Update detection module

hub_module/modules/image/face_detection/pyramidbox_lite_mobile/module.py

@@ -28,6 +28,7 @@ class PyramidBoxLiteMobile(hub.Module):
         self.default_pretrained_model_path = os.path.join(
             self.directory, "pyramidbox_lite_mobile_face_detection")
         self._set_config()
+        self.processor = self
 
     def _set_config(self):
         """
@@ -130,6 +131,9 @@ class PyramidBoxLiteMobile(hub.Module):
         program, feeded_var_names, target_vars = fluid.io.load_inference_model(
             dirname=self.default_pretrained_model_path, executor=exe)
 
+        var = program.global_block().vars['detection_output_0.tmp_1']
+        var.desc.set_dtype(fluid.core.VarDesc.VarType.INT32)
+
         fluid.io.save_inference_model(
             dirname=dirname,
             main_program=program,

hub_module/modules/image/face_detection/pyramidbox_lite_mobile_mask/module.py

@@ -37,6 +37,7 @@ class PyramidBoxLiteMobileMask(hub.Module):
         else:
             self.face_detector = face_detector_module
         self._set_config()
+        self.processor = self
 
     def _set_config(self):
         """

hub_module/modules/image/face_detection/pyramidbox_lite_server/module.py

@@ -28,6 +28,7 @@ class PyramidBoxLiteServer(hub.Module):
         self.default_pretrained_model_path = os.path.join(
             self.directory, "pyramidbox_lite_server_face_detection")
         self._set_config()
+        self.processor = self
 
     def _set_config(self):
         """

hub_module/modules/image/face_detection/pyramidbox_lite_server_mask/module.py

@@ -37,6 +37,7 @@ class PyramidBoxLiteServerMask(hub.Module):
         else:
             self.face_detector = face_detector_module
         self._set_config()
+        self.processor = self
 
     def _set_config(self):
         """

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

@@ -21,7 +21,7 @@ from ssd_mobilenet_v1_pascal.data_feed import reader
 
 @moduleinfo(
     name="ssd_mobilenet_v1_pascal",
-    version="1.1.0",
+    version="1.1.1",
     type="cv/object_detection",
     summary="SSD with backbone MobileNet_V1, trained with dataset Pasecal VOC.",
     author="paddlepaddle",
@@ -275,7 +275,7 @@ class SSDMobileNetv1(hub.Module):
         self.add_module_config_arg()
         self.add_module_input_arg()
         args = self.parser.parse_args(argvs)
-        results = self.face_detection(
+        results = self.object_detection(
             paths=[args.input_path],
             batch_size=args.batch_size,
             use_gpu=args.use_gpu,

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

@@ -21,7 +21,7 @@ from ssd_vgg16_300_coco2017.data_feed import reader
 
 @moduleinfo(
     name="ssd_vgg16_300_coco2017",
-    version="1.0.0",
+    version="1.0.1",
     type="cv/object_detection",
     summary="SSD with backbone VGG16, trained with dataset COCO.",
     author="paddlepaddle",
@@ -264,7 +264,7 @@ class SSDVGG16(hub.Module):
         self.add_module_config_arg()
         self.add_module_input_arg()
         args = self.parser.parse_args(argvs)
-        results = self.face_detection(
+        results = self.object_detection(
             paths=[args.input_path],
             batch_size=args.batch_size,
             use_gpu=args.use_gpu,

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

@@ -21,7 +21,7 @@ from ssd_vgg16_512_coco2017.data_feed import reader
 
 @moduleinfo(
     name="ssd_vgg16_512_coco2017",
-    version="1.0.0",
+    version="1.0.1",
     type="cv/object_detection",
     summary="SSD with backbone VGG16, trained with dataset COCO.",
     author="paddlepaddle",
@@ -278,7 +278,7 @@ class SSDVGG16_512(hub.Module):
         self.add_module_config_arg()
         self.add_module_input_arg()
         args = self.parser.parse_args(argvs)
-        results = self.face_detection(
+        results = self.object_detection(
             paths=[args.input_path],
             batch_size=args.batch_size,
             use_gpu=args.use_gpu,

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

@@ -21,7 +21,7 @@ from yolov3_darknet53_coco2017.yolo_head import MultiClassNMS, YOLOv3Head
 
 @moduleinfo(
     name="yolov3_darknet53_coco2017",
-    version="1.1.0",
+    version="1.1.1",
     type="CV/object_detection",
     summary=
     "Baidu's YOLOv3 model for object detection, with backbone DarkNet53, trained with dataset coco2017.",
@@ -270,7 +270,7 @@ class YOLOv3DarkNet53Coco2017(hub.Module):
         self.add_module_config_arg()
         self.add_module_input_arg()
         args = self.parser.parse_args(argvs)
-        results = self.face_detection(
+        results = self.object_detection(
             paths=[args.input_path],
             batch_size=args.batch_size,
             use_gpu=args.use_gpu,

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

@@ -21,7 +21,7 @@ from yolov3_darknet53_pedestrian.yolo_head import MultiClassNMS, YOLOv3Head
 
 @moduleinfo(
     name="yolov3_darknet53_pedestrian",
-    version="1.0.0",
+    version="1.0.1",
     type="CV/object_detection",
     summary=
     "Baidu's YOLOv3 model for pedestrian detection, with backbone DarkNet53.",
@@ -280,7 +280,7 @@ class YOLOv3DarkNet53Pedestrian(hub.Module):
         self.add_module_config_arg()
         self.add_module_input_arg()
         args = self.parser.parse_args(argvs)
-        results = self.face_detection(
+        results = self.object_detection(
             paths=[args.input_path],
             batch_size=args.batch_size,
             use_gpu=args.use_gpu,

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

@@ -21,7 +21,7 @@ from yolov3_darknet53_vehicles.yolo_head import MultiClassNMS, YOLOv3Head
 
 @moduleinfo(
     name="yolov3_darknet53_vehicles",
-    version="1.0.0",
+    version="1.0.1",
     type="CV/object_detection",
     summary=
     "Baidu's YOLOv3 model for vehicles detection, with backbone DarkNet53.",
@@ -280,7 +280,7 @@ class YOLOv3DarkNet53Vehicles(hub.Module):
         self.add_module_config_arg()
         self.add_module_input_arg()
         args = self.parser.parse_args(argvs)
-        results = self.face_detection(
+        results = self.object_detection(
             paths=[args.input_path],
             batch_size=args.batch_size,
             use_gpu=args.use_gpu,

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

@@ -21,7 +21,7 @@ from yolov3_mobilenet_v1_coco2017.yolo_head import MultiClassNMS, YOLOv3Head
 
 @moduleinfo(
     name="yolov3_mobilenet_v1_coco2017",
-    version="1.0.0",
+    version="1.0.1",
     type="CV/object_detection",
     summary=
     "Baidu's YOLOv3 model for object detection with backbone MobileNet_V1, trained with dataset COCO2017.",
@@ -270,7 +270,7 @@ class YOLOv3MobileNetV1Coco2017(hub.Module):
         self.add_module_config_arg()
         self.add_module_input_arg()
         args = self.parser.parse_args(argvs)
-        results = self.face_detection(
+        results = self.object_detection(
             paths=[args.input_path],
             batch_size=args.batch_size,
             use_gpu=args.use_gpu,

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

@@ -21,7 +21,7 @@ from yolov3_resnet34_coco2017.yolo_head import MultiClassNMS, YOLOv3Head
 
 @moduleinfo(
     name="yolov3_resnet34_coco2017",
-    version="1.0.0",
+    version="1.0.1",
     type="CV/object_detection",
     summary=
     "Baidu's YOLOv3 model for object detection with backbone ResNet34, trained with dataset coco2017.",
@@ -272,7 +272,7 @@ class YOLOv3ResNet34Coco2017(hub.Module):
         self.add_module_config_arg()
         self.add_module_input_arg()
         args = self.parser.parse_args(argvs)
-        results = self.face_detection(
+        results = self.object_detection(
             paths=[args.input_path],
             batch_size=args.batch_size,
             use_gpu=args.use_gpu,

hub_module/modules/image/object_detection/yolov3_resnet50_vd_coco2017/module.py

@@ -21,7 +21,7 @@ from yolov3_resnet50_vd_coco2017.yolo_head import MultiClassNMS, YOLOv3Head
 
 @moduleinfo(
     name="yolov3_resnet50_vd_coco2017",
-    version="1.0.0",
+    version="1.0.1",
     type="CV/object_detection",
     summary=
     "Baidu's YOLOv3 model for object detection with backbone ResNet50, trained with dataset coco2017.",
@@ -274,7 +274,7 @@ class YOLOv3ResNet50Coco2017(hub.Module):
         self.add_module_config_arg()
         self.add_module_input_arg()
         args = self.parser.parse_args(argvs)
-        results = self.face_detection(
+        results = self.object_detection(
             paths=[args.input_path],
             batch_size=args.batch_size,
             use_gpu=args.use_gpu,