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66 lines
2.1 KiB
66 lines
2.1 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 <gflags/gflags.h>
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#include <glog/logging.h>
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#include <gtest/gtest.h>
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#include "paddle/contrib/inference/paddle_inference_api.h"
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DEFINE_string(model, "", "Directory of the inference model.");
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namespace paddle {
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AnakinConfig GetConfig() {
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AnakinConfig config;
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config.model_file = FLAGS_model;
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config.device = 0;
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config.max_batch_size = 1;
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return config;
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}
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TEST(inference, anakin) {
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AnakinConfig config = GetConfig();
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auto predictor =
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CreatePaddlePredictor<AnakinConfig, PaddleEngineKind::kAnakin>(config);
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float data[1 * 3 * 224 * 224] = {1.0f};
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PaddleTensor tensor{.name = "input_0",
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.shape = std::vector<int>({1, 3, 224, 224}),
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.data = PaddleBuf(data, sizeof(data)),
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.dtype = PaddleDType::FLOAT32};
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// For simplicity, we set all the slots with the same data.
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std::vector<PaddleTensor> paddle_tensor_feeds;
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paddle_tensor_feeds.emplace_back(std::move(tensor));
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PaddleTensor tensor_out{.name = "prob_out",
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.shape = std::vector<int>({1000, 1}),
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.data = PaddleBuf(),
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.dtype = PaddleDType::FLOAT32};
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std::vector<PaddleTensor> outputs;
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outputs.emplace_back(std::move(tensor_out));
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ASSERT_TRUE(predictor->Run(paddle_tensor_feeds, &outputs));
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float* data_o = static_cast<float*>(outputs[0].data.data());
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for (size_t j = 0; j < 1000; ++j) {
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LOG(INFO) << "output[" << j << "]: " << data_o[j];
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}
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}
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} // namespace paddle
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