lod input support numpy array

core/general-server/op/general_reader_op.cpp

@@ -131,7 +131,7 @@ int GeneralReaderOp::inference() {
       lod_tensor.dtype = paddle::PaddleDType::FLOAT32;
     }
 
-    if (req->insts(0).tensor_array(i).shape(0) == -1) {
+    if (model_config->_is_lod_feed[i]) {
       lod_tensor.lod.resize(1);
       lod_tensor.lod[0].push_back(0);
       VLOG(2) << "var[" << i << "] is lod_tensor";
@@ -153,6 +153,7 @@ int GeneralReaderOp::inference() {
   // specify the memory needed for output tensor_vector
   for (int i = 0; i < var_num; ++i) {
     if (out->at(i).lod.size() == 1) {
+      int tensor_size = 0;
       for (int j = 0; j < batch_size; ++j) {
         const Tensor &tensor = req->insts(j).tensor_array(i);
         int data_len = 0;
@@ -162,15 +163,19 @@ int GeneralReaderOp::inference() {
           data_len = tensor.float_data_size();
         }
         VLOG(2) << "tensor size for var[" << i << "]: " << data_len;
+        tensor_size += data_len;
 
         int cur_len = out->at(i).lod[0].back();
         VLOG(2) << "current len: " << cur_len;
 
-        out->at(i).lod[0].push_back(cur_len + data_len);
-        VLOG(2) << "new len: " << cur_len + data_len;
+        out->at(i).lod[0].push_back(cur_len + tensor.shape(0));
+        VLOG(2) << "new len: " << cur_len + tensor.shape(0);
+      }
+      out->at(i).data.Resize(tensor_size * elem_size[i]);
+      out->at(i).shape = {out->at(i).lod[0].back()};
+      for (int j = 1; j < req->insts(0).tensor_array(i).shape_size(); ++j) {
+        out->at(i).shape.push_back(req->insts(0).tensor_array(i).shape(j));
       }
-      out->at(i).data.Resize(out->at(i).lod[0].back() * elem_size[i]);
-      out->at(i).shape = {out->at(i).lod[0].back(), 1};
       VLOG(2) << "var[" << i
               << "] is lod_tensor and len=" << out->at(i).lod[0].back();
     } else {

python/paddle_serving_client/__init__.py

@@ -254,23 +254,16 @@ class Client(object):
             for key in feed_i:
                 if key not in self.feed_names_:
                     raise ValueError("Wrong feed name: {}.".format(key))
-                if not isinstance(feed_i[key], np.ndarray):
-                    self.shape_check(feed_i, key)
+                #if not isinstance(feed_i[key], np.ndarray):
+                self.shape_check(feed_i, key)
                 if self.feed_types_[key] == int_type:
                     if i == 0:
                         int_feed_names.append(key)
                         if isinstance(feed_i[key], np.ndarray):
-                            if key in self.lod_tensor_set:
-                                raise ValueError(
-                                    "LodTensor var can not be ndarray type.")
                             int_shape.append(list(feed_i[key].shape))
                         else:
                             int_shape.append(self.feed_shapes_[key])
                     if isinstance(feed_i[key], np.ndarray):
-                        if key in self.lod_tensor_set:
-                            raise ValueError(
-                                "LodTensor var can not be ndarray type.")
-                        #int_slot.append(np.reshape(feed_i[key], (-1)).tolist())
                         int_slot.append(feed_i[key])
                         self.has_numpy_input = True
                     else:
@@ -280,17 +273,10 @@ class Client(object):
                     if i == 0:
                         float_feed_names.append(key)
                         if isinstance(feed_i[key], np.ndarray):
-                            if key in self.lod_tensor_set:
-                                raise ValueError(
-                                    "LodTensor var can not be ndarray type.")
                             float_shape.append(list(feed_i[key].shape))
                         else:
                             float_shape.append(self.feed_shapes_[key])
                     if isinstance(feed_i[key], np.ndarray):
-                        if key in self.lod_tensor_set:
-                            raise ValueError(
-                                "LodTensor var can not be ndarray type.")
-                        #float_slot.append(np.reshape(feed_i[key], (-1)).tolist())
                         float_slot.append(feed_i[key])
                         self.has_numpy_input = True
                     else: