!6267 delete redundant codes in model zoo

Merge pull request !6267 from zhaoting/clean_warnings

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,))