for pylint 3rd

example/resnet50_imagenet2012_THOR/cus_ops/batch_matmul_impl.py

@@ -12,8 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-from mindspore.ops.op_info_register import op_info_register
 """batch_matmul_impl"""
+from mindspore.ops.op_info_register import op_info_register
 
 @op_info_register("""{
     "op_name": "CusBatchMatMul",

example/resnet50_imagenet2012_THOR/cus_ops/cholesky_trsm.py

@@ -12,8 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-from mindspore.ops.op_info_register import op_info_register
 """CusCholeskyTrsm"""
+from mindspore.ops.op_info_register import op_info_register
 
 @op_info_register("""{
     "op_name": "CusCholeskyTrsm",

example/resnet50_imagenet2012_THOR/cus_ops/matmul_cube_fracz_right_mul_impl.py

@@ -109,4 +109,5 @@ NoneType = type(None)
 }""")
 def CusMatMulCubeFraczRightMul(input_x1, input_x2, input_x3, bias=None, output_y={}, trans_a=False, trans_b=False,
                                kernel_name="matmulcube"):
+    """CusMatMulCubeFraczRightMul"""
     return

example/resnet50_imagenet2012_THOR/model/model_thor.py

@@ -530,7 +530,7 @@ class Model:
             valid_dataset (Dataset): Dataset to evaluate the model.
             list_callback (ListCallback): Executor of callback list. Default: None.
             cb_params (_InternalCallbackParam): Callback parameters. Default: None.
- 
+
         Returns:
             Dict, returns the loss value & metrics values for the model in test mode.
         """

example/resnet50_imagenet2012_THOR/model/resnet.py

@@ -126,7 +126,7 @@ def _bn_last(channel):
 def _fc(in_channel, out_channel, damping, loss_scale, frequency):
     weight_shape = (out_channel, in_channel)
     weight = Tensor(kaiming_uniform(weight_shape, a=math.sqrt(5))
-    return Dense_Thor(in_channel, out_channel, has_bias=False, weight_init=weight,
+    return Dense_Thor(in_channel, out_channel, has_bias=False, weight_init=weight, \
                       bias_init=0, damping=damping, loss_scale=loss_scale, frequency=frequency)
 
 

example/resnet50_imagenet2012_THOR/model/thor_layer.py

@@ -196,7 +196,7 @@ class Conv2d_Thor(_Conv):
             self.channels_slice_flag = True
 
         self.padA_flag = False
-        if (self.matrix_A_dim // self.diag_block_dim) * self.diag_block_dim != self.matrix_A_dim
+        if (self.matrix_A_dim // self.diag_block_dim) * self.diag_block_dim != self.matrix_A_dim \
             and self.matrix_A_dim > self.diag_block_dim:
             self.padA_flag = True
             pad_dim = self.diag_block_dim - self.matrix_A_dim % self.diag_block_dim

example/resnet50_imagenet2012_THOR/train.py

@@ -82,9 +82,9 @@ def get_second_order_damping(global_step, damping_init, decay_rate, total_epochs
 
     current_step = global_step
     damping_each_step = np.array(damping_each_step).astype(np.float32)
-    damping = damping_each_step[current_step:]
-    print("damping_is=========", damping)
-    return damping
+    damping_now = damping_each_step[current_step:]
+    print("damping_is=========", damping_now)
+    return damping_now
 
 
 if __name__ == '__main__':