chore: import upstream snapshot with attribution

test/cpp/inference/api/analyzer_zerocopy_tensor_tester.cc

@@ -0,0 +1,63 @@
+// Copyright (c) 2019 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 "paddle/fluid/framework/block_desc.h"
+#include "paddle/fluid/framework/op_desc.h"
+#include "paddle/fluid/framework/program_desc.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/inference/utils/singleton.h"
+#include "test/cpp/inference/api/tester_helper.h"
+
+namespace paddle {
+namespace inference {
+
+TEST(test_zerocopy_tensor, zerocopy_tensor) {
+  AnalysisConfig config;
+  config.SetModel(FLAGS_infer_model + "/inference.pdmodel",
+                  FLAGS_infer_model + "/inference.pdiparams");
+
+  auto predictor = CreatePaddlePredictor(config);
+  int batch_size = 1;
+  int channels = 3;
+  int height = 224;
+  int width = 224;
+  int nums = batch_size * channels * height * width;
+
+  float* input = new float[nums];
+  for (int i = 0; i < nums; ++i) input[i] = 0;
+  auto input_names = predictor->GetInputNames();
+  PaddlePlace p = PaddlePlace::kCPU;
+  PaddlePlace* place = &p;
+  int size;
+
+  auto input_t = predictor->GetInputTensor(input_names[0]);
+  input_t->Reshape({batch_size, channels, height, width});
+  input_t->copy_from_cpu<float>(input);
+  input_t->data<float>(place, &size);
+  input_t->mutable_data<float>(p);
+
+  predictor->ZeroCopyRun();
+
+  std::vector<float> out_data;
+  auto output_names = predictor->GetOutputNames();
+  auto output_t = predictor->GetOutputTensor(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>());
+  out_data.resize(out_num);
+  output_t->copy_to_cpu<float>(out_data.data());
+}
+
+}  // namespace inference
+}  // namespace paddle