!2769 fix get loss error and NoneType error cause by _proceesor_specified_data

Merge pull request !2769 from ougongchang/fix_nonetype

mindspore/train/callback/_summary_collector.py

@@ -239,7 +239,8 @@ class SummaryCollector(Callback):
 
         unexpected_params = set(specified_data) - set(self._DEFAULT_SPECIFIED_DATA)
         if unexpected_params:
-            raise ValueError(f'For `collect_specified_data` the keys {unexpected_params} are unsupported.')
+            raise ValueError(f'For `collect_specified_data` the keys {unexpected_params} are unsupported, '
+                             f'expect the follow keys: {list(self._DEFAULT_SPECIFIED_DATA.keys())}')
 
         if 'histogram_regular' in specified_data:
             check_value_type('histogram_regular', specified_data.get('histogram_regular'), (str, type(None)))
@@ -250,7 +251,8 @@ class SummaryCollector(Callback):
                 check_value_type(item, specified_data.get(item), bool)
 
         if action:
-            result = dict(self._DEFAULT_SPECIFIED_DATA).update(specified_data)
+            result = dict(self._DEFAULT_SPECIFIED_DATA)
+            result.update(specified_data)
         else:
             result = specified_data
         return result
@@ -444,15 +446,12 @@ class SummaryCollector(Callback):
             self._is_parse_loss_success = False
             return None
 
-        if isinstance(output, (int, float)):
+        if isinstance(output, (int, float, Tensor)):
             loss = output
-        elif isinstance(output, (list, tuple)):
+        elif isinstance(output, (list, tuple)) and output:
             # If the output is a list, since the default network returns loss first,
             # we assume that the first one is loss.
             loss = output[0]
-        elif isinstance(output, Tensor) and (not output.shape or output.shape == (1,)):
-            loss_numpy = output.asnumpy()
-            loss = float(np.atleast_1d(loss_numpy)[0])
         else:
             logger.warning("The output type could not be identified, so no loss was recorded in SummaryCollector.")
             self._is_parse_loss_success = False
@@ -461,6 +460,8 @@ class SummaryCollector(Callback):
         if not isinstance(loss, Tensor):
             loss = Tensor(loss)
 
+        precision = 4
+        loss = Tensor(round(np.mean(loss.asnumpy()), precision))
         return loss
 
     def _get_optimizer(self, cb_params):

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

@@ -50,6 +50,9 @@ def get_value():
     _VALUE_CACHE = list()
     return value
 
+_SPECIFIED_DATA = SummaryCollector._DEFAULT_SPECIFIED_DATA
+_SPECIFIED_DATA['collect_metric'] = False
+
 
 class CustomNet(Cell):
     """Define custom netwrok."""
@@ -190,8 +193,8 @@ class TestSummaryCollector:
         data = {'unexpected_key': True}
         with pytest.raises(ValueError) as exc:
             SummaryCollector(summary_dir, collect_specified_data=data)
-        expected_msg = f"For `collect_specified_data` the keys {set(data)} are unsupported."
-        assert expected_msg == str(exc.value)
+        expected_msg = f"For `collect_specified_data` the keys {set(data)} are unsupported"
+        assert expected_msg in str(exc.value)
 
     @pytest.mark.parametrize("custom_lineage_data", [
         123,
@@ -273,12 +276,16 @@ class TestSummaryCollector:
         assert name == 'train_dataset'
 
     @pytest.mark.parametrize("net_output, expected_loss", [
+        (None, None),
         (1, Tensor(1)),
+        (1.5, Tensor(1.5)),
+        (Tensor(1), Tensor(1)),
         ([1], Tensor(1)),
         ([Tensor(1)], Tensor(1)),
-        (Tensor([1]), Tensor(1)),
+        ({}, None),
+        (Tensor([[1, 2], [3, 4]]), Tensor(2.5)),
+        ([Tensor([[3, 4, 3]]), Tensor([3, 4])], Tensor(3.33333)),
         (tuple([1]), Tensor(1)),
-        (None, None)
     ])
     def test_get_loss(self, net_output, expected_loss):
         """Test get loss success and failed."""
@@ -375,3 +382,20 @@ class TestSummaryCollector:
         assert PluginEnum.HISTOGRAM.value == result[0][0]
         assert expected_names == [data[1] for data in result]
         assert expected_values == [data[2] for data in result]
+
+    @pytest.mark.parametrize("specified_data, action, expected_result", [
+        (None, True, SummaryCollector._DEFAULT_SPECIFIED_DATA),
+        (None, False, {}),
+        ({}, True, SummaryCollector._DEFAULT_SPECIFIED_DATA),
+        ({}, False, {}),
+        ({'collect_metric': False}, True, _SPECIFIED_DATA),
+        ({'collect_metric': True}, False, {'collect_metric': True})
+    ])
+    def test_process_specified_data(self, specified_data, action, expected_result):
+        """Test process specified data."""
+        summary_dir = tempfile.mkdtemp(dir=self.base_summary_dir)
+        summary_collector = SummaryCollector(summary_dir,
+                                             collect_specified_data=specified_data,
+                                             keep_default_action=action)
+
+        assert summary_collector._collect_specified_data == expected_result