From 73642ef3d3c2e496e6fa2d61da91c33b0a8a3e58 Mon Sep 17 00:00:00 2001 From: jinyaohui Date: Tue, 28 Apr 2020 15:12:08 +0800 Subject: [PATCH] clean pylint --- mindspore/model_zoo/vgg.py | 1 + mindspore/nn/optim/ftrl.py | 1 - mindspore/nn/optim/lamb.py | 2 +- .../mindspore_test_framework/apps/test_bert_parts.py | 12 ++++++------ tests/mindspore_test_framework/utils/block_util.py | 1 + 5 files changed, 9 insertions(+), 8 deletions(-) diff --git a/mindspore/model_zoo/vgg.py b/mindspore/model_zoo/vgg.py index f3532fab13e..66a73a2e501 100644 --- a/mindspore/model_zoo/vgg.py +++ b/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([ diff --git a/mindspore/nn/optim/ftrl.py b/mindspore/nn/optim/ftrl.py index e6f658acae1..ccc1b3f10be 100644 --- a/mindspore/nn/optim/ftrl.py +++ b/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 diff --git a/mindspore/nn/optim/lamb.py b/mindspore/nn/optim/lamb.py index 01ec9844530..cbeb6fa6744 100755 --- a/mindspore/nn/optim/lamb.py +++ b/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) diff --git a/tests/mindspore_test_framework/apps/test_bert_parts.py b/tests/mindspore_test_framework/apps/test_bert_parts.py index 944ea078420..dcc679b5288 100644 --- a/tests/mindspore_test_framework/apps/test_bert_parts.py +++ b/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)), diff --git a/tests/mindspore_test_framework/utils/block_util.py b/tests/mindspore_test_framework/utils/block_util.py index 75946c3559f..0d597285841 100644 --- a/tests/mindspore_test_framework/utils/block_util.py +++ b/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