for pylint 4th

example/resnet50_imagenet2012_THOR/cus_ops/cus_transpose02314.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-
+"""CusTranspose02314"""
 
 from mindspore.ops import prim_attr_register, PrimitiveWithInfer
 from mindspore.ops.composite import multitype_ops as C

example/resnet50_imagenet2012_THOR/model/thor_layer.py

@@ -37,13 +37,14 @@ C0 = 16
 
 
 def caculate_device_shape(matrix_dim, channel, is_A):
+    ll = (0)
     if is_A:
         if channel // C0 == 0:
             matrix_dim = (matrix_dim / channel) * C0
-        return (int(matrix_dim // C0), int(matrix_dim // C0), C0, C0), int(matrix_dim)
+        ll = (int(matrix_dim // C0), int(matrix_dim // C0), C0, C0), int(matrix_dim)
     else:
-        return (int(matrix_dim // C0), int(matrix_dim // C0), C0, C0), int(matrix_dim)
-
+        ll = (int(matrix_dim // C0), int(matrix_dim // C0), C0, C0), int(matrix_dim)
+    return ll
 
 class _Conv(Cell):
     r"""Applies a N-D convolution over an input signal composed of several input

example/resnet50_imagenet2012_THOR/train.py

@@ -125,7 +125,7 @@ if __name__ == '__main__':
         else:
             lr = Tensor(get_lr(global_step=0, lr_init=config.lr_init, lr_end=config.lr_end, lr_max=config.lr_max,
                                warmup_epochs=config.warmup_epochs, total_epochs=epoch_size, steps_per_epoch=step_size,
-                               lr_decay_mode='poly'))
+                               ))
         opt = THOR(filter(lambda x: x.requires_grad, net.get_parameters()), lr,
                    config.momentum, damping, config.frequency,
                    filter(lambda x: 'matrix_A' in x.name, net.get_parameters()),