!7583 [ME] change some format code.

Merge pull request !7583 from chenzhongming/zomi_master

mindspore/_checkparam.py

@@ -109,7 +109,7 @@ def check_number(arg_value, value, rel, arg_type=int, arg_name=None, prim_name=N
         raise ValueError(f'{arg_name} {prim_name} must be legal value, but got `{arg_value}`.')
     if type_mismatch or not rel_fn(arg_value, value):
         rel_str = Rel.get_strs(rel).format(value)
-        raise type_except(f'{arg_name} {prim_name} should be an {type(arg_type).__name__} and must {rel_str}, '
+        raise type_except(f'{arg_name} {prim_name} should be an {arg_type.__name__} and must {rel_str}, '
                           f'but got `{arg_value}` with type `{type(arg_value).__name__}`.')
 
     return arg_value
@@ -130,7 +130,7 @@ def check_is_number(arg_value, arg_type, arg_name=None, prim_name=None):
         if math.isinf(arg_value) or math.isnan(arg_value) or np.isinf(arg_value) or np.isnan(arg_value):
             raise ValueError(f'{arg_name} {prim_name} must be legal float, but got `{arg_value}`.')
         return arg_value
-    raise TypeError(f'{arg_name} {prim_name} must be float, but got `{type(arg_value).__name__}`')
+    raise TypeError(f'{arg_name} {prim_name} must be {arg_type.__name__}, but got `{type(arg_value).__name__}`')
 
 
 def check_number_range(arg_value, lower_limit, upper_limit, rel, value_type, arg_name=None, prim_name=None):
@@ -146,7 +146,8 @@ def check_number_range(arg_value, lower_limit, upper_limit, rel, value_type, arg
     arg_name = f'`{arg_name}`' if arg_name else ''
     type_mismatch = not isinstance(arg_value, (np.ndarray, np.generic, value_type)) or isinstance(arg_value, bool)
     if type_mismatch:
-        raise TypeError(f'{arg_name} {prim_name} must be `{value_type}`, but got `{type(arg_value).__name__}`.')
+        raise TypeError("{} {} must be `{}`,  but got `{}`.".format(
+            arg_name, prim_name, value_type.__name__, type(arg_value).__name__))
     if not rel_fn(arg_value, lower_limit, upper_limit):
         rel_str = Rel.get_strs(rel).format(lower_limit, upper_limit)
         raise ValueError("{} {} should be in range of {}, but got {:.3e} with type `{}`.".format(

mindspore/common/api.py

@@ -135,7 +135,7 @@ class _MindSporeFunction:
         _exec_init_graph(self.obj, init_phase)
 
     def compile(self, arguments_dict, method_name):
-        """Returns pipline for the given args."""
+        """Returns pipeline for the given args."""
         args_list = tuple(arguments_dict.values())
         arg_names = tuple(arguments_dict.keys())
 

mindspore/nn/cell.py

@@ -32,6 +32,7 @@ from ..ops.functional import cast
 from ..parallel._tensor import _load_tensor_by_layout
 from ..common.tensor import Tensor
 
+
 class Cell(Cell_):
     """
     Base class for all neural networks.
@@ -579,7 +580,7 @@ class Cell(Cell_):
 
     def cast_param(self, param):
         """
-        Cast parameter according to auto mix precison level in pynative mode.
+        Cast parameter according to auto mix precision level in pynative mode.
 
         Args:
             param (Parameter): The parameter to cast.
@@ -594,15 +595,13 @@ class Cell(Cell_):
                 param.set_cast_dtype()
         return param
 
-    def insert_child_to_cell(self, child_name, child):
+    def insert_child_to_cell(self, child_name, child_cell):
         """
-        Adds a child cell to the current cell.
+        Adds a child cell to the current cell with a given name.
-
-        Inserts a subcell with a given name to the current cell.
 
         Args:
             child_name (str): Name of the child cell.
-            child (Cell): The child cell to be inserted.
+            child_cell (Cell): The child cell to be inserted.
 
         Raises:
             KeyError: Child Cell's name is incorrect or duplicated with the other child name.
@@ -612,15 +611,13 @@ class Cell(Cell_):
             raise KeyError("Child cell name is incorrect.")
         if hasattr(self, child_name) and child_name not in self._cells:
             raise KeyError("Duplicate child name '{}'.".format(child_name))
-        if not isinstance(child, Cell) and child is not None:
+        if not isinstance(child_cell, Cell) and child_cell is not None:
             raise TypeError("Child cell type is incorrect.")
-        self._cells[child_name] = child
+        self._cells[child_name] = child_cell
 
     def construct(self, *inputs, **kwargs):
         """
-        Defines the computation to be performed.
+        Defines the computation to be performed. This method must be overridden by all subclasses.
-
-        This method must be overridden by all subclasses.
 
         Note:
             The inputs of the top cell only allow Tensor.

mindspore/train/model.py

@@ -478,7 +478,7 @@ class Model:
                 len_element = len(next_element)
                 next_element = _transfer_tensor_to_tuple(next_element)
                 if self._loss_fn and len_element != 2:
-                    raise ValueError("when loss_fn is not None, train_dataset should"
+                    raise ValueError("when loss_fn is not None, train_dataset should "
                                      "return two elements, but got {}".format(len_element))
                 cb_params.cur_step_num += 1
 

model_zoo/official/cv/lenet/export.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """
-export quantization aware training network to infer `AIR` backend.
+export network to infer `AIR` backend.
 """
 
 import argparse
@@ -27,14 +27,17 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net,
 from src.config import mnist_cfg as cfg
 from src.lenet import LeNet5
 
+
+parser = argparse.ArgumentParser(description='MindSpore MNIST Example')
+parser.add_argument('--device_target', type=str, default="Ascend",
+                    choices=['Ascend', 'GPU'],
+                    help='device where the code will be implemented (default: Ascend)')
+parser.add_argument('--ckpt_path', type=str, default="",
+                    help='if mode is test, must provide path where the trained ckpt file')
+args = parser.parse_args()
+
+
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description='MindSpore MNIST Example')
-    parser.add_argument('--device_target', type=str, default="Ascend",
-                        choices=['Ascend', 'GPU'],
-                        help='device where the code will be implemented (default: Ascend)')
-    parser.add_argument('--ckpt_path', type=str, default="",
-                        help='if mode is test, must provide path where the trained ckpt file')
-    args = parser.parse_args()
     context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
 
     # define fusion network

model_zoo/official/cv/lenet/train.py

@@ -30,23 +30,21 @@ from mindspore.train import Model
 from mindspore.nn.metrics import Accuracy
 from mindspore.common import set_seed
 
+
+parser = argparse.ArgumentParser(description='MindSpore Lenet Example')
+parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
+                    help='device where the code will be implemented (default: Ascend)')
+parser.add_argument('--data_path', type=str, default="./Data",
+                    help='path where the dataset is saved')
+parser.add_argument('--ckpt_path', type=str, default="./ckpt", help='if is test, must provide\
+                    path where the trained ckpt file')
+args = parser.parse_args()
 set_seed(1)
 
+
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description='MindSpore Lenet Example')
-    parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
-                        help='device where the code will be implemented (default: Ascend)')
-    parser.add_argument('--data_path', type=str, default="./Data",
-                        help='path where the dataset is saved')
-    parser.add_argument('--ckpt_path', type=str, default="./ckpt", help='if is test, must provide\
-                        path where the trained ckpt file')
-
-    args = parser.parse_args()
-
-
     context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
-    ds_train = create_dataset(os.path.join(args.data_path, "train"),
+    ds_train = create_dataset(os.path.join(args.data_path, "train"), cfg.batch_size)
-                              cfg.batch_size)
     if ds_train.get_dataset_size() == 0:
         raise ValueError("Please check dataset size > 0 and batch_size <= dataset size")