modify programDesc based on feed and fetch names

paddle/inference/example.cc

@@ -18,33 +18,21 @@ limitations under the License. */
 #include "paddle/inference/inference.h"
 
 DEFINE_string(dirname, "", "Directory of the inference model.");
-DEFINE_string(feed_var_names, "", "Names of feeding variables");
-DEFINE_string(fetch_var_names, "", "Names of fetching variables");
 
 int main(int argc, char** argv) {
   google::ParseCommandLineFlags(&argc, &argv, true);
-  if (FLAGS_dirname.empty() || FLAGS_feed_var_names.empty() ||
-      FLAGS_fetch_var_names.empty()) {
+  if (FLAGS_dirname.empty()) {
     // Example:
     //   ./example --dirname=recognize_digits_mlp.inference.model
-    //             --feed_var_names="x"
-    //             --fetch_var_names="fc_2.tmp_2"
-    std::cout << "Usage: ./example --dirname=path/to/your/model "
-                 "--feed_var_names=x --fetch_var_names=y"
-              << std::endl;
+    std::cout << "Usage: ./example --dirname=path/to/your/model" << std::endl;
     exit(1);
   }
 
   std::cout << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
-  std::cout << "FLAGS_feed_var_names: " << FLAGS_feed_var_names << std::endl;
-  std::cout << "FLAGS_fetch_var_names: " << FLAGS_fetch_var_names << std::endl;
-
   std::string dirname = FLAGS_dirname;
-  std::vector<std::string> feed_var_names = {FLAGS_feed_var_names};
-  std::vector<std::string> fetch_var_names = {FLAGS_fetch_var_names};
 
   paddle::InferenceEngine* engine = new paddle::InferenceEngine();
-  engine->LoadInferenceModel(dirname, feed_var_names, fetch_var_names);
+  engine->LoadInferenceModel(dirname);
 
   paddle::framework::LoDTensor input;
   srand(time(0));

paddle/inference/inference.cc

@@ -25,6 +25,33 @@ limitations under the License. */
 
 namespace paddle {
 
+void InferenceEngine::LoadInferenceModel(const std::string& dirname) {
+  std::string model_filename = dirname + "/__model__.dat";
+  LOG(INFO) << "loading model from " << model_filename;
+  std::ifstream inputfs(model_filename, std::ios::in | std::ios::binary);
+  std::string program_desc_str;
+  inputfs.seekg(0, std::ios::end);
+  program_desc_str.resize(inputfs.tellg());
+  inputfs.seekg(0, std::ios::beg);
+  LOG(INFO) << "program_desc_str's size: " << program_desc_str.size();
+  inputfs.read(&program_desc_str[0], program_desc_str.size());
+  inputfs.close();
+
+  program_ = new framework::ProgramDesc(program_desc_str);
+  GenerateLoadProgram(dirname);
+
+  framework::BlockDesc* global_block = program_->MutableBlock(0);
+  feed_var_names_.clear();
+  fetch_var_names_.clear();
+  for (auto* op : global_block->AllOps()) {
+    if (op->Type() == "feed") {
+      feed_var_names_.insert(feed_var_names_.begin(), op->Output("Out")[0]);
+    } else if (op->Type() == "fetch") {
+      fetch_var_names_.push_back(op->Input("X")[0]);
+    }
+  }
+}
+
 void InferenceEngine::LoadInferenceModel(
     const std::string& dirname,
     const std::vector<std::string>& feed_var_names,

paddle/inference/inference.h

@@ -28,6 +28,7 @@ public:
     delete load_program_;
   }
 
+  void LoadInferenceModel(const std::string& dirname);
   void LoadInferenceModel(const std::string& dirname,
                           const std::vector<std::string>& feed_var_names,
                           const std::vector<std::string>& fetch_var_names);

python/paddle/v2/fluid/io.py

@@ -243,6 +243,28 @@ def save_inference_model(dirname,
 
     # Save only programDesc of inference_program in binary format
     # in another file: __model__.dat
+    global_block = inference_program.global_block()
+    feed_var = global_blok.create_var(
+        name='feed', type=core.VarDesc.VarType.FEED_MINIBATCH, persistable=True)
+
+    for i, name in enumerated(feeded_var_names):
+        out = global_block.var(name)
+        global_block.prepend_op(
+            type='feed',
+            inputs={'X': [feed_var]},
+            outputs={'Out': [out]},
+            attrs={'col': i})
+
+    fetch_var = global_block.create_var(
+        name='fetch', type=core.VarDesc.VarType.FETCH_LIST, persistable=True)
+
+    for i, name in enumerated(fetch_var_names):
+        global_block.append_op(
+            type='fetch',
+            inputs={'X': [var]},
+            outputs={'Out': [fetch_var]},
+            attrs={'col': i})
+
     with open(model_file_name + ".dat", "wb") as fp:
         fp.write(inference_program.desc.serialize_to_string())