modify comment

mindspore/train/callback/_checkpoint.py

@@ -88,7 +88,7 @@ class CheckpointConfig:
                         mode, currently supports 'AES-GCM' and 'AES-CBC'. Default: 'AES-GCM'.
 
     Raises:
-        ValueError: If the input_param is None or 0.
+        ValueError: If input parameter is not the correct type.
 
     Examples:
         >>> class LeNet5(nn.Cell):

mindspore/train/callback/_dataset_graph.py

@@ -27,7 +27,7 @@ class DatasetGraph:
         packages dataset graph into binary data
 
         Args:
-            dataset (MindData): refer to MindDataset
+            dataset (MindDataset): Refer to MindDataset.
 
         Returns:
             DatasetGraph, a object of lineage_pb2.DatasetGraph.

mindspore/train/callback/_loss_monitor.py

@@ -33,7 +33,7 @@ class LossMonitor(Callback):
         per_print_times (int): Print the loss each every time. Default: 1.
 
     Raises:
-        ValueError: If print_step is not an integer or less than zero.
+        ValueError: If per_print_times is not an integer or less than zero.
     """
 
     def __init__(self, per_print_times=1):

mindspore/train/callback/_lr_scheduler_callback.py

@@ -17,11 +17,13 @@
 import math
 import numpy as np
 
+from mindspore import log as logger
 import mindspore.common.dtype as mstype
 from mindspore.common.tensor import Tensor
 from mindspore.train.callback._callback import Callback
 from mindspore.ops import functional as F
 
+
 class LearningRateScheduler(Callback):
     """
     Change the learning_rate during training.
@@ -66,4 +68,4 @@ class LearningRateScheduler(Callback):
         new_lr = self.learning_rate_function(lr, cb_params.cur_step_num)
         if not math.isclose(lr, new_lr, rel_tol=1e-10):
             F.assign(cb_params.optimizer.learning_rate, Tensor(new_lr, mstype.float32))
-            print(f'At step {cb_params.cur_step_num}, learning_rate change to {new_lr}')
+            logger.info(f'At step {cb_params.cur_step_num}, learning_rate change to {new_lr}')

mindspore/train/callback/_time_monitor.py

@@ -16,7 +16,6 @@
 
 import time
 
-from mindspore import log as logger
 from ._callback import Callback
 
 
@@ -25,12 +24,16 @@ class TimeMonitor(Callback):
     Monitor the time in training.
 
     Args:
-        data_size (int): Dataset size. Default: None.
+        data_size (int): How many steps to return time information default is dataset size. Default: None.
+
+    Raises:
+        ValueError: If data_size is not positive int.
     """
 
     def __init__(self, data_size=None):
         super(TimeMonitor, self).__init__()
         self.data_size = data_size
+        self.epoch_time = time.time()
 
     def epoch_begin(self, run_context):
         self.epoch_time = time.time()
@@ -45,8 +48,7 @@ class TimeMonitor(Callback):
                 step_size = cb_params.batch_num
 
         if not isinstance(step_size, int) or step_size < 1:
-            logger.error("data_size must be positive int.")
-            return
+            raise ValueError("data_size must be positive int.")
 
         step_seconds = epoch_seconds / step_size
         print("epoch time: {:5.3f} ms, per step time: {:5.3f} ms".format(epoch_seconds, step_seconds), flush=True)

mindspore/train/model.py

@@ -804,7 +804,10 @@ class Model:
             predict_data (Tensor): One tensor or multiple tensors of predict data.
 
         Returns:
-            Dict, Parameter layout dictionary used for load distributed checkpoint
+            Dict, Parameter layout dictionary used for load distributed checkpoint.
+
+        Raises:
+            RuntimeError: If get_context is not GRAPH_MODE.
 
         Examples:
             >>> # This example should be run with multiple devices. Refer to the tutorial > Distributed Training on

mindspore/train/serialization.py

@@ -286,8 +286,7 @@ def load(file_name):
     """
     Load MindIR.
 
-    The returned object can be executed by a `GraphCell`. However, there are some limitations to the current use
-    of `GraphCell`, see class :class:`mindspore.nn.GraphCell` for more details.
+    The returned object can be executed by a `GraphCell`, see class :class:`mindspore.nn.GraphCell` for more details.
 
     Args:
         file_name (str): MindIR file name.
@@ -296,7 +295,8 @@ def load(file_name):
         Object, a compiled graph that can executed by `GraphCell`.
 
     Raises:
-        ValueError: MindIR file is incorrect.
+        ValueError: MindIR file name is incorrect.
+        RuntimeError: Failed to parse MindIR file.
 
     Examples:
         >>> import numpy as np
@@ -644,7 +644,7 @@ def export(net, *inputs, file_name, file_format='AIR', **kwargs):
 
     Args:
         net (Cell): MindSpore network.
-        inputs (Tensor): Inputs of the `net`.
+        inputs (Tensor): Inputs of the `net`, if the network has multiple inputs, incoming tuple(Tensor).
         file_name (str): File name of the model to be exported.
         file_format (str): MindSpore currently supports 'AIR', 'ONNX' and 'MINDIR' format for exported model.
 
@@ -825,6 +825,7 @@ def _mindir_save_together(net_dict, model):
             return False
     return True
 
+
 def quant_mode_manage(func):
     """
     Inherit the quant_mode in old version.
@@ -840,6 +841,7 @@ def quant_mode_manage(func):
         return func(network, *inputs, file_format=file_format, **kwargs)
     return warpper
 
+
 @quant_mode_manage
 def _quant_export(network, *inputs, file_format, **kwargs):
     """
@@ -1034,12 +1036,11 @@ def build_searched_strategy(strategy_filename):
         strategy_filename (str): Name of strategy file.
 
     Returns:
-        Dictionary, whose key is parameter name and value is slice strategy of this parameter.
+        Dict, whose key is parameter name and value is slice strategy of this parameter.
 
     Raises:
         ValueError: Strategy file is incorrect.
         TypeError: Strategy_filename is not str.
-
     """
     if not isinstance(strategy_filename, str):
         raise TypeError(f"The strategy_filename should be str, but got {type(strategy_filename)}.")
@@ -1162,7 +1163,7 @@ def load_distributed_checkpoint(network, checkpoint_filenames, predict_strategy=
         predict_strategy (dict): Strategy of predication process, whose key is parameter name, and value is a list or
             a tuple that the first four elements are [dev_matrix, tensor_map, param_split_shape, field]. If None,
             it means that the predication process just uses single device. Default: None.
-        train_strategy_filename (str): Train strategy file. Default: None.
+        train_strategy_filename (str): Train strategy proto file name. Default: None.
         dec_key (Union[None, bytes]): Byte type key used for decryption. If the value is None, the decryption
                                       is not required. Default: None.
         dec_mode (str): This parameter is valid only when dec_key is not set to None. Specifies the decryption