fix get_shape/type and a segfault in getter_pass

add test case

mindspore/ccsrc/minddata/dataset/engine/consumers/tree_consumer.cc

@@ -30,8 +30,8 @@
 #include "minddata/mindrecord/include/shard_writer.h"
 #endif
 
-namespace mindspore::dataset {
-
+namespace mindspore {
+namespace dataset {
 // TreeConsumer
 TreeConsumer::TreeConsumer() { tree_adapter_ = std::make_unique<TreeAdapter>(); }
 
@@ -440,7 +440,9 @@ Status SaveToDisk::TransformTensor(const unsigned char *src, const TensorShape &
 }
 #endif
 
-TreeGetters::TreeGetters() : dataset_size_(-1), init_flag_(false) { tree_adapter_ = std::make_unique<TreeAdapter>(); }
+TreeGetters::TreeGetters() : dataset_size_(-1), init_flag_(false), first_row_obtained_(false) {
+  tree_adapter_ = std::make_unique<TreeAdapter>();
+}
 
 Status TreeGetters::Init(std::shared_ptr<DatasetNode> d) {
   root_ = std::move(d);
@@ -473,20 +475,14 @@ Status TreeGetters::GetDatasetSize(int64_t *dataset_size) {
 }
 
 Status TreeGetters::GetOutputTypes(std::vector<DataType> *types) {
-  RETURN_IF_NOT_OK(InternalInit(static_cast<int8_t>(GetterPass::kOutputShapeAndType)));
-  if (first_row_.empty()) RETURN_IF_NOT_OK(GetRow(&first_row_));
-
-  std::transform(first_row_.begin(), first_row_.end(), std::back_inserter(*types),
-                 [](const TensorPtr &t) { return t->type(); });
+  RETURN_IF_NOT_OK(GetFirstRowShapeAndType());
+  *types = first_row_type_;
   return Status::OK();
 }
 
 Status TreeGetters::GetOutputShapes(std::vector<TensorShape> *shapes) {
-  RETURN_IF_NOT_OK(InternalInit(static_cast<int8_t>(GetterPass::kOutputShapeAndType)));
-  if (first_row_.empty()) RETURN_IF_NOT_OK(GetRow(&first_row_));
-
-  std::transform(first_row_.begin(), first_row_.end(), std::back_inserter(*shapes),
-                 [](const TensorPtr &t) { return t->shape(); });
+  RETURN_IF_NOT_OK(GetFirstRowShapeAndType());
+  *shapes = first_row_shape_;
   return Status::OK();
 }
 
@@ -555,6 +551,18 @@ Status TreeGetters::InternalInit() {
   if (!s.IsError()) init_flag_ = true;
   return s;
 }
+Status TreeGetters::GetFirstRowShapeAndType() {
+  RETURN_OK_IF_TRUE(first_row_obtained_);
+  RETURN_IF_NOT_OK(InternalInit(static_cast<int8_t>(GetterPass::kOutputShapeAndType)));
+  TensorRow first_row;
+  RETURN_IF_NOT_OK(GetRow(&first_row));
+  std::transform(first_row.begin(), first_row.end(), std::back_inserter(first_row_type_),
+                 [](const TensorPtr &t) { return t->type(); });
+  std::transform(first_row.begin(), first_row.end(), std::back_inserter(first_row_shape_),
+                 [](const TensorPtr &t) { return t->shape(); });
+  first_row_obtained_ = true;
+  return Status::OK();
+}
 Status BuildVocabConsumer::Init(std::shared_ptr<DatasetNode> d) { return tree_adapter_->Compile(std::move(d), 1); }
 
 Status BuildVocabConsumer::Start() {
@@ -565,4 +573,5 @@ Status BuildVocabConsumer::Start() {
   CHECK_FAIL_RETURN_UNEXPECTED(row.empty(), "The fetched row from BuildVocab should be an EOE.");
   return Status::OK();
 }
-}  // namespace mindspore::dataset
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/engine/consumers/tree_consumer.h

@@ -189,10 +189,14 @@ class TreeGetters : public TreeConsumer {
   virtual Status GetRow(TensorRow *r);
 
  private:
+  Status GetFirstRowShapeAndType();
+
   std::shared_ptr<DatasetNode> root_;
   int64_t dataset_size_;
-  TensorRow first_row_;
-  bool init_flag_;  // indicate whether the tree has initialized
+  std::vector<DataType> first_row_type_;
+  std::vector<TensorShape> first_row_shape_;
+  bool first_row_obtained_;  // whether first row (which could be empty) is obtained by TreeGetter
+  bool init_flag_;           // indicate whether the tree has initialized
 
   Status InternalInit(int8_t type);
   Status InternalInit();

mindspore/ccsrc/minddata/dataset/engine/datasetops/dataset_op.cc

@@ -151,6 +151,8 @@ Status DatasetOp::Remove() {
   if (!child_.empty()) {
     // If we have a parent, then assign child's parent to point to our parent.
     if (!parent_.empty()) {
+      CHECK_FAIL_RETURN_UNEXPECTED(parent_[0]->Children().size() == 1,
+                                   "Removing a node whose parent has more than 1 child is not supported.");
       child_[0]->parent_[0] = parent_[0];
     } else {
       // We don't have a parent, so we are the root node being removed.

mindspore/ccsrc/minddata/dataset/engine/opt/pre/getter_pass.cc

@@ -56,7 +56,12 @@ Status GetterPass::RunOnTree(ExecutionTree *tree, bool *modified) {
 
   // nested private class variables can be directly accessed by its outer class
   for (auto node : pass_.nodes_to_remove_) {
-    RETURN_IF_NOT_OK(node->Remove());
+    DatasetOp *parent;
+    node->Parent(&parent, 0);
+    // only remove node whose is a single child of its parent
+    if (parent != nullptr && parent->Children().size() == 1) {
+      RETURN_IF_NOT_OK(node->Remove());
+    }
   }
 
   // clear the callback for selected ops (map when its GetOutputType/Shape)

tests/ut/python/dataset/test_dataset_numpy_slices.py

@@ -239,6 +239,13 @@ def test_numpy_slices_invalid_empty_data_column():
     assert "Argument data cannot be empty" in str(err.value)
 
 
+def test_numpy_slice_empty_output_shape():
+    logger.info("running test_numpy_slice_empty_output_shape")
+    dataset = de.NumpySlicesDataset([[[1, 2], [3, 4]]], column_names=["col1"])
+    dataset = dataset.batch(batch_size=3, drop_remainder=True)
+    assert dataset.output_shapes() == []
+
+
 if __name__ == "__main__":
     test_numpy_slices_list_1()
     test_numpy_slices_list_2()
@@ -259,3 +266,4 @@ if __name__ == "__main__":
     test_numpy_slices_invalid_column_names_string()
     test_numpy_slices_invalid_empty_column_names()
     test_numpy_slices_invalid_empty_data_column()
+    test_numpy_slice_empty_output_shape()

tests/ut/python/dataset/test_datasets_get_dataset_size.py

@@ -238,6 +238,10 @@ def test_pipeline_get_dataset_size():
     dataset = dataset.repeat(count=2)
     assert dataset.get_dataset_size() == 8
 
+    tf1 = ds.TFRecordDataset(IMAGENET_TFFILE_DIR, shuffle=True)
+    tf2 = ds.TFRecordDataset(IMAGENET_TFFILE_DIR, shuffle=True)
+    assert tf2.concat(tf1).get_dataset_size() == 24
+
 
 if __name__ == '__main__':
     test_imagenet_rawdata_dataset_size()