enforce_dim_check_in_data_feeder

test=develop

python/paddle/fluid/data_feeder.py

@@ -71,10 +71,20 @@ class DataToLoDTensorConverter(object):
             for each_data in data:
                 self._feed_impl_(each_data, lod[1:], lod_level - 1)
 
+    def _check_shape_(self, shape):
+        for s1, s2 in zip(self.shape, shape):
+            if s1 != s2 and s1 >= 0 and s2 >= 0:
+                raise ValueError(
+                    "Shape not match. What is defined in data layer is {}, but receive {}".
+                    format(self.shape, shape))
+
     def done(self):
         arr = numpy.array(self.data, dtype=self.dtype)
-        if self.shape and len(arr.shape) != len(self.shape):
-            arr = arr.reshape(self.shape)
+        if self.shape:
+            if len(arr.shape) != len(self.shape):
+                arr = arr.reshape(self.shape)
+            else:
+                self._check_shape_(arr.shape)
         t = core.LoDTensor()
         t.set(arr, self.place)
         if self.lod_level > 0:
@@ -152,17 +162,8 @@ class DataFeeder(object):
                 raise TypeError("Feed list should contain a list of variable")
             self.feed_dtypes.append(each_var.dtype)
             self.feed_names.append(each_var.name)
-            shape = each_var.shape
-            batch_size_dim = -1
-            for i, s in enumerate(shape):
-                if s < 0:
-                    batch_size_dim = i
-                    break
-            if batch_size_dim == -1:
-                raise ValueError("Variable {0} must has a batch size dimension",
-                                 each_var.name)
             self.feed_lod_level.append(each_var.lod_level)
-            self.feed_shapes.append(shape)
+            self.feed_shapes.append(each_var.shape)
 
         self.place = place