!748 fix np.histogram sometimes calc very large bucket number

Merge pull request !748 from wenkai/wkr02

mindspore/train/summary/_summary_adapter.py

@@ -15,6 +15,7 @@
 """Generate the summary event which conform to proto format."""
 import time
 import socket
+import math
 from enum import Enum, unique
 import numpy as np
 from PIL import Image
@@ -292,6 +293,36 @@ def _get_tensor_summary(tag: str, np_value, summary_tensor):
     return summary_tensor
 
 
+def _calc_histogram_bins(count):
+    """
+    Calculates experience-based optimal bins number for histogram.
+
+    There should be enough number in each bin. So we calc bin numbers according to count. For very small count(1 -
+    10), we assign carefully chosen number. For large count, we tried to make sure there are 9-10 numbers in each
+    bucket on average. Too many bins will slow down performance, so we set max number of bins to 90.
+
+    Args:
+        count (int): Valid number count for the tensor.
+
+    Returns:
+        int, number of histogram bins.
+    """
+    number_per_bucket = 10
+    max_bins = 90
+
+    if not count:
+        return 1
+    if count <= 5:
+        return 2
+    if count <= 10:
+        return 3
+    if count <= 880:
+        # note that math.ceil(881/10) + 1 equals 90
+        return int(math.ceil(count / number_per_bucket) + 1)
+
+    return max_bins
+
+
 def _fill_histogram_summary(tag: str, np_value: np.array, summary_histogram) -> None:
     """
     Package the histogram summary.
@@ -347,7 +378,8 @@ def _fill_histogram_summary(tag: str, np_value: np.array, summary_histogram) ->
 
         return
 
-    counts, edges = np.histogram(np_value, bins='auto', range=(tensor_min, tensor_max))
+    bin_number = _calc_histogram_bins(masked_value.count())
+    counts, edges = np.histogram(np_value, bins=bin_number, range=(tensor_min, tensor_max))
 
     for ind, count in enumerate(counts):
         bucket = summary_histogram.buckets.add()

tests/ut/python/train/summary/test_histogram_summary.py

@@ -22,6 +22,7 @@ import numpy as np
 
 from mindspore.common.tensor import Tensor
 from mindspore.train.summary.summary_record import SummaryRecord, _cache_summary_tensor_data
+from mindspore.train.summary._summary_adapter import _calc_histogram_bins
 from .summary_reader import SummaryReader
 
 CUR_DIR = os.getcwd()
@@ -139,7 +140,7 @@ def test_histogram_summary_same_value():
         event = reader.read_event()
         LOG.debug(event)
 
-        assert len(event.summary.value[0].histogram.buckets) == 1
+        assert len(event.summary.value[0].histogram.buckets) == _calc_histogram_bins(dim1 * dim2)
 
 
 def test_histogram_summary_high_dims():