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104 lines
3.2 KiB
104 lines
3.2 KiB
/* 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 <stddef.h>
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#include <stdint.h>
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#include <stdio.h>
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#include <string>
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#include <vector>
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#include "paddle/fluid/inference/capi/paddle_c_api.h"
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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 zero_copy_run() {
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std::string model_dir = FLAGS_infer_model;
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PD_AnalysisConfig *config = PD_NewAnalysisConfig();
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PD_DisableGpu(config);
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PD_SetCpuMathLibraryNumThreads(config, 10);
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PD_SwitchUseFeedFetchOps(config, false);
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PD_SwitchSpecifyInputNames(config, true);
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PD_SwitchIrDebug(config, true);
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PD_SetModel(config, model_dir.c_str(), nullptr);
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bool use_feed_fetch = PD_UseFeedFetchOpsEnabled(config);
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CHECK(!use_feed_fetch) << "NO";
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bool specify_input_names = PD_SpecifyInputName(config);
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CHECK(specify_input_names) << "NO";
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const int batch_size = 1;
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const int channels = 3;
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const int height = 224;
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const int width = 224;
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float input[batch_size * channels * height * width] = {0};
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int shape[4] = {batch_size, channels, height, width};
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int shape_size = 4;
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int in_size = 2;
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int out_size;
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PD_ZeroCopyData *inputs = new PD_ZeroCopyData[2];
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PD_ZeroCopyData *outputs = nullptr;
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inputs[0].data = static_cast<void *>(input);
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inputs[0].dtype = PD_FLOAT32;
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inputs[0].name = new char[6];
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inputs[0].name[0] = 'i';
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inputs[0].name[1] = 'm';
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inputs[0].name[2] = 'a';
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inputs[0].name[3] = 'g';
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inputs[0].name[4] = 'e';
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inputs[0].name[5] = '\0';
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inputs[0].shape = shape;
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inputs[0].shape_size = shape_size;
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int *label = new int[1];
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label[0] = 0;
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inputs[1].data = static_cast<void *>(label);
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inputs[1].dtype = PD_INT64;
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inputs[1].name = new char[6];
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inputs[1].name[0] = 'l';
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inputs[1].name[1] = 'a';
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inputs[1].name[2] = 'b';
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inputs[1].name[3] = 'e';
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inputs[1].name[4] = 'l';
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inputs[1].name[5] = '\0';
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int label_shape[2] = {1, 1};
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int label_shape_size = 2;
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inputs[1].shape = label_shape;
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inputs[1].shape_size = label_shape_size;
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PD_PredictorZeroCopyRun(config, inputs, in_size, &outputs, &out_size);
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LOG(INFO) << "output size is: " << out_size;
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LOG(INFO) << outputs[0].name;
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for (int j = 0; j < out_size; ++j) {
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LOG(INFO) << "output[" << j
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<< "]'s shape_size is: " << outputs[j].shape_size;
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for (int i = 0; i < outputs[0].shape_size; ++i) {
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LOG(INFO) << "output[" << j << "]'s shape is: " << outputs[j].shape[i];
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}
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LOG(INFO) << "output[" << j
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<< "]'s DATA is: " << *(static_cast<float *>(outputs[j].data));
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}
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delete[] outputs;
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delete[] inputs;
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}
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#ifdef PADDLE_WITH_MKLDNN
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TEST(PD_ZeroCopyRun, zero_copy_run) { zero_copy_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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