Make sure record the first step data in SummaryCollector, and catch the ValueError when the loss is not a Scalar.

mindspore/train/callback/_summary_collector.py

@@ -73,7 +73,7 @@ class SummaryCollector(Callback):
         summary_dir (str): The collected data will be persisted to this directory.
             If the directory does not exist, it will be created automatically.
         collect_freq (int): Set the frequency of data collection, it should be greater then zero,
-            and the unit is `step`. Default: 10.
+            and the unit is `step`. Default: 10. The first step will be recorded at any time.
             It is important to note that if the data sink mode is used, the unit will become the `epoch`.
             It is not recommended to collect data too frequently, which can affect performance.
         collect_specified_data (Union[None, dict]): Perform custom operations on the collected data. Default: None.
@@ -142,9 +142,6 @@ class SummaryCollector(Callback):
         'histogram_regular': None
     }
 
-    # _OPTIMIZER_FAILED means find optimizer failed, so we will not collect data about optimizer.
-    _OPTIMIZER_FAILED = 'Failed'
-
     def __init__(self, summary_dir, collect_freq=10, collect_specified_data=None,
                  keep_default_action=True, custom_lineage_data=None):
         super(SummaryCollector, self).__init__()
@@ -158,7 +155,8 @@ class SummaryCollector(Callback):
         self._check_action(keep_default_action)
 
         self._collect_specified_data = self._process_specified_data(collect_specified_data, keep_default_action)
-        logger.info(f"For `collect_specified_data` the value after processing is: {self._collect_specified_data}.")
+        msg = f"For 'collect_specified_data' the value after processing is: {self._collect_specified_data}."
+        logger.info(msg)
 
         self._check_custom_lineage_data(custom_lineage_data)
         self._custom_lineage_data = custom_lineage_data
@@ -167,6 +165,7 @@ class SummaryCollector(Callback):
         self._has_saved_train_network = False
         self._has_saved_custom_data = False
         self._is_parse_loss_success = True
+        self._first_step = True
 
     def __enter__(self):
         self._record = SummaryRecord(log_dir=self._summary_dir)
@@ -228,7 +227,7 @@ class SummaryCollector(Callback):
         if specified_data is None:
             if action:
                 return self._DEFAULT_SPECIFIED_DATA
-            return None
+            return dict()
 
         check_value_type('collect_specified_data', specified_data, [dict, type(None)])
 
@@ -282,9 +281,13 @@ class SummaryCollector(Callback):
         cb_params = run_context.original_args()
 
         if cb_params.mode == ModeEnum.TRAIN.value:
-            if cb_params.cur_step_num % self._collect_freq:
+
+            # Make sure the first step data is recorded
+            if not self._first_step and cb_params.cur_step_num % self._collect_freq:
                 return
 
+            self._first_step = False
+
             if not self._has_saved_train_network:
                 self._collect_graphs(cb_params)
 
@@ -357,7 +360,8 @@ class SummaryCollector(Callback):
             input_data = input_data[0]
         try:
             self._record.add_value(PluginEnum.IMAGE.value, 'input_data/auto', input_data)
-        except ValueError:
+        except ValueError as ex:
+            logger.warning(str(ex))
             self._collect_specified_data['collect_input_data'] = False
             return
 
@@ -395,7 +399,12 @@ class SummaryCollector(Callback):
         loss = self._get_loss(cb_params)
         if loss is None:
             return
-        self._record.add_value(PluginEnum.SCALAR.value, 'loss/auto', loss)
+
+        try:
+            self._record.add_value(PluginEnum.SCALAR.value, 'loss/auto', loss)
+        except ValueError as exc:
+            logger.warning(str(exc))
+            self._collect_specified_data['collect_metric'] = False
 
     def _get_loss(self, cb_params):
         """
@@ -446,7 +455,9 @@ class SummaryCollector(Callback):
         Returns:
             Union[Optimizer, None], if parse optimizer success, will return a optimizer, else return None.
         """
-        if self._optimizer == self._OPTIMIZER_FAILED:
+        # 'optimizer_failed' means find optimizer failed, so we will not collect data about optimizer.
+        optimizer_failed = 'Failed'
+        if self._optimizer == optimizer_failed:
             return None
 
         if self._optimizer is not None:
@@ -458,9 +469,11 @@ class SummaryCollector(Callback):
             optimizer = self._parse_optimizer_by_network(network)
 
         if optimizer is None or not isinstance(optimizer, Optimizer):
-            logger.warning("Can not find optimizer in network, or the optimizer does not inherit Mindpore's optimizer, "
-                           "so we will not collect data about optimizer in SummaryCollector.")
-            optimizer = self._OPTIMIZER_FAILED
+            logger.warning("Can not find optimizer in network, or the optimizer does not inherit MindSpore's "
+                           "optimizer, so we will not collect data about optimizer in SummaryCollector.")
+            optimizer = None
+
+        self._optimizer = optimizer if optimizer is not None else optimizer_failed
 
         return optimizer
 
@@ -469,6 +482,8 @@ class SummaryCollector(Callback):
         """Parse optimizer from network, if parse success will return a optimizer, else return None."""
         optimizer = None
         for _, cell in network.cells_and_names():
+            if isinstance(cell, Optimizer):
+                return cell
             try:
                 optimizer = getattr(cell, 'optimizer')
             except AttributeError:
@@ -489,11 +504,11 @@ class SummaryCollector(Callback):
         if 'histogram_regular' not in self._collect_specified_data:
             return
 
-        self._optimizer = self._get_optimizer(cb_params)
-        if self._optimizer is None:
+        optimizer = self._get_optimizer(cb_params)
+        if optimizer is None:
             return
 
-        parameters = self._optimizer.parameters
+        parameters = optimizer.parameters
         regular = self._collect_specified_data.get('histogram_regular')
         if regular is not None:
             for parameter in parameters:
@@ -538,7 +553,7 @@ class SummaryCollector(Callback):
             train_lineage[LineageMetadata.loss] = None
 
         optimizer = self._get_optimizer(cb_params)
-        learning_rate = self._get_learning_rate(optimizer)
+        learning_rate = self._get_learning_rate(optimizer) if optimizer is not None else None
 
         if learning_rate is not None:
             train_lineage[LineageMetadata.learning_rate] = list(np.atleast_1d(learning_rate.asnumpy()))[0]