fix the result of the number of decimal points of aicore type execution time

mindinsight/profiler/analyser/analyser.py

@@ -63,6 +63,17 @@ class AicoreTypeAnalyser(BaseAnalyser):
             return self._default_filter(item, filter_condition)
         self._result = list(filter(_inner_filter, self._data))
 
+    def _organize_query_result(self):
+        """
+        Organize the query result.
+
+        Returns:
+            dict, the query result.
+        """
+        for item in self._result:
+            item[1] = float(format(item[1], '.6f'))
+        return super()._organize_query_result()
+
     def _convert_field_type(self, row):
         """
         Convert the field type to the specific type.

mindinsight/profiler/analyser/base_analyser.py

@@ -97,11 +97,7 @@ class BaseAnalyser(ABC):
             self._sort(sort_condition)
         if group_condition:
             self._group(group_condition)
-        return {
-            'col_name': self._display_col_names,
-            'object': self._result,
-            'size': self._size
-        }
+        return self._organize_query_result()
 
     @abstractmethod
     def _load(self):
@@ -244,3 +240,16 @@ class BaseAnalyser(ABC):
         except ValidationError:
             raise ProfilerPathErrorException('The profiling dir is invalid.')
         return normalized_profiling_dir
+
+    def _organize_query_result(self):
+        """
+        Organize the query result.
+
+        Returns:
+            dict, the query result.
+        """
+        return {
+            'col_name': self._display_col_names,
+            'object': self._result,
+            'size': self._size
+        }

tests/st/func/profiler/test_op_analyser.py

@@ -126,34 +126,34 @@ class TestOpAnalyser:
         expect_result = {
             'col_name': ['op_type', 'execution_time', 'execution_frequency', 'percent'],
             'object': [
-                ['UnsortedSegmentSum', 44.60782642857142, 2, 35.28],
-                ['GatherV2', 43.15544147619047, 2, 34.13],
-                ['Slice', 20.376314999999998, 16, 16.12],
-                ['Concat', 5.80845380952381, 4, 4.59],
-                ['Split', 2.7142774761904764, 2, 2.15],
-                ['MatMul', 1.9366814285714287, 15, 1.53],
-                ['Mul', 1.9029486666666666, 32, 1.51],
-                ['StridedSliceGrad', 1.5068342857142858, 2, 1.19],
-                ['TransData', 1.1151575238095237, 30, 0.88],
-                ['ReluGrad', 0.8540685714285714, 5, 0.68],
-                ['Cast', 0.4846848571428572, 15, 0.38],
-                ['ReLU', 0.48328214285714277, 5, 0.38],
-                ['RealDiv', 0.4228071904761905, 15, 0.33],
-                ['StridedSlice', 0.3455687619047618, 2, 0.27],
-                ['Adam', 0.2859357142857143, 11, 0.23],
-                ['BiasAdd', 0.18966285714285713, 5, 0.15],
-                ['BiasAddGrad', 0.07168142857142856, 5, 0.06],
-                ['Tile', 0.04415833333333334, 4, 0.03],
-                ['ReduceSum', 0.030764857142857142, 5, 0.02],
-                ['ApplyFtrl', 0.025453571428571426, 2, 0.02],
-                ['AtomicAddrClean', 0.019368666666666666, 8, 0.02],
-                ['AddN', 0.012836428571428572, 1, 0.01],
-                ['Square', 0.009799333333333334, 1, 0.01],
-                ['SigmoidCrossEntropyWithLogitsGrad', 0.009582142857142859, 2, 0.01],
-                ['TensorAdd', 0.009218380952380952, 3, 0.01],
-                ['SigmoidCrossEntropyWithLogits', 0.004808571428571428, 1, 0.0],
-                ['ReduceMean', 0.004534999999999999, 1, 0.0],
-                ['Assign', 0.0024766666666666665, 2, 0.0],
+                ['UnsortedSegmentSum', 44.607826, 2, 35.28],
+                ['GatherV2', 43.155441, 2, 34.13],
+                ['Slice', 20.376315, 16, 16.12],
+                ['Concat', 5.808454, 4, 4.59],
+                ['Split', 2.714277, 2, 2.15],
+                ['MatMul', 1.936681, 15, 1.53],
+                ['Mul', 1.902949, 32, 1.51],
+                ['StridedSliceGrad', 1.506834, 2, 1.19],
+                ['TransData', 1.115158, 30, 0.88],
+                ['ReluGrad', 0.854069, 5, 0.68],
+                ['Cast', 0.484685, 15, 0.38],
+                ['ReLU', 0.483282, 5, 0.38],
+                ['RealDiv', 0.422807, 15, 0.33],
+                ['StridedSlice', 0.345569, 2, 0.27],
+                ['Adam', 0.285936, 11, 0.23],
+                ['BiasAdd', 0.189663, 5, 0.15],
+                ['BiasAddGrad', 0.071681, 5, 0.06],
+                ['Tile', 0.044158, 4, 0.03],
+                ['ReduceSum', 0.030765, 5, 0.02],
+                ['ApplyFtrl', 0.025454, 2, 0.02],
+                ['AtomicAddrClean', 0.019369, 8, 0.02],
+                ['AddN', 0.012836, 1, 0.01],
+                ['Square', 0.009799, 1, 0.01],
+                ['SigmoidCrossEntropyWithLogitsGrad', 0.009582, 2, 0.01],
+                ['TensorAdd', 0.009218, 3, 0.01],
+                ['SigmoidCrossEntropyWithLogits', 0.004809, 1, 0.0],
+                ['ReduceMean', 0.004535, 1, 0.0],
+                ['Assign', 0.002477, 2, 0.0],
                 ['AssignAdd', 0.001688, 1, 0.0]
             ],
             'size': 29
@@ -178,8 +178,8 @@ class TestOpAnalyser:
         expect_result = {
             'col_name': ['op_type', 'execution_time', 'execution_frequency', 'percent'],
             'object': [
-                ['MatMul', 1.9366814285714287, 15, 1.53],
-                ['Mul', 1.9029486666666666, 32, 1.51]
+                ['MatMul', 1.936681, 15, 1.53],
+                ['Mul', 1.902949, 32, 1.51]
             ],
             'size': 2
         }