Making value 0.0 by default for BC.

Author: datumbox

references/detection/train.py

@@ -93,8 +93,11 @@ def main(args):
         collate_fn=utils.collate_fn)
 
     print("Creating model")
-    model = torchvision.models.detection.__dict__[args.model](num_classes=num_classes,
-                                                              pretrained=args.pretrained)
+    kwargs = {}
+    if "rcnn" in args.model:
+        kwargs["rpn_score_thresh"] = 0.05
+    model = torchvision.models.detection.__dict__[args.model](num_classes=num_classes, pretrained=args.pretrained,
+                                                              **kwargs)
     model.to(device)
 
     model_without_ddp = model

test/test_onnx.py

@@ -368,8 +368,7 @@ def get_test_images(self):
     def test_faster_rcnn(self):
         images, test_images = self.get_test_images()
         dummy_image = [torch.ones(3, 100, 100) * 0.3]
-        model = models.detection.faster_rcnn.fasterrcnn_resnet50_fpn(pretrained=True, min_size=200, max_size=300,
-                                                                     rpn_score_thresh=0.0)
+        model = models.detection.faster_rcnn.fasterrcnn_resnet50_fpn(pretrained=True, min_size=200, max_size=300)
         model.eval()
         model(images)
         # Test exported model on images of different size, or dummy input
@@ -420,8 +419,7 @@ def test_paste_mask_in_image(self):
     def test_mask_rcnn(self):
         images, test_images = self.get_test_images()
         dummy_image = [torch.ones(3, 100, 100) * 0.3]
-        model = models.detection.mask_rcnn.maskrcnn_resnet50_fpn(pretrained=True, min_size=200, max_size=300,
-                                                                 rpn_score_thresh=0.0)
+        model = models.detection.mask_rcnn.maskrcnn_resnet50_fpn(pretrained=True, min_size=200, max_size=300)
         model.eval()
         model(images)
         # Test exported model on images of different size, or dummy input
@@ -471,8 +469,7 @@ def test_heatmaps_to_keypoints(self):
     def test_keypoint_rcnn(self):
         images, test_images = self.get_test_images()
         dummy_images = [torch.ones(3, 100, 100) * 0.3]
-        model = models.detection.keypoint_rcnn.keypointrcnn_resnet50_fpn(pretrained=True, min_size=200, max_size=300,
-                                                                         rpn_score_thresh=0.0)
+        model = models.detection.keypoint_rcnn.keypointrcnn_resnet50_fpn(pretrained=True, min_size=200, max_size=300)
         model.eval()
         model(images)
         self.run_model(model, [(images,), (test_images,), (dummy_images,)],

torchvision/models/detection/faster_rcnn.py

@@ -155,7 +155,7 @@ def __init__(self, backbone, num_classes=None,
                  rpn_nms_thresh=0.7,
                  rpn_fg_iou_thresh=0.7, rpn_bg_iou_thresh=0.3,
                  rpn_batch_size_per_image=256, rpn_positive_fraction=0.5,
-                 rpn_score_thresh=0.05,
+                 rpn_score_thresh=0.0,
                  # Box parameters
                  box_roi_pool=None, box_head=None, box_predictor=None,
                  box_score_thresh=0.05, box_nms_thresh=0.5, box_detections_per_img=100,

torchvision/models/detection/keypoint_rcnn.py

@@ -160,7 +160,7 @@ def __init__(self, backbone, num_classes=None,
                  rpn_nms_thresh=0.7,
                  rpn_fg_iou_thresh=0.7, rpn_bg_iou_thresh=0.3,
                  rpn_batch_size_per_image=256, rpn_positive_fraction=0.5,
-                 rpn_score_thresh=0.05,
+                 rpn_score_thresh=0.0,
                  # Box parameters
                  box_roi_pool=None, box_head=None, box_predictor=None,
                  box_score_thresh=0.05, box_nms_thresh=0.5, box_detections_per_img=100,

torchvision/models/detection/mask_rcnn.py

@@ -163,7 +163,7 @@ def __init__(self, backbone, num_classes=None,
                  rpn_nms_thresh=0.7,
                  rpn_fg_iou_thresh=0.7, rpn_bg_iou_thresh=0.3,
                  rpn_batch_size_per_image=256, rpn_positive_fraction=0.5,
-                 rpn_score_thresh=0.05,
+                 rpn_score_thresh=0.0,
                  # Box parameters
                  box_roi_pool=None, box_head=None, box_predictor=None,
                  box_score_thresh=0.05, box_nms_thresh=0.5, box_detections_per_img=100,