wehub-resource-sync
96 lines
3.5 KiB
C++
96 lines
3.5 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_inference_api.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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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,
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(model_dir + "/inference.pdmodel").c_str(),
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(model_dir + "/inference.pdiparams").c_str());
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PD_Predictor* predictor = PD_PredictorCreate(config);
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PD_OneDimArrayCstr* input_names = PD_PredictorGetInputNames(predictor);
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LOG(INFO) << "The inputs' size is: " << input_names->size;
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EXPECT_EQ(input_names->size, 1u);
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PD_IOInfos* in_infos = PD_PredictorGetInputInfos(predictor);
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EXPECT_EQ(in_infos->size, 1u);
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PD_IOInfos* out_infos = PD_PredictorGetOutputInfos(predictor);
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std::array<int32_t, 4> shape_0 = {1, 3, 224, 224};
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std::array<float, 1 * 3 * 224 * 224> data_0 = {0};
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PD_Tensor* input_0 = PD_PredictorGetInputHandle(predictor, "x");
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PD_TensorReshape(input_0, 4, shape_0.data());
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PD_TensorCopyFromCpuFloat(input_0, data_0.data());
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LOG(INFO) << "Run Inference in CAPI encapsulation. ";
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EXPECT_TRUE(PD_PredictorRun(predictor));
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PD_OneDimArrayCstr* output_names = PD_PredictorGetOutputNames(predictor);
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LOG(INFO) << "output size is: " << output_names->size;
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for (size_t index = 0; index < output_names->size; ++index) {
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LOG(INFO) << "output[" << index
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<< "]'s name is: " << output_names->data[index];
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PD_Tensor* output =
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PD_PredictorGetOutputHandle(predictor, output_names->data[index]);
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PD_OneDimArrayInt32* shape = PD_TensorGetShape(output);
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LOG(INFO) << "output[" << index << "]'s shape_size is: " << shape->size;
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int32_t out_size = 1;
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for (size_t i = 0; i < shape->size; ++i) {
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LOG(INFO) << "output[" << index << "]'s shape is: " << shape->data[i];
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out_size = out_size * shape->data[i];
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}
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float* out_data = new float[out_size];
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PD_TensorCopyToCpuFloat(output, out_data);
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LOG(INFO) << "output[" << index << "]'s DATA is: " << out_data[0];
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delete[] out_data;
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PD_OneDimArrayInt32Destroy(shape);
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PD_TensorDestroy(output);
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}
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PD_PredictorClearIntermediateTensor(predictor);
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PD_PredictorTryShrinkMemory(predictor);
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PD_OneDimArrayCstrDestroy(output_names);
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PD_TensorDestroy(input_0);
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PD_OneDimArrayCstrDestroy(input_names);
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PD_IOInfosDestroy(in_infos);
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PD_IOInfosDestroy(out_infos);
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PD_PredictorDestroy(predictor);
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}
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#ifdef PADDLE_WITH_DNNL
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TEST(PD_PredictorRun, predictor_run) { predictor_run(); }
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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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