add shape check for input shape

python/paddle_serving_client/__init__.py

@@ -74,7 +74,7 @@ class Client(object):
         self.fetch_names_ = []
         self.client_handle_ = None
         self.result_handle_ = None
-        self.feed_shapes_ = []
+        self.feed_shapes_ = {}
         self.feed_types_ = {}
         self.feed_names_to_idx_ = {}
         self.rpath()
@@ -85,7 +85,6 @@ class Client(object):
         lib_path = os.path.join(lib_path, 'lib')
         os.popen('patchelf --set-rpath {} {}'.format(lib_path, client_path))
 
-
     def load_client_config(self, path):
         from .serving_client import PredictorClient
         from .serving_client import PredictorRes
@@ -106,13 +105,16 @@ class Client(object):
             0]] + ["--tryfromenv=" + ",".join(read_env_flags)])
         self.feed_names_ = [var.alias_name for var in model_conf.feed_var]
         self.fetch_names_ = [var.alias_name for var in model_conf.fetch_var]
-        self.feed_shapes_ = [var.shape for var in model_conf.feed_var]
         self.feed_names_to_idx_ = {}
         self.fetch_names_to_type_ = {}
         self.fetch_names_to_idx_ = {}
+        self.lod_tensor_set = set()
         for i, var in enumerate(model_conf.feed_var):
             self.feed_names_to_idx_[var.alias_name] = i
             self.feed_types_[var.alias_name] = var.feed_type
+            self.feed_shapes_[var.alias_name] = var.shape
+            if var.is_lod_tensor:
+                self.lod_tensor_set.add(var.alias_name)
 
         for i, var in enumerate(model_conf.fetch_var):
             self.fetch_names_to_idx_[var.alias_name] = i
@@ -128,9 +130,8 @@ class Client(object):
         predictor_sdk.set_server_endpoints(endpoints)
         sdk_desc = predictor_sdk.gen_desc()
         print(sdk_desc)
-        self.client_handle_.create_predictor_by_desc(
-            sdk_desc.SerializeToString())
-        
+        self.client_handle_.create_predictor_by_desc(sdk_desc.SerializeToString(
+        ))
 
     def get_feed_names(self):
         return self.feed_names_
@@ -138,6 +139,16 @@ class Client(object):
     def get_fetch_names(self):
         return self.fetch_names_
 
+    def shape_check(self, feed, key):
+        seq_shape = 1
+        if key in self.lod_tensor_set:
+            return
+        for shape in self.feed_shapes_[key]:
+            seq_shape *= shape
+        if len(feed[key]) != seq_shape:
+            raise SystemExit("The shape of feed tensor {} not match.".format(
+                key))
+
     def predict(self, feed={}, fetch=[]):
         int_slot = []
         float_slot = []
@@ -145,6 +156,7 @@ class Client(object):
         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:
@@ -158,16 +170,18 @@ class Client(object):
             if key in self.fetch_names_:
                 fetch_names.append(key)
 
-        ret = self.client_handle_.predict(
-            float_slot, float_feed_names, int_slot,
-            int_feed_names, fetch_names, self.result_handle_)
+        ret = self.client_handle_.predict(float_slot, float_feed_names,
+                                          int_slot, int_feed_names, fetch_names,
+                                          self.result_handle_)
 
         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]
+                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]
+                result_map[name] = self.result_handle_.get_float_by_name(name)[
+                    0]
 
         return result_map