chore: import upstream snapshot with attribution

test/cpp/inference/api/analyzer_capi_int_tester.cc

@@ -0,0 +1,105 @@
+/* Copyright (c) 2018 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 <stddef.h>
+#include <stdint.h>
+#include <stdio.h>
+
+#include <string>
+#include <vector>
+
+#include "paddle/fluid/inference/capi/paddle_c_api.h"
+#include "test/cpp/inference/api/tester_helper.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+void zero_copy_run() {
+  std::string model_dir = FLAGS_infer_model;
+  PD_AnalysisConfig *config = PD_NewAnalysisConfig();
+  PD_DisableGpu(config);
+  PD_SetCpuMathLibraryNumThreads(config, 10);
+  PD_SwitchSpecifyInputNames(config, true);
+  PD_SwitchIrDebug(config, true);
+  PD_SetModel(config, model_dir.c_str(), nullptr);
+  bool use_feed_fetch = PD_UseFeedFetchOpsEnabled(config);
+  PADDLE_ENFORCE_EQ(
+      use_feed_fetch, false, common::errors::PreconditionNotMet("NO"));
+  bool specify_input_names = PD_SpecifyInputName(config);
+  PADDLE_ENFORCE_EQ(
+      specify_input_names, true, common::errors::PreconditionNotMet("NO"));
+  const int batch_size = 1;
+  const int channels = 3;
+  const int height = 224;
+  const int width = 224;
+  float input[batch_size * channels * height * width] = {0};
+  int shape[4] = {batch_size, channels, height, width};
+  int shape_size = 4;
+  int in_size = 2;
+  int out_size;
+  PD_ZeroCopyData *inputs = new PD_ZeroCopyData[2];
+  PD_ZeroCopyData *outputs = nullptr;
+  inputs[0].data = static_cast<void *>(input);
+  inputs[0].dtype = PD_FLOAT32;
+  inputs[0].name = new char[6];
+  inputs[0].name[0] = 'i';
+  inputs[0].name[1] = 'm';
+  inputs[0].name[2] = 'a';
+  inputs[0].name[3] = 'g';
+  inputs[0].name[4] = 'e';
+  inputs[0].name[5] = '\0';
+  inputs[0].shape = shape;
+  inputs[0].shape_size = shape_size;
+
+  int *label = new int[1];
+  label[0] = 0;
+  inputs[1].data = static_cast<void *>(label);
+  inputs[1].dtype = PD_INT64;
+  inputs[1].name = new char[6];
+  inputs[1].name[0] = 'l';
+  inputs[1].name[1] = 'a';
+  inputs[1].name[2] = 'b';
+  inputs[1].name[3] = 'e';
+  inputs[1].name[4] = 'l';
+  inputs[1].name[5] = '\0';
+  int label_shape[2] = {1, 1};
+  int label_shape_size = 2;
+  inputs[1].shape = label_shape;
+  inputs[1].shape_size = label_shape_size;
+
+  PD_PredictorZeroCopyRun(config, inputs, in_size, &outputs, &out_size);
+
+  LOG(INFO) << "output size is: " << out_size;
+  LOG(INFO) << outputs[0].name;
+  for (int j = 0; j < out_size; ++j) {
+    LOG(INFO) << "output[" << j
+              << "]'s shape_size is: " << outputs[j].shape_size;
+    for (int i = 0; i < outputs[0].shape_size; ++i) {
+      LOG(INFO) << "output[" << j << "]'s shape is: " << outputs[j].shape[i];
+    }
+    LOG(INFO) << "output[" << j
+              << "]'s DATA is: " << *(static_cast<float *>(outputs[j].data));
+  }
+  delete[] outputs;
+  delete[] inputs;
+}
+
+#ifdef PADDLE_WITH_DNNL
+TEST(PD_ZeroCopyRun, zero_copy_run) { zero_copy_run(); }
+#endif
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle