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add det_mv3_db & LeViT test case in pr-ci-inference (#34803)
* add det_mv3_db & LeViT test case in pr-ci-inference * fix LeViT model dir bugs * fix grammar error
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‎paddle/fluid/inference/tests/infer_ut/CMakeLists.txt‎

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@@ -66,7 +66,7 @@ else()
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if(WITH_MKL)
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set(FLAG_OPENMP "-fopenmp")
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endif()
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 ${FLAG_OPENMP}")
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++17 ${FLAG_OPENMP}")
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endif()
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if(WITH_GPU)

‎paddle/fluid/inference/tests/infer_ut/run.sh‎

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@@ -65,6 +65,18 @@ for model_name in $download_list; do
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download $url_prefix $model_name
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done
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ocr_download_list='ocr_det_mv3_db'
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for model_name in $ocr_download_list; do
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url_prefix="https://paddle-qa.bj.bcebos.com/inference_model/2.1.1/ocr"
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download $url_prefix $model_name
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done
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clas_download_list='LeViT'
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for model_name in $clas_download_list; do
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url_prefix="https://paddle-qa.bj.bcebos.com/inference_model/2.1.1/class"
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download $url_prefix $model_name
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done
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# compile and run test
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cd $current_dir
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mkdir -p build
@@ -92,6 +104,46 @@ if [ $USE_TENSORRT == ON -a $TEST_GPU_CPU == ON ]; then
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fi
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fi
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# ---------tensorrt det_mv3_db on linux---------
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if [ $USE_TENSORRT == ON -a $TEST_GPU_CPU == ON ]; then
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cmake .. -DPADDLE_LIB=${inference_install_dir} \
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-DWITH_MKL=$TURN_ON_MKL \
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-DDEMO_NAME=test_det_mv3_db \
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-DWITH_GPU=$TEST_GPU_CPU \
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-DWITH_STATIC_LIB=OFF \
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-DUSE_TENSORRT=$USE_TENSORRT \
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-DTENSORRT_ROOT=$TENSORRT_ROOT_DIR \
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-DWITH_GTEST=ON
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make -j$(nproc)
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./test_det_mv3_db \
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--modeldir=$DATA_DIR/ocr_det_mv3_db/ocr_det_mv3_db \
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--gtest_output=xml:test_det_mv3_db.xml
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if [ $? -ne 0 ]; then
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echo "test_det_mv3_db runs failed" >> ${current_dir}/build/test_summary.txt
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EXIT_CODE=1
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fi
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fi
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# ---------tensorrt LeViT on linux---------
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if [ $USE_TENSORRT == ON -a $TEST_GPU_CPU == ON ]; then
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cmake .. -DPADDLE_LIB=${inference_install_dir} \
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-DWITH_MKL=$TURN_ON_MKL \
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-DDEMO_NAME=test_LeViT \
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-DWITH_GPU=$TEST_GPU_CPU \
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-DWITH_STATIC_LIB=OFF \
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-DUSE_TENSORRT=$USE_TENSORRT \
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-DTENSORRT_ROOT=$TENSORRT_ROOT_DIR \
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-DWITH_GTEST=ON
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make -j$(nproc)
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./test_LeViT \
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--modeldir=$DATA_DIR/LeViT/LeViT \
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--gtest_output=xml:test_LeViT.xml
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if [ $? -ne 0 ]; then
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echo "test_LeViT runs failed" >> ${current_dir}/build/test_summary.txt
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EXIT_CODE=1
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fi
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fi
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if [[ -f ${current_dir}/build/test_summary.txt ]];then
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echo "=====================test summary======================"
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cat ${current_dir}/build/test_summary.txt

‎paddle/fluid/inference/tests/infer_ut/test_LeViT.cc‎

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,179 @@
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// Copyright (c) 2021 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 "test_suite.h" // NOLINT
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DEFINE_string(modeldir, "", "Directory of the inference model.");
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namespace paddle_infer {
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paddle::test::Record PrepareInput(int batch_size) {
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// init input data
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int channel = 3;
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int width = 224;
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int height = 224;
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paddle::test::Record image_Record;
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int input_num = batch_size * channel * width * height;
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std::vector<float> input_data(input_num, 1);
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image_Record.data = input_data;
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image_Record.shape = std::vector<int>{batch_size, channel, width, height};
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image_Record.type = paddle::PaddleDType::FLOAT32;
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return image_Record;
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}
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TEST(test_LeViT, analysis_gpu_bz1) {
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// init input data
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std::map<std::string, paddle::test::Record> my_input_data_map;
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my_input_data_map["x"] = PrepareInput(1);
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// init output data
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std::map<std::string, paddle::test::Record> infer_output_data,
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truth_output_data;
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// prepare groudtruth config
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paddle_infer::Config config, config_no_ir;
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config_no_ir.SetModel(FLAGS_modeldir + "/inference.pdmodel",
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FLAGS_modeldir + "/inference.pdiparams");
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config_no_ir.SwitchIrOptim(false);
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// prepare inference config
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config.SetModel(FLAGS_modeldir + "/inference.pdmodel",
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FLAGS_modeldir + "/inference.pdiparams");
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// get groudtruth by disbale ir
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paddle_infer::services::PredictorPool pred_pool_no_ir(config_no_ir, 1);
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SingleThreadPrediction(pred_pool_no_ir.Retrive(0), &my_input_data_map,
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&truth_output_data, 1);
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// get infer results
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paddle_infer::services::PredictorPool pred_pool(config, 1);
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SingleThreadPrediction(pred_pool.Retrive(0), &my_input_data_map,
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&infer_output_data);
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// check outputs
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CompareRecord(&truth_output_data, &infer_output_data);
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std::cout << "finish test" << std::endl;
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}
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TEST(test_LeViT, trt_fp32_bz2) {
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// init input data
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std::map<std::string, paddle::test::Record> my_input_data_map;
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my_input_data_map["x"] = PrepareInput(2);
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// init output data
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std::map<std::string, paddle::test::Record> infer_output_data,
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truth_output_data;
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// prepare groudtruth config
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paddle_infer::Config config, config_no_ir;
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config_no_ir.SetModel(FLAGS_modeldir + "/inference.pdmodel",
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FLAGS_modeldir + "/inference.pdiparams");
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config_no_ir.SwitchIrOptim(false);
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// prepare inference config
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config.SetModel(FLAGS_modeldir + "/inference.pdmodel",
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FLAGS_modeldir + "/inference.pdiparams");
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config.EnableUseGpu(100, 0);
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config.EnableTensorRtEngine(
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1 << 20, 2, 6, paddle_infer::PrecisionType::kFloat32, false, false);
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// get groudtruth by disbale ir
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paddle_infer::services::PredictorPool pred_pool_no_ir(config_no_ir, 1);
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SingleThreadPrediction(pred_pool_no_ir.Retrive(0), &my_input_data_map,
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&truth_output_data, 1);
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// get infer results
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paddle_infer::services::PredictorPool pred_pool(config, 1);
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SingleThreadPrediction(pred_pool.Retrive(0), &my_input_data_map,
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&infer_output_data);
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// check outputs
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CompareRecord(&truth_output_data, &infer_output_data);
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std::cout << "finish test" << std::endl;
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}
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TEST(test_LeViT, serial_diff_batch_trt_fp32) {
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int max_batch_size = 5;
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// prepare groudtruth config
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paddle_infer::Config config, config_no_ir;
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config_no_ir.SetModel(FLAGS_modeldir + "/inference.pdmodel",
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FLAGS_modeldir + "/inference.pdiparams");
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config_no_ir.SwitchIrOptim(false);
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paddle_infer::services::PredictorPool pred_pool_no_ir(config_no_ir, 1);
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// prepare inference config
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config.SetModel(FLAGS_modeldir + "/inference.pdmodel",
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FLAGS_modeldir + "/inference.pdiparams");
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config.EnableUseGpu(100, 0);
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config.EnableTensorRtEngine(1 << 20, max_batch_size, 6,
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paddle_infer::PrecisionType::kFloat32, false,
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false);
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paddle_infer::services::PredictorPool pred_pool(config, 1);
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for (int i = 1; i < max_batch_size; i++) {
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// init input data
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std::map<std::string, paddle::test::Record> my_input_data_map;
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my_input_data_map["x"] = PrepareInput(i);
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// init output data
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std::map<std::string, paddle::test::Record> infer_output_data,
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truth_output_data;
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// get groudtruth by disbale ir
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SingleThreadPrediction(pred_pool_no_ir.Retrive(0), &my_input_data_map,
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&truth_output_data, 1);
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// get infer results
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SingleThreadPrediction(pred_pool.Retrive(0), &my_input_data_map,
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&infer_output_data);
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// check outputs
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CompareRecord(&truth_output_data, &infer_output_data);
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}
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std::cout << "finish test" << std::endl;
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}
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TEST(test_LeViT, multi_thread4_trt_fp32_bz2) {
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int thread_num = 4;
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// init input data
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std::map<std::string, paddle::test::Record> my_input_data_map;
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my_input_data_map["x"] = PrepareInput(2);
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// init output data
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std::map<std::string, paddle::test::Record> infer_output_data,
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truth_output_data;
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// prepare groudtruth config
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paddle_infer::Config config, config_no_ir;
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config_no_ir.SetModel(FLAGS_modeldir + "/inference.pdmodel",
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FLAGS_modeldir + "/inference.pdiparams");
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config_no_ir.SwitchIrOptim(false);
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// prepare inference config
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config.SetModel(FLAGS_modeldir + "/inference.pdmodel",
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FLAGS_modeldir + "/inference.pdiparams");
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config.EnableUseGpu(100, 0);
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config.EnableTensorRtEngine(
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1 << 20, 2, 6, paddle_infer::PrecisionType::kFloat32, false, false);
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// get groudtruth by disbale ir
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paddle_infer::services::PredictorPool pred_pool_no_ir(config_no_ir, 1);
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SingleThreadPrediction(pred_pool_no_ir.Retrive(0), &my_input_data_map,
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&truth_output_data, 1);
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// get infer results from multi threads
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std::vector<std::thread> threads;
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services::PredictorPool pred_pool(config, thread_num);
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for (int i = 0; i < thread_num; ++i) {
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threads.emplace_back(paddle::test::SingleThreadPrediction,
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pred_pool.Retrive(i), &my_input_data_map,
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&infer_output_data, 2);
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}
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// thread join & check outputs
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for (int i = 0; i < thread_num; ++i) {
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LOG(INFO) << "join tid : " << i;
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threads[i].join();
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CompareRecord(&truth_output_data, &infer_output_data);
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}
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std::cout << "finish multi-thread test" << std::endl;
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}
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} // namespace paddle_infer
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int main(int argc, char** argv) {
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::testing::InitGoogleTest(&argc, argv);
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::google::ParseCommandLineFlags(&argc, &argv, true);
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return RUN_ALL_TESTS();
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}

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