Fix docs

hub_module/modules/image/object_detection/faster_rcnn_resnet50_coco2017/README.md

@@ -17,7 +17,7 @@ def context(num_classes=81,
             phase='train')
 ```
 
-提取头部特征，用于迁移学习。
+提取特征，用于迁移学习。
 
 **参数**
 
@@ -28,10 +28,28 @@ def context(num_classes=81,
 
 **返回**
 
-* inputs (dict): 模型的输入，keys 包括 'image', 'im\_size'，相应的取值为：
-    * image (Variable): 图像变量
-    * im\_size (Variable): 图片的尺寸
-* outputs (dict): 模型的输出。如果 get\_prediction 为 False，输出 'head\_fatures'，否则输出 'bbox\_out'。
+* inputs (dict): 模型的输入，相应的取值为：
+    当 phase 为 'train'时，包含：
+        * image (Variable): 图像变量
+        * im\_size (Variable): 图像的尺寸
+        * im\_info (Variable): 图像缩放信息
+        * gt\_class (Variable): 检测框类别
+        * gt\_box (Variable): 检测框坐标
+        * is\_crowd (Variable):
+    当 phase 为 'predict'时，包含：
+        * image (Variable): 图像变量
+        * im\_size (Variable): 图像的尺寸
+        * im\_info (Variable): 图像缩放信息
+* outputs (dict): 模型的输出，相应的取值为：
+    当 phase 为 'train'时，包含：
+        * head_features (Variable): 所提取的特征
+        * rpn\_cls\_loss (Variable): 检测框分类损失
+        * rpn\_reg\_loss (Variable): 检测框回归损失
+        * generate\_proposal\_labels (Variable): 图像信息
+    当 phase 为 'predict'时，包含：
+        * head_features (Variable): 所提取的特征
+        * rois (Variable): 提取的roi
+        * bbox\_out (Variable): 预测结果
 * context\_prog (Program): 用于迁移学习的 Program。
 
 ```python

hub_module/modules/image/object_detection/faster_rcnn_resnet50_coco2017/module.py

@@ -34,7 +34,7 @@ from faster_rcnn_resnet50_coco2017.roi_extractor import RoIAlign
     summary=
     "Baidu's Faster R-CNN model for object detection with backbone ResNet50, trained with dataset COCO2017",
     author="paddlepaddle",
-    author_email="")
+    author_email="paddle-dev@baidu.com")
 class FasterRCNNResNet50(hub.Module):
     def _initialize(self):
         # default pretrained model, Faster-RCNN with backbone ResNet50, shape of input tensor is [3, 800, 1333]
@@ -160,7 +160,7 @@ class FasterRCNNResNet50(hub.Module):
                         'is_crowd': var_prefix + is_crowd.name
                     }
                     outputs = {
-                        'head_feat':
+                        'head_features':
                         var_prefix + head_feat.name,
                         'rpn_cls_loss':
                         var_prefix + rpn_loss['rpn_cls_loss'].name,
@@ -178,7 +178,7 @@ class FasterRCNNResNet50(hub.Module):
                         'im_shape': var_prefix + im_shape.name
                     }
                     outputs = {
-                        'head_feat': var_prefix + head_feat.name,
+                        'head_features': var_prefix + head_feat.name,
                         'rois': var_prefix + rois.name,
                         'bbox_out': var_prefix + pred.name
                     }

hub_module/modules/image/object_detection/faster_rcnn_resnet50_fpn_coco2017/README.md

@@ -17,7 +17,7 @@ def context(num_classes=81,
             phase='train')
 ```
 
-提取头部特征，用于迁移学习。
+提取特征，用于迁移学习。
 
 **参数**
 
@@ -28,10 +28,28 @@ def context(num_classes=81,
 
 **返回**
 
-* inputs (dict): 模型的输入，keys 包括 'image', 'im\_size'，相应的取值为：
-    * image (Variable): 图像变量
-    * im\_size (Variable): 图片的尺寸
-* outputs (dict): 模型的输出。如果 get\_prediction 为 False，输出 'head\_fatures'，否则输出 'bbox\_out'。
+* inputs (dict): 模型的输入，相应的取值为：
+    当 phase 为 'train'时，包含：
+        * image (Variable): 图像变量
+        * im\_size (Variable): 图像的尺寸
+        * im\_info (Variable): 图像缩放信息
+        * gt\_class (Variable): 检测框类别
+        * gt\_box (Variable): 检测框坐标
+        * is\_crowd (Variable):
+    当 phase 为 'predict'时，包含：
+        * image (Variable): 图像变量
+        * im\_size (Variable): 图像的尺寸
+        * im\_info (Variable): 图像缩放信息
+* outputs (dict): 模型的输出，相应的取值为：
+    当 phase 为 'train'时，包含：
+        * head_features (Variable): 所提取的特征
+        * rpn\_cls\_loss (Variable): 检测框分类损失
+        * rpn\_reg\_loss (Variable): 检测框回归损失
+        * generate\_proposal\_labels (Variable): 图像信息
+    当 phase 为 'predict'时，包含：
+        * head_features (Variable): 所提取的特征
+        * rois (Variable): 提取的roi
+        * bbox\_out (Variable): 预测结果
 * context\_prog (Program): 用于迁移学习的 Program。
 
 ```python

hub_module/modules/image/object_detection/faster_rcnn_resnet50_fpn_coco2017/module.py

@@ -35,7 +35,7 @@ from faster_rcnn_resnet50_fpn_coco2017.roi_extractor import FPNRoIAlign
     summary=
     "Baidu's Faster-RCNN model for object detection, whose backbone is ResNet50, processed with Feature Pyramid Networks",
     author="paddlepaddle",
-    author_email="")
+    author_email="paddle-dev@baidu.com")
 class FasterRCNNResNet50RPN(hub.Module):
     def _initialize(self):
         # default pretrained model, Faster-RCNN with backbone ResNet50, shape of input tensor is [3, 800, 1333]
@@ -161,7 +161,7 @@ class FasterRCNNResNet50RPN(hub.Module):
                         'is_crowd': var_prefix + is_crowd.name
                     }
                     outputs = {
-                        'head_feat':
+                        'head_features':
                         var_prefix + head_feat.name,
                         'rpn_cls_loss':
                         var_prefix + rpn_loss['rpn_cls_loss'].name,
@@ -179,7 +179,7 @@ class FasterRCNNResNet50RPN(hub.Module):
                         'im_shape': var_prefix + im_shape.name
                     }
                     outputs = {
-                        'head_feat': var_prefix + head_feat.name,
+                        'head_features': var_prefix + head_feat.name,
                         'rois': var_prefix + rois.name,
                         'bbox_out': var_prefix + pred.name
                     }

hub_module/modules/image/object_detection/retinanet_resnet50_fpn_coco2017/module.py

@@ -29,7 +29,7 @@ from retinanet_resnet50_fpn_coco2017.resnet import ResNet
     summary=
     "Baidu's RetinaNet model for object detection, with backbone ResNet50 and FPN.",
     author="paddlepaddle",
-    author_email="")
+    author_email="paddle-dev@baidu.com")
 class RetinaNetResNet50FPN(hub.Module):
     def _initialize(self):
         # default pretrained model of Retinanet_ResNet50_FPN, the shape of input image tensor is (3, 608, 608)

hub_module/modules/image/object_detection/ssd_mobilenet_v1_pascal/module.py

@@ -25,7 +25,7 @@ from ssd_mobilenet_v1_pascal.data_feed import reader
     type="cv/object_detection",
     summary="SSD with backbone MobileNet_V1, trained with dataset Pasecal VOC.",
     author="paddlepaddle",
-    author_email="")
+    author_email="paddle-dev@baidu.com")
 class SSDMobileNetv1(hub.Module):
     def _initialize(self):
         self.default_pretrained_model_path = os.path.join(

hub_module/modules/image/object_detection/ssd_vgg16_300_coco2017/module.py

@@ -25,7 +25,7 @@ from ssd_vgg16_300_coco2017.data_feed import reader
     type="cv/object_detection",
     summary="SSD with backbone VGG16, trained with dataset COCO.",
     author="paddlepaddle",
-    author_email="")
+    author_email="paddle-dev@baidu.com")
 class SSDVGG16(hub.Module):
     def _initialize(self):
         self.default_pretrained_model_path = os.path.join(

hub_module/modules/image/object_detection/ssd_vgg16_512_coco2017/module.py

@@ -25,7 +25,7 @@ from ssd_vgg16_512_coco2017.data_feed import reader
     type="cv/object_detection",
     summary="SSD with backbone VGG16, trained with dataset COCO.",
     author="paddlepaddle",
-    author_email="")
+    author_email="paddle-dev@baidu.com")
 class SSDVGG16_512(hub.Module):
     def _initialize(self):
         self.default_pretrained_model_path = os.path.join(

hub_module/modules/image/object_detection/yolov3_darknet53_coco2017/module.py

@@ -26,7 +26,7 @@ from yolov3_darknet53_coco2017.yolo_head import MultiClassNMS, YOLOv3Head
     summary=
     "Baidu's YOLOv3 model for object detection, with backbone DarkNet53, trained with dataset coco2017.",
     author="paddlepaddle",
-    author_email="")
+    author_email="paddle-dev@baidu.com")
 class YOLOv3DarkNet53Coco2017(hub.Module):
     def _initialize(self):
         self.default_pretrained_model_path = os.path.join(

hub_module/modules/image/object_detection/yolov3_darknet53_pedestrian/module.py

@@ -26,7 +26,7 @@ from yolov3_darknet53_pedestrian.yolo_head import MultiClassNMS, YOLOv3Head
     summary=
     "Baidu's YOLOv3 model for pedestrian detection, with backbone DarkNet53.",
     author="paddlepaddle",
-    author_email="")
+    author_email="paddle-dev@baidu.com")
 class YOLOv3DarkNet53Pedestrian(hub.Module):
     def _initialize(self):
         self.default_pretrained_model_path = os.path.join(

hub_module/modules/image/object_detection/yolov3_darknet53_vehicles/module.py

@@ -26,7 +26,7 @@ from yolov3_darknet53_vehicles.yolo_head import MultiClassNMS, YOLOv3Head
     summary=
     "Baidu's YOLOv3 model for vehicles detection, with backbone DarkNet53.",
     author="paddlepaddle",
-    author_email="")
+    author_email="paddle-dev@baidu.com")
 class YOLOv3DarkNet53Vehicles(hub.Module):
     def _initialize(self):
         self.default_pretrained_model_path = os.path.join(

hub_module/modules/image/object_detection/yolov3_mobilenet_v1_coco2017/module.py

@@ -21,12 +21,12 @@ from yolov3_mobilenet_v1_coco2017.yolo_head import MultiClassNMS, YOLOv3Head
 
 @moduleinfo(
     name="yolov3_mobilenet_v1_coco2017",
-    version="1.1.0",
+    version="1.0.0",
     type="CV/object_detection",
     summary=
     "Baidu's YOLOv3 model for object detection with backbone MobileNet_V1, trained with dataset COCO2017.",
     author="paddlepaddle",
-    author_email="")
+    author_email="paddle-dev@baidu.com")
 class YOLOv3MobileNetV1Coco2017(hub.Module):
     def _initialize(self):
         self.default_pretrained_model_path = os.path.join(

hub_module/modules/image/object_detection/yolov3_resnet34_coco2017/module.py

@@ -21,12 +21,12 @@ from yolov3_resnet34_coco2017.yolo_head import MultiClassNMS, YOLOv3Head
 
 @moduleinfo(
     name="yolov3_resnet34_coco2017",
-    version="1.1.0",
+    version="1.0.0",
     type="CV/object_detection",
     summary=
     "Baidu's YOLOv3 model for object detection with backbone ResNet34, trained with dataset coco2017.",
     author="paddlepaddle",
-    author_email="")
+    author_email="paddle-dev@baidu.com")
 class YOLOv3DarkNet53Coco2017(hub.Module):
     def _initialize(self):
         self.default_pretrained_model_path = os.path.join(

hub_module/scripts/configs/faster_rcnn.yml

-name: faster_rcnn
-dir: "modules/image/object_detection/faster_rcnn"

hub_module/scripts/configs/ssd.yml

-name: ssd
-dir: "modules/image/object_detection/ssd"

hub_module/scripts/configs/yolov3.yml

-name: yolov3
-dir: "modules/image/object_detection/yolov3"