update pretrained_model and inference model link in TIPC config and refine TIPC script

docs/zh_CN/training/metric_learning/metabin.md

@@ -36,7 +36,7 @@
 
 | 模型 | recall@1(\%) | mAP(\%) | 参考recall@1(\%) | 参考mAP(\%) | 预训练模型下载地址 | inference模型下载地址 |
 | -------- | ------------ | ------- |------------ | ------- | ------------------ | --------------------- |
-| ResNet50_MetaBIN | 55.07 | 32.62 | 55.16 | 33.09 | [metabin_resnet50_pretrained.pdparams](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/reid/metabin/metabin_resnet50_pretrained.pdparams)                  | [metabin_resnet50_infer.tar](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/reid/metabin/metabin_resnet50_infer.tar)                     |
+| ResNet50_MetaBIN | 55.25 | 32.97 | 55.16 | 33.09 | [metabin_resnet50_pretrained.pdparams](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/metric_learning/metabin/metabin_resnet50_pretrained.pdparams)                  | [metabin_resnet50_infer.tar](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/metric_learning/metabin/metabin_resnet50_infer.tar)                     |
 
 接下来主要以`MetaBIN_ResNet50_cross_domain.yaml`配置和训练好的模型文件为例，展示在 Market1501 数据集上进行训练, 在 DukeMTMC-reID 数据集上测试、推理的过程。
 
@@ -88,14 +88,14 @@
   -o Global.pretrained_model="./output/RecModel/latest"
   ```
 
-- 以训练好的模型为例，下载 [metabin_resnet50_pretrained.pdparams](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/reid/metabin/metabin_resnet50_pretrained.pdparams) 到 `PaddleClas/pretrained_models` 文件夹中，执行如下命令即可进行评估。
+- 以训练好的模型为例，下载 [metabin_resnet50_pretrained.pdparams](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/metric_learning/metabin/metabin_resnet50_pretrained.pdparams) 到 `PaddleClas/pretrained_models` 文件夹中，执行如下命令即可进行评估。
 
   ```shell
   # 下载模型
   cd PaddleClas
   mkdir pretrained_models
   cd pretrained_models
-  wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/reid/metabin/metabin_resnet50_pretrained.pdparams
+  wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/metric_learning/metabin/metabin_resnet50_pretrained.pdparams
   cd ..
   # 评估
   python3.7 tools/eval.py \
@@ -118,10 +118,9 @@
   ppcls INFO: query feature calculation process: [0/18]
   ppcls INFO: Build query done, all feat shape: [2228, 2048]
   ppcls INFO: re_ranking=False
-  ppcls INFO: [Eval][Epoch 0][Avg]recall1: 0.55072, recall5: 0.68492, recall10: 0.73698, mAP: 0.32624
-
+  ppcls INFO: [Eval][Epoch 0][Avg]recall1: 0.55251, recall5: 0.68268, recall10: 0.72756, mAP: 0.32977
   ```
-  默认评估日志保存在`PaddleClas/output/RecModel/eval.log`中，可以看到我们提供的 `metabin_resnet50_pretrained.pdparams` 模型在 SOP 数据集上的评估指标为recall@1=0.55072，recall@5=0.68492，recall@10=0.73698，mAP=0.32624
+  默认评估日志保存在`PaddleClas/output/RecModel/eval.log`中，可以看到我们提供的 `metabin_resnet50_pretrained.pdparams` 模型在 Market-1501 数据集上的评估指标为recall@1=0.55251，recall@5=0.68268，recall@10=0.72756，mAP=0.32977
 
 - 使用re-ranking功能提升评估精度
 
@@ -145,7 +144,7 @@
   - 或者下载并解压我们提供的 inference 模型
     ```shell
     cd ./deploy
-    wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/reid/metabin/metabin_resnet50_infer.tar
+    wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/metric_learning/metabin/metabin_resnet50_infer.tar
     tar -xf metabin_resnet50_infer.tar
     cd ../
     ```

test_tipc/configs/MetaBIN/MetaBIN_ResNet50_cross_domain_train_amp_infer_python.txt

 ===========================train_params===========================
-model_name:RecModel
+model_name:MetaBIN_ResNet50
 python:python3.7
 gpu_list:0
 -o Global.device:gpu
@@ -13,7 +13,7 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
 null:null
 ##
 trainer:amp_train
-amp_train:tools/train.py -c ppcls/configs/reid/MetaBIN_ResNet50_cross_domain.yaml -o Global.seed=1234 -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.eval_during_train=False -o Global.save_interval=2 -o DataLoader.Train.sampler.num_instances=2 -o DataLoader.Metalearning.Train.sampler.batch_size=16 -o DataLoader.Metalearning.Train.sampler.num_instances=2
+amp_train:tools/train.py -c ppcls/configs/reid/MetaBIN_ResNet50_cross_domain.yaml -o Global.seed=1234 -o DataLoader.Train.loader.num_workers=1 -o DataLoader.Train.loader.use_shared_memory=False -o Global.eval_during_train=False -o Global.save_interval=2 -o DataLoader.Train.sampler.num_instances=2 -o DataLoader.Metalearning.Train.sampler.batch_size=16 -o DataLoader.Metalearning.Train.sampler.num_instances=2
 pact_train:null
 fpgm_train:null
 distill_train:null
@@ -33,7 +33,7 @@ fpgm_export:null
 distill_export:null
 kl_quant:null
 export2:null
-pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/reid/metabin/metabin_resnet50_pretrained.pdparams
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/metric_learning/metabin/metabin_resnet50_pretrained.pdparams
 infer_model:../inference/
 infer_export:True
 infer_quant:Fasle

test_tipc/configs/MetaBIN/MetaBIN_ResNet50_cross_domain_train_infer_python.txt

 ===========================train_params===========================
-model_name:RecModel
+model_name:MetaBIN_ResNet50
 python:python3.7
 gpu_list:0
 -o Global.device:gpu
@@ -13,7 +13,7 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
 null:null
 ##
 trainer:norm_train
-norm_train:tools/train.py -c ppcls/configs/reid/MetaBIN_ResNet50_cross_domain.yaml -o Global.seed=1234 -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.eval_during_train=False -o Global.save_interval=2 -o DataLoader.Train.sampler.num_instances=2 -o DataLoader.Metalearning.Train.sampler.batch_size=16 -o DataLoader.Metalearning.Train.sampler.num_instances=2 -o AMP=None
+norm_train:tools/train.py -c ppcls/configs/reid/MetaBIN_ResNet50_cross_domain.yaml -o Global.seed=1234 -o DataLoader.Train.loader.num_workers=1 -o DataLoader.Train.loader.use_shared_memory=False -o Global.eval_during_train=False -o Global.save_interval=2 -o DataLoader.Train.sampler.num_instances=2 -o DataLoader.Metalearning.Train.sampler.batch_size=16 -o DataLoader.Metalearning.Train.sampler.num_instances=2 -o AMP=None
 pact_train:null
 fpgm_train:null
 distill_train:null
@@ -33,7 +33,7 @@ fpgm_export:null
 distill_export:null
 kl_quant:null
 export2:null
-pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/reid/metabin/metabin_resnet50_pretrained.pdparams
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/metric_learning/metabin/metabin_resnet50_pretrained.pdparams
 infer_model:../inference/
 infer_export:True
 infer_quant:Fasle

test_tipc/prepare.sh

@@ -197,6 +197,11 @@ if [[ ${MODE} = "lite_train_lite_infer" ]] || [[ ${MODE} = "lite_train_whole_inf
         cd tipc_shitu_demo_data
         ln -s demo_test.txt val_list.txt
         cd ../../
+    elif [[ ${model_name} =~ "MetaBIN_ResNet50" ]]; then
+        cd dataset
+        wget -nc https://paddleclas.bj.bcebos.com/data/TIPC/duke_market.zip --no-check-certificate
+        unzip duke_market.zip
+        cd ../
     else
         # pretrain lite train data
         cd dataset

test_tipc/test_train_inference_python.sh

@@ -273,7 +273,7 @@ else
                 # export FLAGS_cudnn_deterministic=True
                 sleep 5
                 eval $cmd
-                if [[ $model_name == *GeneralRecognition* ]]; then
+                if [[ $model_name == *GeneralRecognition* ]] || [[ $model_name == *MetaBIN_ResNet50* ]]; then
                     eval "cat ${save_log}/RecModel/train.log >> ${save_log}.log"
                 else
                     eval "cat ${save_log}/${model_name}/train.log >> ${save_log}.log"
@@ -281,7 +281,7 @@ else
                 status_check $? "${cmd}" "${status_log}" "${model_name}" "${save_log}.log"
                 sleep 5
 
-                if [[ $model_name == *GeneralRecognition* ]]; then
+                if [[ $model_name == *GeneralRecognition* ]] || [[ $model_name == *MetaBIN_ResNet50* ]]; then
                     set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/RecModel/${train_model_name}")
                 else
                     set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${model_name}/${train_model_name}")
@@ -303,7 +303,7 @@ else
                 if [ ${run_export} != "null" ]; then
                     # run export model
                     save_infer_path="${save_log}"
-                    if [[ $model_name == *GeneralRecognition* ]]; then
+                    if [[ $model_name == *GeneralRecognition* ]] || [[ $model_name == *MetaBIN_ResNet50* ]]; then
                         set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/RecModel/${train_model_name}")
                     else
                         set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${model_name}/${train_model_name}")