fix log error

example/graph_to_mindrecord/write_citeseer.sh

 #!/bin/bash
-rm /tmp/citeseer/mindrecord/*
+SRC_PATH=/tmp/citeseer/dataset
+MINDRECORD_PATH=/tmp/citeseer/mindrecord
+
+rm -f $MINDRECORD_PATH/*
 
 python writer.py --mindrecord_script citeseer \
---mindrecord_file "/tmp/citeseer/mindrecord/citeseer_mr" \
+--mindrecord_file "$MINDRECORD_PATH/citeseer_mr" \
 --mindrecord_partitions 1 \
 --mindrecord_header_size_by_bit 18 \
 --mindrecord_page_size_by_bit 20 \
---graph_api_args "/tmp/citeseer/dataset/citeseer.content:/tmp/citeseer/dataset/citeseer.cites"
+--graph_api_args "$SRC_PATH/citeseer.content:$SRC_PATH/citeseer.cites"

example/graph_to_mindrecord/write_cora.sh

 #!/bin/bash
-rm /tmp/cora/mindrecord/*
+SRC_PATH=/tmp/cora/dataset
+MINDRECORD_PATH=/tmp/cora/mindrecord
+
+rm -f $MINDRECORD_PATH/*
 
 python writer.py --mindrecord_script cora \
---mindrecord_file "/tmp/cora/mindrecord/cora_mr" \
+--mindrecord_file "$MINDRECORD_PATH/cora_mr" \
 --mindrecord_partitions 1 \
 --mindrecord_header_size_by_bit 18 \
 --mindrecord_page_size_by_bit 20 \
---graph_api_args "/tmp/cora/dataset/cora_content.csv:/tmp/cora/dataset/cora_cites.csv"
+--graph_api_args "$SRC_PATH/cora_content.csv:$SRC_PATH/cora_cites.csv"

mindspore/ccsrc/dataset/engine/gnn/graph.cc

@@ -51,7 +51,7 @@ Status Graph::CreateTensorByVector(const std::vector<std::vector<T>> &data, Data
     RETURN_STATUS_UNEXPECTED("Data type not compatible");
   }
   if (data.empty()) {
-    RETURN_STATUS_UNEXPECTED("Input data is emply");
+    RETURN_STATUS_UNEXPECTED("Input data is empty");
   }
   std::shared_ptr<Tensor> tensor;
   size_t m = data.size();
@@ -74,7 +74,7 @@ Status Graph::CreateTensorByVector(const std::vector<std::vector<T>> &data, Data
 template <typename T>
 Status Graph::ComplementVector(std::vector<std::vector<T>> *data, size_t max_size, T default_value) {
   if (!data || data->empty()) {
-    RETURN_STATUS_UNEXPECTED("Input data is emply");
+    RETURN_STATUS_UNEXPECTED("Input data is empty");
   }
   for (std::vector<T> &vec : *data) {
     size_t size = vec.size();
@@ -93,6 +93,9 @@ Status Graph::GetEdges(EdgeType edge_type, EdgeIdType edge_num, std::shared_ptr<
 
 Status Graph::GetAllNeighbors(const std::vector<NodeIdType> &node_list, NodeType neighbor_type,
                               std::shared_ptr<Tensor> *out) {
+  if (node_list.empty()) {
+    RETURN_STATUS_UNEXPECTED("Input node_list is empty.");
+  }
   if (node_type_map_.find(neighbor_type) == node_type_map_.end()) {
     std::string err_msg = "Invalid neighbor type:" + std::to_string(neighbor_type);
     RETURN_STATUS_UNEXPECTED(err_msg);
@@ -147,7 +150,7 @@ Status Graph::GetNodeDefaultFeature(FeatureType feature_type, std::shared_ptr<Fe
 Status Graph::GetNodeFeature(const std::shared_ptr<Tensor> &nodes, const std::vector<FeatureType> &feature_types,
                              TensorRow *out) {
   if (!nodes || nodes->Size() == 0) {
-    RETURN_STATUS_UNEXPECTED("Inpude nodes is empty");
+    RETURN_STATUS_UNEXPECTED("Input nodes is empty");
   }
   TensorRow tensors;
   for (auto f_type : feature_types) {

mindspore/ccsrc/dataset/engine/gnn/graph.h

@@ -156,7 +156,7 @@ class Graph {
   std::unordered_map<EdgeIdType, std::shared_ptr<Edge>> edge_id_map_;
 
   std::unordered_map<NodeType, std::unordered_set<FeatureType>> node_feature_map_;
-  std::unordered_map<NodeType, std::unordered_set<FeatureType>> edge_feature_map_;
+  std::unordered_map<EdgeType, std::unordered_set<FeatureType>> edge_feature_map_;
 
   std::unordered_map<FeatureType, std::shared_ptr<Feature>> default_feature_map_;
 };

mindspore/dataset/engine/graphdata.py

@@ -78,7 +78,7 @@ class GraphData:
             >>> import mindspore.dataset as ds
             >>> data_graph = ds.GraphData('dataset_file', 2)
             >>> nodes = data_graph.get_all_nodes(0)
-            >>> neighbors = data_graph.get_all_neighbors(nodes[0], 0)
+            >>> neighbors = data_graph.get_all_neighbors(nodes, 0)
 
         Raises:
             TypeError: If `node_list` is not list or ndarray.
@@ -102,7 +102,7 @@ class GraphData:
             >>> import mindspore.dataset as ds
             >>> data_graph = ds.GraphData('dataset_file', 2)
             >>> nodes = data_graph.get_all_nodes(0)
-            >>> features = data_graph.get_node_feature(nodes[0], [1])
+            >>> features = data_graph.get_node_feature(nodes, [1])
 
         Raises:
             TypeError: If `node_list` is not list or ndarray.