chore: import upstream snapshot with attribution

test/cpp/inference/api/paddle_infer_api_test.cc

@@ -0,0 +1,97 @@
+/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include <glog/logging.h>
+#include <gtest/gtest.h>
+
+#include <functional>
+#include <numeric>
+
+#include "paddle/common/flags.h"
+#include "test/cpp/inference/api/tester_helper.h"
+
+namespace paddle_infer {
+
+TEST(Predictor, use_gpu) {
+  LOG(INFO) << GetVersion();
+  UpdateDllFlag("conv_workspace_size_limit", "4000");
+  std::string model_dir = FLAGS_infer_model + "/model";
+  Config config;
+  config.EnableNewIR(false);
+  config.SetModel(model_dir + "/model", model_dir + "/params");
+  config.EnableUseGpu(100, 0);
+
+  auto predictor = CreatePredictor(config);
+  auto pred_clone = predictor->Clone();
+
+  std::vector<int> in_shape = {1, 3, 318, 318};
+  int in_num = std::accumulate(
+      in_shape.begin(), in_shape.end(), 1, std::multiplies<int>());
+
+  std::vector<float> input(in_num, 0);
+
+  auto input_names = predictor->GetInputNames();
+  auto input_t = predictor->GetInputHandle(input_names[0]);
+
+  input_t->Reshape(in_shape);
+  input_t->CopyFromCpu(input.data());
+  predictor->Run();
+
+  auto output_names = predictor->GetOutputNames();
+  auto output_t = predictor->GetOutputHandle(output_names[0]);
+  std::vector<int> output_shape = output_t->shape();
+  int out_num = std::accumulate(
+      output_shape.begin(), output_shape.end(), 1, std::multiplies<int>());
+
+  std::vector<float> out_data;
+  out_data.resize(out_num);
+  output_t->CopyToCpu(out_data.data());
+  predictor->ClearIntermediateTensor();
+}
+
+TEST(PredictorPool, basic) {
+  LOG(INFO) << GetVersion();
+  UpdateDllFlag("conv_workspace_size_limit", "4000");
+  std::string model_dir = FLAGS_infer_model + "/model";
+  Config config;
+  config.EnableNewIR(false);
+  config.SetModel(model_dir + "/model", model_dir + "/params");
+  config.EnableUseGpu(100, 0);
+
+  services::PredictorPool pred_pool(config, 4);
+  auto pred = pred_pool.Retrieve(2);
+
+  std::vector<int> in_shape = {1, 3, 318, 318};
+  int in_num = std::accumulate(
+      in_shape.begin(), in_shape.end(), 1, std::multiplies<int>());
+  std::vector<float> input(in_num, 0);
+
+  auto in_names = pred->GetInputNames();
+  auto input_t = pred->GetInputHandle(in_names[0]);
+  input_t->name();
+  input_t->Reshape(in_shape);
+  input_t->CopyFromCpu(input.data());
+  pred->Run();
+  auto out_names = pred->GetOutputNames();
+  auto output_t = pred->GetOutputHandle(out_names[0]);
+  auto out_type = output_t->type();
+  LOG(INFO) << GetNumBytesOfDataType(out_type);
+  if (out_type == DataType::FLOAT32) {
+    PlaceType place;
+    int size;
+    output_t->data<float>(&place, &size);
+  }
+}
+
+}  // namespace paddle_infer