use default np.float64 to create ndarray to reduce error of calculation precision

mindinsight/datavisual/data_transform/tensor_container.py

@@ -17,7 +17,6 @@ import numpy as np
 
 from mindinsight.datavisual.common.log import logger
 from mindinsight.datavisual.data_transform.histogram import Histogram, Bucket
-from mindinsight.datavisual.proto_files import mindinsight_anf_ir_pb2 as anf_ir_pb2
 from mindinsight.datavisual.utils.utils import calc_histogram_bins
 from mindinsight.utils.exceptions import ParamValueError
 
@@ -192,10 +191,8 @@ class TensorContainer:
         self._stats = get_statistics_from_tensor(self._np_array)
         original_buckets = calc_original_buckets(self._np_array, self._stats)
         self._count = sum(bucket.count for bucket in original_buckets)
-        # convert the type of max and min value to np.float64 so that it cannot overflow
-        # when calculating width of histogram.
-        self._max = np.float64(self._stats.max)
-        self._min = np.float64(self._stats.min)
+        self._max = self._stats.max
+        self._min = self._stats.min
         self._histogram = Histogram(tuple(original_buckets), self._max, self._min, self._count)
 
     @property
@@ -257,9 +254,4 @@ class TensorContainer:
         Returns:
             numpy.ndarray, ndarray of tensor.
         """
-        data_type_str = anf_ir_pb2.DataType.Name(self.data_type)
-        if data_type_str == 'DT_FLOAT16':
-            return np.array(tuple(tensor), dtype=np.float16).reshape(self.dims)
-        if data_type_str == 'DT_FLOAT32':
-            return np.array(tuple(tensor), dtype=np.float32).reshape(self.dims)
         return np.array(tuple(tensor)).reshape(self.dims)