Adding rpn_score_thresh param directly in fasterrcnn_mobilenet_v3_lar…

…ge_fpn.

references/detection/train.py

@@ -96,7 +96,8 @@ def main(args):
         "trainable_backbone_layers": args.trainable_backbone_layers
     }
     if "rcnn" in args.model:
-        kwargs["rpn_score_thresh"] = args.rpn_score_thresh
+        if args.rpn_score_thresh is not None:
+            kwargs["rpn_score_thresh"] = args.rpn_score_thresh
     model = torchvision.models.detection.__dict__[args.model](num_classes=num_classes, pretrained=args.pretrained,
                                                               **kwargs)
     model.to(device)
@@ -179,9 +180,9 @@ def main(args):
     parser.add_argument('--resume', default='', help='resume from checkpoint')
     parser.add_argument('--start_epoch', default=0, type=int, help='start epoch')
     parser.add_argument('--aspect-ratio-group-factor', default=3, type=int)
-    parser.add_argument('--rpn-score-thresh', default=0.0, type=float, help='rpn score threshold for faster-rcnn')
+    parser.add_argument('--rpn-score-thresh', default=None, type=float, help='rpn score threshold for faster-rcnn')
     parser.add_argument('--trainable-backbone-layers', default=None, type=int,
-                        help='number of trainable layers of backbone ')
+                        help='number of trainable layers of backbone')
     parser.add_argument(
         "--test-only",
         dest="test_only",

torchvision/models/detection/faster_rcnn.py

@@ -414,7 +414,8 @@ def fasterrcnn_mobilenet_v3_large(pretrained=False, progress=True, num_classes=9
 
 
 def fasterrcnn_mobilenet_v3_large_fpn(pretrained=False, progress=True, num_classes=91, pretrained_backbone=True,
-                                      trainable_backbone_layers=None, min_size=320, max_size=640, **kwargs):
+                                      trainable_backbone_layers=None, min_size=320, max_size=640, rpn_score_thresh=0.05,
+                                      **kwargs):
     """
     Constructs a Faster R-CNN model with a MobileNetV3-Large FPN backbone. It works similarly
     to Faster R-CNN with ResNet-50 FPN backbone. See `fasterrcnn_resnet50_fpn` for more details.
@@ -435,6 +436,8 @@ def fasterrcnn_mobilenet_v3_large_fpn(pretrained=False, progress=True, num_class
             Valid values are between 0 and 6, with 6 meaning all backbone layers are trainable.
         min_size (int): minimum size of the image to be rescaled before feeding it to the backbone
         max_size (int): maximum size of the image to be rescaled before feeding it to the backbone
+        rpn_score_thresh (float): during inference, only return proposals with a classification score
+            greater than rpn_score_thresh
     """
     trainable_backbone_layers = _validate_trainable_layers(
         pretrained or pretrained_backbone, trainable_backbone_layers, 6, 3)
@@ -448,7 +451,7 @@ def fasterrcnn_mobilenet_v3_large_fpn(pretrained=False, progress=True, num_class
     aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes)
 
     model = FasterRCNN(backbone, num_classes, rpn_anchor_generator=AnchorGenerator(anchor_sizes, aspect_ratios),
-                       min_size=min_size, max_size=max_size, **kwargs)
+                       min_size=min_size, max_size=max_size, rpn_score_thresh=rpn_score_thresh, **kwargs)
     if pretrained:
         state_dict = load_state_dict_from_url(model_urls['fasterrcnn_mobilenet_v3_large_fpn_coco'], progress=progress)
         model.load_state_dict(state_dict)