wehub-resource-sync
91 lines
2.9 KiB
C++
91 lines
2.9 KiB
C++
/* Copyright (c) 2021 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 <glog/logging.h>
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#include <gtest/gtest.h>
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#include <cstddef>
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#include <cstdint>
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#include <cstdio>
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#include <string>
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#include <vector>
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#include "paddle/common/flags.h"
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#include "paddle/fluid/inference/capi_exp/pd_config.h"
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#include "paddle/fluid/inference/capi_exp/pd_inference_api.h"
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#include "paddle/fluid/inference/capi_exp/pd_utils.h"
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PD_DEFINE_string(infer_model, "", "model path");
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namespace paddle {
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namespace inference {
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namespace analysis {
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void predictor_run() {
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std::string model_dir = FLAGS_infer_model;
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std::string prog_file = model_dir + "/model";
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std::string params_file = model_dir + "/params";
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PD_Config *config = PD_ConfigCreate();
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PD_ConfigDisableGpu(config);
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PD_ConfigSetCpuMathLibraryNumThreads(config, 10);
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PD_ConfigSwitchIrDebug(config, TRUE);
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PD_ConfigSetModel(config, prog_file.c_str(), params_file.c_str());
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PD_Cstr *config_summary = PD_ConfigSummary(config);
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LOG(INFO) << config_summary->data;
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PD_Predictor *predictor = PD_PredictorCreate(config);
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PD_Tensor *tensor = PD_PredictorGetInputHandle(predictor, "data");
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const int batch_size = 1;
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const int channels = 3;
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const int height = 318;
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const int width = 318;
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float *input = new float[batch_size * channels * height * width]();
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std::array<int32_t, 4> shape = {batch_size, channels, height, width};
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PD_TensorReshape(tensor, 4, shape.data());
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PD_TensorCopyFromCpuFloat(tensor, input);
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EXPECT_TRUE(PD_PredictorRun(predictor));
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delete[] input;
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PD_TensorDestroy(tensor);
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PD_CstrDestroy(config_summary);
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PD_PredictorDestroy(predictor);
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}
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TEST(PD_PredictorRun, predictor_run) { predictor_run(); }
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#ifdef PADDLE_WITH_DNNL
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TEST(PD_Config, profile_onednn) {
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std::string model_dir = FLAGS_infer_model;
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std::string prog_file = model_dir + "/model";
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std::string params_file = model_dir + "/params";
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PD_Config *config = PD_ConfigCreate();
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PD_ConfigDisableGpu(config);
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PD_ConfigSetCpuMathLibraryNumThreads(config, 10);
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PD_ConfigSwitchIrDebug(config, TRUE);
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PD_ConfigEnableONEDNN(config);
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bool onednn_enable = PD_ConfigOnednnEnabled(config);
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EXPECT_TRUE(onednn_enable);
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PD_ConfigEnableOnednnBfloat16(config);
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PD_ConfigSetOnednnCacheCapacity(config, 0);
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PD_ConfigSetModel(config, prog_file.c_str(), params_file.c_str());
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PD_ConfigDestroy(config);
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}
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#endif
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} // namespace analysis
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} // namespace inference
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} // namespace paddle
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