docs: rm wsl from release/2.5

docs/zh_CN/training/advanced/knowledge_distillation.md

@@ -583,76 +583,6 @@ Loss:
         weight: 1.0
 ```
 
-<<<<<<< HEAD
-=======
-<a name='1.2.8'></a>
-
-#### 1.2.8 WSL
-
-##### 1.2.8.1 WSL 算法介绍
-
-论文信息：
-
-
-> [Rethinking Soft Labels For Knowledge Distillation: A Bias-variance Tradeoff Perspective](https://arxiv.org/abs/2102.0650)
->
-> Helong Zhou, Liangchen Song, Jiajie Chen, Ye Zhou, Guoli Wang, Junsong Yuan, Qian Zhang
->
-> ICLR, 2021
-
-WSL (Weighted Soft Labels) 损失函数根据教师模型与学生模型关于真值标签的 CE Loss 比值，对每个样本的 KD Loss 分别赋予权重。若学生模型相对教师模型在某个样本上预测结果更好，则对该样本赋予较小的权重。该方法简单、有效，使各个样本的权重可自适应调节，提升了蒸馏精度。
-
-在ImageNet1k公开数据集上，效果如下所示。
-
-| 策略 | 骨干网络 | 配置文件 | Top-1 acc | 下载链接 |
-| --- | --- | --- | --- | --- |
-| baseline | ResNet18 | [ResNet18.yaml](../../../../ppcls/configs/ImageNet/ResNet/ResNet18.yaml) | 70.8% | - |
-| WSL | ResNet18 | [resnet34_distill_resnet18_wsl.yaml](../../../../ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_wsl.yaml) | 72.23%(**+1.43%**) | - |
-
-
-##### 1.2.8.2 WSL 配置
-
-WSL 配置如下所示。在模型构建Arch字段中，需要同时定义学生模型与教师模型，教师模型固定参数，且需要加载预训练模型。在损失函数Loss字段中，需要定义`DistillationGTCELoss`（学生与真值标签之间的CE loss）以及`DistillationWSLLoss`（学生与教师之间的WSL loss），作为训练的损失函数。
-
-
-```yaml
-# model architecture
-Arch:
-  name: "DistillationModel"
-  # if not null, its lengths should be same as models
-  pretrained_list:
-  # if not null, its lengths should be same as models
-  freeze_params_list:
-  - True
-  - False
-  models:
-    - Teacher:
-        name: ResNet34
-        pretrained: True
-
-    - Student:
-        name: ResNet18
-        pretrained: False
-
-  infer_model_name: "Student"
-
-
-# loss function config for traing/eval process
-Loss:
-  Train:
-    - DistillationGTCELoss:
-        weight: 1.0
-        model_names: ["Student"]
-    - DistillationWSLLoss:
-        weight: 2.5
-        model_name_pairs: [["Student", "Teacher"]]
-        temperature: 2
-  Eval:
-    - CELoss:
-        weight: 1.0
-```
-
->>>>>>> 1f6f4797 (docs: refactor & fix link & rename)
 <a name="2"></a>
 
 ## 2. 模型训练、评估和预测