fix waring

mindspore/ccsrc/minddata/dataset/callback/py_ds_callback.cc

@@ -61,27 +61,27 @@ Status PyDSCallback::ExecutePyfunc(py::function f, const CallbackParam &cb_param
   }
   return Status::OK();
 }
-void PyDSCallback::setBegin(py::function f) {
+void PyDSCallback::setBegin(const py::function &f) {
   begin_func_ = f;
   begin_needed_ = true;
 }
-void PyDSCallback::setEnd(py::function f) {
+void PyDSCallback::setEnd(const py::function &f) {
   end_func_ = f;
   end_needed_ = true;
 }
-void PyDSCallback::setEpochBegin(py::function f) {
+void PyDSCallback::setEpochBegin(const py::function &f) {
   epoch_begin_func_ = f;
   epoch_begin_needed_ = true;
 }
-void PyDSCallback::setEpochEnd(py::function f) {
+void PyDSCallback::setEpochEnd(const py::function &f) {
   epoch_end_func_ = f;
   epoch_end_needed_ = true;
 }
-void PyDSCallback::setStepBegin(py::function f) {
+void PyDSCallback::setStepBegin(const py::function &f) {
   step_begin_func_ = f;
   step_begin_needed_ = true;
 }
-void PyDSCallback::setStepEnd(py::function f) {
+void PyDSCallback::setStepEnd(const py::function &f) {
   step_end_func_ = f;
   step_end_needed_ = true;
 }

mindspore/ccsrc/minddata/dataset/callback/py_ds_callback.h

@@ -44,12 +44,12 @@ class PyDSCallback : public DSCallback {
 
   ~PyDSCallback() = default;
 
-  void setBegin(py::function f);
-  void setEnd(py::function f);
-  void setEpochBegin(py::function f);
-  void setEpochEnd(py::function f);
-  void setStepBegin(py::function f);
-  void setStepEnd(py::function f);
+  void setBegin(const py::function &f);
+  void setEnd(const py::function &f);
+  void setEpochBegin(const py::function &f);
+  void setEpochEnd(const py::function &f);
+  void setStepBegin(const py::function &f);
+  void setStepEnd(const py::function &f);
 
   /// \brief actual callback function for begin, needs to be overridden in the derived class
   /// \param cb_param, callback parameter passed in from DatasetOp when calling the callback

mindspore/ccsrc/minddata/dataset/core/tensor.cc

@@ -508,7 +508,9 @@ Status Tensor::GetItemPtr(uchar **ptr, const std::vector<dsize_t> &index, offset
     RETURN_IF_NOT_OK(shape_.ToFlatIndex(index, &flat_idx));
     offset_t length_temp = 0;
     RETURN_IF_NOT_OK(GetStringAt(flat_idx, ptr, &length_temp));
-    if (length != nullptr) *length = length_temp;
+    if (length != nullptr) {
+      *length = length_temp;
+    }
     return Status::OK();
   } else {
     std::string err = "data type not compatible";

mindspore/ccsrc/minddata/dataset/core/tensor.h

@@ -68,7 +68,7 @@ class Tensor {
   Tensor(const Tensor &other) = delete;
   Tensor &operator=(const Tensor &other) = delete;
 
-  /// Create a tensor using shape and type. This constructor should not be used directly, use CreateFromTensor instead
+  /// Create a tensor using shape and type. This constructor should not be used directly, use CreateFromTensor instead.
   /// \note The shape and type information should be known and valid
   /// \note The constructor does not allocate data
   /// \param shape TensorShape

mindspore/ccsrc/minddata/dataset/engine/cache/cache_grpc_client.cc

@@ -73,7 +73,7 @@ Status CacheClientGreeter::DoServiceStop() {
     void *tag;
     while (cq_.Next(&tag, &success)) {
       auto r = reinterpret_cast<CacheClientRequestTag *>(tag);
-      req_.erase(r->seqNo_);
+      (void)req_.erase(r->seqNo_);
     }
   }
   return Status::OK();

mindspore/ccsrc/minddata/dataset/engine/data_schema.cc

@@ -408,7 +408,7 @@ void DataSchema::Print(std::ostream &out) const {
 // Adds a column descriptor to the schema
 Status DataSchema::AddColumn(const ColDescriptor &cd) {
   // Sanity check there's not a duplicate name before adding the column
-  for (int32_t i = 0; i < col_descs_.size(); ++i) {
+  for (auto i = 0; i < col_descs_.size(); ++i) {
     if (col_descs_[i].name() == cd.name()) {
       std::ostringstream ss;
       ss << "column name '" << cd.name() << "' already exists in schema.";

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/album_op.cc

@@ -118,7 +118,7 @@ bool AlbumOp::CheckImageType(const std::string &file_name, bool *valid) {
   return true;
 }
 
-Status AlbumOp::LoadImageTensor(const std::string &image_file_path, uint32_t col_num, TensorRow *row) {
+Status AlbumOp::LoadImageTensor(const std::string &image_file_path, int32_t col_num, TensorRow *row) {
   TensorPtr image;
   std::ifstream fs;
   fs.open(image_file_path, std::ios::binary | std::ios::in);
@@ -168,7 +168,7 @@ Status AlbumOp::LoadImageTensor(const std::string &image_file_path, uint32_t col
   return Status::OK();
 }
 
-Status AlbumOp::LoadStringArrayTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorRow *row) {
+Status AlbumOp::LoadStringArrayTensor(const nlohmann::json &json_obj, int32_t col_num, TensorRow *row) {
   std::vector<std::string> data = json_obj;
 
   MS_LOG(INFO) << "String array label found: " << data << ".";
@@ -178,7 +178,7 @@ Status AlbumOp::LoadStringArrayTensor(const nlohmann::json &json_obj, uint32_t c
   return Status::OK();
 }
 
-Status AlbumOp::LoadStringTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorRow *row) {
+Status AlbumOp::LoadStringTensor(const nlohmann::json &json_obj, int32_t col_num, TensorRow *row) {
   std::string data = json_obj;
   // now we iterate over the elements in json
 
@@ -189,7 +189,7 @@ Status AlbumOp::LoadStringTensor(const nlohmann::json &json_obj, uint32_t col_nu
   return Status::OK();
 }
 
-Status AlbumOp::LoadIntArrayTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorRow *row) {
+Status AlbumOp::LoadIntArrayTensor(const nlohmann::json &json_obj, int32_t col_num, TensorRow *row) {
   TensorPtr label;
   // consider templating this function to handle all ints
   if (data_schema_->column(col_num).type() == DataType::DE_INT64) {
@@ -218,7 +218,7 @@ Status AlbumOp::LoadIntArrayTensor(const nlohmann::json &json_obj, uint32_t col_
   return Status::OK();
 }
 
-Status AlbumOp::LoadFloatArrayTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorRow *row) {
+Status AlbumOp::LoadFloatArrayTensor(const nlohmann::json &json_obj, int32_t col_num, TensorRow *row) {
   TensorPtr float_array;
   // consider templating this function to handle all ints
   if (data_schema_->column(col_num).type() == DataType::DE_FLOAT64) {
@@ -247,7 +247,7 @@ Status AlbumOp::LoadFloatArrayTensor(const nlohmann::json &json_obj, uint32_t co
   return Status::OK();
 }
 
-Status AlbumOp::LoadIDTensor(const std::string &file, uint32_t col_num, TensorRow *row) {
+Status AlbumOp::LoadIDTensor(const std::string &file, int32_t col_num, TensorRow *row) {
   if (data_schema_->column(col_num).type() == DataType::DE_STRING) {
     TensorPtr id;
     RETURN_IF_NOT_OK(Tensor::CreateScalar<std::string>(file, &id));
@@ -263,7 +263,7 @@ Status AlbumOp::LoadIDTensor(const std::string &file, uint32_t col_num, TensorRo
   return Status::OK();
 }
 
-Status AlbumOp::LoadEmptyTensor(uint32_t col_num, TensorRow *row) {
+Status AlbumOp::LoadEmptyTensor(int32_t col_num, TensorRow *row) {
   // hack to get the file name without extension, the 1 is to get rid of the backslash character
   TensorPtr empty_tensor;
   RETURN_IF_NOT_OK(Tensor::CreateEmpty(TensorShape({0}), data_schema_->column(col_num).type(), &empty_tensor));
@@ -275,7 +275,7 @@ Status AlbumOp::LoadEmptyTensor(uint32_t col_num, TensorRow *row) {
 // So we actually have to check what type we want to fill the tensor with.
 // Float64 doesn't work with reinterpret cast here. Otherwise we limit the float in the schema to
 // only be float32, seems like a weird limitation to impose
-Status AlbumOp::LoadFloatTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorRow *row) {
+Status AlbumOp::LoadFloatTensor(const nlohmann::json &json_obj, int32_t col_num, TensorRow *row) {
   TensorPtr float_tensor;
   if (data_schema_->column(col_num).type() == DataType::DE_FLOAT64) {
     double data = json_obj;
@@ -291,7 +291,7 @@ Status AlbumOp::LoadFloatTensor(const nlohmann::json &json_obj, uint32_t col_num
 }
 
 // Loads a tensor with int value, we have to cast the value to type specified in the schema.
-Status AlbumOp::LoadIntTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorRow *row) {
+Status AlbumOp::LoadIntTensor(const nlohmann::json &json_obj, int32_t col_num, TensorRow *row) {
   TensorPtr int_tensor;
   if (data_schema_->column(col_num).type() == DataType::DE_INT64) {
     int64_t data = json_obj;

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/album_op.h

@@ -88,62 +88,62 @@ class AlbumOp : public MappableLeafOp {
   /// \param[in] col_num Column num in schema
   /// \param[in, out] row Tensor row to push to
   /// \return Status The status code returned
-  Status LoadImageTensor(const std::string &image_file, uint32_t col_num, TensorRow *row);
+  Status LoadImageTensor(const std::string &image_file, int32_t col_num, TensorRow *row);
 
   /// \brief Load vector of ints to tensor, append tensor to tensor row
   /// \param[in] json_obj Json object containing multi-dimensional label
   /// \param[in] col_num Column num in schema
   /// \param[in, out] row Tensor row to push to
   /// \return Status The status code returned
-  Status LoadIntArrayTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorRow *row);
+  Status LoadIntArrayTensor(const nlohmann::json &json_obj, int32_t col_num, TensorRow *row);
 
   /// \brief Load vector of floatss to tensor, append tensor to tensor row
   /// \param[in] json_obj Json object containing array data
   /// \param[in] col_num Column num in schema
   /// \param[in, out] row Tensor row to push to
   /// \return Status The status code returned
-  Status LoadFloatArrayTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorRow *row);
+  Status LoadFloatArrayTensor(const nlohmann::json &json_obj, int32_t col_num, TensorRow *row);
 
   /// \brief Load string array into a tensor, append tensor to tensor row
   /// \param[in] json_obj Json object containing string tensor
   /// \param[in] col_num Column num in schema
   /// \param[in, out] row Tensor row to push to
   /// \return Status The status code returned
-  Status LoadStringArrayTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorRow *row);
+  Status LoadStringArrayTensor(const nlohmann::json &json_obj, int32_t col_num, TensorRow *row);
 
   /// \brief Load string into a tensor, append tensor to tensor row
   /// \param[in] json_obj Json object containing string tensor
   /// \param[in] col_num Column num in schema
   /// \param[in, out] row Tensor row to push to
   /// \return Status The status code returned
-  Status LoadStringTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorRow *row);
+  Status LoadStringTensor(const nlohmann::json &json_obj, int32_t col_num, TensorRow *row);
 
   /// \brief Load float value to tensor row
   /// \param[in] json_obj Json object containing float
   /// \param[in] col_num Column num in schema
   /// \param[in, out] row Tensor row to push to
   /// \return Status The status code returned
-  Status LoadFloatTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorRow *row);
+  Status LoadFloatTensor(const nlohmann::json &json_obj, int32_t col_num, TensorRow *row);
 
   /// \brief Load int value to tensor row
   /// \param[in] json_obj Json object containing int
   /// \param[in] col_num Column num in schema
   /// \param[in, out] row Tensor row to push to
   /// \return Status The status code returned
-  Status LoadIntTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorRow *row);
+  Status LoadIntTensor(const nlohmann::json &json_obj, int32_t col_num, TensorRow *row);
 
   /// \brief Load empty tensor to tensor row
   /// \param[in] col_num Column num in schema
   /// \param[in, out] row Tensor row to push to
   /// \return Status The status code returned
-  Status LoadEmptyTensor(uint32_t col_num, TensorRow *row);
+  Status LoadEmptyTensor(int32_t col_num, TensorRow *row);
 
   /// \brief Load id from file name to tensor row
   /// \param[in] file The file name to get ID from
   /// \param[in] col_num Column num in schema
   /// \param[in, out] row Tensor row to push to
   /// \return Status The status code returned
-  Status LoadIDTensor(const std::string &file, uint32_t col_num, TensorRow *row);
+  Status LoadIDTensor(const std::string &file, int32_t col_num, TensorRow *row);
 
   /// \brief Load a tensor row according to a json file
   /// \param[in] row_id_type row_id - id for this tensor row

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/cifar_op.cc

@@ -368,7 +368,7 @@ Status CifarOp::CountTotalRows(const std::string &dir, const std::string &usage,
 Status CifarOp::ComputeColMap() {
   // set the column name map (base class field)
   if (column_name_id_map_.empty()) {
-    for (uint32_t i = 0; i < data_schema_->NumColumns(); ++i) {
+    for (int32_t i = 0; i < data_schema_->NumColumns(); ++i) {
       column_name_id_map_[data_schema_->column(i).name()] = i;
     }
   } else {

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/mindrecord_op.cc

@@ -223,7 +223,7 @@ Status MindRecordOp::GetRowFromReader(TensorRow *fetched_row, uint64_t row_id, i
 
 Status MindRecordOp::LoadTensorRow(TensorRow *tensor_row, const std::vector<uint8_t> &columns_blob,
                                    const mindrecord::json &columns_json, const mindrecord::TaskType task_type) {
-  for (uint32_t i_col = 0; i_col < columns_to_load_.size(); i_col++) {
+  for (int32_t i_col = 0; i_col < columns_to_load_.size(); i_col++) {
     auto column_name = columns_to_load_[i_col];
 
     // Initialize column parameters

mindspore/ccsrc/minddata/dataset/engine/ir/cache/dataset_cache_impl.cc

@@ -31,10 +31,18 @@ Status DatasetCacheImpl::Build() {
 
   CacheClient::Builder builder;
   builder.SetSessionId(session_id_).SetCacheMemSz(cache_mem_sz_).SetSpill(spill_);
-  if (hostname_) builder.SetHostname(hostname_.value());
-  if (port_) builder.SetPort(port_.value());
-  if (num_connections_) builder.SetNumConnections(num_connections_.value());
-  if (prefetch_sz_) builder.SetPrefetchSize(prefetch_sz_.value());
+  if (hostname_) {
+    (void)builder.SetHostname(hostname_.value());
+  }
+  if (port_) {
+    (void)builder.SetPort(port_.value());
+  }
+  if (num_connections_) {
+    (void)builder.SetNumConnections(num_connections_.value());
+  }
+  if (prefetch_sz_) {
+    (void)builder.SetPrefetchSize(prefetch_sz_.value());
+  }
   return builder.Build(&cache_client_);
 }
 

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/epoch_ctrl_node.cc

@@ -71,13 +71,13 @@ Status EpochCtrlNode::ValidateParams() {
 }
 
 // Visitor accepting method for IRNodePass
-Status EpochCtrlNode::Accept(IRNodePass *p, bool *const modified) {
+Status EpochCtrlNode::Accept(IRNodePass *const p, bool *const modified) {
   // Downcast shared pointer then call visitor
   return p->Visit(shared_from_base<EpochCtrlNode>(), modified);
 }
 
 // Visitor accepting method for IRNodePass
-Status EpochCtrlNode::AcceptAfter(IRNodePass *p, bool *const modified) {
+Status EpochCtrlNode::AcceptAfter(IRNodePass *const p, bool *const modified) {
   // Downcast shared pointer then call visitor
   return p->VisitAfter(shared_from_base<EpochCtrlNode>(), modified);
 }

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/epoch_ctrl_node.h

@@ -67,13 +67,13 @@ class EpochCtrlNode : public RepeatNode {
   /// \param[in] p The node to visit
   /// \param[out] modified Indicator if the node was modified
   /// \return Status of the node visit
-  Status Accept(IRNodePass *p, bool *const modified) override;
+  Status Accept(IRNodePass *const p, bool *const modified) override;
 
   /// \brief Base-class override for accepting IRNodePass visitor
   /// \param[in] p The node to visit
   /// \param[out] modified Indicator if the node was modified
   /// \return Status of the node visit
-  Status AcceptAfter(IRNodePass *p, bool *const modified) override;
+  Status AcceptAfter(IRNodePass *const p, bool *const modified) override;
 };
 
 }  // namespace dataset

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/album_node.cc

@@ -83,7 +83,7 @@ Status AlbumNode::Build(std::vector<std::shared_ptr<DatasetOp>> *const node_ops)
 }
 
 // Get the shard id of node
-Status AlbumNode::GetShardId(int32_t *shard_id) {
+Status AlbumNode::GetShardId(int32_t *const shard_id) {
   *shard_id = sampler_->ShardId();
 
   return Status::OK();

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/album_node.h

@@ -59,7 +59,7 @@ class AlbumNode : public MappableSourceNode {
 
   /// \brief Get the shard id of node
   /// \return Status Status::OK() if get shard id successfully
-  Status GetShardId(int32_t *shard_id) override;
+  Status GetShardId(int32_t *const shard_id) override;
 
   /// \brief Base-class override for GetDatasetSize
   /// \param[in] size_getter Shared pointer to DatasetSizeGetter

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/mnist_node.cc

@@ -70,7 +70,7 @@ Status MnistNode::Build(std::vector<std::shared_ptr<DatasetOp>> *const node_ops)
 }
 
 // Get the shard id of node
-Status MnistNode::GetShardId(int32_t *shard_id) {
+Status MnistNode::GetShardId(int32_t *const shard_id) {
   *shard_id = sampler_->ShardId();
 
   return Status::OK();

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/mnist_node.h

@@ -58,7 +58,7 @@ class MnistNode : public MappableSourceNode {
 
   /// \brief Get the shard id of node
   /// \return Status Status::OK() if get shard id successfully
-  Status GetShardId(int32_t *shard_id) override;
+  Status GetShardId(int32_t *const shard_id) override;
 
   /// \brief Base-class override for GetDatasetSize
   /// \param[in] size_getter Shared pointer to DatasetSizeGetter

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/random_node.cc

@@ -118,7 +118,7 @@ Status RandomNode::Build(std::vector<std::shared_ptr<DatasetOp>> *const node_ops
 }
 
 // Get the shard id of node
-Status RandomNode::GetShardId(int32_t *shard_id) {
+Status RandomNode::GetShardId(int32_t *const shard_id) {
   // RandomDataset doesn't support multiple shards
   *shard_id = 0;
   return Status::OK();

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/random_node.h

@@ -80,7 +80,7 @@ class RandomNode : public NonMappableSourceNode {
 
   /// \brief Get the shard id of node
   /// \return Status Status::OK() if get shard id successfully
-  Status GetShardId(int32_t *shard_id) override;
+  Status GetShardId(int32_t *const shard_id) override;
 
   /// \brief Base-class override for GetDatasetSize
   /// \param[in] size_getter Shared pointer to DatasetSizeGetter

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/tf_record_node.cc

@@ -156,7 +156,7 @@ Status TFRecordNode::Build(std::vector<std::shared_ptr<DatasetOp>> *const node_o
 }
 
 // Get the shard id of node
-Status TFRecordNode::GetShardId(int32_t *shard_id) {
+Status TFRecordNode::GetShardId(int32_t *const shard_id) {
   *shard_id = shard_id_;
 
   return Status::OK();
@@ -259,7 +259,7 @@ Status TFRecordNode::Accept(IRNodePass *p, bool *const modified) {
 }
 
 // Visitor accepting method for IRNodePass
-Status TFRecordNode::AcceptAfter(IRNodePass *p, bool *const modified) {
+Status TFRecordNode::AcceptAfter(IRNodePass *const p, bool *const modified) {
   // Downcast shared pointer then call visitor
   return p->VisitAfter(shared_from_base<TFRecordNode>(), modified);
 }

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/tf_record_node.h

@@ -95,7 +95,7 @@ class TFRecordNode : public NonMappableSourceNode {
 
   /// \brief Get the shard id of node
   /// \return Status Status::OK() if get shard id successfully
-  Status GetShardId(int32_t *shard_id) override;
+  Status GetShardId(int32_t *const shard_id) override;
 
   /// \brief Base-class override for GetDatasetSize
   /// \param[in] size_getter Shared pointer to DatasetSizeGetter
@@ -152,7 +152,7 @@ class TFRecordNode : public NonMappableSourceNode {
   /// \param[in] p The node to visit
   /// \param[out] modified Indicator if the node was modified
   /// \return Status of the node visit
-  Status AcceptAfter(IRNodePass *p, bool *const modified) override;
+  Status AcceptAfter(IRNodePass *const p, bool *const modified) override;
 
  private:
   std::vector<std::string> dataset_files_;

mindspore/ccsrc/minddata/dataset/engine/opt/post/repeat_pass.cc

@@ -192,7 +192,7 @@ Status RepeatPass::VisitAfter(std::shared_ptr<TransferNode> node, bool *const mo
 }
 
 // Adds an operator to the cached operator stack save area
-void RepeatPass::AddToCachedNodeStack(std::shared_ptr<DatasetNode> node) { cached_node_stacks_.push(node); }
+void RepeatPass::AddToCachedNodeStack(const std::shared_ptr<DatasetNode> &node) { cached_node_stacks_.push(node); }
 
 // Pops an operator from the cached operator stack save area
 std::shared_ptr<DatasetNode> RepeatPass::PopFromCachedNodeStack() {

mindspore/ccsrc/minddata/dataset/engine/opt/post/repeat_pass.h

@@ -112,7 +112,7 @@ class RepeatPass : public IRNodePass {
   /// \brief Adds an operator to the cached stack save area
   /// \param node - The dataset node to add to cached stack
   /// \return Status The status code returned
-  void AddToCachedNodeStack(std::shared_ptr<DatasetNode> node);
+  void AddToCachedNodeStack(const std::shared_ptr<DatasetNode> &node);
 
   /// \brief Pops an operator from the cached stack save area
   /// \return shared_ptr to the popped dataset node

mindspore/ccsrc/minddata/dataset/kernels/image/crop_op.cc

@@ -41,9 +41,15 @@ Status CropOp::OutputShape(const std::vector<TensorShape> &inputs, std::vector<T
   RETURN_IF_NOT_OK(TensorOp::OutputShape(inputs, outputs));
   outputs.clear();
   TensorShape out = TensorShape{height_, width_};
-  if (inputs[0].Rank() == 2) outputs.emplace_back(out);
-  if (inputs[0].Rank() == 3) outputs.emplace_back(out.AppendDim(inputs[0][2]));
-  if (!outputs.empty()) return Status::OK();
+  if (inputs[0].Rank() == 2) {
+    (void)outputs.emplace_back(out);
+  }
+  if (inputs[0].Rank() == 3) {
+    (void)outputs.emplace_back(out.AppendDim(inputs[0][2]));
+  }
+  if (!outputs.empty()) {
+    return Status::OK();
+  }
   return Status(StatusCode::kMDUnexpectedError,
                 "Crop: invalid input shape, expected 2D or 3D input, but got input dimension is:" +
                   std::to_string(inputs[0].Rank()));

mindspore/ccsrc/minddata/dataset/kernels/image/hwc_to_chw_op.cc

@@ -31,8 +31,12 @@ Status HwcToChwOp::OutputShape(const std::vector<TensorShape> &inputs, std::vect
   outputs.clear();
   TensorShape in = inputs[0];
   TensorShape out = TensorShape{in[2], in[0], in[1]};
-  if (inputs[0].Rank() == 3) outputs.emplace_back(out);
-  if (!outputs.empty()) return Status::OK();
+  if (inputs[0].Rank() == 3) {
+    (void)outputs.emplace_back(out);
+  }
+  if (!outputs.empty()) {
+    return Status::OK();
+  }
   return Status(
     StatusCode::kMDUnexpectedError,
     "HWC2CHW: invalid input shape, expected 3D input, but got input dimension is:" + std::to_string(inputs[0].Rank()));

mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/image_process.cc

@@ -1015,7 +1015,7 @@ std::vector<std::vector<float>> GetDefaultBoxes(BoxesConfig config) {
   }
   scales.push_back(1.0f);
   std::vector<std::vector<float>> default_boxes;
-  for (int i = 0; i < config.feature_size.size(); i++) {
+  for (auto i = 0; i < config.feature_size.size(); i++) {
     float sk1 = scales[i];
     float sk2 = scales[i + 1];
     float sk3 = sqrt(sk1 * sk2);
@@ -1069,10 +1069,10 @@ void ConvertBoxes(std::vector<std::vector<float>> &boxes, const std::vector<std:
 
 std::vector<int> ApplyNms(const std::vector<std::vector<float>> &all_boxes, std::vector<float> &all_scores, float thres,
                           int max_boxes) {
-  int boxes_num = all_boxes.size();
+  size_t boxes_num = all_boxes.size();
   std::vector<float> areas(boxes_num);
   std::vector<int> order(boxes_num);
-  for (int i = 0; i < boxes_num; i++) {
+  for (auto i = 0; i < boxes_num; i++) {
     if (all_boxes[i].size() < 4) {
       return {};
     }

mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/warp_affine.cc

@@ -410,7 +410,7 @@ bool WarpAffineBilinear(const LiteMat &src, LiteMat &dst, const LiteMat &M, int
   int *a = &_a[0], *b = a + dst.width_;
   const int SCALE = 1 << 10;
   const int B_SIZE = 64;
-  int16_t WH[B_SIZE * B_SIZE * 2];
+  int16_t *WH = new int16_t[B_SIZE * B_SIZE * 2];
   int16_t A_Ptr[B_SIZE * B_SIZE];
   int r_delta = SCALE / kTabSz / 2;
   int x, y, x1, y1;
@@ -449,7 +449,7 @@ bool WarpAffineBilinear(const LiteMat &src, LiteMat &dst, const LiteMat &M, int
       Remap(src, lite_part, _HW, _matA, borderType, borderValue);
     }
   }
-
+  delete[] WH;
   delete[] _a;
   return true;
 }

mindspore/ccsrc/minddata/dataset/kernels/image/random_crop_and_resize_op.cc

@@ -61,9 +61,15 @@ Status RandomCropAndResizeOp::OutputShape(const std::vector<TensorShape> &inputs
   RETURN_IF_NOT_OK(TensorOp::OutputShape(inputs, outputs));
   outputs.clear();
   TensorShape out = TensorShape{target_height_, target_width_};
-  if (inputs[0].Rank() == 2) outputs.emplace_back(out);
-  if (inputs[0].Rank() == 3) outputs.emplace_back(out.AppendDim(inputs[0][2]));
-  if (!outputs.empty()) return Status::OK();
+  if (inputs[0].Rank() == 2) {
+    (void)outputs.emplace_back(out);
+  }
+  if (inputs[0].Rank() == 3) {
+    (void)outputs.emplace_back(out.AppendDim(inputs[0][2]));
+  }
+  if (!outputs.empty()) {
+    return Status::OK();
+  }
   return Status(StatusCode::kMDUnexpectedError, "RandomCropAndResize: invalid input shape");
 }
 Status RandomCropAndResizeOp::GetCropBox(int h_in, int w_in, int *x, int *y, int *crop_height, int *crop_width) {

mindspore/ccsrc/minddata/dataset/kernels/image/random_crop_op.cc

@@ -143,9 +143,15 @@ Status RandomCropOp::OutputShape(const std::vector<TensorShape> &inputs, std::ve
   RETURN_IF_NOT_OK(TensorOp::OutputShape(inputs, outputs));
   outputs.clear();
   TensorShape out = TensorShape{crop_height_, crop_width_};
-  if (inputs[0].Rank() == 2) outputs.emplace_back(out);
-  if (inputs[0].Rank() == 3) outputs.emplace_back(out.AppendDim(inputs[0][2]));
-  if (!outputs.empty()) return Status::OK();
+  if (inputs[0].Rank() == 2) {
+    (void)outputs.emplace_back(out);
+  }
+  if (inputs[0].Rank() == 3) {
+    (void)outputs.emplace_back(out.AppendDim(inputs[0][2]));
+  }
+  if (!outputs.empty()) {
+    return Status::OK();
+  }
   return Status(StatusCode::kMDUnexpectedError,
                 "RandomCrop: invalid input shape, expected 2D or 3D input, but got input dimension is:" +
                   std::to_string(inputs[0].Rank()));

mindspore/ccsrc/minddata/dataset/kernels/image/resize_op.cc

@@ -61,9 +61,15 @@ Status ResizeOp::OutputShape(const std::vector<TensorShape> &inputs, std::vector
     outputW = size2_;
   }
   TensorShape out = TensorShape{outputH, outputW};
-  if (inputs[0].Rank() == 2) outputs.emplace_back(out);
-  if (inputs[0].Rank() == 3) outputs.emplace_back(out.AppendDim(inputs[0][2]));
-  if (!outputs.empty()) return Status::OK();
+  if (inputs[0].Rank() == 2) {
+    (void)outputs.emplace_back(out);
+  }
+  if (inputs[0].Rank() == 3) {
+    (void)outputs.emplace_back(out.AppendDim(inputs[0][2]));
+  }
+  if (!outputs.empty()) {
+    return Status::OK();
+  }
   return Status(StatusCode::kMDUnexpectedError, "Resize: invalid input wrong shape.");
 }
 }  // namespace dataset

mindspore/ccsrc/minddata/dataset/kernels/image/uniform_aug_op.h

@@ -49,8 +49,8 @@ class UniformAugOp : public TensorOp {
   std::string Name() const override { return kUniformAugOp; }
 
  private:
-  int32_t num_ops_;
   std::vector<std::shared_ptr<TensorOp>> tensor_op_list_;
+  int32_t num_ops_;
   std::mt19937 rnd_;
 };
 }  // namespace dataset

mindspore/ccsrc/minddata/mindrecord/io/shard_index_generator.cc

@@ -223,7 +223,7 @@ MSRStatus ShardIndexGenerator::CreateShardNameTable(sqlite3 *db, const std::stri
   sql = "INSERT INTO SHARD_NAME (NAME) VALUES (:SHARD_NAME);";
   sqlite3_stmt *stmt = nullptr;
   if (sqlite3_prepare_v2(db, common::SafeCStr(sql), -1, &stmt, 0) != SQLITE_OK) {
-    if (stmt) {
+    if (stmt != nullptr) {
       (void)sqlite3_finalize(stmt);
     }
     MS_LOG(ERROR) << "SQL error: could not prepare statement, sql: " << sql;

mindspore/ccsrc/minddata/mindrecord/io/shard_reader.cc

@@ -877,7 +877,9 @@ std::pair<MSRStatus, std::vector<json>> ShardReader::GetLabels(int page_id, int
       sqlite3_free(errmsg);
     }
     std::vector<json> ret;
-    for (unsigned int i = 0; i < labels_ptr->size(); ++i) ret.emplace_back(json{});
+    for (unsigned int i = 0; i < labels_ptr->size(); ++i) {
+      (void)ret.emplace_back(json{});
+    }
     for (unsigned int i = 0; i < labels_ptr->size(); ++i) {
       json construct_json;
       for (unsigned int j = 0; j < columns.size(); ++j) {

mindspore/dataset/core/validator_helpers.py

@@ -482,8 +482,6 @@ def check_filename(path):
     if filename.startswith(' ') or filename.endswith(' '):
         raise ValueError("filename should not start/end with space.")
 
-    return True
-
 
 def check_dir(dataset_dir):
     """

mindspore/dataset/engine/validators.py

@@ -838,7 +838,6 @@ def check_schema(method):
         [schema_file], _ = parse_user_args(method, *args, **kwargs)
 
         if schema_file is not None:
-            type_check(schema_file, (str,), "schema_file")
             check_file(schema_file)
 
         return method(self, *args, **kwargs)

mindspore/lite/minddata/wrapper/MDToDApi.cc

@@ -269,10 +269,6 @@ extern "C" int MDToDApi_GetNext(MDToDApi *pMDToDApi, MDToDResult_t *results) {
   MS_LOG(INFO) << "Start GetNext [1]" << pMDToDApi;
   // get next row for dataset
   std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
-  if (pMDToDApi->_iter == nullptr) {
-    MS_LOG(ERROR) << "GetNext called with no iteratoe. abort";
-    return -1;
-  }
   // create Execute functions, this replaces Map in Pipeline
 
   bool ret = pMDToDApi->_iter->GetNextRow(&row);

mindspore/lite/minddata/wrapper/album_op_android.cc

@@ -177,7 +177,7 @@ bool AlbumOp::IsReadColumn(const std::string &column_name) {
   return false;
 }
 
-Status AlbumOp::LoadImageTensor(const std::string &image_file_path, uint32_t col_num, TensorPtr *tensor) {
+Status AlbumOp::LoadImageTensor(const std::string &image_file_path, int32_t col_num, TensorPtr *tensor) {
   TensorPtr image;
   TensorPtr rotate_tensor;
   std::ifstream fs;
@@ -257,7 +257,7 @@ int AlbumOp::GetOrientation(const std::string &folder_path) {
   return code;
 }
 
-Status AlbumOp::LoadStringArrayTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorPtr *tensor) {
+Status AlbumOp::LoadStringArrayTensor(const nlohmann::json &json_obj, int32_t col_num, TensorPtr *tensor) {
   std::vector<std::string> data = json_obj.get<std::vector<std::string>>();
 
   MS_LOG(INFO) << "String array label found: " << data << ".";
@@ -265,7 +265,7 @@ Status AlbumOp::LoadStringArrayTensor(const nlohmann::json &json_obj, uint32_t c
   return Status::OK();
 }
 
-Status AlbumOp::LoadStringTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorPtr *tensor) {
+Status AlbumOp::LoadStringTensor(const nlohmann::json &json_obj, int32_t col_num, TensorPtr *tensor) {
   std::string data = json_obj;
   // now we iterate over the elements in json
 
@@ -275,7 +275,7 @@ Status AlbumOp::LoadStringTensor(const nlohmann::json &json_obj, uint32_t col_nu
   return Status::OK();
 }
 
-Status AlbumOp::LoadIntArrayTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorPtr *tensor) {
+Status AlbumOp::LoadIntArrayTensor(const nlohmann::json &json_obj, int32_t col_num, TensorPtr *tensor) {
   // consider templating this function to handle all ints
   if (data_schema_->column(col_num).type() == DataType::DE_INT64) {
     std::vector<int64_t> data;
@@ -302,7 +302,7 @@ Status AlbumOp::LoadIntArrayTensor(const nlohmann::json &json_obj, uint32_t col_
   return Status::OK();
 }
 
-Status AlbumOp::LoadFloatArrayTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorPtr *tensor) {
+Status AlbumOp::LoadFloatArrayTensor(const nlohmann::json &json_obj, int32_t col_num, TensorPtr *tensor) {
   // consider templating this function to handle all ints
   if (data_schema_->column(col_num).type() == DataType::DE_FLOAT64) {
     std::vector<double> data;
@@ -329,7 +329,7 @@ Status AlbumOp::LoadFloatArrayTensor(const nlohmann::json &json_obj, uint32_t co
   return Status::OK();
 }
 
-Status AlbumOp::LoadIDTensor(const std::string &file, uint32_t col_num, TensorPtr *tensor) {
+Status AlbumOp::LoadIDTensor(const std::string &file, int32_t col_num, TensorPtr *tensor) {
   if (data_schema_->column(col_num).type() == DataType::DE_STRING) {
     RETURN_IF_NOT_OK(Tensor::CreateScalar<std::string>(file, tensor));
     return Status::OK();
@@ -341,7 +341,7 @@ Status AlbumOp::LoadIDTensor(const std::string &file, uint32_t col_num, TensorPt
   return Status::OK();
 }
 
-Status AlbumOp::LoadEmptyTensor(uint32_t col_num, TensorPtr *tensor) {
+Status AlbumOp::LoadEmptyTensor(int32_t col_num, TensorPtr *tensor) {
   // hack to get the file name without extension, the 1 is to get rid of the backslash character
   RETURN_IF_NOT_OK(Tensor::CreateEmpty(TensorShape({0}), data_schema_->column(col_num).type(), tensor));
   return Status::OK();
@@ -351,7 +351,7 @@ Status AlbumOp::LoadEmptyTensor(uint32_t col_num, TensorPtr *tensor) {
 // So we actually have to check what type we want to fill the tensor with.
 // Float64 doesn't work with reinterpret cast here. Otherwise we limit the float in the schema to
 // only be float32, seems like a weird limitation to impose
-Status AlbumOp::LoadFloatTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorPtr *tensor) {
+Status AlbumOp::LoadFloatTensor(const nlohmann::json &json_obj, int32_t col_num, TensorPtr *tensor) {
   if (data_schema_->column(col_num).type() == DataType::DE_FLOAT64) {
     double data = json_obj;
     MS_LOG(INFO) << "double found: " << json_obj << ".";
@@ -365,7 +365,7 @@ Status AlbumOp::LoadFloatTensor(const nlohmann::json &json_obj, uint32_t col_num
 }
 
 // Loads a tensor with int value, we have to cast the value to type specified in the schema.
-Status AlbumOp::LoadIntTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorPtr *tensor) {
+Status AlbumOp::LoadIntTensor(const nlohmann::json &json_obj, int32_t col_num, TensorPtr *tensor) {
   if (data_schema_->column(col_num).type() == DataType::DE_INT64) {
     int64_t data = json_obj;
     MS_LOG(INFO) << "int64 found: " << json_obj << ".";

mindspore/lite/minddata/wrapper/album_op_android.h

@@ -93,62 +93,62 @@ class AlbumOp {
   /// \param[in] col_num Column num in schema
   /// \param[in,out] Tensor to push to
   /// \return Status The error code returned
-  Status LoadImageTensor(const std::string &image_file, uint32_t col_num, TensorPtr *tensor);
+  Status LoadImageTensor(const std::string &image_file, int32_t col_num, TensorPtr *tensor);
 
   /// \brief Load vector of ints to tensor, append tensor to tensor
   /// \param[in] json_obj Json object containing multi-dimensional label
   /// \param[in] col_num Column num in schema
   /// \param[in,out] Tensor to push to
   /// \return Status The error code returned
-  Status LoadIntArrayTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorPtr *tensor);
+  Status LoadIntArrayTensor(const nlohmann::json &json_obj, int32_t col_num, TensorPtr *tensor);
 
   /// \brief Load vector of floatss to tensor, append tensor to tensor
   /// \param[in] json_obj Json object containing array data
   /// \param[in] col_num Column num in schema
   /// \param[in,out] Tensor to push to
   /// \return Status The error code returned
-  Status LoadFloatArrayTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorPtr *tensor);
+  Status LoadFloatArrayTensor(const nlohmann::json &json_obj, int32_t col_num, TensorPtr *tensor);
 
   /// \brief Load string array into a tensor, append tensor to tensor
   /// \param[in] json_obj Json object containing string tensor
   /// \param[in] col_num Column num in schema
   /// \param[in,out] Tensor to push to
   /// \return Status The error code returned
-  Status LoadStringArrayTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorPtr *tensor);
+  Status LoadStringArrayTensor(const nlohmann::json &json_obj, int32_t col_num, TensorPtr *tensor);
 
   /// \brief Load string into a tensor, append tensor to tensor
   /// \param[in] json_obj Json object containing string tensor
   /// \param[in] col_num Column num in schema
   /// \param[in,out]  Tensor to push to
   /// \return Status The error code returned
-  Status LoadStringTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorPtr *tensor);
+  Status LoadStringTensor(const nlohmann::json &json_obj, int32_t col_num, TensorPtr *tensor);
 
   /// \brief Load float value to tensor
   /// \param[in] json_obj Json object containing float
   /// \param[in] col_num Column num in schema
   /// \param[in,out]  Tensor to push to
   /// \return Status The error code returned
-  Status LoadFloatTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorPtr *tensor);
+  Status LoadFloatTensor(const nlohmann::json &json_obj, int32_t col_num, TensorPtr *tensor);
 
   /// \brief Load int value to tensor
   /// \param[in] json_obj Json object containing int
   /// \param[in] col_num Column num in schema
   /// \param[in,out] Tensor to push to
   /// \return Status The error code returned
-  Status LoadIntTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorPtr *tensor);
+  Status LoadIntTensor(const nlohmann::json &json_obj, int32_t col_num, TensorPtr *tensor);
 
   /// \brief Load empty tensor to tensor
   /// \param[in] col_num Column num in schema
   /// \param[in,out] Tensor to push to
   /// \return Status The error code returned
-  Status LoadEmptyTensor(uint32_t col_num, TensorPtr *tensor);
+  Status LoadEmptyTensor(int32_t col_num, TensorPtr *tensor);
 
   /// \brief Load id from file name to tensor
   /// \param[in] file The file name to get ID from
   /// \param[in] col_num Column num in schema
   /// \param[in,out] Tensor to push to
   /// \return Status The error code returned
-  Status LoadIDTensor(const std::string &file, uint32_t col_num, TensorPtr *tensor);
+  Status LoadIDTensor(const std::string &file, int32_t col_num, TensorPtr *tensor);
 
   /// \brief Load a tensor according to a json file
   /// \param[in] row_id_type row_id - id for this tensor row

tests/ut/python/dataset/test_schema.py

@@ -48,7 +48,7 @@ def test_schema_exception():
 
     with pytest.raises(TypeError) as info:
         ds.Schema(1)
-    assert "Argument schema_file with value 1 is not of type [<class 'str'>]" in str(info.value)
+    assert "path: 1 is not string" in str(info.value)
 
     with pytest.raises(RuntimeError) as info:
         schema = ds.Schema(SCHEMA_FILE)

third_party/patch/sqlite/sqlite.patch001

@@ -1,6 +1,6 @@
-diff -Npur sqlite-version-3.32.2/src/expr.c sqlite-version-3.32.2-patched/src/expr.c
---- sqlite-version-3.32.2/src/expr.c	2020-06-04 08:58:43.000000000 -0400
-+++ sqlite-version-3.32.2-patched/src/expr.c	2021-04-29 04:06:04.544208700 -0400
+diff -Npur sqlite-version-3.32.2-new/src/expr.c sqlite-version-3.32.2/src/expr.c
+--- sqlite-version-3.32.2-new/src/expr.c	2020-06-04 08:58:43.000000000 -0400
++++ sqlite-version-3.32.2/src/expr.c	2021-08-04 11:57:45.029230992 -0400
 @@ -3813,6 +3813,7 @@ expr_code_doover:
        AggInfo *pAggInfo = pExpr->pAggInfo;
        struct AggInfo_col *pCol;
@@ -32,9 +32,9 @@ diff -Npur sqlite-version-3.32.2/src/expr.c sqlite-version-3.32.2-patched/src/ex
      int i;
      struct SrcCount *p = pWalker->u.pSrcCount;
      SrcList *pSrc = p->pSrc;
-diff -Npur sqlite-version-3.32.2/src/global.c sqlite-version-3.32.2-patched/src/global.c
---- sqlite-version-3.32.2/src/global.c	2020-06-04 08:58:43.000000000 -0400
-+++ sqlite-version-3.32.2-patched/src/global.c	2021-04-29 04:06:04.544208700 -0400
+diff -Npur sqlite-version-3.32.2-new/src/global.c sqlite-version-3.32.2/src/global.c
+--- sqlite-version-3.32.2-new/src/global.c	2020-06-04 08:58:43.000000000 -0400
++++ sqlite-version-3.32.2/src/global.c	2021-08-04 11:57:45.033230992 -0400
 @@ -300,6 +300,11 @@ sqlite3_uint64 sqlite3NProfileCnt = 0;
  int sqlite3PendingByte = 0x40000000;
  #endif
@@ -47,9 +47,9 @@ diff -Npur sqlite-version-3.32.2/src/global.c sqlite-version-3.32.2-patched/src/
  #include "opcodes.h"
  /*
  ** Properties of opcodes.  The OPFLG_INITIALIZER macro is
-diff -Npur sqlite-version-3.32.2/src/resolve.c sqlite-version-3.32.2-patched/src/resolve.c
---- sqlite-version-3.32.2/src/resolve.c	2020-06-04 08:58:43.000000000 -0400
-+++ sqlite-version-3.32.2-patched/src/resolve.c	2021-04-29 04:06:04.545208700 -0400
+diff -Npur sqlite-version-3.32.2-new/src/resolve.c sqlite-version-3.32.2/src/resolve.c
+--- sqlite-version-3.32.2-new/src/resolve.c	2020-06-04 08:58:43.000000000 -0400
++++ sqlite-version-3.32.2/src/resolve.c	2021-08-04 11:57:45.033230992 -0400
 @@ -1715,6 +1715,14 @@ static int resolveSelectStep(Walker *pWa
            return WRC_Abort;
          }
@@ -65,9 +65,9 @@ diff -Npur sqlite-version-3.32.2/src/resolve.c sqlite-version-3.32.2-patched/src
      }
  #endif
  
-diff -Npur sqlite-version-3.32.2/src/select.c sqlite-version-3.32.2-patched/src/select.c
---- sqlite-version-3.32.2/src/select.c	2020-06-04 08:58:43.000000000 -0400
-+++ sqlite-version-3.32.2-patched/src/select.c	2021-04-29 04:07:21.458212191 -0400
+diff -Npur sqlite-version-3.32.2-new/src/select.c sqlite-version-3.32.2/src/select.c
+--- sqlite-version-3.32.2-new/src/select.c	2020-06-04 08:58:43.000000000 -0400
++++ sqlite-version-3.32.2/src/select.c	2021-08-04 12:27:34.737267443 -0400
 @@ -15,20 +15,6 @@
  #include "sqliteInt.h"
  
@@ -89,7 +89,27 @@ diff -Npur sqlite-version-3.32.2/src/select.c sqlite-version-3.32.2-patched/src/
  ** An instance of the following object is used to record information about
  ** how to process the DISTINCT keyword, to simplify passing that information
  ** into the selectInnerLoop() routine.
-@@ -4426,11 +4412,14 @@ static int pushDownWhereTerms(
+@@ -2717,9 +2703,7 @@ static int multiSelect(
+                           selectOpName(p->op)));
+         rc = sqlite3Select(pParse, p, &uniondest);
+         testcase( rc!=SQLITE_OK );
+-        /* Query flattening in sqlite3Select() might refill p->pOrderBy.
+-        ** Be sure to delete p->pOrderBy, therefore, to avoid a memory leak. */
+-        sqlite3ExprListDelete(db, p->pOrderBy);
++        assert( p->pOrderBy==0 );
+         pDelete = p->pPrior;
+         p->pPrior = pPrior;
+         p->pOrderBy = 0;
+@@ -4105,7 +4089,7 @@ static int flattenSubquery(
+     ** We look at every expression in the outer query and every place we see
+     ** "a" we substitute "x*3" and every place we see "b" we substitute "y+10".
+     */
+-    if( pSub->pOrderBy ){
++    if( pSub->pOrderBy && (pParent->selFlags & SF_NoopOrderBy)==0 ){
+       /* At this point, any non-zero iOrderByCol values indicate that the
+       ** ORDER BY column expression is identical to the iOrderByCol'th
+       ** expression returned by SELECT statement pSub. Since these values
+@@ -4426,11 +4410,14 @@ static int pushDownWhereTerms(
  ){
    Expr *pNew;
    int nChng = 0;
@@ -105,7 +125,7 @@ diff -Npur sqlite-version-3.32.2/src/select.c sqlite-version-3.32.2-patched/src/
  #endif
  
  #ifdef SQLITE_DEBUG
-@@ -5553,7 +5542,9 @@ static void explainSimpleCount(
+@@ -5553,7 +5540,9 @@ static void explainSimpleCount(
  static int havingToWhereExprCb(Walker *pWalker, Expr *pExpr){
    if( pExpr->op!=TK_AND ){
      Select *pS = pWalker->u.pSelect;
@@ -116,7 +136,7 @@ diff -Npur sqlite-version-3.32.2/src/select.c sqlite-version-3.32.2-patched/src/
        sqlite3 *db = pWalker->pParse->db;
        Expr *pNew = sqlite3Expr(db, TK_INTEGER, "1");
        if( pNew ){
-@@ -5766,6 +5757,9 @@ int sqlite3Select(
+@@ -5766,6 +5755,9 @@ int sqlite3Select(
    }
    if( sqlite3AuthCheck(pParse, SQLITE_SELECT, 0, 0, 0) ) return 1;
    memset(&sAggInfo, 0, sizeof(sAggInfo));
@@ -126,7 +146,15 @@ diff -Npur sqlite-version-3.32.2/src/select.c sqlite-version-3.32.2-patched/src/
  #if SELECTTRACE_ENABLED
    SELECTTRACE(1,pParse,p, ("begin processing:\n", pParse->addrExplain));
    if( sqlite3SelectTrace & 0x100 ){
-@@ -5804,19 +5798,6 @@ int sqlite3Select(
+@@ -5787,6 +5779,7 @@ int sqlite3Select(
+     sqlite3ExprListDelete(db, p->pOrderBy);
+     p->pOrderBy = 0;
+     p->selFlags &= ~SF_Distinct;
++    p->selFlags |= SF_NoopOrderBy;
+   }
+   sqlite3SelectPrep(pParse, p, 0);
+   if( pParse->nErr || db->mallocFailed ){
+@@ -5804,19 +5797,6 @@ int sqlite3Select(
      generateColumnNames(pParse, p);
    }
  
@@ -146,7 +174,7 @@ diff -Npur sqlite-version-3.32.2/src/select.c sqlite-version-3.32.2-patched/src/
    pTabList = p->pSrc;
    isAgg = (p->selFlags & SF_Aggregate)!=0;
    memset(&sSort, 0, sizeof(sSort));
-@@ -6144,7 +6125,7 @@ int sqlite3Select(
+@@ -6144,7 +6124,7 @@ int sqlite3Select(
    if( (p->selFlags & (SF_Distinct|SF_Aggregate))==SF_Distinct 
     && sqlite3ExprListCompare(sSort.pOrderBy, pEList, -1)==0
  #ifndef SQLITE_OMIT_WINDOWFUNC
@@ -155,7 +183,7 @@ diff -Npur sqlite-version-3.32.2/src/select.c sqlite-version-3.32.2-patched/src/
  #endif
    ){
      p->selFlags &= ~SF_Distinct;
-@@ -6791,6 +6772,14 @@ int sqlite3Select(
+@@ -6791,6 +6771,14 @@ int sqlite3Select(
  select_end:
    sqlite3ExprListDelete(db, pMinMaxOrderBy);
    sqlite3DbFree(db, sAggInfo.aCol);
@@ -170,9 +198,9 @@ diff -Npur sqlite-version-3.32.2/src/select.c sqlite-version-3.32.2-patched/src/
    sqlite3DbFree(db, sAggInfo.aFunc);
  #if SELECTTRACE_ENABLED
    SELECTTRACE(0x1,pParse,p,("end processing\n"));
-diff -Npur sqlite-version-3.32.2/src/sqliteInt.h sqlite-version-3.32.2-patched/src/sqliteInt.h
---- sqlite-version-3.32.2/src/sqliteInt.h	2020-06-04 08:58:43.000000000 -0400
-+++ sqlite-version-3.32.2-patched/src/sqliteInt.h	2021-04-29 04:06:04.547208700 -0400
+diff -Npur sqlite-version-3.32.2-new/src/sqliteInt.h sqlite-version-3.32.2/src/sqliteInt.h
+--- sqlite-version-3.32.2-new/src/sqliteInt.h	2020-06-04 08:58:43.000000000 -0400
++++ sqlite-version-3.32.2/src/sqliteInt.h	2021-08-04 12:28:22.825268422 -0400
 @@ -976,7 +976,12 @@ typedef INT16_TYPE LogEst;
  */
  #if defined(SQLITE_ENABLE_SELECTTRACE)
@@ -211,7 +239,15 @@ diff -Npur sqlite-version-3.32.2/src/sqliteInt.h sqlite-version-3.32.2-patched/s
  ** The datatype ynVar is a signed integer, either 16-bit or 32-bit.
  ** Usually it is 16-bits.  But if SQLITE_MAX_VARIABLE_NUMBER is greater
  ** than 32767 we have to make it 32-bit.  16-bit is preferred because
-@@ -4546,10 +4566,11 @@ extern const unsigned char sqlite3UpperT
+@@ -3105,6 +3125,7 @@ struct Select {
+ #define SF_WhereBegin    0x0080000 /* Really a WhereBegin() call.  Debug Only */
+ #define SF_WinRewrite    0x0100000 /* Window function rewrite accomplished */
+ #define SF_View          0x0200000 /* SELECT statement is a view */
++#define SF_NoopOrderBy   0x0400000 /* ORDER BY is ignored for this query */
+ 
+ /*
+ ** The results of a SELECT can be distributed in several ways, as defined
+@@ -4546,10 +4567,11 @@ extern const unsigned char sqlite3UpperT
  extern const unsigned char sqlite3CtypeMap[];
  extern SQLITE_WSD struct Sqlite3Config sqlite3Config;
  extern FuncDefHash sqlite3BuiltinFunctions;
@@ -224,9 +260,9 @@ diff -Npur sqlite-version-3.32.2/src/sqliteInt.h sqlite-version-3.32.2-patched/s
  #ifdef VDBE_PROFILE
  extern sqlite3_uint64 sqlite3NProfileCnt;
  #endif
-diff -Npur sqlite-version-3.32.2/src/test1.c sqlite-version-3.32.2-patched/src/test1.c
---- sqlite-version-3.32.2/src/test1.c	2020-06-04 08:58:43.000000000 -0400
-+++ sqlite-version-3.32.2-patched/src/test1.c	2021-04-29 04:06:04.548208700 -0400
+diff -Npur sqlite-version-3.32.2-new/src/test1.c sqlite-version-3.32.2/src/test1.c
+--- sqlite-version-3.32.2-new/src/test1.c	2020-06-04 08:58:43.000000000 -0400
++++ sqlite-version-3.32.2/src/test1.c	2021-08-04 11:57:45.037230992 -0400
 @@ -8164,7 +8164,7 @@ int Sqlitetest1_Init(Tcl_Interp *interp)
  #endif
  #endif
@@ -236,9 +272,9 @@ diff -Npur sqlite-version-3.32.2/src/test1.c sqlite-version-3.32.2-patched/src/t
  #endif
  
    for(i=0; i<sizeof(aCmd)/sizeof(aCmd[0]); i++){
-diff -Npur sqlite-version-3.32.2/src/window.c sqlite-version-3.32.2-patched/src/window.c
---- sqlite-version-3.32.2/src/window.c	2020-06-04 08:58:43.000000000 -0400
-+++ sqlite-version-3.32.2-patched/src/window.c	2021-04-29 04:06:04.548208700 -0400
+diff -Npur sqlite-version-3.32.2-new/src/window.c sqlite-version-3.32.2/src/window.c
+--- sqlite-version-3.32.2-new/src/window.c	2020-06-04 08:58:43.000000000 -0400
++++ sqlite-version-3.32.2/src/window.c	2021-08-04 11:57:45.041230992 -0400
 @@ -942,7 +942,7 @@ static int sqlite3WindowExtraAggFuncDept
  */
  int sqlite3WindowRewrite(Parse *pParse, Select *p){
@@ -248,13 +284,13 @@ diff -Npur sqlite-version-3.32.2/src/window.c sqlite-version-3.32.2-patched/src/
      Vdbe *v = sqlite3GetVdbe(pParse);
      sqlite3 *db = pParse->db;
      Select *pSub = 0;             /* The subquery */
-diff -Npur sqlite-version-3.32.2/test/having.test sqlite-version-3.32.2-patched/test/having.test
---- sqlite-version-3.32.2/test/having.test	2020-06-04 08:58:43.000000000 -0400
-+++ sqlite-version-3.32.2-patched/test/having.test	2021-04-29 04:08:11.785214475 -0400
+diff -Npur sqlite-version-3.32.2-new/test/having.test sqlite-version-3.32.2/test/having.test
+--- sqlite-version-3.32.2-new/test/having.test	2020-06-04 08:58:43.000000000 -0400
++++ sqlite-version-3.32.2/test/having.test	2021-08-04 11:57:45.041230992 -0400
 @@ -154,5 +154,24 @@ do_execsql_test 4.3 {
    SELECT a, sum(b) FROM t3 WHERE nondeter(a) GROUP BY a
  } {1 4 2 2}
-
+ 
 +#-------------------------------------------------------------------------
 +reset_db
 +do_execsql_test 5.0 {
@@ -274,11 +310,41 @@ diff -Npur sqlite-version-3.32.2/test/having.test sqlite-version-3.32.2-patched/
 +    SELECT x FROM t2 WHERE a=2 GROUP BY y HAVING 0
 +  ) FROM t1;
 +} {b {}}
-
+ 
+ finish_test
+diff -Npur sqlite-version-3.32.2-new/test/selectA.test sqlite-version-3.32.2/test/selectA.test
+--- sqlite-version-3.32.2-new/test/selectA.test	2020-06-04 08:58:43.000000000 -0400
++++ sqlite-version-3.32.2/test/selectA.test	2021-08-04 12:29:43.021270055 -0400
+@@ -1446,5 +1446,26 @@ do_execsql_test 6.1 {
+   SELECT * FROM (SELECT a FROM t1 UNION SELECT b FROM t2) WHERE a=a;
+ } {12345}
+ 
++# 2020-06-15 ticket 8f157e8010b22af0
++#
++reset_db
++do_execsql_test 7.1 {
++  CREATE TABLE t1(c1);     INSERT INTO t1 VALUES(12),(123),(1234),(NULL),('abc');
++  CREATE TABLE t2(c2);     INSERT INTO t2 VALUES(44),(55),(123);
++  CREATE TABLE t3(c3,c4);  INSERT INTO t3 VALUES(66,1),(123,2),(77,3);
++  CREATE VIEW t4 AS SELECT c3 FROM t3;
++  CREATE VIEW t5 AS SELECT c3 FROM t3 ORDER BY c4;
++}
++do_execsql_test 7.2 {
++  SELECT * FROM t1, t2 WHERE c1=(SELECT 123 INTERSECT SELECT c2 FROM t4) AND c1=123;
++} {123 123}
++do_execsql_test 7.3 {
++  SELECT * FROM t1, t2 WHERE c1=(SELECT 123 INTERSECT SELECT c2 FROM t5) AND c1=123;
++} {123 123}
++do_execsql_test 7.4 {
++  CREATE TABLE a(b);
++  CREATE VIEW c(d) AS SELECT b FROM a ORDER BY b;
++  SELECT sum(d) OVER( PARTITION BY(SELECT 0 FROM c JOIN a WHERE b =(SELECT b INTERSECT SELECT d FROM c) AND b = 123)) FROM c;
++} {}
+ 
  finish_test
-diff -Npur sqlite-version-3.32.2/test/window1.test sqlite-version-3.32.2-patched/test/window1.test
---- sqlite-version-3.32.2/test/window1.test	2020-06-04 08:58:43.000000000 -0400
-+++ sqlite-version-3.32.2-patched/test/window1.test	2021-04-29 04:06:04.549208700 -0400
+diff -Npur sqlite-version-3.32.2-new/test/window1.test sqlite-version-3.32.2/test/window1.test
+--- sqlite-version-3.32.2-new/test/window1.test	2020-06-04 08:58:43.000000000 -0400
++++ sqlite-version-3.32.2/test/window1.test	2021-08-04 11:57:45.041230992 -0400
 @@ -1743,5 +1743,47 @@ do_execsql_test 53.0 {
                 WHERE a.c);
  } {4 4 4 4}