream>
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#include "paddle/fluid/inference/tests/api/tester_helper.h"
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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->param_file = FLAGS_infer_model + "/params";
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cfg->prog_file = FLAGS_infer_model + "/model";
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cfg->use_gpu = false;
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cfg->device = 0;
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cfg->enable_ir_optim = true;
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cfg->specify_input_name = true;
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}
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void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
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PADDLE_ENFORCE_EQ(FLAGS_test_all_data, 0, "Only have single batch of data.");
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PaddleTensor input;
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// channel=3, height/width=318
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std::vector<int> shape({FLAGS_batch_size, 3, 318, 318});
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input.shape = shape;
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input.dtype = PaddleDType::FLOAT32;
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// fill input data, for profile easily, do not use random data here.
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size_t size = FLAGS_batch_size * 3 * 318 * 318;
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input.data.Resize(size * sizeof(float));
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float *input_data = static_cast<float *>(input.data.data());
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for (size_t i = 0; i < size; i++) {
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*(input_data + i) = static_cast<float>(i) / size;
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}
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std::vector<PaddleTensor> input_slots;
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input_slots.assign({input});
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(*inputs).emplace_back(input_slots);
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}
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// Easy for profiling independently.
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TEST(Analyzer_resnet50, profile) {
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AnalysisConfig cfg;
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SetConfig(&cfg);
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std::vector<PaddleTensor> outputs;
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std::vector<std::vector<PaddleTensor>> input_slots_all;
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SetInput(&input_slots_all);
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TestPrediction(cfg, input_slots_all, &outputs, FLAGS_num_threads);
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if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
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PADDLE_ENFORCE_EQ(outputs.size(), 1UL);
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size_t size = GetSize(outputs[0]);
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// output is a 512-dimension feature
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EXPECT_EQ(size, 512 * FLAGS_batch_size);
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}
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}
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// Check the fuse status
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TEST(Analyzer_resnet50, fuse_statis) {
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AnalysisConfig cfg;
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SetConfig(&cfg);
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int num_ops;
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auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
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auto fuse_statis = GetFuseStatis(
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static_cast<AnalysisPredictor *>(predictor.get()), &num_ops);
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ASSERT_TRUE(fuse_statis.count("fc_fuse"));
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EXPECT_EQ(fuse_statis.at("fc_fuse"), 1);
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}
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// Compare result of NativeConfig and AnalysisConfig
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TEST(Analyzer_resnet50, compare) {
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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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CompareNativeAndAnalysis(cfg, input_slots_all);
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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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