fix codecheck

mindspore/ccsrc/minddata/dataset/kernels/image/soft_dvpp/utils/external_soft_dp.h

@@ -17,7 +17,7 @@
 #ifndef EXTERNAL_SOFTDP_H
 #define EXTERNAL_SOFTDP_H
 
-#include <cstdint>
+#include <stdint.h>
 
 struct SoftDpProcsessInfo {
   uint8_t *input_buffer;       // input buffer

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

@@ -636,7 +636,6 @@ std::pair<MSRStatus, std::vector<uint64_t>> ShardReader::GetPagesByCategory(
 
   if (!criteria.first.empty()) {
     auto schema = shard_header_->GetSchemas()[0]->GetSchema();
-
     if (kNumberFieldTypeSet.find(schema["schema"][criteria.first]["type"]) != kNumberFieldTypeSet.end()) {
       sql +=
         " AND " + criteria.first + "_" + std::to_string(column_schema_id_[criteria.first]) + " = " + criteria.second;

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

@@ -660,7 +660,7 @@ MSRStatus ShardWriter::MergeBlobData(const std::vector<string> &blob_fields,
       uint64_t blob_size = b->size();
       // big edian
       for (size_t i = 0; i < buf.size(); ++i) {
-        buf[buf.size() - 1 - i] = (std::numeric_limits<uint8_t>::max() & blob_size);
+        buf[buf.size() - 1 - i] = (std::numeric_limits<uint8_t>::max()) & blob_size;
         blob_size >>= 8u;
       }
       std::copy(buf.begin(), buf.end(), (*output)->begin() + idx);

mindspore/mindrecord/shardutils.py

@@ -125,13 +125,12 @@ def check_parameter(func):
                 else:
                     check_filename(value)
             if name == 'num_consumer':
-                if value is not None:
-                    if isinstance(value, int):
-                        if value < MIN_CONSUMER_COUNT or value > MAX_CONSUMER_COUNT():
-                            raise ParamValueError("Consumer number should between {} and {}."
-                                                  .format(MIN_CONSUMER_COUNT, MAX_CONSUMER_COUNT()))
-                    else:
-                        raise ParamValueError("Consumer number is illegal.")
+                if value is None:
+                    raise ParamValueError("Consumer number is illegal.")
+                if isinstance(value, int):
+                    if value < MIN_CONSUMER_COUNT or value > MAX_CONSUMER_COUNT():
+                        raise ParamValueError("Consumer number should between {} and {}."
+                                              .format(MIN_CONSUMER_COUNT, MAX_CONSUMER_COUNT()))
                 else:
                     raise ParamValueError("Consumer number is illegal.")
         return func(*args, **kw)

mindspore/train/model.py

@@ -282,9 +282,15 @@ class Model:
             scaling_sens /= self._device_number
         return scaling_sens
 
-    def _exec_preprocess(self, network, is_train, phase, dataset,
-                         dataset_sink_mode, sink_size=-1, epoch_num=1, dataset_helper=None):
+    def _exec_preprocess(self, is_train, dataset, dataset_sink_mode, sink_size=-1, epoch_num=1, dataset_helper=None):
         """Initializes dataset."""
+        if is_train:
+            network = self._train_network
+            phase = 'train'
+        else:
+            network = self._eval_network
+            phase = 'eval'
+
         if dataset_sink_mode and not is_train:
             dataset.__loop_size__ = 1
 
@@ -330,9 +336,7 @@ class Model:
                 self._train_network.set_broadcast_flag()
 
             train_dataset.__no_send__ = True
-            train_dataset_helper, train_network = self._exec_preprocess(self._train_network,
-                                                                        is_train=True,
-                                                                        phase='train',
+            train_dataset_helper, train_network = self._exec_preprocess(is_train=True,
                                                                         dataset=train_dataset,
                                                                         dataset_sink_mode=True,
                                                                         sink_size=sink_size)
@@ -346,9 +350,7 @@ class Model:
                 raise RuntimeError('If define `valid_dataset`, metric fn can not be None or empty.')
 
             valid_dataset.__no_send__ = True
-            valid_dataset_helper, eval_network = self._exec_preprocess(self._eval_network,
-                                                                       is_train=False,
-                                                                       phase='eval',
+            valid_dataset_helper, eval_network = self._exec_preprocess(is_train=False,
                                                                        dataset=valid_dataset,
                                                                        dataset_sink_mode=True)
             self._eval_network = eval_network
@@ -456,9 +458,7 @@ class Model:
         for i in range(epoch):
             cb_params.cur_epoch_num = i + 1
             list_callback.epoch_begin(run_context)
-            dataset_helper, train_network = self._exec_preprocess(self._train_network,
-                                                                  is_train=True,
-                                                                  phase='train',
+            dataset_helper, train_network = self._exec_preprocess(is_train=True,
                                                                   dataset=train_dataset,
                                                                   dataset_sink_mode=True,
                                                                   sink_size=sink_size,
@@ -506,9 +506,7 @@ class Model:
             list_callback (Callback): Executor of callback list. Default: None.
             cb_params (_InternalCallbackParam): Callback parameters. Default: None.
         """
-        dataset_helper, _ = self._exec_preprocess(self._train_network,
-                                                  is_train=True,
-                                                  phase='train',
+        dataset_helper, _ = self._exec_preprocess(is_train=True,
                                                   dataset=train_dataset,
                                                   dataset_sink_mode=False,
                                                   epoch_num=epoch)
@@ -640,9 +638,7 @@ class Model:
         """
         run_context = RunContext(cb_params)
 
-        dataset_helper, eval_network = self._exec_preprocess(self._eval_network,
-                                                             is_train=False,
-                                                             phase='eval',
+        dataset_helper, eval_network = self._exec_preprocess(is_train=False,
                                                              dataset=valid_dataset,
                                                              dataset_sink_mode=True)
         self._eval_network = eval_network
@@ -680,9 +676,7 @@ class Model:
         run_context = RunContext(cb_params)
         cb_params.dataset_sink_mode = False
         list_callback.begin(run_context)
-        dataset_helper, _ = self._exec_preprocess(self._eval_network,
-                                                  is_train=False,
-                                                  phase='eval',
+        dataset_helper, _ = self._exec_preprocess(is_train=False,
                                                   dataset=valid_dataset,
                                                   dataset_sink_mode=False)
         for next_element in dataset_helper: