clean pylint

mindspore/model_zoo/vgg.py

@@ -61,6 +61,7 @@ class Vgg(nn.Cell):
 
     def __init__(self, base, num_classes=1000, batch_norm=False, batch_size=1):
         super(Vgg, self).__init__()
+        _ = batch_size
         self.layers = _make_layer(base, batch_norm=batch_norm)
         self.flatten = nn.Flatten()
         self.classifier = nn.SequentialCell([

mindspore/nn/optim/ftrl.py

@@ -14,7 +14,6 @@
 # ============================================================================
 """FTRL"""
 from mindspore.ops import functional as F, composite as C, operations as P
-from mindspore.common.parameter import Parameter
 from mindspore.common import Tensor
 import mindspore.common.dtype as mstype
 from mindspore._checkparam import Validator as validator

mindspore/nn/optim/lamb.py

@@ -110,8 +110,8 @@ def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, global_step, para
 
 def _check_param_value(decay_steps, warmup_steps, start_learning_rate,
                        end_learning_rate, power, beta1, beta2, eps, weight_decay, prim_name):
-
     """Check the type of inputs."""
+    _ = warmup_steps
     validator.check_float_positive('start_learning_rate', start_learning_rate, prim_name)
     validator.check_float_legal_value('start_learning_rate', start_learning_rate, prim_name)
     validator.check_float_positive('end_learning_rate', end_learning_rate, prim_name)

tests/mindspore_test_framework/apps/test_bert_parts.py

@@ -173,8 +173,8 @@ test_sets = [
                                                   embedding_size=768,
                                                   embedding_shape=[1, 128, 768],
                                                   use_one_hot_embeddings=True,
-                                                  initializer_range=0.02), 1, 1), {
-                      'init_param_with': lambda shp: np.ones(shp).astype(np.float32)}),
+                                                  initializer_range=0.02), 1, 1),
+                  {'init_param_with': lambda shp: np.ones(shp).astype(np.float32)}),
         'desc_inputs': [input_ids],
         'desc_bprop': [[128]]}),
     ('EmbeddingLookup_multi_outputs_init_param', {
@@ -182,8 +182,8 @@ test_sets = [
                                   embedding_size=768,
                                   embedding_shape=[1, 128, 768],
                                   use_one_hot_embeddings=False,
-                                  initializer_range=0.02), {
-                      'init_param_with': lambda shp: np.ones(shp).astype(np.float32)}),
+                                  initializer_range=0.02),
+                  {'init_param_with': lambda shp: np.ones(shp).astype(np.float32)}),
         'desc_inputs': [input_ids],
         'desc_bprop': [[1, 128, 768], [128]]}),
     ('EmbeddingLookup_multi_outputs_grad_with_no_sens', {
@@ -191,8 +191,8 @@ test_sets = [
                                   embedding_size=768,
                                   embedding_shape=[1, 128, 768],
                                   use_one_hot_embeddings=False,
-                                  initializer_range=0.02), {
-                      'init_param_with': lambda shp: np.ones(shp).astype(np.float32)}),
+                                  initializer_range=0.02),
+                  {'init_param_with': lambda shp: np.ones(shp).astype(np.float32)}),
         'desc_inputs': [input_ids]}),
     ('GetMaskedLMOutput_grad_with_no_sens', {
         'block': GetMaskedLMOutput(BertConfig(batch_size=1)),

tests/mindspore_test_framework/utils/block_util.py

@@ -69,6 +69,7 @@ class IthOutputCell(nn.Cell):
         return predict
 
 def get_output_cell(network, num_input, output_index, training=True):
+    _ = num_input
     net = IthOutputCell(network, output_index)
     set_block_training(net, training)
     return net