delete redundant codes

model_zoo/official/cv/alexnet/README.md

@@ -50,7 +50,7 @@ Dataset used: [CIFAR-10](<http://www.cs.toronto.edu/~kriz/cifar.html>)
 - Hardware（Ascend/GPU）
   - Prepare hardware environment with Ascend or GPU processor. 
 - Framework
-  - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
+  - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
   - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html) 
   - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)

model_zoo/official/cv/deeplabv3/src/data/data_generator.py

@@ -13,8 +13,8 @@
 # limitations under the License.
 # ============================================================================
 
-import cv2
 import numpy as np
+import cv2
 import mindspore.dataset as de
 cv2.setNumThreads(0)
 

model_zoo/official/cv/faster_rcnn/src/FasterRcnn/bbox_assign_sample_stage2.py

@@ -114,7 +114,7 @@ class BboxAssignSampleForRcnn(nn.Cell):
         bboxes = self.select(self.cast(self.tile(self.reshape(self.cast(valid_mask, mstype.int32), \
                              (self.num_bboxes, 1)), (1, 4)), mstype.bool_), \
                              bboxes, self.check_anchor_two)
-        # 1 dim = gt, 2 dim = bbox
+
         overlaps = self.iou(bboxes, gt_bboxes_i)
 
         max_overlaps_w_gt_index, max_overlaps_w_gt = self.max_gt(overlaps)

model_zoo/official/cv/googlenet/train.py

@@ -166,15 +166,12 @@ if __name__ == '__main__':
                 parameter_name = x.name
                 if parameter_name.endswith('.bias'):
                     # all bias not using weight decay
-                    # print('no decay:{}'.format(parameter_name))
                     no_decay_params.append(x)
                 elif parameter_name.endswith('.gamma'):
                     # bn weight bias not using weight decay, be carefully for now x not include BN
-                    # print('no decay:{}'.format(parameter_name))
                     no_decay_params.append(x)
                 elif parameter_name.endswith('.beta'):
                     # bn weight bias not using weight decay, be carefully for now x not include BN
-                    # print('no decay:{}'.format(parameter_name))
                     no_decay_params.append(x)
                 else:
                     decay_params.append(x)

model_zoo/official/cv/inceptionv3/README.md

@@ -54,7 +54,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 - Hardware（Ascend/GPU）
   - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. 
 - Framework
-  - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
+  - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
   - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html) 
   - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)

model_zoo/official/cv/lenet/README.md

@@ -56,7 +56,7 @@ Dataset used: [MNIST](<http://yann.lecun.com/exdb/mnist/>)
 - Hardware（Ascend/GPU/CPU）
   - Prepare hardware environment with Ascend, GPU, or CPU processor. 
 - Framework
-  - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
+  - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
   - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html) 
   - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)

model_zoo/official/cv/lenet/src/lenet.py

@@ -22,8 +22,8 @@ class LeNet5(nn.Cell):
     Lenet network
 
     Args:
-        num_class (int): Num classes. Default: 10.
-        num_channel (int): Num channels. Default: 1.
+        num_class (int): Number of classes. Default: 10.
+        num_channel (int): Number of channels. Default: 1.
 
     Returns:
         Tensor, output tensor

model_zoo/official/cv/lenet_quant/src/lenet_fusion.py

@@ -21,7 +21,7 @@ class LeNet5(nn.Cell):
     Lenet network
 
     Args:
-        num_class (int): Num classes. Default: 10.
+        num_class (int): Number of classes. Default: 10.
 
     Returns:
         Tensor, output tensor

model_zoo/official/cv/lenet_quant/src/lenet_quant.py

@@ -22,7 +22,7 @@ class LeNet5(nn.Cell):
     Lenet network
 
     Args:
-        num_class (int): Num classes. Default: 10.
+        num_class (int): Number of classes. Default: 10.
 
     Returns:
         Tensor, output tensor

model_zoo/official/cv/maskrcnn/src/maskrcnn/bbox_assign_sample_stage2.py

@@ -118,7 +118,7 @@ class BboxAssignSampleForRcnn(nn.Cell):
         bboxes = self.select(self.cast(self.tile(self.reshape(self.cast(valid_mask, mstype.int32), \
                              (self.num_bboxes, 1)), (1, 4)), mstype.bool_), \
                              bboxes, self.check_anchor_two)
-        # 1 dim = gt, 2 dim = bbox
+
         overlaps = self.iou(bboxes, gt_bboxes_i)
 
         max_overlaps_w_gt_index, max_overlaps_w_gt = self.max_gt(overlaps)

model_zoo/official/cv/mobilenetv2/README.md

@@ -51,7 +51,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 - Hardware（Ascend/GPU/CPU）
   - Prepare hardware environment with Ascend、GPU or CPU processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
 - Framework
-  - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
+  - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
   - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
   - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)

model_zoo/official/cv/mobilenetv2/src/mobilenetV2.py

@@ -145,7 +145,7 @@ class MobileNetV2Backbone(nn.Cell):
     MobileNetV2 architecture.
 
     Args:
-        class_num (Cell): number of classes.
+        class_num (int): number of classes.
         width_mult (int): Channels multiplier for round to 8/16 and others. Default is 1.
         has_dropout (bool): Is dropout used. Default is false
         inverted_residual_setting (list): Inverted residual settings. Default is None
@@ -233,7 +233,7 @@ class MobileNetV2Head(nn.Cell):
     MobileNetV2 architecture.
 
     Args:
-        class_num (Cell): number of classes.
+        class_num (int): Number of classes. Default is 1000.
         has_dropout (bool): Is dropout used. Default is false
     Returns:
         Tensor, output tensor.
@@ -284,11 +284,13 @@ class MobileNetV2(nn.Cell):
     MobileNetV2 architecture.
 
     Args:
-        class_num (Cell): number of classes.
+        class_num (int): number of classes.
         width_mult (int): Channels multiplier for round to 8/16 and others. Default is 1.
         has_dropout (bool): Is dropout used. Default is false
         inverted_residual_setting (list): Inverted residual settings. Default is None
         round_nearest (list): Channel round to . Default is 8
+        backbone(nn.Cell): Backbone of MobileNetV2.
+        head(nn.Cell): Classification head of MobileNetV2.
     Returns:
         Tensor, output tensor.
 

model_zoo/official/cv/mobilenetv2/src/models.py

@@ -29,8 +29,8 @@ class CrossEntropyWithLabelSmooth(_Loss):
     CrossEntropyWith LabelSmooth.
 
     Args:
-        smooth_factor (float): smooth factor, default=0.
-        num_classes (int): num classes
+        smooth_factor (float): smooth factor. Default is 0.
+        num_classes (int): number of classes. Default is 1000.
 
     Returns:
         None.

model_zoo/official/cv/mobilenetv2_quant/src/utils.py

@@ -83,8 +83,8 @@ class CrossEntropyWithLabelSmooth(_Loss):
     CrossEntropyWith LabelSmooth.
 
     Args:
-        smooth_factor (float): smooth factor, default=0.
-        num_classes (int): num classes
+        smooth_factor (float): smooth factor for label smooth. Default is 0.
+        num_classes (int): number of classes. Default is 1000.
 
     Returns:
         None.

model_zoo/official/cv/mobilenetv3/Readme.md

@@ -45,7 +45,7 @@ Dataset used: [imagenet](http://www.image-net.org/)
 - Hardware（GPU）
   - Prepare hardware environment with GPU processor.
 - Framework
-  - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
+  - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
   - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html) 
   - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)

model_zoo/official/cv/mobilenetv3/src/mobilenetV3.py

@@ -83,7 +83,7 @@ class SE(nn.Cell):
     SE warpper definition.
 
     Args:
-        num_out (int): Output channel.
+        num_out (int): Numbers of output channels.
         ratio (int): middle output ratio.
 
     Returns:
@@ -301,7 +301,7 @@ class MobileNetV3(nn.Cell):
     def _make_layer(self, kernel_size, exp_ch, out_channel, use_se, act_func, stride=1):
         mid_planes = exp_ch
         out_planes = out_channel
-        #num_in, num_mid, num_out, kernel_size, stride=1, act_type='relu', use_se=False):
+
         layer = ResUnit(self.inplanes, mid_planes, out_planes,
                         kernel_size, stride=stride, act_type=act_func, use_se=use_se)
         self.inplanes = out_planes

model_zoo/official/cv/mobilenetv3/train.py

@@ -68,8 +68,8 @@ class CrossEntropyWithLabelSmooth(_Loss):
     CrossEntropyWith LabelSmooth.
 
     Args:
-        smooth_factor (float): smooth factor, default=0.
-        num_classes (int): num classes
+        smooth_factor (float): smooth factor for label smooth. Default is 0.
+        num_classes (int): number of classes. Default is 1000.
 
     Returns:
         None.

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

@@ -47,7 +47,6 @@ def create_dataset(dataset_path, config, do_train, repeat_num=1):
             C.RandomCropDecodeResize(config.image_size),
             C.RandomHorizontalFlip(prob=0.5),
             C.RandomColorAdjust(brightness=0.4, saturation=0.5)  # fast mode
-            # C.RandomColorAdjust(brightness=0.4, contrast=0.5, saturation=0.5, hue=0.2)
         ]
     else:
         trans = [

model_zoo/official/cv/resnet_thor/src/dataset_helper.py

@@ -151,7 +151,7 @@ class _DatasetIter:
 
 
 class _DatasetIterMSLoopSink(_DatasetIter):
-    """Iter for context (device_target=Ascend)"""
+    """Iter for context when device_target is Ascend"""
     def __init__(self, dataset, sink_size, epoch_num, iter_first_order):
         super().__init__(dataset, sink_size, epoch_num)
         sink_count = 1
@@ -179,7 +179,7 @@ class _DatasetIterMSLoopSink(_DatasetIter):
 
 
 class _DatasetIterMS(_DatasetIter):
-    """Iter for MS(enable_loop_sink=False)."""
+    """Iter for MS when enable_loop_sink is False."""
     def __init__(self, dataset, sink_size, epoch_num):
         super().__init__(dataset, sink_size, epoch_num)
         if sink_size > 0:

model_zoo/official/cv/resnet_thor/src/resnet_thor.py

@@ -283,7 +283,7 @@ class ResNet(nn.Cell):
                               frequency=frequency, batch_size=batch_size)
         self.bn1 = _bn(64)
         self.relu = P.ReLU()
-        # self.maxpool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
+
         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
 
         self.layer1 = self._make_layer(block,

model_zoo/official/cv/resnext50/README.md

@@ -56,7 +56,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 - Hardware（Ascend/GPU）
   - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. 
 - Framework
-  - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
+  - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
   - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html) 
   - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)

model_zoo/official/cv/resnext50/src/utils/optimizers__init__.py

@@ -23,15 +23,12 @@ def get_param_groups(network):
         parameter_name = x.name
         if parameter_name.endswith('.bias'):
             # all bias not using weight decay
-            # print('no decay:{}'.format(parameter_name))
             no_decay_params.append(x)
         elif parameter_name.endswith('.gamma'):
             # bn weight bias not using weight decay, be carefully for now x not include BN
-            # print('no decay:{}'.format(parameter_name))
             no_decay_params.append(x)
         elif parameter_name.endswith('.beta'):
             # bn weight bias not using weight decay, be carefully for now x not include BN
-            # print('no decay:{}'.format(parameter_name))
             no_decay_params.append(x)
         else:
             decay_params.append(x)

model_zoo/official/cv/shufflenetv2/Readme.md

@@ -40,7 +40,7 @@ Dataset used: [imagenet](http://www.image-net.org/)
 - Hardware(GPU)
   - Prepare hardware environment with GPU processor.
 - Framework
-  - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
+  - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below:
   - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
   - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)

model_zoo/official/cv/shufflenetv2/src/dataset.py

@@ -66,7 +66,6 @@ def create_dataset(dataset_path, do_train, rank, group_size, repeat_num=1):
     trans += [
         toBGR(),
         C.Rescale(1.0 / 255.0, 0.0),
-        # C.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
         C.HWC2CHW(),
         C2.TypeCast(mstype.float32)
     ]

model_zoo/official/cv/shufflenetv2/src/shufflenetv2.py

@@ -79,7 +79,6 @@ class ShuffleV2Block(nn.Cell):
 
     def channel_shuffle(self, x):
         batchsize, num_channels, height, width = P.Shape()(x)
-        ##assert (num_channels % 4 == 0)
         x = P.Reshape()(x, (batchsize * num_channels // 2, 2, height * width,))
         x = P.Transpose()(x, (1, 0, 2,))
         x = P.Reshape()(x, (2, -1, num_channels // 2, height, width,))

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

@@ -162,7 +162,6 @@ def create_voc_label(is_training):
     voc_dir = config.voc_dir
     cls_map = {name: i for i, name in enumerate(config.coco_classes)}
     sub_dir = 'train' if is_training else 'eval'
-    # sub_dir = 'train'
     voc_dir = os.path.join(voc_dir, sub_dir)
     if not os.path.isdir(voc_dir):
         raise ValueError(f'Cannot find {sub_dir} dataset path.')

model_zoo/official/cv/vgg16/train.py

@@ -14,7 +14,6 @@
 # ============================================================================
 """
 #################train vgg16 example on cifar10########################
-python train.py --data_path=$DATA_HOME --device_id=$DEVICE_ID
 """
 import argparse
 import datetime

model_zoo/official/cv/yolov3_darknet53/src/transforms.py

@@ -146,7 +146,7 @@ def _preprocess_true_boxes(true_boxes, anchors, in_shape, num_classes,
     # input_shape is [h, w]
     true_boxes[..., 0:2] = boxes_xy / input_shape[::-1]
     true_boxes[..., 2:4] = boxes_wh / input_shape[::-1]
-    # true_boxes = [xywh]
+    # true_boxes [x, y, w, h]
 
     grid_shapes = [input_shape // 32, input_shape // 16, input_shape // 8]
     # grid_shape [h, w]

model_zoo/official/cv/yolov3_darknet53_quant/src/transforms.py

@@ -153,7 +153,7 @@ def _preprocess_true_boxes(true_boxes, anchors, in_shape, num_classes,
     # input_shape is [h, w]
     true_boxes[..., 0:2] = boxes_xy / input_shape[::-1]
     true_boxes[..., 2:4] = boxes_wh / input_shape[::-1]
-    # true_boxes = [xywh]
+    # true_boxes [x, y, w, h]
 
     grid_shapes = [input_shape // 32, input_shape // 16, input_shape // 8]
     # grid_shape [h, w]

model_zoo/official/cv/yolov3_resnet18/src/dataset.py

@@ -44,7 +44,6 @@ def preprocess_fn(image, box, is_training):
         num_layers = anchors.shape[0] // 3
         anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
         true_boxes = np.array(true_boxes, dtype='float32')
-        # input_shape = np.array([in_shape, in_shape], dtype='int32')
         input_shape = np.array(in_shape, dtype='int32')
         boxes_xy = (true_boxes[..., 0:2] + true_boxes[..., 2:4]) // 2.
         boxes_wh = true_boxes[..., 2:4] - true_boxes[..., 0:2]

model_zoo/official/nlp/bert/pretrain_eval.py

@@ -105,7 +105,7 @@ class BertPretrainEva(nn.Cell):
 
 def get_enwiki_512_dataset(batch_size=1, repeat_count=1, distribute_file=''):
     '''
-    Get enwiki seq_length=512 dataset
+    Get enwiki dataset when seq_length is 512.
     '''
     ds = de.TFRecordDataset([cfg.data_file], cfg.schema_file, columns_list=["input_ids", "input_mask", "segment_ids",
                                                                             "masked_lm_positions", "masked_lm_ids",

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

@@ -490,7 +490,7 @@ class BertAttention(nn.Cell):
 
         # use_relative_position, supplementary logic
         if self.use_relative_positions:
-            # 'relations_keys' = [F|T, F|T, H]
+            # relations_keys is [F|T, F|T, H]
             relations_keys = self._generate_relative_positions_embeddings()
             relations_keys = self.cast_compute_type(relations_keys)
             # query_layer_t is [F, B, N, H]
@@ -533,7 +533,7 @@ class BertAttention(nn.Cell):
 
         # use_relative_position, supplementary logic
         if self.use_relative_positions:
-            # 'relations_values' = [F|T, F|T, H]
+            # relations_values is [F|T, F|T, H]
             relations_values = self._generate_relative_positions_embeddings()
             relations_values = self.cast_compute_type(relations_values)
             # attention_probs_t is [F, B, N, T]

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

@@ -165,7 +165,6 @@ def LoadNewestCkpt(load_finetune_checkpoint_dir, steps_per_epoch, epoch_num, pre
         name_ext = os.path.splitext(filename)
         if name_ext[-1] != ".ckpt":
             continue
-        #steps_per_epoch = ds.get_dataset_size()
         if filename.find(prefix) == 0 and not filename[pre_len].isalpha():
             index = filename[pre_len:].find("-")
             if index == 0 and max_num == 0:

model_zoo/official/nlp/bert_thor/README.md

@@ -49,7 +49,7 @@ The classical first-order optimization algorithm, such as SGD, has a small amoun
 - Hardware（Ascend/GPU）
   - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. 
 - Framework
-  - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
+  - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
   - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html) 
   - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)

model_zoo/official/nlp/bert_thor/pretrain_eval.py

@@ -110,7 +110,7 @@ class BertPretrainEva(nn.Cell):
 
 def get_enwiki_512_dataset(batch_size=1, repeat_count=1, distribute_file=''):
     '''
-    Get enwiki seq_length=512 dataset
+    Get enwiki dataset when seq_length is 512.
     '''
     ds = de.TFRecordDataset([cfg.data_file], cfg.schema_file, columns_list=["input_ids", "input_mask", "segment_ids",
                                                                             "masked_lm_positions", "masked_lm_ids",

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

@@ -566,7 +566,7 @@ class BertAttention(nn.Cell):
 
         # use_relative_position, supplementary logic
         if self.use_relative_positions:
-            # 'relations_keys' = [F|T, F|T, H]
+            # relations_keys is [F|T, F|T, H]
             relations_keys = self._generate_relative_positions_embeddings()
             relations_keys = self.cast_compute_type(relations_keys)
             # query_layer_t is [F, B, N, H]
@@ -609,7 +609,7 @@ class BertAttention(nn.Cell):
 
         # use_relative_position, supplementary logic
         if self.use_relative_positions:
-            # 'relations_values' = [F|T, F|T, H]
+            # relations_values is [F|T, F|T, H]
             relations_values = self._generate_relative_positions_embeddings()
             relations_values = self.cast_compute_type(relations_values)
             # attention_probs_t is [F, B, N, T]

model_zoo/official/nlp/bert_thor/src/dataset_helper.py

@@ -155,7 +155,7 @@ class _DatasetIter:
 
 
 class _DatasetIterMSLoopSink(_DatasetIter):
-    """Iter for context (device_target=Ascend)"""
+    """Iter for context, the device_target is Ascend."""
 
     def __init__(self, dataset, sink_size, epoch_num, iter_first_order):
         super().__init__(dataset, sink_size, epoch_num)

model_zoo/official/nlp/bert_thor/src/thor_for_bert.py

@@ -198,7 +198,7 @@ class THOR(Optimizer):
             g = F.depend(g, fake_G)
             new_grads = new_grads + (g, pooler_bias)
 
-            # for cls1 fc layer: mlm
+            # cls1 fully connect layer for masked language model(mlm)
             mlm_fc_idx = encoder_layers_num * self.num_hidden_layers + 8
             matrix_idx = self.num_hidden_layers * 6 + 4
             g = gradients[mlm_fc_idx]
@@ -327,7 +327,7 @@ class THOR(Optimizer):
             g = self.cast(g, mstype.float32)
             new_grads = new_grads + (g, pooler_bias)
 
-            # for cls1 fc layer: mlm
+            # cls1 fully connect layer for masked language model(mlm)
             mlm_fc_idx = encoder_layers_num * self.num_hidden_layers + 8
             matrix_idx = self.num_hidden_layers * 6 + 4
             g = gradients[mlm_fc_idx]

model_zoo/official/nlp/bert_thor/src/thor_for_bert_arg.py

@@ -203,7 +203,7 @@ class THOR(Optimizer):
             g = F.depend(g, fake_G)
             new_grads = new_grads + (g, pooler_bias)
 
-            # for cls1 fc layer: mlm
+            # cls1 fully connect layer for masked language model(mlm)
             mlm_fc_idx = encoder_layers_num * self.num_hidden_layers + 8
             matrix_idx = self.num_hidden_layers * 6 + 4
             g = gradients[mlm_fc_idx]
@@ -333,7 +333,7 @@ class THOR(Optimizer):
             g = self.cast(g, mstype.float32)
             new_grads = new_grads + (g, pooler_bias)
 
-            # for cls1 fc layer: mlm
+            # cls1 fully connect layer for masked language model(mlm)
             mlm_fc_idx = encoder_layers_num * self.num_hidden_layers + 8
             matrix_idx = self.num_hidden_layers * 6 + 4
             g = gradients[mlm_fc_idx]

model_zoo/official/nlp/bert_thor/src/utils.py

@@ -129,7 +129,6 @@ def LoadNewestCkpt(load_finetune_checkpoint_dir, steps_per_epoch, epoch_num, pre
         name_ext = os.path.splitext(filename)
         if name_ext[-1] != ".ckpt":
             continue
-        # steps_per_epoch = ds.get_dataset_size()
         if filename.find(prefix) == 0 and not filename[pre_len].isalpha():
             index = filename[pre_len:].find("-")
             if index == 0 and max_num == 0:

model_zoo/official/nlp/lstm/eval.py

@@ -14,7 +14,6 @@
 # ============================================================================
 """
 #################train lstm example on aclImdb########################
-python eval.py --ckpt_path=./lstm-20-390.ckpt
 """
 import argparse
 import os

model_zoo/official/nlp/lstm/src/imdb.py

@@ -103,7 +103,7 @@ class ImdbParser():
         vocab = set(chain(*tokenized_features))
         self.__vacab[seg] = vocab
 
-        # word_to_idx: {'hello': 1, 'world':111, ... '<unk>': 0}
+        # word_to_idx looks like {'hello': 1, 'world':111, ... '<unk>': 0}
         word_to_idx = {word: i + 1 for i, word in enumerate(vocab)}
         word_to_idx['<unk>'] = 0
         self.__word2idx[seg] = word_to_idx
@@ -147,7 +147,7 @@ class ImdbParser():
 
     def get_datas(self, seg):
         """
-        return features, labels, and weight
+        get features, labels, and weight by gensim.
         """
         features = np.array(self.__features[seg]).astype(np.int32)
         labels = np.array(self.__labels[seg]).astype(np.int32)

model_zoo/official/nlp/lstm/train.py

@@ -14,7 +14,6 @@
 # ============================================================================
 """
 #################train lstm example on aclImdb########################
-python train.py --preprocess=true --aclimdb_path=your_imdb_path --glove_path=your_glove_path
 """
 import argparse
 import os

model_zoo/official/nlp/mass/README.md

@@ -472,7 +472,7 @@ More detail about LR scheduler could be found in `src/utils/lr_scheduler.py`.
 - Hardware（Ascend/GPU）
   - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. 
 - Framework
-  - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
+  - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
   - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html) 
   - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)

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

@@ -94,7 +94,6 @@ class TileBeam(nn.Cell):
         # add an dim
         input_tensor = self.expand(input_tensor, 1)
         # get tile shape: [1, beam, ...]
-        # shape = self.shape(input_tensor)
         tile_shape = (1,) + (self.beam_width,)
         for _ in range(len(shape) - 1):
             tile_shape = tile_shape + (1,)
@@ -349,7 +348,7 @@ class BeamSearchDecoder(nn.Cell):
 
         # add length penalty scores
         penalty_len = self.length_penalty(state_length)
-        # return penalty_len
+        # get penalty length
         log_probs = self.real_div(state_log_probs, penalty_len)
 
         # sort according to scores

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

@@ -383,7 +383,6 @@ class BertNetworkWithLoss_td(nn.Cell):
         if is_predistill:
             new_param_dict = {}
             for key, value in param_dict.items():
-                # new_key = re.sub('tinybert_', 'bert_', key)
                 new_key = re.sub('tinybert_', 'bert_', 'bert.' + key)
                 new_param_dict[new_key] = value
             load_param_into_net(self.bert, new_param_dict)
@@ -391,7 +390,6 @@ class BertNetworkWithLoss_td(nn.Cell):
             new_param_dict = {}
             for key, value in param_dict.items():
                 new_key = re.sub('tinybert_', 'bert_', key)
-                # new_key = re.sub('tinybert_', 'bert_', 'bert.'+ key)
                 new_param_dict[new_key] = value
             load_param_into_net(self.bert, new_param_dict)
         self.cast = P.Cast()

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

@@ -502,7 +502,7 @@ class BertAttention(nn.Cell):
         attention_scores = self.matmul_trans_b(query_layer, key_layer)
         # use_relative_position, supplementary logic
         if self.use_relative_positions:
-            # 'relations_keys' = [F|T, F|T, H]
+            # relations_keys is [F|T, F|T, H]
             relations_keys = self._generate_relative_positions_embeddings()
             relations_keys = self.cast_compute_type(relations_keys)
             # query_layer_t is [F, B, N, H]
@@ -539,7 +539,7 @@ class BertAttention(nn.Cell):
         context_layer = self.matmul(attention_probs, value_layer)
         # use_relative_position, supplementary logic
         if self.use_relative_positions:
-            # 'relations_values' = [F|T, F|T, H]
+            # relations_values is [F|T, F|T, H]
             relations_values = self._generate_relative_positions_embeddings()
             relations_values = self.cast_compute_type(relations_values)
             # attention_probs_t is [F, B, N, T]

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

@@ -258,7 +258,7 @@ class BeamSearchDecoder(nn.Cell):
 
         # add length penalty scores
         penalty_len = self.length_penalty(state_length)
-        # return penalty_len
+        # get penalty length
         log_probs = self.real_div(state_log_probs, penalty_len)
 
         # sort according to 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"""
+        """Defines the gradients clip."""
         if clip_type != 0 and clip_type != 1:
             return grads
 

model_zoo/official/recommend/wide_and_deep_multitable/src/wide_and_deep.py

@@ -156,6 +156,7 @@ class WideDeepModel(nn.Cell):
         emb64_multi_size = 20900
         indicator_size = 16
         deep_dim_list = [1024, 1024, 1024, 1024, 1024]
+
         wide_reg_coef = [0.0, 0.0]
         deep_reg_coef = [0.0, 0.0]
         wide_lr = 0.2

model_zoo/research/cv/ghostnet/Readme.md

@@ -43,7 +43,7 @@ Dataset used: [Oxford-IIIT Pet](https://www.robots.ox.ac.uk/~vgg/data/pets/)
 - Hardware（Ascend/GPU)
   - Prepare hardware environment with Ascend or GPU. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
 - Framework
-  - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
+  - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
   - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
   - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)

model_zoo/research/cv/ghostnet/src/ghostnet600.py

@@ -335,7 +335,6 @@ class GhostNet(nn.Cell):
 
         self.blocks = []
         for layer_cfg in self.cfgs:
-            #print (layer_cfg)
             self.blocks.append(self._make_layer(kernel_size=layer_cfg[0],
                                                 exp_ch=_make_divisible(
                                                     self.inplanes * layer_cfg[3]),

model_zoo/research/cv/ghostnet_quant/Readme.md

@@ -48,7 +48,7 @@ Dataset used: [Oxford-IIIT Pet](https://www.robots.ox.ac.uk/~vgg/data/pets/)
 - Hardware（Ascend/GPU）
   - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
 - Framework
-  - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
+  - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
   - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
   - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)

model_zoo/research/cv/ghostnet_quant/src/ghostnet.py

@@ -105,7 +105,7 @@ class SE(nn.Cell):
     SE warpper definition.
 
     Args:
-        num_out (int): Output channel.
+        num_out (int): output channel.
         ratio (int): middle output ratio.
 
     Returns:

model_zoo/research/cv/resnet50_adv_pruning/Readme.md

@@ -36,7 +36,7 @@ Dataset used: [Oxford-IIIT Pet](https://www.robots.ox.ac.uk/~vgg/data/pets/)
 - Hardware（Ascend/GPU）
   - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
 - Framework
-  - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
+  - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
   - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
   - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)

model_zoo/research/cv/ssd_ghostnet/README.md

@@ -23,7 +23,7 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
 - Hardware（Ascend/GPU）
   - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
 - Framework
-  - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
+  - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
   - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
   - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)

model_zoo/research/cv/ssd_ghostnet/src/ssd_ghostnet.py

@@ -84,7 +84,6 @@ class ConvBNReLU(nn.Cell):
 
     def construct(self, x):
         output = self.features(x)
-        # print(output.shape)
         return output
 
 
@@ -267,8 +266,6 @@ class GhostModule(nn.Cell):
     def construct(self, x):
         x1 = self.primary_conv(x)
         x2 = self.cheap_operation(x1)
-        # print(x1.shape)
-        # print(x2.shape)
         return self.concat((x1, x2))
 
 
@@ -342,7 +339,6 @@ class GhostBottleneck(nn.Cell):
             out = self.add(shortcut, out)
         if self.last_relu:
             out = self.relu(out)
-        # print(out.shape)
         return out
 
     def _get_pad(self, kernel_size):
@@ -410,7 +406,6 @@ class InvertedResidual(nn.Cell):
             x = self.add(identity, x)
         if self.last_relu:
             x = self.relu(x)
-        # print(x.shape)
         return x
 
 
@@ -675,7 +670,6 @@ class SSDWithGhostNet(nn.Cell):
     def __init__(self, model_cfgs, multiplier=1., round_nearest=8):
         super(SSDWithGhostNet, self).__init__()
         self.cfgs = model_cfgs['cfg']
-        # self.inplanes = 16   ## for "1x"
         self.inplanes = 20  # for "1.3x"
         first_conv_in_channel = 3
         first_conv_out_channel = _make_divisible(multiplier * self.inplanes)
@@ -686,7 +680,6 @@ class SSDWithGhostNet(nn.Cell):
 
         layer_index = 0
         for layer_cfg in self.cfgs:
-            # print(layer_cfg)
             if layer_index == 11:
                 hidden_dim = int(round(self.inplanes * 6))
                 self.expand_layer_conv_11 = ConvBNReLU(
@@ -711,7 +704,6 @@ class SSDWithGhostNet(nn.Cell):
     def _make_layer(self, kernel_size, exp_ch, out_channel, use_se, act_func, stride=1):
         mid_planes = exp_ch
         out_planes = out_channel
-        # num_in, num_mid, num_out, kernel_size, stride=1, act_type='relu', use_se=False):
         layer = GhostBottleneck(self.inplanes, mid_planes, out_planes,
                                 kernel_size, stride=stride, act_type=act_func, use_se=use_se)
         self.inplanes = out_planes

model_zoo/research/cv/ssd_ghostnet/train.py

@@ -23,10 +23,8 @@ from mindspore import context, Tensor
 from mindspore.communication.management import init
 from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, LossMonitor, TimeMonitor
 from mindspore.train import Model, ParallelMode
-# from mindspore.context import ParallelMode
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from src.ssd_ghostnet import SSD300, SSDWithLossCell, TrainingWrapper, ssd_ghostnet
-# from src.config_ghostnet_1x import config
 from src.config_ghostnet_13x import config
 from src.dataset import create_ssd_dataset, data_to_mindrecord_byte_image, voc_data_to_mindrecord
 from src.lr_schedule import get_lr
@@ -124,7 +122,6 @@ def main():
 
         backbone = ssd_ghostnet()
         ssd = SSD300(backbone=backbone, config=config)
-        # print(ssd)
         net = SSDWithLossCell(ssd, config)
         init_net_param(net)
 

model_zoo/utils/graph_to_mindrecord/writer.py

@@ -149,7 +149,6 @@ if __name__ == "__main__":
     # pass mr_api arguments
     os.environ['graph_api_args'] = args.graph_api_args
 
-    # import mr_api
     try:
         mr_api = import_module(args.mindrecord_script + '.mr_api')
     except ModuleNotFoundError:

tests/st/model_zoo_tests/wide_and_deep/python_file_for_ci/wide_and_deep.py

@@ -13,22 +13,19 @@
 # limitations under the License.
 # ============================================================================
 """wide and deep model"""
+import numpy as np
 from mindspore import nn
 from mindspore import Parameter, ParameterTuple
 import mindspore.common.dtype as mstype
 from mindspore.ops import functional as F
 from mindspore.ops import composite as C
 from mindspore.ops import operations as P
-# from mindspore.nn import Dropout
 from mindspore.nn.optim import Adam, FTRL
-# from mindspore.nn.metrics import Metric
 from mindspore.common.initializer import Uniform, initializer
-# from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
 from mindspore.parallel._utils import _get_device_num, _get_parallel_mode, _get_gradients_mean
 from mindspore.context import ParallelMode
 from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
 from mindspore.communication.management import get_group_size
-import numpy as np
 
 np_type = np.float32
 ms_type = mstype.float32
@@ -110,8 +107,6 @@ class DenseLayer(nn.Cell):
 
     def construct(self, x):
         x = self.act_func(x)
-        # if self.training:
-        #    x = self.dropout(x)
         x = self.mul(x, self.scale_coef)
         if self.convert_dtype:
             x = self.cast(x, mstype.float16)

tests/st/networks/models/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
@@ -21,8 +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']
 

tests/st/networks/models/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.vision.c_transforms as C
-import numpy as np
 
 from .ei_dataset import HwVocRawDataset
 from .utils import custom_transforms as tr