fix slim bugs

deploy/slim/README.md

@@ -66,7 +66,7 @@ cd PaddleClas
 以CPU为例，若使用GPU，则将命令中改成`cpu`改成`gpu`
 
 ```bash
-python3.7 deploy/slim/slim.py -m train -c ppcls/configs/slim/ResNet50_vd_quantalization.yaml -o Global.device=cpu
+python3.7 deploy/slim/slim.py -m train -c ppcls/configs/slim/ResNet50_vd_quantization.yaml -o Global.device=cpu
 ```
 
 其中`yaml`文件解析详见[参考文档](../../docs/zh_CN/tutorials/config_description.md)。为了保证精度，`yaml`文件中已经使用`pretrained model`.
@@ -81,7 +81,7 @@ python3.7 -m paddle.distributed.launch \
     --gpus="0,1,2,3" \
       deploy/slim/slim.py \
       -m train \
-      -c ppcls/configs/slim/ResNet50_vd_quantalization.yaml
+      -c ppcls/configs/slim/ResNet50_vd_quantization.yaml
 ```
 
 ##### 3.1.2 离线量化
@@ -131,7 +131,7 @@ python3.7 -m paddle.distributed.launch \
 python3.7 deploy/slim/slim.py \
     -m export \
     -c ppcls/configs/slim/ResNet50_vd_prune.yaml \
-        -o Global.save_inference_dir=./inference
+    -o Global.save_inference_dir=./inference
 ```
 
 

deploy/slim/README_en.md

@@ -67,7 +67,7 @@ The training command is as follow:
 If using GPU, change the `cpu` to `gpu` in the following command.
 
 ```bash
-python3.7 deploy/slim/slim.py -m train -c ppcls/configs/slim/ResNet50_vd_quantalization.yaml -o Global.device=cpu
+python3.7 deploy/slim/slim.py -m train -c ppcls/configs/slim/ResNet50_vd_quantization.yaml -o Global.device=cpu
 ```
 
 The description of `yaml` file can be found  in this [doc](../../docs/en/tutorials/config_en.md). To get better accuracy, the `pretrained model`is used in `yaml`.
@@ -82,7 +82,7 @@ python3.7 -m paddle.distributed.launch \
     --gpus="0,1,2,3" \
       deploy/slim/slim.py \
       -m train \
-      -c ppcls/configs/slim/ResNet50_vd_quantalization.yaml
+      -c ppcls/configs/slim/ResNet50_vd_quantization.yaml
 ```
 
 ##### 3.1.2 Offline quantization
@@ -132,7 +132,7 @@ After getting the compressed model, we can export it as inference model for pred
 python3.7 deploy/slim/slim.py \
     -m export \
     -c ppcls/configs/slim/ResNet50_vd_prune.yaml \
-        -o Global.save_inference_dir=./inference
+    -o Global.save_inference_dir=./inference
 ```
 
 ### 5. Deploy

log/endpoints.log

@@ -0,0 +1,5 @@
+PADDLE_TRAINER_ENDPOINTS: 
+127.0.0.1:51466
+127.0.0.1:58283
+127.0.0.1:34005
+127.0.0.1:58331

ppcls/configs/slim/ResNet50_vd_quantization.yaml

@@ -42,7 +42,7 @@ Optimizer:
   momentum: 0.9
   lr:
     name: Cosine
-    learning_rate: 0.1
+    learning_rate: 0.01
   regularizer:
     name: 'L2'
     coeff: 0.00007