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clean pylint

This commit is contained in:
jinyaohui 2020-04-28 15:12:08 +08:00
parent c1813671db
commit 73642ef3d3
5 changed files with 9 additions and 8 deletions
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1
mindspore/model_zoo/vgg.py
View File

@ -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([

1
mindspore/nn/optim/ftrl.py
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@ -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

2
mindspore/nn/optim/lamb.py
View File

@ -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)

12
tests/mindspore_test_framework/apps/test_bert_parts.py
View File

@ -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)),

1
tests/mindspore_test_framework/utils/block_util.py
View File

@ -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