!12749 amend_resnet18

From: @jiangzg001
Reviewed-by: @liangchenghui,@c_34
Signed-off-by: @c_34

model_zoo/official/cv/resnet/README.md

@@ -492,8 +492,8 @@ result: {'top_5_accuracy': 0.9342589628681178, 'top_1_accuracy': 0.7680657810499
 | Loss Function              | Softmax Cross Entropy                                       |
 | outputs                    | probability                                                 |
 | Loss                       | 0.0002519517                                                    |
-| Speed                      | 10 ms/step（8pcs）                     |
-| Total time                 | 3 mins                          |
+| Speed                      | 13 ms/step（8pcs）                     |
+| Total time                 | 4 mins                          |
 | Parameters (M)             | 11.2                                                        |
 | Checkpoint for Fine tuning | 86M (.ckpt file)                                         |
 | Scripts                    | [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet) |
@@ -512,8 +512,8 @@ result: {'top_5_accuracy': 0.9342589628681178, 'top_1_accuracy': 0.7680657810499
 | Loss Function              | Softmax Cross Entropy                                       |
 | outputs                    | probability                                                 |
 | Loss                       | 2.15702                                                   |
-| Speed                      | 140ms/step（8pcs）                     |
-| Total time                 | 131 mins                          |
+| Speed                      | 110ms/step（8pcs）  (may need to set_numa_enbale in dataset.py)                    |
+| Total time                 | 110 mins                        |
 | Parameters (M)             | 11.7                                                       |
 | Checkpoint for Fine tuning | 90M (.ckpt file)                                         |
 | Scripts                    | [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet) |

model_zoo/official/cv/resnet/README_CN.md

@@ -459,8 +459,8 @@ result:{'top_5_accuracy':0.9342589628681178, 'top_1_accuracy':0.768065781049936}
 | 损失函数              | Softmax交叉熵                                       |
 | 输出                    | 概率                                                 |
 | 损失                       | 0.0002519517                                                   |
-| 速度                      | 10毫秒/步（8卡）                     |
-| 总时长                 | 3分钟                          |
+| 速度                      | 13毫秒/步（8卡）                     |
+| 总时长                 | 4分钟                          |
 | 参数(M)             | 11.2                                                         |
 | 微调检查点 | 86（.ckpt文件）                                         |
 | 脚本                    | [链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet) |
@@ -479,8 +479,8 @@ result:{'top_5_accuracy':0.9342589628681178, 'top_1_accuracy':0.768065781049936}
 | 损失函数              | Softmax交叉熵                                       |
 | 输出                    | 概率                                                 |
 | 损失                       | 2.15702                                                       |
-| 速度                      | 140毫秒/步（8卡）                     |
-| 总时长                 | 131分钟                          |
+| 速度                      | 110毫秒/步（8卡） (可能需要在datasetpy中增加set_numa_enbale绑核操作)                    |
+| 总时长                 | 110分钟                          |
 | 参数(M)             | 11.7                                                         |
 | 微调检查点| 90M（.ckpt文件）                                         |
 | 脚本                    | [链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet) |

model_zoo/official/cv/resnet/src/resnet.py

@@ -176,8 +176,8 @@ class ResidualBlock(nn.Cell):
         in_channel (int): Input channel.
         out_channel (int): Output channel.
         stride (int): Stride size for the first convolutional layer. Default: 1.
-        use_se (bool): enable SE-ResNet50 net. Default: False.
-        se_block(bool): use se block in SE-ResNet50 net. Default: False.
+        use_se (bool): Enable SE-ResNet50 net. Default: False.
+        se_block(bool): Use se block in SE-ResNet50 net. Default: False.
 
     Returns:
         Tensor, output tensor.
@@ -276,8 +276,9 @@ class ResidualBlockBase(nn.Cell):
         in_channel (int): Input channel.
         out_channel (int): Output channel.
         stride (int): Stride size for the first convolutional layer. Default: 1.
-        use_se (bool): enable SE-ResNet50 net. Default: False.
-        se_block(bool): use se block in SE-ResNet50 net. Default: False.
+        use_se (bool): Enable SE-ResNet50 net. Default: False.
+        se_block(bool): Use se block in SE-ResNet50 net. Default: False.
+        res_base (bool): Enable parameter setting of resnet18. Default: True.
 
     Returns:
         Tensor, output tensor.
@@ -290,9 +291,9 @@ class ResidualBlockBase(nn.Cell):
                  in_channel,
                  out_channel,
                  stride=1,
-                 res_base=True,
                  use_se=False,
-                 se_block=False):
+                 se_block=False,
+                 res_base=True):
         super(ResidualBlockBase, self).__init__()
         self.res_base = res_base
         self.conv1 = _conv3x3(in_channel, out_channel, stride=stride, res_base=self.res_base)
@@ -341,8 +342,10 @@ class ResNet(nn.Cell):
         out_channels (list): Output channel in each layer.
         strides (list):  Stride size in each layer.
         num_classes (int): The number of classes that the training images are belonging to.
-        use_se (bool): enable SE-ResNet50 net. Default: False.
-        se_block(bool): use se block in SE-ResNet50 net in layer 3 and layer 4. Default: False.
+        use_se (bool): Enable SE-ResNet50 net. Default: False.
+        se_block(bool): Use se block in SE-ResNet50 net in layer 3 and layer 4. Default: False.
+        res_base (bool): Enable parameter setting of resnet18. Default: True.
+
     Returns:
         Tensor, output tensor.
 
@@ -432,7 +435,7 @@ class ResNet(nn.Cell):
             in_channel (int): Input channel.
             out_channel (int): Output channel.
             stride (int): Stride size for the first convolutional layer.
-            se_block(bool): use se block in SE-ResNet50 net. Default: False.
+            se_block(bool): Use se block in SE-ResNet50 net. Default: False.
         Returns:
             SequentialCell, the output layer.
 

model_zoo/official/cv/resnet/train.py

@@ -110,9 +110,7 @@ if __name__ == '__main__':
             set_algo_parameters(elementwise_op_strategy_follow=True)
             if args_opt.net == "resnet50" or args_opt.net == "se-resnet50":
                 context.set_auto_parallel_context(all_reduce_fusion_config=[85, 160])
-            elif args_opt.net == "resnet18":
-                context.set_auto_parallel_context(all_reduce_fusion_config=[40, 61])
-            else:
+            elif args_opt.net == "resnet101":
                 context.set_auto_parallel_context(all_reduce_fusion_config=[180, 313])
             init()
         # GPU target