diff --git a/mindspore/_checkparam.py b/mindspore/_checkparam.py
index fefaa2db6d..bd9a615ea8 100644
--- a/mindspore/_checkparam.py
+++ b/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(
diff --git a/mindspore/common/api.py b/mindspore/common/api.py
index 7f368faea9..6643993614 100644
--- a/mindspore/common/api.py
+++ b/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())
diff --git a/mindspore/nn/cell.py b/mindspore/nn/cell.py
index b6191f8833..e309d0f37d 100755
--- a/mindspore/nn/cell.py
+++ b/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.
-
- Inserts a subcell with a given name to the current cell.
+ Adds a child cell to the current cell with a given name.
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.
-
- This method must be overridden by all subclasses.
+ Defines the computation to be performed. This method must be overridden by all subclasses.
Note:
The inputs of the top cell only allow Tensor.
diff --git a/mindspore/train/model.py b/mindspore/train/model.py
index 0ef0a766a5..104b696664 100755
--- a/mindspore/train/model.py
+++ b/mindspore/train/model.py
@@ -477,7 +477,7 @@ 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
diff --git a/model_zoo/official/cv/lenet/export.py b/model_zoo/official/cv/lenet/export.py
index 9cdec74ee6..c3861ac2f3 100644
--- a/model_zoo/official/cv/lenet/export.py
+++ b/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
diff --git a/model_zoo/official/cv/lenet/train.py b/model_zoo/official/cv/lenet/train.py
index ea96a6da02..980b5e26b9 100644
--- a/model_zoo/official/cv/lenet/train.py
+++ b/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
-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()
+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__":
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
- ds_train = create_dataset(os.path.join(args.data_path, "train"),
- cfg.batch_size)
+ ds_train = create_dataset(os.path.join(args.data_path, "train"), cfg.batch_size)
if ds_train.get_dataset_size() == 0:
raise ValueError("Please check dataset size > 0 and batch_size <= dataset size")