Merge pull request #1482 from FlyingQianMM/develop_qh

rename pdx.model to pdx.det in example codes

examples/defect_detection/README.md

@@ -87,7 +87,7 @@ eval_dataset = pdx.datasets.CocoDetection(
 # 初始化模型，并进行训练
 # 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
 num_classes = len(train_dataset.labels)
-model = pdx.models.MaskRCNN(
+model = pdx.det.MaskRCNN(
     num_classes=num_classes, backbone='ResNet50', with_fpn=True)
 ```
 ``` bash

examples/defect_detection/code/train.py

@@ -34,7 +34,7 @@ eval_dataset = pdx.datasets.CocoDetection(
 # 初始化模型，并进行训练
 # 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
 num_classes = len(train_dataset.labels)
-model = pdx.models.MaskRCNN(
+model = pdx.det.MaskRCNN(
     num_classes=num_classes, backbone='ResNet50', with_fpn=True)
 
 # API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/detector.py#L155

examples/fireSmoke_detection/README.md

@@ -157,7 +157,7 @@ PaddleX提供了5种目标检测模型：FasterRCNN、YOLOv3、PP-YOLO、PP-YOLO
 * eval_transforms：验证预处理参数，可以增加、修改预处理方法和参数；
 * train_dataset：训练使用数据集，修改图片路径、标签路径以及是否进行数据shuffle；
 * eval_dataset：验证使用数据集，修改图片路径、标签路径以及是否进行数据shuffle；
-* pdx.models.*：设置不同的模型，可选[FasterRCNN、YOLOv3、PP-YOLO、PP-YOLO-tiny、PP-YOLOv2，这里选择PP-YOLOv2；
+* pdx.det.*：设置不同的模型，可选[FasterRCNN、YOLOv3、PP-YOLO、PP-YOLO-tiny、PP-YOLOv2，这里选择PP-YOLOv2；
 * model.train：设置训练epoch、训练和验证数据集、batch size、学习率learning rate、warmup step、lr衰减lr_decay_epoch、模型保存间隔save_interval_epoch、模型保存路径save_dir，详细介绍[训练参数](https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/apis/models/detection.md)。
 
 PaddleX提供了单卡/多卡训练模型，满足用户多种训练需求
@@ -347,6 +347,3 @@ python infer.py
 ## 开源数据
 
 * 非常感谢[gengyanlei](https://github.com/gengyanlei/fire-smoke-detect-yolov4)和[Thomas-yanxin](https://aistudio.baidu.com/aistudio/datasetdetail/90352/0)开源的火灾和烟雾数据集
-
-  
-

examples/meter_reader/README.md

@@ -166,7 +166,7 @@ eval_dataset = pdx.datasets.CocoDetection(
 
 ```python
 num_classes = len(train_dataset.labels)
-model = pdx.models.PPYOLOv2(
+model = pdx.det.PPYOLOv2(
     num_classes=num_classes, backbone='ResNet50_vd_dcn')
 
 ```
@@ -250,7 +250,7 @@ eval_dataset = pdx.datasets.SegDataset(
 
 ```python
 num_classes = len(train_dataset.labels)
-model = pdx.models.DeepLabV3P(num_classes=num_classes, backbone='ResNet50_vd', use_mixed_loss=True)
+model = pdx.seg.DeepLabV3P(num_classes=num_classes, backbone='ResNet50_vd', use_mixed_loss=True)
 
 ```
 

examples/rebar_count/README.md

@@ -105,7 +105,7 @@ eval_dataset = pdx.datasets.VOCDetection(
 # 初始化模型，并进行训练
 # 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
 num_classes = len(train_dataset.labels)
-model = pdx.models.YOLOv3(num_classes=num_classes, backbone='DarkNet53')
+model = pdx.det.YOLOv3(num_classes=num_classes, backbone='DarkNet53')
 ```
 ``` bash
 # API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/detector.py#L155

examples/rebar_count/code/train.py

@@ -39,7 +39,7 @@ eval_dataset = pdx.datasets.VOCDetection(
 # 初始化模型，并进行训练
 # 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
 num_classes = len(train_dataset.labels)
-model = pdx.models.YOLOv3(num_classes=num_classes, backbone='DarkNet53')
+model = pdx.det.YOLOv3(num_classes=num_classes, backbone='DarkNet53')
 
 # API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/detector.py#L155
 # 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html

examples/robot_grab/README.md

@@ -109,7 +109,7 @@ eval_dataset = pdx.datasets.CocoDetection(
 # 初始化模型，并进行训练
 # 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
 num_classes = len(train_dataset.labels)
-model = pdx.models.MaskRCNN(
+model = pdx.det.MaskRCNN(
     num_classes=num_classes, backbone='ResNet50', with_fpn=True)
 ```
 ``` bash

examples/robot_grab/code/train.py

@@ -32,7 +32,7 @@ eval_dataset = pdx.datasets.CocoDetection(
 # 初始化模型，并进行训练
 # 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
 num_classes = len(train_dataset.labels)
-model = pdx.models.MaskRCNN(
+model = pdx.det.MaskRCNN(
     num_classes=num_classes, backbone='ResNet50', with_fpn=True)
 
 # API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/detector.py#L155