wehub-resource-sync
114 lines
3.9 KiB
C++
114 lines
3.9 KiB
C++
// 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 <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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TEST(PD_PredictorRun, predictor_run) {
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auto model_dir = FLAGS_infer_model;
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PD_Config *config = PD_ConfigCreate();
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PD_ConfigSetModel(config,
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(model_dir + "/__model__").c_str(),
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(model_dir + "/param").c_str());
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PD_ConfigDisableGpu(config);
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PD_Predictor *predictor = PD_PredictorCreate(config);
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size_t input_num = PD_PredictorGetInputNum(predictor);
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LOG(INFO) << "Input num: " << input_num;
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size_t output_num = PD_PredictorGetOutputNum(predictor);
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LOG(INFO) << "Output num: " << output_num;
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PD_OneDimArrayCstr *input_names = PD_PredictorGetInputNames(predictor);
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EXPECT_EQ(input_names->size, 2u);
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LOG(INFO) << "Predictor start run!";
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PD_Tensor *inputs[2]; // NOLINT
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inputs[0] = PD_PredictorGetInputHandle(predictor, input_names->data[0]);
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inputs[1] = PD_PredictorGetInputHandle(predictor, input_names->data[1]);
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LOG(INFO) << "Predictor start run!";
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// inputs[0]: word, use lod memory in stack
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std::array<int32_t, 2> shape_0 = {11, 1};
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std::array<int64_t, 11 * 1> data_0 = {
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12673, 9763, 905, 284, 45, 7474, 20, 17, 1, 4, 9};
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std::array<size_t, 2> lod_layer_0 = {0, 11};
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PD_OneDimArraySize layer_0;
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layer_0.size = 2;
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layer_0.data = lod_layer_0.data();
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PD_OneDimArraySize *layer_0_ptr = &layer_0;
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PD_TwoDimArraySize lod_0;
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lod_0.size = 1;
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lod_0.data = &layer_0_ptr;
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PD_TensorReshape(inputs[0], 2, shape_0.data());
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PD_TensorCopyFromCpuInt64(inputs[0], data_0.data());
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PD_TensorSetLod(inputs[0], &lod_0);
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// inputs[1]: mention, use lod memory in heap
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std::array<int32_t, 2> shape_1 = {11, 1};
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std::array<int64_t, 11 * 1> data_1 = {27, 0, 0, 33, 34, 33, 0, 0, 0, 1, 2};
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PD_TwoDimArraySize *lod_1_ptr = new PD_TwoDimArraySize();
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lod_1_ptr->size = 1;
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lod_1_ptr->data = new PD_OneDimArraySize *[1];
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lod_1_ptr->data[0] = new PD_OneDimArraySize();
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lod_1_ptr->data[0]->size = 2;
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lod_1_ptr->data[0]->data = new size_t[2];
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lod_1_ptr->data[0]->data[0] = 0;
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lod_1_ptr->data[0]->data[1] = 11;
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PD_TensorReshape(inputs[1], 2, shape_1.data());
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PD_TensorCopyFromCpuInt64(inputs[1], data_1.data());
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PD_TensorSetLod(inputs[1], lod_1_ptr);
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// retrieve the lod memory
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delete[] lod_1_ptr->data[0]->data;
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delete lod_1_ptr->data[0];
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delete[] lod_1_ptr->data;
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delete lod_1_ptr;
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lod_1_ptr = nullptr;
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LOG(INFO) << "Predictor start run!";
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bool success = PD_PredictorRun(predictor);
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EXPECT_TRUE(success);
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LOG(INFO) << "Predictor run success!";
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PD_OneDimArrayCstr *output_names = PD_PredictorGetOutputNames(predictor);
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PD_Tensor *output =
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PD_PredictorGetOutputHandle(predictor, output_names->data[0]);
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PD_TwoDimArraySize *output_lod = PD_TensorGetLod(output);
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PD_TwoDimArraySizeDestroy(output_lod);
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PD_TensorDestroy(output);
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PD_OneDimArrayCstrDestroy(output_names);
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PD_TensorDestroy(inputs[0]);
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PD_TensorDestroy(inputs[1]);
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PD_OneDimArrayCstrDestroy(input_names);
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PD_PredictorDestroy(predictor);
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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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