rename config -> configs

test_tipc/config/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5_train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt → test_tipc/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5_train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt

@@ -38,8 +38,8 @@ infer_model:../inference/
 infer_export:True
 infer_quant:Fasle
 inference:python/predict_rec.py -c configs/inference_rec.yaml
--o Global.use_gpu:False
--o Global.enable_mkldnn:True|False
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:False
 -o Global.cpu_num_threads:1
 -o Global.batch_size:1
 -o Global.use_tensorrt:False

test_tipc/config/PPHGNet/PPHGNet_small_train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt → test_tipc/configs/PPHGNet/PPHGNet_small_train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt

@@ -38,10 +38,10 @@ infer_model:../inference/
 infer_export:True
 infer_quant:Fasle
 inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=236
--o Global.use_gpu:False
+-o Global.use_gpu:True|False
 -o Global.enable_mkldnn:False
--o Global.cpu_num_threads:1|6
--o Global.batch_size:1|16
+-o Global.cpu_num_threads:1
+-o Global.batch_size:1
 -o Global.use_tensorrt:False
 -o Global.use_fp16:False
 -o Global.inference_model_dir:../inference

test_tipc/config/PPLCNet/PPLCNet_x1_0_train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt → test_tipc/configs/PPLCNet/PPLCNet_x1_0_train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt

@@ -38,10 +38,10 @@ infer_model:../inference/
 infer_export:True
 infer_quant:Fasle
 inference:python/predict_cls.py -c configs/inference_cls.yaml
--o Global.use_gpu:False
+-o Global.use_gpu:True|False
 -o Global.enable_mkldnn:False
--o Global.cpu_num_threads:1|6
--o Global.batch_size:1|16
+-o Global.cpu_num_threads:1
+-o Global.batch_size:1
 -o Global.use_tensorrt:False
 -o Global.use_fp16:False
 -o Global.inference_model_dir:../inference

test_tipc/config/PPLCNetV2/PPLCNetV2_base_train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt → test_tipc/configs/PPLCNetV2/PPLCNetV2_base_train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt

@@ -38,10 +38,10 @@ infer_model:../inference/
 infer_export:True
 infer_quant:Fasle
 inference:python/predict_cls.py -c configs/inference_cls.yaml
--o Global.use_gpu:False
+-o Global.use_gpu:True|False
 -o Global.enable_mkldnn:False
--o Global.cpu_num_threads:1|6
--o Global.batch_size:1|16
+-o Global.cpu_num_threads:1
+-o Global.batch_size:1
 -o Global.use_tensorrt:False
 -o Global.use_fp16:False
 -o Global.inference_model_dir:../inference

test_tipc/docs/benchmark_train.md

@@ -9,7 +9,7 @@
 
 ```shell
 # 运行格式：bash test_tipc/prepare.sh  train_benchmark.txt  mode
-bash test_tipc/prepare.sh test_tipc/config/MobileNetV2/MobileNetV2_train_infer_python.txt benchmark_train
+bash test_tipc/prepare.sh test_tipc/configs/MobileNetV2/MobileNetV2_train_infer_python.txt benchmark_train
 ```
 
 ## 1.2 功能测试
@@ -17,14 +17,14 @@ bash test_tipc/prepare.sh test_tipc/config/MobileNetV2/MobileNetV2_train_infer_p
 
 ```shell
 # 运行格式：bash test_tipc/benchmark_train.sh train_benchmark.txt mode
-bash test_tipc/benchmark_train.sh test_tipc/config/MobileNetV2/MobileNetV2_train_infer_python.txt benchmark_train
+bash test_tipc/benchmark_train.sh test_tipc/configs/MobileNetV2/MobileNetV2_train_infer_python.txt benchmark_train
 
 ```
 
 `test_tipc/benchmark_train.sh`支持根据传入的第三个参数实现只运行某一个训练配置，如下：
 ```shell
 # 运行格式：bash test_tipc/benchmark_train.sh train_benchmark.txt mode params
-bash test_tipc/benchmark_train.sh test_tipc/config/MobileNetV2/MobileNetV2_train_infer_python.txt benchmark_train  dynamic_bs8_fp32_DP_N1C1
+bash test_tipc/benchmark_train.sh test_tipc/configs/MobileNetV2/MobileNetV2_train_infer_python.txt benchmark_train  dynamic_bs8_fp32_DP_N1C1
 ```
 dynamic_bs8_fp32_DP_N1C1为test_tipc/benchmark_train.sh传入的参数，格式如下：
 `${modeltype}_${batch_size}_${fp_item}_${run_mode}_${device_num}`
@@ -33,7 +33,7 @@ dynamic_bs8_fp32_DP_N1C1为test_tipc/benchmark_train.sh传入的参数，格式
 
 ## 2. 日志输出
 
-运行后将保存模型的训练日志和解析日志，使用 `test_tipc/config/MobileNetV2/MobileNetV2_train_infer_python.txt` 参数文件的训练日志解析结果是：
+运行后将保存模型的训练日志和解析日志，使用 `test_tipc/configs/MobileNetV2/MobileNetV2_train_infer_python.txt` 参数文件的训练日志解析结果是：
 
 ```
 {"model_branch": "dygaph", "model_commit": "7c39a1996b19087737c05d883fd346d2f39dbcc0", "model_name": "cls_MobileNetV2_bs8_fp32_SingleP_DP", "batch_size": 8, "fp_item": "fp32", "run_process_type": "SingleP", "run_mode": "DP", "convergence_value": "5.413110", "convergence_key": "loss:", "ips": 19.333, "speed_unit": "samples/s", "device_num": "N1C1", "model_run_time": "0", "frame_commit": "8cc09552473b842c651ead3b9848d41827a3dbab", "frame_version": "0.0.0"}

test_tipc/docs/test_inference_cpp.md

@@ -246,7 +246,7 @@ make -j
 
 * 可执行以下命令，自动完成上述准备环境中的所需内容
 ```shell
-bash test_tipc/prepare.sh test_tipc/config/ResNet/ResNet50_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt cpp_infer
+bash test_tipc/prepare.sh test_tipc/configs/ResNet/ResNet50_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt cpp_infer
 ```
 ### 2.3 功能测试
 
@@ -260,7 +260,7 @@ bash test_tipc/test_inference_cpp.sh ${your_params_file} cpp_infer
 以`ResNet50`的`Linux GPU/CPU C++推理测试`为例，命令如下所示。
 
 ```shell
-bash test_tipc/test_inference_cpp.sh test_tipc/config/ResNet/ResNet50_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt cpp_infer
+bash test_tipc/test_inference_cpp.sh test_tipc/configs/ResNet/ResNet50_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt cpp_infer
 ```
 
 输出结果如下，表示命令运行成功。

test_tipc/docs/test_lite_arm_cpu_cpp.md

@@ -15,7 +15,7 @@ Lite_arm_cpp_cpu 预测功能测试的主程序为`test_lite_arm_cpu_cpp.sh`，
 先运行 `prepare_lite_arm_cpu_cpp.sh` 准备数据和模型，然后运行 `test_lite_arm_cpu_cpp.sh` 进行测试，最终在 `./output` 目录下生成 `lite_*.log` 后缀的日志文件。
 
 ```shell
-bash test_tipc/prepare_lite_arm_cpu_cpp.sh test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_lite_arm_cpu_cpp.txt
+bash test_tipc/prepare_lite_arm_cpu_cpp.sh test_tipc/configs/MobileNetV3/MobileNetV3_large_x1_0_lite_arm_cpu_cpp.txt
 ```
 
 运行预测指令后，在`./output`文件夹下自动会保存运行日志，包括以下文件：

test_tipc/docs/test_paddle2onnx.md

@@ -21,10 +21,10 @@ PaddleServing预测功能测试的主程序为`test_paddle2onnx.sh`，可以测
 下方展示以PPHGNet_small为例的测试命令与结果。
 
 ```shell
-bash test_tipc/prepare.sh ./test_tipc/config/ResNet/ResNet50_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt paddle2onnx_infer
+bash test_tipc/prepare.sh ./test_tipc/configs/ResNet/ResNet50_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt paddle2onnx_infer
 
 # 用法:
-bash test_tipc/test_paddle2onnx.sh ./test_tipc/config/ResNet/ResNet50_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt
+bash test_tipc/test_paddle2onnx.sh ./test_tipc/configs/ResNet/ResNet50_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt
 ```
 
 #### 运行结果

test_tipc/docs/test_train_fleet_inference_python.md

@@ -34,7 +34,7 @@ Linux GPU/CPU 多机多卡训练推理测试的主程序为`test_train_inference
 
 #### 2.1.1 修改配置文件
 
-首先，修改配置文件`test_tipc/config/PPLCNet/PPLCNet_x1_0_train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt`中的`gpu_list`设置：假设两台机器的`ip`地址分别为`192.168.0.1`和`192.168.0.2`，则对应的配置文件`gpu_list`字段需要修改为`gpu_list:192.168.0.1,192.168.0.2;0,1`。
+首先，修改配置文件`test_tipc/configs/PPLCNet/PPLCNet_x1_0_train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt`中的`gpu_list`设置：假设两台机器的`ip`地址分别为`192.168.0.1`和`192.168.0.2`，则对应的配置文件`gpu_list`字段需要修改为`gpu_list:192.168.0.1,192.168.0.2;0,1`。
 
 **`ip`地址查看命令为`ifconfig`，在`inet addr:`字段后的即为ip地址**。
 
@@ -44,7 +44,7 @@ Linux GPU/CPU 多机多卡训练推理测试的主程序为`test_train_inference
 运行`prepare.sh`准备数据和模型，数据准备命令如下所示。
 
 ```shell
-bash test_tipc/prepare.sh test_tipc/config/PPLCNet/PPLCNet_x1_0_train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt lite_train_lite_infer
+bash test_tipc/prepare.sh test_tipc/configs/PPLCNet/PPLCNet_x1_0_train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt lite_train_lite_infer
 ```
 
 **注意：** 由于是多机训练，这里需要在所有节点上都运行一次启动上述命令来准备数据。
@@ -60,7 +60,7 @@ export FLAGS_START_PORT=17000
 
 接下来就可以开始执行测试，命令如下所示。
 ```shell
-bash test_tipc/test_train_inference_python.sh  test_tipc/config/PPLCNet/PPLCNet_x1_0_train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt
+bash test_tipc/test_train_inference_python.sh  test_tipc/configs/PPLCNet/PPLCNet_x1_0_train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt
 ```
 
 **注意：** 由于是多机训练，这里需要在所有的节点上均运行启动上述命令进行测试。

test_tipc/docs/test_train_inference_python.md

@@ -58,36 +58,36 @@ Linux端基础训练预测功能测试的主程序为`test_train_inference_pytho
 
 - 模式1：lite_train_lite_infer，使用少量数据训练，用于快速验证训练到预测的走通流程，不验证精度和速度；
 ```shell
-bash test_tipc/prepare.sh ./test_tipc/config/ResNet/ResNet50_vd_train_infer_python.txt 'lite_train_lite_infer'
-bash test_tipc/test_train_inference_python.sh ./test_tipc/config/ResNet/ResNet50_vd_train_infer_python.txt 'lite_train_lite_infer'
+bash test_tipc/prepare.sh ./test_tipc/configs/ResNet/ResNet50_vd_train_infer_python.txt 'lite_train_lite_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ResNet/ResNet50_vd_train_infer_python.txt 'lite_train_lite_infer'
 ```
 
 - 模式2：lite_train_whole_infer，使用少量数据训练，一定量数据预测，用于验证训练后的模型执行预测，预测速度是否合理；
 ```shell
-bash test_tipc/prepare.sh ./test_tipc/config/ResNet/ResNet50_vd_train_infer_python.txt  'lite_train_whole_infer'
-bash test_tipc/test_train_inference_python.sh ./test_tipc/config/ResNet/ResNet50_vd_train_infer_python.txt 'lite_train_whole_infer'
+bash test_tipc/prepare.sh ./test_tipc/configs/ResNet/ResNet50_vd_train_infer_python.txt  'lite_train_whole_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ResNet/ResNet50_vd_train_infer_python.txt 'lite_train_whole_infer'
 ```
 
 - 模式3：whole_infer，不训练，全量数据预测，走通开源模型评估、动转静，检查inference model预测时间和精度;
 ```shell
-bash test_tipc/prepare.sh ./test_tipc/config/ResNet/ResNet50_vd_train_infer_python.txt 'whole_infer'
+bash test_tipc/prepare.sh ./test_tipc/configs/ResNet/ResNet50_vd_train_infer_python.txt 'whole_infer'
 # 用法1:
-bash test_tipc/test_train_inference_python.sh ./test_tipc/config/ResNet/ResNet50_vd_train_infer_python.txt 'whole_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ResNet/ResNet50_vd_train_infer_python.txt 'whole_infer'
 # 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
-bash test_tipc/test_train_inference_python.sh ./test_tipc/config/ResNet/ResNet50_vd_train_infer_python.txt 'whole_infer' '1'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ResNet/ResNet50_vd_train_infer_python.txt 'whole_infer' '1'
 ```
 
 - 模式4：whole_train_whole_infer，CE： 全量数据训练，全量数据预测，验证模型训练精度，预测精度，预测速度；
 ```shell
-bash test_tipc/prepare.sh ./test_tipc/config/ResNet/ResNet50_vd_train_infer_python.txt 'whole_train_whole_infer'
-bash test_tipc/test_train_inference_python.sh ./test_tipc/config/ResNet/ResNet50_vd_train_infer_python.txt 'whole_train_whole_infer'
+bash test_tipc/prepare.sh ./test_tipc/configs/ResNet/ResNet50_vd_train_infer_python.txt 'whole_train_whole_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ResNet/ResNet50_vd_train_infer_python.txt 'whole_train_whole_infer'
 ```
 
 - 模式5:  klquant_whole_infer，测试离线量化；
 
 ```shell
-bash test_tipc/prepare.sh ./test_tipc/config/ResNet/ResNet50_vd_train_infer_python.txt 'klquant_whole_infer'
-bash test_tipc/test_train_inference_python.sh ./test_tipc/config/ResNet/ResNet50_vd_train_infer_python.txt 'klquant_whole_infer'
+bash test_tipc/prepare.sh ./test_tipc/configs/ResNet/ResNet50_vd_train_infer_python.txt 'klquant_whole_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ResNet/ResNet50_vd_train_infer_python.txt 'klquant_whole_infer'
 ```
 
 

test_tipc/docs/test_train_pact_inference_python.md

@@ -79,7 +79,7 @@ Linux GPU/CPU PACT量化训练推理测试的主程序为`test_train_inference_p
 ### 2.2 准备数据和模型
 
 ```bash
-bash test_tipc/prepare.sh test_tipc/config/PPLCNetV2/PPLCNetV2_base_train_pact_infer_python.txt lite_train_lite_infer
+bash test_tipc/prepare.sh test_tipc/configs/PPLCNetV2/PPLCNetV2_base_train_pact_infer_python.txt lite_train_lite_infer
 ```
 
 在线量化的操作流程，可参考[文档](../../deploy/slim/README.md)。
@@ -89,7 +89,7 @@ bash test_tipc/prepare.sh test_tipc/config/PPLCNetV2/PPLCNetV2_base_train_pact_i
 以`MobileNetV3_large_x1_0`的`Linux GPU/CPU PACT在线量化训练推理测试`为例，命令如下所示。
 
 ```bash
-bash test_tipc/test_train_inference_python.sh test_tipc/config/PPLCNetV2/PPLCNetV2_base_train_pact_infer_python.txt lite_train_lite_infer
+bash test_tipc/test_train_inference_python.sh test_tipc/configs/PPLCNetV2/PPLCNetV2_base_train_pact_infer_python.txt lite_train_lite_infer
 ```
 
 输出结果如下，表示命令运行成功。

test_tipc/docs/test_train_ptq_inference_python.md

@@ -80,7 +80,7 @@ Linux GPU/CPU KL离线量化推理测试的主程序为`test_ptq_inference_pytho
 ### 2.2 准备数据和模型
 
 ```bash
-bash test_tipc/prepare.sh test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_train_ptq_infer_python.txt whole_infer
+bash test_tipc/prepare.sh test_tipc/configs/MobileNetV3/MobileNetV3_large_x1_0_train_ptq_infer_python.txt whole_infer
 ```
 
 离线量化的操作流程，可参考[文档](../../deploy/slim/README.md)。
@@ -90,7 +90,7 @@ bash test_tipc/prepare.sh test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_tr
 以`MobileNetV3_large_x1_0`的`Linux GPU/CPU KL离线量化训练推理测试`为例，命令如下所示。
 
 ```bash
-bash test_tipc/test_ptq_inference_python.sh test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_train_ptq_infer_python.txt whole_infer
+bash test_tipc/test_ptq_inference_python.sh test_tipc/configs/MobileNetV3/MobileNetV3_large_x1_0_train_ptq_infer_python.txt whole_infer
 ```
 
 输出结果如下，表示命令运行成功。