unify module name (#2530)

* unify module name * update * update
PaddlePaddle · Nov 25, 2024 · 303f30d · 303f30d
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diff --git a/docs/module_usage/tutorials/cv_modules/anomaly_detection.en.md b/docs/module_usage/tutorials/cv_modules/anomaly_detection.en.md
@@ -68,7 +68,7 @@ tar -xf ./dataset/mvtec_examples.tar -C ./dataset/
 A single command can complete data validation:
 
 ```bash
-python main.py -c paddlex/configs/anomaly_detection/STFPM.yaml \
+python main.py -c paddlex/configs/image_anomaly_detection/STFPM.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/mvtec_examples
 ```
@@ -118,7 +118,7 @@ After executing the above command, PaddleX will validate the dataset and collect
 A single command is sufficient to complete model training, taking the training of STFPM as an example:
 
 ```bash
-python main.py -c paddlex/configs/anomaly_detection/STFPM.yaml \
+python main.py -c paddlex/configs/image_anomaly_detection/STFPM.yaml \
     -o Global.mode=train \
     -o Global.dataset_dir=./dataset/mvtec_examples
 ```
@@ -150,7 +150,7 @@ Other related parameters can be set by modifying the `Global` and `Train` fields
 After completing model training, you can evaluate the specified model weight file on the validation set to verify the model's accuracy. Using PaddleX for model evaluation, you can complete the evaluation with a single command:
 
 ```bash
-python main.py -c paddlex/configs/anomaly_detection/STFPM.yaml \
+python main.py -c paddlex/configs/image_anomaly_detection/STFPM.yaml \
     -o Global.mode=evaluate \
     -o Global.dataset_dir=./dataset/mvtec_examples
 ```
@@ -172,7 +172,7 @@ After completing model training and evaluation, you can use the trained model we
 #### 4.4.1 Model Inference
 * To perform inference prediction through the command line, simply use the following command. Before running the following code, please download the [demo image](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/uad_grid.png) to your local machine.
 ```bash
-python main.py -c paddlex/configs/anomaly_detection/STFPM.yaml \
+python main.py -c paddlex/configs/image_anomaly_detection/STFPM.yaml \
     -o Global.mode=predict \
     -o Predict.model_dir="./output/best_model/inference" \
     -o Predict.input="uad_grid.png"

diff --git a/docs/module_usage/tutorials/cv_modules/anomaly_detection.md b/docs/module_usage/tutorials/cv_modules/anomaly_detection.md
@@ -69,7 +69,7 @@ tar -xf ./dataset/mvtec_examples.tar -C ./dataset/
 一行命令即可完成数据校验：
 
 ```bash
-python main.py -c paddlex/configs/anomaly_detection/STFPM.yaml \
+python main.py -c paddlex/configs/image_anomaly_detection/STFPM.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/mvtec_examples
 ```
@@ -116,7 +116,7 @@ python main.py -c paddlex/configs/anomaly_detection/STFPM.yaml \
 一条命令即可完成模型的训练，以此处STFPM的训练为例：
 
 ```bash
-python main.py -c paddlex/configs/anomaly_detection/STFPM.yaml \
+python main.py -c paddlex/configs/image_anomaly_detection/STFPM.yaml \
     -o Global.mode=train \
     -o Global.dataset_dir=./dataset/mvtec_examples
 ```
@@ -147,7 +147,7 @@ python main.py -c paddlex/configs/anomaly_detection/STFPM.yaml \
 在完成模型训练后，可以对指定的模型权重文件在验证集上进行评估，验证模型精度。使用 PaddleX 进行模型评估，一条命令即可完成模型的评估：
 
 ```bash
-python main.py -c paddlex/configs/anomaly_detection/STFPM.yaml \
+python main.py -c paddlex/configs/image_anomaly_detection/STFPM.yaml \
     -o Global.mode=evaluate \
     -o Global.dataset_dir=./dataset/mvtec_examples
 ```
@@ -169,7 +169,7 @@ python main.py -c paddlex/configs/anomaly_detection/STFPM.yaml \
 #### 4.4.1 模型推理
 * 通过命令行的方式进行推理预测，只需如下一条命令，运行以下代码前，请您下载[示例图片](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/uad_grid.png)到本地。
 ```bash
-python main.py -c paddlex/configs/anomaly_detection/STFPM.yaml \
+python main.py -c paddlex/configs/image_anomaly_detection/STFPM.yaml \
     -o Global.mode=predict \
     -o Predict.model_dir="./output/best_model/inference" \
     -o Predict.input="uad_grid.png"

diff --git a/docs/module_usage/tutorials/cv_modules/face_feature.en.md b/docs/module_usage/tutorials/cv_modules/face_feature.en.md
@@ -84,7 +84,7 @@ tar -xf ./dataset/face_rec_examples.tar -C ./dataset/
 A single command can complete data validation:
 
 ```bash
-python main.py -c paddlex/configs/face_recognition/MobileFaceNet.yaml \
+python main.py -c paddlex/configs/face_feature/MobileFaceNet.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/face_rec_examples
 ```
@@ -186,7 +186,7 @@ images/Miyako_Miyazaki_0002.jpg images/Munir_Akram_0002.jpg 0
 Model training can be completed with a single command. Here is an example of training MobileFaceNet:
 
 ```bash
-python main.py -c paddlex/configs/face_recognition/MobileFaceNet.yaml \
+python main.py -c paddlex/configs/face_feature/MobileFaceNet.yaml \
     -o Global.mode=train \
     -o Global.dataset_dir=./dataset/face_rec_examples
 ```
@@ -240,7 +240,7 @@ After completing model training and evaluation, you can use the trained model we
 #### 4.4.1 Model Inference
 * To perform inference predictions through the command line, you only need the following command. Before running the following code, please download the [example image](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/face_recognition_001.jpg) to your local machine.
 ```bash
-python main.py -c paddlex/configs/face_recognition/MobileFaceNet.yaml \
+python main.py -c paddlex/configs/face_feature/MobileFaceNet.yaml \
     -o Global.mode=predict \
     -o Predict.model_dir="./output/best_model/inference" \
     -o Predict.input="face_recognition_001.jpg"

diff --git a/docs/module_usage/tutorials/cv_modules/face_feature.md b/docs/module_usage/tutorials/cv_modules/face_feature.md
@@ -83,7 +83,7 @@ tar -xf ./dataset/face_rec_examples.tar -C ./dataset/
 一行命令即可完成数据校验：
 
 ```bash
-python main.py -c paddlex/configs/face_recognition/MobileFaceNet.yaml \
+python main.py -c paddlex/configs/face_feature/MobileFaceNet.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/face_rec_examples
 ```
@@ -184,7 +184,7 @@ images/Miyako_Miyazaki_0002.jpg images/Munir_Akram_0002.jpg 0
 一条命令即可完成模型的训练，以此处MobileFaceNet的训练为例：
 
 ```bash
-python main.py -c paddlex/configs/face_recognition/MobileFaceNet.yaml \
+python main.py -c paddlex/configs/face_feature/MobileFaceNet.yaml \
     -o Global.mode=train \
     -o Global.dataset_dir=./dataset/face_rec_examples
 ```
@@ -216,7 +216,7 @@ python main.py -c paddlex/configs/face_recognition/MobileFaceNet.yaml \
 在完成模型训练后，可以对指定的模型权重文件在验证集上进行评估，验证模型精度。使用 PaddleX 进行模型评估，一条命令即可完成模型的评估：
 
 ```bash
-python main.py -c paddlex/configs/face_recognition/MobileFaceNet.yaml \
+python main.py -c paddlex/configs/face_feature/MobileFaceNet.yaml \
     -o Global.mode=evaluate \
     -o Global.dataset_dir=./dataset/face_rec_examples
 ```
@@ -238,7 +238,7 @@ python main.py -c paddlex/configs/face_recognition/MobileFaceNet.yaml \
 #### 4.4.1 模型推理
 * 通过命令行的方式进行推理预测，只需如下一条命令，运行以下代码前，请您下载[示例图片](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/face_recognition_001.jpg)到本地。
 ```bash
-python main.py -c paddlex/configs/face_recognition/MobileFaceNet.yaml \
+python main.py -c paddlex/configs/face_feature/MobileFaceNet.yaml \
     -o Global.mode=predict \
     -o Predict.model_dir="./output/best_model/inference" \
     -o Predict.input="face_recognition_001.jpg"

diff --git a/docs/module_usage/tutorials/cv_modules/image_feature.en.md b/docs/module_usage/tutorials/cv_modules/image_feature.en.md
@@ -76,7 +76,7 @@ tar -xf ./dataset/Inshop_examples.tar -C ./dataset/
 #### 4.1.2 Data Validation
 A single command can complete data validation:
 ```bash
-python main.py -c paddlex/configs/general_recognition/PP-ShiTuV2_rec.yaml \
+python main.py -c paddlex/configs/image_feature/PP-ShiTuV2_rec.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/Inshop_examples
 ```
@@ -174,13 +174,13 @@ CheckDataset:
   ......
 </code></pre>
 <p>Then execute the command:</p>
-<pre><code class="language-bash">python main.py -c paddlex/configs/general_recognition/PP-ShiTuV2_rec.yaml \
+<pre><code class="language-bash">python main.py -c paddlex/configs/image_feature/PP-ShiTuV2_rec.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/image_classification_labelme_examples
 </code></pre>
 <p>After the data conversion is executed, the original annotation files will be renamed to <code>xxx.bak</code> in the original path.</p>
 <p>The above parameters also support being set by appending command line arguments:</p>
-<pre><code class="language-bash">python main.py -c paddlex/configs/general_recognition/PP-ShiTuV2_rec.yaml \
+<pre><code class="language-bash">python main.py -c paddlex/configs/image_feature/PP-ShiTuV2_rec.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/image_classification_labelme_examples \
     -o CheckDataset.convert.enable=True \
@@ -206,13 +206,13 @@ CheckDataset:
   ......
 </code></pre>
 <p>Then execute the command:</p>
-<pre><code class="language-bash">python main.py -c paddlex/configs/general_recognition/PP-ShiTuV2_rec.yaml \
+<pre><code class="language-bash">python main.py -c paddlex/configs/image_feature/PP-ShiTuV2_rec.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/Inshop_examples
 </code></pre>
 <p>After the data splitting is executed, the original annotation files will be renamed to <code>xxx.bak</code> in the original path.</p>
 <p>The above parameters also support being set by appending command line arguments:</p>
-<pre><code class="language-bash">python main.py -c paddlex/configs/general_recognition/PP-ShiTuV2_rec.yaml  \
+<pre><code class="language-bash">python main.py -c paddlex/configs/image_feature/PP-ShiTuV2_rec.yaml  \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/Inshop_examples \
     -o CheckDataset.split.enable=True \
@@ -228,7 +228,7 @@ CheckDataset:
 Model training can be completed with a single command, taking the training of the image feature model PP-ShiTuV2_rec as an example:
 
 ```bash
-python main.py -c paddlex/configs/general_recognition/PP-ShiTuV2_rec.yaml \
+python main.py -c paddlex/configs/image_feature/PP-ShiTuV2_rec.yaml \
     -o Global.mode=train \
     -o Global.dataset_dir=./dataset/Inshop_examples
 ```
@@ -259,7 +259,7 @@ Other related parameters can be set by modifying the `Global` and `Train` fields
 After completing model training, you can evaluate the specified model weight file on the validation set to verify the model's accuracy. Using PaddleX for model evaluation can be done with a single command:
 
 ```bash
-python main.py -c paddlex/configs/general_recognition/PP-ShiTuV2_rec.yaml \
+python main.py -c paddlex/configs/image_feature/PP-ShiTuV2_rec.yaml \
     -o Global.mode=evaluate \
     -o Global.dataset_dir=./dataset/Inshop_examples
 ```
@@ -283,7 +283,7 @@ After completing model training and evaluation, you can use the trained model we
 To perform inference prediction through the command line, simply use the following command. Before running the following code, please download the [demo image](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_recognition_001.jpg) to your local machine.
 
 ```bash
-python main.py -c paddlex/configs/general_recognition/PP-ShiTuV2_rec.yaml  \
+python main.py -c paddlex/configs/image_feature/PP-ShiTuV2_rec.yaml  \
     -o Global.mode=predict \
     -o Predict.model_dir="./output/best_model/inference" \
     -o Predict.input="general_image_recognition_001.jpg"

diff --git a/docs/module_usage/tutorials/cv_modules/image_feature.md b/docs/module_usage/tutorials/cv_modules/image_feature.md
@@ -78,7 +78,7 @@ tar -xf ./dataset/Inshop_examples.tar -C ./dataset/
 一行命令即可完成数据校验：
 
 ```bash
-python main.py -c paddlex/configs/general_recognition/PP-ShiTuV2_rec.yaml \
+python main.py -c paddlex/configs/image_feature/PP-ShiTuV2_rec.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/Inshop_examples
 ```
@@ -178,13 +178,13 @@ CheckDataset:
   ......
 </code></pre>
 <p>随后执行命令：</p>
-<pre><code class="language-bash">python main.py -c  paddlex/configs/general_recognition/PP-ShiTuV2_rec.yaml  \
+<pre><code class="language-bash">python main.py -c  paddlex/configs/image_feature/PP-ShiTuV2_rec.yaml  \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/image_classification_labelme_examples
 </code></pre>
 <p>数据转换执行之后，原有标注文件会被在原路径下重命名为 <code>xxx.bak</code>。</p>
 <p>以上参数同样支持通过追加命令行参数的方式进行设置：</p>
-<pre><code class="language-bash">python main.py -c paddlex/configs/general_recognition/PP-ShiTuV2_rec.yaml  \
+<pre><code class="language-bash">python main.py -c paddlex/configs/image_feature/PP-ShiTuV2_rec.yaml  \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/image_classification_labelme_examples \
     -o CheckDataset.convert.enable=True \
@@ -210,13 +210,13 @@ CheckDataset:
   ......
 </code></pre>
 <p>随后执行命令：</p>
-<pre><code class="language-bash">python main.py -c paddlex/configs/general_recognition/PP-ShiTuV2_rec.yaml  \
+<pre><code class="language-bash">python main.py -c paddlex/configs/image_feature/PP-ShiTuV2_rec.yaml  \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/Inshop_examples
 </code></pre>
 <p>数据划分执行之后，原有标注文件会被在原路径下重命名为 <code>xxx.bak</code>。</p>
 <p>以上参数同样支持通过追加命令行参数的方式进行设置：</p>
-<pre><code class="language-bash">python main.py -c paddlex/configs/general_recognition/PP-ShiTuV2_rec.yaml  \
+<pre><code class="language-bash">python main.py -c paddlex/configs/image_feature/PP-ShiTuV2_rec.yaml  \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/Inshop_examples \
     -o CheckDataset.split.enable=True \
@@ -232,7 +232,7 @@ CheckDataset:
 一条命令即可完成模型的训练，以此处图像特征模型 PP-ShiTuV2_rec 的训练为例：
 
 ```
-python main.py -c paddlex/configs/general_recognition/PP-ShiTuV2_rec.yaml \
+python main.py -c paddlex/configs/image_feature/PP-ShiTuV2_rec.yaml \
     -o Global.mode=train \
     -o Global.dataset_dir=./dataset/Inshop_examples
 ```
@@ -263,7 +263,7 @@ python main.py -c paddlex/configs/general_recognition/PP-ShiTuV2_rec.yaml \
 在完成模型训练后，可以对指定的模型权重文件在验证集上进行评估，验证模型精度。使用 PaddleX 进行模型评估，一条命令即可完成模型的评估：
 
 ```bash
-python main.py -c paddlex/configs/general_recognition/PP-ShiTuV2_rec.yaml \
+python main.py -c paddlex/configs/image_feature/PP-ShiTuV2_rec.yaml \
     -o Global.mode=evaluate \
     -o Global.dataset_dir=./dataset/Inshop_examples
 ```
@@ -286,7 +286,7 @@ python main.py -c paddlex/configs/general_recognition/PP-ShiTuV2_rec.yaml \
 通过命令行的方式进行推理预测，只需如下一条命令。运行以下代码前，请您下载[示例图片](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_recognition_001.jpg)到本地。
 
 ```bash
-python main.py -c paddlex/configs/general_recognition/PP-ShiTuV2_rec.yaml  \
+python main.py -c paddlex/configs/image_feature/PP-ShiTuV2_rec.yaml  \
     -o Global.mode=predict \
     -o Predict.model_dir="./output/best_model/inference" \
     -o Predict.input="general_image_recognition_001.jpg"

diff --git a/docs/module_usage/tutorials/cv_modules/image_multilabel_classification.en.md b/docs/module_usage/tutorials/cv_modules/image_multilabel_classification.en.md
@@ -86,7 +86,7 @@ tar -xf ./dataset/mlcls_nus_examples.tar -C ./dataset/
 A single command can complete data validation:
 
 ```bash
-python main.py -c paddlex/configs/multilabel_classification/PP-LCNet_x1_0_ML.yaml \
+python main.py -c paddlex/configs/image_multilabel_classification/PP-LCNet_x1_0_ML.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/mlcls_nus_examples
 ```
@@ -175,13 +175,13 @@ CheckDataset:
   ......
 </code></pre>
 <p>Then execute the command:</p>
-<pre><code class="language-bash">python main.py -c paddlex/configs/multilabel_classification/PP-LCNet_x1_0_ML.yaml \
+<pre><code class="language-bash">python main.py -c paddlex/configs/image_multilabel_classification/PP-LCNet_x1_0_ML.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/det_coco_examples
 </code></pre>
 <p>After the data conversion is executed, the original annotation files will be renamed to <code>xxx.bak</code> in the original path.</p>
 <p>The above parameters also support being set by appending command line arguments:</p>
-<pre><code class="language-bash">python main.py -c paddlex/configs/multilabel_classification/PP-LCNet_x1_0_ML.yaml \
+<pre><code class="language-bash">python main.py -c paddlex/configs/image_multilabel_classification/PP-LCNet_x1_0_ML.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/det_coco_examples \
     -o CheckDataset.convert.enable=True \
@@ -207,13 +207,13 @@ CheckDataset:
   ......
 </code></pre>
 <p>Then execute the command:</p>
-<pre><code class="language-bash">python main.py -c paddlex/configs/multilabel_classification/PP-LCNet_x1_0_ML.yaml \
+<pre><code class="language-bash">python main.py -c paddlex/configs/image_multilabel_classification/PP-LCNet_x1_0_ML.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/det_coco_examples
 </code></pre>
 <p>After the data splitting is executed, the original annotation files will be renamed to <code>xxx.bak</code> in the original path.</p>
 <p>These parameters can also be set by appending command-line arguments:</p>
-<pre><code class="language-bash">python main.py -c paddlex/configs/multilabel_classification/PP-LCNet_x1_0_ML.yaml \
+<pre><code class="language-bash">python main.py -c paddlex/configs/image_multilabel_classification/PP-LCNet_x1_0_ML.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/det_coco_examples \
     -o CheckDataset.split.enable=True \
@@ -224,7 +224,7 @@ CheckDataset:
 ### 4.2 Model Training
 A single command can complete the model training. Taking the training of the image multi-label classification model PP-LCNet_x1_0_ML as an example:
 ```bash
-python main.py -c paddlex/configs/multilabel_classification/PP-LCNet_x1_0_ML.yaml \
+python main.py -c paddlex/configs/image_multilabel_classification/PP-LCNet_x1_0_ML.yaml \
     -o Global.mode=train \
     -o Global.dataset_dir=./dataset/mlcls_nus_examples
 ```
@@ -257,7 +257,7 @@ the following steps are required:
 After completing model training, you can evaluate the specified model weights file on the validation set to verify the model's accuracy. Using PaddleX for model evaluation can be done with a single command:
 
 ```bash
-python main.py -c paddlex/configs/multilabel_classification/PP-LCNet_x1_0_ML.yaml \
+python main.py -c paddlex/configs/image_multilabel_classification/PP-LCNet_x1_0_ML.yaml \
     -o Global.mode=evaluate \
     -o Global.dataset_dir=./dataset/mlcls_nus_examples
 ```
@@ -280,7 +280,7 @@ After completing model training and evaluation, you can use the trained model we
 * Inference predictions can be performed through the command line with just one command. Before running the following code, please download the [demo image](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/multilabel_classification_005.png) to your local machine.
 
 ```bash
-python main.py -c paddlex/configs/multilabel_classification/PP-LCNet_x1_0_ML.yaml  \
+python main.py -c paddlex/configs/image_multilabel_classification/PP-LCNet_x1_0_ML.yaml  \
     -o Global.mode=predict \
     -o Predict.model_dir="./output/best_model/inference" \
     -o Predict.input="multilabel_classification_005.png"