add prediction demo and script on windows (#21248)

paddle/fluid/inference/api/demo_ci/CMakeLists.txt

@@ -69,6 +69,12 @@ if(WITH_GPU)
   endif(NOT WIN32)
 endif()
 
+if (USE_TENSORRT AND WITH_GPU)
+  set(TENSORRT_ROOT ${PADDLE_LIB_THIRD_PARTY_PATH}tensorrt)
+  set(TENSORRT_INCLUDE_DIR ${TENSORRT_ROOT}/include)
+  set(TENSORRT_LIB_DIR ${TENSORRT_ROOT}/lib)
+endif()
+
 if (NOT WIN32)
   if (USE_TENSORRT AND WITH_GPU)
       include_directories("${TENSORRT_INCLUDE_DIR}")
@@ -108,9 +114,12 @@ if(WITH_MKL)
     endif(WIN32)
   endif()
 else()
-  set(MATH_LIB ${PADDLE_LIB_THIRD_PARTY_PATH}openblas/lib/libopenblas${CMAKE_STATIC_LIBRARY_SUFFIX})
   if(WIN32)
     set(MATH_DLL ${PADDLE_LIB_THIRD_PARTY_PATH}openblas/lib/openblas${CMAKE_SHARED_LIBRARY_SUFFIX})
+    # Note: fix the openblas static library not work on windows, and change the static library to import library.
+    set(MATH_LIB ${PADDLE_LIB_THIRD_PARTY_PATH}openblas/lib/openblas${CMAKE_STATIC_LIBRARY_SUFFIX})
+  else()
+    set(MATH_LIB ${PADDLE_LIB_THIRD_PARTY_PATH}openblas/lib/libopenblas${CMAKE_STATIC_LIBRARY_SUFFIX})
   endif()
 endif()
 

paddle/fluid/inference/api/demo_ci/untar_model.py

@@ -0,0 +1,35 @@
+# 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.
+
+import tarfile, os
+import sys
+
+
+def untar(fname, dirs):
+    """
+    extract the tar.gz file
+    :param fname: the name of tar.gz file
+    :param dirs: the path of decompressed file 
+    :return: bool
+    """
+    try:
+        t = tarfile.open(name=fname, mode='r:gz')
+        t.extractall(path=dirs)
+        return True
+    except Exception as e:
+        print(e)
+        return False
+
+
+untar(sys.argv[1], sys.argv[2])

paddle/fluid/inference/api/demo_ci/windows_mobilenet.cc

@@ -0,0 +1,92 @@
+// 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 <gflags/gflags.h>
+#include <glog/logging.h>
+#include <algorithm>
+#include <fstream>
+#include <iostream>
+#include <numeric>
+#include <string>
+#include <vector>
+
+#include "paddle/include/paddle_inference_api.h"
+
+DEFINE_string(modeldir, "", "Directory of the inference model.");
+DEFINE_bool(use_gpu, false, "Whether use gpu.");
+
+namespace paddle {
+namespace demo {
+
+void RunAnalysis() {
+  // 1. create AnalysisConfig
+  AnalysisConfig config;
+  if (FLAGS_modeldir.empty()) {
+    LOG(INFO) << "Usage: path\\mobilenet --modeldir=path/to/your/model";
+    exit(1);
+  }
+
+  // CreateConfig(&config);
+  if (FLAGS_use_gpu) {
+    config.EnableUseGpu(100, 0);
+  }
+  config.SetModel(FLAGS_modeldir + "/__model__",
+                  FLAGS_modeldir + "/__params__");
+
+  // use ZeroCopyTensor, Must be set to false
+  config.SwitchUseFeedFetchOps(false);
+
+  // 2. create predictor, prepare input data
+  std::unique_ptr<PaddlePredictor> predictor = CreatePaddlePredictor(config);
+  int batch_size = 1;
+  int channels = 3;
+  int height = 300;
+  int width = 300;
+  int nums = batch_size * channels * height * width;
+
+  float* input = new float[nums];
+  for (int i = 0; i < nums; ++i) input[i] = 0;
+
+  // 3. create input tensor, use ZeroCopyTensor
+  auto input_names = predictor->GetInputNames();
+  auto input_t = predictor->GetInputTensor(input_names[0]);
+  input_t->Reshape({batch_size, channels, height, width});
+  input_t->copy_from_cpu(input);
+
+  // 4. run predictor
+  predictor->ZeroCopyRun();
+
+  // 5. get out put
+  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(out_data.data());
+  delete[] input;
+}
+
+}  // namespace demo
+}  // namespace paddle
+
+int main(int argc, char** argv) {
+  google::ParseCommandLineFlags(&argc, &argv, true);
+  paddle::demo::RunAnalysis();
+  std::cout << "=========================Runs successfully===================="
+            << std::endl;
+  return 0;
+}