!18173 [MD] Apply r1.2 codecheck fixes to master

Merge pull request !18173 from harshvardhangupta/codecheck_fix_master

mindspore/ccsrc/minddata/dataset/api/python/pybind_conversion.cc

@@ -248,10 +248,12 @@ Status ToJson(const py::handle &padded_sample, nlohmann::json *const padded_samp
 }
 
 Status toPadInfo(py::dict value, std::map<std::string, std::pair<TensorShape, std::shared_ptr<Tensor>>> *pad_info) {
+  constexpr size_t kExpectedTupleSize = 2;
   for (auto p : value) {
     if (!p.second.is_none()) {
       auto tp = py::reinterpret_borrow<py::tuple>(p.second);
-      CHECK_FAIL_RETURN_UNEXPECTED(tp.size() == 2, "tuple in pad_info must be (list,int) or (list,float)");
+      CHECK_FAIL_RETURN_UNEXPECTED(tp.size() == kExpectedTupleSize,
+                                   "tuple in pad_info must be (list,int) or (list,float)");
       TensorShape shape = tp[0].is_none() ? TensorShape::CreateUnknownRankShape() : TensorShape(tp[0]);
       std::shared_ptr<Tensor> pad_val = nullptr;
       if (py::isinstance<py::str>(tp[1])) {

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

@@ -205,9 +205,9 @@ Status AlbumOp::LoadIntArrayTensor(const nlohmann::json &json_obj, uint32_t col_
     std::vector<int32_t> data;
 
     // Iterate over the integer list and add those values to the output shape tensor
-    auto items = json_obj.items();
-    using it_type = decltype(items.begin());
-    (void)std::transform(items.begin(), items.end(), std::back_inserter(data), [](it_type j) { return j.value(); });
+    auto items1 = json_obj.items();
+    using it_type = decltype(items1.begin());
+    (void)std::transform(items1.begin(), items1.end(), std::back_inserter(data), [](it_type j) { return j.value(); });
 
     RETURN_IF_NOT_OK(Tensor::CreateFromVector(data, &label));
   } else {
@@ -234,9 +234,9 @@ Status AlbumOp::LoadFloatArrayTensor(const nlohmann::json &json_obj, uint32_t co
     std::vector<float> data;
 
     // Iterate over the integer list and add those values to the output shape tensor
-    auto items = json_obj.items();
-    using it_type = decltype(items.begin());
-    (void)std::transform(items.begin(), items.end(), std::back_inserter(data), [](it_type j) { return j.value(); });
+    auto items1 = json_obj.items();
+    using it_type = decltype(items1.begin());
+    (void)std::transform(items1.begin(), items1.end(), std::back_inserter(data), [](it_type j) { return j.value(); });
 
     RETURN_IF_NOT_OK(Tensor::CreateFromVector(data, &float_array));
   } else {
@@ -282,8 +282,8 @@ Status AlbumOp::LoadFloatTensor(const nlohmann::json &json_obj, uint32_t col_num
     MS_LOG(INFO) << "double found: " << json_obj << ".";
     RETURN_IF_NOT_OK(Tensor::CreateScalar<double>(data, &float_tensor));
   } else if (data_schema_->column(col_num).type() == DataType::DE_FLOAT32) {
-    float data = json_obj;
-    RETURN_IF_NOT_OK(Tensor::CreateScalar<float>(data, &float_tensor));
+    float data1 = json_obj;
+    RETURN_IF_NOT_OK(Tensor::CreateScalar<float>(data1, &float_tensor));
     MS_LOG(INFO) << "float found: " << json_obj << ".";
   }
   row->push_back(std::move(float_tensor));

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

@@ -33,7 +33,7 @@ namespace dataset {
 // Constructor for TransferNode
 TransferNode::TransferNode(std::shared_ptr<DatasetNode> child, std::string queue_name, std::string device_type,
                            int32_t device_id, bool send_epoch_end, int32_t total_batch, bool create_data_info_queue)
-    : prefetch_size_(16),
+    : prefetch_size_(GlobalContext::config_manager()->prefetch_size()),
       queue_name_(std::move(queue_name)),
       device_type_(std::move(device_type)),
       send_epoch_end_(send_epoch_end),

mindspore/dataset/engine/datasets.py

@@ -204,7 +204,6 @@ class Dataset:
         self.num_parallel_workers = num_parallel_workers
         self.cache = cache
 
-        # todo check the following:
         self._device_iter = 0
         self._input_indexs = ()
         self.saved_output_types = None
@@ -2922,7 +2921,6 @@ class _ToDevice:
         self._to_device.Init(ir_tree)
         self._runtime_context.AssignConsumer(self._to_device)
 
-        # todo remove next when ContextManager is done
         ITERATORS_LIST.append(weakref.ref(self))
         _unset_iterator_cleanup()
 
@@ -4043,7 +4041,6 @@ class TFRecordDataset(SourceDataset):
                  shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, shard_equal_rows=False, cache=None):
         super().__init__(num_parallel_workers=num_parallel_workers, num_samples=num_samples, shuffle=shuffle,
                          num_shards=num_shards, shard_id=shard_id, cache=cache)
-        # todo push down to c++
         self.dataset_files = self._find_files(dataset_files)
         self.dataset_files.sort()
 

mindspore/dataset/engine/iterators.py

@@ -86,10 +86,8 @@ class Iterator:
                 self._transform_tensor = lambda t: Tensor.from_numpy(t.as_array())
         self.__index = 0
 
-        # todo remove next when ContextManager is done
         ITERATORS_LIST.append(weakref.ref(self))
         _unset_iterator_cleanup()
-        #######
 
     def __iter__(self):
         return self
@@ -184,7 +182,6 @@ class TupleIterator(Iterator):
         if columns is not None:
             if not isinstance(columns, list):
                 columns = [columns]
-            # todo: move next to IR
             dataset = dataset.project(columns)
         super().__init__(dataset, num_epochs, output_numpy, do_copy)