Merge pull request #1359 from HexToString/grpc_update

增加Fetch为空的处理

core/general-client/include/general_model.h

@@ -53,6 +53,9 @@ class ModelRes {
                             res._int32_value_map.end());
     _shape_map.insert(res._shape_map.begin(), res._shape_map.end());
     _lod_map.insert(res._lod_map.begin(), res._lod_map.end());
+    _tensor_alias_names.insert(_tensor_alias_names.end(),
+                               res._tensor_alias_names.begin(),
+                               res._tensor_alias_names.end());
   }
   ModelRes(ModelRes&& res) {
     _engine_name = std::move(res._engine_name);
@@ -69,6 +72,10 @@ class ModelRes {
                       std::make_move_iterator(std::end(res._shape_map)));
     _lod_map.insert(std::make_move_iterator(std::begin(res._lod_map)),
                     std::make_move_iterator(std::end(res._lod_map)));
+    _tensor_alias_names.insert(
+        _tensor_alias_names.end(),
+        std::make_move_iterator(std::begin(res._tensor_alias_names)),
+        std::make_move_iterator(std::end(res._tensor_alias_names)));
   }
   ~ModelRes() {}
   const std::vector<int64_t>& get_int64_by_name(const std::string& name) {
@@ -105,6 +112,10 @@ class ModelRes {
     _engine_name = engine_name;
   }
   const std::string& engine_name() { return _engine_name; }
+
+  const std::vector<std::string>& tensor_alias_names() {
+    return _tensor_alias_names;
+  }
   ModelRes& operator=(ModelRes&& res) {
     if (this != &res) {
       _engine_name = std::move(res._engine_name);
@@ -121,6 +132,10 @@ class ModelRes {
                         std::make_move_iterator(std::end(res._shape_map)));
       _lod_map.insert(std::make_move_iterator(std::begin(res._lod_map)),
                       std::make_move_iterator(std::end(res._lod_map)));
+      _tensor_alias_names.insert(
+          _tensor_alias_names.end(),
+          std::make_move_iterator(std::begin(res._tensor_alias_names)),
+          std::make_move_iterator(std::end(res._tensor_alias_names)));
     }
     return *this;
   }
@@ -132,6 +147,7 @@ class ModelRes {
   std::map<std::string, std::vector<int32_t>> _int32_value_map;
   std::map<std::string, std::vector<int>> _shape_map;
   std::map<std::string, std::vector<int>> _lod_map;
+  std::vector<std::string> _tensor_alias_names;
 };
 
 class PredictorRes {
@@ -193,11 +209,16 @@ class PredictorRes {
   }
   const std::string& variant_tag() { return _variant_tag; }
   const std::vector<std::string>& get_engine_names() { return _engine_names; }
+  const std::vector<std::string>& get_tensor_alias_names(const int model_idx) {
+    _tensor_alias_names = _models[model_idx].tensor_alias_names();
+    return _tensor_alias_names;
+  }
 
  private:
   std::vector<ModelRes> _models;
   std::string _variant_tag;
   std::vector<std::string> _engine_names;
+  std::vector<std::string> _tensor_alias_names;
 };
 
 class PredictorClient {

core/general-client/src/general_model.cpp

@@ -168,8 +168,6 @@ int PredictorClient::numpy_predict(
   Timer timeline;
   int64_t preprocess_start = timeline.TimeStampUS();
 
-  int fetch_name_num = fetch_name.size();
-
   _api.thrd_initialize();
   std::string variant_tag;
   _predictor = _api.fetch_predictor("general_model", &variant_tag);
@@ -329,10 +327,12 @@ int PredictorClient::numpy_predict(
       auto output = res.outputs(m_idx);
       ModelRes model;
       model.set_engine_name(output.engine_name());
-
-      int idx = 0;
-      for (auto &name : fetch_name) {
+      // 在ResponseOp处，已经按照fetch_name对输出数据进行了处理
+      // 所以，输出的数据与fetch_name是严格对应的，按顺序处理即可。
+      for (int idx = 0; idx < output.tensor_size(); ++idx) {
         // int idx = _fetch_name_to_idx[name];
+        const std::string name = output.tensor(idx).alias_name();
+        model._tensor_alias_names.push_back(name);
         int shape_size = output.tensor(idx).shape_size();
         VLOG(2) << "fetch var " << name << " index " << idx << " shape size "
                 << shape_size;
@@ -347,13 +347,7 @@ int PredictorClient::numpy_predict(
             model._lod_map[name][i] = output.tensor(idx).lod(i);
           }
         }
-        idx += 1;
-      }
 
-      idx = 0;
-
-      for (auto &name : fetch_name) {
-        // int idx = _fetch_name_to_idx[name];
         if (_fetch_name_to_type[name] == P_INT64) {
           VLOG(2) << "ferch var " << name << "type int64";
           int size = output.tensor(idx).int64_data_size();
@@ -373,7 +367,6 @@ int PredictorClient::numpy_predict(
               output.tensor(idx).int_data().begin(),
               output.tensor(idx).int_data().begin() + size);
         }
-        idx += 1;
       }
       predict_res_batch.add_model_res(std::move(model));
     }

core/general-client/src/pybind_general_model.cpp

@@ -69,7 +69,10 @@ PYBIND11_MODULE(serving_client, m) {
            })
       .def("variant_tag", [](PredictorRes &self) { return self.variant_tag(); })
       .def("get_engine_names",
-           [](PredictorRes &self) { return self.get_engine_names(); });
+           [](PredictorRes &self) { return self.get_engine_names(); })
+      .def("get_tensor_alias_names", [](PredictorRes &self, int model_idx) {
+        return self.get_tensor_alias_names(model_idx);
+      });
 
   py::class_<PredictorClient>(m, "PredictorClient", py::buffer_protocol())
       .def(py::init())

core/general-server/op/general_response_op.cpp

@@ -74,10 +74,19 @@ int GeneralResponseOp::inference() {
   // and the order of Output is the same as the prototxt FetchVar.
   // otherwise, you can only get the Output by the corresponding of
   // Name -- Alias_name.
-  fetch_index.resize(req->fetch_var_names_size());
-  for (int i = 0; i < req->fetch_var_names_size(); ++i) {
-    fetch_index[i] =
-        model_config->_fetch_alias_name_to_index[req->fetch_var_names(i)];
+  if (req->fetch_var_names_size() > 0) {
+    fetch_index.resize(req->fetch_var_names_size());
+    for (int i = 0; i < req->fetch_var_names_size(); ++i) {
+      fetch_index[i] =
+          model_config->_fetch_alias_name_to_index[req->fetch_var_names(i)];
+    }
+  } else {
+    fetch_index.resize(model_config->_fetch_alias_name.size());
+    for (int i = 0; i < model_config->_fetch_alias_name.size(); ++i) {
+      fetch_index[i] =
+          model_config
+              ->_fetch_alias_name_to_index[model_config->_fetch_alias_name[i]];
+    }
   }
 
   for (uint32_t pi = 0; pi < pre_node_names.size(); ++pi) {
@@ -105,7 +114,7 @@ int GeneralResponseOp::inference() {
     // fetch_index is the real index in FetchVar of Fetchlist
     // for example, FetchVar = {0:A, 1:B, 2:C}
     // FetchList = {0:C,1:A}, at this situation.
-    // fetch_index = [2,0], C`index = 2 and A`index = 0 
+    // fetch_index = [2,0], C`index = 2 and A`index = 0
     for (auto &idx : fetch_index) {
       Tensor *tensor = output->add_tensor();
       tensor->set_name(in->at(idx).name);

python/paddle_serving_client/client.py

@@ -289,16 +289,18 @@ class Client(object):
                 log_id=0):
         self.profile_.record('py_prepro_0')
 
-        if feed is None or fetch is None:
-            raise ValueError("You should specify feed and fetch for prediction")
+        if feed is None:
+            raise ValueError("You should specify feed for prediction")
 
         fetch_list = []
         if isinstance(fetch, str):
             fetch_list = [fetch]
         elif isinstance(fetch, list):
             fetch_list = fetch
+        elif fetch == None:
+            pass
         else:
-            raise ValueError("Fetch only accepts string and list of string")
+            raise ValueError("Fetch only accepts string or list of string")
 
         feed_batch = []
         if isinstance(feed, dict):
@@ -339,16 +341,13 @@ class Client(object):
         string_feed_names = []
         string_lod_slot_batch = []
         string_shape = []
+
         fetch_names = []
 
         for key in fetch_list:
             if key in self.fetch_names_:
                 fetch_names.append(key)
 
-        if len(fetch_names) == 0:
-            raise ValueError(
-                "Fetch names should not be empty or out of saved fetch list.")
-
         feed_dict = feed_batch[0]
         for key in feed_dict:
             if ".lod" not in key and key not in self.feed_names_:
@@ -443,6 +442,8 @@ class Client(object):
         model_engine_names = result_batch_handle.get_engine_names()
         for mi, engine_name in enumerate(model_engine_names):
             result_map = {}
+            if len(fetch_names) == 0:
+                fetch_names = result_batch_handle.get_tensor_alias_names(mi)
             # result map needs to be a numpy array
             for i, name in enumerate(fetch_names):
                 if self.fetch_names_to_type_[name] == int64_type:

python/paddle_serving_client/httpclient.py

@@ -93,9 +93,13 @@ class HttpClient(object):
         self.try_request_gzip = False
         self.try_response_gzip = False
         self.total_data_number = 0
+        self.headers = {}
         self.http_proto = True
+        self.headers["Content-Type"] = "application/proto"
         self.max_body_size = 512 * 1024 * 1024
         self.use_grpc_client = False
+        self.url = None
+
         # 使用连接池能够不用反复建立连接
         self.requests_session = requests.session()
         # 初始化grpc_stub
@@ -190,8 +194,21 @@ class HttpClient(object):
 
     def set_port(self, port):
         self.port = port
+        self.server_port = port
         self.init_grpc_stub()
 
+    def set_url(self, url):
+        if isinstance(url, str):
+            self.url = url
+        else:
+            print("url must be str")
+
+    def add_http_headers(self, headers):
+        if isinstance(headers, dict):
+            self.headers.update(headers)
+        else:
+            print("headers must be a dict")
+
     def set_request_compress(self, try_request_gzip):
         self.try_request_gzip = try_request_gzip
 
@@ -200,6 +217,10 @@ class HttpClient(object):
 
     def set_http_proto(self, http_proto):
         self.http_proto = http_proto
+        if self.http_proto:
+            self.headers["Content-Type"] = "application/proto"
+        else:
+            self.headers["Content-Type"] = "application/json"
 
     def set_use_grpc_client(self, use_grpc_client):
         self.use_grpc_client = use_grpc_client
@@ -232,31 +253,26 @@ class HttpClient(object):
         return self.fetch_names_
 
     def get_legal_fetch(self, fetch):
-        if fetch is None:
-            raise ValueError("You should specify feed and fetch for prediction")
 
         fetch_list = []
         if isinstance(fetch, str):
             fetch_list = [fetch]
         elif isinstance(fetch, (list, tuple)):
             fetch_list = fetch
+        elif fetch == None:
+            pass
         else:
-            raise ValueError("Fetch only accepts string and list of string")
+            raise ValueError("Fetch only accepts string/list/tuple of string")
 
         fetch_names = []
         for key in fetch_list:
             if key in self.fetch_names_:
                 fetch_names.append(key)
-
-        if len(fetch_names) == 0:
-            raise ValueError(
-                "Fetch names should not be empty or out of saved fetch list.")
-            return {}
         return fetch_names
 
     def get_feedvar_dict(self, feed):
         if feed is None:
-            raise ValueError("You should specify feed and fetch for prediction")
+            raise ValueError("You should specify feed for prediction")
         feed_dict = {}
         if isinstance(feed, dict):
             feed_dict = feed
@@ -402,17 +418,19 @@ class HttpClient(object):
             # 此时先统一处理为一个list
             # 由于输入比较特殊，shape保持原feedvar中不变
             data_value = []
-            data_value.append(feed_dict[key])
             if isinstance(feed_dict[key], (str, bytes)):
                 if self.feed_types_[key] != bytes_type:
                     raise ValueError(
                         "feedvar is not string-type,feed can`t be a single string."
                     )
+                if isinstance(feed_dict[key], bytes):
+                    feed_dict[key] = feed_dict[key].decode()
             else:
                 if self.feed_types_[key] == bytes_type:
                     raise ValueError(
                         "feedvar is string-type,feed can`t be a single int or others."
                     )
+            data_value.append(feed_dict[key])
         # 如果不压缩，那么不需要统计数据量。
         if self.try_request_gzip:
             self.total_data_number = self.total_data_number + data_bytes_number(
@@ -453,20 +471,25 @@ class HttpClient(object):
 
         feed_dict = self.get_feedvar_dict(feed)
         fetch_list = self.get_legal_fetch(fetch)
-        headers = {}
         postData = ''
 
         if self.http_proto == True:
             postData = self.process_proto_data(feed_dict, fetch_list, batch,
                                                log_id).SerializeToString()
-            headers["Content-Type"] = "application/proto"
+
         else:
             postData = self.process_json_data(feed_dict, fetch_list, batch,
                                               log_id)
-            headers["Content-Type"] = "application/json"
 
         web_url = "http://" + self.ip + ":" + self.server_port + self.service_name
+        if self.url != None:
+            if "http" not in self.url:
+                self.url = "http://" + self.url
+            if "self.service_name" not in self.url:
+                self.url = self.url + self.service_name
+            web_url = self.url
         # 当数据区长度大于512字节时才压缩.
+        self.headers.pop("Content-Encoding", "nokey")
         try:
             if self.try_request_gzip and self.total_data_number > 512:
 
@@ -474,20 +497,21 @@ class HttpClient(object):
                     postData = gzip.compress(postData)
                 else:
                     postData = gzip.compress(bytes(postData, 'utf-8'))
-                headers["Content-Encoding"] = "gzip"
+                self.headers["Content-Encoding"] = "gzip"
             if self.try_response_gzip:
-                headers["Accept-encoding"] = "gzip"
+                self.headers["Accept-encoding"] = "gzip"
         # 压缩异常，使用原始数据
         except:
             print("compress error, we will use the no-compress data")
-            headers.pop("Content-Encoding", "nokey")
+            self.headers.pop("Content-Encoding", "nokey")
         # requests支持自动识别解压
         try:
             result = self.requests_session.post(
                 url=web_url,
-                headers=headers,
+                headers=self.headers,
                 data=postData,
-                timeout=self.timeout_ms / 1000)
+                timeout=self.timeout_ms / 1000,
+                verify=False)
             result.raise_for_status()
         except:
             print("http post error")