!9201 Fix minor style issues for MD

From: @ezphlow
Reviewed-by: @nsyca,@mikef
Signed-off-by:

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

@@ -781,7 +781,6 @@ inline Status Tensor::CreateFromVector<std::string>(const std::vector<std::strin
     num_bytes -= str.length() + 1;
   }
   // store one more offset value so we can get the length of the last string
-  // length[last_element] = offset_arr[last_element + 1] - offset_arr[last_element]
   offset_arr[i] = offset;
 
   (*out)->data_end_ = (*out)->data_ + offset_arr[i];

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

@@ -546,9 +546,9 @@ Status BatchOp::GetDatasetSize(int64_t *dataset_size) {
   RETURN_IF_NOT_OK(child_[0]->GetDatasetSize(&num_rows));
   if (num_rows > 0 && start_batch_size_ > 0) {
     if (drop_) {
-      num_rows = floor(num_rows / (1.0 * start_batch_size_));
+      num_rows = static_cast<int64_t>(floor(num_rows / (1.0 * start_batch_size_)));
     } else {
-      num_rows = ceil(num_rows / (1.0 * start_batch_size_));
+      num_rows = static_cast<int64_t>(ceil(num_rows / (1.0 * start_batch_size_)));
     }
   }
   *dataset_size = num_rows;

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

@@ -52,16 +52,8 @@ Status ParallelOp::CreateWorkerConnector(int32_t worker_connector_size) {
 
 // A print method typically used for debugging
 void ParallelOp::Print(std::ostream &out, bool show_all) const {
-  // Summary 1-liner print
-  if (!show_all) {
-    // Call super class printer
-    DatasetOp::Print(out, show_all);
-    out << " [workers: " << num_workers_ << "]";
-  } else {
-    // Detailed print
-    DatasetOp::Print(out, show_all);
-    out << "\nNum workers: " << num_workers_;
-  }
+  DatasetOp::Print(out, show_all);
+  out << " [workers: " << num_workers_ << "]";
 }
 
 // Override base class reset to provide reset actions specific to the ParallelOp class.

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

@@ -220,7 +220,7 @@ Status ShuffleOp::operator()() {
     }
 
     // Since we overloaded eoeReceived function, we are responsible to flow the EOE up the
-    // pipepline manually now that we are done draining the shuffle buffer
+    // pipeline manually now that we are done draining the shuffle buffer
     MS_LOG(DEBUG) << "Shuffle operator sending EOE.";
     auto eoe_buffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
     RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(eoe_buffer)));
@@ -283,7 +283,7 @@ Status ShuffleOp::InitShuffleBuffer() {
     shuffle_buffer_state_ = kShuffleStateDrain;
   }
 
-  MS_LOG(DEBUG) << "Shuffle operator finished intializing the shuffle buffer.";
+  MS_LOG(DEBUG) << "Shuffle operator finished initializing the shuffle buffer.";
   return Status::OK();
 }
 

mindspore/ccsrc/minddata/dataset/kernels/data/data_utils.cc

@@ -111,7 +111,9 @@ Status OneHotEncoding(std::shared_ptr<Tensor> input, std::shared_ptr<Tensor> *ou
     *output = out;
     return Status::OK();
   } catch (const std::exception &e) {
-    RETURN_STATUS_UNEXPECTED("Unexpected error in OneHotOp");
+    std::string err_msg = "Unexpected error in OneHotOp: ";
+    err_msg += e.what();
+    RETURN_STATUS_UNEXPECTED(err_msg);
   }
 }
 

mindspore/ccsrc/minddata/dataset/kernels/data/random_choice_op.cc

@@ -89,7 +89,8 @@ RandomChoiceOp::RandomChoiceOp(const std::vector<std::shared_ptr<TensorOp>> &ops
     : ops_(ops), gen_(GetSeed()), rand_int_(0, ops.size() - 1) {
   if (ops_.empty()) {
     MS_LOG(ERROR) << "op_list in RandomChoiceOp is empty.";
-  } else if (ops_.size() == 1) {
+  }
+  if (ops_.size() == 1) {
     MS_LOG(WARNING) << "op_list has only 1 op, this op would be picked every time.";
   }
   is_deterministic_ = false;

mindspore/dataset/engine/datasets.py

@@ -343,8 +343,8 @@ class Dataset:
         Add a blocking condition to the input Dataset.
 
         Args:
-            num_batch (int): the number of batches without blocking at the start of each epoch.
             condition_name (str): The condition name that is used to toggle sending next row.
+            num_batch (int): the number of batches without blocking at the start of each epoch.
             callback (function): The callback funciton that will be invoked when sync_update is called.
 
         Raises:
@@ -1452,9 +1452,10 @@ class Dataset:
             num_batch (Union[int, None]): The number of batches (rows) that are released.
                 When num_batch is None, it will default to the number specified by the
                 sync_wait operator (default=None).
-            data (Union[dict, None]): The data passed to the callback (default=None).
+            data (Any): The data passed to the callback, user defined (default=None).
         """
-        if isinstance(num_batch, int) and num_batch <= 0:
+        if (not isinstance(num_batch, int) and num_batch is not None) or \
+            (isinstance(num_batch, int) and num_batch <= 0):
             # throwing exception, disable all sync_wait in pipeline
             self.disable_sync()
             raise RuntimeError("Sync_update batch size can only be positive, got : {}.".format(num_batch))