planning: add an inference demo for one planning demo pytorch model.

modules/planning/tools/BUILD

@@ -14,4 +14,13 @@ cc_binary(
     ],
 )
 
+cc_binary(
+    name = "inference_demo",
+    srcs = ["inference_demo.cc"],
+    deps = [
+        "@com_github_gflags_gflags//:gflags",
+	"//third_party:libtorch",
+    ],
+)
+
 cpplint()

modules/planning/tools/inference_demo.cc

+/******************************************************************************
+ * Copyright 2019 The Apollo 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 "torch/script.h"
+#include "torch/torch.h"
+
+DEFINE_string(model_file,
+              "/apollo/modules/planning/tools/planning_demo_model.pt",
+              "pytorch model file.");
+
+int main(int argc, char **argv) {
+  google::ParseCommandLineFlags(&argc, &argv, true);
+
+  torch::jit::script::Module model;
+  torch::Device device(torch::kCPU);
+
+  torch::set_num_threads(1);
+  model = torch::jit::load(FLAGS_model_file, device);
+
+  std::vector<torch::jit::IValue> torch_inputs;
+
+  int input_dim = 2 * 3 * 224 * 224 + 2 * 14;
+  std::vector<double> feature_values(input_dim, 0.5);
+
+  std::vector<torch::jit::IValue> inputs;
+  std::vector<torch::jit::IValue> tuple;
+  tuple.push_back(torch::zeros({2, 3, 224, 224}));
+  tuple.push_back(torch::zeros({2, 14}));
+  inputs.push_back(torch::ivalue::Tuple::create(tuple));
+
+  auto torch_output = model.forward(inputs);
+  std::cout << torch_output << std::endl;
+  std::cout << "isDoubleList:" << torch_output.isDoubleList() << std::endl;
+  std::cout << "isTensorList:" << torch_output.isTensorList() << std::endl;
+  std::cout << "isTensor:" << torch_output.isTensor() << std::endl;
+  auto torch_output_tensor = torch_output.toTensor();
+  std::cout << "tensor dim:" << torch_output_tensor.dim() << std::endl;
+  std::cout << "tensor sizes:" << torch_output_tensor.sizes() << std::endl;
+  std::cout << "tensor toString:" << torch_output_tensor.toString()
+            << std::endl;
+  std::cout << "tensor [0,0,0] element:" << torch_output_tensor[0][0][0]
+            << std::endl;
+  std::cout << "tensor [0,0,1] element:" << torch_output_tensor[0][0][1]
+            << std::endl;
+
+  return 0;
+}