!17449 clearn codechekc for master thor optimizer

From: @sl_wang
Reviewed-by: @kisnwang,@c_34
Signed-off-by: @c_34

mindspore/nn/layer/thor_layer.py

@@ -221,9 +221,9 @@ class _ConvThor(Cell):
         self.dilation = dilation
         self.group = Validator.check_positive_int(group)
         self.has_bias = has_bias
-        self._validate_kernel_size(kernel_size)
-        self._validate_stride(stride)
-        self._validate_dilation(dilation)
+        self.__validate_kernel_size(kernel_size)
+        self.__validate_stride(stride)
+        self.__validate_dilation(dilation)
         if in_channels % group != 0:
             raise ValueError("Attr 'in_channels' of 'Conv2DThor' Op must be divisible by "
                              "attr 'group' of 'Conv2DThor' Op.")
@@ -243,7 +243,7 @@ class _ConvThor(Cell):
                 logger.warning("Value of 'has_bias' is False, value of 'bias_init' will be ignored.")
             self.bias = None
 
-    def _validate_kernel_size(self, kernel_size):
+    def __validate_kernel_size(self, kernel_size):
         """validate kernel size."""
         if (not isinstance(kernel_size[0], int)) or (not isinstance(kernel_size[1], int)) or \
                 isinstance(kernel_size[0], bool) or isinstance(kernel_size[1], bool) or \
@@ -251,14 +251,14 @@ class _ConvThor(Cell):
             raise ValueError("Attr 'kernel_size' of 'Conv2D' Op passed "
                              + str(self.kernel_size) + ", should be a int or tuple and equal to or greater than 1.")
 
-    def _validate_stride(self, stride):
+    def __validate_stride(self, stride):
         """validate stride."""
         if (not isinstance(stride[0], int)) or (not isinstance(stride[1], int)) or \
                 isinstance(stride[0], bool) or isinstance(stride[1], bool) or stride[0] < 1 or stride[1] < 1:
             raise ValueError("Attr 'stride' of 'Conv2D' Op passed "
                              + str(self.stride) + ", should be a int or tuple and equal to or greater than 1.")
 
-    def _validate_dilation(self, dilation):
+    def __validate_dilation(self, dilation):
         """validate dilation."""
         if (not isinstance(dilation[0], int)) or (not isinstance(dilation[1], int)) or \
                 isinstance(dilation[0], bool) or isinstance(dilation[1], bool) or dilation[0] < 1 or dilation[1] < 1:

mindspore/nn/optim/thor.py

@@ -42,6 +42,7 @@ op_add = P.AddN()
 apply_decay = C.MultitypeFuncGraph("apply_decay")
 _momentum_opt = C.MultitypeFuncGraph("momentum_opt")
 
+
 @apply_decay.register("Number", "Bool", "Tensor", "Tensor")
 def _tensor_apply_decay(weight_decay, if_apply, weight, gradient):
     """Get grad with weight_decay."""
@@ -63,6 +64,7 @@ GRADIENT_CLIP_VALUE = 1.0
 clip_grad = C.MultitypeFuncGraph("clip_grad")
 hyper_map_op = C.HyperMap()
 
+
 @clip_grad.register("Number", "Number", "Tensor")
 def _clip_grad(clip_type, clip_value, grad):
     """
@@ -97,6 +99,8 @@ def clip_gradient(enable_clip_grad, gradients):
     return gradients
 
 C0 = 16
+
+
 def caculate_device_shape(matrix_dim, channel, is_a):
     ll = (0)
     if is_a:

mindspore/train/train_thor/convert_utils.py

@@ -25,12 +25,13 @@ class ConvertNetUntils():
     Convert net to thor layer net
     """
     def __init__(self):
-        self._convert_method_map = {nn.Dense: self._convert_dense,
-                                    nn.Embedding: self._convert_embedding,
-                                    nn.Conv2d: self._convert_conv2d}
+        self._convert_method_map = {nn.Dense: ConvertNetUntils._convert_dense,
+                                    nn.Embedding: ConvertNetUntils._convert_embedding,
+                                    nn.Conv2d: ConvertNetUntils._convert_conv2d}
 
 
-    def _convert_dense(self, subcell):
+    @staticmethod
+    def _convert_dense(subcell):
         """
         convert dense cell to second_order cell
         """
@@ -65,7 +66,8 @@ class ConvertNetUntils():
         return new_subcell
 
 
-    def _convert_embedding(self, subcell):
+    @staticmethod
+    def _convert_embedding(subcell):
         """
         convert embedding cell to second_order cell
         """
@@ -76,7 +78,8 @@ class ConvertNetUntils():
         return new_subcell
 
 
-    def _convert_conv2d(self, subcell):
+    @staticmethod
+    def _convert_conv2d(subcell):
         """
         convert conv2d cell to second_order cell
         """
@@ -140,7 +143,8 @@ class ConvertModelUtils():
     convert model to thor model utils
     """
 
-    def convert_to_thor_model(self, model, network, loss_fn=None, optimizer=None, metrics=None, amp_level="O0",
+    @staticmethod
+    def convert_to_thor_model(model, network, loss_fn=None, optimizer=None, metrics=None, amp_level="O0",
                               loss_scale_manager=None, keep_batchnorm_fp32=False):
 
         """