unify predict and batch predict interface and call batch_predict inside the predict function

python/paddle_serving_client/__init__.py

@@ -155,44 +155,23 @@ class Client(object):
             raise SystemExit("The shape of feed tensor {} not match.".format(
                 key))
 
-    def predict(self, feed={}, fetch=[]):
-        int_slot = []
-        float_slot = []
-        int_feed_names = []
-        float_feed_names = []
-        fetch_names = []
-
-        for key in feed:
-            self.shape_check(feed, key)
-            if key not in self.feed_names_:
-                continue
-            if self.feed_types_[key] == int_type:
-                int_feed_names.append(key)
-                int_slot.append(feed[key])
-            elif self.feed_types_[key] == float_type:
-                float_feed_names.append(key)
-                float_slot.append(feed[key])
-
-        for key in fetch:
-            if key in self.fetch_names_:
-                fetch_names.append(key)
+    def predict(self, feed=None, fetch=None):
+        fetch_list = []
+        if isinstance(fetch, str):
+            fetch_list = [fetch]
+        elif isinstance(fetch, list):
+            fetch_list = fetch
+        else:
+            raise ValueError("fetch only accepts string and list of string")
+
+        feed_batch = []
+        if isinstance(feed, dict):
+            feed_batch.append(feed)
+        elif isinstance(feed, list):
+            feed_batch = feed
+        else:
+            raise ValueError("feed only accepts dict and list of dict")
 
-        ret = self.client_handle_.predict(float_slot, float_feed_names,
-                                          int_slot, int_feed_names, fetch_names,
-                                          self.result_handle_, self.pid)
-
-        result_map = {}
-        for i, name in enumerate(fetch_names):
-            if self.fetch_names_to_type_[name] == int_type:
-                result_map[name] = self.result_handle_.get_int64_by_name(name)[
-                    0]
-            elif self.fetch_names_to_type_[name] == float_type:
-                result_map[name] = self.result_handle_.get_float_by_name(name)[
-                    0]
-
-        return result_map
-
-    def batch_predict(self, feed_batch=[], fetch=[]):
         int_slot_batch = []
         float_slot_batch = []
         int_feed_names = []
@@ -200,10 +179,20 @@ class Client(object):
         fetch_names = []
         counter = 0
         batch_size = len(feed_batch)
-        for feed in feed_batch:
+
+        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 {}
+
+        for feed_i in feed_batch:
             int_slot = []
             float_slot = []
-            for key in feed:
+            for key in feed_i:
                 if key not in self.feed_names_:
                     continue
                 if self.feed_types_[key] == int_type:
@@ -213,15 +202,11 @@ class Client(object):
                 elif self.feed_types_[key] == float_type:
                     if counter == 0:
                         float_feed_names.append(key)
-                    float_slot.append(feed[key])
+                    float_slot.append(feed_i[key])
             counter += 1
             int_slot_batch.append(int_slot)
             float_slot_batch.append(float_slot)
 
-        for key in fetch:
-            if key in self.fetch_names_:
-                fetch_names.append(key)
-
         result_batch = self.result_handle_
         res = self.client_handle_.batch_predict(
             float_slot_batch, float_feed_names, int_slot_batch, int_feed_names,