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96 lines
3.1 KiB
96 lines
3.1 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 <cuda_runtime.h>
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#include <glog/logging.h>
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#include <gtest/gtest.h>
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#include <cstring>
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#include <numeric>
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#include "gflags/gflags.h"
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#include "paddle/fluid/inference/tests/api/trt_test_helper.h"
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namespace paddle_infer {
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TEST(Predictor, use_gpu) {
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LOG(INFO) << GetVersion();
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UpdateDllFlag("conv_workspace_size_limit", "4000");
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std::string model_dir = FLAGS_infer_model + "/model";
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Config config;
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config.SetModel(model_dir + "/model", model_dir + "/params");
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config.EnableUseGpu(100, 0);
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auto predictor = CreatePredictor(config);
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auto pred_clone = predictor->Clone();
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std::vector<int> in_shape = {1, 3, 318, 318};
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int in_num = std::accumulate(in_shape.begin(), in_shape.end(), 1,
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[](int &a, int &b) { return a * b; });
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std::vector<float> input(in_num, 0);
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auto input_names = predictor->GetInputNames();
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auto input_t = predictor->GetInputHandle(input_names[0]);
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input_t->Reshape(in_shape);
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input_t->CopyFromCpu(input.data());
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predictor->Run();
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auto output_names = predictor->GetOutputNames();
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auto output_t = predictor->GetOutputHandle(output_names[0]);
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std::vector<int> output_shape = output_t->shape();
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int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
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std::multiplies<int>());
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std::vector<float> out_data;
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out_data.resize(out_num);
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output_t->CopyToCpu(out_data.data());
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predictor->ClearIntermediateTensor();
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}
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TEST(PredictorPool, basic) {
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LOG(INFO) << GetVersion();
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UpdateDllFlag("conv_workspace_size_limit", "4000");
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std::string model_dir = FLAGS_infer_model + "/model";
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Config config;
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config.SetModel(model_dir + "/model", model_dir + "/params");
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config.EnableUseGpu(100, 0);
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services::PredictorPool pred_pool(config, 4);
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auto pred = pred_pool.Retrive(2);
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std::vector<int> in_shape = {1, 3, 318, 318};
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int in_num = std::accumulate(in_shape.begin(), in_shape.end(), 1,
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[](int &a, int &b) { return a * b; });
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std::vector<float> input(in_num, 0);
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auto in_names = pred->GetInputNames();
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auto input_t = pred->GetInputHandle(in_names[0]);
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input_t->name();
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input_t->Reshape(in_shape);
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input_t->CopyFromCpu(input.data());
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pred->Run();
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auto out_names = pred->GetOutputNames();
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auto output_t = pred->GetOutputHandle(out_names[0]);
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auto out_type = output_t->type();
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LOG(INFO) << GetNumBytesOfDataType(out_type);
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if (out_type == DataType::FLOAT32) {
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PlaceType place;
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int size;
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output_t->data<float>(&place, &size);
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}
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}
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} // namespace paddle_infer
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