fix pylint warnings in model_zoo

model_zoo/official/cv/deeplabv3/src/md_dataset.py

@@ -13,10 +13,10 @@
 # limitations under the License.
 # ============================================================================
 """Dataset module."""
+import numpy as np
 from PIL import Image
 import mindspore.dataset as de
 import mindspore.dataset.transforms.vision.c_transforms as C
-import numpy as np
 
 from .ei_dataset import HwVocRawDataset
 from .utils import custom_transforms as tr

model_zoo/official/cv/faster_rcnn/src/network_define.py

@@ -110,8 +110,6 @@ class LossCallBack(Callback):
 
 class LossNet(nn.Cell):
     """FasterRcnn loss method"""
-    def __init__(self):
-        super(LossNet, self).__init__()
     def construct(self, x1, x2, x3, x4, x5, x6):
         return x1 + x2
 

model_zoo/official/cv/maskrcnn/src/network_define.py

@@ -117,8 +117,6 @@ class LossCallBack(Callback):
 
 class LossNet(nn.Cell):
     """MaskRcnn loss method"""
-    def __init__(self):
-        super(LossNet, self).__init__()
     def construct(self, x1, x2, x3, x4, x5, x6, x7):
         return x1 + x2
 

model_zoo/official/cv/ssd/src/dataset.py

@@ -20,8 +20,8 @@ from __future__ import division
 import os
 import json
 import xml.etree.ElementTree as et
-import cv2
 import numpy as np
+import cv2
 
 import mindspore.dataset as de
 import mindspore.dataset.transforms.vision.c_transforms as C

model_zoo/official/cv/ssd/src/init_params.py

@@ -14,8 +14,8 @@
 # ============================================================================
 """Parameters utils"""
 
-from mindspore.common.initializer import initializer, TruncatedNormal
 import numpy as np
+from mindspore.common.initializer import initializer, TruncatedNormal
 
 def init_net_param(network, initialize_mode='TruncatedNormal'):
     """Init the parameters in net."""

model_zoo/official/cv/warpctc/src/warpctc_for_train.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """Automatic differentiation with grad clip."""
+import numpy as np
 from mindspore.parallel._utils import (_get_device_num, _get_mirror_mean,
                                        _get_parallel_mode)
 from mindspore.train.parallel_utils import ParallelMode
@@ -24,7 +25,6 @@ from mindspore.nn.cell import Cell
 from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
 import mindspore.nn as nn
 from mindspore.common.tensor import Tensor
-import numpy as np
 
 compute_norm = C.MultitypeFuncGraph("compute_norm")
 

model_zoo/official/gnn/gat/src/gat.py

@@ -297,6 +297,9 @@ class AttentionHead(nn.Cell):
         self.activation = activation
 
     def construct(self, input_feature, bias_mat, training=True):
+        """
+        Attention Head for Graph Attention Networks.
+        """
         if training is True:
             input_feature = self.in_drop(input_feature)
 

model_zoo/official/gnn/gat/src/utils.py

@@ -38,7 +38,7 @@ class MaskedSoftMaxLoss(nn.Cell):
         self.num_params = len(self.params)
 
     def construct(self, logits):
-        # calc l2 loss
+        """calc l2 loss"""
         l2_loss = 0
         for i in range(self.num_params):
             l2_loss = l2_loss + self.l2_coeff * P.L2Loss()(self.params[i])
@@ -69,6 +69,7 @@ class MaskedAccuracy(nn.Cell):
         self.mask = Tensor(mask, dtype=mstype.float32)
 
     def construct(self, logits):
+        """Calculate accuracy"""
         logits = P.Reshape()(logits, (-1, self.num_class))
         labels = P.Reshape()(self.label, (-1, self.num_class))
         mask = P.Reshape()(self.mask, (-1,))

model_zoo/official/gnn/gcn/src/gcn.py

@@ -66,6 +66,9 @@ class GraphConvolution(nn.Cell):
         self.matmul = P.MatMul()
 
     def construct(self, adj, input_feature):
+        """
+        GCN graph convolution layer.
+        """
         dropout = input_feature
         if self.dropout_flag:
             dropout = self.dropout(dropout)

model_zoo/official/gnn/gcn/src/metrics.py

@@ -39,6 +39,7 @@ class Loss(nn.Cell):
         self.param = param
 
     def construct(self, preds):
+        """Calculate loss"""
         param = self.l2_loss(self.param)
         loss = self.weight_decay * param
         preds = self.cast(preds, mstype.float32)

model_zoo/official/nlp/bert/pretrain_eval.py

@@ -88,6 +88,7 @@ class BertPretrainEva(nn.Cell):
 
 
     def construct(self, input_ids, input_mask, token_type_id, masked_pos, masked_ids, masked_weights, nsp_label):
+        """Calculate prediction scores"""
         bs, _ = self.shape(input_ids)
         probs = self.bert(input_ids, input_mask, token_type_id, masked_pos)
         index = self.argmax(probs)

model_zoo/official/nlp/bert/src/bert_for_finetune.py

@@ -99,7 +99,7 @@ class BertFinetuneCell(nn.Cell):
                   token_type_id,
                   label_ids,
                   sens=None):
-
+        """Bert Finetune"""
 
         weights = self.weights
         init = False
@@ -195,6 +195,7 @@ class BertSquadCell(nn.Cell):
                   unique_id,
                   is_impossible,
                   sens=None):
+        """BertSquad"""
         weights = self.weights
         init = self.alloc_status()
         loss = self.network(input_ids,
@@ -313,6 +314,7 @@ class BertSquad(nn.Cell):
         self.squeeze = P.Squeeze(axis=-1)
 
     def construct(self, input_ids, input_mask, token_type_id, start_position, end_position, unique_id, is_impossible):
+        """interface for SQuAD finetuning task"""
         logits = self.bert(input_ids, input_mask, token_type_id)
         if self.is_training:
             unstacked_logits_0 = self.squeeze(logits[:, :, 0:1])

model_zoo/official/nlp/bert/src/bert_for_pre_training.py

@@ -103,6 +103,7 @@ class GetMaskedLMOutput(nn.Cell):
                   input_tensor,
                   output_weights,
                   positions):
+        """Get output log_probs"""
         flat_offsets = self.reshape(
             self.rng * self.seq_length_tensor, self.shape_flat_offsets)
         flat_position = self.reshape(positions + flat_offsets, self.last_idx)
@@ -248,6 +249,7 @@ class BertNetworkWithLoss(nn.Cell):
                   masked_lm_positions,
                   masked_lm_ids,
                   masked_lm_weights):
+        """Get pre-training loss"""
         prediction_scores, seq_relationship_score = \
             self.bert(input_ids, input_mask, token_type_id, masked_lm_positions)
         total_loss = self.loss(prediction_scores, seq_relationship_score,

model_zoo/official/nlp/bert/src/bert_model.py

@@ -137,6 +137,7 @@ class EmbeddingLookup(nn.Cell):
         self.shape = tuple(embedding_shape)
 
     def construct(self, input_ids):
+        """Get output and embeddings lookup table"""
         extended_ids = self.expand(input_ids, -1)
         flat_ids = self.reshape(extended_ids, self.shape_flat)
         if self.use_one_hot_embeddings:
@@ -205,6 +206,7 @@ class EmbeddingPostprocessor(nn.Cell):
                                                   name='full_position_embeddings')
 
     def construct(self, token_type_ids, word_embeddings):
+        """Postprocessors apply positional and token type embeddings to word embeddings."""
         output = word_embeddings
         if self.use_token_type:
             flat_ids = self.reshape(token_type_ids, self.shape_flat)
@@ -288,6 +290,7 @@ class RelaPosMatrixGenerator(nn.Cell):
         self.cast = P.Cast()
 
     def construct(self):
+        """Generates matrix of relative positions between inputs."""
         range_vec_row_out = self.cast(F.tuple_to_array(F.make_range(self._length)), mstype.int32)
         range_vec_col_out = self.range_mat(range_vec_row_out, (self._length, -1))
         tile_row_out = self.tile(range_vec_row_out, (self._length,))
@@ -342,9 +345,9 @@ class RelaPosEmbeddingsGenerator(nn.Cell):
         self.matmul = P.BatchMatMul()
 
     def construct(self):
+        """Generate embedding for each relative position of dimension depth."""
         relative_positions_matrix_out = self.relative_positions_matrix()
 
-        # Generate embedding for each relative position of dimension depth.
         if self.use_one_hot_embeddings:
             flat_relative_positions_matrix = self.reshape(relative_positions_matrix_out, (-1,))
             one_hot_relative_positions_matrix = self.one_hot(
@@ -495,7 +498,7 @@ class BertAttention(nn.Cell):
                                            use_one_hot_embeddings=use_one_hot_embeddings)
 
     def construct(self, from_tensor, to_tensor, attention_mask):
-        # reshape 2d/3d input tensors to 2d
+        """reshape 2d/3d input tensors to 2d"""
         from_tensor_2d = self.reshape(from_tensor, self.shape_from_2d)
         to_tensor_2d = self.reshape(to_tensor, self.shape_to_2d)
         query_out = self.query_layer(from_tensor_2d)
@@ -784,6 +787,7 @@ class BertTransformer(nn.Cell):
         self.out_shape = (batch_size, seq_length, hidden_size)
 
     def construct(self, input_tensor, attention_mask):
+        """Multi-layer bert transformer."""
         prev_output = self.reshape(input_tensor, self.shape)
 
         all_encoder_layers = ()
@@ -915,7 +919,7 @@ class BertModel(nn.Cell):
         self._create_attention_mask_from_input_mask = CreateAttentionMaskFromInputMask(config)
 
     def construct(self, input_ids, token_type_ids, input_mask):
-
+        """Bidirectional Encoder Representations from Transformers."""
         # embedding
         if not self.token_type_ids_from_dataset:
             token_type_ids = self.token_type_ids

model_zoo/official/nlp/bert/src/finetune_eval_model.py

@@ -110,6 +110,7 @@ class BertNERModel(nn.Cell):
         self.origin_shape = (config.batch_size, config.seq_length, self.num_labels)
 
     def construct(self, input_ids, input_mask, token_type_id):
+        """Return the final logits as the results of log_softmax."""
         sequence_output, _, _ = \
             self.bert(input_ids, token_type_id, input_mask)
         seq = self.dropout(sequence_output)

model_zoo/official/nlp/bert/src/fused_layer_norm.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """fused layernorm"""
+import numpy as np
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore.common.parameter import Parameter
@@ -21,7 +22,6 @@ from mindspore.ops.primitive import constexpr
 import mindspore.common.dtype as mstype
 from mindspore.nn.cell import Cell
 
-import numpy as np
 
 
 __all__ = ['FusedLayerNorm']
@@ -101,6 +101,7 @@ class FusedLayerNorm(Cell):
         self.use_batch_norm = use_batch_norm
 
     def construct(self, input_x):
+        """Applies Layer Normalization over a mini-batch of inputs"""
         if self.use_batch_norm and self.training:
             ones = P.Fill()(mstype.float32, F.shape(input_x)[:self.begin_norm_axis], 1.0)
             zeros = P.Fill()(mstype.float32, F.shape(input_x)[:self.begin_norm_axis], 0.0)

model_zoo/official/nlp/mass/src/transformer/components.py

@@ -52,6 +52,7 @@ class LayerNorm(nn.Cell):
         self.get_shape = P.Shape()
 
     def construct(self, input_tensor):
+        """layer norm"""
         shape = self.get_shape(input_tensor)
         batch_size = shape[0]
         max_len = shape[1]

model_zoo/official/nlp/tinybert/src/fused_layer_norm.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """fused layernorm"""
+import numpy as np
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore.common.parameter import Parameter
@@ -21,7 +22,6 @@ from mindspore.ops.primitive import constexpr
 import mindspore.common.dtype as mstype
 from mindspore.nn.cell import Cell
 
-import numpy as np
 
 
 __all__ = ['FusedLayerNorm']

model_zoo/official/nlp/transformer/src/beam_search.py

@@ -241,6 +241,7 @@ class BeamSearchDecoder(nn.Cell):
         return cur_input_ids, state_log_probs, state_seq, state_finished, state_length
 
     def construct(self, enc_states, enc_attention_mask):
+        """Get beam search result."""
         cur_input_ids = self.start_ids
         # beam search states
         state_log_probs = self.init_scores

model_zoo/official/nlp/transformer/src/transformer_for_train.py

@@ -55,7 +55,7 @@ class ClipGradients(nn.Cell):
                   grads,
                   clip_type,
                   clip_value):
-        # return grads
+        """return grads"""
         if clip_type != 0 and clip_type != 1:
             return grads
 

model_zoo/official/nlp/transformer/src/transformer_model.py

@@ -131,6 +131,7 @@ class EmbeddingLookup(nn.Cell):
         self.shape = P.Shape()
 
     def construct(self, input_ids):
+        """Get a embeddings lookup table with a fixed dictionary and size."""
         input_shape = self.shape(input_ids)
 
         flat_ids = self.reshape(input_ids, self.shape_flat)
@@ -200,6 +201,7 @@ class EmbeddingPostprocessor(nn.Cell):
         self.shape = P.Shape()
 
     def construct(self, word_embeddings):
+        """Postprocessors apply positional embeddings to word embeddings."""
         input_shape = self.shape(word_embeddings)
         input_len = input_shape[1]
 
@@ -377,7 +379,7 @@ class MultiheadAttention(nn.Cell):
         self.softmax_cast = P.Cast()
 
     def construct(self, from_tensor, to_tensor, attention_mask=None):
-        # reshape 2d/3d input tensors to 2d
+        """reshape 2d/3d input tensors to 2d"""
         from_tensor_2d = self.reshape(from_tensor, self.shape_from_2d)
         to_tensor_2d = self.reshape(to_tensor, self.shape_to_2d)
         query_out = self.query_layer(from_tensor_2d)
@@ -476,6 +478,7 @@ class SelfAttention(nn.Cell):
         self.reshape = P.Reshape()
         self.shape = (-1, hidden_size)
     def construct(self, input_tensor, memory_tensor, attention_mask):
+        """Apply self-attention."""
         input_tensor = self.reshape(input_tensor, self.shape)
         memory_tensor = self.reshape(memory_tensor, self.shape)
 
@@ -831,6 +834,7 @@ class CreateAttentionMaskFromInputMask(nn.Cell):
         self.batch_matmul = P.BatchMatMul()
 
     def construct(self, input_mask):
+        """Create attention mask according to input mask."""
         input_shape = self.shape(input_mask)
         shape_right = (input_shape[0], 1, input_shape[1])
         shape_left = input_shape + (1,)
@@ -876,6 +880,7 @@ class PredLogProbs(nn.Cell):
     def construct(self,
                   input_tensor,
                   output_weights):
+        """Get log probs."""
         input_tensor = self.reshape(input_tensor, self.shape_flat_sequence_tensor)
         input_tensor = self.cast(input_tensor, self.compute_type)
         output_weights = self.cast(output_weights, self.compute_type)
@@ -962,7 +967,10 @@ class TransformerDecoderStep(nn.Cell):
         self.cast_compute_type = CastWrapper(dst_type=compute_type)
 
     def construct(self, input_ids, enc_states, enc_attention_mask):
-        # input_ids: [batch_size * beam_width]
+        """
+        Multi-layer transformer decoder step.
+        input_ids: [batch_size * beam_width]
+        """
         # process embedding
         input_embedding, embedding_tables = self.tfm_embedding_lookup(input_ids)
         input_embedding = self.tfm_embedding_processor(input_embedding)
@@ -1122,6 +1130,7 @@ class TransformerModel(nn.Cell):
             self.encdec_mask = Tensor(ones, dtype=mstype.float32)
 
     def construct(self, source_ids, source_mask, target_ids=None, target_mask=None):
+        """Transformer with encoder and decoder."""
         # process source sentence
         src_word_embeddings, embedding_tables = self.tfm_embedding_lookup(source_ids)
         src_embedding_output = self.tfm_embedding_postprocessor_for_encoder(src_word_embeddings)

model_zoo/official/nlp/transformer/train.py

@@ -69,6 +69,7 @@ class LossCallBack(Callback):
             time_stamp_init = True
 
     def step_end(self, run_context):
+        """Monitor the loss in training."""
         global time_stamp_first
         time_stamp_current = get_ms_timestamp()
         cb_params = run_context.original_args()

model_zoo/official/recommend/deepfm/src/callback.py

@@ -68,6 +68,7 @@ class LossCallBack(Callback):
         self._per_print_times = per_print_times
 
     def step_end(self, run_context):
+        """Monitor the loss in training."""
         cb_params = run_context.original_args()
         loss = cb_params.net_outputs.asnumpy()
         cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1

model_zoo/official/recommend/deepfm/src/dataset.py

@@ -19,8 +19,8 @@ import os
 import math
 from enum import Enum
 
-import pandas as pd
 import numpy as np
+import pandas as pd
 import mindspore.dataset.engine as de
 import mindspore.common.dtype as mstype
 

model_zoo/official/recommend/deepfm/src/deepfm.py

@@ -147,6 +147,7 @@ class DenseLayer(nn.Cell):
         return act_func
 
     def construct(self, x):
+        """Dense Layer for Deep Layer of DeepFM Model."""
         x = self.act_func(x)
         if self.training:
             x = self.dropout(x)